Inventory Optimization and Management of Health Products and Technologies in Kenya: A Multi-County Study on access to Quality affordable Health Products and Technologies

Inventory Optimization and Management of Health Products and Technologies in Kenya: A Multi-County Study on access to Quality affordable Health Products and Technologies | 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 Inventory Optimization and Management of Health Products and Technologies in Kenya: A Multi-County Study on access to Quality affordable Health Products and Technologies Shadrack Mururu Meme, Carol Kawila, Kezia Njoroge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7250053/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Health Products and Technologies (HPTs) are critical pillars of the health system and essential to achieving Kenya's Universal Health Coverage (UHC). UHC prioritizes access to high-quality medical care with minimal financial hardship. Despite efforts to enhance HPTs management, counties like Kisumu, Machakos, Nyeri, Kiambu, and Isiolo in Kenya face inefficiencies. Challenges include long lead times for receiving commodities and low order fill rates, which hinder access to quality and affordable health HPTs, impacting service delivery. This study aimed to determine the influence of inventory optimization on the management of HPTs. The Utilization Management Theory guided the research. The research was conducted in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo counties, using the pragmatism paradigm to support a mixed-methods design. Quantitative data utilized a descriptive research design, while qualitative data employed an exploratory design. A census sampling method was used in the study, where 141 staff managing HPTs at level 4 and 5 public health facilities were targeted. Participants were drawn from clinical, pharmacy, service delivery, and administration departments. Key informant interviews were conducted with County Directors of Health and County Pharmacists. Data collection involved pre-tested questionnaires and Key Informant interview guides to ensure validity and reliability. Quantitative data was analyzed using descriptive and inferential statistics, while qualitative data was thematically analyzed. Diagnostic tests, including normality test, homoscedasticity, autocorrelation, and multicollinearity checks, ensured assumptions were met. The study adhered to research ethics throughout the investigation; informed consent was sought from the respondents; data confidentiality was observed by ensuring no personal identifiers were collected from the respondents; instead, a unique serial number was used to identify the participants. Data was collected and stored in secure areas accessible only to the researcher. The study was approved by the Institutional Scientific Ethical Review Committee of Kenya Methodist University (KeMU/ISERC/HSM/26/2023), and NACOSTI offered a research permit NACOSTI/P/23/31850. The study found that the model explained 53.5% (R Square value of 0.535) of the variance in the management of HPTs. This meant that the model had strong explanatory power, but there was still a significant portion of variance (46.5%) that was not accounted for by these predictors. The study concluded that inventory optimization significantly impacts the management of HPTs. Effective tools such as ABC Analysis, FEFO, and robust safety stock policies can address existing inefficiencies. Integrating these practices with supportive digital systems and tailored policies is vital for access to quality and affordable HPTs, thus improving service delivery. Inventory Optimization Management of Health Products and Technologies Affordability Availability and Quality 1.0 Introduction Health Products and Technologies (HPTs) refer to medications, vaccines, devices, and medical procedures, essential for disease prevention, diagnosis, treatment, and rehabilitation, World Health Organization. HPTs focus on ensuring effective healthcare delivery by prioritizing the availability of high-quality medications in appropriate quantities. Maintaining optimized HPTs and good inventory management is critical, as poor inventory control can lead to overstocking or understocking, wasting resources, and increasing morbidity and mortality due to a lack of life-saving medications (Kaupa & Naude, 2021 ) Even though Kenya uses bin cards in the management of inventory, the country has not embraced modern inventory optimization techniques that help in preventing stock outs while minimizing associated costs. (Friday et al., 2021 ). Globally, several countries have taken significant steps to improve HPT management. In Asia, nations like Pakistan, Vietnam, the Philippines, and India have developed national priority lists to guide the procurement of essential medications at reasonable costs across all healthcare levels (Bigio et al., 2023 ). Computerized logistics management information systems (LMIS) are increasingly used to enhance inventory management, ensuring the timely availability of critical products. In the United States, states such as New York, New Mexico, and Texas have implemented standardized tools like bin cards, stock cards, and requisition forms for inventory tracking, applicable across public health facilities, whether manual or computerized (Eckelman et al., 2020 ). Africa faces significant challenges in managing HPTs effectively. Limited access to medications in hospitals and clinics is a major concern. In South Africa, poor inventory management is a key barrier to improving HPT access Tuomala and Grant, 2022 ), while frequent stock-outs in Zimbabwe prevent patients from accessing critical drugs (Kanyepe, 2022 ). These issues highlight the broader challenges in ensuring efficient HPT management across the continent. In Kenya, the management of HPTs remains problematic, reflecting issues common in developing nations (Ministry of Health [MOH], 2014). Despite efforts by the Ministry of Health to enhance HPT coordination, availability of essential medications and diagnostics remains low, with averages of 14% and 19%, respectively, even after devolution (Njoroge, 2019 ). Ineffective inventory management contributes to poor health outcomes, Muiruri ( 2017 ) finding that nearly 30% of deaths in Embu County result from mismanagement of essential medicines and medical supplies. Additionally, the study underscored the critical role of inventory optimization in achieving universal access to high-quality medications and healthcare technologies. The study suggested that efforts to improve HPT management in Kenya must address inventory control inefficiencies to enhance healthcare delivery and reduce preventable deaths (Mbatia, 2021 ). Despite policy initiatives and investment, public health facilities often have low HPT availability, stemming from fragmented supply chains, underused information systems, and resource gaps. Limited research exists on how inventory practices influence HPT management at the county level. This multicounty study sought to address that gap in Kenya. 1.1 Statement of the problem The health sector plays a critical role in any nation’s economy. In Kenya, provision of quality, affordable healthcare is emphasized under the Constitution and Vision 2030 under the social pillar, prompting the Ministry of Health to prioritize Universal Health Coverage (UHC). Health Products and Technologies (HPTs) were designated as a key component of UHC, supported by regulatory policies under the Ministry of Health (MOH, 2014). Efforts by national and county governments have included investments in healthcare infrastructure, with Kisumu, Kiambu, Machakos, Nyeri, and Isiolo Counties establishing HPT Units (HPTUs) staffed by multidisciplinary teams to manage health products and technologies. These Counties have leveraged devolution to improve the availability, quality, and affordability of HPTs through decentralized financing, streamlined procurement, partnerships, and health information systems like DHIS2, which enhance supply monitoring and reduce costs. Inventory optimization is critical in improving service delivery by ensuring availability of quality and affordable health products and technologies by eliminating wastage through pilferage and expiries, reducing stock holding costs, and promoting efficiency in the health commodities supply chain. HPT management in public hospitals in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo remains ineffective. For instance, delays in HPT supply and intermittent shortages of essential medicines and diagnostic kits, compromise service delivery, which has led to the mushrooming of private retail chemists and medical diagnostic laboratories around the public hospitals where patients receive the services at astronomical costs. Budget reductions and inadequate financing have limited access to essential commodities such as laboratory reagents and surgical consumables. Additionally, inadequate training for healthcare workers on forecasting and supply chain management further exacerbated the problem. This led to increased mortality from communicable diseases due to shortages of essential medicines and diagnostic tools. Nonetheless, there is limited knowledge on how inventory optimization influences HPT management in public hospitals. This study aimed to bridge the knowledge gap by conducting a multi-county study on the relationship between inventory optimization and the management of health products and technologies in Kenya. 1.2 Objective of the study This study examined the relationship between inventory optimization and the management of health products and technologies in Kenya. 2.0 Literature Review Effective inventory optimization is vital for achieving universal health coverage (UHC), a core objective of the World Health Organization (WHO). Globally, unreliable inventory systems present a significant barrier to the management of Health Products and Technologies (HPTs), which are critical for medical service delivery. Inventory optimization balances service-level goals across stock-keeping units (SKUs) at the same time, managing demand and supply uncertainties. 