Search

This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers. [ABSTRACT FROM AUTHOR]

Mishra and Raghunathan (2004) and Kim (2008) conducted a study on the same model but reached conflicting results on under what circumstances vendor-managed inventory (VMI) benefits the retailer as opposed to retailer-managed inventory (RMI) in a setting where two products are partially substitutable. However, we note that their conflicting results were due to different assumptions about unmet demand after product substitution, which is influenced by consumers response to retail stockouts. This paper then extends their studies by incorporating consumer response to retail stockouts and re-examining the impacts of VMI on the retailer's performance as well as on each manufacturer's. Therefore, this paper reconciles the conflicts in the existing scholarly work and provides a more complete understanding of when each supply chain member is better off with VMI than RMI in a two-product setting. Additionally, the incorporation of consumer response to retail stockouts sheds light on how product substitution and consumers' store loyalty affect the value of VMI to each supply chain member. [ABSTRACT FROM AUTHOR]

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher. [ABSTRACT FROM AUTHOR]

This paper considers the multi-vehicle production routeing problem with a maximum-level replenishment policy. This is a well-established problem within vendor managed inventory where production, inventory and routeing decisions are made simultaneously. We present a novel method to solve the problem that outperforms existing methods both in terms of solution gaps and the number of best-known solutions. The proposed matheuristic is tested on three different sets of benchmark instances consisting of 1218 instances and finds or improves the best-known solution for 632 of them. For the remaining instances, the matheuristic is less than 2.5% from the best-known solutions. The method is particularly proficient on large instances and is also efficient for the inventory routeing problem. The success of the method is largely due to its improvement phase where a novel path-flow-inspired mathematical model is introduced. Here, a route set obtained from the current solution is used and retailers can be simultaneously inserted and removed from a route, making the method flexible even when a small route set is used. In addition, we introduce a new production subproblem that approximates the costs of using a vehicle instead of approximating the costs of visiting a retailer, making it very fast to solve. [ABSTRACT FROM AUTHOR]

Our work delves into supply chain coordination involving a single supplier selling a single product to multiple downstream buyers. We consider the case when the supplier proposes implementing vendor-managed inventory (VMI) for each buyer. The VMI contract involves the buyers charging a penalty to the supplier on the excess inventory whenever the supplier exceeds some pre-decided inventory threshold. Unlike the works covered predominantly in the VMI literature involving penalty, where both the penalty and the threshold are exogenous, we develop a model to determine both endogenously. In our study, the supplier deciding on the penalty and the inventory threshold needs to ensure that each of the buyers' interests is protected, that is, none of the buyers becomes worse off from the optimal position, thereby ensuring the willing participation of all the buyers. We further compared our model with the centralized Joint Economic Lot Size (JELS) model and other existing models. We show that our model has a cost equivalence with the JELS model with unequal order intervals. We also show the superiority of our proposed model over the existing VMI models in terms of the overall costs. Finally, we examine the impact of size and ordering cost on each buyers' costs through numerical analysis. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Selling fresh food products can be challenging due to their perishability, which often results in significant losses. To address this issue and maximize profits, we have developed a contract that takes into account the costs of investing in preservation technology over a finite planning horizon. The arrangements incentivize the supplier and the retailer to establish coordination and determine not only the optimal price and schedule for replenishment but also the optimal investment required in preservation technology. We investigate the effectiveness of vendor managed inventory (VMI) and consignment with revenue-sharing models through an analysis of pricing and inventory decisions, followed by evaluating the channel performance and the distribution of profits. Contract parameters are defined under the equilibrium state to achieve advantageous relationships among supply chain partners by improving profits for both channel members. The finding shows that a VMI and consignment mechanism with a side payment arrangement could help coordinate channels in a non-cooperative setting. Still, performing the contract is easier in a cooperative setting. Both members could achieve optimal decisions for the wide-channel system without any additional payments, leading to increased profitability for all supply chain members. In an alternative approach, the retailer has the option to offer a subsidy alongside the revenue-sharing-only trading terms within a VMI and consignment by incorporating a generalized revenue-sharing scheme to facilitate coordination with the supplier. [ABSTRACT FROM AUTHOR]

Background: Traditional procurement and price negotiation methods in public hospitals in Thailand involve group purchasing agreements and bulk orders, posing challenges to improving inventory management efficiency. The vendor-managed inventory (VMI) model is a promising alternative for enhancing hospital performance, especially during crises. This study aimed to assess the potential cost savings in implementing VMI in a large general hospital in Thailand. Methods: A simulation modeling approach was used to compare the current inventory system with three VMI models: VMI1, focused on improving inventory turnover rate (ITR); VMI2, emphasized frequent replenishment with a 1-month supply; and VMI3, eliminated safety stock. Results: The results demonstrated significant cost savings, with potential reductions in total inventory management expenses. Specifically, VMI1 improved ITR from 6.31 to 7.76, reducing average inventory by 36% and cutting management costs by 40%. VMI2, with an ITR of 12.80, reduced inventory by 44% and saved 47% in management costs, while VMI3 achieved a 70% reduction in inventory and a 69% saving in management costs. Conclusions: This study highlights the VMI's transformative potential in hospital inventory management, demonstrating significant cost savings. However, in the public sector, the feasibility of procurement regulations requires further exploration. [ABSTRACT FROM AUTHOR]

