Purpose: This study examined the influence of inventory information systems on warehouse performance at the Medical Stores Department (MSD), Tanzania. Specifically, the study assessed user efficiency, error reduction, and system integration as key dimensions of inventory information systems affecting warehouse operations. Methodology: The study employed a quantitative approach and descriptive research design. The target population consisted of 100 employees from the Procurement Management Unit and warehouse departments at MSD Headquarters and Mabibo Zonal Office. A sample of 80 respondents was selected using stratified and simple random sampling. Data were collected through structured questionnaires using a five-point Likert scale and analysed using IBM SPSS Version 28. Multiple regression analysis was applied to determine the effect of inventory information systems on warehouse performance. Findings: The findings revealed that inventory information systems had a positive and statistically significant effect on warehouse performance (β = .360, t = 3.326, p = .001). Effective system utilization improved transaction speed and accuracy, reduced manual recording errors, strengthened stock accuracy, and enhanced information flow between procurement and warehouse functions. System integration also supported real-time stock tracking and inventory monitoring. Unique Contribution to Theory, Practice and Policy: The study recommends strengthening user capacity, improving automated inventory controls, and enhancing integration between EPICOR-10, eLMIS, procurement, warehousing, inventory control, and distribution functions. These measures are expected to improve inventory visibility, reduce stock discrepancies, strengthen coordination, and enhance warehouse performance at MSD.
Counterfeit products are disrupting automotive supply chains globally, especially in the aftermarket sector. This study investigated and sought to address the challenge of counterfeits in the automotive aftermarket sector of Zimbabwe by applying the Extended Resource-Based Theory. This study employed the Interpretivism philosophy, the qualitative research approach, and a case study design. The researcher used interviews and a Focus Group Discussion (FGD) as research instruments. Purposive sampling was used to draw a sample of 23 participants that was made up of aftermarket businesspeople, automotive companies, a regional automotive association, policymakers, automotive clients, logisticians, researchers, and academia. The saturation point determined the cut-off point for the number of samples. Data analysis was carried out using Thematic Analysis. The study concluded that counterfeits are causing negative financial impacts on clients, automotive aftermarket businesses, and governments. Law enforcement enhancement, policy reviews, and stakeholder collaborations were identified as critical success factors in the fight against counterfeits in the automotive sector after-market. The major finding from the study was that application of the Extended Resource-Based Theory in the fight against counterfeits has the potential to mobilize strategic resources that can help bring back sanity to the local automotive aftermarket sector supply chains. The recommendations include conducting awareness campaigns that educate customers, training law enforcement agents in the requisite skills to combat counterfeits, and investing in technologies that detect and deter the smuggling and trading of counterfeits. Lastly, further research is recommended to establish the impact of policy on the fight against counterfeit automotive products and the role of technology in this fight. This paper contributes to policy and practice by advancing the application of the ERBT theory in driving collaborations to solve contemporary business challenges, such as counterfeiting in the automotive aftermarket sector supply chains.
Monopsony theory predicts that a single buyer facing multiple sellers should command decisive pricing power. This paper challenges that assumption in the context of specialized silicon procurement, where a sole buyer often confronts a monopolistic or near-monopolistic supplier. Drawing on practitioner experience in strategic sourcing and supply chain leadership within the consumer electronics industry, this paper identifies and formalizes the Monopsony Paradox: the counterintuitive phenomenon whereby single-buyer leverage diminishes – rather than intensifies – as supplier specialization and mutual dependency increase. This research makes three distinct contributions to supply chain management theory and practice. First, it defines and validates the Monopsony Paradox as a distinct theoretical phenomenon, providing a structured explanation of inverted buyer leverage in specialized silicon markets. Second, it develops the Triple-Constraint Negotiation Framework (TCNF), a novel procurement approach that simultaneously optimizes cost extraction, supply security, and supplier viability – representing a fundamental departure from conventional zero-sum negotiation models. Third, it provides empirical validation through detailed case analysis demonstrating how TCNF enabled a substantial reduction in manufacturing value-add while qualifying alternative suppliers and preserving vendor financial stability, with implications applicable across monopolistic procurement categories. The framework is illustrated through sensor silicon procurement in the consumer electronics industry, where significant double-digit cost reductions were achieved in the vendor’s manufacturing value-add while simultaneously qualifying an alternative supply network and preserving the incumbent vendor's financial health. Unlike conventional procurement models that treat vendor negotiations as zero-sum, this framework argues that short-term profit maximization through aggressive cost extraction can be counterproductive when the buyer depends on a sole-source vendor for mission-critical components. The paper offers practical implications for procurement leaders operating in bilateral dependency environments across semiconductor, display, battery, and other specialized technology supply chains.
