Inventory Planning and Optimization in a Globally Connected World: A Comprehensive Analysis

Purpose This study aims to explore the interplay between supply chain collaboration and disruption and their joint impact on supply chain resilience. It also aims to identify the configurational conditions under which supply chain resilience is achieved, providing a holistic framework for enhancing supply chain management practices amidst disruptions. Design/methodology/approach This study uses a fuzzy-set qualitative comparative analysis (fsQCA) to investigate the complex causal mechanisms linking supply chain collaboration and disruption with supply chain resilience. The research integrates three dimensions of collaboration with three dimensions of disruption, offering a comprehensive understanding of supply chain resilience factors. Findings This study reveals that supply chain resilience is not solely dependent on individual elements but is shaped by unique configurations of collaboration and disruption factors. Information sharing emerges as a core factor in high resilience, while the absence of supply and facility disruptions significantly influences resilience outcomes. The research highlights the importance of multiple paths leading to supply chain resilience and the asymmetrical impact of collaboration and disruption. Research limitations/implications The study acknowledges limitations due to data sourced exclusively from China and the use of static data, suggesting the need for cross-temporal and international samples to enhance broader applicability. Future research should consider dynamic temporal changes and diverse theoretical perspectives to comprehensively examine the factors influencing supply chain resilience. Practical implications The research underscores the critical role of information sharing in bolstering supply chain resilience and advises firms to prioritize it in their strategies. It also highlights the importance of mitigating supply and facility disruptions through supplier diversification and robust contingency planning, offering actionable insights for enhancing operational efficiency and risk management in supply chain management. Social implications This research has significant social implications, particularly in the context of global supply chain disruptions. By identifying key factors that enhance supply chain resilience, it can help businesses better prepare for and respond to crises, thereby reducing economic instability and social disruption. Improved resilience can lead to more stable employment, maintain the flow of essential goods and services and contribute to overall societal well-being during times of supply chain stress. Originality/value This study offers original insights by applying fsQCA to explore the multifaceted relationship between supply chain collaboration, disruption and resilience. Its value lies in revealing the complex causal configurations that lead to high or low levels of supply chain resilience, challenging traditional linear perspectives and providing a nuanced understanding that can guide both academic research and practical supply chain management strategies.

With the acceleration of globalization and the increasingly fierce competition in the market, demand forecasting and inventory management in supply chain management have become particularly important. Traditional supply chain management methods are difficult to cope with the complex and changing market demand, resulting in the frequent occurrence of inaccurate forecasts, excessive or insufficient inventory, and other problems. This paper aims to explore the application of intelligent technology in supply chain demand forecasting and inventory management, focusing on analyzing how technologies such as big data, artificial intelligence, and machine learning can improve forecasting accuracy and inventory management efficiency. Through the analysis of actual cases, this paper demonstrates the implementation effect of intelligent demand forecasting technology and proposes corresponding inventory management optimization strategies. The study shows that intelligent supply chain management can not only effectively reduce operating costs, but also improve service levels, thus enhancing the competitiveness of enterprises in a dynamic market environment

Inventory optimization is pivotal in mitigating economic losses and enhancing efficiency in supply chain management. This paper examines the challenges associated with traditional inventory practices, including inefficiencies, demand variability, and supply chain disruptions, which lead to substantial financial and operational setbacks. Building on theoretical foundations and insights from existing research, a comprehensive inventory optimization framework is proposed, integrating advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud-based systems. The framework emphasizes data-driven decision-making, real-time tracking, lean inventory management, and sustainability, offering scalable solutions for diverse industries. Practical recommendations for implementation include investments in advanced technologies, collaborative supply chain practices, and continuous performance monitoring. The proposed framework aims to reduce costs, improve operational resilience, and align inventory management with broader economic and environmental goals. By adopting this innovative approach, organizations can navigate the complexities of modern supply chains and achieve long-term success.. Keywords: Inventory Optimization, Supply Chain Management, Economic Losses, Advanced Technologies, Lean Inventory, Sustainability.

