Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

The strategic integration of agile and lean supply

Lean supply is closely associated with enabling flow and the elimination of wasteful variation within the supply chain. However, lean operations depend on level scheduling and the growing need to accommodate variety and demand uncertainty has resulted in the emergence of the concept of agility. This paper explores the role of inventory and capacity in accommodating such variation and identifies how TRIZ separation principles and TOC tools may be combined in the integrated development of responsive and efficient supply chains. A detailed apparel industry case study is used to illustrate the application of these concepts and tools

Global supply chains of high value low volume products

Imperial Users onl

Multiobjective optimization as a decision aid for managing build-to-order supply chains

This paper provides an overview of multiobjective optimization (MOO) as a decision aid in build-to-order supply chains (BTO-SC). The main features of BTO-SCs are discussed along with capabilities of MOO to enhance decision making at different points along the chain. Key decision points across a typical BTO-SC are identified and potential applications of MOO are discussed. A sample application is presented and future avenues for further research highlighted

System Dynamics Simulation to Test Operational Policies in the Milk-Cheese Supply Chain Case study: Piar Municipality, Bolivar State, Venezuela.

With the purpose of detecting the impact that variations of demand cause in the milk-cheese supply chain, and determining how the operational policies of capacity, inventories or labor force can mitigate this impact, a system dynamics simulation model has been designed based on a survey conducted on a sample of cheese manufacturers and their links with milk farms, transportation companies and cheese distributors. This supply chain will be consolidated when a milk center that will collect the raw milk is completed. From this center, and after adequate treatment, milk will be distributed to the different cheese manufacturers in the supply chain. Managing adequately the milk-cheese supply chain represents an important challenge due to the short life of these products. Although this study was done in a region in Latin America, its results can be applicable to food supply chains by introducing some modifications. The milk-cheese supply chain in this case study contemplates three milk producers, one milk center, five cheese producers and several distributing agents. These companies operate individually under normal conditions, but they have understood that their integration in a supply chain improves the competitiveness of all its members. That is to say, the sum is greater than the parts. For its initial design a simulation software model is used in which the resources of the supply chain are optimized. Later the product of this optimization facilitates some initial values to be used in the system dynamics model in which causeeffect or influence relationships have been previously established considering the most representative variables. Finally, changes in operational policies that can reduce the level of pending orders in the supply chain are tested using other simulation software. The main contribution of this research is that it can serve as support or contribute to reduce the uncertainty in the decision making process of the supply chain management due to the speed with which individual or combined policies can be analyzed. In response to a variation of demand the most adequate policy may be selected and that can be done before the policy is implemented

Supply chain uncertainty:a review and theoretical foundation for future research

Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)