11 It enables healthcare facilities to maintain sufficient stock levels, identify supply risks, and request replenishments, ultimately reducing costs and enhancing system efficiency (Tadayonrad & Ndiaye, 2023 ). This balance is achieved through continuous or periodic inventory review policies that prevent stockouts and ensure operational continuity (Boxley et al., 2019 ). Various inventory optimization approaches have been applied to enhance HPT management worldwide. The ABC Analysis is widely used, categorizing inventory into three groups based on their significance in value or usage (Knapp & Mueller, 2010 ). Essential life-saving medical supplies, categorized as “A” items, require constant availability to prevent morbidity and mortality. In contrast, “B” and “C” items are less critical and managed with different priority levels. Studies in Malaysia, Ghana, and Uganda demonstrate that ABC analysis improves essential medicine stock levels, reduces stockouts, and decreases holding costs, contributing to financial sustainability in healthcare systems (Mohajan, 2017 ). Despite its benefits, the method faces challenges in resource-constrained settings where health information systems and data accuracy are inadequate (Mwihia, 2020 ). Another critical approach is the First Expiry First Out (FEFO) principle, which ensures consumables are used before expiration, minimizing waste and enhancing access to life-saving products (Okungu, 2019 ). Studies from Nigeria and Zambia highlight FEFO’s effectiveness in reducing vaccine wastage and maintaining a steady supply of antiretroviral therapy (ART), significantly improving patient outcomes (Rahi, 2017 ). However, FEFO implementation may face hurdles, such as inaccurate inventory records and inadequate training for healthcare staff (Rowan & Laffey, 2021 ). In Africa, challenges in managing HPTs often stem from insufficient inventory systems and resource constraints. Effective record-keeping and Safety Stock Policies as crucial for maintaining HPT availability. Safety stock acts as a buffer to address unexpected demand, ensuring uninterrupted access to life-saving medicines. Research from Tanzania confirms that safety stock policies effectively reduce HPT stockouts in primary healthcare facilities (Selemani, 2020 ). Accurate record-keeping is equally critical for tracking stock transactions and identifying inventory management issues, with studies in Kenya and other African countries identifying poor record-keeping as a significant obstacle to effective HPT management (Shangala, 2020 ). The Economic Order Quantity (EOQ) model is another valuable inventory management tool, balancing ordering and holding costs to maintain optimal stock levels. Studies in the United Kingdom and South Sudan demonstrated EOQ’s role in minimizing costs, reducing waste, and ensuring a steady supply of essential medical products, contributing significantly to UHC (Rowan & Laffey, 2021 ; Saha & Ray, 2019 ). Meta-analyses from diverse global healthcare settings further highlight EOQ’s potential to address stockouts and overstocking, improving access to critical tools and medicines (Rahman & Zailani, 2017 ). Also, the Just-In-Time (JIT) inventory management approach has proven effective in optimizing HPT availability. JIT reduces holding costs, prevents wastage, and ensures timely supply of medical products. Research in Japan and Tunisia shows that JIT improves supply chain efficiency, enabling healthcare facilities to respond swiftly to patient needs (Shangala, 2020 ). Studies in Uganda found that JIT reduced lead times, increased inventory turnover rates, and enhanced healthcare delivery efficiency, advancing UHC goals (Gafa, 2023 ). In Kenya, traditional and technology-driven inventory optimization strategies are yet to implemented to address HPT management challenges (Mudogo et al., 2023 ) The country lacks policies to promote adoption of JIT systems with predictive analytics has allowed healthcare facilities to forecast demand patterns and maintain optimal inventory levels, significantly reducing wastage and supply chain inefficiencies (Onyancha, 2022 ). Similarly, safety stock policies and accurate record-keeping have been identified as essential for maintaining HPT availability and addressing stockouts in primary healthcare facilities are poorly implemented in Kenya (Shangala, 2020 ). On the other hand, inventory optimization methods like FEFO, ABC analysis, and EOQ have demonstrated significant benefits globally in promoting availability of quality and affordable health Products and technologies faces a lot of implementation challenges in Kenya such as inadequate training, data inaccuracies, and resource constraints. equitable access to quality healthcare. 3.0 Methodology The study took place in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo counties to determine the influence of financing on management of health products and technologies in selected Counties, Kenya. The research was anchored on the Pragmatism paradigm because it is basically based on the existing body of knowledge that is fixed, observable, and objective, as well as multiple, socially constructed by individuals. Pragmatists believe that the nature of knowledge is both quantifiable (objective knowledge) using scientific research as well as gained through in-depth understanding (Park et al., 2020). A pragmatist’s beliefs are both single and multiple. A descriptive cross-sectional research design and interviews, both anchored by the pragmatism paradigm, were used in the study. Staff dealing with the management of health products and technologies directly or indirectly at the service delivery level 4 and 5 public health facilities in Kisumu, Nyeri, Isiolo, Kiambu, and Machakos Counties were the study's target group. The study also targeted the county health management team from the selected counties as the key informants. The questionnaire and key informant interview guides were used to collect data. Pretesting of the data collection tools was conducted at Kajiado County. A census sampling method was used in the study, where 141 staff managing HPTs at level 4 and 5 public health facilities were targeted. Key informant interviews were conducted with the County Health Management Team. Ethical considerations were considered. informed consent to participate was obtained from the participants in the study, and all the respondents gave consent to participate in the study. The questionnaire and interview guide used in this study were developed specifically for this research and is provided as Supplementary File 1 4.0 Results and Discussions A total of 141 structured questionnaires were distributed to respondents via Google Forms, out of which 106 were completed, yielding a 75.0% response rate. The data collection instrument was evaluated, with inventory optimization and the overall tool (r = 0.935) demonstrating excellent reliability. A Cronbach’s Alpha (r) value of 0.7 and above is generally considered reliable and acceptable for research purposes (Sürücü & Maslakci, 2020 ). Descriptive statistics Inventory optimization factors with major effects on the management of HPT The study established the inventory optimization factors with a major effect on the management of HPTs as shown in Table 1 below. Table 1 Inventory optimization and management of HPTs County ABC analysis First-expiry-first- out Safety stock policy and record keeping Economic order quantity Just in time N Chi-Square (χ 2 ) P- Value Kiambu 2(15.4%) 3(23.1%) 6(46.2%) 0(0.0%) 2(15.4%) 13 32.87 0.008 Isiolo 7(50.0%) 6(42.9%) 0(0.0%) 1(7.1%) 0(0.0%) 14 Machakos 10(27.0%) 13(35.1%) 6(16.2%) 2(5.4%) 6(16.2%) 37 Kisumu 2(8.7%) 7(30.4%) 13(56.5%) 1(4.3%) 0(0.0%) 23 Nyeri 4(21.1%) 9(47.4%) 6(31.6%) 0(0.0%) 0(0.0%) 19 25(23.6%) 38(35.8%) 31(29.2%) 4(3.8%) 8(7.5%) 106 The results indicate that the most influential factors are First-Expiry-First-Out (FEFO) and Safety Stock Policy, and Record Keeping. FEFO was identified as the most prevalent method, with 38 responses (35.8%), followed by Safety Stock Policy and Record Keeping at 31 responses (29.2%). Conversely, Economic Order Quantity (EOQ) and Just-in-Time (JIT) practices were less commonly used, with 4 responses (3.8%) and 8 responses (7.5%), respectively. The Chi-Square test (χ² = 32.87, P-value = 0.008) indicates that the distribution of these factors was statistically significant across counties. Kiambu and Kisumu counties favored Safety Stock Policy and Record Keeping, with 46.2% and 56.5% responses, respectively. Isiolo favored ABC Analysis, with 50% of responses. While Machakos and Nyeri counties preferred First-Expiry-First-Out (FEFO), with 35.1% and 47.4% of responses, respectively. These findings align with studies by Kiarie and Mbugu (2022) in Nairobi County, who found that FEFO reduces wastage and ensures the availability of effective medication, and Kilimo et al., (2022) in Mombasa County, who emphasized the importance of Safety Stock Policy in maintaining supply continuity. On the other hand, Kagwiri et al. ( 2023 ) in Nakuru County, the study found Economic Order Quantity to be more influential, noting its role in better financial planning and reducing carrying costs. Software to streamline and integrate the management of HPT The study identified the software used for streamlining and integrating the management of HPT. The study categorized the software into four levels: Basic, Moderate, Advanced, and Specialized, as indicated in Table 2 below. Table 2 Software to streamline and integrate the management of HPT County Basic software Moderate software Advanced software Specialized software N Chi-Square P (χ 2 ) Value Kiambu 7(53.8%) 3(23.1%) 3(23.1%) 0(0.0%) 13 33.302 0.001 Isiolo 5(35.7%) 5(35.7%) 4(28.6%) 0(0.0%) 14 Machakos 4(10.8%) 17(45.9%) 13(35.1%) 3(8.1%) 37 Kisumu 4(17.4%) 12(52.2%) 7(30.4%) 0(0.0%) 23 Nyeri 14(73.7%) 4(21.1%) 0(0.0%) 1(5.3%) 19 34(32.1%) 41(38.7%) 27(25.5%) 4(3.8%) 106 The results showed that Moderate Software was the most commonly used (38.7% of responses), followed by Basic Software (32.1%), Advanced Software (25.5%), and Specialized Software (3.8%). Nyeri, Kiambu, and Isiolo predominantly used Basic Software (73.7%, 53.8%, and 35.7%, respectively), and Machakos and Kisumu preferred Moderate Software, with 45.9% and 52.2% of responses. The Chi-Square test (χ² = 33.302, P-value = 0.001) confirmed a statistically significant impact of software type on HPT management. The findings were consistent with those of Lahariya ( 2020 ) in rural India and Kayiwa, (2020) in Uganda, who observed that moderate software balances functionality and simplicity. However, Malakoane et al. ( 2020 ) in South Africa found that despite the advantages, advanced software adoption was limited by cost and technical constraints, agreeing with study findings where only 25.5% and 3.8% were using advanced and specialized software, respectively. Frequency of ABC analysis conducted for HPT The research sought to underscore the frequency of ABC analysis conducted for HPT. Table 3 captures the findings on the frequency of ABC analysis. Table 3 Frequency of ABC analysis conducted for HPT Description County N Mean Rank Kruskal-Wallis H P Value Frequency of ABC analysis conducted for HPT Kiambu 12 44.92 Isiolo 14 46.11 Machakos 37 55.57 Kisumu 22 51.34 Nyeri 19 57.37 Total 104 2.843 .584 The study also found that Nyeri had the highest mean rank (57.37), followed by Machakos (55.57) and Kisumu (51.34). The Kruskal-Wallis H test revealed no statistically significant differences (H = 2.843, P-value = 0.584), indicating that ABC analysis was applied with relative consistency across the counties. These results aligned with the findings of Shami et al. (2021), who observed that ABC analysis was evenly implemented across districts in their study. However, Banerjee (2024) found regional disparities in the frequency of ABC analysis in Northern India. Criteria for Determining Optimal HPT Stock Levels The study assessed the clarity and effectiveness of the criteria for determining optimal HPT stock levels, which are captured in Table 4 . Table 4 Criteria for Determining Optimal HPT Stock Levels County N Mean Rank Kruskal-Wallis H P Value. Clarity of the criteria used to determine optimal stock levels for HPT Kiambu 13 60.27 Isiolo 14 53.86 Machakos 37 55.46 Kisumu 23 47.00 Nyeri 19 52.66 Total 106 2.089 0.719 The Kiambu county had the highest mean rank (60.27), followed by Machakos (55.46) and Isiolo (53.86). Despite differences in the rankings, the Kruskal-Wallis H test (H = 2.089, P-value = 0.719) indicated no statistically significant differences in the perceived clarity and effectiveness of these criteria across the counties. These results agreed with Kayiwa et al. ( 2020 ) and Rumisha, et al. ( 2020 ) in Tanzania, who found no significant regional differences in understanding stock management criteria. However, Bwanga and Chanda ( 2020 ) found significant discrepancies in Zambia, where some regions lacked adequate training and resources. Frequency