We introduce vendor-managed inventory (VMI) modes and retailer-managed inventory modes and use conditional value-at-risk to quantify the inventory manager's risk aversion. Then, we compare the supply chain (SC) members' optimal decisions and expected utilities when the online retail platform or the supplier conducts sales effort under different inventory modes. The cost-sharing contracts are introduced to optimize the SC performance. Some interesting findings are achieved: for VMI SC, SC members' sales-effort preferences change with the wholesale price; the inventory manager's risk-aversion degree has a significant impact on the SC members' preferences for inventory management modes; there always exists appropriate cost-sharing ratio regions in which the SC members' expected utilities can be improved. The corresponding research results can help to improve the operational effectiveness of different kinds of SCs. [ABSTRACT FROM AUTHOR]

There are calls in the extant literature for further exploration into the factors influencing customer satisfaction in industrial vending solutions (IVS), a distinct vendor-managed inventory method. This study delves into these factors, identifying primary drivers of satisfaction, perceptions of service quality, and indicators of trust and commitment in B2B IVS. It examines how IVS execution impacts perceived service quality through in-depth semi-structured interviews with B2B customers, focusing on efficiency, user-friendliness, and timeliness as key satisfaction drivers. Trust in the customer–supplier relationship positively affects commitment to the supplier. Successful IVS implementation enhances inventory control, customer service, and cost management. Supply chain managers can use these insights to evaluate vending solutions. Future research could explore supplier perceptions of vendor-managed inventory (VMI) success and conduct larger quantitative studies. This study distinguishes itself by focusing on the primary drivers of customer satisfaction and the perception of service quality in B2B IVS environments. It offers a conceptual framework for managing customer satisfaction, product development, and marketing strategies in IVS, addressing a gap in the literature on IVS within VMI contexts. [ABSTRACT FROM AUTHOR]

In a collaborative supply chain arrangement like vendor‐managed inventory, information on product demand at the point of sale is expected to be shared among members of the supply chain. However, in practice, obtaining such information can be costly, and some members may be unwilling or unable to provide the necessary access to the data. As such, large collaborative supply chains with multiple members may operate under a mixed‐information scenario where point‐of‐sale demand information is not known for all customers. Other sources of demand information exist and are becoming more available along supply chains using Industry 4.0 technologies and can serve as a substitute, but the data may be noisy, distorted, and partially missing. Under mixed information, leveraging existing customers' point‐of‐sale demand to improve the intermittent demand forecast of customers with missing information has yet to be explored. We propose a supervised demand forecasting method that uses multivariate time series clustering to map multiple sources of demand data. Members with missing downstream demand data have their resulting demand forecast improved by averaging over customers with similar delivery patterns for their final demand forecast. Our results show up to a 10% accuracy improvement over traditional intermittent demand forecasting methods with missing information. [ABSTRACT FROM AUTHOR]

Effective vendor-buyer collaboration is a successful supply chain management (SCM) cornerstone. By working together, both parties can achieve significant benefits. This collaboration approach fosters open communication, which allows for better communication, improved efficiency, service levels, and reduced costs, i.e., a win-win situation. Ultimately, this partnership strengthens the entire supply chain, increasing efficiency and profitability for vendors and buyers. This study aims to investigate the dynamics of collaboration between supply chain partners. It focuses on a scenario where a single vendor supplies a single product to multiple buyers through a vendor-managed inventory (VMI) and consignment stock (CS) agreement. Managing inventory and product quality is a key challenge, as the components may be imperfect. Buyers are responsible for inspecting these items, but the process is susceptible to misclassification. To address these issues, the research aims to develop an optimization model that incorporates costs associated with collaboration, quality, inspection, and potential misclassification. In addition, developing a methodology to identify the optimal solution minimizes overall costs and maximizes benefits for both vendors and buyers. Furthermore, a numerical example is presented for tangible illustration, offering practical insights into the benefits of partner collaboration in SCM. The findings underscore the efficacy of implementing a collaborative approach between vendors and buyers to minimize supply chain costs. This collaborative policy necessitates a willingness from both parties to forgo their individual cost-minimization strategies in favor of optimizing the overall system cost through cooperation. Through this sacrifice, synergistic benefits emerge, resulting in enhanced efficiency, reduced expenses, and improved overall performance within the supply chain ecosystem. [ABSTRACT FROM AUTHOR]