Multi-echelon supply chain networks face increasing complexity in demand forecasting and inventory optimization due to volatile market conditions and dynamic customer preferences. Traditional forecasting methods often struggle to capture long-range dependencies and complex temporal patterns inherent in supply chain data. Transformer-based architectures, originally developed for natural language processing (NLP), have emerged as powerful tools for time series forecasting in supply chain management (SCM). These models leverage self-attention mechanisms to process sequential data and capture intricate relationships across multiple time steps. This review examines the application of transformer models in demand forecasting and inventory optimization within multi-echelon supply chain networks. The paper analyzes how transformer architectures address challenges such as bullwhip effect amplification, demand volatility, and coordination across supply chain tiers. Key findings indicate that transformer-based approaches outperform conventional methods including autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) networks, and traditional machine learning (ML) algorithms in forecast accuracy and computational efficiency. The review synthesizes recent developments in transformer architectures specifically adapted for supply chain contexts, including modifications for handling sparse data, incorporating external factors, and enabling real-time decision support. Furthermore, the paper explores integration strategies between demand forecasting and inventory optimization, examining how transformer predictions inform safety stock calculations, reorder point determination, and dynamic replenishment policies. Emerging trends such as attention mechanism interpretability, federated learning (FL) for collaborative forecasting, and hybrid models combining transformers with reinforcement learning (RL) are discussed. The review identifies critical gaps in current research, including limited validation in real-world multi-echelon settings, computational scalability challenges, and the need for robust frameworks addressing demand uncertainty propagation across supply chain levels.

Purpose The purpose of this paper is twofold: first, to present a set of critical factors for total quality management (TQM) and supply chain management (SCM) practices through an extensive literature review; and, second, to identify the relationships among them by comparing the identified TQM and SCM practices in order to explore the concept of supply chain quality management (SCQM). Design/methodology/approach To meet the goals of this work, a review of published quality research papers was carried out. For this, the authors identified papers on TQM and SCM practices and observed how these practices improve the business performance of organizations. Further, on the basis of identified practices, a conceptual model of SCQM was developed. Findings The results presented a set of six critical factors each for TQM and SCM practices. Further, it was found that management support and commitment, customer focus, information and supplier partnership are the most common practices found in both TQM and SCM practices. The integration of TQM and SCM (SCQM) throughout the supply chain has the strongest impact on the organizational performance. Research limitations/implications The study used data from various research papers of reputed journals which considered the study of India’s manufacturing industry as well as other countries, which may not provide a clear picture. However, this can be overcome by applying the findings of this paper to collect data from Indian manufacturing industry in future studies. Practical implications Indian manufacturing industry can improve its competitive image through the synergy of TQM and SCM. The result of this paper will help in providing a greater understanding of identified TQM and SCM practices that will lead to the successful implementation of TQM and SCM strategies to enhance business performance in terms of the improved levels of customer service. Originality/value Much of the attention is given on TQM and SCM practices, and very few studies have been undertaken to integrate TQM and SCM practices. But as far as the authors know, there is no study undertaken to integrate TQM and SCM practices in India for the manufacturing class. Therefore, this study compares TQM and SCM practices and considers their integration and can be thus treated as filling a gap in the extant literature. Therefore, the findings of this review paper will contribute in future research.

This research delves extensively into the value-added role of Artificial Intelligence (AI) in enriching and simplifying the nuances of supply chain management (SCM), with specific reference to two very critical areas: demand forecasting and inventory optimization. As supply chains expand and increase in complexity, traditional management techniques tend to fall short of meeting the needs created by dynamic market forces and breakdowns in the supply chain. Taking a quantitative research orientation, this research was successful in gathering data from a sample size of 150 professors who were deployed in Pakistan using a self-administered questionnaire with a Likert scale. The results show that, while overall perceptions and awareness towards the use of AI in the context of SCM are generally positive, statistical findings revealed that the impact of AI on the accuracy of demand forecasting was not found significant within the confines of this given sample. Nevertheless, it must be noted that high correlations were established between key capabilities of AI, including greater supply chain transparency, enhanced decision-making efficiency, and increased resilience in times of crisis. This research underscores the critical need for an expansive use of AI technologies and the requirement of empirical validation in actual supply chain environments. In addition, it underscores the primary need for training and awareness programs to facilitate organizations to be able to leverage and avail themselves fully of the immense potential that AI can offer in the context of SCM.