of stocking policy review and adjustment Table 5 below shows the frequency of stocking policy review and adjustment across counties. Table 5 Frequency of stocking policy review and adjustment Description County N Mean Rank Kruskal-Wallis H P Value Frequency of stocking policy review and adjustment Kiambu 13 54.96 1.961 0.743 Isiolo 14 47.32 Machakos 37 56.64 Kisumu 23 48.74 Nyeri 19 56.71 Total 106 Nyeri County had the highest frequency (mean rank = 56.71), followed closely by Machakos (mean rank = 56.64). However, the Kruskal-Wallis H test (H = 1.961, P-value = 0.743) revealed no statistically significant differences, suggesting that stock policy reviews and adjustments occurred similarly across the counties. These findings resonate with Banerjee ( 2021 ), who observed consistency in regional policy review practices. However, Ooms et al. ( 2021 ) found regional variations in the Eastern Province, suggesting that localized management practices may influence the frequency of stocking policy adjustments. Accuracy and reliability of the system used for the record-keeping of HPT inventory Table 6 below captures the results of the accuracy and reliability of systems used to record the inventory of Health Products and Technologies (HPT) across five counties. Table 6 Accuracy and reliability of the system used for the record-keeping of HPT inventory County N Mean Rank Kruskal-Wallis H P Value Accuracy and reliability of the system used for the record-keeping of HPT inventory Kiambu 13 48.62 Isiolo 14 54.61 Machakos 37 56.55 Kisumu 23 56.28 Nyeri 19 46.71 Total 106 2.113 0.715 The Kruskal-Wallis H test revealed no significant differences in perceptions of accuracy and reliability (H = 2.113, p = 0.715). Machakos (mean rank = 56.55) and Kisumu (mean rank = 56.28) were rated highest, while Nyeri (mean rank = 46.71) was rated lowest. These findings implied consistent perceptions of record-keeping systems across the counties. This aligned with Bwanga and Chanda ( 2020 ) who found no significant regional differences in record-keeping perceptions in Uganda. Conversely, Batamuriza et al. ( 2020 ) reported notable disparities in Rwanda due to technological infrastructure and training variations. The findings agreed with those of key interviews that inventory optimization plays a critical role in the management of HPTs in public hospitals: “…Inventory management is largely manual, with multiple records used for receiving and distributing commodities. The First Expiry First Out (FEFO) principle is followed, and excess HPTs are redistributed while maintaining good storage practices. Nyeri County highlighted out-of-pocket expenses as a barrier to healthcare due to increased workload. During the UHC Pilot, Drawing Rights at KEMSA ensured consistent HPT supply, with KEMSA’s fill rate exceeding 85%. However, the push system led to expiries, despite the use of IMS and stock cards…” (KII, Male, 005, 24th June, 2024 Inferential statistics : Correlation analysis Table 7 shows a Bivariate Pearson correlation analysis measuring the relationship between inventory optimization and management of HPTs. Table 7 Correlations Y X4 Y Pearson Correlation 1 Sig. (2-tailed) N 106 X4 Pearson Correlation .636 ** 1 Sig. (2-tailed) .000 N 106 106 Y = HPTs management; X4 = inventory management Source: Field data (2024) The study revealed a strong, positive, and statistically significant correlation (r = 0.636, P = 0.000) between inventory optimization and HPT management. Effective inventory practices are strongly linked to improved HPT management, with results unlikely due to chance (P value was less than 0.05). Optimizing inventory significantly enhances the management of health products and technologies. The study findings aligned with those of Balkhi, et al. ( 2022 ) demonstrated that applying Just-in-Time (JIT) and safety stock policies reduced medication wastage and ensured availability in public hospitals across India, emphasizing the importance of structured inventory systems. In agreement, Maduhu ( 2022 ) in Tanzania highlighted that FEFO and robust record-keeping practices minimized stockouts and enhanced efficiency in health supply chains, particularly for essential medications. Conversely, Kagwiri et al. ( 2023 ) in Nakuru County observed Economic Order Quantity (EOQ) as a more significant factor for improving inventory-related decision-making and cost management than FEFO or safety stock. They argued that the impact of optimization practices varies based on resource allocation and implementation capacity. Regression explored the predictive ability of inventory optimization in the management of HPTs. Model summary The study evaluated the fit and performance of the regression model. The model summary was crucial for understanding how well the model explained the variability in the dependent variable based on the independent variable, as shown in Table 8 below. Table 8 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .732 a .535 .512 4.886 .535 23.029 5 100 .000 a. Predictor: (Constant), Inventory optimization b. Dependent Variable: Management of HPTs From the model summary analysis, the model explained 53.5% of the variance in managing HPTs (R Square = 0.535). This meant that the model had a strong explanatory power, indicating that the model was very effective at predicting the dependent variable, and the chosen independent variable was appropriate and sufficient for explaining the dependent variable. Therefore, the researcher was confident in the robustness of the model based on the independent variables included in the study. Nevertheless, a significant portion of variance (46.5%) was still not accounted for by the predictor. To check whether the model occurred by chance or rather potential overestimation of the R Square, the Adjusted R Square was analysed. The study estimated an adjusted R Square value of 51.2% (Adjusted R Square of 0.512). This indicated that 51.2% of the variance in the management of HPTs is explained by the model, slightly less than the R Square, but still a strong effect. The model reached statistical significance (P = .000). 4.0 Conclusions of the study The study concluded that inventory optimization has a statistically significant influence on the management of Health Products and Technologies (HPTs) in public hospitals in Kenya. Consequently, the study rejected the null hypothesis that inventory optimization has no significant influence on the management of Health Products and Technologies in selected Counties and failed to reject the alternative hypothesis. Improvement in inventory optimization leads to efficiency in the management of Health Products and Technologies, resulting in improved access to quality and affordable essential Health Products and Technologies, thus improving service delivery in health facilities for the achievement of Universal Health Coverage. Inventory optimization significantly improves management of HPTs in public hospitals. Lean inventory strategies, digitalization, and supportive policy frameworks are recommended to address inefficiencies and improve access to essential health technologies. 5.0 Recommendations of the study The study recommends optimization of inventory management through the adoption of lean inventory management to increase the availability of quality and affordable health products and technologies. Public health facilities should make monthly orders as opposed to quarterly orders that require storage, thus increasing the stock holding costs, increasing the risk of wastage through expiries and pilferage. Other recommended strategies for inventory optimization include implementing ABC Analysis and adopting the First Expiry First-Out (FEFO) method to minimize wastage. Regular audits would ensure adherence. Hospitals should establish tailored safety stock policies to prevent stockouts and invest in digital record-keeping systems. The study also recommends optimizing the Economic Order Quantity (EOQ) model that balances ordering and holding costs, as well as developing policies to support the adoption of Just-in-Time (JIT) practices that minimize holding costs for highly specialized and expensive HPTs such as orthopedic implants and radiopharmaceuticals in public health facilities. Regular inventory audits and record keeping are recommended in healthcare supply chain policies to improve efficiency. Policymakers should provide the necessary support for standardization across all public health facilities to promote the availability of quality and affordable HPTs for improved service delivery. Implications on theories, policies, and practice Policy Implications The study highlights the importance of integrating effective inventory management strategies into healthcare policies. Policymakers must review the current practices of quarterly orders and adopt monthly orders with one month’s working stock and one month's buffer stock. This includes mandating the use of tools like ABC Analysis, FEFO, and the Economic Order Quantity (EOQ) model, which would require investments in training, technology, and infrastructure. Additionally, policymakers should provide financial and technical support to hospitals to ensure the implementation of digital record-keeping systems and tailored safety stock policies. Policies that encourage regular audits and monitoring will also be crucial for maintaining the effectiveness of these strategies. Theoretical Implications The findings align with inventory management theories that emphasize the importance of systematic approaches in controlling costs and optimizing resources. The Economic Order Quantity (EOQ) and Just-in-Time (JIT) models support theories related to supply chain efficiency, highlighting the balance between cost savings and product availability. Similarly, the ABC analysis, First Expiry, First-Out (FEFO), and safety stock models connect with inventory control theories focused on minimizing waste and ensuring stock availability under varying demand conditions. These findings further reinforce the relevance of these theoretical models in the healthcare context, contributing to the growing body of research on healthcare supply chain management. Practical Implications Practically, the recommendations will enhance the day-to-day operations of public hospitals. Adopting ABC Analysis and FEFO, alongside tailored stock policies, can streamline inventory management processes, reduce waste, and prevent stockouts, improving the availability of critical health products. Investing in digital record-keeping systems will provide real-time data, enhancing decision-making and accountability. Regular audits will ensure compliance and allow hospitals to refine their practices. Furthermore, implementing Just-in-Time (JIT) inventory practices will reduce holding costs and enhance operational efficiency. These practices, supported by comprehensive policies, will lead to more effective and sustainable management of health products and technologies in public hospitals. Study Limitation The study was limited by focusing only on five counties Kisumu, Kiambu, Nyeri, Machakos, and Isiolo selected for their unique health challenges and UHC pilot status. This excluded the remaining 42 counties, private, and faith-based facilities, potentially limiting the generalizability of findings. Additionally, the focus on level 4 and 5 hospitals, due to their advanced HPT management structures, overlooked insights from level 2 and 3 facilities, which may face distinct challenges. Further, the study utilized a mixed-method design, which, while offering comprehensive