Financial policy directly affects the availability and cost of a firm's working capital which has significant impacts on the firm's operations, profitability and risk. The capacity strategy also relies on the financing resources and is an integral part of the operations planning. This paper is the first research that examines the financial policy, capacity strategy, and operations planning in a holistic manner. We first develop an optimization model that integrates these three levels of decisions with the consideration of demand uncertainty and credit market risk. We also study the competition among firms who may use different financial policies and capacity strategies. In particular, we use the theory of variational inequality to model the equilibria of multiple competing firms under uncertain demand and credit conditions. We also provide numerical case studies which generate important managerial insights.
The rapid expansion of artificial intelligence (AI) and digital transformation has made understanding the conditions for its effective adoption in small and medium-sized enterprises (SME) operations an increasingly important and timely research priority. This study examines the economic, technological, social, and regulatory imperatives shaping the adoption of artificial intelligence in the operational activities of SMEs. The research focuses on Eastern Europe, Caucasus and Central Asia (EECCA) countries and selected former socialist bloc economies, using a combination of qualitative analysis and quantitative modeling. To strengthen the empirical analysis, a multiyear country-level dataset (2015-2024) is employed, allowing for a parsimonious regression approach. Two model specifications are estimated to examine the association between macro-level innovation indicators and technological readiness conditions. The results indicate that research and development expenditure represents the most robust and consistent factor associated with technological readiness, while entrepreneurial activity plays a complementary role. The findings suggest that national innovation environments shape the preconditions for AI adoption in SME operational and supply chain activities. The results are interpreted as exploratory evidence of macro-level patterns rather than predictive relationships. The study contributes to operations and supply chain management research by linking macro-level innovation conditions to the early stages of operational capability formation.
This study examines the association between supply chain capabilities and firm-level financial performance among Tadawul-listed manufacturing firms in Saudi Arabia. Drawing on the Resource-Based View and Dynamic Capabilities Theory, the study focuses on five capability dimensions: supply chain integration, complexity management, strategic alignment, IT-enabled process capability, and operational innovation. Firm-level capability measures were derived from survey data collected during 2024-2025 and linked to audited financial indicators, namely return on assets (ROA), return on equity (ROE), and return on sales (ROS), for the period 2020-2025. To address model saturation in annual regressions and improve statistical efficiency, the revised analysis employs pooled panel regression on firm-year observations, with specification choice guided by fixed-effects and random-effects testing and inference based on robust standard errors. The analysis places primary interpretive weight on the 2024-2025 period, where temporal alignment between capability measurement and financial outcomes is strongest, while earlier years are treated as retrospective association checks. The revised framework provides more credible evidence on how digital and innovation-oriented supply chain capabilities relate to audited financial performance in an emerging market context.
Agricultural supply chains (ASC) in the Mekong Delta face significant challenges in balancing economic efficiency, social development, and environmental sustainability. This study develops a multi-objective mixed-integer linear programming (MILP) model to optimize the citrus supply chain (CSC) by simultaneously minimizing total costs, maximizing employment, and reducing CO2 emissions. The augmented epsilon-constraint method was applied to generate Pareto-optimal solutions and to identify trade-offs among competing objectives. The results indicate that within a predictable range, moderate increases in employment can be achieved with stable marginal costs and emissions. However, beyond a critical knee point, additional social benefits come at disproportionately high economic costs with limited environmental gains. The optimal trade-off solution was identified at v = 4.75, yielding a balanced outcome of 3,304.02 million VND in cost, 718 jobs, and 2,811.87 kg of CO2 emissions. This research contributes to theory by extending multi-objective optimization in agri-food logistics, provides methodological insights through the use of the augmented epsilon-constraint approach, and offers practical implications for policymakers and managers in designing sustainable supply chain strategies in the Mekong Delta.
The convergence of fashion and supply chain management (SCM) has gained increasing scholarly attention in response to growing global concerns over agility, sustainability, and responsiveness in the fashion industry. This bibliometric analysis aims to systematically map the evolution of literature at the intersection of fashion-related products encompassing apparel, garments, textiles, and clothing and SCM practices within the business and management domain. Using the Scopus database and a rigorously defined search protocol, we extracted and analyzed peerreviewed journal articles published in top-tier journals. Analytical tools such as Biblioshiny (R package) and VOSviewer were employed to assess publication trends, identify influential authors, institutions, and journals, and explore thematic clusters. Our findings reveal five Adaptation in Fashion SC; (2) Circular Economy and Sustainability Transitions in Fashion SC; (3) Optimization and Coordination in Fashion SC; (4) Digital and Circular Transformation in Sustainable Fashion SC and (5) Ethics, Entrepreneurship, and Green Innovation in Fashion SC. The study also highlights research gaps and proposes directions for future inquiry. This work offers precious insights for SCM concepts have shaped, and been shaped by, the evolving demands of the global fashion ecosystem.
