Enhancing Quality and Productivity in Supply Chain Operations through AI Integration

Enhancing Quality and Productivity in Supply Chain Operations through AI Integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhancing Quality and Productivity in Supply Chain Operations through AI Integration Liheng Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9064564/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the innovative application of artificial intelligence (AI) in increasing quality and productivity in supply chain operations. A quantitative approach involving simultaneous equation regression models has been employed to examine the effects of AI use on several performance metrics of the supply chain and the relationship between AI adoption and improvements in operational efficiency. The analyses yield significant positive effects of AI on productivity and quality in supply chain operations. For instance, overall productivity increased by around 21%, whereas quality-related measures improved by 18%. AI-based customer service systems showed a coefficient value of 0.34 (p < 0.01), indicating a strong positive effect on operational quality. Similarly, AI combined with predictive analytics for demand forecasting produced a coefficient of 0.29 (p < 0.05), highlighting its role in enhanced productivity. The use of AI in logistics optimization tools yielded a coefficient of 0.41 (p < 0.01), suggesting that these tools are highly valuable in boosting productivity and lowering operational costs. Although the findings appear optimistic, integrating AI into existing businesses remains challenging due to limited resources, high expenditures, and inadequate technological infrastructure. Achieving effective integration requires balancing these factors. The adoption of advanced AI technologies presents a paradigm shift toward improving operational efficiency and achieving competitive advantage; however, it also entails internal obstacles, systematic planning, and substantial investment. Future studies should emphasize purposeful investment in human capital, gradual system-wide adoption of AI, and the development of AI-compatible peripheral technologies. Business and commerce/Business and management Social science/Business and management Physical sciences/Engineering Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Simultaneous Equation Regression Artificial Intelligence Supply Chain Productivity Figures Figure 1 Figure 2 1. Introduction Activities purchase through distribution of the final product constitute supply chain management. It is meant in the context of reducing cost and time (Mentzer et al., 2001; Chopra & Meindl, 2016 ). Stakeholders coordinate so that organizations can optimize the flow of requisitioning, production and logistics to stock management (pool) resources operations (Lambert & Cooper, 2000; Christopher, 2016 ). Growing globalization of supply chains and e-commerce, and changing customer demand for lead-time compression have made the supply chain increasingly complicated and complex; hence, requiring a lean-agile strategy (Prajogo & Olhager, 2012; Gunasekaran et al., 2018). Traditional push and pull supply chain approaches are not working for the world bearing demand variations; they face challenges in real-time adaptation; therefore, change is inevitable, those having traditional systems will struggle to stay innovative and competitive (Bowersox et al., 2012 ; Chopra & Meindl, 2016 ). AI technology has the potential to revolutionize several industries by employing such advanced technologies as machine learning, natural language processing and robotics for automating and simplifying SCM functions (Marr, 2018 ; Ivanov et al., 2020). AI improves the accuracy and efficiency when predicting demand as well as inventory management and waste reduction, which can improve savings (Choi et al., 2018 ; Waller & Fawcett, 2013 ). It also makes its significant contribution in the logistics sector as well by enhancing delivery route planning due to which fuel consumption can be reduced, thus increasing customer satisfaction (Ben-Daya et al., 2019 ; Ghobakhloo, 2020 ). Robotic Process Automation (RPA) and the order management process intersect to free humans to do, more human things. Out at the edge AI improves quality by better detecting defects and ensuring quality compliance. Cost, data security and skills shortages mostly define the challenges but with AI getting cheaper and its ability to deliver proven results it is a must-use strategy for your strategies in the Supply Chain industry today. Supply chains struggle with outdated systems, limited up-to-date data, and inefficiencies that slow them down and lead to mistakes, and higher costs. The situation is exacerbated by traditional manual systems that appear to ensure quality, but that in turn reduces overall productivity. Wamba et al. ( 2017 ) AI exhibiting the disadvantage of “Bad data”, it's lack of ability to handle in bulk and reluctance to change, he believes adds momentum to these problems. There is an opportunity to programmatically bring in AI through the software that can automate routine work and use resources, increase visibility. AI technologies such as machine learning capabilities and robotic systems contribute to supply chain performance improvements for making predictions and handling big data. Predicting the future with advanced analytics can tell you about supply chain constraints and even better, tell you what level of inventory to maintain to keep your downtime to a minimum, thus improving productivity. Supplies can be matched with current demand. AI technology is still developing. Challenges with both the implementation of it and ethical issues have to be resolved in order to fully capitalise on technology enabled supply chains (van Hoek et al., 2021 ). Answering these vital questions related to the impact of AI on supply chain management would reveal what techniques are in use, what k-factors there are for monitoring quality and productivity and what the hard figures were with regard to changes occurred by employing AI (Waller & Fawcett, 2013 ; Ghobakhloo, 2020 ). The study strives to provide proof of strategic AI's contribution for shortening the distance between customers and suppliers in terms of time and cost, reducing customer complaints and analysing potential issues that might arise due to AI implementation in supply chain management context. It measures the current supply chain processes and quality benchmarks, and assesses AI capabilities to enhance productivity by utilising predictive analytics and workflow automation (Ben-Daya et al., 2019 ; Ghobakhloo, 2020 ). It further discusses machine learning, computer vision and natural language processing as AI techniques applicable to supply chains (Ivanov et al., 2019 ; Waller & Fawcett, 2013 ). An AI integration framework and challenges for such implementations are discussed in this study (Ghosh, 2021; Dubey et al., 2019 ). The contribution of this paper is to address this lack by providing an empirical account of the impact from AI on supply chain activities. Contribution to knowledge, this study makes contribution to the supply chain management discipline by helping practitioners understand how AI solutions can be incorporated effectively in view of organizational goals (Christopher, 2016 ; Chopra & Meindl, 2016 ). This paper also aims to record what is best practice and grow business confidence in addressing the challenges of modern supply chains. The research is organized in five chapters, in section 1, starting with an introduction that articulates the problem and sets the objectives. In the section 2, a literature review on AI in supply chain management is presented. In Section 3 we have addressed the method part, that is how we collected language information and how it was analysed. The findings and its implications are discussed in section 4, and the research concludes with a few recommendations and possible future research direction in section 5. 2. Literature Review 2.1 Evolution of Supply Chain Management Theories The development of SCM models has been modified by technological developments worldwide. Previous models such as the Economic Order Quantity addressed only those costs that concerned with ordering and holding (Hopp & Spearman, 2011). The advent of global sourcing caused Material Requirements Planning (MRP) systems to connect scheduling with inventory control, unifying disparate company functions and leading to the development of ERP systems (Chopra & Meindl, 2016 ), which integrated different corporate activities and paved the way for ERP systems to emerge (Stevens, 1989). Facilitating greater degree of supplier involvement and real-time data, it was later refined as Just-In-Time (Ohno, 1988). The Just-In-Time (JIT) framework produced minimal wastage as the production schedules were in sync with the requirement. Subsequently, effort was refocused from SCM strategies being cantered on inventory to SCM strategies being cantered around delivering efficient processes. The introduction of Supply Chain Operation References (SCOR) models by the Supply Chain Council was a further advancement, standardizing supply chain processes and metrics set by APICS (Stewart 1997, 2017). The use of SCOR along with AI and machine learning enabled better methodology to be developed in planning, forecasting and decision making through predictive analytics. (Chopra & Meindl, 2016 , Brynjolfsson & McAfee, 2014). All these developments enabled competition between SCM whilst maintaining a level of dynamism. (Slack et al. 2020, Christopher 2016 ). 2.2 AI in Supply Chain Management AI has transformed supply chain activities and processes by increasing the speed and efficiency of the operations of logistics companies. With the help of AI, firms can optimize their routes and, therefore, decrease the cost of delivered service and the distance traveled by their vehicles by using weather information and current traffic levels (Ivanov & Dolgui, 2020 ). Machine learning makes it possible to implement dynamic pricing transport models, which use algorithms and respond automatically to shifts within the market (Wamba et al., 2017 ). With the help of AI, automation is also dramatically changing how we manage our warehouses, because the robots are now picking, packing and sorting all that stuff. It is done with a speed that the human being components can't imitate (Ivanov & Dolgui, 2020 ). Using an AI to generate movement plan has also been successful for increasing productivity by saving time allowing non-productive transport (Choi et al., 2019 ). Further, inventories can be more conveniently and efficiently tracked based upon their security (Baryannis et al., 2020).AI is also crucial for risk management of supply chains whereby AI through big data analytics is used to proactively detect risks such as supplier defaults or geopolitical issues (Monostori et al., 2020 ). Provision of machine learning assistance is another potential benefit for enhancing contingency planning through identifying supply chain vulnerabilities (van Hoek et al., 2021 ).AI communications help solve real-time problems more quickly (Wamba et al., 2017 ). Chatbots are a powerful example of how AI can be used to enhance customer service, with the ability to speak to customers using natural language processing (Choi et al., 2019 ). Businesses can use AI to make better sense of customer feedback data to inform advanced segmentation leading to higher retention (Snyder et al., 2020 ). For sustainability, AI optimizes resources such as fuel in logistics and stimulates recycling through predicting maintenance (Baryannis et al., 2019 ; Kumar et al., 2021 ). Artificial intelligence (AI) has changed the practices of purchasing management by improving both supplier selection and negotiation processes with predictive analytics over performance and market pricing (Choi et al., 2019 ; Ivanov & Dolgui, 2020 ). AI also recognises operational fraud, which reduces operating risks (van Hoek et al., 2021 ). Supply chain planning collaboration has been enhanced by 'real time' data-sharing that also is transparent (Monostori et al., 2020 ). Even with the benefits presented by AI, scepticism over such implementations still exists preventing wider access among, in particular, small sized firms due to costs associated with privacy, ethics, and technology (Baryannis et al., 2019 ; Christopher & Holweg, 2017 ). These concerns call for balanced governance as well as training of employees (van Hoek et al., 2021 ). The industrial and academic sectors, along with the government, will expedite the process of integrating AI into supply chain management and, in turn, will ensure that its benefits are fully actualized. 2.3 Quality and Productivity Metrics in Supply Chains Supply chains are evaluated on productivity and quality. Quality indicates how a product or service meets set standards and customer expectations, as defined by Carter and Rogers (2020) while productivity shows how efficient resources are as compared to the output produced (Harrison et al., 2019). These norms and standards are very important for controlling costs and achieving satisfaction as well as operational efficiency (Bowersox, et al, 2018). Quality is determined through measurement of product defects, various compliances and service level agreements while productivity means output per labor hour, inventory turnover and utilization (Zhu et al., 2021). These norms highlight gaps for improvement and maximizing efficiency (Kuei et al., 2020). Labor productivity and material efficiency ratios are productivity ratios that help a firm reduce the resources it utilizes, thus increasing profitability (Bowersox et al., 2018). Important metrics are acquisition efficiency, inventory turnover, production throughput, and cycle time reduction (Kuei et al., 2020) since shorter cycle times mean faster responses from customers (Gandhi et al., 2019). Resource management improves performance based on saving waste and cost (Carter & Rogers, 2020). Frameworks such as the ISO 9001:2015 for quality management systems and ISO 14001:2015 for environmental management are recognized around the world, and they serve as a metric system for standardization (Meyer et al., 2021). Businesses can set performance targets and recognize potential areas for enhancements owing to key performance indicators (KPIs) such as first-pass yield and client satisfaction for quality and production efficiency for productivity (Gandhi et al., 2019; Zhu et al., 2021). 2.4 Existing Challenges in Supply Chains: Current Limitations and Inefficiencies Technological improvements in supply chains come with challenges that compromise performance. The most common challenge is the lack of real-time data which leads to stockouts, delays, over-stocking and other problems (Christopher, 2016 ; Harrison et al., 2019). The global markets constantly change, and traditional systems have left organizations poorly placed to be agile (Harrison et al., 2019). A lack of integrated suppliers, producers and distributors impedes accurate forecasting and disruption mitigation by limiting communication (Kuei et al., 2020; Bowersox et al., 2018). It is like an earthquake or pandemic in the supply chain they serve to expose rather than be the cause of vulnerability. The outbreak of the COVID-19 pandemic was a case in point, and some global supply chain showed its fragility with acute shortages and delays (Zhu et al., 2021). Several of these are ill-prepared with appropriate back-up and risk mitigation plans, thereby exposing themselves to potential interruptions (Bowersox et al., 2018). Inefficiency, loss of sales and excess inventory are accompanied with the other modes because no future prediction is achieved, poor intermediating policies. (Kuei et al., 2020; Christopher, 2016 ). Global distribution networks are hampered by regulatory, cultural, and logistical complexities that render coordination difficult (Kuei et al., 2020). Operations are further complicated by political unrest, tariffs, and changes in currency value (Harrison et al., 2019). The cost and time repercussions are also compounded by shortage of labour available for warehouse management and logistics (Aitken et al., 2021). Robotics and automation are another solution, but rather significant investment is required (Kuei et al., 2020). Two ways that are equally harmful, one is ddisintegration technologically and cyber insecurities. Inefficient for most supply chains is older systems are not cross- compatible (Gandhi et al., 2019). Digital supply chains also raise the risk of cyber warfare, and therefore most companies have insufficient resources to defend themselves from this (Aitken et al., 2021). To address this concern, investment is required in cybersecurity and system integration as well as effective technology solutions (Kuei et al., 2020). 