Technical Analysis and Solutions to Reduce Scrap Rate In Liquid Filler Machine: Case Study: In Moha Soft Drink Production Line

Technical Analysis and Solutions to Reduce Scrap Rate In Liquid Filler Machine: Case Study: In Moha Soft Drink Production Line | 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 Research Article Technical Analysis and Solutions to Reduce Scrap Rate In Liquid Filler Machine: Case Study: In Moha Soft Drink Production Line Samuel Berhe Gebremedhin, Goytom Desta Gebreyesus, Misgna Arefaine Gebreabzgi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7752571/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 The carbonated beverage industry relies on automated filler machines for efficient production. Despite technological advancements, scrap generation and rejected bottles during filling remain a challenge, leading to product waste and quality control issues. This study focuses on the filler machine at the Moha Soft Drink Factory in Mekelle, where scrap accounts for approximately 3.85% of production, thereby reducing profitability. The research aims to identify the root causes of scrap, correlate key technical parameters of the filler machine, and propose solutions. Quality control tools, including Pareto and fishbone diagrams, together with statistical regression analysis, were employed. Data were collected through observations, focus group discussions (FGDs), interviews, and instruments such as flow meters, pressure gauges, and temperature probes to monitor variables including speed, pressure, and temperature, viscosity, and CO₂ levels. Pareto analysis revealed underfilling, uncrowned bottles, and contamination as the top contributors to scrap, accounting for 88.1% of total rejects. Root cause analysis and regression modeling identified counter pressure (bar) and speed (bph) as significant predictors of scrap rate, explaining 37.5% of its variability (p = 0.002). The regression coefficients for counter pressure and speed were 0.561 and 0.000057, respectively, indicating their influence on scrap rate. Adjusting these parameters within optimal limits is recommended to reduce scrap, particularly from underfilling, uncrowned bottles, and contamination. Implementing these corrective measures is expected to enhance product quality and operational efficiency. Technical Analysis Solutions Scrap rate Liquid filler machines Quality Control Tools Management Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION The carbonated beverage industry plays a vital role in the global economy, producing billions of liters of soft drinks annually to meet growing consumer demand. Central to this production process are automated filler machines, which ensure speed, accuracy, and consistency in bottling operations. Their efficiency directly affects not only production output but also product quality and overall profitability [1]. However, despite continuous technological advancement, filler machines remain prone to scrap generation during the filling process. Scrap, defined as defective or rejected bottles, constitutes a major source of production loss, leading to material waste, increased operational costs, and potential customer dissatisfaction. In practice, scrap arises from multiple interrelated factors, including excessive filling speed, variations in temperature and pressure, misaligned guiding systems, poor maintenance practices, and the deterioration of machine components such as nozzles, pistons, and seals. For manufacturers, even small percentages of defective products can accumulate into substantial financial and operational burdens [2]. This is particularly true in highly competitive markets, where cost efficiency and quality are decisive for sustaining competitiveness. Moha Soft Drink Factory, one of the leading producers in Ethiopia, faces a recurring challenge with scrap in its liquid filler machines. The company targets a production yield of 98.5%, yet actual operations show an average monthly scrap rate of about 3.85%. This level of wastage significantly reduces profitability, disrupts production timelines, and undermines product quality assurance. Addressing these inefficiencies is therefore critical not only for the company’s operational performance but also for maintaining its market position and reputation. Although quality control and improvement methodologies such as Six Sigma and Lean manufacturing are well documented in industrial contexts, there is a noticeable gap in their specific application to filler machines within the soft drink industry. Most existing studies address broader aspects of process optimization or focus on general manufacturing efficiency, leaving limited evidence on tailored strategies to reduce scrap in beverage filling operations [3, 4]. Moreover, while some works have examined individual parameters such as speed or pressure, the integrated analysis of multiple technical factors, temperature, viscosity, CO₂ concentration, counter pressure, and filling speed, in relation to scrap rate, has not been sufficiently explored [5]. This study seeks to bridge these gaps by conducting a systematic technical analysis of filler machine operations at Moha Soft Drink Factory. The research employs both qualitative and quantitative approaches, utilizing quality control tools such as Pareto and fishbone diagrams alongside statistical regression analysis. By identifying and correlating key technical indicators with scrap rates, the study examines the root causes of defects, including underfilling, uncrowned bottles, and contamination, which collectively account for the majority of rejects. The objectives of this research are to identify significant technical parameters influencing scrap generation, to uncover the underlying mechanical, operational, and environmental factors that lead to recurring defects, and to propose targeted corrective measures for minimizing scrap. By achieving these objectives, the study contributes to enhancing operational efficiency, reducing costs, and ensuring consistent product quality. Beyond the immediate case study, the findings also offer broader implications for beverage manufacturers seeking to optimize filler machine performance and establish more sustainable production practices. 2. LITERATURE REVIEW A comprehensive review of existing literature provides the foundation for understanding scrap reduction in the soft drink industry and situates this study within the broader discourse on manufacturing efficiency. Previous research has examined a range of approaches, from technical analysis to improvement methodologies, to enhance the performance of filler machines and minimize production waste. A paper conducted by [6] a meta-analysis to assess the effectiveness of technical analysis in predicting soft drink demand, synthesizing findings from multiple studies to evaluate the predictive power of technical indicators. Similarly, [7] offered a comparative analysis of technical indicators and trading strategies within the soft drink industry, highlighting their potential impact on performance and profitability. These works emphasize the value of applying systematic analytical approaches in beverage production while also revealing the need for more targeted studies on production machinery, particularly filler machines. The integration of technical analysis with established improvement methodologies has received considerable attention. An article by [8] demonstrated how technical analysis can be combined with Six Sigma, showing tangible improvements in quality control, defect reduction, and machine performance. In a related direction, [9] proposed a predictive maintenance framework that leverages technical analysis and data analytics to anticipate potential machine failures, aligning with broader goals of optimizing filler machine operations. Further studies by [10, 9, 11] and [12] incorporated Design of Experiments (DOE) into technical analysis, highlighting the importance of statistical optimization techniques for identifying key variables influencing filler machine efficiency. Collectively, these studies underscore the potential of combining technical tools with structured improvement frameworks to address production inefficiencies. Recent years have also seen the emergence of advanced technologies in technical analysis. [13], presented a framework that integrates artificial intelligence (AI) techniques with technical analysis to develop predictive models capable of detecting potential machine failures and scheduling preventive maintenance. [14], compared technical analysis with machine learning approaches, critically evaluating their accuracy and reliability in forecasting filler machine performance indicators such as defect rates, production rates, and downtime. In parallel, Wu and D introduced genetic algorithms to optimize filler machine performance, adding to the growing evidence that advanced computational methods can significantly improve predictive accuracy and process optimization. These developments highlight the evolving role of AI, machine learning, and related techniques in advancing the efficiency of filler machines. Another stream of literature focuses on the technical parameters directly affecting filler machine performance. Carbonation, pressure, temperature, viscosity, and filling speed are consistently identified as critical variables in ensuring beverage quality. A paper by [3] emphasized the influence of carbonation and pressure conditions on filling efficiency, while [15] and [8] highlighted the need to balance filling speed, temperature, and accuracy to maintain consistent product characteristics. Again, [16] further demonstrated how pressure and temperature affect CO₂ solubility, noting that excessively high pressures may lead to over-carbonation and product instability, while precise control is necessary to achieve the desired sensory attributes of carbonated beverages. These studies collectively confirm the importance of managing filler machine parameters to ensure product uniformity and reduce scrap. Within this context, the application of technical analysis in filler machines has gained recognition as a practical approach to minimizing scrap rates. [11] underscored the significance of examining the intricate components and operational parameters of filler machines to identify opportunities for performance improvement. By systematically analyzing variables such as counter pressure, filling speed, temperature, and CO₂ content, technical analysis enables manufacturers to uncover root causes of defects and implement targeted interventions. Overall, the reviewed literature demonstrates substantial progress in applying technical analysis, improvement methodologies, and emerging technologies to enhance soft drink production. However, it also reveals key gaps: limited focus on filler machines as a critical stage of production, insufficient integration of multiple technical parameters into unified models, and a lack of case-specific studies in contexts such as Ethiopia. Addressing these gaps, the present study applies technical analysis to identify, quantify, and reduce scrap in the Moha Soft Drink Factory, aiming to provide practical and context-specific solutions that contribute to improved operational efficiency and product quality. 3. METHODOLOGY 3.1 Research Design A technical research design was employed to investigate and reduce scrap in soft drink production. The study analyzed historical and real-time operational data from filler machines to identify key performance parameters influencing scrap generation. Specific indicators such as fill level, counter pressure, temperature, machine speed, viscosity, and CO₂ concentration were examined. Statistical tools, particularly regression analysis, were applied to establish the relationships between these variables and scrap rates. 3.2 Research Approach A descriptive research approach guided the study. The process involved systematic data collection, identification of technical indicators, execution of root cause analysis, proposal of corrective measures, and evaluation of their potential effectiveness. This approach allowed for both the quantification of parameter effects and the qualitative interpretation of operational challenges. 