Introduction: Considering the perishability and substitutability of products are among the most significant challenges in the design and optimization of decision-making in Vendor Managed Inventory (VMI) systems. This challenge becomes more pronounced when there is uncertainty in product demand. Therefore, the primary objective of this research is to present a stochastic dynamic programming approach for optimal control of decisions in VMI systems with dynamic demand uncertainty, optimizing the ordering levels and inventory of perishable products in a two-tier network including vendors and buyers.Methods: After defining the problem of interest in developing VMI systems, considering demand uncertainty and product perishability, the problem is formulated in a multi-period modeling framework, and a stochastic dynamic programming (SDP) approach is used for its formulation. In the proposed SDP model, the objective function is to maximize expected profit by taking into account various costs such as ordering and holding, where the holding cost is dependent on the remaining product life; meaning that as the product approaches its expiration date, the holding cost increases. The proposed SDP model is executed in a recursive manner, and MATLAB software is used for its implementation. Each step of the SDP model is a simpler linear optimization model that is efficiently solved using the CPLEX solver.Results and discussion (Findings): Numerical results demonstrate the computational effectiveness of the SDP method in solving this problem. Using this approach, it is possible to control different costs in a VMI system and make optimal decisions at different stages under any state, thereby improving profit at the end of the time periods.Product substitution in the event of a shortage ensures that, firstly, in the case of a shortage at one center and the supplier's inability to replenish, the center offers its excess inventory to prevent the shortage. Secondly, if the inventory in a distribution center approaches its expiration date, spoilage is prevented. Therefore, the substitution capability generally leads to a reduction in shortage costs and spoilage costs. Results related to product shelf life indicate that by considering the remaining shelf life of products in a VMI-based inventory control system, information between the distribution and supply layers can be used to reduce prices, transfer inventory to another center, and even change inventory control policies to not only prevent product spoilage but also reduce shortage and reordering costs. To model this feature, a full shelf life is initially defined for each newly supplied product, and then a set of periods is defined. For each planning period, if the product is not delivered to the end customer (remains in inventory), one period/day is deducted from the initially defined shelf life, and over time, it moves from a fresh state to the category of older products, which are subject to price reductions. Furthermore, comparing the proposed model with substitution capability in the case of a shortage to the case where a shortage is not considered, it is observed that there is a 10.5% improvement in profit.Conclusion: In the management of modern VMI systems, considering the dynamism in demand behavior and the associated uncertainty is very important and can significantly affect the profitability of enterprises. This importance is doubled when the system in question is managed for the inventory control of perishable products. The SDP approach, by considering potential scenarios of demand uncertainty, enabling substitution, and ultimately paying attention to product shelf life, provides optimal decisions in different situations and not only reduces the risk of decision-making but also leads to a noticeable improvement in final profit compared to nominal quantity models and classical optimization models in the literature.

Objectives: To employ a drug supply chain information system to optimize drug management practices, reducing costs and improving efficiency in financial and asset management. Methods: A digital artificial intelligence + vendor managed inventory (AI+VMI)-based systemfor drug supply chain information management in hospitals has been established. The system enables digitalization and intelligentization of purchasing plans, reconciliations, and consumption settlements while generating purchase, sales, inventory reports as well as various query reports. The indicators for evaluating the effectiveness before and after project implementation encompass drug loss reporting, inventory discrepancies, inter-hospital medication retrieval frequency, drug expenditure, and cloud pharmacy service utilization. Results: The successful implementation of this system has reduced the hospital inventory rate to approximately 20% and decreased the average annual inventory error rate from 0.425? to 0.025?, significantly boosting drug supply chain efficiency by 42.4%. It has also minimized errors in drug application, allocation, and distribution while increasing adverse reaction reports. Drug management across multiple hospital districts has been standardized, leading to improved access to medicines and enhanced patient satisfaction. Conclusion: The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel. [ABSTRACT FROM AUTHOR]

In the era of widespread e-commerce adoption, businesses encounter challenges in managing e-channel demand and production constraints. This study explores an e-channel with an e-retailer with no pattern demand, a primary supplier, and a backup supplier to address these issues through outsourcing. The primary supplier, facing limited production capacity and potential failure, seeks assistance from the backup supplier under a vendor-managed inventory (VMI) strategy. Two contracts, a conventional contract with penalties for rejecting units and an option contract allowing negotiations, are compared. Results indicate that the option contract promotes a win-win scenario, enhancing overall e-channel efficiency and earnings compared to the conventional contract. Negotiations facilitated by the backup supplier contribute to e-channel coordination. However, if the option price exceeds the penalty difference, the conventional contract may be more attractive to the primary supplier. [ABSTRACT FROM AUTHOR]