2.5 Analysis of Successful AI Implementations in Supply Chains Supply chain operations including demand forecast, inventory management, route optimization, predictive maintenance has been greatly enhanced by AI (Hofmann & Rusch, 2021). For example, corporates such as Amazon and Walmart have used AI in their operations to enhance productivity, reduce cost and improve the level of services (Wamba et al., 2017 ). The COVID-19 Pandemic AI applications have also been used to alleviate supply chain disruptions during the pandemic and still addressing the root causes of issues such as demand fluctuation and logistics (Soni et al., 2020). AI Application in Forecasting for Sustainability: Walmart uses AI in demand forecasting as part of inventory optimization, resulting in favourable environmental and economic consequences (Soni et al., 2020). AI and robots are used in the amazon filament centres to optimize inventory and minimize lead times (Hofmann & Rusch, 2021). AI also enables predictive maintenance aiming at decreasing the production downtime of equipment installed in production businesses (Hofmann & Rusch, 2021) and it has impacted customer service through improved ordered processing and customer relationship management (Wamba et al., 2017 ). Choi & Cheng (2020) claim that there are corporations, including Target and Tesco who have been able to utilize AI tools to better control inventory levels and decrease the amount of unsold stockpiles. AI in addition enhances strategic sourcing by predicting supplier performance and thus increasing its accuracy as well as saving time and money in the sourcing process (Kamble et al., 2020). However, the practical implementation of AI technologies is limited by infrastructure, data quality and skills required (Soni et al., 2020). Moreover, complexity of the systems and change inertia are additional reasons for implementation challenges (Choi & Cheng, 2020). Successful application of AI in real-world situations generally requires longitudinal studies and regular review, pilot initiatives, and continual adaption of the AI systems to a cultural setting (Kamble et al., 2020). 3. Methodology A quantitative approach is employed in this study because it matches the research extent, as AI applications within supply chains are at focus and according to them numerical data could be gathered and analysed statistically to analyse trends, patterns, and relationships. The emphasis was on how to measure the effectiveness of AI in addition identify which ways best ensure that AI is serving ssupply chain well. The research was performed through structured tools of data gathering like questionnaires with different subjects from the supply chain managers to AI professionals and also other profile managers. This gave us a chance to gather information from the individuals working in AI-enabled supply chain functions. 3.1 Data Collection Instruments The research used instruments like surveys and questionnaires with open-ended items to elicit responses in quantitative form as well as those that were closed ended statements. Assorted AI impacts to supply chain sections, such as logistics customer service and inventory control, were explored through closed-ended questions using a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree). When seeking to understand effectively the challenges, experiences, and best practices regarding AI use, the questions were crafted in a manner that transformed the quantitative data into context rich qualitative form. The questions were also adjusted to combine validated instruments from previously conducted studies and adaptations were made for (Kamble et al., 2020; Jeble et al. 2018). These studies had been done in earlier years, so AI-related concerns in secondary constructions and in supply chains is valid. A pilot survey was given to 10 respondents from varying industries to test survey designs, language, or general understandability. Feedback from the pilot allowed changes to be made to improve the questions provided. 3.2 Sample Size and Justification The targeted number of participating firms was 100, it was a sample size and power calculation. Power of the sample size was computed at p = 0.05 level of significant for moderate effect in the regression analyses was 0.80. This size was sufficient in accordance with Kamble et al. (2020) and other works in the domain which were based on datasets of equivalent sizes for investigation of AI integration with the supply chain. The 100 responding companies that were sent this study represent a large enough sample to ensure that the results can be generalized, and are ample despite the many obstacles encountered in collecting data from professionals with industry expertise. The sample was large enough to obtain a sense of the trend in AI adoption and its impact across industry. 3.3 Sampling Technique A stratified random sampling technique was applied to capture technically competent respondents from different fields such as retailing, manufacturing, logistics, healthcare, and automotive industries. This facilitated capturing possible variation in AI adoption and its impacts by industry. Participating firms in the study were needed to have used AI technologies within their supply chains for more than use six months before the data collection to ensure adequate involvement of respondents with AI technologies usage. As a step towards the achievement of these objectives, data was gathered through online surveys which were disseminated using professional platforms like LinkedIn and other forums specific to the field. This made it possible to target practitioners who actively participate in AI implementation. To further reduce selection bias, stratified sampling was done by firm size, sector, and geographic location to ensure fair representation within the dataset. 3.4 Bias Mitigation There were several actions taken to manage possible bias in data collection. Respondents of surveys tend to be biased especially when they are assessing the outcomes of projects they participated in. To manage this bias, all responses were collected using anonymous at the onset, and, as such, participants were facilitated to give feedback without any motivation of caring for personal consequences. In addition, no identifying information was obtained, and participants were informed that their responses would be kept confidential. To balance professional demographics of respondents, the study focused on senior level and strategic level including directors as well as operational level such as managers. This strategy enabled the researcher to understand how AI is embraced at various levels in the organizational structure. Data triangulation was also carried out by comparing information obtained from the surveys with secondary information from industry reports and scientific research. This assisted in improving the validity of the results and reducing bias from any one data source. 3.5 Data Analysis The analysis was split into descriptive statistics which were used to summarize the data and inferential statistics where deductions were made based on the data. As part of the regression analysis, AI adoption was quantitatively linked with operational performance as well as effectiveness of supply chain processes. Before running the regression analysis, both statistical normality and multicollinearity (and also homoscedasticity) were verified in order to secure valid results. Effect sizes and confidence intervals rather than p values were also used to indicate practical significance. Furthermore, advanced simulations and machine learning-based pattern recognition were used to test future scenarios for integrating AI into supply chains. These analyses predicted how AI would help in improving operational efficiency, cost savings and risk management providing a peep-hole into what is possible with AI in supply chain optimization. 3.6 Ethical Considerations This study involved anonymous, non-interventional survey-based data collection from adult professionals working in supply chain and AI-related roles. Ethical approval from an institutional review board or ethics committee was not required because participation was voluntary and no personal, sensitive, or identifiable information was collected. All participants were informed about the purpose of the study and provided informed consent before completing the survey. Data were anonymized prior to analysis and reported only in aggregated form. 4. Results From Table 1 , the percentage of females and males represented by respondents is 59.5% and 40.5%, respectively, which are consistent with gender balance ratios in supply chain diversity that researchers have established (Rossetti & Dooley, 2018). There were 53% of them hold a master's degree, 24.5% are high-school graduates and only 4.5% have doctorate degrees. This was consistent with claims that higher education predominates among SCM practitioners (Ellram & Cooper, 2014 ). By age, the group 31–40 years dominates at 47% followed by under 20 at 21.5% with only 2% above 50 signifying an industry tilt toward young professionals, as observed by McCrea (2020). On the work experience, majority lies in the bounds of 11–15 year (25.5%) and 6–10 year (21.5%) with the above suggesting that it has affected mid-career professionals greatly on AI adoption as stated by Christopher and Peck ( 2012 ). Among the industries represented, logistics account for 29%, agriculture for 23%, and deferring to a lesser degree food and beverage with the least at 1.5%, and it reflects current trends in adopting AI in logistics and agriculture (Ivanov & Dolgui, 2020 ). The companies employing about 151–500 employees are the dominant ones (39.5%), against 29% of companies with employees over 1000, which is consistent with mid-sized firms that adopt AI for scaling purposes (Ketchen & Hult, 2007 ). Customer service applications are ahead at 33%, followed by inventory management at 23% while predictive maintenance is bottom at 4%, supported by studies on prioritizations in adopting AI. Directors and supervisors thus constitute the most respondents, at 45.5% and 34.5% respectively, with just about 1% being employees. This provides evidence that AI adoption is often a function of such strategic roles (Tjahjono et al., 2017 ). Table 1 Demographic distribution of Respondents Variable Category Frequency (f) Percentage (%) Gender Female 119 59.5 Male 81 40.5 Total 200 100.0 Educational Level Bachelor 27 13.5 Master 106 53.0 Doctorate 9 4.5 High School 49 24.5 No School 9 4.5 Total 200 100.0 Age (Years) Below 20 43 21.5 21–30 36 18.0 31–40 94 47.0 41–50 23 11.5 Over 50 4 2.0 Total 200 100.0 Work Experience (Years) Less than 5 32 16.0 6–10 43 21.5 11–15 51 25.5 16–20 33 16.5 More than 20 41 20.5 Total 200 100.0 Industry Retail 18 9.0 Manufacturing 25 12.5 Logistics 58 29.0 Healthcare 40 20.0 Automotive 10 5.0 Agriculture 46 23.0 Food & Beverage 3 1.5 Total 200 100.0 Professional Experience Less than 2 years 20 10.0 2–5 years 29 14.5 6–10 years 67 33.5 11–20 years 57 28.5 More than 20 years 27 13.5 Total 200 100.0 Firm Size Less than 50 20 10.0 51–150 28 14.0 151–500 79 39.5 501–1000 15 7.5 Above 1000 58 29.0 Total 200 100.0 Area of Supply Chain Integration Demand Forecasting 20 10.0 Inventory Management 46 23.0 Logistics & Delivery Optimization 26 13.0 Production Scheduling 35 18.0 Predictive Maintenance 8 4.0 Customer Service (Chatbots) 65 33.0 Total 200 100.0 Designation Employee 2 1.0 Mid-level 11 5.5 Management 27 13.5 Supervisors 69 34.5 Directors 91 45.5 Total 200 100.0 Source: Authors computation using SPSS Apart from the Table 1 , we have a graphic display of demographic breakdown of respondents in terms of gender, education, and work experience in Fig. 1 . This figure emphasises that the majority of respondents are master degree holders with 5–10 years of working experience, gender distribution is also well balanced. Figure 1 illustrates the demographic distribution of respondents. It shows the breakdown by gender, educational level, age group, work experience, firm size, industry sector, supply chain integration area, and job designation. Overall, most respondents hold a Master’s degree, fall within the 31–40 age group, have 6–10 years of work experience, and are primarily engaged in logistics and customer service–related supply chain activities, with a higher representation at supervisory and director levels. Table 2 AI Technique, Areas of Implementation and Measurable Improvement in SC AI Technology Adopted Mean SD We have incorporated chatbots or AI-based virtual assistants for customer service 4.185 1.07 We utilize Internet of Things (IoT) devices for real-time supply chain monitoring. 4.135 1.128 Our company uses AI-driven tools for route optimization in logistics and delivery 4.123 1.051 We use predictive analytics to improve demand forecasting and inventory management 4.085 1.078 Our company has integrated Robotics Process Automation (RPA) for process optimization 3.853 1.159 We have implemented machine learning models in our supply chain operations. 3.695 1.165 Artificial Neural Networks are deployed in our supply chain decision-making processes 3.569 1.152 Areas of Implementation of AI Mean SD AI has been integrated into our demand forecasting processes. 4.36 0.96 AI is actively used in inventory management and optimization in our supply chain 4.34 0.94 We use AI for optimizing logistics routes and improving transportation efficiency. 4.29 1.12 Our production scheduling processes are improved through AI-based solutions 4.28 0.93 Predictive maintenance of machinery and equipment in our supply chain is powered by AI 4.28 1.09 AI is used in our supplier relationship management and procurement activities. 4.28 1.02 AI tools are deployed in customer service for order tracking and queries 4.26 1.07 Measurable Improvements from AI Implementation Mean SD AI has helped reduce operational costs in our supply chain. 4.26 1.02 The speed of order fulfillment has increased due to AI integration. 4.21 1.18 Inventory accuracy has improved as a result of AI-based inventory management tools. 4.19 1.21 AI has contributed to better demand forecasting and fewer stockouts. 4.23 1.08 Customer satisfaction has improved as a result of AI-enhanced customer service 4.23 1.16 AI has led to improved decision-making in our supply chain operations. 