“The costs of demand variability can put you out of business.” That blunt assessment, recently offered to us by the director of sales and operations planning at a Fortune 500 company, reflects what managers already know: Peaks in demand can drive high overtime costs, stockouts, and lost sales, while slowdowns leave capacity idle and increase excess inventory. The impact on customer service levels — not to mention the bottom line — can be significant. But how can companies best manage this variability, especially when deciding which potential new customers to target?
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Consider a manufacturing company that is expanding its capacity to produce an additional 20 units each month and is pursuing two potential new customers for that increased output. Both prospects have an average demand of 20 units per month, but prospect A’s demand is stable, while prospect B’s is highly variable. Prospect A strikes executives as an ideal customer, because it has stable demand that exactly matches the company’s proposed capacity expansion. But a closer look at the aggregate demand patterns of the manufacturer’s entire customer base reveals that B is superior: It needs more product when other customers in the company’s portfolio need less and thus smooths out overall demand. (See “Looking at Demand Across a Customer Portfolio.”)
This simplified example reflects a more integrated way to analyze customers — one that considers not only the absolute increase in sales or the variability of the new customer’s demand, but also the aggregate variability of the entire portfolio, with the new customer included. We recently analyzed this issue using a large database of supply chain network relationships.1 Our findings show that a portfolio approach can help companies effectively manage their aggregate demand variability. The portfolio approach works upstream as well, helping companies understand how their demand affects the aggregate demand variability of their suppliers. Companies can work with their suppliers to take steps to reduce variability by changing order schedules, adjusting inventory levels, or taking other measures.
1. N. Osadchiy, W. Schmidt, and J. Wu, “The Bullwhip Effect in Supply Networks,” Management Science, forthcoming.
2. R.P. Rumelt, “Strategy, Structure, and Economic Performance” (Cambridge, Massachusetts: Harvard University Press, 1974), xiv, 235; and K. Palepu, “Diversification Strategy, Profit Performance and the Entropy Measure,” Strategic Management Journal 6, no. 3 (July-September 1985): 239-255.
3. W.F. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk,” Journal of Finance 19, no. 3 (September 1964): 425-442.
4. H.L. Lee, V. Padmanabhan, and S. Whang, “Information Distortion in a Supply Chain: The Bullwhip Effect,” Management Science 43, no. 4 (April 1997): 546-558.