Published papers

The purpose of this paper is to investigate a supplier-retailer supply chain that experiences disruptions in supplier during the planning horizon. There are multiple options to supply a raw material, to manufacture or assemble the product, and to transport the product to the customer. While determine what suppliers, parts, processes, and transportation modes to select at each stage in the supply chain, options disruption must be considered. In this paper, we show that changes to the original plan induced by a disruption may impose considerable deviation costs throughout the system. When the production plan and the supply chain coordination scheme are designed in a static manner, as is most often the case, both will have to be adjusted under a disruption scenario. Using dynamic policies, we derive conditions under which the supply chain can be coordinated so that the maximum potential profit is realized.

In this paper we deal with a model for optimizing the costs of an integrated production, distribution and package reprocessing problem. As a contribution to integrated production and distribution model, a reverse material Žow of returnable packages material (RPM) Žow and a retreating process of the RPM are incorporated into the model and extended to a closed-loop supply chain. A linear programming mathematical model is constructed for the beverage industry, although the model could also be applied to other industry that includes a RPM feedback Žow. The optimization model provides a number of decision variables such as production quantity and time, RPM ratio, production line allocation transportation routing, RPM retreating scheduling. The proposed model allows to introduce and change multiple parameters in each of the different parts of the closed loop supply chain. Finally, a numerical example involving a bottling industry of standardized packaging is solved utilizing the optimization software Gurobi, and a minimal cost solution is presented.

Traditional demand forecasting methods are categorized as scientific methods (e.g., time series analysis or regression analysis) or methods based on experience and tacit knowledge (e.g., delphi method or market research). Recently, research that combines these forecasting methods has become a hot topic and categorized as a demand forecasting method based on the prediction market. It is known that the prediction market was able to accurately forecast the vote ratio for the US presidential election. In the field of supply chain management, the research is applied to forecast the future demand of products. In this study, we propose a demand forecasting method that uses a voting system based on a collective intelligence mechanism. We examine forecasting accuracy using real business data from a five-month period. According to statistical tests, we show that the forecasting method we propose performs more accurately than the existing method used in our company.

In the printer manufacturing industry, it is known that business impact is especially large because of the due date penalty or opportunity loss (including sales loss of toner cartridge and drum cartridge) when an inventory shortage occurs. Furthermore, in this industry, due to occurrence of sudden and large demand, it is not clear how to analyze demand distribution from historical sales data and how much inventory should be kept. We reviewed various literature regarding the demand forecasting method for sudden and large demand, but we were unable to find a proper method, and therefore decided to develop a new method ourselves. In this research, we propose a new demand forecasting method based on extreme value distribution. In the method proposed, we first classify historical sales data, then we estimate the parameters of the extreme value distribution, and finally forecast future demand. Additionally, we tested our method using real business data. As a result, we found that the method proposed performs better than the current method being used by businesses and can avoid inventory shortages when sudden and large demand occurs.

Platform-based Product Development (PPD) approach is an effective way to achieve mass customization. The product platform configuration in PPD is to determine the number and composition of platforms for a product family. Different types of design strategy have often been found to satisfy product requirements, i.e., the matching-designed with higher platform development cost and lower customization cost, the over-designed or under-designed platforms with different performance in these two types of cost. Traditional research balances the cost trade-off within the design domain, and few studies include the procurement cost from suppliers. Involving the supplier selection requirements into product platform configuration was examined in this study with focus on module-based platforms. The proposed model contains development cost, sourcing cost, and customization cost, under given product architecture and given number of modules and associated options. We developed a solving algorithm applying the linearization method with Gurobi solver to solve the proposed nonlinear MIP (mixed integer programming) model. Numerical experiments are carried out and sensitivity analyses are performed to justify the proposed model. The results show that the optimal combination of suppliers and the optimal number of platforms depends on the given parameters, such as various cost parameters included in the model, product demand and lifetime. Sensitivity analyses show that the optimal number of product platforms will decrease as the development cost increases, while the number of platforms increases as the customization cost increases. The over-designed platform is more prevalent in the presence of high development cost and high customization cost. In contrast, the matching-designed platform is more suitable for low development cost and high customization cost.

Platform-based product development is a cost-efficient approach to satisfy wide range of customer preferences. One important problem is the product platform configuration, in which two types of platform configuration are widely used, either module selection or module integration. The platform configuration based on module selection provides a broader solution space of platform selection, while module integration facilitates product platform commonality to gain economic benefits. Up to date, most research focused on the platform configuration based on module selection with a given module set. In this paper, we propose a new model to determine the optimal platform configuration for an external product family, considering both module selection and integration. The demand for the external product family is uncertain and assumed to follow normal distribution. A hybrid methodology combining simulated annealing and variable neighbourhood search is proposed. A numerical examination is carried out for the purpose of evaluation. The results show that the proposed model can provide a practically good solution. Main contributions of this research are two folds, one is the balance point between module selection and module integration in the platform configuration and the other is that we take into account the operation cost of module acquisition to the platform configuration problem.