3.3 Sampling Purposive (homogeneous) sampling was used to target technically skilled personnel with direct experience in operating and maintaining filler machines. The selected site, Moha Soft Drink Factory, was chosen due to its production scale and the availability of detailed technical and operational data. 3.4 Data Collection A combination of qualitative and quantitative methods was adopted. Structured questionnaires provided measurable data, while interviews and focus group discussions offered contextual insights into recurring issues. Observations during production runs further enhanced data reliability. Instruments such as flow meters, pressure gauges, and temperature probes were employed to monitor variables including speed, pressure, temperature, viscosity, and CO₂ concentration. Secondary sources such as company records, operational manuals, and historical scrap logs supplemented the primary data. 3.5 Scrap Data Scrap data spanning four months were collected and analyzed. These data included daily measurements of technical parameters, speed, temperature, CO₂ level, viscosity, and counter pressure, alongside recorded scrap rates. Both manual logs and automated systems were used as sources, ensuring data accuracy and completeness. 3.6 Data Analysis The collected data were analyzed using statistical methods. Pareto analysis was applied to prioritize the most frequent and impactful defects, while regression and ANOVA were employed to quantify relationships between technical parameters and scrap rates. These statistical approaches enabled the identification of significant predictors and the development of explanatory models. 3.7 Root Cause Analysis To move beyond surface-level defects, root cause analysis was conducted using fishbone diagrams and the 5 Whys technique. These tools facilitated the identification of underlying mechanical, operational, and material causes of scrap. The analysis emphasized systemic issues rather than isolated events, supporting the development of corrective strategies. 3.8 Key Variables Critical variables influencing scrap generation were categorized into three groups: equipment settings, raw material quality, and the production environment. Their significance was assessed based on correlation strength, frequency, and operational importance. A survey of 24 technical staff yielded 19 valid responses (a response rate of 79.08%), providing further insights into staff perceptions of scrap causes and potential improvement measures. Table 1 Summary of data distributed and returned Data collected by the questionnaire Name of plant No. questions circulated No. questions returned Respondent rate Moha soft drink 24 19 19/24 = 79.08% 4. RESULT AND DISCUSSION This study applied technical analysis tools to investigate the root causes of quality-related scrap issues in the filler machine section of Moha Soft Drink Factory. The findings, based on four months of data collection and analysis, provide strong evidence of recurring defects and inefficiencies that affect product quality and operational performance. 4.1 Scrap Data Analysis Table 2 Monthly Scrap Report Month Under fill Uncrowned Contaminated Full breakage Out of space Total scrap Feb-21 640 300 261 72 75 1348 Feb-22 642 291 260 159 60 1412 Feb-24 1004 639 294 189 32 2158 Feb-25 988 887 270 90 21 2356 Feb-26 766 564 252 57 27 1766 Feb-27 832 822 361 249 11 2475 Feb-28 389 311 132 112 12 824 March-30 213 92 33 25 31 494 March-3 521 644 342 281 13 3001 March-4 262 543 285 256 63 1509 March-5 892 655 340 271 81 2239 March-6 345 722 78 17 12 2874 March-7 654 203 232 191 64 1553 April-8 665 385 299 138 76 1563 April-10 1000 566 232 107 19 1024 April-11 1121 852 211 132 61 2377 April-12 1543 67 365 171 19 1265 April-13 102 132 122 241 66 1583 April-14 1392 311 301 113 117 1234 April-16 203 111 321 43 112 790 April-17 511 564 281 112 171 2839 April-18 1262 970 321 241 71 1865 May-19 882 260 375 173 42 1222 May-21 1433 788 182 96 65 1764 May-22 833 287 311 187 32 1941 May-23 732 243 315 175 56 1521 May-24 1103 621 286 211 31 1188 May-26 678 521 257 89 26 1670 May-27 966 522 382 101 42 1013 Total 20598 14508 8124 4518 1296 49044 The scrap data collected over four months revealed recurring defects in the filler machine operation, which directly affected production quality and efficiency. Table 2 summarizes the distribution of defects by type, including underfill, uncrowned bottles, contamination, full breakage, and out-of-space issues. Among these, underfill defects were the most frequent, with a total of 20,598 cases, followed by uncrowned bottles with 14,508 cases, and contamination with 8,124 cases. Full breakage and out-of-space defects were less common, contributing 4,518 and 1,296 cases, respectively, across the study period. A clear pattern emerges from these results: the majority of rejects were concentrated in only a few categories. Underfill, uncrowned, and contamination together accounted for 88.1% of all scrap, confirming that these are the most critical defects in the production line. This finding was further reinforced by Pareto analysis, as illustrated in Fig. 1 . The Pareto diagram shows that these three categories dominate the defect profile, consistent with the 80/20 principle, where a small number of causes are responsible for most of the problems. The high frequency of underfill defects indicates recurring challenges with maintaining consistent filling levels, which may be linked to counter-pressure imbalances, damaged or worn-out components such as pistons and filling valves, or foam formation during the filling process. Similarly, the large number of uncrowned bottles points to mechanical or material-related issues in the crowning system, including motor or sensor failures, misaligned conveyors, or defective corks. Contamination defects, while less frequent than underfill or uncrowned, also represent a major concern because they directly compromise product safety and consumer trust. Possible causes include residual cleaning chemicals, rust, malfunctioning spray jets, untreated bottles, and lapses in inspection processes. Overall, the analysis of scrap data demonstrates that efforts to reduce waste and improve efficiency should focus primarily on underfill, uncrowned, and contamination defects. Addressing these three categories would yield the greatest improvements in product quality and operational performance, while also reducing unnecessary costs and losses in the production line. 4.2 Cause and effect diagram of underfill Underfill emerged as the most frequent defect in the scrap data, with 20,598 recorded cases over the study period. This high occurrence indicates a persistent challenge in achieving consistent filling levels, which significantly affects both product quality and efficiency. To investigate the underlying factors, a cause-and-effect analysis was conducted using the Why-Why method and a fishbone diagram. Table 3 summarizes the Why-Why analysis for underfill. The first-level causes include filling valve damage, water droplets on bottles, variations in bottle volume, damaged pistons, and foam formation during the filling process. Further probing revealed that these issues often stem from prolonged machine operation without proper maintenance, malfunctioning of the bottle washer or dryer, variability in bottles supplied by customers, and the use of worn or defective expanding tubes and sliding components. Foam formation, in particular, was linked to damaged expanding tubes and unbalanced counter pressure, both of which compromise the precision of filling operations. Table 2 why-why analysis for underfill No Why-why analysis Possible causes 1 Why did underfill happen? - filling valve damage -existence of a water drop on the bottle -bottle volume difference -Use a damaged piston -occurrences of foam 2 Why filling valve damaged? - long-time machine work 3 Why water drop exist on the bottle? - speed of bottle washer m/n - damage dry m/n 4 Why do bottle volume differences occur? - from customer -human error 5 Why does foam occur? - Use a damage-expanding tube -damage sliding 6 Why expanding tube damaged? - unbalanced counter pressure -closing oaring loss lubricant The fishbone diagram (Fig. 2 ) provides a visual representation of these causes, categorizing them under machine-related, material-related, process-related, and human-related factors. Machine-related issues, such as valve and piston wear, appear as the dominant contributors, highlighting the importance of timely maintenance and replacement of critical components. Process-related issues, including foam formation and counter-pressure imbalance, also play a significant role, suggesting that operational settings require closer monitoring and adjustment. Overall, the analysis indicates that underfill defects are not caused by a single factor but rather by the interaction of equipment deterioration, process conditions, and input variability. Addressing these issues requires a dual strategy: implementing stricter maintenance schedules to prevent component wear, and optimizing process parameters such as counter pressure and lubrication. By doing so, the frequency of underfill defects can be significantly reduced, leading to improved filling accuracy and reduced scrap rates. 4.2.1 Cause and effect diagram of uncrowned and its remedial solution Uncrowned bottles represented the second most frequent category of scrap, with 14,508 recorded cases. This defect occurs when the bottle is not properly sealed with a crown cork, rendering the product unsuitable for distribution. Beyond the financial cost of wasted material, uncrowned bottles directly threaten consumer safety and product integrity, making them a critical issue for quality control. To investigate the root causes, a Why-Why analysis was conducted and is presented in Table 3 . Table 3 Why-Why analysis for uncrowned s/n Why-why analysis Possible causes 1 Why was un crowned? -electrical failures of the crowner motor -Crowner sensor failure -stop vibration system -The cork was bent and attached to the conveyor -for food safety 2 Why electrical motors fail? - use of an old machine -improper maintenance 3 Why was the cork bent? - conveyor system does not change the cork face 4 Why conveyor system cork face? - improper size of cork -cork thickness 5 Why crowner sensor fail? - conveyor problem -improper maintenance 6 Why for food safety? - Breakdown of the bottle The Why-Why analysis highlights several recurring issues. Mechanical problems, such as motor and sensor failures, are primarily linked to aging equipment and inadequate maintenance. Material-related issues, including variations in cork size and thickness, also contribute to preventing the proper placement and sealing of crowns. In addition, operational factors, such as conveyor misalignment and vibration system malfunctions, increase the likelihood of cork misplacement. Finally, quality control practices also play a role, as bottles that break during the crowning process are rejected to ensure food safety. The fishbone diagram (Fig. 3 ) provides a structured representation of these causes, showing how machine-related, material-related, and operational issues converge to produce uncrowned defects. The diagram underscores the interplay between equipment reliability and input quality. Even when machines function correctly, inconsistent cork dimensions or improper handling by conveyors can result in uncrowned bottles. Overall, uncrowned defects arise from both mechanical reliability issues and material quality inconsistencies. Addressing these problems requires a two-pronged strategy: (i) strengthening preventive maintenance practices for conveyor motors, sensors, and conveyor systems, and (ii) implementing stricter quality checks on cork materials to ensure size and thickness compliance. Improving these areas will not only reduce scrap from uncrowned bottles but also enhance sealing consistency, thereby safeguarding product safety and consumer satisfaction. 