Contributors

Abstract
 Purpose: The aim of the study was to investigating the effects of sustainability initiatives on supply chain resilience and performance
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: The investigation into sustainability initiatives' effects on supply chain resilience and performance revealed significant benefits. Companies that integrated sustainable practices into their supply chain operations experienced improved resilience against disruptions, such as natural disasters or supply chain disruptions. Additionally, these initiatives often led to enhanced operational performance, including cost savings, efficiency improvements, and better relationships with stakeholders. Overall, the findings suggest that integrating sustainability into supply chain management can contribute positively to both resilience and performance outcomes.
 Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV) theory, institutional theory & contingency theory may be used to anchor future studies on investigating the effects of sustainability initiatives on supply chain resilience and performance. Practitioners should prioritize the implementation of integrated sustainability strategies that align with organizational goals and supply chain objectives. Policymakers play a critical role in creating an enabling environment for sustainable supply chain management practices.

COVID-19 pandemic prompted supply chain (SC) disruptions and heightened demand for crucial items like facemasks and ventilators. Lockdowns and border closures hindered raw material supply and manufacturing capacity expansion. Consequently, manufacturers faced challenges in inventory, transport, and delivery, resulting in higher shortage costs, elevated SC expenses, and reduced SC efficacy. Using an integrated agent-based model (ABM) and optimisation, this paper examines COVID-19's multifaceted impacts on facemask SCs. It assesses four primary resilience strategies: enhancing manufacturing capacity, improving raw material supply, increasing transportation and distribution facilities, and maintaining dynamic inventory policy. Moreover, the model tested the proposed strategies under different scenarios by optimising the inventory policy and transportation strategies, leading to improved facemask production and delivery during extreme events. Our study found that increased production capacity through an optimal inventory and transportation strategy for a long period reduced the multiple impacts of the pandemic on facemask SCs, resulting in diminished total SC costs and increased consumer access to finished products. Based on demand forecasts, maintaining dynamically optimal reordering points and order up to levels can help maximise raw material supply and inventory levels, thereby minimising risks. Using these findings, future risks related to outbreaks and pandemics can be more effectively planned.

This study pioneers the integration of large language models (LLMs) into simulation-based inventory optimization under supply chain disruptions. We develop and compare three human–AI collaborative approaches with varying degrees of automation: fully automated direct optimization (FADO), two-stage automated simulation and optimization (TASO), and simulation-based human-specified optimization (SHSO). Through extensive numerical experiments, we evaluate their performance using the probability of correct selection and solution quality under different operational conditions. Results show that while FADO offers the greatest automation and ease of implementation, its accuracy and stability are limited. TASO enhances computational robustness but remains sensitive to the granularity of parameters. SHSO consistently achieves statistically optimal and stable outcomes by combining LLM-driven simulation with human-specified algorithms. These findings advance the theoretical understanding of human–AI collaboration in operations and provide actionable guidance for managers seeking to enhance decision reliability and supply chain resilience.