Public procurement is increasingly recognized as a strategic policy instrument for stimulating innovation, addressing societal challenges, and creating public value. Despite growing policy support and scholarly interest, the implementation of innovation-oriented procurement remains uneven, with public organizations frequently struggling to translate innovation ambitions into practice. This study systematically reviews the literature on Public Procurement Innovation (PPI) to identify the drivers and constraints influencing innovation-oriented procurement, examine the conditions shaping its implementation, and explain the persistence of the innovation paradox. Drawing on Institutional Theory and Innovation Systems Theory, the study employs a systematic literature review methodology to analyse 133 peer-reviewed articles published between 2006 and 2024 and indexed in the Scopus database. The findings reveal that innovation procurement outcomes are shaped by four interrelated dimensions: institutional conditions, regulatory frameworks, organizational capabilities, and market dynamics. Policy commitment, governance coordination, flexible procurement instruments, organizational competencies, supplier engagement, and supportive innovation ecosystems emerge as important enablers of innovation-oriented procurement. Conversely, fragmented governance structures, procedural rigidity, capability deficits, risk aversion, information asymmetries, and weak market responsiveness frequently constrain implementation. The review demonstrates that innovation outcomes depend not on individual factors alone but on the alignment of conditions across these dimensions. The study contributes to the literature by advancing a multidimensional explanation of the innovation paradox in public procurement and developing a conceptual taxonomy that integrates institutional, regulatory, organizational, and market perspectives into a unified analytical framework. The findings offer theoretical insights into the governance of innovation-oriented procurement and practical implications for policymakers and procurement practitioners seeking to strengthen innovation outcomes through public procurement.
Global supply chains face unprecedented complexity and volatility, accelerating demand for adaptive management systems beyond human-in-the-loop AI. This paper examines agentic AI — AI systems capable of autonomous, goal-directed behavior across multi-step tasks — as applied to end-to-end supply chain orchestration. Unlike conventional AI tools that recommend actions for human execution, agentic AI systems independently perceive supply chain states, reason about optimal interventions, and execute decisions across procurement, inventory, logistics, and risk management functions. Drawing on emerging literature and practitioner experience managing supply chain operations at scale, this paper makes four principal contributions. First, it traces the evolution from assistive AI to autonomous agents, establishing a conceptual foundation for understanding the transformative shift underway. Second, it examines the architectural components of multi-agent supply chain systems, detailing how specialized agents collaborate within enterprise infrastructure. Third, it maps key application domains where agentic AI is generating measurable operational impact. Fourth, it proposes the STAR Framework — comprising Structure, Trust, Adaptation, and Resilience — as a governance architecture for responsible deployment. The paper further introduces an Autonomy Decision Matrix that calibrates agent authority to risk exposure and decision certainty, and critically examines the Deskilling Hypothesis as a counter-theoretical challenge to autonomous supply chain management. Three illustrative case applications validate the framework's practical relevance. Implications for practitioners, organizational designers, and researchers are discussed.
This This study develops a comprehensive multi-sourcing framework enabling organizations to mitigate Calendar Year 2025 datacentre infrastructure tariff exposure through strategic supplier diversification across multiple countries of origin. Enterprise server, storage, and networking procurement faces unprecedented cost volatility driven by Section 301 supplemental tariffs imposing 25-27.6 percent duties on China-origin products while alternative origins including USMCA partners, Southeast Asian manufacturing hubs, and emerging Indian production facilities offer zero-tariff treatment. Drawing upon supply chain risk management theory and portfolio optimization methodology, this research proposes a Total Cost of Ownership framework that integrates origin-dependent tariff structures, logistics costs, quality considerations, and risk premiums to enable systematic supplier allocation decisions. This research makes three primary contributions to supply chain management literature. First, it extends portfolio optimization theory to origin-based supplier allocation decisions under trade policy uncertainty. Second, it develops an enhanced TCO framework incorporating origin-dependent tariff structures as primary decision variables. Third, it provides the first systematic examination of India as an emerging datacentre infrastructure manufacturing origin for international procurement. Implementation guidance encompasses a sixteen-week execution program, financial justification models demonstrating 12-18 percent TCO reduction potential, and governance mechanisms for dynamic portfolio management.
