Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterized by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic program that finds the optimal manufacturing technology for meeting each market’s demand, each operation’s optimal production quantity, and each selected technology’s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.This work was developed under an Accenture Open Innovation University [grant number I-01326] and was also partially supported by grant RTI2018-097580-B-I00 of the Ministry of Economy and Competitiveness of Spain.Peer ReviewedPostprint (published version

Research on the Comparison between the Different Policies by Service Level and Inventory Level Performance of Auto Parts in N.A.C.C. (North Automobile Components Company)

As after sales services become more and more popular, particularly preventive or corrective maintenance, the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers. The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs. The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain. This reuse is primarily driven by extending the economic life of goods, initially regarded as waste and therefore without added value, by transforming them into valuable spare parts that can be reused; secondly, for environmental or regulatory reasons, demanding responsibility for the treatment of products at the end of their life; and thirdly, to improve the availability of parts for maintenance, especially parts that the organization can no longer purchase or that are impacted by other issues. It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition. In this paper, we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts, identify the thresholds of inventory management policies, and finally, compare the different policies by service level and inventory level performance for the N.A.C.C. company

Modeling strategic customers using simulations - with examples from airline revenue management

AbstractA condition for airline revenue management is the possibility of identifying and differentiating customer segments (refer to Chiang et al. (2007) for a state of the art). Traditionally, customer differentiation has been realized by the time of request in days before departure as well as by restrictions connected to the tickets sold. Customer segments have been regarded to be fixed over time, based on myopic customer behavior. With the market transparency increased through the Internet as well as the rise of no-frills offers and flat-rates, customer behavior has changed during the last decades. Strategic customer behavior describes a tendency to remember previous buying experiences, adapt expectations and observe the market over longer periods of time before deciding on what (and whether) to buy. The empirical consequences of strategic customer behavior for traditional as well as state-of-the-art revenue management have been little examined. A major reason for this is that measuring the degree of strategic versus myopic tendencies of demand in real customers is difficult and expensive. In this paper, we formulate a mathematical model of strategic customer behavior including parameters defining the propensity to delay buying as well as learning and communicating. We test the empirical consequences of our model using a stochastic simulation, in which customers act as agents deciding whether and when to buy. Thereby, we provide first results on how different ways of strategic behavior affect the success of methods of revenue management, highlighting the possible weaknesses and strengths of approaches when confronted with strategic customers. Of course, strategic customer behavior is not limited to airline revenue management. Useful models of strategic behavior as implemented in the simulation can be applied to analyze a wide range of situations: conditions for this transfer as well as examples are provided as part of the outlook