Demand forecasting and smoothing capacity planning for products with high random demand volatility

Abstract

For those products that are heavily competitive in the marketplace, demand volatility and unpredictability have been growing. This has resulted in a sizeable deviation in demand forecasts when using the traditional forecasting methods. Accordingly, this study aims to develop a real option approach-based forecasting model for predicting demand during the upcoming planning horizon for products with high random volatility on demand. The real option approach can effectively deal with the long-term trends and random variation involved in a given demand stochastic diffusion process. Additionally, this study proposes taking Monte Carlo simulation as a numerical method to solve the demand-forecasting model. Monte Carlo simulation not only can accurately approximate almost any type of stochastic processes, but also can competently handle the path-dependant relationship existing between successive demands. Subsequently, these demand forecasts are used to determine the provisioned smoothing capacity during the upcoming planning horizon. To this end, this study also proposes several effective and practical smoothing capacity-planning approaches in accordance with the specified production strategy. Based on a numerical example, the integrated planning approach can obtain a plausible result.

Keywords:

• Demand forecasting
• Capacity planning
• Smoothing capacity
• Real option approach

Acknowledgements

The authors would like to thank the National Science Council of the Republic of China for supporting this research under Contract No. NSC-94-2213-E-158-003.