Supply chain organizations are hitting a wall with AI (Artificial Intelligence) automation initiatives because their data is a fragmented mess. Inconsistent terminology and siloed systems create a structural bottleneck that kills AI effectiveness before it can even scale. This study delivers the essential semantic infrastructure with a specialized ontology framework specifically designed to bridge these existing gaps. It transforms AI from a disconnected tool into a scalable, explainable, and high-performing asset that actually works across the entire supply chain network. Utilizing a rigorous Design Science Research (DSR) approach, this study moves beyond theory to build a high-functioning solution for real-world operational failures. By analysing documented semantic gaps in current supply chains, a domain-specific ontology was engineered to formalize every relationship, attribute, and constraint within the operation. This framework was then stress-tested through scenario analysis across procurement, forecasting, inventory management, and logistics to ensure it demonstrates potential utility and logical coherence. The results are clear, this ontology successfully bridges the gap between fragmented systems, ensuring data remains consistent and actionable across the entire network. By embedding logical rules and explicit relationships directly into the data structure, the framework facilitates interoperability across heterogeneous platforms. This doesn't just make systems talk to each other; it empowers AI to perform complex reasoning and enhances traceability for every decision made, ensuring total transparency. This research redefines the “ontology” as a strategic mandate for AI success rather than a mere modelling exercise. It proves that semantic architecture is the primary driver of AI scalability. For industry leaders, this provides a structured pathway for harmonizing data definitions and building a foundation that can support large-scale, high-stakes AI integration.
The concept of Integrated Intelligent Transportation System (ITS) or Smart Traffic Management System operating within a Connected Digital Ecosystem (such as Digital Twins) represents the zenith of modern ITS and smart city planning - moving from standalone, reactive traffic control to a unified, urban predictive ecosystem, that is proactive. This article explains how a single pane of glass for urban mobility, created by leveraging digital infrastructure and systems engineering, effectively address critical challenges of rapid urbanization including growing congestion, environmental and safety issues to benefit quick commerce and e-commerce businesses whose supply chains, earnings and margins are heavily affected by their last-mile logistical efficiencies. The article shows that ITS reduces urban travel times by 25 and congestion by 30%, while increasing intersection efficiency by 40% and cutting unnecessary stops by 20%. By integrating AI and IoT, these systems optimize logistical operations and reduce company costs by 10–30%. This study offers practical insights for traffic regulators and e-commerce professionals aiming to optimize last-mile logistics, accelerate business growth, and secure customer loyalty by elevating the delivery experience.
Purpose: Effective stores management is a foundational pillar of supply chain performance, yet scholarly discourse on how to quantify its effectiveness within broader supply chain management (SCM) frameworks remains fragmented and inconclusive. This paper undertakes a systematic secondary analysis to examine, contrast, and synthesise key quantitative and qualitative dimensions through which store management systems can be evaluated within the SCM context. Methodology: Drawing on peer-reviewed literature published between 2018 and 2025 and sourced from Scopus-indexed and DOAJ-indexed journals, the study maps the principal performance measurement frameworks, including the Supply Chain Operations Reference (SCOR) model, the Balanced Scorecard adapted for SCM, and emergent digitally-enabled metrics, against store-level operational variables such as inventory accuracy, order fulfilment rates, shrinkage control, and demand forecast alignment. Findings: The analysis reveals significant convergence around five quantifiable dimensions of store management effectiveness: inventory turnover efficiency, service level attainment, stockout frequency, lead time variability, and cost-to-serve ratios. The paper further introduces the Contextual Innovation Performance Model (CIPM) as an integrative analytical lens, arguing that contextual organisational variables mediate the relationship between measurement system design and actual performance outcomes. Unique Contribution to Theory, Practice and Policy: The study contributes to theory by resolving definitional ambiguity around store effectiveness and proposes a multi-dimensional Store Management Effectiveness Index (SMEI) as a practical benchmarking instrument for researchers and practitioners.
