Abstract
This study proposes a production risk management model based on knowledge utilized forecasting. The demand uncertainty of business has been a serious problem for manufactures. As the market globalization has increased the scale and speed of their business, it becomes too big and complex to manage those uncertainty business risks. There are often the cases that misleading demand forecast cause not only business opportunity loss because of stock shortage but also serious overstock at the end of product lifecycle. It is important for manufactures to obtain a reliable production planning that manage their business risk between forecast and sales result. This study proposes a dynamic reproduction decision support system by measuring reproduction risk based on knowledge utilized product’s lifecycle sales forecast. It gives us proper timing and volume for reproduction even just after products release. As a case study, this model is applied to Japanese book publishing planning and verified how it works. The results showed the twice efficient performance as much as present position under the same risk volume.
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References
Tanaka K, Miyata H, Takechi S. Knowledge based forecasting for non-linear trend products. In: Proceedings of the ISPE international conference concurrent engineering; 2009.
Miyata H, Tanaka K, Sato I, Nichi Y. Reprint recommendation, processing system outputs specific number of reprint candidate book, JP2008282269-A; 2008.
Mentzer JT, Bienstock CC. Sales forecasting, management. Thousand Oaks, CA: Sage; 1996.
Agrawal D, Schorling C. Market share forecasting: an empirical comparison of artificial neural networks and multinomial logit model. J Retail. 1996;72(4):383–407.
Trappey CV, Wu H-Y. An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles. Adv Eng Inform. 2008;22(4):421–30.
Chu C-W, Zhang GP. A comparative study of linear and nonlinear models for aggregate retail sales forecasting, Int. J Prod Econ. 2003;86:217–31.
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Tanaka, K., Zhang, J. (2010). Managing Demand Uncertainty with Knowledge Utilized Forecasting. In: Pokojski, J., Fukuda, S., Salwiński, J. (eds) New World Situation: New Directions in Concurrent Engineering. Advanced Concurrent Engineering. Springer, London. https://doi.org/10.1007/978-0-85729-024-3_7
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DOI: https://doi.org/10.1007/978-0-85729-024-3_7
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