Introduction: Considering the perishability and substitutability of products are among the most significant challenges in the design and optimization of decision-making in Vendor Managed Inventory (VMI) systems. This challenge becomes more pronounced when there is uncertainty in product demand. Therefore, the primary objective of this research is to present a stochastic dynamic programming approach for optimal control of decisions in VMI systems with dynamic demand uncertainty, optimizing the ordering levels and inventory of perishable products in a two-tier network including vendors and buyers. Methods: After defining the problem of interest in developing VMI systems, considering demand uncertainty and product perishability, the problem is formulated in a multi-period modeling framework, and a stochastic dynamic programming (SDP) approach is used for its formulation. In the proposed SDP model, the objective function is to maximize expected profit by taking into account various costs such as ordering and holding, where the holding cost is dependent on the remaining product life; meaning that as the product approaches its expiration date, the holding cost increases. The proposed SDP model is executed in a recursive manner, and MATLAB software is used for its implementation. Each step of the SDP model is a simpler linear optimization model that is efficiently solved using the CPLEX solver. Results and discussion (Findings): Numerical results demonstrate the computational effectiveness of the SDP method in solving this problem. Using this approach, it is possible to control different costs in a VMI system and make optimal decisions at different stages under any state, thereby improving profit at the end of the time periods. Product substitution in the event of a shortage ensures that, firstly, in the case of a shortage at one center and the supplier's inability to replenish, the center offers its excess inventory to prevent the shortage. Secondly, if the inventory in a distribution center approaches its expiration date, spoilage is prevented. Therefore, the substitution capability generally leads to a reduction in shortage costs and spoilage costs. Results related to product shelf life indicate that by considering the remaining shelf life of products in a VMI-based inventory control system, information between the distribution and supply layers can be used to reduce prices, transfer inventory to another center, and even change inventory control policies to not only prevent product spoilage but also reduce shortage and reordering costs. To model this feature, a full shelf life is initially defined for each newly supplied product, and then a set of periods is defined. For each planning period, if the product is not delivered to the end customer (remains in inventory), one period/day is deducted from the initially defined shelf life, and over time, it moves from a fresh state to the category of older products, which are subject to price reductions. Furthermore, comparing the proposed model with substitution capability in the case of a shortage to the case where a shortage is not considered, it is observed that there is a 10.5% improvement in profit. Conclusion: In the management of modern VMI systems, considering the dynamism in demand behavior and the associated uncertainty is very important and can significantly affect the profitability of enterprises. This importance is doubled when the system in question is managed for the inventory control of perishable products. The SDP approach, by considering potential scenarios of demand uncertainty, enabling substitution, and ultimately paying attention to product shelf life, provides optimal decisions in different situations and not only reduces the risk of decision-making but also leads to a noticeable improvement in final profit compared to nominal quantity models and classical optimization models in the literature. [ABSTRACT FROM AUTHOR]

These Supply chain involves various stages that includes manufacture, distribution, storage, and transit of goods. It is frequently overseen by a large number of businesses, suppliers, and other stakeholders. The Administration for International Trade states that more than 76% of global trade is done by supply chain transactions, demonstrating its enormous importance in the worldwide economy. Decentralized solutions and blockchain technology are rapidly gaining attraction in a number of industries, with the FMCG industry showing particular interest. Blockchain presents a compelling alternative for FMCG supply chain optimization as it has many benefits like traceability, transparency, and security. When used, blockchain improves information exchange's traceability and transparency, and smart contracts allow for automated contract execution, which lowers payment latency and simplifies complicated contracts. Blockchain has enormous potential to transform FMCG management for supply-chain, comprising provenance tracking, inventory control, demand forecasting, and communication. The difficulties in conventional supply-chain are addressed by integrating with blockchain technology. Coordination blockchain into supply chains for fast-moving buyer merchandise is imperative for progressing effectiveness, cutting costs, and cultivating believe and collaboration among supply chain members. This alter has the potential to definitely change the FMCG supply chain environment whereas giving a reasonable course forward for expanded effectiveness, security, and transparency. [ABSTRACT FROM AUTHOR]

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy. [ABSTRACT FROM AUTHOR]

The search for competitiveness has led organizations to recognize the importance of implementing collaborative methods. In this context, Collaborative Planning, Forecasting, and Replenishment (CPFR) seek to relate customer demands to replenishment needs throughout the supply chain. The literature points to implementation difficulties, such as trust between partners and commitment, and to overcome these difficulties, the digital technologies of Industry 4.0 are emerging as an essential support. This paper aims to research the applicability and contribution of Industry 4.0 digital technologies to the CPFR, considering each stage of the CPFR. A systematic literature review identified an unexplored gap: the use and contribution of Industry 4.0 digital technologies to the functioning of the CPFR. Considering the sequence of the CPFR stages and their dependence on each other, a roadmap can be developed for using Industry 4.0 digital technologies in each stage of the CPFR. It was, therefore, possible to create a roadmap indicating, based on current literature, which Industry 4.0 digital technologies can enhance SCM at each stage of the CPFR. This roadmap has two significant contributions: (i) to guide companies in prioritizing the implementation of these technologies in support of supply chain objectives and (ii) to serve as a basis for further empirical studies, highlighting the development of this roadmap through its application in real situations. [ABSTRACT FROM AUTHOR]