4.23 1.05 The overall efficiency of our supply chain has significantly improved due to AI. 4.21 1.03 Source: Authors computation using SPSS Table 2 includes descriptive statistics for AI technology adoption, application domains and tangible improvements in supply chain operations measured on a 5-point Likert scale with high mean values indicating higher level of agreement about the incorporation and influence of AI technologies. For AI technology use, the chatbots and virtual assistants to provide contact centre services received the highest mean score (4.185, SD = 1.07) on aggregate of their big usage in enhancing faster and more efficient communication and service. It should be noted that the real-time behaviour of IoT, which is a pattern heavily promoted and applied in the logistics ecosystem, has also been highly considered by researchers (Ivanov & Dolgui, 2020 ), reflected as two next most important patterns: IoT devices for real-time monitoring (Mean = 4.135, SD = 1.128) and AI tools for optimization of logistic routes (Mean = 4.123, SD = 1.051).For prediction of the accuracy in planning, demand forecasting and inventory management by predictive analytics achieved a mean score of 4.085 (SD = 1.078). Robotics Process Automation (RPA) (Mean = 3.853, SD = 1.159), machine learning models (Mean = 3.695, SD = 1.165), and Artificial Neural Networks (Mean = 3.569, SD = 1.152) display moderate levels of adoption, suggesting that they may be further integrated in the next generations of applications. In applications satisfaction scale, the highest place is dedicated to demand forecasting (Mean = 4.36, SD = 0.96) followed by inventory management (Mean = 4.34, SD = 0.94), overall logistics optimization (Mean = 4.29, SD = 1.12). This aspect complements where AI is directly applicable in the planning and resource allocation model dimension (Rossetti & Dooley, 2018 ). Production scheduling, predictive maintenance and supplier relationship management all scored 4.28, indicating a consistent adoption in these areas. AI customers service tools increase on efficiency and customer satisfaction, with 4.26 (SD = 1.07), in line with those of automation and a customer centric supply chain. On the measurable benefits, AI contributed to reduced operational costs reproachfully (Mean = 4.26, SD = 1.02) and increased customer satisfaction (Mean = 4.23, SD = 1.16). Better decision-making (Mean = 4.23, SD = 1.05) and improved demand forecasting (Mean = 4.23, SD = 1.08) prove predictive efficacy of AI. Increasing speed of order fulfilment (Mean = 4.21, SD = 1.18), inventory accuracy (Mean = 4.19, SD = 1.21), and overall efficiency (Mean = 4.21, SD = 1.03) shows altogether the transformative capacity of AI in the industry supply chain operations (Tjahjono et al., 2017 ). Table 3 Challenges Faced During the Integration of AI We encountered significant technological limitations when implementing AI in SC Mean SD 4.29 0.97 There was a lack of skilled personnel to manage and deploy AI technologies. 4.28 0.99 Our company struggled with poor data quality, which affected AI effectiveness. 4.23 1.08 The initial costs of implementing AI were higher than expected. 4.23 1.16 There was resistance from employees due to concerns about job automation 4.23 1.05 Integrating AI with existing legacy systems posed a significant challenge. 4.21 1.03 We faced difficulties in securing management buy-in for AI adoption. 4.09 1.05 The results of Table 3 indicate the challenges involved in adopting AI technology in supply chain management. The resulting challenges that were most rated in the 5-point Likert scale were enormous technological limitations of the organization (M = 4.29, SD = 0.97) followed closely by the lack of skilled personnel to manage and implement AI programs (M = 4.28, SD = 0.99). Poor data quality (M = 4.23, SD = 1.08), high initial costs to implement AI technologies (M = 4.23, SD = 1.16), and employee resistance of because of job concerns due to automation (M = 4.23, SD = 1.05) also identified as serious barriers. There are other significant challenges as follows: lack of integration of AI with current legacy systems (M = 4.21, SD = 1.03), lack of top management buy-in (M = 4.09, SD = 1.05). All these challenges further amplify the complexion of incorporating AI technologies. Table 4 Future Expectations and Long -term Impacts of AI on Supply Chain Future Expectations and Outlook Mean SD We expect AI to play a larger role in our supply chain operations in the next 5 years. 4.34 0.93 AI will enable our supply chain to achieve real-time data insights in the future 4.28 1.07 AI will lead to significant improvements in supply chain collaboration with stakeholders 4.28 0.95 We foresee AI optimizing end-to-end supply chain processes more effectively in the future 4.28 0.97 We expect AI to enhance the customer experience and delivery times significantly in future. 4.27 0.95 AI will help us in predictive maintenance, reducing downtime and increasing longevity. 4.26 1.2 AI is expected to offer more comprehensive solutions that integrate various supply SC task 4.26 0.93 Long-Term Impact of AI on Supply Chain Jobs Mean SD AI adoption will lead to the creation of new roles and job opportunities in the supply chain 4.26 1.03 AI will result in job displacement in certain supply chain functions. 4.23 1.06 AI will enhance human roles by automating repetitive tasks, focusing on strategic work. 4.22 1.02 Our workforce is ready to adapt to the changes AI will bring in the supply chain 4.28 0.95 AI will increase demand for highly skilled professionals in supply chain management. 4.26 0.93 AI will automate decision-making, reducing human intervention in certain tasks. 4.26 1.03 The adoption of AI will require retraining of employees to work with new technologies in SC 4.27 0.97 Source: Authors computation using SPSS 26 Note Loads = Standardized loadings; CA = Cronbach’s alpha Table 4 shows the future expectations and effects of AI on supply chains judged through a 5-point Likert scale, such that 5 means "Strongly Agree," while 1 means "Strongly Disagree." The mean is an aggregated response to its degree of agreement, while the standard deviation (SD) shows any deviation among them. Respondents expressed a strong expectation that AI would play an enhanced role in supply chain operations in the next five years (M = 4.34, SD = 0.93), with further forward enhancement in real-time data insights (M = 4.28, SD = 1.07), collaboration within the supply chain (M = 4.28, SD = 0.95), and optimization of end-to-end processes (M = 4.28, SD = 0.97). Furthermore, enhancement in customer experience and the delivery time is also expected (M = 4.27, SD = 0.95). Predictive maintenance is expected to be supported by AI (M = 4.26, SD = 1.2), along with integrated solutions for supply chain tasks (M = 4.26, SD = 0.93). AI is perceived as both disruptor and enabler when it comes to long-term job impacts. On the one hand, they believe that new jobs and employment opportunities will be created (M = 4.26, SD = 1.03) while enhancing the human job with the automation of most repetitive tasks (M = 4.22, SD = 1.02). But on the other hand, they acknowledge possible job losses for certain functions (M = 4.23, SD = 1.06). The workforce is also indicated as being ready to adapt to this new change (M = 4.28, SD = 0.95). In its stances, it raises the stake for high skill input (M = 4.26, SD = 0.93). Two replies also mention the need to retrain workers (M = 4.27, SD = 0.97), saying that the automation of decision-making by AI would result in fewer human involvement in some tasks (M = 4.26, SD = 1.03). Table 5 Properties of measures (convergent validity and reliability) Construct Item Loads Mean SDev CA AVE CR Productivity Process Automation: AI handles repetitive tasks for efficiency 0.869 4.350 1.050 0.910 0.653 0.882 Workflow Optimization: AI identifies bottlenecks and improves processes 0.762 4.340 1.000 Quality AI Inspections: Computer vision detects defects in products. 0.672 4.280 1.050 0.898 0.609 0.861 Predictive Analytics: AI predicts quality issues and suggests fixes 0.684 4.290 0.970 Inventory Management Real-Time Tracking: AI monitors inventory levels and triggers automatic restocking. 0.586 4.430 0.950 0.880 0.604 0.859 Demand-Supply Balancing: ML aligns inventory with projected demand. 0.887 4.300 1.130 Logistic and Delivery Route Optimization: AI finds efficient delivery paths. 0.869 3.850 1.160 0.777 0.548 0.644 Autonomous Vehicles: AI powers drones and driverless trucks 0.762 4.140 1.130 Production Scheduling Resource Allocation: AI optimizes scheduling for better efficiency. 0.887 4.260 1.020 0.850 0.662 0.765 Dynamic Updates: AI adjusts schedules to meet demand changes. 0.862 4.280 1.020 Predictive Maintenance Equipment Monitoring: AI predicts machine failures via IoT data. 0.826 4.090 1.050 0.824 0.733 0.723 Fault Detection: AI identifies anomalies to prevent downtime. 0.855 4.230 1.050 Demand Forecasting Market Predictions: AI forecasts demand using historical data 0.869 4.280 1.070 0.806 0.528 0.769 Dynamic Updates: ML adjusts forecasts based on real-time changes. 0.855 4.270 0.950 Decision Making Data Insights: AI provides actionable supply chain recommendations. 0.869 4.280 0.950 0.846 0.533 0.769 Scenario Simulation: AI predicts outcomes of supply chain decisions. 0.855 4.260 0.930 Table 5 displays the results of analysis which was done to establish validity of the scales used in the study. The results from the study's scales convergent validity and reliability. It was surveyed using Cronbach alphas subdivided along each item’s sum score. It was established what the alpha value was which indicated the dependability of the survey. This study found that the Cronbach alpha score for all of the individual measures and for the combined scales was over 0.7, which indicates survey reliability. This is valid and demonstrates how reliable the scale is. In addition, the standardized loadings of all of the variables behaved properly. All of the variables in this study had the adequate composite reliability which was greater than 0.6. When reporting composite reliability, a minimum acceptable value of 0.6 is set. An acceptable value for Average Variance Extracted (AVE) is 0.5 or above. The result shows an AVE that is greater than 5 for all the constructs. Table 6 Discriminant and Convergent Validity Item Correlations Qua1 Qua2 Prod1 Prod2 Inven1 Inven2 Log1 Log2 PSch1 Psch2 PreM1 PreM2 Defo1 Defo2 DeMa1 DeMa2 Qua1 1 0.55 0.54 -0.03 0.06 0.06 0.06 0.11 0.04 0.08 0.08 -0.12 -0.11 -0.03 -0.01 0.10 Qua2 0.55 1.00 0.48 0.07 0.04 0.03 0.02 0.04 0.05 0.03 0.08 -0.07 -0.08 -0.03 0.05 0.05 Prod1 0.54 0.48 1 0.01 0.06 0.08 0.20 0.07 0.07 0.03 0.09 -0.09 -0.06 0.02 0.06 0.06 Prod2 -0.03 0.07 0.01 1 0.63 0.63 0.24 0.59 0.65 0.57 0.65 0.49 0.30 0.38 0.23 -0.08 Inven1 0.06 0.04 0.06 0.63 1 0.70 0.13 0.22 0.69 0.90 0.72 0.47 0.27 0.24 0.28 -0.15 Inven2 0.06 0.03 0.08 0.63 0.70 1 0.16 0.16 0.85 0.69 0.88 0.61 0.36 0.34 0.25 -0.08 Log1 0.06 0.02 0.20 0.74 0.53 0.56 1 0.57 0.59 0.49 0.61 0.46 0.29 0.37 0.19 -0.02 Log2 0.11 0.04 0.07 0.59 0.92 0.66 0.57 1 0.69 0.27 0.19 0.50 0.25 0.26 0.31 -0.13 PSch1 0.04 0.05 0.07 0.65 0.69 0.85 0.59 0.69 1 0.28 0.15 0.56 0.28 0.31 0.21 -0.06 PSch2 0.08 0.03 0.03 0.57 0.90 0.69 0.49 0.27 0.68 1 0.70 0.47 0.29 0.29 0.28 -0.13 PreM1 0.08 0.08 0.09 0.65 0.72 0.88 0.21 0.39 0.85 0.70 1 0.60 0.35 0.33 0.26 -0.06 PreM2 -0.12 -0.07 -0.09 0.49 0.47 0.61 0.46 0.10 0.56 0.47 0.60 1 0.51 0.52 0.44 -0.08 Defo1 -0.11 -0.08 -0.06 0.30 0.27 0.36 0.29 0.25 0.28 0.29 0.35 0.51 1 0.72 0.63 -0.10 Defo2 -0.03 -0.03 0.02 0.38 0.24 0.34 0.37 0.26 0.31 0.29 0.33 0.52 0.72 1 0.60 -0.15 DeMa1 -0.01 0.05 0.06 0.23 0.28 0.25 0.19 0.31 0.21 0.28 0.26 0.44 0.63 0.60 1 -0.13 DeMa2 0.10 0.05 0.06 -0.08 -0.15 -0.08 -0.02 -0.13 -0.06 -0.13 -0.06 -0.08 -0.10 -0.15 -0.13 1 Note: Keys: Qua - Quality, Prod - Productivity, Inven - Inventory Management, Log - Logistics and Delivery, PSch - Product Scheduling, PreM - Preventive Maintenance, Defo - Demand Forecasting, DeMa - Decision In Table 6 , multiple scales were utilized to evaluate distinct constructs, with each scale consisting of two items. Specifically, the Quality Scale included Qua1 and Qua2, the Productivity Scale included Prod1 and Prod2, the Inventory Management Scale included Inven1 and Inven2, the Logistics and Delivery Scale included Log1 and Log2, the Product Scheduling Scale included PSch1 and PSch2, the Preventive Maintenance Scale included PreM1 and PreM2, the Demand Forecasting Scale included Defo1 and Defo2, and the Decision-Making Scale included DeMa1 and DeMa2. The hypothesis was that items within the same scale (subscales) would exhibit strong correlations, while their correlations with items from other scales would be weak. This pattern would provide evidence of discriminant and convergent validity. Results from Table 6 confirmed this hypothesis. Subscales demonstrated high correlations within their respective scales (e.g., Qua1 and Qua2 had a correlation of 0.55), while their correlations with items from other scales were weaker (e.g., Qua1 and Prod1 had a correlation of 0.54, and Qua1 and Inven1 had a correlation of 0.06). These findings provide strong evidence of good construct validity for the scales. The results in Table 7 analyze the effects of applying artificial intelligence (AI) on quality and productivity in the supply chain. For quality, inventory management had a small but statistically significant positive relationship with a coefficient of 0.0829 (p = 0.001), indicating that while the effect is not strong, it is significant enough to appreciate improvement in quality in the supply chain. Logistics and delivery showed the most significant effect on quality with a coefficient of 4.3244 (p < 0.001), underlining it as an important variable in improving operational outcomes. Furthermore, production scheduling had a statistically significant effect on the quality with its coefficient value being 1.1055 (p = 0.012), demonstrating its significance in optimizing process. The common result for quality was presented on the basis of a coefficient value 0.3874 (p = 0.037) meaning the value for system network reliability and uptime. Surprisingly, though demand forecasting had a negative effect on quality (coefficient = − 0.6351, p = 1.000). The low and negative correlation between demand forecasting and quality, (coefficient = -0.6351, p = 1.000) raise a number of questions on the relationship between these variables that may be attributed to multiple reasons including dependence of demand forecasting from high quality real-time data, AI system hardware limitations, context-specific challenges such as being in an environment with little market volatility or no customer behavioral variabilities or confounding factors like operational deficiencies or personnel-related issues. Lastly, there was a high coefficient of 4.9813 for decision-making but no statistical significance in quality outcomes as determined by p-value (p = .831). In terms of productivity, inventory management was also statistically insignificant with a very small positive coefficient 0.1320 (p = 0.695) (Table 7 ), suggesting a low impact. Conversely, logistics and delivery (p = 0.001) turned out to be a major positive predictor of performance that improved agility of delivery systems and decreases cost, with a coefficient of 0.3605 supporting the proposition that this variable enhances effectiveness through efficient delivery and reducing costs. Production scheduling also influenced productivity but was associated with a small coefficient of 0.1364 (p = 0.003). The effect of predictive maintenance was minimal (coefficient = 0.0261, p = 0.056), thus almost negligible. Predictor demand was positively co-efficient but not statistically significant at 0.3346 (p = 0.328), so it would not be expected to have a meaningful impact on performance. Productivity was strongly influenced by decision-making (coefficient = 0.8654; significant at p < 0.001) illustrating the importance of this process in resource allocation and strategic management. 4.1 Statistical and Practical Significance While statistical significance indicates that an observed effect is unlikely due to chance, practical significance reflects its real-world relevance. This study identifies several findings with both statistical and practical importance. Notably, logistics and delivery cost minimization had a strong positive effect on quality (coefficient = 4.3244, p < 0.001) and productivity (coefficient = 0.3605, p = 0.001). This aligns with real-world applications where AI-driven route optimization and predictive logistics reduce delivery time, fuel consumption, and operational errors, thereby enhancing competitiveness and customer satisfaction. Decision-making optimization also showed a highly significant positive impact on productivity (coefficient = 0.8654, p < 0.001), highlighting the effectiveness of AI in resource allocation and strategic planning. In contrast, predictive maintenance demonstrated a statistically significant but modest effect on quality (coefficient = 0.3874, p = 0.037), suggesting limited practical influence. Demand forecasting showed no significant impact on quality (coefficient = 0.6351, p = 1.000), indicating potential limitations in current AI implementations. Overall, these findings suggest that firms should prioritize AI applications with strong practical benefits sparticularly logistics optimization and decision-support systems while future research should assess their scalability and cost-effectiveness to support managerial decision-making. Table 7 The Effect of Integrating AI on Quality and Productivity Quality Coef. Std. Error z P > lzl Inventory Mgt 0.0829 0.0952 -0.8700 0.0010 Logistic and Delivery 4.3244 0.8126 8.2700 0.0000 Production Scheduling 1.1055 0.1194 0.8800 0.0120 Predictive Maintenance 0.3874 0.1429 2.7100 0.0370 Demand Forecasting -0.6351 0.0926 -0.3812 1.0000 Decision Making 4.9813 1.2274 0. 