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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. [ABSTRACT FROM AUTHOR]

In the area of materials stock-flow analysis, the cumulative graph is an effective method to visualize and analyze flow patterns and embedded problems, leading to identifying ways to improve material flows in production and service systems. However, since the conventional approach of cumulative flow graphs assumes a tandem flow shoptype one-way flow, the graph cannot be used in complicated flow patterns where each stage is comprised of parallel processes and materials flow back-and-forth between these stages. This paper proposes a method to apply a cumulative flow graph to the case of such parallel flow pattern, where a single stage is comprised of a main system and a subsystem, and materials repeatedly flow between these systems before finally flowing out from the process. For the purpose of identifying fundamental problems in the material flow, the method proposed focuses on depicting stock quantity in each system on the cumulative flow graph. The paper finally applies the method proposed to the actual case of a physical distribution system comprised of the main and secondary warehouses, and the validity of the method proposed is discussed and clarified using practical examples. [ABSTRACT FROM AUTHOR]