A new data-driven framework to select the optimal replenishment strategy in complex supply chains

When dealing with uncertainties in the supply chain and ensuring customer satisfaction, efficient management of lead time plays a significant role. Likewise, besides managing inventory and pricing strategies adeptly in multi-retailer supply chains, it has become inevitable for firms to embrace green and sustainable business practices. In this context, this paper considers a two-level supply chain consisting of a single manufacturer and multiple retailers in which the manufacturer produces a single product and delivers it to the retailers in equal-sized batches. Each retailer faces a price and green-sensitive market demand. The lead time is assumed to be a random variable that follows a normal distribution. Shortages for retailer inventory are allowed to occur and are completely backlogged. The centralized model and a decentralized model based on the leader-follower Stackelberg gaming approach are developed. A price discount mechanism between the manufacturer and retailers is proposed. For the acceptance of this contract, the upper and lower limits of the price discount rate are established. Numerical outcomes exhibit that the price discount mechanism effectively coordinates the supply chain and enhances both environmental and economical performances. A sensitivity analysis with respect to some key parameters is performed, and certain managerial insights are emphasized.

Sustainable multi-biofuel production with stochastic lead time and optimum energy utilization under flexible manufacturing

Business executives have recently indicated that supply chain disruptions represent a major threat to their firm’s profitability and revenue growth (Smyrlis, 2006; Hendricks and Singhal, 2009). In the literature, a number of conceptual studies have suggested that supply chain complexity is a main driver of supply chain disruptions (Choi & Krause, 2006; Christopher & Lee, 2004; Juettner et al., 2003). Other studies have suggested that the degree of coupling in the supply chain is a major determinant of supply chain disruptions (Agrawal and Nahimias, 1997; Ahuja, 2000). Building on the theoretical foundation of Normal Accident Theory (NAT), this paper proposes a nuanced relationship between supply chain complexity and coupling and their collective impact on supply chain disruptions. Through an empirical examination, we seek to validate the theoretical proposition that complex, tightly coupled systems are inherently prone to failures or accidents. This study contributes significantly to the supply chain management discourse, providing a detailed analysis of the mechanisms underlying supply chain vulnerabilities and offering practical guidance for developing more resilient and responsive supply chain systems. Our findings invite future research to further explore the intricacies of supply chain disruptions, complexity, and coupling, aiming to refine and expand upon the mitigation strategies discussed herein.

Supply chain management is a discipline dealing with organization, coordination, and optimization of relations within supply chains. Complexity and dynamics of supply chains are not always proportional to their reliability, and supply chain risk management becomes a very important tool in minimizing risk and uncertainties caused by, or impacting on, logistics-related activities or resources in the supply chain. Because risk modeling presents a very important segment of risk management, the paper includes a description of the main characteristics of supply chains and a model for risk assessment based on the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) methods. The main intention of the research presented here is to propose approaches based on application of the AHP and FAHP methods which are used as a tool for ranking supply chain risk categories, determining its share in total risk, and as a method for the supply chain risk assessment. The proposed approach is based on the experience and the knowledge of experts from insurance companies which are professionally engaged in the process of risk assessment, and possibility of its application is tested on a numerical example.

We examine the effect of stochastic lead times on Just-in-Time (JIT) delivery. We find that with stochastic lead times there is a possibility of order crossover, and what order crossover does is to transform the original lead times into effective lead times, which is an AR(1) process that is an autoregressive process of Order 1. The mean of this process is the same as the mean of the original lead time, but its variance could be much smaller. The implication is that when we consider order crossover in the analysis, the cost could be less than otherwise (but never less than that with deterministic lead times). The literature on JIT with stochastic lead times has never considered order crossover, which produces the effective delivery times (EDT). Here, we demonstrate some important properties of the EDT: that it is a Cauchy sequence, and hence it converges; that it is an AR(1) process; and that it stochastically dominates the parent lead time distribution.

PurposeThe purpose of this paper is to discuss complexity assessment in supply chains, to describe a methodology for measuring supply chain complexity in distributive trade and to illustrate the measurement of supply chain complexity and mechanisms to cope with supply chain complexity in distributive trade.Design/methodology/approachThe paper discusses literature on measuring supply chain complexity and uses five case studies among wholesale companies to illustrate a methodology to measure supply chain complexity and to discuss strategies to cope with supply chain complexity.FindingsThe study confirms the multifaceted nature of supply chain complexity. The paper identifies eight drivers of supply chain complexity and uses these to illustrate the measurement of supply chain complexity in a wholesale environment. The paper identifies six strategies used by wholesalers for coping with supply chain complexity and identifies interrelations between supply chain complexity drivers and these strategies.Research limitations/implicationsThe research is based on case studies in wholesale companies; future research may include survey research, including other sectors to analyze industry differences but may also focus on other parts of the supply chain.Practical implicationsThe study provides insights into how to identify and measure complexity in a supply chain and what can be done to manage supply chain complexity.Originality/valueThe measurement approach is new to supply chain management and is based on multiple drivers of supply chain complexity. The research is focused on wholesalers, which is a segment that has received limited academic attention in supply chain research to date.