This study investigates the influence of social norms on stock management practices within sachet water production companies in the Western Region, integrating the Theory of Planned Behavior (TPB) to analyze how subjective norms, specifically community expectations and cultural practices, shape behavioral intentions and operational decisions. Using a quantitative survey design, and structural equation modeling (SEM) to analyze data from 520 respondents, the study revealed that community influence and cultural practices significantly enhance firms' responsiveness to demand fluctuations driven by social events, which in turn leads to increased stock management challenges such as overstocking or stockouts. However, these challenges prompt the adoption of mitigation strategies, which are found to have a strong positive effect on business outcomes, including sales volume and profitability. The measurement model demonstrated excellent reliability and validity, with Cronbach's Alpha values ranging from 0.82 to 0.88 and AVE values above the threshold of 0.50. The structural model exhibited a good fit (chi(2)/df = 2.14, CFI = 0.95, RMSEA = 0.05, SRMR = 0.04), supporting the robustness of the findings. Path analysis results showed large effect sizes for key relationships, particularly Mitigation Strategies -> Business Outcome (f(2) = 0.59), indicating that adaptive behaviors effectively translate norm-driven challenges into performance improvements. These findings underscore the critical role of social norms in shaping inventory decisions and highlight the importance of incorporating behavioral insights into supply chain training, planning tools, and incentive systems, particularly in culturally embedded and resource-constrained environments.
In the highly competitive global electronics sector, manufacturing productivity is paramount. Manual processes within otherwise automated assembly lines frequently become critical bottlenecks, constraining throughput and hindering growth. This research presents a case study of a Printed Circuit Board (PCB) assembly facility in Thailand that faced a significant capacity shortfall. The primary objective is to analyze the implementation and impact of a Kaizen-driven, low-cost automation (LCA) solution designed to resolve a specific manual bottleneck in an adhesive application process. The methodology combines a detailed analysis of the industrial case study with established theoretical frameworks of lean manufacturing, including the Toyota Production System (TPS), time study, and the ECRS (Eliminate, Combine, Rearrange, Simplify) principles. The intervention, executed through three iterative Kaizen cycles, involved developing a custom automated dispensing machine using repurposed, on-site assets. The results demonstrate a profound improvement in operational performance: the bottleneck process cycle time was reduced by 73.6%, from 11 minutes 53 seconds to 3 minutes 8 seconds per unit. This directly enabled an increase in total production capacity to 245 units per week, exceeding the demand target and yielding an estimated annual cost saving of 98,700 THB. This study's principal contribution is its empirical demonstration of how a culture of continuous improvement, coupled with creative problem-solving, can leverage existing resources to achieve significant productivity gains without substantial capital investment, offering a viable and practical model for small and medium-sized enterprises (SMEs) and other resource-constrained manufacturers.
Designing an effective supply chain strategy that supports the firm's competitive strategy is increasingly complex, as priorities vary significantly across firms, markets, and industries and continue to evolve over time. While recent literature has gradually addressed the dimensions of effective supply chain strategy, knowledge remains fragmented and an integrated framework is still lacking. This study introduces the EASIER model (acronym of efficiency, agility, sustainability, integration, digital enablement, and resilience), a holistic and adaptable tool that bridges academic theory and practical application. Designed for simplicity and ease of implementation, EASIER serves both as a diagnostic and strategic decision-making model. It enhances managerial alignment by clearly mapping trade-offs and linking supply chain priorities to the firm's strategic objectives. Accordingly, by helping firms to assess their current and desired performance across key supply chain competences, the framework guides investment, transformation, and management attention toward the capabilities most critical to enabling full implementation of the business plan. In doing so, it advances the discourse on supply chain strategy and offers a practical, future-oriented pathway for leveraging supply chain management as a source of competitive advantage.
According to the law of demand, price and demand are inversely related. With the increase in price, the demand for an item decreases and vice versa. It is observed that the demand for an item is also influenced by the instantaneous stock level. In this paper, an attempt is made to develop a price and lot-size policy for perishable goods in the case of multi-item, whose demand has been considered to be dependent upon price as well as the stock level, and the goods are of a deteriorating nature. The deteriorating item loses its economic value with time. A mathematical model has been formulated to minimize total supply chain cost under trade credit policy. Illustrative numerical problems and further sensitivity analysis have been carried out. The results obtained show that model behaviour is justified for real-life experiences. It has been observed that the total supply chain cost for all three items increases gradually with the increase in the rate of deterioration. The cycle time, credit period, and lot size decrease with the increase in the deterioration rate. The production inventory model with trade credit financing can be useful for the supply chain managers to reduce supply chain cost and increase supply chain surplus.