4.2.2 Cause-and-Effect Analysis of Contamination Contamination defects accounted for 8,124 cases during the study period, ranking third among the major sources of scrap. Although less frequent than underfill and uncrowned bottles, contamination is especially critical because it directly affects product safety, consumer trust, and regulatory compliance. Even a small number of contaminated bottles can result in significant reputational and financial damage, making this category an important focus of analysis. The root causes of contamination were examined through cause-and-effect analysis, which revealed several interrelated factors. Key contributors included the use of untreated or incompatible bottles, bypassing of inspection stages, residual cleaning chemicals such as caustic soda, rust formation inside machines, and malfunctioning spray jets. Inadequate light screening during inspection and occasional human error were also identified as contributors to the persistence of contamination. Machine-related causes, such as malfunctioning spray jets and rust development, were linked to insufficient preventive maintenance and the use of outdated equipment. Process-related causes included bypassed or ineffective inspection steps, which allowed defective bottles to proceed to filling. Material-related causes, such as untreated or poor-quality bottles, introduced external risks that compromised cleanliness. Finally, human-related factors, particularly lapses in inspection and handling, compounded the problem. The fishbone diagram for contamination illustrates these categories, emphasizing how technical, process, material, and human factors converge to create this defect type. Unlike underfill or uncrowned bottles, which are primarily mechanical in origin, contamination highlights the importance of both technical systems and human vigilance. Addressing contamination defects requires a comprehensive approach. Preventive maintenance must be intensified to ensure the reliability of spray jets and prevent rust formation. Process integrity must be strengthened by enforcing bottle inspection protocols and ensuring that no stages are bypassed. Material quality checks should be applied to incoming bottles to verify compatibility and cleanliness. Finally, staff training and stricter supervision can minimize human error during the inspection and filling process. Taken together, these measures would significantly reduce contamination-related scrap, improve overall product quality, and enhance consumer safety assurance. 4.3 Correlation between the Technical parameter and the scrape rate To explore how operational conditions influence scrap generation, correlation and regression analyses were conducted between technical parameters and scrap rates. The parameters considered included temperature, counter pressure, filling speed, viscosity, and CO₂ preservation. Table 4 presents the dataset collected over four months, showing daily measurements of these variables alongside the corresponding scrap rate percentages. The descriptive data reveal that fluctuations in machine settings corresponded with variations in scrap rates. For instance, higher counter pressure readings and faster machine speeds were frequently associated with increases in scrap percentages. In contrast, parameters such as viscosity and CO₂ levels showed less consistent patterns. These preliminary observations suggest that some variables exert a stronger influence on scrap generation than others, warranting more advanced statistical testing. Table 4 Technical parameters versus scrape rate Date Temperature (°C) Counter Pressure (bar) Speed (bph) CO2 Preservation Viscosity (cP) Scrap Rate (%) 01/01/2018 4.3 2.1 11880 3.3 3 1.7 02/01/2018 4.4 2.3 11900 3.4 3.1 1.8 03/01/2018 4.1 2.3 18500 3.3 3.2 2.2 04/01/2018 4.6 2.1 12000 3.3 3.1 2.2 05/01/2018 4.5 2.1 12100 3.2 3.2 2.3 06/01/2018 4.5 2.3 19500 3.4 3.3 2.4 07/01/2018 4.7 3 11500 3.2 3 2 01/02/2018 4.5 2.3 12000 3.5 3.1 1.9 02/02/2018 4.5 2.2 11800 3.5 3.3 1.8 03/02/2018 4.6 2.3 11880 3.5 3.2 2.5 04/02/2018 4.5 2.2 12000 3.6 3.2 1.7 05/02/2018 4.6 2.1 11700 3.4 3.1 1.6 06/02/2018 4.6 2.2 11800 3.5 3 2 07/02/2018 4.5 2.3 11222 3.8 3.2 2.2 01/03/2018 4.4 3 18888 3.9 3 3.3 02/03/2018 4.5 2.4 12000 3.4 3.3 2.5 03/03/2018 4.6 2.9 11500 3.4 3.2 2.4 04/03/2018 4.5 2.3 12000 3.5 3 1.9 05/03/2018 4.6 2.3 11700 3.6 3.2 2.3 06/03/2018 4.5 2.2 11800 3.8 3.1 2.2 07/03/2018 4.6 2.3 11750 3.4 3.2 2.5 08/03/2018 4.5 2.4 13005 3.6 3 1.8 01/04/2018 4.3 2.6 11900 4 3 2.3 02/04/2018 4.5 2.5 12000 3.2 3.1 2.2 03/04/2018 4.6 2.8 11900 3.2 3.3 2.5 04/04/2018 4.5 2.8 11500 3.1 3.2 2.3 05/04/2018 4.6 2.9 12500 3.2 3.4 2.3 06/04/2018 4.5 2.3 12100 3.4 3 2.5 07/04/2018 4.5 2.4 12500 3.8 3.2 2.4 4.3.1 Regression Analysis A regression analysis was conducted to examine the relationship between scrap rate and key technical parameters, counter pressure, temperature, viscosity, speed, and CO₂ preservation using data collected from the filler machine. By applying Minitab 17, the study evaluated the strength and direction of these relationships, revealing how each parameter influences scrap generation. This analysis, supported by prior research (Wooten, 2019; Rebecca, 2020), provides a data-driven foundation for process optimization and quality control by identifying critical variables for reducing scrap and improving efficiency. Table 5 Best Subsets Regression: Scrap Rate (versus Temperature, Counter Pressure, speed, and viscosity) and the Response is Scrap Rate (%) extracted from Table 4 . S/n Vars R-sq. R-sq. (adj) R-sq. (pred) Mallows Cp S T o Pr S CO 2 V 1 1 24.8 22.1 5.6 6.9 0.30889 X 2 1 17.6 14.6 0.0 10.0 0.32349 X 3 2 37.5 32.7 14.4 3.6 0.28712 X 4 2 29.9 24.5 3.7 6.8 0.30401 X X 5 3 40.8 33.7 17.3 4.2 0.28497 X X 6 3 39.3 32.0 7.8 4.8 0.28851 X X X 7 4 44.0 34.7 5.4 4.8 0.28088 X X X 8 4 43.6 34.2 16.1 5.0 0.28384 X X X 9 5 45.9 34.1 0.5 6.0 0.28409 X X X X X The Best Subsets Regression analysis revealed that scrap rate in the filler machine is significantly influenced by multiple technical parameters, with the combination of temperature, counter pressure, speed, and viscosity offering the strongest predictive power (R² = 45.9%). While adding variables generally improved model accuracy, careful consideration of Mallows' C p and adjusted R² emphasized the need to balance complexity and model fit. The final model, refined using backward elimination, retained only the most statistically significant predictors, enhancing interpretability and reducing the risk of over-fitting. This approach underscores the importance of multi-variable control in optimizing production quality and minimizing scrap. Regression Analysis: Scrap Rate (versus Temperature, Counter Pressure, Speed (bph), viscosity) 4.3.2 Backward Elimination Results Using backward elimination, only counter pressure and speed remained as statistically significant predictors of scrap rate. The final regression equation was derived as: Scrap Rate (%) = 0.126 + 0.561 × Counter Pressure (bar) + 0.000057 × Speed (bph)} The coefficients indicate that counter pressure exerts the stronger influence on scrap rate. Specifically, an increase of one bar in counter pressure increases the scrap rate by approximately 0.561%, while an increase of 1,000 bottles per hour in speed contributes an additional 0.057% to the scrap rate. While speed has a relatively smaller coefficient, its effect becomes significant when production runs at very high speeds. Table 6 Backward Elimination of Terms Step1 Step2 Step3 Step4 Coef. P Coef. P Coef. p Coef. P Constant -5.01 -2.88 -0.859 0.126 Temperature 0.498 0.381 C.pressure 0.529 0.015 0.575 0.007 0.585 0.006 0.561 0.008 Speed 0.000064 0.039 0.000050 0.054 0.000053 0.042 0.000057 0.030 CO2 0.390 0.132 0.365 0.152 0.281 0.249 Viscosity 0.488 0.338 0.571 0.253 S 0.284087 0.282882 0.284770 0.287124 R-sq. 45.88% 44.00% 40.80% 37.7% R-sq.(adj) 34.11% 34.67% 33.70% 14.45% R-sq.(pred) Mallow’s low(cp) 5.2% 5.30% 6 17.28% 4.8 4.16 3.56% 3.56 Given Alpha to remove = 0.1; The final model includes only Counter Pressure (bar) and Speed (bph) as significant predictors of Scrap Rate (%). The other are not significant since large p-values, but counter pressure and speed have small values as shown in the above table. 4.3.3 ANOVA Results The ANOVA table (Table 7 ) confirmed that the regression model is statistically significant, with an overall p-value of 0.002. Within the model, counter pressure was highly significant (p = 0.008), while speed was moderately significant (p = 0.030). This reinforces the conclusion that both parameters play critical roles in determining scrap rate variability. Table 7 Analysis of Variance Source DF Adj.SS Adj.MS F-value P-value Regression 2 1.2862 0.64310 7.8 0.002 Pressure 1 0.6862 0.68206 8.27 0.008 Speed 1 0.4345 0.43445 5.27 0.030 Error 26 2.1434 0.08244 Total 28 3.4297 4.3.4 Model Summary and Diagnostics The model explained 37.5% of the observed variation in scrap rate, with an adjusted R² of 32.7%. Although the predictive power was moderate (predicted R² = 14.5%), the results provide valuable insights into the main drivers of scrap. Diagnostics identified observations 6 and 15 as unusual data points with higher residuals, suggesting possible outliers or process anomalies that merit further investigation Table 8 Model summery S R-sq. R-sq.(adj.) R-sq.(pred.) 0.287124 37.5% 32.7% 14.45% The relatively low predicted R² suggests that while the model captures significant trends, additional unmeasured variables, such as operator skill, raw material consistency, or machine wear, may also influence scrap rates. 4.3.5 Interpretation The regression results emphasize that counter pressure and speed are the dominant parameters affecting scrap in the filler machine. Small deviations in counter pressure settings directly increase the risk of underfill and foam-related defects, while excessive machine speed amplifies operational errors, leading to both underfill and uncrowned defects. Other parameters such as temperature, viscosity, and CO₂ preservation did not show statistically significant relationships in this dataset, though they may still play secondary roles under different conditions. Table 9 Coefficients Term Coef. SE Coef. T-value P-value VIF Constant 0.126 0.531 0.24 0.815 Pressure 0.561 0.195 0.88 0.008 1.02 Speed 0.000057 0.000025 2.3 0.303 1.02 Table 9 shows that pressure significantly affects the scrap rate (p = 0.008), while speed does not (p = 0.303); both predictors have low VIFs (1.02), indicating no multi-collinearity, and the constant term is not statistically significant. And the coefficient for Pressure' is 0.561, statistically significant with a t-value of 0.88 (p = 0.008), while Speed shows a coefficient of 0.000057 with a t-value of 2.3 (p = 0.303). Overall, the statistical analysis confirms that effective control of counter pressure and careful regulation of machine speed are essential strategies for reducing scrap. These findings provide a strong basis for operational improvements, including parameter optimization and enhanced process monitoring. 4.3.6 Integrated Discussion and Interpretation The combined findings from scrap data analysis, cause-and-effect studies, and regression modeling provide a comprehensive understanding of the major drivers of scrap in the Moha Soft Drink Factory filler machine. The results clearly show that three defect categories—underfill, uncrowned bottles, and contamination- account for the majority of scrap, together contributing 88.1% of total rejects. These defects, therefore, represent the “vital few” that must be prioritized for corrective actions. The cause-and-effect analyses highlighted the complexity of factors contributing to these defects. Underfill was primarily associated with machine component deterioration (filling valves, pistons, and expanding tubes) and process parameters such as counter pressure and foam formation. Uncrowned bottles were strongly linked to crown motor and sensor failures, conveyor malfunctions, and material inconsistencies in cork size and thickness. Contamination, while less frequent, stemmed from both technical failures (e.g., spray jet malfunctions, rust formation) and process lapses (e.g., bypassed inspections, untreated bottles), underscoring the need for improved preventive maintenance and quality checks. Regression analysis reinforced these findings by quantifying the relationship between technical parameters and scrap rates. Among all variables studied, counter pressure and filling speed emerged as statistically significant predictors, together explaining 37.5% of the variability in scrap. This indicates that while mechanical and material issues contribute to scrap, process control through parameter optimization is equally important. In particular, deviations in counter pressure settings were found to strongly influence underfill and foam formation, while high machine speeds increased the likelihood of both underfill and uncrowned defects. Taken together, the analyses demonstrate that scrap generation is not caused by isolated issues but rather by the interplay of technical, material, and operational factors. The evidence suggests that meaningful scrap reduction requires a multifaceted strategy: preventive maintenance to reduce mechanical failures, strict material quality checks to minimize input variability, and tighter process control of critical parameters, especially counter pressure and speed. By addressing these areas simultaneously, Moha Soft Drink Factory can substantially reduce waste, enhance product quality, and improve production efficiency. Moreover, the results of this study contribute practical insights to the broader soft drink industry, where filler machines remain critical bottlenecks in production and significant sources of cost inefficiency. 