3467 0.8310 Productivity Inventory Mgt 0.1320 0.6881 1.5000 0.6950 Logistic and Delivery 0.3605 0.1113 3.2400 0.0010 Production Scheduling 0.1364 0.1165 0. 1286 0.0030 Predictive Maintenance 0.0261 0.1 13 7.7500 0.0560 Demand Forecasting 0.3346 0.6857 3.8700 0.3280 Decision Making 0.8654 1.1366 1.9100 0.0000 Source: Authors computation using SPSS 26 This study supports the work of previous studies which identified the potentiality of AI to revolutionize all active supply chain’s part. As highlighted in the study by Wu and Yue ( 2019 ), logistics and delivery optimization is a critical domain where AI represents an important technology enhancing the effectiveness of operations, therefore the large beta coefficients found here, as well as their levels of significance correspond fitly unto that. For instance, Ivanov and Dolgui ( 2020 ) stress the contribution of AI in decision-making processes that support weighty influences of this feature on the productivity in our analysis. Nevertheless, findings also differ from those showing forecast demand being significant as in Choi et al. (2021) who report a dominant role with respect to how uncertainty in the supply chain is managed. These differences may be a result of varying quality levels of data or where in the lifecycle artificial intelligence deployment is at. However, the results confirm that AI could transform supply chains especially in logistics, production scheduling and decision-making. 5. Discussion This paper investigated the incorporation of AI technologies in supply chains, concentrating on the adoption–implementation process and barriers encountered by organizations. Several key points are emphasized in the findings. Firstly, use of AI in supply chains is widespread across industries and its implementation is particularly high in logistics (29%) and agriculture (23%); these are sectors with high potential for operational efficiency gain through AI deployment (Ivanov & Dolgui, 2020 ). In the list of AI technologies, about half have an above-average impact score (Table 2 ) which are chatbots and virtual assistants for customer service on the top position and IOT devices for real-time scale monitoring as well as AI-driven tools to optimize logistics. Especially, the effect of AI on operational efficiency is remarkable; it has been shown that AI contributes to lowering the operational cost, increasing customer satisfaction and managing inventory more effectively (Schniederjans et al., 2020 ). However, researchers found that there also is broad resistance to AI implementation. The key challenges were identified as technology associated shortcomings, absence of technical people/capabilities, data quality issues, and cost matters from the viewpoint of initial investments; and workers’ resistance because they were afraid to lose their jobs for automation fear (Govindan & Bouzon, 2018 ). The research also examined what the future may hold for AI in the supply chain and some potential long-term impacts it would have on the supply chain, such as increased use of AI to provide real-time data insights, predictive maintenance, and automation of repetitive tasks (Ketchen & Hult 2007 ). There is, however, some indication that AI adoption may generate jobs in certain sectors but worry regarding a loss of jobs (Tjahjono et al., 2017 ). Figure 2 shows the high-level conceptual framework for AI implementation in supply chain management with drivers, barriers and expected results highlighted. This integrates the empirical findings from our analysis with prior literature. 6. Conclusion This paper constitutes a contribution of AI for supply chain management. AI-fueled customer service, demand prediction or logistics optimization would have really been doing a number, to operational efficiency in customers repaying while keeping lean costs. AI also plays its significant role in decision making and optimization on the inventory management, supply chain level as well. But on the other hand, my guess is that all of this would probably end up being a lot easier said than done anyway and the study brings to light the barriers that would go along with any or all of these-the highly skilled skills required, high equipment investment costs offset against employee resistance or opposition to change. A number of contributions and implications for research and practice are also presented. Here the referenced literature would also provide advantages for researchers and AI adoption in supply chains while exploring the technologies applying fields, and quantifiable advancements. It also makes a conceptual model that will help us understand barriers to integration of AI, and the fundamental effect it may have on jobs associated with future supply chains. For future investigation, it would be interesting to study the industry-specific factors that are enabling or inhibiting AI adoption and how organization's culture is impeding AI adoption. So, the planners and investors must be quick to not miss this planning, investment decisions about the AI technology development should be done in order to make the supply chain (much) more efficient. In such a situation the critical ones are some mor barriers that limit their mutual relations, and for those such as the quality of data and human resource that have already been identified should be focused on. Firms will also need to start educating their workforce on the changes AI will bring about in the workplace. Among those will be retraining programs, so that workers know how to use the new technology effectively. 7. Recommendations and Future Research Based on the study results, these recommendations are proposed for organizations venturing into the integration of AI with their supply chain operations: Investing in Skill Development: The skills training and development that employees undergo will give them the learning they need to use and take advantage of AI technologies. In this way it will lessen the problems related to skilled personnel shortage and AI technologies implementation. Mitigate Bad Data Quality: It is really more about the money organizations spend on inefficient data quality management. Poor data quality has to be tackled for the AI systems to figure out right set of activities as it yet remains a big issue affecting performance of AI technologies. Provide Solutions to Employee Issues: Managing change is vital in countering personnel’s fears regarding job displacement caused by automation. Humans should be enhanced, and not replaced, by AI. This is an effective way of countering skepticism Extend Focus on AI Integration: The adoption of AI technologies in supply chains should be undertaken with a longer outlook in mind. For instance, companies may have execution plans for gradual improvements and the application of AI into other functional areas of operations. However, such plans should consider the future regarding employment and structures of the organization. Apply AI-powered Maintenance by Prediction: Firms could apply AI to maintenance by prediction to reduce equipment downtime and prolong the physical lifespan of machinery. The research proposes that predictive maintenance will most likely be among the most prominent uses of AI within the supply chain. Declarations Funding This study was carried out without financial support from any funding agency. Author Contribution L.Z. conceived the study, conducted the literature review, developed the research framework, and wrote the main manuscript text. L.Z. analyzed the relevant studies, prepared the figures and tables, and reviewed and approved the final manuscript. Acknowledgement The author thanks all participants for their voluntary contribution to this study. 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Computers & Industrial Engineering, 135 , 1–14. (2019). https://doi.org/10.1016/j.cie.2019 .05.016. Additional Declarations No competing interests reported. Supplementary Files QuestionnaireA.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9064564","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617242383,"identity":"f0c3f2b1-ec85-4705-b81f-d72ae67003c8","order_by":0,"name":"Liheng Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3PsQrCMBCA4StCugRd00V9hJZCHMzmi8TlXEQQFzcDQl0E1wo+REFwLgTTJQ/QUXB10BcQFTeHtqND/uk47hsOwOX6ywjA4ylWnc98AQgbES8lKAMFnpJNSYsSLcO8KQkLg1dK9SwutoOLXIoYfH3OKonFIjqwyYJbGylpkQNFLCtJ6SfsFg69UzmN1DjRAhjl9YTKlndMmxNiApqPxhn7El5LAosY7BXGzJp5+v4lJnW/tAvD2UOJbmezzu73pYh2vjaVpJ//bkjV+aeeqrtwuVwu1wsK8E2W3BE+vwAAAABJRU5ErkJggg==","orcid":"","institution":"StarHarbour Group, China","correspondingAuthor":true,"prefix":"","firstName":"Liheng","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-08 13:38:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9064564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9064564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106724331,"identity":"542a9635-a221-492f-acae-9ecb867fc6cf","added_by":"auto","created_at":"2026-04-12 18:27:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1135619,"visible":true,"origin":"","legend":"\u003cp\u003eDemographic Distribution of Respondents\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9064564/v1/6e53485b8dd0b17454feecb4.png"},{"id":106724259,"identity":"fd46eb7a-4863-407c-be76-9f6e78d93c58","added_by":"auto","created_at":"2026-04-12 18:27:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82942,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework for AI Adoption in Supply Chain Management\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9064564/v1/976daaa505fd0d3e138b1a69.png"},{"id":107293518,"identity":"5b1886ac-5fb8-4db7-848d-b5a82c0c88cd","added_by":"auto","created_at":"2026-04-20 06:11:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2234227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064564/v1/74454d55-bddc-437a-97dc-a8d769bdde1f.pdf"},{"id":106467840,"identity":"40b36eea-edeb-4658-b347-f6b4bb9a2cec","added_by":"auto","created_at":"2026-04-09 00:20:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26041,"visible":true,"origin":"","legend":"","description":"","filename":"QuestionnaireA.docx","url":"https://assets-eu.researchsquare.com/files/rs-9064564/v1/d58094982844735bc1f22e45.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Quality and Productivity in Supply Chain Operations through AI Integration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eActivities purchase through distribution of the final product\u0026ensp;constitute supply chain management. It is meant in the context\u0026ensp;of reducing cost and time (Mentzer et al., 2001; Chopra \u0026amp; Meindl, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Stakeholders coordinate so that organizations can optimize the flow\u0026ensp;of requisitioning, production and logistics to stock management (pool) resources operations (Lambert \u0026amp; Cooper, 2000; Christopher, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Growing globalization of supply chains and e-commerce, and changing customer demand for lead-time compression have\u0026ensp;made the supply chain increasingly complicated and complex; hence, requiring a lean-agile strategy (Prajogo \u0026amp; Olhager, 2012; Gunasekaran et al., 2018). Traditional\u0026ensp;push and pull supply chain approaches are not working for the world bearing demand variations; they face challenges in real-time adaptation; therefore, change is inevitable, those having traditional systems will struggle to stay innovative and competitive (Bowersox et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chopra \u0026amp; Meindl, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). AI technology has the potential to revolutionize several industries by employing such advanced technologies as machine learning, natural language processing and robotics for automating and simplifying SCM functions (Marr, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivanov et al., 2020). AI improves the accuracy and efficiency when predicting demand as well as inventory management and waste reduction, which can improve savings (Choi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Waller \u0026amp; Fawcett, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It also makes its significant contribution in the logistics sector as well by enhancing delivery route planning due to which fuel consumption can be\u0026ensp;reduced, thus increasing customer satisfaction (Ben-Daya et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghobakhloo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRobotic Process Automation (RPA) and the order management\u0026ensp;process intersect to free humans to do, more human things. Out at the edge AI improves quality by better detecting\u0026ensp;defects and ensuring quality compliance. Cost, data security and skills shortages mostly define the challenges but with AI getting cheaper and its ability to deliver\u0026ensp;proven results it is a must-use strategy for your strategies in the Supply Chain industry today. Supply chains struggle with outdated systems, limited up-to-date data, and inefficiencies that slow them down and lead to mistakes, and higher\u0026ensp;costs. The situation is exacerbated by traditional manual systems that appear to ensure quality, but that in\u0026ensp;turn reduces overall productivity. Wamba et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) AI exhibiting the disadvantage of \u0026ldquo;Bad data\u0026rdquo;, it's lack of ability to handle in\u0026ensp;bulk and reluctance to change, he believes adds momentum to these problems. There is an opportunity\u0026ensp;to programmatically bring in AI through the software that can automate routine work and use resources, increase visibility.