The study explores the strategic pricing and quality improvement decisions under uncertain demand in a three-layer textile and garment supply chain. According to whether the fabric manufacturer (FM) invests in quality or not and whether the garment manufacturer (GM) or garment retailer (GR) is willing to share the costs or not, five game models are constructed to investigate the impact of different members' cost sharing on the optimal decisions and profits. By conducting a theoretical and numerical analysis, we find that: (1) The GM's or GR's cost sharing plays a positive effect on the quality improvement, as for whose cost sharing performs better in improving the quality depending on the proportion of cost sharing, and the quality improvement is highest with both members share the costs simultaneously. (2) The FM receives the highest profit when both members share the costs simultaneously, however, whose cost sharing is more profitable for the FM is also related to the proportion of cost sharing; in short, the FM always benefits from the cost sharing, no matter one member does this or two members do this. (3) The GM (GR) gains the highest profit when only the GR (GM) shares the costs, and the results indicate that if one member has shared the costs, whether the other member engaging in cost sharing could benefit the former depending on their proportions. Specifically, when the GM (GR) chooses to share the costs and the proportion is relatively low, the GR(GM) joining in cost sharing is beneficial to the former; otherwise, is harmful. [ABSTRACT FROM AUTHOR]

Within the framework of institutional theory, this study explores how the proactiveness of focal (or buying) manufacturing firms, demonstrated through the adoption of various sustainability practices, influences the sustainability performance of suppliers. Additionally, it examines how the leadership capability of buying firms, particularly in terms of supply chain leadership, can play a crucial role in this link. Through empirical analysis of a sample taken from the fourth round of the High‐Performance Management (HPM) project, our study reveals compelling evidence indicating that buying firms, by adopting diverse sustainability practices, such as internal initiatives, monitoring efforts and collaborative approaches with suppliers, are able to create distinct institutional pressures. These pressures serve as a conduit for the diffusion of a shared set of sustainability goals, values and norms among suppliers, ultimately contributing to the development of sustainability competences and improving their overall sustainability performance. Furthermore, our findings suggest that when buying firms undertake a leading position, they can effectively translate isomorphism pressures into sustainability improvements on the supplier side. Overall, this study sheds light on important and understudied aspects of sustainability practices in buyer–supplier relationships and underscores the critical role that supply chain leadership can play in promoting sustainable practices across the entire supply chain. [ABSTRACT FROM AUTHOR]

Vendor managed inventory (VMI) agreements are a supply chain collaboration principle in which the supplier is responsible for keeping the inventory at the buyer between given lower and upper inventory limits. VMI gives the supplier a lot of autonomy while making replenishments, allowing the supplier to efficiently ensure availability at the buyer. But different suppliers may apply different approaches for staying between the limits under VMI. Relatedly, the objectives of a supplier are not completely aligned with those of the buyer. We develop a methodology for setting appropriate lower and upper inventory limits, taking into account the heterogeneity in the supplier base. To this end, by distinguishing between various possible strategic supplier objectives and various approaches for staying within the inventory limits, we arrive at four possible supplier types. We develop a methodology for classifying suppliers into one of these four types based on historic inventory trajectories and argue how to set inventory limits tailored to each supplier type. A case study of the approach at a large high-tech manufacturer reveals that the approach accurately identifies the type of each of the 47 VMI suppliers of the manufacturer. Moreover, a simulation with company data shows that tailoring inventory limits to the suppliers' types may lead to a cost reduction of 4–6 % without reducing the service level, as compared to a benchmark method that does not take into account supplier types. [ABSTRACT FROM AUTHOR]

The article discusses the evolution of revenue management and pricing beyond the airline and hotel industries. It highlights various industries that have adopted revenue management strategies, including entertainment, grocery management, the restaurant industry, electricity and energy, delivery services, the retail sector, olive oil pricing, and telecommunications. Each industry is explored through different research papers that examine specific aspects of revenue management and pricing. The article provides valuable insights into the application of revenue management in diverse sectors, offering potential growth opportunities and pricing strategies for each industry. [Extracted from the article]

In the supplier–retailer–consumer system, the retailer's replenishment and pricing strategies impact the entire transaction process, forming a comprehensive trading market. Suppliers offer advance-cash-credit payments to retailers, while retailers provide customers with cash-credit payment options. In the current health-conscious consumer market, purchasing decisions are influenced not only by commodity prices but also by the freshness of products, particularly perishable goods. Growing awareness of climate change and the advent of carbon emission policies have raised concerns about the environmental costs of business transactions. This study focuses on perishable products whose demand is influenced by both price and freshness. It explores the adoption of various payment methods by suppliers and retailers, as well as the impact of carbon emission cap-and-trade policies or carbon tax policies on management and pricing strategies. Suitable inventory models are established to determine the optimal replenishment and pricing strategies for maximizing the current value of total profit. We illustrate that the current value of total profit demonstrates joint concavity concerning both the selling price and the replenishment time. Finally, we verify the proposed models using numerical examples and present the findings of sensitivity analyses. The findings of this study yield several valuable insights for inventory management of perishable goods. [ABSTRACT FROM AUTHOR]