5. CONCLUSION This study identified underfill, uncrowned bottles, and contamination as the dominant defects in Moha Soft Drink Factory’s filler machine, together accounting for 88.1% of total rejects. Root cause analysis linked these defects to machine wear, material variability, and process inconsistencies, while regression modeling showed counter pressure and filling speed as significant predictors of scrap rate, explaining 37.5% of its variation. The originality of this work lies in integrating technical parameter analysis with structured quality control tools to address scrap reduction in a beverage filler context, offering new empirical evidence from a developing economy. Its novelty and generalizability are demonstrated by showing that optimizing counter pressure and speed is transferable to other beverage manufacturers beyond Ethiopia. Practically, the findings provide actionable solutions, preventive maintenance, stricter material checks, and real-time parameter monitoring that reduce scrap, enhance efficiency, and strengthen competitiveness. By combining empirical analysis with practical applicability, this study contributes a data-driven framework for sustainable quality improvement in the soft drink industry. Declarations Ethical Approval and Consent to Participate This research study did not comprise any human or animal participants. Hence, the ethical approval and consent from participants are not applicable. Consent for Publication This study did not involve any person’s data. Therefore, the consent publication is not applicable. Funding This research was funded by Mekelle University for data collection, analysis, and report preparation. However, no additional funding was provided for publication costs. Consequently, the authors have chosen a subscription-based publishing model and declare that they have no competing interests related to this publication. Availability of Data and Materials All data generated and analyzed throughout this study that support the findings are available from the corresponding author, Samuel Berhe Gebremedhin (SBG), upon reasonable request. For the sake of protecting the privacy of study participants and obeying confidentiality agreements, the data cannot be shared openly. Nevertheless, in case researchers are interested in accessing specific details or verifying the results of the study may contact the corresponding author. Access will be granted on a case-by-case basis while guaranteeing adherence to privacy and confidentiality protocols. Completing Interests The authors declare that they have no competing interests. Author’s Contributions SBG initiated and conducted the research and drafted the manuscript. GDG contributed by providing advice throughout the study and support in preparing the manuscript. MAG also participates in result analysis and manuscript writing. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank Mekelle University for its support and contributions to this research. References "I. W. R. Taifa, E. D. Makundi, and G. S. Mwaluko, “Production quality improvement for the soft drinks bottling industry through Six Sigma methodology,” International Journal of Industrial and Systems Engineering, vol. 41, no. 2, pp. 159–179, 2022.". "A. K. Mohanty, S. R. Mishra, and S. K. Mahapatra, “Implementation of Lean Six Sigma approach to minimize waste at a food manufacturing industry,” Journal of Advanced Manufacturing Technology, vol. 34, no. 1, pp. 11–24, 2024.". "J. F. S. Santos and J. C. Leite, “Application of the Six Sigma methodology and use of the DMAIC method to reduce the loss rate of aluminium cans in a beverage industry to reduce operational costs: Case study: Latax Refrigerantes Ltda,” International Journ". L. a. Park, "Integration on the Technical analysis and six sigma methodologies in filler machine operation," journal of industries, pp. 23,123-129, 2012. "R. Müller, L. M. Gonçalves, and T. A. Souza, “Filling process optimization through modifications in machine settings,” Processes, vol. 10, no. 11, pp. 2273–2288, 2022.". "D. R. Emerson, “Liquid filling machine trends: Today’s equipment is more versatile,” Food Engineering Magazine, vol. 94, no. 3, pp. 28–32, Mar. 2021.". "D. J. Poland, L. Puglisi, and D. Ravi, “Industrial machines health prognosis using a transformer-based framework,” arXiv preprint arXiv:2411.14443, Nov. 2024.". M. Li, "a framework for optimizing filler machine performance using a combination of technical analysis and artificial intelligence (AI) techniques.," Journal of production and economics managment, pp. 34,111-124, 2020. T. Kim, "a predictive maintenance framework for filler machines using a combination of technical analysis and data analytics techniques.," journal of engineering, pp. 50-70, 2020. Z. a. Wang, "an optimization approach for filler machine performance by integrating technical analysis with Design of Experiments (DOE) methodology.," journal of future market, pp. 121-233, 2018. W. a. Zung, "An Emperical study on the application of Technical anaysis in soft drink industy," international journal of production economics, pp. 153,123-128, 2020. wang, "a comparative study on the effectiveness of technical analysis and machine learning techniques in predicting filler machine performance," journal of quality control and engineering, pp. 89-100, 2019. "GEA Group, “Drinktec 2025: Next-level beverage filling,” GEA Trade Press News, 2025.". "Siemens AG, “AI-supported predictive maintenance: Siemens and Sachsenmilch are breaking new ground in the food and beverage industry,” Manufacturing Tomorrow, Jun. 2025.". G. a. Kim, "Technical analysis in soft drink industry:Acomparative study," journal of agriculture and applied economics, pp. 45,321-338, 2021. "Krones AG, “AI ensures accurate fill levels,” Krones Magazine – Innovation, 2024.". Additional Declarations No competing interests reported. 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. 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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-7752571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522942978,"identity":"c478c322-8503-4363-8a89-df45f4310f3e","order_by":0,"name":"Samuel Berhe 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11:49:57","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120193,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7752571/v1/c94419f3afd31a49ca74b903.html"},{"id":92802516,"identity":"47f08d4e-4c7d-436a-b619-0acc4a01c42f","added_by":"auto","created_at":"2025-10-05 11:41:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17685,"visible":true,"origin":"","legend":"\u003cp\u003ePareto diagram analysis of the scraps\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7752571/v1/303418840933f77492695e23.png"},{"id":92802517,"identity":"782c464c-4edb-4f04-99bc-540fc65aff04","added_by":"auto","created_at":"2025-10-05 11:41:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19310,"visible":true,"origin":"","legend":"\u003cp\u003ecause and effect diagram under fill\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7752571/v1/86ec4cbc7cc7c42611948ba3.png"},{"id":92803023,"identity":"f73d8681-dad7-4d2f-b53c-1901a124e126","added_by":"auto","created_at":"2025-10-05 11:49:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24338,"visible":true,"origin":"","legend":"\u003cp\u003ecauses and effect diagram for uncrown\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7752571/v1/c8138daace9191a4b4aa8268.png"},{"id":93597768,"identity":"9867eafd-5113-4ff1-8bbe-5f309b52e3cc","added_by":"auto","created_at":"2025-10-15 14:22:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1467864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7752571/v1/15ee9602-8de3-4e9c-9f75-a9086e03d38f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Technical Analysis and Solutions to Reduce Scrap Rate In Liquid Filler Machine: Case Study: In Moha Soft Drink Production Line","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe carbonated beverage industry plays a vital role in the global economy, producing billions of liters of soft drinks annually to meet growing consumer demand. Central to this production process are automated filler machines, which ensure speed, accuracy, and consistency in bottling operations. Their efficiency directly affects not only production output but also product quality and overall profitability [1]. However, despite continuous technological advancement, filler machines remain prone to scrap generation during the filling process. Scrap, defined as defective or rejected bottles, constitutes a major source of production loss, leading to material waste, increased operational costs, and potential customer dissatisfaction.\u003c/p\u003e\u003cp\u003eIn practice, scrap arises from multiple interrelated factors, including excessive filling speed, variations in temperature and pressure, misaligned guiding systems, poor maintenance practices, and the deterioration of machine components such as nozzles, pistons, and seals. For manufacturers, even small percentages of defective products can accumulate into substantial financial and operational burdens [2]. This is particularly true in highly competitive markets, where cost efficiency and quality are decisive for sustaining competitiveness.\u003c/p\u003e\u003cp\u003eMoha Soft Drink Factory, one of the leading producers in Ethiopia, faces a recurring challenge with scrap in its liquid filler machines. The company targets a production yield of 98.5%, yet actual operations show an average monthly scrap rate of about 3.85%. This level of wastage significantly reduces profitability, disrupts production timelines, and undermines product quality assurance. Addressing these inefficiencies is therefore critical not only for the company\u0026rsquo;s operational performance but also for maintaining its market position and reputation.\u003c/p\u003e\u003cp\u003eAlthough quality control and improvement methodologies such as Six Sigma and Lean manufacturing are well documented in industrial contexts, there is a noticeable gap in their specific application to filler machines within the soft drink industry. Most existing studies address broader aspects of process optimization or focus on general manufacturing efficiency, leaving limited evidence on tailored strategies to reduce scrap in beverage filling operations [3, 4]. Moreover, while some works have examined individual parameters such as speed or pressure, the integrated analysis of multiple technical factors, temperature, viscosity, CO₂ concentration, counter pressure, and filling speed, in relation to scrap rate, has not been sufficiently explored [5].\u003c/p\u003e\u003cp\u003eThis study seeks to bridge these gaps by conducting a systematic technical analysis of filler machine operations at Moha Soft Drink Factory. The research employs both qualitative and quantitative approaches, utilizing quality control tools such as Pareto and fishbone diagrams alongside statistical regression analysis. By identifying and correlating key technical indicators with scrap rates, the study examines the root causes of defects, including underfilling, uncrowned bottles, and contamination, which collectively account for the majority of rejects.