insights, posed challenges in integrating findings from both research approaches. It was resource-intensive, requiring significant time, financial investment, and specialized expertise. Declarations Ethics approval and consent to participate The study was approved by the Institutional Scientific Ethical Review Committee of Kenya Methodist University (KeMU/ISERC/HSM/26/2023), and the National Council for Science, Technology and Innovation (NACOSTI) offered research permit NACOSTI/P/23/31850. Authorization to collect data from each county was obtained from the director of health. This study was conducted following the ethical principles of the Declaration of Helsinki (as revised in 2024) for research involving human participants, as stipulated by the World Medical Association. Consent for publication Not Applicable Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The researcher had no competing interests Funding No finding was provided for this study. Authors' contributions Shadrack Mururu Meme conducted the research study and development of the manuscript. Dr Caroline Kawila – Assisted in designing the objectives and review of the manuscript Dr Kezia Njoroge- Assisted in designing the objectives and review of the manuscript. Acknowledgement I wish to acknowledge the following for their support toward the development of this manuscript. Kenya Methodist University department of health systems within the school of Health Sciences. Chief Officers and Directors for Health from Machakos, Kiambu, Isiolo, Kisumu and Nyeri counties for their approval to conduct the study in their counties. County Health Management Team for agreeing to be key informants in this study. Chief Executive Officer and Head of Commercial Services at Mission for Essential Drugs and Supplies for allowing me time to go out and conduct the research. Supervisors Dr. Caroline Kawila and Dr. Kezia Njoroge for their guidance and support during the development of the manuscript References Balkhi, B., Alshahrani, A. & Khan, A. (2022). Just-in-time approach in healthcare inventory management: Does it really work?. Saudi Pharmaceutical Journal , 30 (12), 1830–1835. https://doi.org/10.1016/j.jsps.2022.10.013 Banerjee, S. (2021). Determinants of rural-urban differential in healthcare utilization among the elderly population in India. 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BMC health services research , 20 (1), 1–14. https://doi.org/10.1186/s12913-019-4862-y Mbatia, E. M. (2021). Determinants of Maternal Child Health Commodities Management in Public Health Facilities in Meru County. [Masters Thesis, Kenya Methodist University, Kenya]. http://repository.kemu.ac.ke/handle/123456789/739 Ministry of Health (2014). Kenya Health Policy 2014–2030 Towards attaining the highest standard of health. Ministry of Health. https://www.health.go.ke/ . Mohajan, H. K. (2017). Two criteria for good measurements in research: Validity and reliability. Annals of Spiru Haret University. Economic Series , 17 (4), 59–82. https://www.ceeol.com/search/article-detail?id=673569 Mudogo, M. C., Jerusa, O., Eric, S., Justus, M. I., Mary, A., Mercy, A., … Wilber, O. (2023). Routine Supportive Supervision and Management of Medicines and Other Health Products and Technologies in Vihiga County, Kenya. Pharmacology & Pharmacy , 14 (2), 43–57. https://www.scirp.org/journal/paperinformation?paperid=123378 Muiruri, C. (2017). Factors influencing availability of essential medicines in public health facilities in Kenya: A case of Embu County [Masters Thesis, University of Nairobi]. https://erepository.uonbi.ac.ke/bitstream/handle/11295/101916 Mwihia, F. (2020). Performance of Public Hospitals in Kenya: the essential role of management . [Doctoral dissertation, University of Nairobi]. https://erepository.uonbi.ac.ke/handle/11295/153966 Njoroge, H. M. (2019). Determinants of public primary health facilities preparedness for service delivery in Nyandarua County, Kenya [Masters Thesis, Kenya Methodist University]. http://repository.kemu.ac.ke/handle/123456789/739 Okungu, V. (2019). Assessing the Capacity of County Health Departments in Kenya using the World Health Organization’s Health Systems Framework: Implications for Service Delivery and Outcomes. International Journal of Health Services Research and Policy , 4 (1), 31–42. extension://mjdgandcagmikhlbjnilkmfnjeamfikk/https://dergipark.org.tr/en/download/article-file/683475 Onyancha, B. N. (2022). Determinants of Technical Efficiency of Public Hospitals in Kiambu County [Doctoral dissertation, University of Nairobi, Kenya]. https://erepository.uonbi.ac.ke/handle/11295/162338 Ooms, G. I., van Oirschot, J., Okemo, D., Waldmann, B., Erulu, E., Mantel-Teeuwisse, A. K., … Reed, T. (2021). Availability, affordability and stock-outs of commodities for the treatment of snakebite in Kenya. PLOS Neglected Tropical Diseases , 15 (8), e0009702. https://doi.org/10.1371/journal.pntd.0009702 Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences , 6 (2), 1–5. https://doi.org10.4172/2162-6359.1000403 Rahman, M. K., & Zailani, S. (2017). The effectiveness and outcomes of the Muslim-friendly medical tourism supply chain. Journal of Islamic Marketing , 8 (4), 732–752. https://www.emerald.com/insight/content/doi/ 10.1108/jima-11-2015-0082/full/html Rowan, N. J. & Laffey, J. G. (2021). Unlocking the surge in demand for personal and protective equipment (PPE) and improvised face coverings arising from coronavirus disease (COVID-19) pandemic–implications for efficacy, re-use and sustainable waste management. Science of the Total Environment , 752, 142259. https://doi.org/10.1016/j.scitotenv.2020.142259 Rumisha, S. F., Lyimo, E. P., Mremi, I. R., Tungu, P. K., Mwingira, V. S., Mbata, D., … Mboera, L. E. (2020). Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania. BMC medical informatics and decision making , 20 (340), 1–22. https://doi.org/10.1186/s12911-020-01366-w Saha, E. & Ray, P. K. (2019). Modelling and analysis of inventory management systems in healthcare: A review and reflections. Computers & Industrial Engineering , 9 (3), 299–312. https://www.sciencedirect.com/science/article/abs/pii/S0360835219305108 Selemani, I. S. (2020). Indigenous knowledge and rangelands’ biodiversity conservation in Tanzania: success and failure. Biodiversity and conservation , 29 (14), 3863–3876. https://doi.org/10.1007/s10531-020-02060-z Shammi, M., Bodrud-Doza, M., Islam, A. R. M. T. & Rahman, M. M. (2021). Strategic assessment of COVID-19 pandemic in Bangladesh: comparative lockdown scenario analysis, public perception, and management for sustainability. Environment, Development and Sustainability , 23 (1), 6148–6191. https://doi.org/10.1007/s10668-020-00867-y Shangala, V. (2020). Effect of Hospital Management Information System Functionalities on the Performance of Health Care Institutions in Kenya: A Case of the Nairobi Hospital [Doctoral dissertation, Daystar University]. https://repository.daystar.ac.ke/ Sürücü, L., & Maslakci, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal , 8 (3), 2694–2726. https://doi.org/10.15295/bmij.v8i3.1540 Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics , 3 (1), 100026. https://doi.org/10.1016/j.sca.2023.100026 Tuomala, V., & Grant, D. B. (2022). Exploring supply chain issues affecting food access and security among urban poor in South Africa. The International Journal of Logistics Management , 33 (5), 27–48. https://doi.org/10.1108/IJLM-01-2021-0007 World Health Organization. (2022). WHO guideline on self-care interventions for health and well-being. World Health Organization. Additional Declarations No competing interests reported. Supplementary Files FINALREVISEDQUESTIONNAIREFORBMCJOURNAL28.07.2025.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 30 Jul, 2025 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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HPTs focus on ensuring effective healthcare delivery by prioritizing the availability of high-quality medications in appropriate quantities. Maintaining optimized HPTs and good inventory management is critical, as poor inventory control can lead to overstocking or understocking, wasting resources, and increasing morbidity and mortality due to a lack of life-saving medications (Kaupa \u0026amp; Naude, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Even though Kenya uses bin cards in the management of inventory, the country has not embraced modern inventory optimization techniques that help in preventing stock outs while minimizing associated costs. (Friday et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, several countries have taken significant steps to improve HPT management. In Asia, nations like Pakistan, Vietnam, the Philippines, and India have developed national priority lists to guide the procurement of essential medications at reasonable costs across all healthcare levels (Bigio et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Computerized logistics management information systems (LMIS) are increasingly used to enhance inventory management, ensuring the timely availability of critical products. In the United States, states such as New York, New Mexico, and Texas have implemented standardized tools like bin cards, stock cards, and requisition forms for inventory tracking, applicable across public health facilities, whether manual or computerized (Eckelman et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAfrica faces significant challenges in managing HPTs effectively. Limited access to medications in hospitals and clinics is a major concern. In South Africa, poor inventory management is a key barrier to improving HPT access Tuomala and Grant, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while frequent stock-outs in Zimbabwe prevent patients from accessing critical drugs (Kanyepe, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These issues highlight the broader challenges in ensuring efficient HPT management across the continent.\u003c/p\u003e\u003cp\u003eIn Kenya, the management of HPTs remains problematic, reflecting issues common in developing nations (Ministry of Health [MOH], 2014). Despite efforts by the Ministry of Health to enhance HPT coordination, availability of essential medications and diagnostics remains low, with averages of 14% and 19%, respectively, even after devolution (Njoroge, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ineffective inventory management contributes to poor health outcomes, Muiruri (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) finding that nearly 30% of deaths in Embu County result from mismanagement of essential medicines and medical supplies. Additionally, the study underscored the critical role of inventory optimization in achieving universal access to high-quality medications and healthcare technologies. The study suggested that efforts to improve HPT management in Kenya must address inventory control inefficiencies to enhance healthcare delivery and reduce preventable deaths (Mbatia, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite policy initiatives and investment, public health facilities often have low HPT availability, stemming from fragmented supply chains, underused information systems, and resource gaps. Limited research exists on how inventory practices influence HPT management at the county level. This multicounty study sought to address that gap in Kenya.