Not long ago, logistics executives at Procter & Gamble (P&G) examined the order patterns for one of their best-selling products, Pampers. Its sales at retail stores were fluctuating, but the variabilities were certainly not excessive. However, as they examined the distributors’ orders, the executives were surprised by the degree of variability. When they looked at P&G’s orders of materials to their suppliers, such as 3M, they discovered that the swings were even greater. At first glance, the variabilities did not make sense. While the consumers, in this case, the babies, consumed diapers at a steady rate, the demand order variabilities in the supply chain were amplified as they moved up the supply chain. P&G called this phenomenon the “bullwhip” effect. (In some industries, it is known as the “whiplash” or the “whipsaw” effect.)
When Hewlett-Packard (HP) executives examined the sales of one of its printers at a major reseller, they found that there were, as expected, some fluctuations over time. However, when they examined the orders from the reseller, they observed much bigger swings. Also, to their surprise, they discovered that the orders from the printer division to the company’s integrated circuit division had even greater fluctuations.
What happens when a supply chain is plagued with a bullwhip effect that distorts its demand information as it is transmitted up the chain? In the past, without being able to see the sales of its products at the distribution channel stage, HP had to rely on the sales orders from the resellers to make product forecasts, plan capacity, control inventory, and schedule production. Big variations in demand were a major problem for HP’s management. The common symptoms of such variations could be excessive inventory, poor product forecasts, insufficient or excessive capacities, poor customer service due to unavailable products or long backlogs, uncertain production planning (i.e., excessive revisions), and high costs for corrections, such as for expedited shipments and overtime. HP’s product division was a victim of order swings that were exaggerated by the resellers relative to their sales; it, in turn, created additional exaggerations of order swings to suppliers.
In the past few years, the Efficient Consumer Response (ECR) initiative has tried to redefine how the grocery supply chain should work.1 One motivation for the initiative was the excessive amount of inventory in the supply chain.
1. This initiative was engineered by Kurt Salmon Associates but propelled by executives from a group of innovative companies like Procter & Gamble and Campbell Soup Company. See: Kurt Salmon Associates, “ECR: Enhancing Consumer Value in the Grocery Industry (Washington, D.C.: report, January 1993); and F.A. Crawford, “ECR: A Mandate for Food Manufacturers?” Food Processing, volume 55, February 1994, pp. 34–42.
2. J.A. Cooke, “The $30 Billion Promise,” Traffic Management, volume 32, December 1993, pp. 57–59.
3. J. Sterman, “Modeling Managerial Behavior: Misperception of Feedback in a Dynamic Decision-Making Experiment,” Management Science, volume 35, number 3, 1989, pp. 321–339.
4. Sterman (1989); and P. Senge, The Fifth Discipline: The Art and Practice of the Learning Organization (New York: Doubleday/Currency, 1990).
5. For a theoretical treatment of this subject, see: H.L. Lee, P. Padmanabhan, and S. Whang, “Information Distortion in a Supply Chain: The Bullwhip Effect,” Management Science, 1997, forthcoming.
6. M. Millstein, “P&G to Restructure Logistics and Pricing,” Supermarket News, 27 June 1994, pp. 1, 49.
7. V. Carroll, H.L. Lee, and A.G. Rao, “Implications of Salesforce Productivity, Heterogeneity and Demotivation: A Navy Recruiter Case Study,” Management Science, volume 32, number 11, 1986, pp. 1371–1388.
8. Salmon (1993).
9. P. Sellers, “The Dumbest Marketing Ploy,” Fortune, volume 126, 5 October 1992, pp. 88–93.
10. P. Kotler, Marketing Management: Analysis, Planning, Implementation, and Control (Englewood Cliffs, New Jersey: Prentice Hall, 1997).
11. R.D. Buzzell, J.A. Quelch, and W.J. Salmon, “The Costly Bargain of Trade Promotion,” Harvard Business Review, volume 68, March–April 1990, pp. 141–148.
12. Sellers (1992).
14. Lee et al. (1997).
15. L. Lode, “The Role of Inventory in Delivery Time Competition,” Management Science, volume 38, number 2, 1992, pp. 182–197.
16. Personal communication with Hewlett-Packard.
17. K. Kelly, “Burned by Busy Signals: Why Motorola Ramped up Production Way Past Demand,” Business Week, 6 March 1995, p. 36.
18. Rory J. O’Connor, “Rumor Bolsters IBM Shares,” San Jose Mercury News, 8 October 1994, p. 9D.
19. M. Reid, “Change at the Check-Out,” The Economist, volume 334, 4 March 1995, pp. 3–18.
20. A. Clark and H. Scarf, “Optimal Policies for a Multi-Echelon Inventory Problem,” Management Science, volume 6, number 4, 1960, pp. 465–490.
21. E.K. Clemons and M. Row, “McKesson Drug Company — A Strategic Information System,” Journal of Management Information Systems, volume 5, Summer 1988, pp. 36–50.
22. Millstein (1994).
23. T. Smart, “Jack Welch’s Cyber-Czar,” Business Week, 5 August 1996, pp. 82–83.
24. G. Stern, “Retailers of P&G to Get New Plan on Bills, Shipment,” Wall Street Journal, 22 June 1994.
25. Reid (1995).
26. H.L. Richardson, “How Much Should You Outsource?,” Transportation and Distribution, volume 35, September 1994, pp. 61–62.
27. Z. Schiller, “Ed Artzt’s Elbow Grease Has P&G Shining,” Business Week, 10 October 1994, pp. 84–86.
28. R. Mathews, “CRP Moves Towards Reality,” Progressive Grocer, volume 73, July 1994, pp. 43–44.