\u003c/p\u003e\u003cp\u003eThe objectives of this research are to identify significant technical parameters influencing scrap generation, to uncover the underlying mechanical, operational, and environmental factors that lead to recurring defects, and to propose targeted corrective measures for minimizing scrap. By achieving these objectives, the study contributes to enhancing operational efficiency, reducing costs, and ensuring consistent product quality. Beyond the immediate case study, the findings also offer broader implications for beverage manufacturers seeking to optimize filler machine performance and establish more sustainable production practices.\u003c/p\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cp\u003eA comprehensive review of existing literature provides the foundation for understanding scrap reduction in the soft drink industry and situates this study within the broader discourse on manufacturing efficiency. Previous research has examined a range of approaches, from technical analysis to improvement methodologies, to enhance the performance of filler machines and minimize production waste.\u003c/p\u003e\u003cp\u003eA paper conducted by [6] a meta-analysis to assess the effectiveness of technical analysis in predicting soft drink demand, synthesizing findings from multiple studies to evaluate the predictive power of technical indicators. Similarly, [7] offered a comparative analysis of technical indicators and trading strategies within the soft drink industry, highlighting their potential impact on performance and profitability. These works emphasize the value of applying systematic analytical approaches in beverage production while also revealing the need for more targeted studies on production machinery, particularly filler machines.\u003c/p\u003e\u003cp\u003eThe integration of technical analysis with established improvement methodologies has received considerable attention. An article by [8] demonstrated how technical analysis can be combined with Six Sigma, showing tangible improvements in quality control, defect reduction, and machine performance. In a related direction, [9] proposed a predictive maintenance framework that leverages technical analysis and data analytics to anticipate potential machine failures, aligning with broader goals of optimizing filler machine operations. Further studies by [10, 9, 11] and [12] incorporated Design of Experiments (DOE) into technical analysis, highlighting the importance of statistical optimization techniques for identifying key variables influencing filler machine efficiency. Collectively, these studies underscore the potential of combining technical tools with structured improvement frameworks to address production inefficiencies.\u003c/p\u003e\u003cp\u003eRecent years have also seen the emergence of advanced technologies in technical analysis. [13], presented a framework that integrates artificial intelligence (AI) techniques with technical analysis to develop predictive models capable of detecting potential machine failures and scheduling preventive maintenance. [14], compared technical analysis with machine learning approaches, critically evaluating their accuracy and reliability in forecasting filler machine performance indicators such as defect rates, production rates, and downtime. In parallel, Wu and D introduced genetic algorithms to optimize filler machine performance, adding to the growing evidence that advanced computational methods can significantly improve predictive accuracy and process optimization. These developments highlight the evolving role of AI, machine learning, and related techniques in advancing the efficiency of filler machines.\u003c/p\u003e\u003cp\u003eAnother stream of literature focuses on the technical parameters directly affecting filler machine performance. Carbonation, pressure, temperature, viscosity, and filling speed are consistently identified as critical variables in ensuring beverage quality. A paper by [3] emphasized the influence of carbonation and pressure conditions on filling efficiency, while [15] and [8] highlighted the need to balance filling speed, temperature, and accuracy to maintain consistent product characteristics. Again, [16] further demonstrated how pressure and temperature affect CO₂ solubility, noting that excessively high pressures may lead to over-carbonation and product instability, while precise control is necessary to achieve the desired sensory attributes of carbonated beverages. These studies collectively confirm the importance of managing filler machine parameters to ensure product uniformity and reduce scrap.\u003c/p\u003e\u003cp\u003eWithin this context, the application of technical analysis in filler machines has gained recognition as a practical approach to minimizing scrap rates. [11] underscored the significance of examining the intricate components and operational parameters of filler machines to identify opportunities for performance improvement. By systematically analyzing variables such as counter pressure, filling speed, temperature, and CO₂ content, technical analysis enables manufacturers to uncover root causes of defects and implement targeted interventions.\u003c/p\u003e\u003cp\u003eOverall, the reviewed literature demonstrates substantial progress in applying technical analysis, improvement methodologies, and emerging technologies to enhance soft drink production. However, it also reveals key gaps: limited focus on filler machines as a critical stage of production, insufficient integration of multiple technical parameters into unified models, and a lack of case-specific studies in contexts such as Ethiopia. Addressing these gaps, the present study applies technical analysis to identify, quantify, and reduce scrap in the Moha Soft Drink Factory, aiming to provide practical and context-specific solutions that contribute to improved operational efficiency and product quality.\u003c/p\u003e"},{"header":"3. METHODOLOGY","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Design\u003c/h2\u003e\u003cp\u003eA technical research design was employed to investigate and reduce scrap in soft drink production. The study analyzed historical and real-time operational data from filler machines to identify key performance parameters influencing scrap generation. Specific indicators such as fill level, counter pressure, temperature, machine speed, viscosity, and CO₂ concentration were examined. Statistical tools, particularly regression analysis, were applied to establish the relationships between these variables and scrap rates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Research Approach\u003c/h2\u003e\u003cp\u003eA descriptive research approach guided the study. The process involved systematic data collection, identification of technical indicators, execution of root cause analysis, proposal of corrective measures, and evaluation of their potential effectiveness. This approach allowed for both the quantification of parameter effects and the qualitative interpretation of operational challenges.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Sampling\u003c/h2\u003e\u003cp\u003ePurposive (homogeneous) sampling was used to target technically skilled personnel with direct experience in operating and maintaining filler machines. The selected site, Moha Soft Drink Factory, was chosen due to its production scale and the availability of detailed technical and operational data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Data Collection\u003c/h2\u003e\u003cp\u003eA combination of qualitative and quantitative methods was adopted. Structured questionnaires provided measurable data, while interviews and focus group discussions offered contextual insights into recurring issues. Observations during production runs further enhanced data reliability. Instruments such as flow meters, pressure gauges, and temperature probes were employed to monitor variables including speed, pressure, temperature, viscosity, and CO₂ concentration. Secondary sources such as company records, operational manuals, and historical scrap logs supplemented the primary data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Scrap Data\u003c/h2\u003e\u003cp\u003eScrap data spanning four months were collected and analyzed. These data included daily measurements of technical parameters, speed, temperature, CO₂ level, viscosity, and counter pressure, alongside recorded scrap rates. Both manual logs and automated systems were used as sources, ensuring data accuracy and completeness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Data Analysis\u003c/h2\u003e\u003cp\u003eThe collected data were analyzed using statistical methods. Pareto analysis was applied to prioritize the most frequent and impactful defects, while regression and ANOVA were employed to quantify relationships between technical parameters and scrap rates. These statistical approaches enabled the identification of significant predictors and the development of explanatory models.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Root Cause Analysis\u003c/h2\u003e\u003cp\u003eTo move beyond surface-level defects, root cause analysis was conducted using fishbone diagrams and the 5 Whys technique. These tools facilitated the identification of underlying mechanical, operational, and material causes of scrap. The analysis emphasized systemic issues rather than isolated events, supporting the development of corrective strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Key Variables\u003c/h2\u003e\u003cp\u003eCritical variables influencing scrap generation were categorized into three groups: equipment settings, raw material quality, and the production environment. Their significance was assessed based on correlation strength, frequency, and operational importance. A survey of 24 technical staff yielded 19 valid responses (a response rate of 79.08%), providing further insights into staff perceptions of scrap causes and potential improvement measures.\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\u003eSummary of data distributed and returned\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eData collected by the questionnaire\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName of plant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. questions circulated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo. questions returned\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRespondent rate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoha soft drink\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19/24\u0026thinsp;=\u0026thinsp;79.08%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. RESULT AND DISCUSSION","content":"\u003cp\u003eThis study applied technical analysis tools to investigate the root causes of quality-related scrap issues in the filler machine section of Moha Soft Drink Factory. The findings, based on four months of data collection and analysis, provide strong evidence of recurring defects and inefficiencies that affect product quality and operational performance.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Scrap Data Analysis\u003c/h2\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\u003eMonthly Scrap Report\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=\"left\" 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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnder fill\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUncrowned\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eContaminated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFull breakage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOut of space\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal scrap\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2356\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFeb-28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarch-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eApril-18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1865\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1941\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMay-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e20598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49044\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 scrap data collected over four months revealed recurring defects in the filler machine operation, which directly affected production quality and efficiency. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the distribution of defects by type, including underfill, uncrowned bottles, contamination, full breakage, and out-of-space issues. Among these, underfill defects were the most frequent, with a total of 20,598 cases, followed by uncrowned bottles with 14,508 cases, and contamination with 8,124 cases. Full breakage and out-of-space defects were less common, contributing 4,518 and 1,296 cases, respectively, across the study period.\u003c/p\u003e\u003cp\u003eA clear pattern emerges from these results: the majority of rejects were concentrated in only a few categories. Underfill, uncrowned, and contamination together accounted for 88.1% of all scrap, confirming that these are the most critical defects in the production line. This finding was further reinforced by Pareto analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Pareto diagram shows that these three categories dominate the defect profile, consistent with the 80/20 principle, where a small number of causes are responsible for most of the problems.\u003c/p\u003e\u003cp\u003eThe high frequency of underfill defects indicates recurring challenges with maintaining consistent filling levels, which may be linked to counter-pressure imbalances, damaged or worn-out components such as pistons and filling valves, or foam formation during the filling process. Similarly, the large number of uncrowned bottles points to mechanical or material-related issues in the crowning system, including motor or sensor failures, misaligned conveyors, or defective corks. Contamination defects, while less frequent than underfill or uncrowned, also represent a major concern because they directly compromise product safety and consumer trust. Possible causes include residual cleaning chemicals, rust, malfunctioning spray jets, untreated bottles, and lapses in inspection processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the analysis of scrap data demonstrates that efforts to reduce waste and improve efficiency should focus primarily on underfill, uncrowned, and contamination defects. Addressing these three categories would yield the greatest improvements in product quality and operational performance, while also reducing unnecessary costs and losses in the production line.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Cause and effect diagram of underfill\u003c/h2\u003e\u003cp\u003eUnderfill emerged as the most frequent defect in the scrap data, with 20,598 recorded cases over the study period. This high occurrence indicates a persistent challenge in achieving consistent filling levels, which significantly affects both product quality and efficiency. To investigate the underlying factors, a cause-and-effect analysis was conducted using the Why-Why method and a fishbone diagram.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the Why-Why analysis for underfill. The first-level causes include filling valve damage, water droplets on bottles, variations in bottle volume, damaged pistons, and foam formation during the filling process. Further probing revealed that these issues often stem from prolonged machine operation without proper maintenance, malfunctioning of the bottle washer or dryer, variability in bottles supplied by customers, and the use of worn or defective expanding tubes and sliding components. Foam formation, in particular, was linked to damaged expanding tubes and unbalanced counter pressure, both of which compromise the precision of filling operations.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ewhy-why analysis for underfill\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\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy-why analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePossible causes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy did underfill happen?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003efilling valve damage\u003c/p\u003e\u003cp\u003e-existence of a water drop on the bottle\u003c/p\u003e\u003cp\u003e-bottle volume difference\u003c/p\u003e\u003cp\u003e-Use a damaged piston\u003c/p\u003e\u003cp\u003e-occurrences of foam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy filling valve damaged?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003elong-time machine work\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy water drop exist on the bottle?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003espeed of bottle washer m/n\u003c/p\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003edamage dry m/n\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy do bottle volume differences occur?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003efrom customer\u003c/p\u003e\u003cp\u003e-human error\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy does foam occur?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003eUse a damage-expanding tube\u003c/p\u003e\u003cp\u003e-damage sliding\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy expanding tube damaged?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003eunbalanced counter pressure\u003c/p\u003e\u003cp\u003e-closing oaring loss lubricant\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 fishbone diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) provides a visual representation of these causes, categorizing them under machine-related, material-related, process-related, and human-related factors. Machine-related issues, such as valve and piston wear, appear as the dominant contributors, highlighting the importance of timely maintenance and replacement of critical components. Process-related issues, including foam formation and counter-pressure imbalance, also play a significant role, suggesting that operational settings require closer monitoring and adjustment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the analysis indicates that underfill defects are not caused by a single factor but rather by the interaction of equipment deterioration, process conditions, and input variability. Addressing these issues requires a dual strategy: implementing stricter maintenance schedules to prevent component wear, and optimizing process parameters such as counter pressure and lubrication. By doing so, the frequency of underfill defects can be significantly reduced, leading to improved filling accuracy and reduced scrap rates.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Cause and effect diagram of uncrowned and its remedial solution\u003c/h2\u003e\u003cp\u003eUncrowned bottles represented the second most frequent category of scrap, with 14,508 recorded cases. This defect occurs when the bottle is not properly sealed with a crown cork, rendering the product unsuitable for distribution. Beyond the financial cost of wasted material, uncrowned bottles directly threaten consumer safety and product integrity, making them a critical issue for quality control. To investigate the root causes, a Why-Why analysis was conducted and is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWhy-Why analysis for uncrowned\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\u003es/n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy-why analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePossible causes\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\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy was un crowned?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-electrical failures of the crowner motor\u003c/p\u003e\u003cp\u003e-Crowner sensor failure\u003c/p\u003e\u003cp\u003e-stop vibration system\u003c/p\u003e\u003cp\u003e-The cork was bent and attached to the conveyor\u003c/p\u003e\u003cp\u003e-for food safety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy electrical motors fail?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003euse of an old machine\u003c/p\u003e\u003cp\u003e-improper maintenance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy was the cork bent?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003econveyor system does not change the cork face\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy conveyor system cork face?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003eimproper size of cork\u003c/p\u003e\u003cp\u003e-cork thickness\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy crowner sensor fail?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003econveyor problem\u003c/p\u003e\u003cp\u003e-improper maintenance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhy for food safety?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003eBreakdown of the bottle\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 Why-Why analysis highlights several recurring issues. Mechanical problems, such as motor and sensor failures, are primarily linked to aging equipment and inadequate maintenance. Material-related issues, including variations in cork size and thickness, also contribute to preventing the proper placement and sealing of crowns. In addition, operational factors, such as conveyor misalignment and vibration system malfunctions, increase the likelihood of cork misplacement. Finally, quality control practices also play a role, as bottles that break during the crowning process are rejected to ensure food safety.\u003c/p\u003e\u003cp\u003eThe fishbone diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) provides a structured representation of these causes, showing how machine-related, material-related, and operational issues converge to produce uncrowned defects. The diagram underscores the interplay between equipment reliability and input quality. Even when machines function correctly, inconsistent cork dimensions or improper handling by conveyors can result in uncrowned bottles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, uncrowned defects arise from both mechanical reliability issues and material quality inconsistencies. Addressing these problems requires a two-pronged strategy: (i) strengthening preventive maintenance practices for conveyor motors, sensors, and conveyor systems, and (ii) implementing stricter quality checks on cork materials to ensure size and thickness compliance. Improving these areas will not only reduce scrap from uncrowned bottles but also enhance sealing consistency, thereby safeguarding product safety and consumer satisfaction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Cause-and-Effect Analysis of Contamination\u003c/h2\u003e\u003cp\u003eContamination defects accounted for 8,124 cases during the study period, ranking third among the major sources of scrap. Although less frequent than underfill and uncrowned bottles, contamination is especially critical because it directly affects product safety, consumer trust, and regulatory compliance. Even a small number of contaminated bottles can result in significant reputational and financial damage, making this category an important focus of analysis.\u003c/p\u003e\u003cp\u003eThe root causes of contamination were examined through cause-and-effect analysis, which revealed several interrelated factors. Key contributors included the use of untreated or incompatible bottles, bypassing of inspection stages, residual cleaning chemicals such as caustic soda, rust formation inside machines, and malfunctioning spray jets. Inadequate light screening during inspection and occasional human error were also identified as contributors to the persistence of contamination.\u003c/p\u003e\u003cp\u003eMachine-related causes, such as malfunctioning spray jets and rust development, were linked to insufficient preventive maintenance and the use of outdated equipment. Process-related causes included bypassed or ineffective inspection steps, which allowed defective bottles to proceed to filling. Material-related causes, such as untreated or poor-quality bottles, introduced external risks that compromised cleanliness. Finally, human-related factors, particularly lapses in inspection and handling, compounded the problem.