As the Internet rapidly grows, manufacturers tend to open a direct channel to distribute the products in addition to traditional channels. Meantime, retailers may have difficulty in making immediate payments due to limited capital. Confronting with the channel encroachment, financial pressure, and uncertain demand, retailers may also show a risk averse attitude, which was rarely considered in literature. This study considers a dual-channel supply chain wherein a retailer may be capital constrained and is risk averse. Two scenarios are respectively modeled as a Stackelberg game. One is that the retailer has adequate capital, the other is that the retailer is capital constrained and a manufacturer prefers to provide a deferred payment contract. The results show that the manufacturer present a high degree of willingness to offer the delayed payment to an extremely risk-averse retailer. Further, when such a payment contract is offered, the manufacturer's or even the entire supply chain's profits may decrease. Moreover, a deferred payment contract may benefit the entire supply chain only if the unit production cost is relatively large. Finally, a mixed contract that combines revenue sharing and deferred payment is introduced to achieve the coordination of the dual-channel supply chain. [ABSTRACT FROM AUTHOR]

It is essential to secure a sustainable flow of cash along the supply chain in the modern corporate environment. Due to the growing significance of cash flow, the concept of the cash-flow bullwhip effect has recently drawn academic attention to finding solutions to the cash shortage. The supply chain's response to the bullwhip effect, known as the cash-flow bullwhip effect, causes cash-flow volatility to be amplified from downstream to upstream. A number of research studies have looked into the sources and effects of the cash-flow bullwhip effect, but none have concentrated on solutions. This study investigated for the first time the impacts of the vendor-managed inventory policy as a tool for mitigating the cash-flow bullwhip effect. The findings show that the vendor-managed inventory policy typically shortens the cash-conversion cycle of each supply chain member and can, therefore, be implemented as a policy for mitigating the cash-flow bullwhip effect. [ABSTRACT FROM AUTHOR]

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs. [ABSTRACT FROM AUTHOR]

We investigate the combined planning problem involving the routing of technicians and the stocking of spare parts for servicing geographically distributed repair tasks. The problem incorporates many operational uncertainties, such as future repair requests and the required spare parts to replace malfunctioned components. We model the problem as a sequential decision problem where decisions are made at the end of each day about the next day's technician route and spare part inventory in the van. We show that exact methods are intractable because of the inherent high-dimensional state, decision, and transition spaces involved. To overcome these challenges, we present two novel algorithmic techniques. First, we suggest a hybrid value function approximation method that combines a genetic search with a graph neural network capable of reasoning, learning, and decision making in high-dimensional, discrete decision spaces. Second, we introduce a unique state-encoding method that employs multiattribute graphs and spatial markers, eliminating the need for manually designed basis functions and allowing efficient learning. We illustrate the general adaptive learning capacity by solving a variety of instance settings without instance-specific hyperparameter tuning. An extensive numerical study demonstrates that our hybrid learning technique outperforms other benchmark policies and adapts well to changes in the environment. We also generate a wide range of insights that not only shed light on the algorithmic components but also offer guidance on how to execute on-site repair tasks more efficiently. The techniques showcased are versatile and hold potential for application in other dynamic and stochastic problems, particularly in the realm of transportation planning. Funding: This work was supported by Deutsche Forschungsgemeinschaft (DFG). The Research Training Group 2201 [Grant 277991500], "Advanced Optimization in a Networked Economy," funded by the DFG, has provided partial support for this work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0434. [ABSTRACT FROM AUTHOR]

Vehicle routing problem is a well-known optimization problem in the logistics area. A special case of the vehicle routing problem is the station replenishment problem in which different types of fuel types have to be transported from the depots to the customers. In this paper we study the replenishment problem of a European petrol company. The problem contains several additional constraints such as time windows, different sized compartment vehicles, and restrictions on the vehicles that can serve a customer. We introduce a mixed integer linear programming model of the problem. To reduce the size complexity of the MILP model the customers are clustered and, based on the clusters, additional constraints are added to the MILP model. The resulting MILP model is tested on real problems of the company. The results show that combining the MILP model with clustering improves the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Retailers often sell their own store brands that compete with the national brands. Store brand competition induces the supplier to quote lower wholesale prices for national brands. In multi‐period selling environments, retailers may complement this wholesale price reduction by carrying strategic inventories. Hence, store brand carrying retailers face the following dilemma. On the one hand, strong store brand competition might be sufficient to lower the national brand's wholesale price substantially. In this case, the retailer need not carry any strategic inventory. On the other hand, the wholesale price reduction induced solely by store brand competition might not be substantial. In this case, the retailer might want to carry either national brand or store brand strategic inventory to complement the wholesale price reduction. We address the above dilemma by investigating the effect of store brand competition on the strategic inventory decision using a two‐period game‐theoretic framework. We enhance the current understanding of the retailer's strategic inventory by incorporating the impact of store brand competition. Our analysis reveals that it is never optimal for the retailer to carry store brand as strategic inventory even when the store brand and the national brand are close substitutes. We also find that a store brand selling retailer may become worse off when being endowed with the option of carrying strategic inventory. However, when the holding cost is sufficiently low, we show that the strategic inventory and store brand competition complement each other and benefit the retailer. [ABSTRACT FROM AUTHOR]