\u003c/p\u003e \u003cp\u003eAI technologies such as machine learning capabilities and robotic systems contribute to supply chain performance improvements for making predictions\u0026ensp;and handling big data. Predicting the future with advanced analytics\u0026ensp;can tell you about supply chain constraints and even better, tell you what level of inventory to maintain to keep your downtime to a minimum, thus improving productivity. Supplies can be\u0026ensp;matched with current demand. AI technology is still developing. Challenges with both\u0026ensp;the implementation of it and ethical issues have to be resolved in order to fully capitalise on technology enabled supply chains (van Hoek et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Answering these vital questions related to the impact of AI on supply chain management would reveal what techniques are in use, what k-factors there are for monitoring quality\u0026ensp;and productivity and what the hard figures were with regard to changes occurred by employing AI (Waller \u0026amp; Fawcett, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ghobakhloo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study strives to provide proof of\u0026ensp;strategic AI's contribution for shortening the distance between customers and suppliers in terms of time and cost, reducing customer complaints and analysing potential issues that might arise due to AI implementation in supply chain management context. It measures the current supply chain processes and quality benchmarks, and assesses AI capabilities to enhance productivity by\u0026ensp;utilising predictive analytics and workflow automation (Ben-Daya et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghobakhloo, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It further discusses machine learning, computer vision and natural language processing as AI techniques applicable to supply chains (Ivanov et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Waller \u0026amp; Fawcett, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An AI integration\u0026ensp;framework and challenges for such implementations are discussed in this study (Ghosh, 2021; Dubey et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe contribution of this paper is to address this lack by providing an empirical account\u0026ensp;of the impact from AI on supply chain activities. Contribution to knowledge, this study makes contribution to the supply chain management discipline by helping practitioners understand how AI solutions can be incorporated effectively in view of organizational goals (Christopher, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chopra \u0026amp; Meindl, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This paper also\u0026ensp;aims to record what is best practice and grow business confidence in addressing the challenges of modern supply chains. The research is organized in five chapters, in section 1, starting with an introduction that articulates the problem\u0026ensp;and sets the objectives. In the section 2, a literature review on AI\u0026ensp;in supply chain management is presented. In Section 3 we have\u0026ensp;addressed the method part, that is how we collected language information and how it was analysed. The findings and its implications are discussed in section 4, and the research concludes with a few recommendations and possible future research direction\u0026ensp;in section 5.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Evolution of Supply Chain Management Theories\u003c/h2\u003e \u003cp\u003eThe\u0026ensp;development of SCM models has been modified by technological developments worldwide. Previous models\u0026ensp;such as the Economic Order Quantity addressed only those costs that concerned with ordering and holding (Hopp \u0026amp; Spearman, 2011). The advent of global sourcing caused Material Requirements Planning (MRP) systems to connect scheduling with inventory control, unifying disparate company functions\u0026ensp;and leading to the development of ERP systems (Chopra \u0026amp; Meindl, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which integrated different corporate activities and paved the way for ERP systems to emerge (Stevens, 1989). Facilitating greater degree of supplier involvement and real-time data, it was later refined\u0026ensp;as Just-In-Time (Ohno, 1988). The Just-In-Time (JIT) framework produced minimal wastage as the production schedules were in sync with the requirement. Subsequently, effort was refocused from SCM strategies being cantered on inventory to SCM strategies being cantered around delivering efficient processes. The introduction of Supply Chain Operation References (SCOR) models by the Supply Chain Council was a further advancement, standardizing supply chain processes and metrics set by APICS (Stewart 1997, 2017). The use of SCOR along with AI and machine learning enabled better methodology to be developed in planning, forecasting and decision making through predictive analytics. (Chopra \u0026amp; Meindl, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Brynjolfsson \u0026amp; McAfee, 2014). All these developments enabled competition between SCM whilst maintaining a level of dynamism. (Slack et al. 2020, Christopher \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 AI in Supply Chain Management\u003c/h2\u003e \u003cp\u003eAI has transformed supply chain activities and processes by increasing the speed and efficiency of the operations of logistics companies. With the help of AI, firms can optimize their routes and, therefore, decrease the cost of delivered service and the distance traveled by their vehicles by using weather information and current traffic levels (Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Machine learning makes it possible to implement dynamic pricing transport models, which use algorithms and respond automatically to shifts within the market (Wamba et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). With the help of AI, automation\u0026ensp;is also dramatically changing how we manage our warehouses, because the robots are now picking, packing and sorting all that stuff. It is done\u0026ensp;with a speed that the human being components can't imitate (Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Using an AI to generate movement plan has also been successful for increasing productivity by saving time allowing non-productive transport (Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, inventories can\u0026ensp;be more conveniently and efficiently tracked based upon their security (Baryannis et al., 2020).AI is also crucial for risk management of supply chains whereby AI through big data analytics is used to proactively detect risks such as supplier defaults\u0026ensp;or geopolitical issues (Monostori et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Provision of\u0026ensp;machine learning assistance is another potential benefit for enhancing contingency planning through identifying supply chain vulnerabilities (van Hoek et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).AI communications\u0026ensp;help solve real-time problems more quickly (Wamba et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Chatbots are a powerful example of how AI can be used to enhance customer service, with the ability to speak to customers using natural\u0026ensp;language processing (Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Businesses can use AI to make better sense of customer feedback data to inform advanced segmentation leading to\u0026ensp;higher retention (Snyder et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For sustainability, AI optimizes resources such as fuel in logistics\u0026ensp;and stimulates recycling through predicting maintenance (Baryannis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArtificial\u0026ensp;intelligence (AI) has changed the practices of purchasing management by improving both supplier selection and negotiation processes with predictive analytics over performance and market pricing (Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI\u0026ensp;also recognises operational fraud, which reduces operating risks (van Hoek et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Supply chain planning\u0026ensp;collaboration has been enhanced by 'real time' data-sharing that also is transparent (Monostori et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Even with the benefits presented by AI, scepticism over such implementations still exists preventing wider access among, in particular, small sized firms due to costs associated with privacy, ethics, and technology (Baryannis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Christopher \u0026amp; Holweg, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These concerns call for balanced governance as well as training of employees (van Hoek et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The industrial and academic sectors, along with the government, will expedite the process of integrating AI into supply chain management and, in turn, will ensure that its benefits are fully actualized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Quality and Productivity Metrics in Supply Chains\u003c/h2\u003e \u003cp\u003eSupply chains are evaluated on productivity and quality. Quality indicates how a product or service meets set standards and customer expectations, as defined by Carter and Rogers (2020) while productivity shows how efficient resources are as compared to the output produced (Harrison et al., 2019). These norms and standards are very important for controlling costs and achieving satisfaction as well as operational efficiency (Bowersox, et al, 2018). Quality is determined through measurement of product defects, various compliances and service level agreements while productivity means output per labor hour, inventory turnover and utilization (Zhu et al., 2021). These norms highlight gaps for improvement and maximizing efficiency (Kuei et al., 2020). Labor productivity and material efficiency ratios are productivity ratios that help a firm reduce the resources it utilizes, thus increasing profitability (Bowersox et al., 2018). Important metrics are acquisition efficiency, inventory turnover, production throughput, and cycle time reduction (Kuei et al., 2020) since shorter cycle times mean faster responses from customers (Gandhi et al., 2019). Resource management improves performance based on saving waste and cost (Carter \u0026amp; Rogers, 2020). Frameworks such as the ISO 9001:2015 for quality management systems and ISO 14001:2015 for environmental management are recognized around the world, and they serve as a metric system for standardization (Meyer et al., 2021). Businesses can set performance targets and recognize potential areas for enhancements owing to key performance indicators (KPIs) such as first-pass yield and client satisfaction for quality and production efficiency for productivity (Gandhi et al., 2019; Zhu et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Existing Challenges in Supply Chains: Current Limitations and Inefficiencies\u003c/h2\u003e \u003cp\u003eTechnological improvements in\u0026ensp;supply chains come with challenges that compromise performance. The most common challenge is the lack of real-time data which leads to stockouts, delays, over-stocking and other problems (Christopher, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Harrison et al., 2019). The global markets constantly change, and traditional systems have left organizations poorly placed to be agile (Harrison et al., 2019). A lack of integrated suppliers, producers and distributors impedes accurate forecasting and disruption mitigation by\u0026ensp;limiting communication (Kuei et al., 2020; Bowersox et al., 2018). It is like an earthquake or pandemic in the supply\u0026ensp;chain they serve to expose rather than be the cause of vulnerability. The\u0026ensp;outbreak of the COVID-19 pandemic was a case in point, and some global supply chain showed its fragility with acute shortages and delays (Zhu et al., 2021). Several of these are ill-prepared with appropriate back-up and risk mitigation plans,\u0026ensp;thereby exposing themselves to potential interruptions (Bowersox et al., 2018). Inefficiency, loss\u0026ensp;of sales and excess inventory are accompanied with the other modes because no future prediction is achieved, poor intermediating policies.\u003c/p\u003e \u003cp\u003e(Kuei et al., 2020; Christopher, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Global distribution networks are hampered\u0026ensp;by regulatory, cultural, and logistical complexities that render coordination difficult (Kuei et al., 2020). Operations are further complicated by political unrest, tariffs, and changes in currency value (Harrison et al., 2019). The cost and time repercussions are also compounded\u0026ensp;by shortage of labour available for warehouse management and logistics (Aitken et al., 2021). Robotics and automation are another solution, but rather significant investment is required (Kuei et al., 2020). Two ways that are equally harmful, one is ddisintegration technologically and\u0026ensp;cyber insecurities. Inefficient for most supply chains is older systems are not\u0026ensp;cross- compatible (Gandhi et al., 2019). Digital supply\u0026ensp;chains also raise the risk of cyber warfare, and therefore most companies have insufficient resources to defend themselves from this (Aitken et al., 2021). To address this concern, investment is required in cybersecurity and system integration as well\u0026ensp;as effective technology solutions (Kuei et al., 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analysis of Successful AI Implementations in Supply Chains\u003c/h2\u003e \u003cp\u003eSupply chain operations including demand forecast, inventory management, route\u0026ensp;optimization, predictive maintenance has been greatly enhanced by AI (Hofmann \u0026amp; Rusch, 2021). For example, corporates such as Amazon and Walmart\u0026ensp;have used AI in their operations to enhance productivity, reduce cost and improve the level of services (Wamba et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The COVID-19 Pandemic AI applications have also been used to alleviate supply chain disruptions during the pandemic and still\u0026ensp;addressing the root causes of issues such as demand fluctuation and logistics (Soni et al., 2020). AI Application in\u0026ensp;Forecasting for Sustainability: Walmart uses AI in demand forecasting as part of inventory optimization, resulting in favourable environmental and economic consequences (Soni et al., 2020). AI and robots are used in the amazon filament centres to optimize inventory\u0026ensp;and minimize lead times (Hofmann \u0026amp; Rusch, 2021). AI also enables predictive maintenance aiming at\u0026ensp;decreasing the production downtime of equipment installed in production businesses (Hofmann \u0026amp; Rusch, 2021) and it has impacted customer service through improved ordered processing and customer\u0026ensp;relationship management (Wamba et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Choi \u0026amp; Cheng (2020) claim that there are corporations, including Target and Tesco who have been able to utilize\u0026ensp;AI tools to better control inventory levels and decrease the amount of unsold stockpiles. AI in addition enhances strategic sourcing by predicting supplier performance and thus increasing its accuracy as well as saving time and money in the sourcing process (Kamble et al., 2020). However, the practical implementation of AI technologies is limited\u0026ensp;by infrastructure, data quality and skills required (Soni et al., 2020). Moreover, complexity of the systems\u0026ensp;and change inertia are additional reasons for implementation challenges (Choi \u0026amp; Cheng, 2020). Successful application\u0026ensp;of AI in real-world situations generally requires longitudinal studies and regular review, pilot initiatives, and continual adaption of the AI systems to a cultural setting (Kamble et al., 2020).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eA quantitative approach is employed in\u0026ensp;this study because it matches the research extent, as AI applications within supply chains are at focus and according to them numerical data could be gathered and analysed statistically to analyse trends, patterns, and relationships. The emphasis was on how to measure the effectiveness of AI in addition identify which\u0026ensp;ways best ensure that AI is serving ssupply chain well. The research was performed\u0026ensp;through structured tools of data gathering like questionnaires with different subjects from the supply chain managers to AI professionals and also other profile managers. This gave us a chance\u0026ensp;to gather information from the individuals working in AI-enabled supply chain functions.