\u003c/p\u003e\u003cp\u003eThe fishbone diagram for contamination illustrates these categories, emphasizing how technical, process, material, and human factors converge to create this defect type. Unlike underfill or uncrowned bottles, which are primarily mechanical in origin, contamination highlights the importance of both technical systems and human vigilance.\u003c/p\u003e\u003cp\u003eAddressing contamination defects requires a comprehensive approach. Preventive maintenance must be intensified to ensure the reliability of spray jets and prevent rust formation. Process integrity must be strengthened by enforcing bottle inspection protocols and ensuring that no stages are bypassed. Material quality checks should be applied to incoming bottles to verify compatibility and cleanliness. Finally, staff training and stricter supervision can minimize human error during the inspection and filling process.\u003c/p\u003e\u003cp\u003eTaken together, these measures would significantly reduce contamination-related scrap, improve overall product quality, and enhance consumer safety assurance.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Correlation between the Technical parameter and the scrape rate\u003c/h2\u003e\u003cp\u003eTo explore how operational conditions influence scrap generation, correlation and regression analyses were conducted between technical parameters and scrap rates. The parameters considered included temperature, counter pressure, filling speed, viscosity, and CO₂ preservation. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the dataset collected over four months, showing daily measurements of these variables alongside the corresponding scrap rate percentages.\u003c/p\u003e\u003cp\u003eThe descriptive data reveal that fluctuations in machine settings corresponded with variations in scrap rates. For instance, higher counter pressure readings and faster machine speeds were frequently associated with increases in scrap percentages. In contrast, parameters such as viscosity and CO₂ levels showed less consistent patterns. These preliminary observations suggest that some variables exert a stronger influence on scrap generation than others, warranting more advanced statistical testing.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTechnical parameters versus scrape rate\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCounter Pressure (bar)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpeed (bph)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCO2 Preservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eViscosity (cP)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eScrap Rate (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e01/01/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e02/01/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e03/01/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e13005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e01/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e02/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e03/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e04/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e05/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e07/04/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Regression Analysis\u003c/h2\u003e\u003cp\u003eA regression analysis was conducted to examine the relationship between scrap rate and key technical parameters, counter pressure, temperature, viscosity, speed, and CO₂ preservation using data collected from the filler machine. By applying Minitab 17, the study evaluated the strength and direction of these relationships, revealing how each parameter influences scrap generation. This analysis, supported by prior research (Wooten, 2019; Rebecca, 2020), provides a data-driven foundation for process optimization and quality control by identifying critical variables for reducing scrap and improving efficiency.\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 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBest Subsets Regression: Scrap Rate (versus Temperature, Counter Pressure, speed, and viscosity) and the Response is Scrap Rate (%) extracted from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS/n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVars\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-sq.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR-sq. 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colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eX\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 Best Subsets Regression analysis revealed that scrap rate in the filler machine is significantly influenced by multiple technical parameters, with the combination of temperature, counter pressure, speed, and viscosity offering the strongest predictive power (R\u0026sup2; = 45.9%). While adding variables generally improved model accuracy, careful consideration of Mallows' C\u003csub\u003ep\u003c/sub\u003e and adjusted R\u0026sup2; emphasized the need to balance complexity and model fit. The final model, refined using backward elimination, retained only the most statistically significant predictors, enhancing interpretability and reducing the risk of over-fitting. This approach underscores the importance of multi-variable control in optimizing production quality and minimizing scrap.\u003c/p\u003e\u003cp\u003eRegression Analysis: Scrap Rate (versus Temperature, Counter Pressure, Speed (bph), viscosity)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Backward Elimination Results\u003c/h2\u003e\u003cp\u003eUsing backward elimination, only counter pressure and speed remained as statistically significant predictors of scrap rate. The final regression equation was derived as:\u003c/p\u003e\u003cp\u003eScrap Rate (%)\u0026thinsp;=\u0026thinsp;0.126\u0026thinsp;+\u0026thinsp;0.561 \u0026times; Counter Pressure (bar)\u0026thinsp;+\u0026thinsp;0.000057 \u0026times; Speed (bph)}\u003c/p\u003e\u003cp\u003eThe coefficients indicate that counter pressure exerts the stronger influence on scrap rate. Specifically, an increase of one bar in counter pressure increases the scrap rate by approximately 0.561%, while an increase of 1,000 bottles per hour in speed contributes an additional 0.057% to the scrap rate. While speed has a relatively smaller coefficient, its effect becomes significant when production runs at very high speeds.\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 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBackward Elimination of Terms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eStep1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eStep2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eStep3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eStep4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCoef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC.pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViscosity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.253\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.284087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.282882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.284770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.287124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-sq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.88%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-sq.(adj)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.45%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-sq.(pred)\u003c/p\u003e\u003cp\u003eMallow\u0026rsquo;s low(cp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.30%\u003c/p\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.56%\u003c/p\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eGiven Alpha to remove\u0026thinsp;=\u0026thinsp;0.1;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe final model includes only Counter Pressure (bar) and Speed (bph) as significant predictors of Scrap Rate (%). The other are not significant since large p-values, but counter pressure and speed have small values as shown in the above table.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 ANOVA Results\u003c/h2\u003e\u003cp\u003eThe ANOVA table (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e) confirmed that the regression model is statistically significant, with an overall p-value of 0.002. Within the model, counter pressure was highly significant (p\u0026thinsp;=\u0026thinsp;0.008), while speed was moderately significant (p\u0026thinsp;=\u0026thinsp;0.030). This reinforces the conclusion that both parameters play critical roles in determining scrap rate variability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of Variance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdj.SS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdj.MS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4 Model Summary and Diagnostics\u003c/h2\u003e\u003cp\u003eThe model explained 37.5% of the observed variation in scrap rate, with an adjusted R\u0026sup2; of 32.7%. Although the predictive power was moderate (predicted R\u0026sup2; = 14.5%), the results provide valuable insights into the main drivers of scrap. Diagnostics identified observations 6 and 15 as unusual data points with higher residuals, suggesting possible outliers or process anomalies that merit further investigation\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel summery\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR-sq.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR-sq.(adj.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR-sq.(pred.)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.287124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.45%\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 relatively low predicted R\u0026sup2; suggests that while the model captures significant trends, additional unmeasured variables, such as operator skill, raw material consistency, or machine wear, may also influence scrap rates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.3.5 Interpretation\u003c/h2\u003e\u003cp\u003eThe regression results emphasize that counter pressure and speed are the dominant parameters affecting scrap in the filler machine. Small deviations in counter pressure settings directly increase the risk of underfill and foam-related defects, while excessive machine speed amplifies operational errors, leading to both underfill and uncrowned defects. Other parameters such as temperature, viscosity, and CO₂ preservation did not show statistically significant relationships in this dataset, though they may still play secondary roles under different conditions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\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\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\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\u003eSE Coef.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpeed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows that pressure significantly affects the scrap rate (p\u0026thinsp;=\u0026thinsp;0.008), while speed does not (p\u0026thinsp;=\u0026thinsp;0.303); both predictors have low VIFs (1.02), indicating no multi-collinearity, and the constant term is not statistically significant. And the coefficient for Pressure' is 0.561, statistically significant with a t-value of 0.88 (p\u0026thinsp;=\u0026thinsp;0.008), while Speed shows a coefficient of 0.000057 with a t-value of 2.3 (p\u0026thinsp;=\u0026thinsp;0.303).\u003c/p\u003e\u003cp\u003eOverall, the statistical analysis confirms that effective control of counter pressure and careful regulation of machine speed are essential strategies for reducing scrap. These findings provide a strong basis for operational improvements, including parameter optimization and enhanced process monitoring.