Risk management has become an increasingly important topic in supply chain management (SCM), but decision-making under conditions of risk remains under-researched. This study used an experiment based on purchasing and the game of roulette to assess dynamic decision-making behaviors in SCM. An analysis of four separate risk domains - including a distinction between quantity- and variability-based decisions - indicated that decision-makers assess risk attitudes independently for each domain and that these attitudes affect subsequent decisions in their respective domains. These findings indicate that SCM represents a distinct decision context. For practitioners, this research demonstrates why standardized risk management approaches that assume consistent risk attitudes may fail, as a manager who appears risk-averse in one type of decision may simultaneously be risk-seeking elsewhere. Organizations can leverage these insights by matching employee risk profiles to decision types and implementing domain-specific training programs.
In the era of digital transformation, blockchain-based collaborative logistics platforms initially emerged as promising solutions to streamline inter-organizational exchanges. However, several major initiatives have recently faced critical crises, exposing the vulnerabilities of their business models. Addressing this issue, this study explores the factors that led to the collapse of TradeLens, a flagship platform developed by Maersk and IBM. Using stakeholder theory as an analytical framework, the study adopts a qualitative single-case study approach, combining ten semi-structured interviews with industry experts and an in-depth analysis of secondary sources. Thematic analysis reveals five critical dynamics: initial governance imbalances, asymmetric value capture, heightened coopetitive tensions, institutional rigidity, and a cumulative dynamic leading to crisis. The findings enhance the understanding of failures in collaborative blockchain ecosystems, highlighting the need for inclusive governance, equitable value creation, and strong institutional adaptability. From a managerial perspective, the research offers concrete recommendations for the sustainable design of blockchain logistics platforms. It also opens avenues for future research on adaptive governance and stakeholder salience in distributed environments.
Indonesia's shift from liquefied petroleum gas (LPG) to city gas represents a vital move toward cleaner energy and long-term fiscal health, yet faces obstacles such as disjointed policies, limited infrastructure, and persistent user habits. This study adopts a qualitative system dynamics method to build a comprehensive view of the transition through a validated Causal Loop Diagram (CLD) encompassing three key areas: (1) subsidy policy, (2) consumer behavior, and (3) infrastructure development. The CLD of subsidy policy shows that price incentives alone are ineffective without synchronized infrastructure readiness and well-timed policies. The CLD of consumer behavior underscores the role of service quality, reliability, and satisfaction in maintaining adoption. Meanwhile, the CLD of system growth identifies technical, regulatory, and financial factors that influence the speed and fairness of infrastructure development. A combined CLD demonstrates how these areas interact-revealing, for instance, how pricing strategies impact user trust and behavior, or how satisfaction drives future demand. These findings highlight the need for integrated, rather than piecemeal, policy actions. The study proposes phased reforms in subsidies aligned with infrastructure rollout, improved residential access, and strategic public funding to accelerate city gas adoption. Although qualitative, the model offers a strong base for future simulations, policy experimentation, and deeper exploration of behavioral and institutional dynamics in Indonesia's energy shift.
The increasing complexity and volatility of global supply chains have heightened the need for innovative technologies that enhance collaboration and support sustainable operations. While blockchain technology (BCT) has gained significant attention for its potential to improve transparency, accountability, and information sharing, empirical evidence on its practical impact remains limited. This study investigates how three widely discussed BCT use cases, traceability, data-sharing, and smart contracts, influence supply chain collaboration (SCC) and socio-environmental performance (SEP). By focusing on these distinct applications, the study offers a nuanced perspective on the mechanisms through which blockchain enables inter-organizational coordination and advances sustainability objectives. A survey with supply chain professionals was conducted, and the data were analyzed using partial least squares structural equation modeling. The results reveal that all three BCT use cases exert significant positive effects on SCC and SEP, though their relative impact differs. Notably, traceability emerges as the strongest driver of improved socio-environmental outcomes through enhanced collaboration. This study contributes to the emerging body of knowledge on digital supply chain transformation by clarifying the differentiated value of blockchain functionalities. It further provides actionable insights for managers and policymakers seeking to implement blockchain-enabled sustainability strategies.
This study aims to systematically review the attributes of Methodologies (ASCPEMs). To accomplish this, we employed a systematic literature review methodology, focusing on peer-reviewed journal articles published between 2007 and June 2023. By examining 123 papers, we observed a growing number of publications on ASCPEMs. A similarity analysis enabled the identification of common characteristics among the methods studied, including the agrifood supply chain processes analyzed, evaluation objectives, aspects assessed, application cases, analysis methods, information sources, and data collection techniques. These characteristics outline prevailing trends in food supply performance. The findings elaborate on these trends for each characteristic, supported by reference examples. This review, the first of its kind to characterize ASCPEMs, also identifies future research directions and emerging gaps in information management technology. Among the findings, it was noted that information and Industry 4.0 technologies are sparingly utilized, and data collection methods are predominantly manual, resulting in delays in performance analysis.