Sue Abdinnour (“ Hawker Beechcraft Uses a New Solution Approach to Balance Assembly Lines ”) is a professor of operations management and holds the Verne and Helen Omer Professorship in Business in the W. Frank Barton School of Business at Wichita State University. Her work has been published in journals such as Decision Sciences, International Journal of Production Research, European Journal of Operational Research, and the Annals of Operations Research. Sue engages in consulting that assists companies in modeling their processes and improving their operations. She has won several research and teaching awards during her career. Marc Albers (“ Optimizing Railway Crew Scheduling at DB Schenker ”) works as a consultant for Bayer Business Services. Prior to joining Bayer he worked at the University of Cologne, where he received his doctoral degree. His fields of interest include crew scheduling, supply chain management, and business intelligence. Jude Buquid (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) is the director of inventory management and logistics planning at HD Supply Facilities Maintenance. He has more than 15 years of experience across the supply chain in duties that include outbound fulfillment and freight cost optimization, applying academic theory to the realities of modern inventory management, and striking the right balance of speed and cost at which products can be delivered. He is currently focused on optimizing the company's network footprint from an inventory perspective as well as developing strategies to increase throughput through distribution centers. He holds a BS in logistics and economics from the University of Maryland at College Park and an MSBA in finance from San Diego State University. Philip Hans Franses (“ Averaging Model Forecasts and Expert Forecasts: Why Does It Work? ”) is professor of econometrics and professor of marketing research, both affiliated with the Erasmus School of Economics. His research interests concern marketing, empirical finance, applied econometrics, and forecasting. He publishes on these topics in a variety of academic journals including Interfaces, the Journal of Marketing Research, and the Journal of Econometrics. Currently he serves as the Dean of the Erasmus School of Economics, serving 5,000 students and 400 staff. Kevin C. Furman (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) has been a leader of various programs and teams related to optimization and logistics research and software application development across multiple ExxonMobil affiliates. He received his PhD in chemical engineering from the University of Illinois at Urbana–Champaign. His eight years at ExxonMobil have focused on research, development, and leadership in the areas of OR and process systems engineering. David Gibson (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) is the manager of logistics planning at HD Supply Facilities Maintenance and has been working within the field of operations and inventory management for the past seven years. His areas of expertise include process improvement, working capital optimization, and distribution center slotting efficiency. He is a leader in reshaping the corporate culture at HD Supply Facilities Maintenance by demonstrating innovative ways that data can be used to make decisions. He is a graduate of San Diego State University and holds a BS in financial services. Knut Haase (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a professor in the School of Business, Economics, and Social Sciences at the University of Hamburg. He received his PhD and his habilitation at the University of Kiel. His current research areas focus on crew scheduling, discrete choice models, line planning, pilgrim scheduling, and revenue management. Paul Huppert (“ HD Supply Facilities Maintenance Uses Diluted Discounts to Optimize Purchasing Opportunities ”) received his degree in business administration from San Diego State University. He is a supply chain manager for HD Supply. He has over 12 years experience in real-world inventory optimization including the implementation of supply chain systems that support the function. He is certified by the Institute of Business Forecasting as a Professional Forecaster. Silke Jütte (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a research assistant in the Department of Supply Chain Management at the University of Cologne. She has studied business mathematics at the University of Kaiserslautern and worked as a process manager at DHL. She is writing her PhD thesis in the field of crew scheduling. Gary R. Kocis (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is a senior technologist with ExxonMobil Research and Engineering Company, presently working in the supply chain optimization field. During his 10 years with ExxonMobil, he has developed and applied optimization-based solutions to refinery planning and scheduling, and logistics optimization. He has 22 years of experience as a mathematical programming practitioner, having worked in systems development for Shell Oil Company and the ROMeo online optimization tool for Invensys. Michael K. McDonald (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is an ExxonMobil Global feedstocks trader. He has been trading refining intermediates and products, and natural gas for 15 years. Jin-Hwa Song (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) leads optimization research at Corporate Strategic Research, ExxonMobil Research and Engineering Company. He received his PhD from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Since joining ExxonMobil, he has been working on various projects related to optimization and decision science, as well as science-build research programs with several universities. Ulrich W. Thonemann (“ Optimizing Railway Crew Scheduling at DB Schenker ”) is a professor in the Department of Supply Chain Management at the University of Cologne. He received his PhD from Stanford University and worked as a management consultant at McKinsey & Company. His current research areas focus on crew scheduling, inventory optimization, and behavioral operations. Joris van de Klundert (“ ANWB Automates and Improves Service Personnel Dispatching ”) holds an MSc in computer science from Erasmus University Rotterdam and a PhD in OR from Maastricht University. He has served as the founding director of Mateum, a Maastricht University spin-off delivering OR-based software and consultancy services. After being appointed professor of value chain optimization at Maastricht University, he returned to Erasmus University Rotterdam in 2009 to chair the Department of Health Services Management and Organisation. His main research interests are in the contributions of management science to networks of health-service organizations. Peter van Huigenbosch (“ ANWB Automates and Improves Service Personnel Dispatching ”) is trained as a military officer and holds a bachelor's degree in automotive mechanics. He has worked in various positions at ANWB since 1992. He became involved in dispatch services while doing his MBA, which he earned in 2003. He is logistics manager at ANWB responsible for all supporting departments involved in assisting 1,250,000 stranded motorists annually. Philip H. Warrick (“ Feedstock Routing in the ExxonMobil Downstream Sector ”) is a Distinguished Engineering Associate with ExxonMobil Research and Engineering Company. He is responsible for the supply chain technology team. His 30-year career at ExxonMobil has concentrated on the downstream of the oil business, including responsibilities for supply, transportation and refinery operations and planning, cargo trading, and the development and application of optimization technology to refining and logistics. Laurens Wormer (“ ANWB Automates and Improves Service Personnel Dispatching ”) holds an MSc in OR from Maastricht University. He works for Mateum, a company providing advanced planning and optimization solutions for complex and innovative business processes. His main interests lie in developing decision support systems with state-of-the-art OR techniques that improve customer satisfaction and operational efficiency in health, home-care, and service organizations.