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Statement of the problem\u003c/h2\u003e\u003cp\u003eThe health sector plays a critical role in any nation\u0026rsquo;s economy. In Kenya, provision of quality, affordable healthcare is emphasized under the Constitution and Vision 2030 under the social pillar, prompting the Ministry of Health to prioritize Universal Health Coverage (UHC). Health Products and Technologies (HPTs) were designated as a key component of UHC, supported by regulatory policies under the Ministry of Health (MOH, 2014). Efforts by national and county governments have included investments in healthcare infrastructure, with Kisumu, Kiambu, Machakos, Nyeri, and Isiolo Counties establishing HPT Units (HPTUs) staffed by multidisciplinary teams to manage health products and technologies. These Counties have leveraged devolution to improve the availability, quality, and affordability of HPTs through decentralized financing, streamlined procurement, partnerships, and health information systems like DHIS2, which enhance supply monitoring and reduce costs. Inventory optimization is critical in improving service delivery by ensuring availability of quality and affordable health products and technologies by eliminating wastage through pilferage and expiries, reducing stock holding costs, and promoting efficiency in the health commodities supply chain. HPT management in public hospitals in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo remains ineffective. For instance, delays in HPT supply and intermittent shortages of essential medicines and diagnostic kits, compromise service delivery, which has led to the mushrooming of private retail chemists and medical diagnostic laboratories around the public hospitals where patients receive the services at astronomical costs. Budget reductions and inadequate financing have limited access to essential commodities such as laboratory reagents and surgical consumables. Additionally, inadequate training for healthcare workers on forecasting and supply chain management further exacerbated the problem. This led to increased mortality from communicable diseases due to shortages of essential medicines and diagnostic tools. Nonetheless, there is limited knowledge on how inventory optimization influences HPT management in public hospitals. This study aimed to bridge the knowledge gap by conducting a multi-county study on the relationship between inventory optimization and the management of health products and technologies in Kenya.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Objective of the study\u003c/h2\u003e\u003cp\u003eThis study examined the relationship between inventory optimization and the management of health products and technologies in Kenya.\u003c/p\u003e\u003c/div\u003e"},{"header":"2.0 Literature Review","content":"\u003cp\u003eEffective inventory optimization is vital for achieving universal health coverage (UHC), a core objective of the World Health Organization (WHO). Globally, unreliable inventory systems present a significant barrier to the management of Health Products and Technologies (HPTs), which are critical for medical service delivery. Inventory optimization balances service-level goals across stock-keeping units (SKUs) at the same time, managing demand and supply uncertainties. \u003csup\u003e11\u003c/sup\u003e It enables healthcare facilities to maintain sufficient stock levels, identify supply risks, and request replenishments, ultimately reducing costs and enhancing system efficiency (Tadayonrad \u0026amp; Ndiaye, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This balance is achieved through continuous or periodic inventory review policies that prevent stockouts and ensure operational continuity (Boxley et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVarious inventory optimization approaches have been applied to enhance HPT management worldwide. The ABC Analysis is widely used, categorizing inventory into three groups based on their significance in value or usage (Knapp \u0026amp; Mueller, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Essential life-saving medical supplies, categorized as \u0026ldquo;A\u0026rdquo; items, require constant availability to prevent morbidity and mortality. In contrast, \u0026ldquo;B\u0026rdquo; and \u0026ldquo;C\u0026rdquo; items are less critical and managed with different priority levels. Studies in Malaysia, Ghana, and Uganda demonstrate that ABC analysis improves essential medicine stock levels, reduces stockouts, and decreases holding costs, contributing to financial sustainability in healthcare systems (Mohajan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite its benefits, the method faces challenges in resource-constrained settings where health information systems and data accuracy are inadequate (Mwihia, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother critical approach is the First Expiry First Out (FEFO) principle, which ensures consumables are used before expiration, minimizing waste and enhancing access to life-saving products (Okungu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies from Nigeria and Zambia highlight FEFO\u0026rsquo;s effectiveness in reducing vaccine wastage and maintaining a steady supply of antiretroviral therapy (ART), significantly improving patient outcomes (Rahi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, FEFO implementation may face hurdles, such as inaccurate inventory records and inadequate training for healthcare staff (Rowan \u0026amp; Laffey, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Africa, challenges in managing HPTs often stem from insufficient inventory systems and resource constraints. Effective record-keeping and Safety Stock Policies as crucial for maintaining HPT availability. Safety stock acts as a buffer to address unexpected demand, ensuring uninterrupted access to life-saving medicines. Research from Tanzania confirms that safety stock policies effectively reduce HPT stockouts in primary healthcare facilities (Selemani, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Accurate record-keeping is equally critical for tracking stock transactions and identifying inventory management issues, with studies in Kenya and other African countries identifying poor record-keeping as a significant obstacle to effective HPT management (Shangala, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Economic Order Quantity (EOQ) model is another valuable inventory management tool, balancing ordering and holding costs to maintain optimal stock levels. Studies in the United Kingdom and South Sudan demonstrated EOQ\u0026rsquo;s role in minimizing costs, reducing waste, and ensuring a steady supply of essential medical products, contributing significantly to UHC (Rowan \u0026amp; Laffey, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saha \u0026amp; Ray, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meta-analyses from diverse global healthcare settings further highlight EOQ\u0026rsquo;s potential to address stockouts and overstocking, improving access to critical tools and medicines (Rahman \u0026amp; Zailani, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, the Just-In-Time (JIT) inventory management approach has proven effective in optimizing HPT availability. JIT reduces holding costs, prevents wastage, and ensures timely supply of medical products. Research in Japan and Tunisia shows that JIT improves supply chain efficiency, enabling healthcare facilities to respond swiftly to patient needs (Shangala, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies in Uganda found that JIT reduced lead times, increased inventory turnover rates, and enhanced healthcare delivery efficiency, advancing UHC goals (Gafa, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Kenya, traditional and technology-driven inventory optimization strategies are yet to implemented to address HPT management challenges (Mudogo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) The country lacks policies to promote adoption of JIT systems with predictive analytics has allowed healthcare facilities to forecast demand patterns and maintain optimal inventory levels, significantly reducing wastage and supply chain inefficiencies (Onyancha, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, safety stock policies and accurate record-keeping have been identified as essential for maintaining HPT availability and addressing stockouts in primary healthcare facilities are poorly implemented in Kenya (Shangala, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, inventory optimization methods like FEFO, ABC analysis, and EOQ have demonstrated significant benefits globally in promoting availability of quality and affordable health Products and technologies faces a lot of implementation challenges in Kenya such as inadequate training, data inaccuracies, and resource constraints. equitable access to quality healthcare.\u003c/p\u003e"},{"header":"3.0 Methodology","content":"\u003cp\u003eThe study took place in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo counties to determine the influence of financing on management of health products and technologies in selected Counties, Kenya. The research was anchored on the Pragmatism paradigm because it is basically based on the existing body of knowledge that is fixed, observable, and objective, as well as multiple, socially constructed by individuals. Pragmatists believe that the nature of knowledge is both quantifiable (objective knowledge) using scientific research as well as gained through in-depth understanding (Park et al., 2020). A pragmatist\u0026rsquo;s beliefs are both single and multiple.\u003c/p\u003e\u003cp\u003eA descriptive cross-sectional research design and interviews, both anchored by the pragmatism paradigm, were used in the study. Staff dealing with the management of health products and technologies directly or indirectly at the service delivery level 4 and 5 public health facilities in Kisumu, Nyeri, Isiolo, Kiambu, and Machakos Counties were the study's target group. The study also targeted the county health management team from the selected counties as the key informants. The questionnaire and key informant interview guides were used to collect data. Pretesting of the data collection tools was conducted at Kajiado County. A census sampling method was used in the study, where 141 staff managing HPTs at level 4 and 5 public health facilities were targeted. Key informant interviews were conducted with the County Health Management Team. Ethical considerations were considered. informed consent to participate was obtained from the participants in the study, and all the respondents gave consent to participate in the study. The questionnaire and interview guide used in this study were developed specifically for this research and is provided as Supplementary File 1\u003c/p\u003e"},{"header":"4.0 Results and Discussions","content":"\u003cp\u003eA total of 141 structured questionnaires were distributed to respondents via Google Forms, out of which 106 were completed, yielding a 75.0% response rate. The data collection instrument was evaluated, with inventory optimization and the overall tool (r\u0026thinsp;=\u0026thinsp;0.935) demonstrating excellent reliability. A Cronbach\u0026rsquo;s Alpha (r) value of 0.7 and above is generally considered reliable and acceptable for research purposes (S\u0026uuml;r\u0026uuml;c\u0026uuml; \u0026amp; Maslakci, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDescriptive statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInventory optimization factors with major effects on the management of HPT\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study established the inventory optimization factors with a major effect on the management of HPTs as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\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\u003e\u003cem\u003eInventory optimization and management of HPTs\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eABC analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFirst-expiry-first- out\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSafety stock policy and record keeping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEconomic order quantity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJust in time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003cp\u003e(χ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\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\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2(15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3(23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(46.