In today's highly competitive business landscape, SMEs face the challenges of meeting customer demands and expectations. To remain competitive, attract and satisfy customers, manufacturing SMEs must adopt innovative practices within their supply chain. This research investigates the influence of supply chain innovative practices (SCIPs) on customer satisfaction within the context of manufacturing SMEs. A cross-sectional questionnaire survey was utilised to collect the data from 172 customers of manufacturing SMEs in Mwanza, Tanzania to gain comprehensive insights into the influence of SCIPs on customer satisfaction. Descriptive statistics and binary logistic regression (BLR) were used for data analysis. The research findings revealed that manufacturing SMEs adopt SCIPs, including digitalization and IoT integration, vendor-managed inventory, customisation, Just-In-Time, collaborative relationships, and lean manufacturing to a moderate extent. Moreover, the findings demonstrate a significant and positive correlation between SCIPs and customer satisfaction. We conclude that SMEs that successfully adopt SCIPs experience higher levels of customer satisfaction, which, in turn, positively influences customer loyalty and brand reputation. Therefore, manufacturing SMEs can capitalise on customer satisfaction, loyalty, and sustainable growth by strategizing SCIPs along their supply chain. The study contributes to the existing knowledge by providing valuable insights into the importance of SCIPs in improving customer satisfaction in manufacturing SMEs. [ABSTRACT FROM AUTHOR]

Copyright of Manajemen IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah is the property of IPB University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to analyse the manufacturer-supplier relational perspective under the influence of exogenous financial risk. Following corporate finance theory, a multi-objective decision model for supplier selection and order allocation is developed to maximise the total profit of the manufacturer, and minimise the implicit equity stake and financial risk faced by selected suppliers. A two-echelon supply chain is explored under the influence of foreign exchange risk, default risk, market risk and price fluctuation risk, and solved using an NSGA-III algorithm. Three case scenarios are analysed to explore the influence of a set of financial risk on the manufacturer-supplier relationship and the behaviour of suppliers concerning risk profile, both in the short and long-term horizon. The results are analysed from both the manufacturer as well as supplier perspective, and the optimal conditions are discussed under the cascading risk circumstances. The study provides multiple insights into the impact of financial risk on supply chain relationship and will be valuable for dealing with similar uncertain economic environment. The research is likely to be of benefit beyond supply chain managers, like investors and financial risk managers in making informed decisions. The need to focus on systemic risk in supply chains is evident from the study. [ABSTRACT FROM AUTHOR]

Retailers play a vital role in supply chain management because they deal directly with consumers. Occasionally, retailers may cover the entire system's statistics and not disclose these data to the manufacturer. Therefore, asymmetry is generated in the data throughout the system. The main motive of this research was to prevent unreliability throughout the system using a vendor-managed inventory policy. This research shows that by applying a cap and trade policy, the total carbon emitted from the production and transportation sectors can be controlled in the atmosphere. Finally, numerical and sensitivity analyses, along with pictorial representations of various parameters, are performed to examine the optimal results of this study. In addition, the retailer's lead time demand for items is assumed to be random rather than fixed and follows uniform and normal distribution functions. Under these two distribution functions, the optimal retailer lot size, service provided by the retailer to customers, and retailer reorder points are assessed. Furthermore, an evaluation of the total carbon released from an environmental viewpoint is illustrated using numerical findings. The numerical results show that this research is 50.24% more economically beneficial than the methods used in previous studies, whereas the mean value of demand follows a uniform distribution.