Background Recent pandemics, epidemics and outbreaks continually underscore the critical importance of resilient supply chains in healthcare, particularly in clinical laboratories where timely access to supplies is essential for patient care. Supply chain disruptions, whether due to global crises or localized challenges, can significantly impact laboratory operations, leading to delayed diagnoses, compromised patient outcomes, and increased costs. It is estimated that 70%-80% of a patients’ EMR is clinical laboratory results. Despite this, supply chain resilience in clinical laboratories remains an under-addressed issue and remains a buzzword. Methods To evaluate the cost and importance of supply chain resilience in the clinical laboratory, we conducted a comprehensive review and interviews with 9 Healthcare Supply Chain experts analyzing the impact of supply chain disruptions on laboratory operations and patient care. Data was also collected from a diverse range of clinical laboratories, including hospitals, independent labs, and research institutions, spanning different geographic regions as well as a survey of 50 Laboratory experts with a 50% response rate. Key metrics assessed included: -Frequency and duration of supply chain disruptions. -Financial costs incurred due to disruptions, including expenses related to expedited shipping, alternative sourcing, and inventory management. -Impact on laboratory workflow, turnaround times, and patient care outcomes. -Strategies employed to mitigate supply chain risks and enhance resilience. Results Our review revealed findings regarding the cost and importance of supply chain resilience in the clinical laboratory: -Supply chain disruptions were frequent, with 85% of laboratories experiencing at least one significant disruption in the past year and 100% in experienced disruptions 2022. -The average duration of disruptions was 2-3 weeks, leading to substantial delays in test processing and patient care. -Financial costs associated with disruptions averaged $50,000 per laboratory per annum, including expenses for rush orders, and premium-priced alternatives. -Common strategies employed were increased inventory or safety stocks, multi-vendor sourcing, and supply chain mapping. Conclusions The findings of this study underscore the critical importance of supply chain resilience in the clinical laboratory. Supply chain disruptions are not just inconveniences; they pose significant risks to patient safety, operational efficiency, and financial sustainability. Investing in supply chain resilience is imperative for laboratories to fulfill their mission of delivering timely and accurate patient results. To continue to enhance supply chain resilience, laboratories must adopt a proactive approach, leveraging data-driven insights and collaborative partnerships. This includes diversifying supplier networks and fostering closer collaboration between laboratory and procurement teams.