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2(15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e32.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6(42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1(7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14\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\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10(27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13(35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2(5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6(16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37\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\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2(8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7(30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13(56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1(4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23\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\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9(47.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25(23.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38(35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4(3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8(7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e106\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results indicate that the most influential factors are First-Expiry-First-Out (FEFO) and Safety Stock Policy, and Record Keeping. FEFO was identified as the most prevalent method, with 38 responses (35.8%), followed by Safety Stock Policy and Record Keeping at 31 responses (29.2%). Conversely, Economic Order Quantity (EOQ) and Just-in-Time (JIT) practices were less commonly used, with 4 responses (3.8%) and 8 responses (7.5%), respectively. The Chi-Square test (χ\u0026sup2; = 32.87, P-value\u0026thinsp;=\u0026thinsp;0.008) indicates that the distribution of these factors was statistically significant across counties. Kiambu and Kisumu counties favored Safety Stock Policy and Record Keeping, with 46.2% and 56.5% responses, respectively. Isiolo favored ABC Analysis, with 50% of responses. While Machakos and Nyeri counties preferred First-Expiry-First-Out (FEFO), with 35.1% and 47.4% of responses, respectively.\u003c/p\u003e\u003cp\u003eThese findings align with studies by Kiarie and Mbugu (2022) in Nairobi County, who found that FEFO reduces wastage and ensures the availability of effective medication, and Kilimo et al., (2022) in Mombasa County, who emphasized the importance of Safety Stock Policy in maintaining supply continuity. On the other hand, Kagwiri et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Nakuru County, the study found Economic Order Quantity to be more influential, noting its role in better financial planning and reducing carrying costs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSoftware to streamline and integrate the management of HPT\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study identified the software used for streamlining and integrating the management of HPT. The study categorized the software into four levels: Basic, Moderate, Advanced, and Specialized, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eSoftware to streamline and integrate the management of HPT\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBasic software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModerate software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdvanced software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpecialized software\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(χ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7(53.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3(23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3(23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e33.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5(35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5(35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4(28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13(35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3(8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4(17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12(52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14(73.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4(21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1(5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41(38.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27(25.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4(3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results showed that Moderate Software was the most commonly used (38.7% of responses), followed by Basic Software (32.1%), Advanced Software (25.5%), and Specialized Software (3.8%). Nyeri, Kiambu, and Isiolo predominantly used Basic Software (73.7%, 53.8%, and 35.7%, respectively), and Machakos and Kisumu preferred Moderate Software, with 45.9% and 52.2% of responses. The Chi-Square test (χ\u0026sup2; = 33.302, P-value\u0026thinsp;=\u0026thinsp;0.001) confirmed a statistically significant impact of software type on HPT management. The findings were consistent with those of Lahariya (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in rural India and Kayiwa, (2020) in Uganda, who observed that moderate software balances functionality and simplicity. However, Malakoane et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in South Africa found that despite the advantages, advanced software adoption was limited by cost and technical constraints, agreeing with study findings where only 25.5% and 3.8% were using advanced and specialized software, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFrequency of ABC analysis conducted for HPT\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe research sought to underscore the frequency of ABC analysis conducted for HPT. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e captures the findings on the frequency of ABC analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eFrequency of ABC analysis conducted for HPT\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKruskal-Wallis H\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\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eFrequency of ABC analysis conducted for HPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.92\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=\"c2\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.11\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=\"c2\"\u003e\u003cp\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.57\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=\"c2\"\u003e\u003cp\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.34\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=\"c2\"\u003e\u003cp\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.37\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=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.584\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 study also found that Nyeri had the highest mean rank (57.37), followed by Machakos (55.57) and Kisumu (51.34). The Kruskal-Wallis H test revealed no statistically significant differences (H\u0026thinsp;=\u0026thinsp;2.843, P-value\u0026thinsp;=\u0026thinsp;0.584), indicating that ABC analysis was applied with relative consistency across the counties. These results aligned with the findings of Shami et al. (2021), who observed that ABC analysis was evenly implemented across districts in their study. However, Banerjee (2024) found regional disparities in the frequency of ABC analysis in Northern India.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCriteria for Determining Optimal HPT Stock Levels\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study assessed the clarity and effectiveness of the criteria for determining optimal HPT stock levels, which are captured in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eCriteria for Determining Optimal HPT Stock Levels\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKruskal-Wallis H\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\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eClarity of the criteria used to determine optimal stock levels for HPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.27\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=\"c2\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.86\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=\"c2\"\u003e\u003cp\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.46\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=\"c2\"\u003e\u003cp\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.00\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=\"c2\"\u003e\u003cp\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.66\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=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.719\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 Kiambu county had the highest mean rank (60.27), followed by Machakos (55.46) and Isiolo (53.86). Despite differences in the rankings, the Kruskal-Wallis H test (H\u0026thinsp;=\u0026thinsp;2.089, P-value\u0026thinsp;=\u0026thinsp;0.719) indicated no statistically significant differences in the perceived clarity and effectiveness of these criteria across the counties. These results agreed with Kayiwa et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Rumisha, et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Tanzania, who found no significant regional differences in understanding stock management criteria. However, Bwanga and Chanda (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found significant discrepancies in Zambia, where some regions lacked adequate training and resources.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFrequency of stocking policy review and adjustment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below shows the frequency of stocking policy review and adjustment across counties.