Supply chain collaboration

In the past, research in operations management focused on single-firm analysis. Its goal was to provide managers in practice with suitable tools to improve the performance of their firm by calculating optimal inventory quantities, among others. Nowadays, business decisions are dominated by the globalization of markets and increased competition among firms. Further, more and more products reach the customer through supply chains that are composed of independent firms. Following these trends, research in operations management has shifted its focus from single-firm analysis to multi-firm analysis, in particular to improving the efficiency and performance of supply chains under decentralized control. The main characteristics of such chains are that the firms in the chain are independent actors who try to optimize their individual objectives, and that the decisions taken by a firm do also affect the performance of the other parties in the supply chain. These interactions among firms’ decisions ask for alignment and coordination of actions. Therefore, game theory, the study of situations of cooperation or conflict among heterogenous actors, is very well suited to deal with these interactions. This has been recognized by researchers in the field, since there are an ever increasing number of papers that applies tools, methods and models from game theory to supply chain problems

Supply chain management of blood products: a literature review.

This paper presents a review of the literature on inventory and supply chain management of blood products. First, we identify different perspectives on approaches to classifying the existing material. Each perspective is presented as a table in which the classification is displayed. The classification choices are exemplified through the citation of key references or by expounding the features of the perspective. The main contribution of this review is to facilitate the tracing of published work in relevant fields of interest, as well as identifying trends and indicating which areas should be subject to future research.OR in health services; Supply chain management; Inventory; Blood products; Literature review;

Mitigating the Bullwhip Effect and Enhancing Supply Chain Performance through Demand Information Sharing: An ARENA Simulation Study

The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes.   Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing. Keywords: Supply Chain Management, Bullwhip effect, Inventory management, ARENA simulation, Information sharing, forecasting technique, Demand variability. DOI: 10.7176/JESD/14-14-07 Publication date:August 31st 202

Supply Chain Modifications to Improve Additive Manufacturing

Additive manufacturing (AM) offers unique production characteristics which among those, toollessness and production of complex geometries are potentially significant to operations efficiency. Previous research has illustrated the potential sufficiency of this technology to affect the supply chains‟ arrangements and enabling decentralized production configurations. While, one of the important advantages of AM enabled distributed production is the increased flexibility, which is a necessity in today‟s competitive and ever changing global supply chains, number of obstacles have kept this method from wide implementation. In this paper, we study the possible supply chain modifications to decrease the cost of an AM-enabled decentralized production system. In other words, we perform a cost-benefit analysis on various AM supply chain strategies in a spare parts context to realize the independent operational factors affecting the implementation cost of additive manufacturing. Moreover, we analyze the ways to adapt the supply chain management to enable full potential of AM considering the present technology.Mechanical Engineerin

Carbon emissions Inventory Games

Carbon emissions reduction has been the center of attention in many organizations during the past few decades. Many international entities developed rules and regulations to monitor and control carbon emissions especially under supply chain context. Furthermore, researchers investigated techniques and methods on how reduce carbon emissions under operational adjustment which can be done by cooperation or coordination. The main contribution of this thesis is to measure to what extend cooperation can contribute to carbon emissions. Many research addresses the advantage of cooperation in reducing cost. However, there isn't a plenty of research addressing the effect of cooperation on carbon emissions when the incentive of the cooperation is to reduce cost only. The aim of this thesis is to show if joint replenishment leads to a reduction in carbon emissions and this to be considered as an advantage to be added to cooperation. Moreover, if a savings occur from cooperation, the aim will be to address the issue of allocating the savings among parties engaged in the coalition. The thesis methodology adapted and extended cooperative EOQ model and basic inventory model (EOQ) in order to formulate and build an adjusted model to measure carbon emissions. The adjusted model will be used to calculate carbon emission in centralized and decentralized systems with incentives to reduce cost and no incentives to reduce emission. The calculation shall yield the optimum ordering quantity which in turn yields the savings between the two systems. Finally core allocation principles will be leveraged to propose a fair allocation of savings. Furthermore, the model will be extended to consider some regulation and different environments to which it will cater for carbon-tax regulation and full Truckload system contexts. Findings indicate that applying inventory game theory leads to a reduction of carbon emissions along with cost. Additionally, the total carbon emissions in centralized system will always be less then decentralized system under all conditions. Moreover, the proposed proportional allocation which was proven to be a core allocation model will be based on the frequency of ordering and the amount of holding emissions

Improving the coordination in the humanitarian supply chain: exploring the role of options contract

The uncertainty associated with the location, severity and timing of disaster makes it difficult for the humanitarian organization (HO) to predict demand for the aid material and thereby making the relief material procurement even more challenging. This research explores whether options contract can be used as a mechanism to aid the HO in making procurement of relief material less challenging by addressing two main issues: inventory risk for buyers and over-production risk for suppliers. Furthermore, a contracting mechanism is designed to achieve coordination between the HO and aid material suppliers in the humanitarian supply chain through optimal pricing. The options contract is modelled as a stylized version of the newsvendor problem that allows the HO to adjust their order quantity after placing the initial order at the beginning of the planning horizon. This flexibility helps to mitigate the risk of both overstocking and understocking for the HO as well as the risk of overproduction for the supplier. Our results indicate that the optimal values for decision parameters are not “point estimates” but a range of prices, which can facilitate negotiation between the two parties for appropriate selection of contract parameters under an options contract. The results imply that options contract can aid in the decentralized approach of fixing the prices between the HO and the supplier, which in turn would help in achieving systemic coordination

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly