This study develops an SCM educational material which aims at training SCM professionals, and also focuses on effectiveness verification of this material which provides an opportunity for understandings of supply chain trade-off problems. This SCM educational material provides learning opportunities through a virtual experience which a computer simulation game brings. This simulator is a web-based program that generates a virtual environment of supply chain. It also consists of rules which regulate the decision-making of learners. Learners play as team, and each one is responsible for a supply chain related to each department; sales, logistics, production, and purchase. They virtually experience cooperative decision-making process in supply chain. While they resolve trade-off problems derived from inter-department conflicts of interests, they can gain knowledge of SCM through this learning experience. Test experiments on undergraduate are conducted to verify the existence of a learning effects and utility of this educational material.

It becomes difficult to improve efficiency of production planning process due to the uncertainty and fluidity of production environment. This paper analyses an actual planning process in an automotive parts manufacturer and finds the following 3 problems. Those are the monthly daily production plan is made based on the informal notice that is not fixed, the inventory levels are decided by the planner instead of the manager, and the order related information is not used effectively in the order fluctuation analysis. The problems are solved from the point of Kaizen approach, and 6 tactics involving management and the information are proposed; shortening of lead time, order analysis, informal notice and fixed order, inventory planning, response to order valuation, and role allotment. By applying these tactics to the manufacturer’s situation, 3 improvements are proposed and validated. Some improvement effects are verified by applying the actual data, and availability of improvement ideas as an example of new production planning is confirmed.

Our intuitive predictions and judgments under uncertainty are often mediated by judgmental heuristics that sometimes lead to biases. To eliminate or reduce such biases in decision making, which is referred to as “debiasing,” a number of studies have suggested and examined various kinds of debiasing techniques. Two issues are considered: (1) To review the current research on debiasing and the effectiveness of huge amounts of debiasing techniques for each heuristics, and (2) To investigate the effect of three debiasing methods (education/training, detecting the reason of error, and involvement) on three heuristics (base rate neglect, illusory correlation, and conjunction–disjunction bias). The results identified not only the most effective debiasing technique among three methods, but also which specific heuristic was affected most strongly by each debiasing technique. The findings are discussed in terms of contingency perspective in decision aid, including debiasing and training program in organizations.

I discuss Portfolio optimization problem for individual investors with relatively small funds, in this study. The stock’s smallest round lot can prevent such investors from trading freely. Then, To formulate models reflected the smallest round lot, I describe the models applied stochastic network and Integer programming. I introduce two models adopted Variance and Conditional Value-at-Risk (C-VaR) as measure of risk, I demonstrate these models can be solved within available time in practical use by using optimization package. The performance of the models in simulation based on historical data is inspected, I show that the model applied Variance as risk measure is superior to in stock market downturn and the model applied C-VaR is superior to in its upturn.