Demand Forecasting for Seasonal Demand Patterns: Case Study of a Pharmaceutical Company

Autor/innen

  • Md. Mamunur Rahman Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
  • Sudipa Sarker Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

Schlagwörter:

Time series forecasting, seasonal demand, tracking signal, non-linear optimization, smoothing constants

Abstract

In this paper the authors have developed a heuristic model that addresses demand forecasting for products those follow seasonal patterns. The model is checked against various renowned forecasting methods by comparing forecast errors. The proposed heuristic model is found to give better results than the Winters’ and other exponential models. Non-linear optimization is used to choose the values of smoothing parameters rather than depending on human judgment or experience. This paper also focuses the role of control chart, and tracking signal to analyze biases and trend of forecast errors that justifies the appropriateness of the applied forecasting model.

Autor/innen-Biografien

  • Md. Mamunur Rahman, Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
    Lecturer, Department of Mechancial and Production Engineering, Ahsanullah Univeristy of Science and Technology, Dhaka 1208, Bangladesh. 
  • Sudipa Sarker, Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
    Assistant Professor, Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh.Currently Ph.D candidate at KTH Royal Institute of Technology

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Veröffentlicht

2015-01-05

Ausgabe

Bd. 4 Nr. 3 (2014)

Rubrik

Research Articles

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