This paper presents a vendor-managed inventory model for a three-layer supply chain comprised of suppliers, manufacturers, and retailers. This model considers an imperfect production system with a deterministic percentage of defective and deteriorated items, exponential demand for perishable products, and operational costs. The VMI model maximizes the benefits, assuming a Stackelberg approach to find the optimal values of replenishment lot sizes, production rates, selling prices, and product cycle length. A solution algorithm is developed to find the optimal solutions. A case study of the Dairy Industry for highly perishable products is provided to illustrate the applications, evaluate the performance, and obtain managerial insights. The results show that the percentage of defective and deteriorated items and remanufacturing rates are crucial in replenishment, production, and selling policies. The VMI agreement includes remanufacturing defective products to obtain new dairy derivates. The deteriorated products can be sold as near-expired products or inputs for compost, fertilizers, etc. Finally, some conclusions and future research lines are provided. [ABSTRACT FROM AUTHOR]

To deal with disruption at the supply side, the use of backup suppliers is among the commonly employed strategies. In this research, we derived a dual sourcing model using capacity reservation contracts for a supply chain consisting of a retailer, a primary supplier who may experience disruption, and a reliable backup supplier. This dual sourcing model is a reactive strategy that balances flexibility and redundancy to improve supply chain resilience. The aim of the proposed model is to help find optimal solutions for the reserved capacities of the retailer to the suppliers and the constructed capacities of the suppliers. It has been derived in this research that the above-mentioned reserved/constructed capacities exist uniquely. Sensitivity analyses were also conducted to examine the impact of contract parameters on decision variables. This research contributes to existing knowledge by providing deeper insights into the application of the capacity reservation contract to handle supply disruption. [ABSTRACT FROM AUTHOR]

In recent years, finding mitigation strategies for supply chain disruptions has become one of the most critical challenges for businesses. This issue is crucial for complex product industries because of their role in the modern economy, few suppliers, and their need for high investment in research and development (R&D). This paper studies a resilient supply chain in complex product systems to overcome its specific challenges through supplier reliability enhancement and cooperative contracts. Utilising a game theoretic approach and analytical models, this paper aims to improve the supply chain performance from the resilience perspective while considering R&D investment, supplier learning effect, buyer fairness concern, and market sensitivity to the product's technology. Investment in supplier reliability enhancement with different contracts is proposed to mitigate disruption risks for a two-echelon supply chain. Analytical mathematical models have been developed, and a simulation approach has been used in optimisation. The results show how proposed contracts effectively increase supply chain performance from financial and resilience perspectives. Moreover, the market sensitivity to the product's technological level and the sensitivity to the price could adversely affect performance. The buyer's fairness concern also improves the profit loss while decreasing the service level slightly. [ABSTRACT FROM AUTHOR]

For the best implementation of (vendor-managed inventory) VMI, a vendor must choose the optimal combination of suppliers that can better meet the criteria of environmental problems. In VMI, a vendor controls some retailers' inventory decisions. This study aims to select the best suppliers in the green VMI and assign orders to them. In this study, the vendor selects suppliers by accounting for various factors, including quality factors, delivery reliability, and the reduction of the return percentage of raw materials in the developed mathematical model. For this purpose, this paper applies the Bayesian best worst approach (BWM), as one of the multi criteria decision making (MCDM) techniques, for ranking these criteria and the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the proposed model. Since the developed model is a non-differentiable, non-convex, and mixed-integer function, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study with actual data collected from the electronic supply chain. [ABSTRACT FROM AUTHOR]

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system. The proposed model simultaneously focuses on green and regular production methods with an optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, cycles do not depend on each other, and consequently, each model parameter can be adjusted to be responsive to the dynamic nature of demand rate and/or price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. This paper accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production, the lower the total amount of emissions generated by the system, i.e., the system becomes more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the minimum total cost per unit time when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower in the first cycle (subsequent cycles) than that of the existing literature, i.e., 33.59% (16.13%) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. In addition, the system earns further revenue by utilizing a mixed transportation policy that combines the Truck Load (TL) and Less than Truck Load (LTL) services. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights. [ABSTRACT FROM AUTHOR]

Due to high real estate costs in urban areas, shop floor space is scarce in most brick‐and‐mortar stores. Maneuvering newly arrived merchandise through narrow aisles during shelf replenishment is time‐consuming for the sales staff and impedes customers. Therefore, many retail chains nowadays aim for store‐friendly shipments (SFS). By mirroring the layout of a store in the buildup of its dedicated shipments, the need for a zigzag movement through the store when replenishing shelves can be avoided. On the negative side, however, additional effort arises in the distribution centers. A suitable warehousing system to assemble SFS without excessive effort is a pocket (or pouch or bag) sorter, where each item is put into its separate bag. These bags, filled with items, are automatically transported while hanging from an overhead conveyor and can be sorted into any sequence before being delivered to the workstations that build SFS. This article investigates the assembly of SFS with a pocket sorter and presents scheduling procedures to enhance the efficiency of this process for a given set of store orders. We demonstrate that, despite its notorious complexity, the problem can be solved by simple decision rules with good performance. In a case study, we show that this approach can dramatically reduce the completion times of store orders, resulting in savings of more than 60% of the total working hours compared to a simple real‐world policy. Another 30% of reduction can be obtained by standardized store layouts. [ABSTRACT FROM AUTHOR]