Purpose: This study examined the influence of inventory control practices on public organisational performance in Tanzania, with specific focus on the Tanzania Immigration Department Kurasini Office, Dar es Salaam. The specific objectives were to examine the influence of inventory tracking on organisational performance, determine the effect of reorder point management on organisational performance, and examine the influence of stock levels maintenance on organisational performance at the Immigration Office in Kurasini, Dar es Salaam. Methodology: The study employed an explanatory research design utilising a quantitative research approach to establish causal relationships between inventory control practices and organisational performance. The target population comprised 208 employees and management staff working at the Immigration Office in Kurasini, Dar es Salaam, including employees directly involved in inventory management, procurement, logistics, and operations, as well as administrative and support staff. A sample size of 67 respondents was determined using Yamane's (1967) formula with a 10% margin of error. Simple random sampling technique was employed to select participants from the target population, ensuring equal probability of selection and minimising selection bias. Primary data were collected through structured questionnaires administered to the sampled respondents. Data were analysed using descriptive statistics and multiple linear regression analysis. Descriptive data usually serve to support inferential analysis. Given that this study focuses on examining influence, the regression results provide clearer evidence. Findings: The findings indicate that inventory tracking (β = 0.394, p = 0.005), stock levels maintenance (β = 0.287, p = 0.006), and re-order point management (β = 0.241, p = 0.027) all exert a positive and statistically significant influence on organizational performance. Therefore, the emphasis is placed on the inferential results rather than descriptive statistics. Unique Contribution to Theory, Practice and Policy: The study extend the resource based view theory by empirical confirming that core inventory control practices, inventory tracking, reorder point management and stock level maintenance significantly influence organization performance. The study recommends the implementation of digital inventory management systems, staff training programmes on modern inventory control practices, establishment of clear reorder point policies, and regular monitoring and evaluation of inventory control procedures.
Sustainability, a key concept being implemented in all aspects of commercial activities around the world, aligns with nations’ objective of transition to a greener and cleaner world. A key facet of this transition is “circular flow” or what is commonly understood as recycling. This article establishes the benefits of integrating the same principles in logistics, through a comprehensive analysis of an Environmental, Social, and Governance or ESG aligned circular warehouse model implemented across six GoBolt warehouses in North America. Combining reusable plastic pallets and Gaylords, waste compaction and corrugated recycling, and 5S-based (Sort, Set in Order, Shine, Standardize, Sustain) workflow redesign yielded an average annual savings of $54,100 per warehouse, with an estimated 139 tons per year of CO? avoided per warehouse. Additionally, there was 12% productivity enhancement due to reduced picker walking and standardized 5S practices. Comparison with benchmarks of CHEP, iGPS, IFCO, and IKEA indicates that an in-house circular model can deliver competitive carbon reductions and attractive payback periods.
A large number of lifestyle brands, such as Aarong, Yellow, Cats Eye, etc., are driving the apparel market of Bangladesh. Within this market, a huge number of capex procurements are required annually to meet companies' requirements. Each completed requisition includes a lead time, which is a critical factor impacting overall operational efficiency. This study determines the impact of the metrics, requisition quantity, and lead time on delivery performance. The purpose of the study was to discover the relationship between key variables that ease decision making and to establish the development of a concrete knowledge base intended to support strategies for managing diverse requisition quantity and lead time. Consequently, the study served the purpose of improving supply chain efficiency. To discover the relationship, supply chain and procurement theories were addressed. A total of 8,099 procurement records were analysed through the lens of a positivist research paradigm to ensure objectivity. A quantitative method was employed to conduct tests that generate accurate results supporting the purpose of this study. Predictions for the test were formulated to develop connection between lead time, requisition quantity on delivery performance. Chi-square tests were carried out to find out the connection. Findings revealed that the lead time and requisition quantity had a significant impact on delivery performance. Altogether, the study contributes to procurement and supply chain management literature by presenting evidence-based analysis into how operational factors and distinct item types may affect delivery efficiency.