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.3.6 Integrated Discussion and Interpretation\u003c/h2\u003e\u003cp\u003eThe combined findings from scrap data analysis, cause-and-effect studies, and regression modeling provide a comprehensive understanding of the major drivers of scrap in the Moha Soft Drink Factory filler machine. The results clearly show that three defect categories\u0026mdash;underfill, uncrowned bottles, and contamination- account for the majority of scrap, together contributing 88.1% of total rejects. These defects, therefore, represent the \u0026ldquo;vital few\u0026rdquo; that must be prioritized for corrective actions.\u003c/p\u003e\u003cp\u003eThe cause-and-effect analyses highlighted the complexity of factors contributing to these defects. Underfill was primarily associated with machine component deterioration (filling valves, pistons, and expanding tubes) and process parameters such as counter pressure and foam formation. Uncrowned bottles were strongly linked to crown motor and sensor failures, conveyor malfunctions, and material inconsistencies in cork size and thickness. Contamination, while less frequent, stemmed from both technical failures (e.g., spray jet malfunctions, rust formation) and process lapses (e.g., bypassed inspections, untreated bottles), underscoring the need for improved preventive maintenance and quality checks.\u003c/p\u003e\u003cp\u003eRegression analysis reinforced these findings by quantifying the relationship between technical parameters and scrap rates. Among all variables studied, counter pressure and filling speed emerged as statistically significant predictors, together explaining 37.5% of the variability in scrap. This indicates that while mechanical and material issues contribute to scrap, process control through parameter optimization is equally important. In particular, deviations in counter pressure settings were found to strongly influence underfill and foam formation, while high machine speeds increased the likelihood of both underfill and uncrowned defects.\u003c/p\u003e\u003cp\u003eTaken together, the analyses demonstrate that scrap generation is not caused by isolated issues but rather by the interplay of technical, material, and operational factors. The evidence suggests that meaningful scrap reduction requires a multifaceted strategy: preventive maintenance to reduce mechanical failures, strict material quality checks to minimize input variability, and tighter process control of critical parameters, especially counter pressure and speed.\u003c/p\u003e\u003cp\u003eBy addressing these areas simultaneously, Moha Soft Drink Factory can substantially reduce waste, enhance product quality, and improve production efficiency. Moreover, the results of this study contribute practical insights to the broader soft drink industry, where filler machines remain critical bottlenecks in production and significant sources of cost inefficiency.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study identified underfill, uncrowned bottles, and contamination as the dominant defects in Moha Soft Drink Factory\u0026rsquo;s filler machine, together accounting for 88.1% of total rejects. Root cause analysis linked these defects to machine wear, material variability, and process inconsistencies, while regression modeling showed counter pressure and filling speed as significant predictors of scrap rate, explaining 37.5% of its variation. The originality of this work lies in integrating technical parameter analysis with structured quality control tools to address scrap reduction in a beverage filler context, offering new empirical evidence from a developing economy. Its novelty and generalizability are demonstrated by showing that optimizing counter pressure and speed is transferable to other beverage manufacturers beyond Ethiopia. Practically, the findings provide actionable solutions, preventive maintenance, stricter material checks, and real-time parameter monitoring that reduce scrap, enhance efficiency, and strengthen competitiveness. By combining empirical analysis with practical applicability, this study contributes a data-driven framework for sustainable quality improvement in the soft drink industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research study did not comprise any human or animal participants. Hence, the ethical approval and consent from participants are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any person\u0026rsquo;s data. Therefore, the consent publication is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Mekelle University for data collection, analysis, and report preparation. However, no additional funding was provided for publication costs. Consequently, the authors have chosen a subscription-based publishing model and declare that they have no competing interests related to this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed throughout this study that support the findings are available from the corresponding author, Samuel Berhe Gebremedhin (SBG), upon reasonable request. For the sake of protecting the privacy of study participants and obeying confidentiality agreements, the data cannot be shared openly. Nevertheless, in case researchers are interested in accessing specific details or verifying the results of the study may contact the corresponding author. Access will be granted on a case-by-case basis while guaranteeing adherence to privacy and confidentiality protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompleting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBG initiated and conducted the research and drafted the manuscript. GDG contributed by providing advice throughout the study and support in preparing the manuscript. MAG also participates in result analysis and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Mekelle University for its support and contributions to this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026quot;I. W. R. Taifa, E. D. Makundi, and G. S. Mwaluko, \u0026ldquo;Production quality improvement for the soft drinks bottling industry through Six Sigma methodology,\u0026rdquo; International Journal of Industrial and Systems Engineering, vol. 41, no. 2, pp. 159\u0026ndash;179, 2022.\u0026quot;. \u003c/li\u003e\n\u003cli\u003e\u0026quot;A. K. Mohanty, S. R. Mishra, and S. K. Mahapatra, \u0026ldquo;Implementation of Lean Six Sigma approach to minimize waste at a food manufacturing industry,\u0026rdquo; Journal of Advanced Manufacturing Technology, vol. 34, no. 1, pp. 11\u0026ndash;24, 2024.\u0026quot;. \u003c/li\u003e\n\u003cli\u003e\u0026quot;J. F. S. Santos and J. C. Leite, \u0026ldquo;Application of the Six Sigma methodology and use of the DMAIC method to reduce the loss rate of aluminium cans in a beverage industry to reduce operational costs: Case study: Latax Refrigerantes Ltda,\u0026rdquo; International Journ\u0026quot;. \u003c/li\u003e\n\u003cli\u003eL. a. Park, \u0026quot;Integration on the Technical analysis and six sigma methodologies in filler machine operation,\u0026quot; journal of industries, pp. 23,123-129, 2012. \u003c/li\u003e\n\u003cli\u003e\u0026quot;R. M\u0026uuml;ller, L. M. Gon\u0026ccedil;alves, and T. A. Souza, \u0026ldquo;Filling process optimization through modifications in machine settings,\u0026rdquo; Processes, vol. 10, no. 11, pp. 2273\u0026ndash;2288, 2022.\u0026quot;. \u003c/li\u003e\n\u003cli\u003e\u0026quot;D. R. Emerson, \u0026ldquo;Liquid filling machine trends: Today\u0026rsquo;s equipment is more versatile,\u0026rdquo; Food Engineering Magazine, vol. 94, no. 3, pp. 28\u0026ndash;32, Mar. 2021.\u0026quot;. \u003c/li\u003e\n\u003cli\u003e\u0026quot;D. J. Poland, L. Puglisi, and D. Ravi, \u0026ldquo;Industrial machines health prognosis using a transformer-based framework,\u0026rdquo; arXiv preprint arXiv:2411.14443, Nov. 2024.\u0026quot;. \u003c/li\u003e\n\u003cli\u003eM. Li, \u0026quot;a framework for optimizing filler machine performance using a combination of technical analysis and artificial intelligence (AI) techniques.,\u0026quot; Journal of production and economics managment, pp. 34,111-124, 2020. \u003c/li\u003e\n\u003cli\u003eT. Kim, \u0026quot;a predictive maintenance framework for filler machines using a combination of technical analysis and data analytics techniques.,\u0026quot; journal of engineering, pp. 50-70, 2020. \u003c/li\u003e\n\u003cli\u003eZ. a. Wang, \u0026quot;an optimization approach for filler machine performance by integrating technical analysis with Design of Experiments (DOE) methodology.,\u0026quot; journal of future market, pp. 121-233, 2018. \u003c/li\u003e\n\u003cli\u003eW. a. Zung, \u0026quot;An Emperical study on the application of Technical anaysis in soft drink industy,\u0026quot; international journal of production economics, pp. 153,123-128, 2020. \u003c/li\u003e\n\u003cli\u003ewang, \u0026quot;a comparative study on the effectiveness of technical analysis and machine learning techniques in predicting filler machine performance,\u0026quot; journal of quality control and engineering, pp. 89-100, 2019. \u003c/li\u003e\n\u003cli\u003e\u0026quot;GEA Group, \u0026ldquo;Drinktec 2025: Next-level beverage filling,\u0026rdquo; GEA Trade Press News, 2025.\u0026quot;. \u003c/li\u003e\n\u003cli\u003e\u0026quot;Siemens AG, \u0026ldquo;AI-supported predictive maintenance: Siemens and Sachsenmilch are breaking new ground in the food and beverage industry,\u0026rdquo; Manufacturing Tomorrow, Jun. 2025.\u0026quot;. \u003c/li\u003e\n\u003cli\u003eG. a. Kim, \u0026quot;Technical analysis in soft drink industry:Acomparative study,\u0026quot; journal of agriculture and applied economics, pp. 45,321-338, 2021. \u003c/li\u003e\n\u003cli\u003e\u0026quot;Krones AG, \u0026ldquo;AI ensures accurate fill levels,\u0026rdquo; Krones Magazine \u0026ndash; Innovation, 2024.\u0026quot;. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Technical Analysis, Solutions, Scrap rate, Liquid filler machines, Quality Control Tools, Management","lastPublishedDoi":"10.21203/rs.3.rs-7752571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7752571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe carbonated beverage industry relies on automated filler machines for efficient production. Despite technological advancements, scrap generation and rejected bottles during filling remain a challenge, leading to product waste and quality control issues. This study focuses on the filler machine at the Moha Soft Drink Factory in Mekelle, where scrap accounts for approximately 3.85% of production, thereby reducing profitability. The research aims to identify the root causes of scrap, correlate key technical parameters of the filler machine, and propose solutions. Quality control tools, including Pareto and fishbone diagrams, together with statistical regression analysis, were employed. Data were collected through observations, focus group discussions (FGDs), interviews, and instruments such as flow meters, pressure gauges, and temperature probes to monitor variables including speed, pressure, and temperature, viscosity, and CO₂ levels. Pareto analysis revealed underfilling, uncrowned bottles, and contamination as the top contributors to scrap, accounting for 88.1% of total rejects. Root cause analysis and regression modeling identified counter pressure (bar) and speed (bph) as significant predictors of scrap rate, explaining 37.5% of its variability (p\u0026thinsp;=\u0026thinsp;0.002). The regression coefficients for counter pressure and speed were 0.561 and 0.000057, respectively, indicating their influence on scrap rate. Adjusting these parameters within optimal limits is recommended to reduce scrap, particularly from underfilling, uncrowned bottles, and contamination. Implementing these corrective measures is expected to enhance product quality and operational efficiency.\u003c/p\u003e","manuscriptTitle":"Technical Analysis and Solutions to Reduce Scrap Rate In Liquid Filler Machine: Case Study: In Moha Soft Drink Production Line","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:41:52","doi":"10.21203/rs.3.rs-7752571/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":"901d9d4b-e504-488c-acdc-b906888c4ed7","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T19:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-05 11:41:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7752571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7752571","identity":"rs-7752571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}