Mesoeconomics represents a critical intermediate layer of economic analysis, bridging the gap between microeconomics and macroeconomics. With the growing complexity of global supply chains, traditional economic frameworks and data analysis techniques have proven insufficient. The increasing availability and use of big data technologies have highlighted the relevance of mesoeconomic approaches in analyzing networked economic interactions across international supply chains. The research aims to explore how mesoeconomic frameworks can be effectively utilized to analyze global supply chain networks through big data analytics. It seeks to identify the advantages of applying mesoeconomic analysis in understanding the structure, flow, and dynamics of supply chains, as well as the challenges associated with data integration and analytics. The research employs a qualitative and conceptual analysis of mesoeconomic theory in the context of supply chains, supplemented by a review of current big data analytics methods used by companies. The analysis includes an examination of various data sources (primary and secondary), network structures, and classification techniques that support the processing and interpretation of large-scale data related to goods and services flows. The findings suggest that mesoeconomics, when combined with big data analytics, offers a robust framework for capturing the complexities of global supply chains. This approach enhances the understanding of relationships among products, producers, and processes, enabling more efficient and informed decision-making. However, challenges such as data quality, interoperability, privacy, and security remain significant barriers to the full realization of big data’s potential in supply chain analysis.

We consider an inventory system for continuous decaying items with stochastic lead time and Poisson demand. Shortage is allowed and all the unsatisfied demands are backlogged. Moreover, replenishment is one for one. Our objective is to minimize the long-run total expected cost of the system. Firstly, we have developed the mathematical model with deterministic lead time. Since the stochastic lead time makes the model complex especially when lead time has complicated probability distribution and it is difficult to prove convexity of the objective function, we have applied simulation modeling approach. The simulation model has no limitation on lead time or any other parameters. The simulation model is validated by comparing its outputs and analytical model's results for the deterministic lead time case. Furthermore, we use optimizer module of the applied software to find near optimal solutions for a number of examples with stochastic lead time.

Effective inventory management is a cornerstone of successful supply chain operations, ensuring the alignment of stock levels with fluctuating customer demands. Central to this is demand forecasting, a process that utilizes historical data, statistical tools, and market analysis to predict future demand patterns. This paper explores the role of demand forecasting in optimizing inventory levels, reducing operational costs, and improving supply chain performance. Through a detailed review of existing literature, various forecasting techniques—including exponential smoothing, regression models, and bootstrapping approaches—are categorized and analyzed based on their functionalities and applications. Forecasting serves as a fundamental tool for optimizing inventory levels, mitigating the impact of stochastic demand rates, and minimizing associated costs in supply chain management. Key findings emphasize the significance of accurate demand forecasting in mitigating challenges posed by stochastic demand rates, market uncertainties, and extended lead times. The benefits of effective forecasting, such as enhanced inventory management, cost reduction, improved customer service, and strategic resource allocation, are outlined. Furthermore, this study underscores the importance of adapting forecasting methodologies to dynamic market conditions and integrating innovative approaches to sustain competitive advantage. The research concludes by advocating for the continuous advancement of forecasting techniques to address evolving supply chain complexities and support strategic decision-making in a competitive global market. The paper categorizes various forecasting techniques based on their applications, highlighting their significance in addressing challenges posed by fluctuating market demands and lead times. Thus, this research underscores the importance of accurate forecasting in achieving optimal inventory management and operational efficiency within complex supply chains.

The design and optimization of combined location-inventory model for deteriorating products are a main focus in supply chain management. There were many combined location-inventory design models in this field, but these models are under the assumptions of adequate capacity facilities, invariable lead time, unique product, and uncorrelated retailer’s demands. These assumptions have a big gap in the practical situation. In this paper, we design a combined location-inventory model for deteriorating products under capacitated facilities, stochastic lead time, multiple products, and correlated retailers’ stochastic demands assumptions. These constraints are near to actual supply chain circumstance. The problem is modeled as conic quadratic mix-integer programming (CQMIP) to minimize the total expected cost. We explain how to formulate these problems as conic quadratic mixed-integer problems, and in order to obtain better computational results we use extended cover cuts. Simultaneously we compare our method with the previous Lagrange methods; the result is that the new CQMIP method can get better solution.