The integration of Artificial Intelligence (AI) into Supply-Chain Management (SCM) has revolutionized operations, offering avenues for enhanced efficiency and decision-making. AI has become pivotal in tackling various Supply-Chain Management challenges, notably enhancing demand forecasting precision and automating warehouse operations for improved efficiency and error reduction. However, a critical debate arises concerning the choice between less accurate explainable models and more accurate yet unexplainable models in Supply-Chain Management applications. This paper explores this debate within the context of various Supply-Chain Management challenges and proposes a methodology for developing models tailored to different Supply-Chain Management problems. Drawing from academic research and modelling, the paper discusses the applications of AI in demand forecasting, inventory optimization, warehouse automation, transportation management, supply chain planning, supplier management, quality control, risk management, and customer service. Additionally, it examines the trade-offs between model interpretability and accuracy, highlighting the need for a nuanced approach. The proposed methodology advocates for the development of explainable models for tasks where interpretability is crucial, such as risk management and supplier selection, while leveraging unexplainable models for tasks prioritizing accuracy, like demand forecasting and predictive maintenance. Through this approach, stakeholders gain insights into Supply-Chain Management processes, fostering better decision-making and accountability.

Global competition has forced companies, including small and medium enterprises (SMEs), to improve their competitive advantage. Supply chain management practices are the ways to improve the competitive advantage, particularly in the global competition context. However, there is still doubt SMEs can compete globally, considering they face limited resources, skilled workforce, and business networks. Therefore, this study aims to examine the influence of supply chain management practices, covering cross-functional integration, partnership, responsiveness, and resilience. Moreover, this study has examined which practices dominate in improving competitive advantage. The quantitative study involved 445 SMEs located in East Java, Indonesia. The respondents are supervisors or higher levels and work in departments related to supply chain management, as they can provide the relevant information and possess complete knowledge of management practices. The data were collected via a questionnaire designed with a five-point Likert scale. The responses were analyzed using SmartPLS software 4.0. The results show that cross-function integration improves supply chain partnership, responsiveness, and resilience (β = 0.705, 0.382, 0.324; t-value = 25.177, 6.697, 5.783). Supply chain partnerships affect supply chain responsiveness, resilience, and competitive advantage (β = 0.327, 0.257, 0.249; t-value = 5.933, 4.536, 5.651). Moreover, supply chain responsiveness improves supply chain resilience and competitive advantage (β = 0.285, 0.106; t-value = 5.690, 2.099). Supply chain resilience improves competitive advantage (β = 0.435 and t-value = 8.987). SMEs can enhance their competitive advantage by integrating their internal cross-functional integration and adopting supply chain partnership, responsiveness, and resilience. Acknowledgments The authors would like to thank DIKTI 2023 and Research and Community Outreach Petra Christian University for providing the grant to fund this research.

Existence in a globalized economy presents the most significant challenges that the supply chain management has never experienced before, such as demand in variation, disruption in supply, transparency deficiencies, and issues of sustainability. This review explicitly looks at how machine learning, deep learning and blockchain technologies can be used to transform the activity in the supply chains in a revolutionizing way. The work applies the PRISMA methodology to analyze the current trends of the supply chain systems with respect to intelligent supply chain system systematically and within the framework of new trends and future directions. The machine learning algorithms have amazing performance in demand forecasting, inventory optimization and predictive maintenance, whereas deep learning architectures perform better in complicated pattern recognition, computer vision tasks and natural language processing to supply chain analytics. Distributed ledger systems, smart contracts, and decentralized consensus mechanisms are all innovations that blockchain technology suggests which will bring unprecedented levels of transparency, traceability, and security. According to this review, existing research has a number of gaps such as limited integration frameworks involving each of the three technologies, lack of confirmation of scalability in practice, and little attention to the problems of adoption in small and medium enterprises. The article shows that hybrid solutions that integrate machine learning predictive functionalities with blockchain trust infrastructures have a higher performance on providing end-to-end supply chain visibility and optimization. The potential opportunities are autonomous supply chain orchestration, circular economy enabling and robust network design.