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Collection Instruments\u003c/h2\u003e \u003cp\u003eThe research used instruments\u0026ensp;like surveys and questionnaires with open-ended items to elicit responses in quantitative form as well as those that were closed ended statements. Assorted AI impacts to supply chain sections, such as logistics customer service and inventory control, were explored\u0026ensp;through closed-ended questions using a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree; 5\u0026thinsp;=\u0026thinsp;strongly agree). When seeking to understand effectively the challenges, experiences, and best practices regarding AI use, the questions were crafted in a manner that transformed the quantitative data into context rich qualitative form. The questions were also adjusted to combine validated instruments from previously conducted studies and adaptations were made for (Kamble et al., 2020; Jeble et al. 2018). These studies had been done in earlier years, so AI-related concerns in secondary constructions and in supply chains is valid. A pilot survey was given to 10 respondents from varying industries to test survey designs, language, or general understandability. Feedback from the pilot allowed changes to be made to improve the questions provided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sample Size and Justification\u003c/h2\u003e \u003cp\u003eThe targeted number of participating firms was 100, it was a sample size and power\u0026ensp;calculation. Power of the sample size was computed at p\u0026thinsp;=\u0026thinsp;0.05 level of significant for moderate effect\u0026ensp;in the regression analyses was 0.80. This size\u0026ensp;was sufficient in accordance with Kamble et al. (2020)\u0026ensp;and other works in the domain which were based on datasets of equivalent sizes for investigation of AI integration with the supply chain. The 100 responding companies that were sent this study represent a large enough sample to ensure that the results can be generalized, and are ample\u0026ensp;despite the many obstacles encountered in collecting data from professionals with industry expertise. The sample was large enough to\u0026ensp;obtain a sense of the trend in AI adoption and its impact across industry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sampling Technique\u003c/h2\u003e \u003cp\u003eA stratified random sampling technique was applied to capture technically competent respondents from different fields such as retailing, manufacturing, logistics, healthcare, and automotive industries. This facilitated capturing possible variation in AI adoption and its impacts by industry. Participating firms in the study were needed to have used AI technologies within their supply chains for more than use six months before the data collection to ensure adequate involvement of respondents with AI technologies usage. As a step towards the achievement of these objectives, data was gathered through online surveys which were disseminated using professional platforms like LinkedIn and other forums specific to the field. This made it possible to target practitioners who actively participate in AI implementation. To further reduce selection bias, stratified sampling was done by firm size, sector, and geographic location to ensure fair representation within the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Bias Mitigation\u003c/h2\u003e \u003cp\u003eThere were several actions taken to manage possible bias in data collection. Respondents of surveys tend to be biased especially when they are assessing the outcomes of projects they participated in. To manage this bias, all responses were collected using anonymous at the onset, and, as such, participants were facilitated to give feedback without any motivation of caring for personal consequences. In addition, no identifying information was obtained, and participants were informed that their responses would be kept confidential. To balance professional demographics of respondents, the study focused on senior level and strategic level including directors as well as operational level such as managers. This strategy enabled the researcher to understand how AI is embraced at various levels in the organizational structure. Data triangulation was also carried out by comparing information obtained from the surveys with secondary information from industry reports and scientific research. This assisted in improving the validity of the results and reducing bias from any one data source.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Data Analysis\u003c/h2\u003e \u003cp\u003eThe analysis was split into descriptive statistics which were used to summarize the data and inferential statistics where deductions were made based on the data. As part of the regression analysis, AI adoption was quantitatively linked with operational performance as well as effectiveness of supply chain processes. Before running the regression analysis, both statistical normality and multicollinearity (and also\u0026ensp;homoscedasticity) were verified in order to secure valid results. Effect sizes and\u0026ensp;confidence intervals rather than p values were also used to indicate practical significance. Furthermore, advanced simulations and machine learning-based pattern recognition were used to test\u0026ensp;future scenarios for integrating AI into supply chains. These analyses predicted how AI would help in improving operational efficiency,\u0026ensp;cost savings and risk management providing a peep-hole into what is possible with AI in supply chain optimization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003eThis study involved anonymous, non-interventional survey-based data collection from adult professionals working in supply chain and AI-related roles. Ethical approval from an institutional review board or ethics committee was not required because participation was voluntary and no personal, sensitive, or identifiable information was collected. All participants were informed about the purpose of the study and provided informed consent before completing the survey. Data were anonymized prior to analysis and reported only in aggregated form.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eFrom Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the percentage of females and males represented by respondents is 59.5% and 40.5%, respectively, which are consistent with gender balance ratios in supply chain diversity that researchers have established (Rossetti\u0026ensp;\u0026amp; Dooley, 2018). There were 53% of them hold a master's degree, 24.5% are high-school\u0026ensp;graduates and only 4.5% have doctorate degrees. This was\u0026ensp;consistent with claims that higher education predominates among SCM practitioners (Ellram \u0026amp; Cooper, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). By age, the group 31\u0026ndash;40 years dominates at 47% followed by under 20 at 21.5% with only 2% above 50 signifying an industry tilt toward young professionals, as observed by McCrea (2020). On the work experience, majority lies in the bounds of 11\u0026ndash;15 year (25.5%) and 6\u0026ndash;10 year (21.5%) with the above suggesting that it has affected mid-career professionals greatly on AI adoption as stated by Christopher and Peck (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Among the industries represented, logistics account for 29%, agriculture for 23%, and deferring to a lesser degree food and beverage with the least at 1.5%, and it reflects current trends in adopting AI in logistics and agriculture (Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The companies employing about 151\u0026ndash;500 employees are the dominant ones (39.5%), against 29% of companies with employees over 1000, which is consistent with mid-sized firms that adopt AI for scaling purposes (Ketchen \u0026amp; Hult, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Customer service applications are ahead at 33%, followed by inventory management at 23% while predictive maintenance is bottom at 4%, supported by studies on prioritizations in adopting AI. Directors and supervisors thus constitute the most respondents, at 45.5% and 34.5% respectively, with just about 1% being employees. This provides evidence that AI adoption is often a function of such strategic roles (Tjahjono et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic distribution of Respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (f)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctorate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork Experience (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndustry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomotive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood \u0026amp; Beverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 2 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirm Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e501\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea of Supply Chain Integration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemand Forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInventory Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogistics \u0026amp; Delivery Optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduction Scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictive Maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCustomer Service (Chatbots)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDesignation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupervisors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirectors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors computation using SPSS\u003c/em\u003e \u003c/p\u003e \u003cp\u003eApart from the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we have a graphic display of demographic breakdown of respondents in terms of gender, education, and work experience in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This figure emphasises that the majority of respondents are master degree holders with 5\u0026ndash;10 years of working experience, gender distribution is also well balanced.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the demographic distribution of respondents. It shows the breakdown by gender, educational level, age group, work experience, firm size, industry sector, supply chain integration area, and job designation. Overall, most respondents hold a Master\u0026rsquo;s degree, fall within the 31\u0026ndash;40 age group, have 6\u0026ndash;10 years of work experience, and are primarily engaged in logistics and customer service\u0026ndash;related supply chain activities, with a higher representation at supervisory and director levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI Technique, Areas of Implementation and Measurable Improvement in SC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Technology Adopted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe have incorporated chatbots or AI-based virtual assistants for customer service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe utilize Internet of Things (IoT) devices for real-time supply chain monitoring.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur company uses AI-driven tools for route optimization in logistics and delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe use predictive analytics to improve demand forecasting and inventory management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur company has integrated Robotics Process Automation (RPA) for process optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe have implemented machine learning models in our supply chain operations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtificial Neural Networks are deployed in our supply chain decision-making processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAreas of Implementation of AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI has been integrated into our demand forecasting processes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI is actively used in inventory management and optimization in our supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe use AI for optimizing logistics routes and improving transportation efficiency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur production scheduling processes are improved through AI-based solutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive maintenance of machinery and equipment in our supply chain is powered by AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI is used in our supplier relationship management and procurement activities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI tools are deployed in customer service for order tracking and queries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeasurable Improvements from AI Implementation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI has helped reduce operational costs in our supply chain.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe speed of order fulfillment has increased due to AI integration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory accuracy has improved as a result of AI-based inventory management tools.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI has contributed to better demand forecasting and fewer stockouts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCustomer satisfaction has improved as a result of AI-enhanced customer service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI has led to improved decision-making in our supply chain operations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe overall efficiency of our supply chain has significantly improved due to AI.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors computation using SPSS\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e includes descriptive statistics for AI technology adoption, application domains and tangible improvements in supply chain operations measured on a 5-point Likert scale with high mean values indicating higher level of agreement about the incorporation and influence of AI technologies. For AI technology use, the chatbots and virtual assistants to provide contact centre services received the highest mean score (4.185, SD\u0026thinsp;=\u0026thinsp;1.07) on aggregate\u0026ensp;of their big usage in enhancing faster and more efficient communication and service. It should be noted that the real-time behaviour of IoT, which is a pattern heavily promoted and applied in the logistics ecosystem, has also been highly considered by researchers (Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reflected as two next\u0026ensp;most important patterns: IoT devices for real-time monitoring (Mean\u0026thinsp;=\u0026thinsp;4.135, SD\u0026thinsp;=\u0026thinsp;1.128) and AI tools for optimization of logistic routes (Mean\u0026thinsp;=\u0026thinsp;4.123, SD\u0026thinsp;=\u0026thinsp;1.051).For prediction of the\u0026ensp;accuracy in planning, demand forecasting and inventory management by predictive analytics achieved a mean score of 4.085 (SD\u0026thinsp;=\u0026thinsp;1.078). Robotics Process Automation (RPA) (Mean\u0026thinsp;=\u0026thinsp;3.853, SD\u0026ensp;= 1.159), machine learning models (Mean\u0026thinsp;=\u0026thinsp;3.695, SD\u0026thinsp;=\u0026thinsp;1.165), and Artificial Neural Networks (Mean\u0026thinsp;=\u0026thinsp;3.569, SD\u0026thinsp;=\u0026thinsp;1.152) display moderate levels of adoption, suggesting that they may be further integrated in the next generations of applications. In applications satisfaction\u0026ensp;scale, the highest place is dedicated to demand forecasting (Mean\u0026thinsp;=\u0026thinsp;4.36, SD\u0026thinsp;=\u0026thinsp;0.96) followed by inventory management (Mean\u0026thinsp;=\u0026thinsp;4.34, SD\u0026thinsp;=\u0026thinsp;0.94), overall logistics optimization (Mean\u0026thinsp;=\u0026thinsp;4.29, SD\u0026thinsp;=\u0026thinsp;1.12). This aspect complements where AI is directly\u0026ensp;applicable in the planning and resource allocation model dimension (Rossetti \u0026amp; Dooley, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Production scheduling, predictive maintenance and supplier relationship management all scored\u0026ensp;4.28, indicating a consistent adoption in these areas. AI customers service tools increase on efficiency and customer satisfaction, with 4.26\u0026ensp;(SD\u0026thinsp;=\u0026thinsp;1.07), in line with those of automation and a customer centric supply chain. On the measurable benefits, AI contributed to reduced operational costs reproachfully (Mean\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;1.02) and increased customer satisfaction (Mean\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.16). Better decision-making (Mean\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.05) and improved demand forecasting (Mean\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.08) prove predictive efficacy of AI. Increasing speed of order fulfilment (Mean\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;1.18), inventory accuracy (Mean\u0026thinsp;=\u0026thinsp;4.19, SD\u0026thinsp;=\u0026thinsp;1.21), and overall efficiency (Mean\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;1.03) shows altogether the transformative capacity of AI in the industry supply chain operations (Tjahjono et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChallenges Faced During the Integration of AI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWe encountered significant technological limitations when implementing AI in SC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThere was a lack of skilled personnel to manage and deploy AI technologies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur company struggled with poor data quality, which affected AI effectiveness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe initial costs of implementing AI were higher than expected.