Supply Chain Management and Management Science: A Successful Marriage

The last century has witnessed extant studies on the applications of Management Science (MS) to a diverse set of Supply Chain Management (SCM) issues. This paper provides an overview of the contribution of MS within SCM. A framework is developed in this paper with a sampling of MS contributions to major SCM dimensions. Future research directions are presented

Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the predecided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis

Determining Optimal Lot Size, Reorder Point, and Quality Features for a Food Item in a Cold Warehouse: Data-Driven Optimization Approach

We propose a nonlinear optimization model for determining the optimum lot size and reorder point for a food item distributed through a cold warehouse as well as the optimum quality features, namely temperature, humidity, packaging type, and level of environmental conditions. The item's quality is estimated based on the features mentioned earlier, and then it is used as a constraint in the optimization process. An assumption was made that the inventory is managed under a continuous review policy and the warehouse has limited space. The model seeks to minimize the annual total cost of managing the warehouse. The model will be a nonlinear mixed programming one, which is solved by Pyomo as a leading library in Python language programming. Numerical examples are used to demonstrate the use of the model and, through sensitivity analysis, develop insights into the operation of cold warehouses. This sensitive analysis opens the doors to managerial insight from which managers and policymakers can highly benefit.Comment: 12 pages, 5 figure

Role of Optimal Production Plan at the Focal Firm in Optimization of the Supply Chain

Supply chain management and optimization is a critical aspect of modern enterprises and an expanding area of research. Modeling and optimization are the traditional tools of supply chain management. The techniques have been used by many companies for planning, manufacturing, and other decision areas in supply chains. Current study is motivated by the fact that optimization studies in supply chain management have mostly considered network optimization. Supply chain management however, requires alignment between the supply chain partners at the tactical level. As a first step towards achieving this goal, current study presents a model that incorporates the activity level planning at the focal firm in a supply chain. This paper presents a new mixed integer programming model that incorporates optimization of production planning at the focal firm while optimizing the strategic alignment of the supply chain entities. The model represents a four step, multi-echelon supply chain including supplier, warehouse, manufacturer, and retailer. The manufacturer in this network represents the focal firm. This model is an attempt to integrate the production planning decisions in the network optimization decisions

A multi-objective possibilistic programming approach for locating distribution centers and allocating customers demands in supply chains

In this paper, we present a multi-objective possibilistic programming model to locate distribution centers (DCs) and allocate customers' demands in a supply chain network design (SCND) problem. The SCND problem deals with determining locations of facilities (DCs and/or plants), and also shipment quantities between each two consecutive tier of the supply chain. The primary objective of this study is to consider different risk factors which are involved in both locating DCs and shipping products as an objective function. The risk consists of various components: the risks related to each potential DC location, the risk associated with each arc connecting a plant to a DC and the risk of shipment from a DC to a customer. The proposed method of this paper considers the risk phenomenon in fuzzy forms to handle the uncertainties inherent in these factors. A possibilistic programming approach is proposed to solve the resulted multi-objective problem and a numerical example for three levels of possibility is conducted to analyze the model

A Pre Review On The Interaction Component In Formulating Sustainable Supply Chain Management

The purpose of this paper is to explore the basic interaction components in formulating sustainable supply chain management. Sustainable Supply Chain is one of the advance technology approaches that compete with high productivity and less cost. In order, to achieve best performance on sustainability, the process of Supply Chain Management must accomplish and through with the management of industry. Process of Supply Chain Management includes five elements, which are; planning, procurement, production, delivery and response level. All the processes have their own interaction on manufacturing. The interaction framework in this paper can be used by manufacturers and researchers to gain more understanding on process of sustainable supply chain management

Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

Supply Chain Intelligence

This chapter provides on overall picture of business intelligence (BI) and supply chain analytics (SCA) as a means to support supply chain management (SCM) and decision-making. Based on the literature review, we clarify the needs of BI and performance measurement in the SCM sphere, and discuss its potential to enhance decision-making in strategic, tactical and operational levels. We also make a closer look in to SCA in different areas and functions of SCM. Our findings indicate that the main challenge for harnessing the full potential of SCA is the lack of holistic and integrated BI approaches that originates from the fact that each functional area is using its own IT applications without necessary integration in to the company’s overall BI system. Following this examination, we construct a holistic framework that illustrates how an integrated, managerially planned BI system can be developed. Finally, we discuss the main competency requirements, as well as the challenges still prohibiting the great majority of firms from building smart and comprehensive BI systems for SCM.fi=vertaisarvioitu|en=peerReviewed