This study explores the development of intelligent demand forecasting methods in the context of the rise of rational consumption awareness, to establish the optimal spare parts reuse plan. As consumers pay more attention to sustainability and environmental protection, price and quality are no longer the only considerations; global inflation has also made consumer behavior more conservative, emphasizing budget control and long-term value. In response to the sluggish market demand, this study proposes a intelligent demand forecasting method to help companies understand consumer demand and design product reuse plans that are in line with their values. This study adopts the concept of "integrated forecasting" and designs two methods: (1) Hybrid Stacking (HS) Method: Combining traditional time series and machine learning techniques to improve forecast accuracy; (2) External Information Integration (EI-HS) Method: Incorporating external information into the HS model as an improvement baseline to further reduce forecast errors. Finally, the results of MASE and RGRMSE indicate that the methods proposed in this study hold strong application potential. The research results emphasize that the development of intelligent demand forecasting methods in the era of rational consumption is of key significance and points out the direction of sustainable improvement and expansion in the future, providing companies with more accurate forecasting tools and spare parts reuse decision-making basis.
This research aims to investigate the impact of supply chain resilience and integration on the performance of FMCG firms in Bangladesh. The primary objective is to synthesize existing literature to comprehend how these two interdependent capabilities enhance operational efficiency, delivery reliability, flexibility, innovation, and customer satisfaction in a developing economy. This research used a narrative and integrative review methodology, examining peer-reviewed journal articles, conference proceedings, and industry reports mostly published in the last decade. The technique included the thematic classification of literature into resilience, integration, and performance dimensions, followed by an iterative process of synthesis and interpretation. The study highlights findings that supply chain resilience, which includes adaptation, flexibility, visibility, redundancy, and recovery capacity, allows organizations to successfully absorb and recover from disturbances. The outcomes of the study emphasize the significant role of Supply chain integration to facilitate coordination, information exchange, and cooperation, hence enhancing resilience. Therefore, the research offers actionable insights for managers in cultivating robust and cohesive supply chains, governments in fostering enabling infrastructure and technology integration, and industry stakeholders in advancing collaborative networks.
Unsold products represent a persistent operational and financial challenge for manufacturers, wholesalers, and retailers. This paper aims to address the problem of unsold inventory management by developing a decision-support framework for clearance strategies. Based on the newsvendor model, the proposed approach jointly optimises supply chain profit from regular market sales and clearance sales. The optimisation relies on a qualitative risk–performance assessment of alternative clearance strategies, incorporating managers’ industrial knowledge. A numerical illustration highlights the main drivers influencing the selection of an optimal clearance policy, namely acceptable risk levels, expected clearance revenues, and capacity constraints. The results provide actionable managerial insights and are synthesised into a practical decision-making approach to support practitioners in selecting effective clearance strategies. The proposed framework is applicable to both seasonal and non-perishable products.
The objective of this research is to comprehensively assess the impact of digitization and enhanced visibility in supply chain performance improvement of the manufacturing industry. The methodological approach adopted was systematic in examining contemporary empirical research and frameworks focused on supply chain digitization and visibility. The research shows that various indicators of performance are significantly improved by combining processes such as supply chain digitization with better visibility, including reduced lead times, more effective inventory management, and increased responsiveness in supply chain. Although many of these breakthroughs have been accomplished, there are still numerous practical challenges, like harmonizing data systems, protecting data, and dealing with resistance from the users. The study will be of value to the practitioners because it emphasizes the pivotal role played by blending the use of digital solutions with visibility practices in simplifying supply chain processes. The study suggests that the digital strategies to supply chain management could lead to greater resilience and adaptability, and as such, enhance better efficiency in supply chain management. This analysis provides a new insight into the combination of digitization and visibility in supply chains, a unified picture of the combined contributions to operational success.
Reverse supply chain (RSC) operations have become increasingly critical for retail firms seeking to enhance sustainability, reduce costs, and retain customer loyalty. While traditional supply chain research emphasizes forward logistics, customer satisfaction in reverse logistics remains underexplored. This study investigates the relationship between RSC processes and customer satisfaction using qualitative semi-structured interviews with six U.S.-based supply chain professionals. It applies Qualitative Comparative Analysis (QCA) and the SERVQUAL model to analyze the configurations of RSC conditions that influence customer experience. Four key drivers emerged: return process efficiency, communication and transparency, employee competence, and alignment with customer expectations. A truth table developed using fuzzy set QCA 3.0 reveals five RSC condition combinations that consistently lead to high satisfaction. Customer communication and a hasslefree return process were necessary in all successful configurations. This study highlights best practices, including flexible return options, use of return metrics, staff training, and clear return policies. It contributes to the existing literature by reconceptualizing the reverse supply chain as not merely a cost recovery or operational function, but as a strategic service platform for generating competitive advantage in retail logistics. Further, this research offers a novel analytical framework capable of identifying multiple high-performance pathways rather than prescribing a one-size-fits-all solution. The emphasis on equifinality, or the possibility of different configurations leading to the same positive outcome, reflects the complexity and diversity of modern supply chain environments and aligns with the contemporary demands of supply chain managers.
Every aspect of modern human living has now become significantly dependent on the use of data and analytics to reach optimum utility levels. Agriculture, modernized and mechanized over the ages, now makes judicious use of data analytics, particularly on aspects of agricultural supply chain, to improve efficiency. This article focuses on the merits of using supply chain analytics in agriculture. Global agricultural information can be organized and made universally accessible and useful. This article brings out how and why enormous volumes of data should be harnessed to effectively enhance agri SC in a sustainable manner, and benefit not just the customers but also the environment. This article establishes the rationale behind applying SC Analytics (SCA) in Agriculture and prepares the floor for further research on how volumes of agricultural data can be used effectively by corporate giants to implement Blockchain and Al technologies in agri SC.
Semiconductors are crucial components of modern technology, with uses ranging from communications to national security. As demand for semiconductors rises, it is critical to understand procurement methods, which have a considerable impact on pricing and supply chain dynamics. The study provides an in-depth analysis of the literature on the evolution of purchasing models in the semiconductor industry, including historical and current viewpoints. It analyzes four key purchasing models: direct purchasing, distributor purchasing, value-added resellers (VAR), and online purchasing, each with unique strategic advantages and problems that impact buyer-supplier relationships, cost, and delivery consistency. This study contributes to the literature through providing a comprehensive review of the semiconductor supply chain while exploring available purchasing models and identifying factors that help shape purchasing decisions. The research delves into the historical development of various models, their current implementation, and potential future trends. Additionally, the research identifies existing gaps in the literature and suggests areas for future investigation to further enhance supply chain efficiency and effectiveness. The findings from this study will provide valuable insights for the semiconductor industry, offering strategies to optimize their supply chain, improve resilience, and adapt to emerging challenges.
Purpose: The performance of mining and extraction firms in Kenya have been below par. The industry recorded a decline in production of most of the minerals such as titanium and soda ash and a declining trend towards contribution to the gross domestic product. This study sought to examine the influence of green supply chain on the performance of mining and extraction firms in Kenya. Methodology: The research employed a descriptive design and targeted a sample of 201 respondents from registered mining and extraction firms in Kenya. The study used stratified random sampling techniques for respondent selection. Data were gathered using structured questionnaires. Pilot study was carried out to determine the reliability and validity of the research instrument. Statistical package for social sciences version 27 was used to analyze the data. Descriptive, correlation and regression statistics were used to evaluate the relationship between green supply chain and firm performance. Findings: The study found that green supply chain have a statistically significant and positive effect on firm performance. These practices were associated with reduced operational costs, enhanced regulatory compliance, improved stakeholder relationships, and greater environmental responsibility. The regression analysis confirmed a strong positive correlation between green supply chain adoption and improved organizational outcomes. Unique Contribution to Theory, Practice, and Policy: The study extends the application of the Natural Resource-Based View (NRBV) by empirically validating the strategic value of green supply chain management practices in the mining and extraction sector, a context that has been underexplored in sustainability literature. The findings provide actionable insights for industry practitioners on how adopting green supply chain strategies can yield operational and competitive benefits, including efficiency gains and enhanced market reputation. The study underscores the need for supportive regulatory frameworks and targeted capacity-building initiatives to foster broader adoption of green supply chain practices in Kenya's mining sector. It advocates for policy instruments that incentivize eco-innovation, stakeholder engagement, and long-term environmental stewardship.
