​Resilient supply chain network design under competition : a case study

This research, motivated by a real-life case study in a highly competitive automobile supply chain, experimentally studies the impact of disruption on the competitiveness of supply chains. The studied supply chain faces two major risks: disruption of suppliers and tough competition from competitors. Any disruption in upstream level of the supply chain leads to an inability to meet demand downstream and causes market share to be lost to the competitors. For such a setting, a resilient topology is redesigned that can recover from and react quickly to any disruptive incidents. To this aim, we speculate there are three policies that can be used to mitigate the disruption risk, namely keeping emergency stock at the retailers, reserving back-up capacity at the suppliers, and multiple-sourcing. The problem is addressed using a mixed integer non-linear model to find the most profitable network and mitigation policies. We design a piecewise linear method to solve the model. Based on the data extracted from an automotive supply chain, practical insights of the research are extracted in a controlled experiment. Our analysis suggests that implementing risk mitigation policies not only work to the advantage of the supply chain by sustaining and improving its market share but also benefit customers by stabilizing retail prices in the market. Using the case study, we analyze the contribution of each risk strategy in stabilizing the supply chain's profit, market share, and retail price. Our analysis reveals that downstream “emergency stock” is the most preferable risk mitigation strategy if suppliers are unreliable

Models for Flexible Supply Chain Network Design

Arguably Supply Chain Management (SCM) is one of the central problems in Operations Research and Management Science (OR/MS). Supply Chain Network Design (SCND) is one of the most crucial strategic problems in the context of SCM. SCND involves decisions on the number, location, and capacity, of production/distribution facilities of a manufacturing company and/or its suppliers operating in an uncertain environment. Specifically, in the automotive industry, manufacturing companies constantly need to examine and improve their supply chain strategies due to uncertainty in the parameters that impact the design of supply chains. The rise of the Asian markets, introduction of new technologies (hybrid and electric cars), fluctuations in exchange rates, and volatile fuel costs are a few examples of these uncertainties. Therefore, our goal in this dissertation is to investigate the need for accurate quantitative decision support methods for decision makers and to show different applications of OR/MS models in the SCND realm. In the first technical chapter of the dissertation, we proposed a framework that enables the decision makers to systematically incorporate uncertainty in their designs, plan for many plausible future scenarios, and assess the quality of service and robustness of their decisions. Further, we discuss the details of the implementation of our framework for a case study in the automotive industry. Our analysis related to the uncertainty quantification, and network's design performance illustrates the benefits of using our framework in different settings of uncertainty. Although this chapter is focused on our case study in the automotive industry, it can be generalized to the SCND problem in any industry. We have outline the shortcomings of the current literature in incorporating the correlation among design parameters of the supply chains in the second technical chapter. In this chapter, we relax the traditional assumption of knowing the distribution of the uncertain parameters. We develop a methodology based on Distributionally Robust Optimization (DRO) with marginal uncertainty sets to incorporate the correlation among uncertain parameters into the designing process. Further, we propose a delayed generation constraint algorithm to solve the NP-hard correlated model in significantly less time than that required by commercial solvers. Further, we show that the price of ignoring this correlation in the parameters increases when we have less information about the uncertain parameters and that the correlated model gives higher profit when exchange rates are high compared to the stochastic model (with the independence assumption). We extended our models in previous chapters by presenting capacity options as a mechanism to hedge against uncertainty in the input parameters. The concept of capacity options similar to financial options constitute the right, but not the obligation, to buy more commodities from suppliers with a predetermined price, if necessary. In capital-intensive industries like the automotive industry, the lost capital investment for excess capacity and the opportunity costs of underutilized capacity have been important drivers for improving flexibility in supply contracts. Our proposed mechanism for high tooling cost parts decreases the total costs of the SCND and creates flexibility within the structure of the designed SCNs. Moreover, we draw several insights from our numerical analyses and discuss the possibility of price negotiations between suppliers and manufacturers over the hedging fixed costs and variable costs. Overall, the findings from this dissertation contribute to improve the flexibility, reliability, and robustness of the SCNs for a wide-ranging set of industries.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145819/1/nsalehi_1.pd

Modeling Multilevel Supply Chain Systems to Optimize Order Quantities and Order Points Through Mathematical Models, Discrete Event simulation and Physical Simulations

Managing supply chains in today\u27s distributed manufacturing environment has become more complex. To remain competitive in today\u27s global marketplace, organizations must streamline their supply chains. The practice of coordinating the design, procurement, flow of goods, services, information and finances, from raw material flows to parts supplier to manufacturer to distributor to retailer and finally to consumer requires synchronized planning and execution. Efficient and effective supply chain management assists an organization in getting the right goods and services to the place needed at the right time, in the proper quantity and at acceptable cost. Managing this process involves developing and overseeing relationships with suppliers and customers, controlling inventory, and forecasting demand, all requiring constant feedback from every link in the chain. Base Stock Model and (Q, r) models are applied to three tier single-product supply chain to calculate order quantities and reorder point at various locations within the supply chain. Two physical simulations are designed to study the above supply chain. One of these simulations is specifically designed to validate the results from Base Stock model. A computer based discrete event simulation model is created to study the three tier supply chain and to validate the results of the Base Stock model. Results from these mathematical models, physical simulation models and computer based simulation model are compared. In addition, the physical simulation model studies the impact of lean implementation through various performance metrics and the results demonstrate the power of physical simulations as a pedagogical tool for training. Contribution of present work in understanding the supply chain integration is discussed and future research topics are presented

Book of abstracts of the ICIEOM-CIO-IIIE International Conference 2015

BOOK OF ABSTRACTS OF THE ICIEOM-CIO-IIIE INTERNATIONAL CONFERENCE 2015: ENGINEERING SYSTEMS AND NETWORKS: The way ahead for industrial engineering and operations managemen

Production Engineering and Management

It is our pleasure to introduce the 8th edition of the International Conference on Production Engineering and anagement (PEM), an event that is the result of the joint effort of the OWL University of Applied Sciences and the University of Trieste. The conference has been established as an annual meeting under the Double Degree Master Program “Production Engineering and Management” by the two partner universities. This year the conference is hosted at the university campus in Lemgo, Germany. The main goal of the conference is to offer students, researchers and professionals in Germany, Italy and abroad, an opportunity to meet and exchange information, discuss experiences, specific practices and technical solutions for planning, design, and management of manufacturing and service systems and processes. As always, the conference is a platform aimed at presenting research projects, introducing young academics to the tradition of symposiums and promoting the exchange of ideas between the industry and the academy. This year’s special focus is on Supply Chain Design and Management in the context of Industry 4.0, which are currently major topics of discussion among experts and professionals. In fact, the features and problems of Industry 4.0 have been widely discussed in the last editions of the PEM conference, in which sustainability and efficiency also emerged as key factors. With the further study and development of Direct Digital Manufacturing technologies in connection with new Management Practices and Supply Chain Designs, the 8th edition of the PEM conference aims to offer new and interesting scientific contributions. The conference program includes 25 speeches organized in seven sessions. Two are specifically dedicated to “Direct Digital Manufacturing in the context of Industry 4.0”. The other sessions are covering areas of great interest and importance to the participants of the conference, which are related to the main focus: “Supply Chai n Design and Management”, “Industrial Engineering and Lean Management”, “Wood Processing Technologies and Furniture Production”, and “Management Practices and Methodologies”. The proceedings of the conference include the articles submitted and accepted after a careful double-blind refereeing process