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eFrequency of stocking policy review and adjustment\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKruskal-Wallis H\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\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eFrequency of stocking policy review and adjustment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.32\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=\"c2\"\u003e\u003cp\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.64\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=\"c2\"\u003e\u003cp\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.74\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=\"c2\"\u003e\u003cp\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.71\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=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106\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\u003cp\u003eNyeri County had the highest frequency (mean rank\u0026thinsp;=\u0026thinsp;56.71), followed closely by Machakos (mean rank\u0026thinsp;=\u0026thinsp;56.64). However, the Kruskal-Wallis H test (H\u0026thinsp;=\u0026thinsp;1.961, P-value\u0026thinsp;=\u0026thinsp;0.743) revealed no statistically significant differences, suggesting that stock policy reviews and adjustments occurred similarly across the counties. These findings resonate with Banerjee (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who observed consistency in regional policy review practices. However, Ooms et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found regional variations in the Eastern Province, suggesting that localized management practices may influence the frequency of stocking policy adjustments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy and reliability of the system used for the record-keeping of HPT inventory\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e below captures the results of the accuracy and reliability of systems used to record the inventory of Health Products and Technologies (HPT) across five counties.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eAccuracy and reliability of the system used for the record-keeping of HPT inventory\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCounty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Rank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKruskal-Wallis H\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\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAccuracy and reliability of the system used for the record-keeping of HPT inventory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKiambu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.62\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=\"c2\"\u003e\u003cp\u003eIsiolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.61\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=\"c2\"\u003e\u003cp\u003eMachakos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.55\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=\"c2\"\u003e\u003cp\u003eKisumu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.28\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=\"c2\"\u003e\u003cp\u003eNyeri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.71\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=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.715\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 Kruskal-Wallis H test revealed no significant differences in perceptions of accuracy and reliability (H\u0026thinsp;=\u0026thinsp;2.113, p\u0026thinsp;=\u0026thinsp;0.715). Machakos (mean rank\u0026thinsp;=\u0026thinsp;56.55) and Kisumu (mean rank\u0026thinsp;=\u0026thinsp;56.28) were rated highest, while Nyeri (mean rank\u0026thinsp;=\u0026thinsp;46.71) was rated lowest. These findings implied consistent perceptions of record-keeping systems across the counties. This aligned with Bwanga and Chanda (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who found no significant regional differences in record-keeping perceptions in Uganda. Conversely, Batamuriza et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported notable disparities in Rwanda due to technological infrastructure and training variations.\u003c/p\u003e\u003cp\u003eThe findings agreed with those of key interviews that inventory optimization plays a critical role in the management of HPTs in public hospitals:\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;\u0026hellip;Inventory management is largely manual, with multiple records used for receiving and distributing commodities. The First Expiry First Out (FEFO) principle is followed, and excess HPTs are redistributed while maintaining good storage practices. Nyeri County highlighted out-of-pocket expenses as a barrier to healthcare due to increased workload. During the UHC Pilot, Drawing Rights at KEMSA ensured consistent HPT supply, with KEMSA\u0026rsquo;s fill rate exceeding 85%. However, the push system led to expiries, despite the use of IMS and stock cards\u0026hellip;\u0026rdquo; (KII, Male, 005, 24th June, 2024\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInferential statistics\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows a Bivariate Pearson correlation analysis measuring the relationship between inventory optimization and management of HPTs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eCorrelations\u003c/em\u003e\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eX4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eX4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.636\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eY\u0026thinsp;=\u0026thinsp;HPTs management; X4\u0026thinsp;=\u0026thinsp;inventory management\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Field data (2024)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe study revealed a strong, positive, and statistically significant correlation (r\u0026thinsp;=\u0026thinsp;0.636, P\u0026thinsp;=\u0026thinsp;0.000) between inventory optimization and HPT management. Effective inventory practices are strongly linked to improved HPT management, with results unlikely due to chance (P value was less than 0.05). Optimizing inventory significantly enhances the management of health products and technologies. The study findings aligned with those of Balkhi, et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that applying Just-in-Time (JIT) and safety stock policies reduced medication wastage and ensured availability in public hospitals across India, emphasizing the importance of structured inventory systems. In agreement, Maduhu (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Tanzania highlighted that FEFO and robust record-keeping practices minimized stockouts and enhanced efficiency in health supply chains, particularly for essential medications. Conversely, Kagwiri et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Nakuru County observed Economic Order Quantity (EOQ) as a more significant factor for improving inventory-related decision-making and cost management than FEFO or safety stock. They argued that the impact of optimization practices varies based on resource allocation and implementation capacity. Regression explored the predictive ability of inventory optimization in the management of HPTs.\u003c/p\u003e\u003cp\u003e\u003cem\u003eModel summary\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe study evaluated the fit and performance of the regression model. The model summary was crucial for understanding how well the model explained the variability in the dependent variable based on the independent variable, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e below.\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 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eModel Summary\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eR Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAdjusted R Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStd. Error of the Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003eChange Statistics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR Square Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF Change\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003edf1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003edf2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eSig. F Change\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\u003e.732\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003ea. Predictor: (Constant), Inventory optimization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eb. Dependent Variable: Management of HPTs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFrom the model summary analysis, the model explained 53.5% of the variance in managing HPTs (R Square\u0026thinsp;=\u0026thinsp;0.535). This meant that the model had a strong explanatory power, indicating that the model was very effective at predicting the dependent variable, and the chosen independent variable was appropriate and sufficient for explaining the dependent variable. Therefore, the researcher was confident in the robustness of the model based on the independent variables included in the study. Nevertheless, a significant portion of variance (46.5%) was still not accounted for by the predictor.\u003c/p\u003e\u003cp\u003eTo check whether the model occurred by chance or rather potential overestimation of the R Square, the Adjusted R Square was analysed. The study estimated an adjusted R Square value of 51.2% (Adjusted R Square of 0.512). This indicated that 51.2% of the variance in the management of HPTs is explained by the model, slightly less than the R Square, but still a strong effect. The model reached statistical significance (P\u0026thinsp;=\u0026thinsp;.000).\u003c/p\u003e"},{"header":"4.0 Conclusions of the study","content":"\u003cp\u003eThe study concluded that inventory optimization has a statistically significant influence on the management of Health Products and Technologies (HPTs) in public hospitals in Kenya. Consequently, the study rejected the null hypothesis that inventory optimization has no significant influence on the management of Health Products and Technologies in selected Counties and failed to reject the alternative hypothesis. Improvement in inventory optimization leads to efficiency in the management of Health Products and Technologies, resulting in improved access to quality and affordable essential Health Products and Technologies, thus improving service delivery in health facilities for the achievement of Universal Health Coverage. Inventory optimization significantly improves management of HPTs in public hospitals. Lean inventory strategies, digitalization, and supportive policy frameworks are recommended to address inefficiencies and improve access to essential health technologies.\u003c/p\u003e"},{"header":"5.0 Recommendations of the study","content":"\u003cp\u003eThe study recommends optimization of inventory management through the adoption of lean inventory management to increase the availability of quality and affordable health products and technologies. Public health facilities should make monthly orders as opposed to quarterly orders that require storage, thus increasing the stock holding costs, increasing the risk of wastage through expiries and pilferage. Other recommended strategies for inventory optimization include implementing ABC Analysis and adopting the First Expiry First-Out (FEFO) method to minimize wastage. Regular audits would ensure adherence. Hospitals should establish tailored safety stock policies to prevent stockouts and invest in digital record-keeping systems. The study also recommends optimizing the Economic Order Quantity (EOQ) model that balances ordering and holding costs, as well as developing policies to support the adoption of Just-in-Time (JIT) practices that minimize holding costs for highly specialized and expensive HPTs such as orthopedic implants and radiopharmaceuticals in public health facilities. Regular inventory audits and record keeping are recommended in healthcare supply chain policies to improve efficiency. Policymakers should provide the necessary support for standardization across all public health facilities to promote the availability of quality and affordable HPTs for improved service delivery.