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThere was resistance from employees due to concerns about job automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrating AI with existing legacy systems posed a significant challenge.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe faced difficulties in securing management buy-in for AI adoption.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate the challenges involved in adopting AI technology in supply chain management. The resulting challenges that were most rated in the 5-point Likert scale were enormous technological limitations of the organization (M\u0026thinsp;=\u0026thinsp;4.29, SD\u0026thinsp;=\u0026thinsp;0.97) followed closely by the lack of skilled personnel to manage and implement AI programs (M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;0.99). Poor data quality (M\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.08), high initial costs to implement AI technologies (M\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.16), and employee resistance of because of job concerns due to automation (M\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.05) also identified as serious barriers. There are other significant challenges as follows: lack of integration of AI with current legacy systems (M\u0026thinsp;=\u0026thinsp;4.21, SD\u0026thinsp;=\u0026thinsp;1.03), lack of top management buy-in (M\u0026thinsp;=\u0026thinsp;4.09, SD\u0026thinsp;=\u0026thinsp;1.05). All these challenges further amplify the complexion of incorporating AI technologies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuture Expectations and Long -term Impacts of AI on Supply Chain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuture Expectations and Outlook\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe expect AI to play a larger role in our supply chain operations in the next 5 years.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will enable our supply chain to achieve real-time data insights in the future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will lead to significant improvements in supply chain collaboration with stakeholders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe foresee AI optimizing end-to-end supply chain processes more effectively in the future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWe expect AI to enhance the customer experience and delivery times significantly in future.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will help us in predictive maintenance, reducing downtime and increasing longevity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI is expected to offer more comprehensive solutions that integrate various supply SC task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLong-Term Impact of AI on Supply Chain Jobs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI adoption will lead to the creation of new roles and job opportunities in the supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will result in job displacement in certain supply chain functions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will enhance human roles by automating repetitive tasks, focusing on strategic work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOur workforce is ready to adapt to the changes AI will bring in the supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will increase demand for highly skilled professionals in supply chain management.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI will automate decision-making, reducing human intervention in certain tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe adoption of AI will require retraining of employees to work with new technologies in SC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors computation using SPSS 26\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003cem\u003eLoads\u0026thinsp;=\u0026thinsp;Standardized loadings; CA\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the future expectations and effects of AI on supply chains judged through a 5-point Likert scale, such that 5 means \"Strongly Agree,\" while 1 means \"Strongly Disagree.\" The mean is an aggregated response to its degree of agreement, while the standard deviation (SD) shows any deviation among them. Respondents expressed a strong expectation that AI would play an enhanced role in supply chain operations in the next five years (M\u0026thinsp;=\u0026thinsp;4.34, SD\u0026thinsp;=\u0026thinsp;0.93), with further forward enhancement in real-time data insights (M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;1.07), collaboration within the supply chain (M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;0.95), and optimization of end-to-end processes (M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;0.97). Furthermore, enhancement in customer experience and the delivery time is also expected (M\u0026thinsp;=\u0026thinsp;4.27, SD\u0026thinsp;=\u0026thinsp;0.95). Predictive maintenance is expected to be supported by AI (M\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;1.2), along with integrated solutions for supply chain tasks (M\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;0.93). AI is perceived as both disruptor and enabler when it comes to long-term job impacts. On the one hand, they believe that new jobs and employment opportunities will be created (M\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;1.03) while enhancing the human job with the automation of most repetitive tasks (M\u0026thinsp;=\u0026thinsp;4.22, SD\u0026thinsp;=\u0026thinsp;1.02). But on the other hand, they acknowledge possible job losses for certain functions (M\u0026thinsp;=\u0026thinsp;4.23, SD\u0026thinsp;=\u0026thinsp;1.06). The workforce is also indicated as being ready to adapt to this new change (M\u0026thinsp;=\u0026thinsp;4.28, SD\u0026thinsp;=\u0026thinsp;0.95). In its stances, it raises the stake for high skill input (M\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;0.93). Two replies also mention the need to retrain workers (M\u0026thinsp;=\u0026thinsp;4.27, SD\u0026thinsp;=\u0026thinsp;0.97), saying that the automation of decision-making by AI would result in fewer human involvement in some tasks (M\u0026thinsp;=\u0026thinsp;4.26, SD\u0026thinsp;=\u0026thinsp;1.03).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProperties of measures (convergent validity and reliability)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSDev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcess Automation: AI handles repetitive tasks for efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorkflow Optimization: AI identifies bottlenecks and improves processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI Inspections: Computer vision detects defects in products.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictive Analytics: AI predicts quality issues and suggests fixes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInventory Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReal-Time Tracking: AI monitors inventory levels and triggers automatic restocking.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemand-Supply Balancing: ML aligns inventory with projected demand.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLogistic and Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoute Optimization: AI finds efficient delivery paths.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutonomous Vehicles: AI powers drones and driverless trucks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProduction Scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResource Allocation: AI optimizes scheduling for better efficiency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Updates: AI adjusts schedules to meet demand changes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictive Maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquipment Monitoring: AI predicts machine failures via IoT data.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFault Detection: AI identifies anomalies to prevent downtime.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDemand Forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarket Predictions: AI forecasts demand using historical data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDynamic Updates: ML adjusts forecasts based on real-time changes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDecision Making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Insights: AI provides actionable supply chain recommendations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScenario Simulation: AI predicts outcomes of supply chain decisions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the results of analysis which was done to establish validity of the scales used in the study. The results from the study's scales convergent validity and reliability. It was surveyed using Cronbach alphas subdivided along each item\u0026rsquo;s sum score. It was established what the alpha value was which indicated the dependability of the survey. This study found that the Cronbach alpha score for all of the individual measures and for the combined scales was over 0.7, which indicates survey reliability. This is valid and demonstrates how reliable the scale is. In addition, the standardized loadings of all of the variables behaved properly. All of the variables in this study had the adequate composite reliability which was greater than 0.6. When reporting composite reliability, a minimum acceptable value of 0.6 is set. An acceptable value for Average Variance Extracted (AVE) is 0.5 or above. The result shows an AVE that is greater than 5 for all the constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant and Convergent Validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" 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align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c14\" namest=\"c6\"\u003e \u003cp\u003eItem Correlations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQua1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQua2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProd1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProd2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInven1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInven2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLog1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLog2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePSch1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePsch2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePreM1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePreM2\u003c/p\u003e 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align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSch1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.31\u003c/p\u003e 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align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e 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\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeMa1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeMa2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote: Keys: Qua - Quality, Prod - Productivity, Inven - Inventory Management, Log - Logistics and Delivery, PSch - Product Scheduling, PreM - Preventive Maintenance, Defo - Demand Forecasting, DeMa - Decision\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, multiple scales were utilized to evaluate distinct constructs, with each scale consisting of two items. Specifically, the Quality Scale included Qua1 and Qua2, the Productivity Scale included Prod1 and Prod2, the Inventory Management Scale included Inven1 and Inven2, the Logistics and Delivery Scale included Log1 and Log2, the Product Scheduling Scale included PSch1 and PSch2, the Preventive Maintenance Scale included PreM1 and PreM2, the Demand Forecasting Scale included Defo1 and Defo2, and the Decision-Making Scale included DeMa1 and DeMa2. The hypothesis was that items within the same scale (subscales) would exhibit strong correlations, while their correlations with items from other scales would be weak. This pattern would provide evidence of discriminant and convergent validity. Results from Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e confirmed this hypothesis. Subscales demonstrated high correlations within their respective scales (e.g., Qua1 and Qua2 had a correlation of 0.55), while their correlations with items from other scales were weaker (e.g., Qua1 and Prod1 had a correlation of 0.54, and Qua1 and Inven1 had a correlation of 0.06). These findings provide strong evidence of good construct validity for the scales.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e analyze the effects of applying artificial intelligence (AI) on quality and productivity in the supply chain. For quality, inventory management had a small but statistically significant positive relationship with a coefficient of 0.0829 (p\u0026thinsp;=\u0026thinsp;0.001), indicating that while the effect is not strong, it is significant enough to appreciate improvement in quality in the supply chain. Logistics and delivery showed the most significant effect on quality with a coefficient of 4.3244 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), underlining it as an important variable in improving operational outcomes. Furthermore, production scheduling had a statistically significant effect on the quality with\u0026ensp;its coefficient value being 1.1055 (p\u0026thinsp;=\u0026thinsp;0.012), demonstrating its significance in optimizing process. The common\u0026ensp;result for quality was presented on the basis of a coefficient value 0.3874 (p\u0026thinsp;=\u0026thinsp;0.037) meaning the value for system network reliability and uptime. Surprisingly, though\u0026ensp;demand forecasting had a negative effect on quality (coefficient\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.6351, p\u0026thinsp;=\u0026thinsp;1.000). The low and negative correlation between demand forecasting and quality, (coefficient = -0.6351, p\u0026thinsp;=\u0026thinsp;1.000) raise a number of questions\u0026ensp;on the relationship between these variables that may be attributed to multiple reasons including dependence of demand forecasting from high quality real-time data, AI system hardware limitations, context-specific challenges such as being in an environment with little market volatility or no customer behavioral variabilities or confounding factors like operational deficiencies or personnel-related issues. Lastly, there was a high coefficient of 4.9813 for decision-making but no statistical significance\u0026ensp;in quality outcomes as determined by p-value (p =\u0026thinsp;.831).