Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Taming of Whip-Lash Effect in Forecasting for Automotive Spare Parts Industry using Minitab and Excel Spread Sheet

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
A. Naga Phaneendra, V. Diwakar Reddy, G. Krishnaiah

Naga A Phaneendra, Diwakar V Reddy and G Krishnaiah. Taming of Whip-Lash Effect in Forecasting for Automotive Spare Parts Industry using Minitab and Excel Spread Sheet. International Journal of Computer Applications 161(9):12-17, March 2017. BibTeX

	author = {A. Naga Phaneendra and V. Diwakar Reddy and G. Krishnaiah},
	title = {Taming of Whip-Lash Effect in Forecasting for Automotive Spare Parts Industry using Minitab and Excel Spread Sheet},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {9},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {12-17},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017913210},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper investigates the proliferation and exaggeration of order variances (i.e., Whip-Lash Effect) in supply chain. The supply chain engages manufacturer, distributor and retailer for goods transactions with (or) without sharing reliable information which leads to backorders (or) over orders. The paper analyses the orders placed by a distributor to manufacturer in a stationary demand scenario. The previous demand data of twenty four months is collected from the automotive spare parts industry and forecasted for the next two years using Triple exponential smoothing model namely Holts winters model. The smoothing parameters that influence the forecast data are analyzed. Finally the optimum smoothing parameter that minimizes the Whip-Lash Effect is predicted in Minitab software by considering various levels of alpha, beta and gamma values and the results are tabulated in excel spread sheets.


  1. Baganha, M.P., Cohen, M.A., 1998. The stabilizing effect of inventory in supply chains. Operations Research 46 (3), 572–583.
  2. Cooke, J.A., 1993. The $30 Billion promise. Traffic Management 32, 57–59.
  3. Forrester, J., 1958. Industrial dynamics, a major breakthrough for decision makers. Harvard Business Review 36, 37–66 (July and August).
  4. Forrester, J., 1961. Industrial Dynamics. MIT Press, Cambridge, MA
  5. Holt, C.C., Modigliani, F., Muth, J., Simon, H.A., 1960. Planning Production, Inventories and Work Force. Prentice-Hall, Englewood Cliffs, New York
  6. Holmstro¨m, J., 1997. Product range management: A case study of supply chain operations in the European grocery industry. Supply Chain Management 2 (3), 107–115.
  7. Lee, H.L., Padmanabhan, V., Whang, S., 1997a. The Bullwhip effect in supply chains Sloan Management Review 38 (3), 93–102.
  8. Magee, J.F., 1956. Guides to inventory control (Part II). Harvard Business Review, 106– 116.
  9. Magee, J.F., Boodman, D., 1967. Production Planning and Inventory Control, seconded. McGraw-Hill, New York.
  10. Ouyang, Y., 2007. The effect of information sharing on supply chain stability and the bullwhip effect. European Journal of Operational Research 182 (3), 1107–1121.
  11. Ouyang, Y., Daganzo, C.F., 2006a. Characterization of the bullwhip effect in linear, time-invariant supply chains: some formulae and tests. Management Science 52 (10), 1544– 1556.
  12. Ouyang, Y., Daganzo, C.F., 2006b. Counteracting the bullwhip effect with decentralized negotiations and advance demand information. Physica A 363 (1), 14–23.
  13. Ouyang, Y., Daganzo, C.F., 2009. Robust stability analysis of decentralized supply chains. In: Kempf, K., et al. (Eds.), Production Planning Handbook, Springer’s International Series in Operations Research and Management Science, Springer, in press.
  14. Ouyang, Y., Daganzo, C.F., 2008. Robust tests for the bullwhip effect in supply chains with stochastic dynamics. European Journal of Operational Research 185 (1), 340–353.
  15. Schmenner, R.W., 2001. Looking ahead by looking back: Swift, even flow in the history of manufacturing. Production and Operations Management 10 (1), 87–95.
  16. Sterman, J., 1989. Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment.Management Science 35 (3), 321–339.


Supply chain Management, Whip-Lash Effect, Minitab,