This study presents a comprehensive bibliometric analysis of logistics performance research from 2003 to 2022, utilizing R Studio, Biblioshiny, and VOSviewer. Unlike previous literature, this review maps the intellectual structure and thoroughly examines the field's evolution. The findings reveal sustained growth in scholarly output, with China and the United States identified as the most productive and influential contributors. Notably, several developing countries, including Malaysia, Turkey, and India, also emerge as prominent contributors. Bibliographic coupling analysis identified six primary research clusters: operational optimization, strategic management, sustainable logistics, reverse logistics, economic perspectives, and industrial/organizational frontiers. The study offers several key contributions: it provides a structural overview of leading publications, authors, and collaborative networks; identifies the most salient emerging research paths; and employs mapping techniques-including tree, thematic, and conceptual maps-to delineate the field's development. These findings serve as a strategic guide for researchers and policymakers to identify foundational knowledge and future research directions.
This study assesses the institutionalisation of sustainable procurement (SP) practices in South African state-owned enterprises (SOEs), evaluating their alignment with economic, social, and environmental sustainability objectives. It further explores the challenges hindering SP implementation and the variations in adoption across Schedule 2 and Schedule 3 SOEs. We employed a mixed-methods approach based on semi-structured interviews with 51 procurement professionals across 26 SOEs. The quantitative data was analyzed using descriptive and inferential statistical techniques in SPSS while the open data using thematic analysis. The findings indicate that economic sustainability receives the highest emphasis, social sustainability is moderately integrated, and environmental sustainability is the weakest. Notably, Schedule 3 SOEs demonstrate greater adherence to government mandated sustainability policies, while Schedule 2 SOEs prioritize financial autonomy and cost-driven procurement strategies. Governance issues, corruption, and lack of SP training further impede comprehensive sustainability adoption. This study advances the empirical understanding of SP practices in SOEs within a developing economy. It also provides practical recommendations for government policymakers, SOE leadership, and procurement professionals, emphasizing the need for a comprehensive national SP framework, regulatory enforcement, and capacity-building initiatives to enhance SP integration.
This study proposes a novel network Data Envelopment Analysis (DEA) model to evaluate supply chain performance from both cost and revenue perspectives. Although DEA is a widely used method for evaluating the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs, traditional DEA models encounter difficulties when applied to multi-stage systems involving intermediate products, such as supply chains. To overcome these challenges, we extend the well-established Range Adjusted Measure (RAM) to develop a customized network DEA model that effectively captures the internal structure of supply chains. Our model enables accurate efficiency evaluation across different production stages and offers valuable insights into inefficiency sources. A case example of global supply chain network is conducted, incorporating data from suppliers, factories, sales distributors, and customers across multiple regions. We conducted five distinct evaluation scenarios that reflect various strategic objectives and supply configurations. Through these scenarios, we analyze the performance of diverse supply chain structures, identify inefficiency factors, and explore decision-making strategies such as suggested sourcing of parts factories and selection of parts and assy factories. Furthermore, we compare the consistency and difference between the overall and the stage-level scores in each scenario, thereby demonstrating the necessity and effectiveness of our proposed model for evaluating supply chain efficiency. Consequently, the results indicate that the model not only evaluates performance but also supports strategic supply chain design and improvement.
The rapid expansion of the e-commerce industry in Malaysia has led to a significant increase in packaging consumption, particularly plastic, which contributes to growing environmental concerns. Although sustainable packaging alternatives are accessible and consumer awareness regarding environmental protection is increasing, a gap exists between consumers' awareness and their actual purchasing behavior concerning products with sustainable packaging. This study aims to identify and analyze the key factors influencing the intention to purchase products with sustainable packaging in the context of e-commerce. Based on the Theory of Planned Behavior (TPB), the research investigates the role of customer perception, government initiatives, subjective norms, willingness to pay, and environmental awareness. A quantitative research design was employed, and data were gathered through structured questionnaires distributed to 384 respondents. The data were analyzed using the Statistical Package for the Social Sciences (SPSS). The results demonstrate that customer perception, government involvement, willingness to pay, and environmental awareness significantly influence consumer purchase intention. However, subjective norms do not demonstrate a significant effect. These findings offer important insights for e-commerce businesses and policymakers seeking to promote sustainable consumption behavior and implement effective strategies for environmental sustainability. By applying these insights, stronger cooperation between businesses, packaging manufacturers, and policymakers can accelerate the transition toward sustainable packaging. This, in turn, will help reduce plastic waste, benefiting both the environment and the economy.