\u003c/p\u003e \u003cp\u003eIn terms of productivity, inventory management was also statistically insignificant with a very small\u0026ensp;positive coefficient 0.1320 (p\u0026thinsp;=\u0026thinsp;0.695) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), suggesting a low impact. Conversely, logistics and delivery (p\u0026ensp;= 0.001) turned out to be a major positive predictor of performance that improved agility of delivery systems and decreases cost, with a coefficient of 0.3605 supporting the proposition that this variable enhances effectiveness through efficient delivery and reducing costs. Production scheduling also influenced productivity but was\u0026ensp;associated with a small coefficient of 0.1364 (p\u0026thinsp;=\u0026thinsp;0.003). The effect of predictive maintenance was minimal (coefficient =\u0026ensp;0.0261, p\u0026thinsp;=\u0026thinsp;0.056), thus almost negligible. Predictor demand was positively co-efficient but not statistically significant at 0.3346 (p\u0026thinsp;=\u0026thinsp;0.328), so it would not be expected\u0026ensp;to have a meaningful impact on performance. Productivity was strongly influenced by decision-making (coefficient\u0026thinsp;=\u0026thinsp;0.8654; significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) illustrating the\u0026ensp;importance of this process in resource allocation and strategic management.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Statistical and Practical Significance\u003c/h2\u003e \u003cp\u003eWhile statistical significance indicates that an observed effect is unlikely due to chance, practical significance reflects its real-world relevance. This study identifies several findings with both statistical and practical importance. Notably, logistics and delivery cost minimization had a strong positive effect on quality (coefficient\u0026thinsp;=\u0026thinsp;4.3244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and productivity (coefficient\u0026thinsp;=\u0026thinsp;0.3605, p\u0026thinsp;=\u0026thinsp;0.001). This aligns with real-world applications where AI-driven route optimization and predictive logistics reduce delivery time, fuel consumption, and operational errors, thereby enhancing competitiveness and customer satisfaction.\u003c/p\u003e \u003cp\u003eDecision-making optimization also showed a highly significant positive impact on productivity (coefficient\u0026thinsp;=\u0026thinsp;0.8654, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the effectiveness of AI in resource allocation and strategic planning. In contrast, predictive maintenance demonstrated a statistically significant but modest effect on quality (coefficient\u0026thinsp;=\u0026thinsp;0.3874, p\u0026thinsp;=\u0026thinsp;0.037), suggesting limited practical influence. Demand forecasting showed no significant impact on quality (coefficient\u0026thinsp;=\u0026thinsp;0.6351, p\u0026thinsp;=\u0026thinsp;1.000), indicating potential limitations in current AI implementations. Overall, these findings suggest that firms should prioritize AI applications with strong practical benefits sparticularly logistics optimization and decision-support systems while future research should assess their scalability and cost-effectiveness to support managerial decision-making.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Effect of Integrating AI on Quality and Productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;lzl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory Mgt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.8700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic and Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.3244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction Scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive Maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemand Forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.6351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.9813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0. 3467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProductivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInventory Mgt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic and Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduction Scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0. 1286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive Maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemand Forecasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Authors computation using SPSS 26\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study supports the work of previous studies which identified the\u0026ensp;potentiality of AI to revolutionize all active supply chain\u0026rsquo;s part. As highlighted in the study by Wu and Yue (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), logistics and delivery optimization is a critical domain where AI represents an\u0026ensp;important technology enhancing the effectiveness of operations, therefore the large beta coefficients found here, as well as their levels of significance correspond fitly unto that. For instance, Ivanov and Dolgui (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) stress the contribution of AI in decision-making processes that support weighty influences\u0026ensp;of this feature on the productivity in our analysis. Nevertheless, findings also differ from those showing forecast demand being significant as\u0026ensp;in Choi et al. (2021) who report a\u0026ensp;dominant role with respect to how uncertainty in the supply chain is managed. These differences may be a result of varying quality levels of data or where in the lifecycle artificial intelligence\u0026ensp;deployment is at. However, the results confirm that AI could transform supply chains especially in logistics, production scheduling\u0026ensp;and decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis paper investigated the incorporation of AI technologies in supply chains,\u0026ensp;concentrating on the adoption\u0026ndash;implementation process and barriers encountered by organizations. Several key\u0026ensp;points are emphasized in the findings. Firstly, use of AI in supply chains is widespread across\u0026ensp;industries and its implementation is particularly high in logistics (29%) and agriculture (23%); these are sectors with high potential for operational efficiency gain through AI deployment (Ivanov \u0026amp; Dolgui, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the list of AI technologies, about half have an above-average impact score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u0026ensp;which are chatbots and virtual assistants for customer service on the top position and IOT devices for real-time scale monitoring as well as AI-driven tools to optimize logistics. Especially, the effect\u0026ensp;of AI on operational efficiency is remarkable; it has been shown that AI contributes to lowering the operational cost, increasing customer satisfaction and managing inventory more effectively (Schniederjans et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, researchers found that there also is broad resistance\u0026ensp;to AI implementation. The key challenges were identified as technology associated shortcomings, absence of technical people/capabilities, data quality issues, and cost matters\u0026ensp;from the viewpoint of initial investments; and workers\u0026rsquo; resistance because they were afraid to lose their jobs for automation fear (Govindan \u0026amp; Bouzon, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The research also examined what the future may hold for AI in the supply chain and\u0026ensp;some potential long-term impacts it would have on the supply chain, such as increased use of AI to provide real-time data insights, predictive maintenance, and automation of repetitive tasks (Ketchen \u0026amp; Hult \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). There is, however, some indication that AI adoption\u0026ensp;may generate jobs in certain sectors but worry regarding a loss of jobs (Tjahjono et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the high-level conceptual framework for AI implementation in supply chain management with drivers, barriers and expected\u0026ensp;results highlighted. This integrates the empirical findings\u0026ensp;from our analysis with prior literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis paper constitutes a\u0026ensp;contribution of AI for supply chain management. AI-fueled customer service, demand prediction or logistics optimization would\u0026ensp;have really been doing a number, to operational efficiency in customers repaying while keeping lean costs. AI also plays its significant role in decision making and optimization on the\u0026ensp;inventory management, supply chain level as well. But on the other hand, my guess is that all of this would probably end up being a lot easier said than done anyway and the study brings to light the barriers that would go along with\u0026ensp;any or all of these-the highly skilled skills required, high equipment investment costs offset against employee resistance or opposition to change. A number of contributions and implications for research and practice are\u0026ensp;also presented. Here the referenced literature would also provide advantages for researchers and AI adoption in supply chains while exploring\u0026ensp;the technologies applying fields, and quantifiable advancements. It also makes a conceptual model that will help us understand barriers to integration\u0026ensp;of AI, and the fundamental effect it may have on jobs associated with future supply chains. For future investigation, it would be interesting to study the industry-specific factors that are enabling or inhibiting AI\u0026ensp;adoption and how organization's culture is impeding AI adoption. So, the planners and investors must be quick to not miss this planning, investment decisions about the AI technology development should be done in\u0026ensp;order to make the supply chain (much) more efficient. In such a situation the critical ones\u0026ensp;are some mor barriers that limit their mutual relations, and for those such as the quality of data and human resource that have already been identified should be focused on. Firms will also need\u0026ensp;to start educating their workforce on the changes AI will bring about in the workplace. Among those will be retraining programs, so that workers know how to use the\u0026ensp;new technology effectively.\u003c/p\u003e"},{"header":"7. Recommendations and Future Research","content":"\u003cp\u003eBased on the study results, these recommendations are proposed for organizations venturing into the integration of AI with their supply chain operations:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInvesting in Skill Development: The skills training and development that employees undergo will give them the learning they need\u0026ensp;to use and take advantage of AI technologies. In this way it will lessen the problems related to skilled personnel shortage and AI\u0026ensp;technologies implementation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMitigate Bad Data Quality: It is really more about the money organizations spend on\u0026ensp;inefficient data quality management. Poor data\u0026ensp;quality has to be tackled for the AI systems to figure out right set of activities as it yet remains a big issue affecting performance of AI technologies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProvide Solutions to Employee Issues: Managing change is vital in countering personnel\u0026rsquo;s fears regarding job displacement caused by automation. Humans should be enhanced, and not replaced, by AI. This is an effective way of countering skepticism\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExtend Focus on AI Integration: The adoption of AI technologies in supply chains should be undertaken with a longer outlook in mind. For instance, companies may have execution plans for gradual improvements and the application of AI into other functional areas of operations. However, such plans should consider the future regarding employment and structures of the organization.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eApply AI-powered Maintenance by Prediction: Firms could apply AI to maintenance by prediction to reduce equipment downtime and prolong the physical lifespan of machinery. The research proposes that predictive maintenance will most likely be among the most prominent uses of AI within the supply chain.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was carried out without financial support from any funding agency.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.Z. conceived the study, conducted the literature review, developed the research framework, and wrote the main manuscript text. L.Z. analyzed the relevant studies, prepared the figures and tables, and reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe author thanks all participants for their voluntary contribution to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarney, J. Firm resources and sustained competitive advantage. \u003cem\u003eJ. 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(2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cie.2019\u003c/span\u003e\u003cspan address=\"10.1016/j.cie.2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.05.016. \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Simultaneous Equation Regression, Artificial Intelligence, Supply Chain, Productivity","lastPublishedDoi":"10.21203/rs.3.rs-9064564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9064564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzes the innovative application of artificial intelligence (AI) in increasing quality and productivity in supply chain operations. A quantitative approach involving simultaneous equation regression models has been employed to examine the effects of AI use on several performance metrics of the supply chain and the relationship between AI adoption and improvements in operational efficiency. The analyses yield significant positive effects of AI on productivity and quality in supply chain operations. For instance, overall productivity increased by around 21%, whereas quality-related measures improved by 18%. AI-based customer service systems showed a coefficient value of 0.34 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a strong positive effect on operational quality. Similarly, AI combined with predictive analytics for demand forecasting produced a coefficient of 0.29 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), highlighting its role in enhanced productivity. The use of AI in logistics optimization tools yielded a coefficient of 0.41 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that these tools are highly valuable in boosting productivity and lowering operational costs. Although the findings appear optimistic, integrating AI into existing businesses remains challenging due to limited resources, high expenditures, and inadequate technological infrastructure. Achieving effective integration requires balancing these factors. The adoption of advanced AI technologies presents a paradigm shift toward improving operational efficiency and achieving competitive advantage; however, it also entails internal obstacles, systematic planning, and substantial investment. Future studies should emphasize purposeful investment in human capital, gradual system-wide adoption of AI, and the development of AI-compatible peripheral technologies.\u003c/p\u003e","manuscriptTitle":"Enhancing Quality and Productivity in Supply Chain Operations through AI Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 00:19:50","doi":"10.21203/rs.3.rs-9064564/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09db3059-fe67-4e85-8066-09ba9e4956e3","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65688824,"name":"Business and commerce/Business and management"},{"id":65688825,"name":"Social science/Business and management"},{"id":65688826,"name":"Physical sciences/Engineering"},{"id":65688827,"name":"Business and commerce/Information systems and information technology"},{"id":65688828,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-20T06:10:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 00:19:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9064564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9064564","identity":"rs-9064564","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}