A web-based approach for exceptions management in the supply chain

There has been a paradigm shift in the functioning of global supply chains, particularly in thepost-pandemic era. This shift is characterized by the convergence of technology. deployment, geopolitical dynamics, and an increased awareness of environmental and social responsibilities. These transformative changes compel businesses to navigate a complex. web of challenges while simultaneously creating many opportunities for global supply chains. This includes delegating responsibilities to the supplier side, empowering suppliers, and promoting digital supply chain networks. Ongoing trade tensions between major economies, such as the United States and China, have resulted in fluctuations in global logistics patterns. Alterations in tariffs and trade agreements impact shipping routes, transportation costs, and the overall flow of goods. Consequently, supply chains must focus on end-to-end supply chains originating from low-cost sources to withstand unforeseen events like black swan occurrences and geopolitical and trade wars. The pandemic has underscored the critical importance of building resilient supply chains. Many firms reevaluate their supply chain strategies, emphasizing diversification to mitigate risk. This involves reassessing suppliers, exploring alternative sourcing options, and increasing safety stock levels. Geopolitical uncertainties and disruptions have prompted reconsidering global sourcing strategies, with reshoring and nearshoring gaining prominence. Companies seek to mitigate risks associated with distant and complex supply chains by bringing production closer to target markets, enhancing responsiveness, reducing lead times, and fortifying supply chain resilience amid uncertain business conditions. From the traditional supply chain model, there is a continuous effort to integrate information technology to transform value streams and increase processes' transparency. Blockchain is playing a significant role, from tracking the provenance of raw materials to reducing transaction fraud. This technology fosters a new level of trust among supply chain partners, safeguarding against potential disruptions and reinforcing the integrity of the entire supply chain ecosystem. These rapid transformations amid uncertain conditions have prompted a reevaluation of supply chain resiliency with a greater focus on risk mitigation strategies. In global logistics, owning ships and manufacturing/leasing containers for extended periods have become the guiding principles. The recent Brexit has also impacted global transport's European and UK legs. The European industry is grappling with significant shortages of resources, including drivers and trained supply chain manpower, affecting last-mile delivery and land transportation in numerous geographies. The growth of e-commerce has significantly influenced supply chains, leading to innovations in last-mile delivery solutions. Companies are investing in warehouse automation, drone deliveries, and other technologies to meet the demands of the rapidly expanding e-commerce market. x Furthermore, a seismic shift toward sustainability reshapes the global supply chain landscape. Businesses recognize the imperative to address environmental, social, and governance (ESG) concerns. Initiatives to reduce carbon footprints, optimize transportation routes, and embrace ethical sourcing practices are becoming integral components of supply chain strategies. Sustainability is no longer merely a corporate buzzword but a critical factor in ensuring long-term viability and meeting the expectations of socially conscious consumers. Overall, global supply chains are undergoing significant transformation driven by technological innovation, environmental consciousness, and a commitment to building resilient networks. Businesses that embrace these trends are not only adapting to the challenges of today but are also positioning themselves to thrive in the dynamic landscape of tomorrow. The key to success is navigating change, leveraging technology, and building sustainable businesses.

In light of the information revolution, this study aims to clarify the impact of artificial intelligence and supply chain resilience on the supply chain performance of engineering, electrical, and information technology companies registered with the Jordan Chamber of Industry. This study expands knowledge by exploring the relationships between artificial intelligence and the moderating supply chain dynamism. This study looks at artificial intelligence as an important resource, in addition to resilience supply chains, an important resource in raising the supply chain performance for companies. The questionnaire was conducted via e-mail and the study sample included (208) companies registered with the Jordanian Chamber of Industry and Commerce. The data was analyzed using the smart (Pls) software and its direct link with artificial intelligence and supply chain resilience. In addition, the analysis shows that there is a direct relationship between the mediating variables supply chain dynamism and supply chain resilience and supply chain performance. These results provide an insight into the relationship between artificial intelligence and supply chains, and the Moderating variable on the performance of a company's supply chains, which may be an entry point for companies to enhance their performance due to the importance of this sector to the Jordanian economy.KeywordsArtificial intelligenceSupply chain ResilienceSupply chain performanceSupply chain dynamismEngineering electricalInformation technologyJordan

The supply chain is an extremely important and complex system, which is widely related to the social system and the management system. The hierarchical scheduling and production planning of a dynamic supply chain can enable enterprises in the supply chain to obtain, and maintain, stable and lasting competitive advantages; thus, improving the overall competitiveness of the supply chain. Simultaneously, research on supply chains is also a hotspot in system engineering. At present, research on complex supply chain systems with dual channels is insufficient, and analysis of dynamic supply chains with supply failure of goods is rare. In the epidemic era, dynamic supply chain management was affected by the epidemic and became a very challenging topic. The control of the supply chain system under the impact of an epidemic situation is particularly important. This paper focused on H∞ the fault-tolerant control of a complex supply chain system with dual channels. First, from the perspective of engineering practice and green production, we established a new green supply chain model. This supply chain model includes the dual-channel supply of goods and also considers the supply failure of goods. At the same time, production delay and supply delay are also considered in this model. Second, a supply scheduling scheme was designed through cybernetics to ensure the stability of a supply chain with dual channels under supply failure of goods, and we completed supply chain management. Finally, the effectiveness of our results was verified by specific Chinese enterprise data.

Supply chain management is struggling with a bunch of issues that appear during supply chain members coordination. Raising of supply chain complexity leads to the necessity of developing new software applications, which can be used for analysis of supply chain dynamics, storing data about it’s past and present states, predicting future behavior. This paper discusses current challenges in supply chain management and presents a model for the multi-agent framework in order to investigate supply chain dynamics.