\u003c/p\u003e\u003cp\u003e\u003cem\u003eImplications on theories, policies, and practice\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u003cp\u003eThe study highlights the importance of integrating effective inventory management strategies into healthcare policies. Policymakers must review the current practices of quarterly orders and adopt monthly orders with one month\u0026rsquo;s working stock and one month's buffer stock. This includes mandating the use of tools like ABC Analysis, FEFO, and the Economic Order Quantity (EOQ) model, which would require investments in training, technology, and infrastructure. Additionally, policymakers should provide financial and technical support to hospitals to ensure the implementation of digital record-keeping systems and tailored safety stock policies. Policies that encourage regular audits and monitoring will also be crucial for maintaining the effectiveness of these strategies.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTheoretical Implications\u003c/strong\u003e\u003cp\u003eThe findings align with inventory management theories that emphasize the importance of systematic approaches in controlling costs and optimizing resources. The Economic Order Quantity (EOQ) and Just-in-Time (JIT) models support theories related to supply chain efficiency, highlighting the balance between cost savings and product availability. Similarly, the ABC analysis, First Expiry, First-Out (FEFO), and safety stock models connect with inventory control theories focused on minimizing waste and ensuring stock availability under varying demand conditions. These findings further reinforce the relevance of these theoretical models in the healthcare context, contributing to the growing body of research on healthcare supply chain management.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePractical Implications\u003c/strong\u003e\u003cp\u003ePractically, the recommendations will enhance the day-to-day operations of public hospitals. Adopting ABC Analysis and FEFO, alongside tailored stock policies, can streamline inventory management processes, reduce waste, and prevent stockouts, improving the availability of critical health products. Investing in digital record-keeping systems will provide real-time data, enhancing decision-making and accountability. Regular audits will ensure compliance and allow hospitals to refine their practices. Furthermore, implementing Just-in-Time (JIT) inventory practices will reduce holding costs and enhance operational efficiency. These practices, supported by comprehensive policies, will lead to more effective and sustainable management of health products and technologies in public hospitals.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Limitation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study was limited by focusing only on five counties Kisumu, Kiambu, Nyeri, Machakos, and Isiolo selected for their unique health challenges and UHC pilot status. This excluded the remaining 42 counties, private, and faith-based facilities, potentially limiting the generalizability of findings. Additionally, the focus on level 4 and 5 hospitals, due to their advanced HPT management structures, overlooked insights from level 2 and 3 facilities, which may face distinct challenges. Further, the study utilized a mixed-method design, which, while offering comprehensive insights, posed challenges in integrating findings from both research approaches. It was resource-intensive, requiring significant time, financial investment, and specialized expertise.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Scientific Ethical Review Committee of Kenya Methodist University (KeMU/ISERC/HSM/26/2023), and the National Council for Science, Technology and Innovation (NACOSTI) offered research permit NACOSTI/P/23/31850. Authorization to collect data from each county was obtained from the director of health. This study was conducted following the ethical principles of the Declaration of Helsinki (as revised in 2024) for research involving human participants, as stipulated by the World Medical Association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researcher had no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo finding was provided for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eShadrack Mururu Meme conducted the research study and development of the manuscript.\u003c/li\u003e\n \u003cli\u003eDr Caroline Kawila – Assisted in designing the objectives and review of the manuscript \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDr Kezia Njoroge- Assisted in designing the objectives and review of the manuscript.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI wish to acknowledge the following for their support toward the development of this manuscript.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eKenya Methodist University department of health systems within the school of Health Sciences.\u003c/li\u003e\n \u003cli\u003eChief Officers and Directors for Health from Machakos, Kiambu, Isiolo, Kisumu and Nyeri counties for their approval to conduct the study in their counties.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCounty Health Management Team for agreeing to be key informants in this study.\u003c/li\u003e\n \u003cli\u003eChief Executive Officer and Head of Commercial Services at Mission for Essential Drugs and Supplies for allowing me time to go out and conduct the research.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSupervisors Dr. Caroline Kawila and Dr. Kezia Njoroge for their guidance and support during the development of the manuscript\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBalkhi, B., Alshahrani, A. \u0026amp; Khan, A. (2022). Just-in-time approach in healthcare inventory management: Does it really work?. \u003cem\u003eSaudi Pharmaceutical Journal\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(12), 1830\u0026ndash;1835. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jsps.2022.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jsps.2022.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanerjee, S. (2021). 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(2022). \u003cem\u003eWHO guideline on self-care interventions for health and well-being.\u003c/em\u003e World Health Organization.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Inventory Optimization, Management of Health Products and Technologies, Affordability, Availability, and Quality","lastPublishedDoi":"10.21203/rs.3.rs-7250053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7250053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealth Products and Technologies (HPTs) are critical pillars of the health system and essential to achieving Kenya's Universal Health Coverage (UHC). UHC prioritizes access to high-quality medical care with minimal financial hardship. Despite efforts to enhance HPTs management, counties like Kisumu, Machakos, Nyeri, Kiambu, and Isiolo in Kenya face inefficiencies. Challenges include long lead times for receiving commodities and low order fill rates, which hinder access to quality and affordable health HPTs, impacting service delivery. This study aimed to determine the influence of inventory optimization on the management of HPTs. The Utilization Management Theory guided the research. The research was conducted in Kisumu, Kiambu, Machakos, Nyeri, and Isiolo counties, using the pragmatism paradigm to support a mixed-methods design. Quantitative data utilized a descriptive research design, while qualitative data employed an exploratory design. A census sampling method was used in the study, where 141 staff managing HPTs at level 4 and 5 public health facilities were targeted. Participants were drawn from clinical, pharmacy, service delivery, and administration departments. Key informant interviews were conducted with County Directors of Health and County Pharmacists. Data collection involved pre-tested questionnaires and Key Informant interview guides to ensure validity and reliability. Quantitative data was analyzed using descriptive and inferential statistics, while qualitative data was thematically analyzed. Diagnostic tests, including normality test, homoscedasticity, autocorrelation, and multicollinearity checks, ensured assumptions were met. The study adhered to research ethics throughout the investigation; informed consent was sought from the respondents; data confidentiality was observed by ensuring no personal identifiers were collected from the respondents; instead, a unique serial number was used to identify the participants. Data was collected and stored in secure areas accessible only to the researcher. The study was approved by the Institutional Scientific Ethical Review Committee of Kenya Methodist University (KeMU/ISERC/HSM/26/2023), and NACOSTI offered a research permit NACOSTI/P/23/31850. The study found that the model explained 53.5% (R Square value of 0.535) of the variance in the management of HPTs. This meant that the model had strong explanatory power, but there was still a significant portion of variance (46.5%) that was not accounted for by these predictors. The study concluded that inventory optimization significantly impacts the management of HPTs. Effective tools such as ABC Analysis, FEFO, and robust safety stock policies can address existing inefficiencies. Integrating these practices with supportive digital systems and tailored policies is vital for access to quality and affordable HPTs, thus improving service delivery.\u003c/p\u003e","manuscriptTitle":"Inventory Optimization and Management of Health Products and Technologies in Kenya: A Multi-County Study on access to Quality affordable Health Products and Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 16:03:37","doi":"10.21203/rs.3.rs-7250053/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-25T20:29:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T14:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260480864928283358434419218262743650285","date":"2025-08-11T06:02:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T07:37:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52077756465244095095616119406794133160","date":"2025-08-07T07:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160267322706241497959001163335384186366","date":"2025-08-07T06:56:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81216952702109319705257359824734604839","date":"2025-08-06T19:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254507450544112794636978050245454885255","date":"2025-08-06T19:11:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T19:07:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T09:15:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T10:34:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health Research Policy and Systems","date":"2025-07-30T07:48:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75441664-210f-4e7c-92eb-e718d388b993","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-16T01:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 16:03:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7250053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7250053","identity":"rs-7250053","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}