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Demand Forecasting of the Short-Lifecycle Dairy Products

  • Rahul S. Mor
  • Swatantra Kumar Jaiswal
  • Sarbjit Singh
  • Arvind Bhardwaj
Chapter

Abstract

Predictions of future market demands for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. For business operations in dairy industry, the accuracy of the forecast is of crucial importance because of the volatile demand pattern, influenced by an environment of rapid and dynamic response. The current study aims to compare the forecasting models like moving average, regression, multiple regression, and the Holt–Winters model based on accuracy measures, applied to demand forecasting of a time series formed by a group of perishable dairy products in milk processing industry. Further, the metric analysis of various error-measuring techniques is also applied to select the least error-producing model for such products as a performance measure. Findings of the study will help dairy industry to achieve high order fill rate, good inventory control as well as high profits. However, the selection of these models depends upon the knowledge, availability of data, and context of forecasting.

Keywords

Demand forecasting Error measures Dairy industry Short-lifecycle products Seasonality Food processing 

Nomenclature

Ft

Forecasted demand for the period ‘t

Yt

Actual demand for the period ‘t

ui

Level factor for the period ‘t

vi

Trend factor for the period ‘t

Si

Seasonal factor for the period ‘t

α

Smoothing constant for demand or level

β

Smoothing constant for trend

γ

Smoothing constant for seasonality

MAD

Mean absolute deviation

MSE

Mean square error

RMSE

Root-mean-square error

MAPE

Mean absolute percentage error

M.A.

Moving average

H-W

Holt and Winters

Notes

Acknowledgements

The authors would like to thank all the key resource persons from dairy industry. Further, the authors would like to express their sincere gratitude for the remarks and recommendations made by anonymous reviewers and editor which radically improved the quality of this work.

Supplementary material

461165_1_En_6_MOESM1_ESM.xlsx (852 kb)
Supplementary material 1 (XLSX 852 kb)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rahul S. Mor
    • 1
  • Swatantra Kumar Jaiswal
    • 1
  • Sarbjit Singh
    • 1
  • Arvind Bhardwaj
    • 1
  1. 1.Department of Industrial & Production EngineeringNational Institute of TechnologyJalandharIndia

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