In a time of shortening product life cycles, complex corporate joint ventures, and stiffening requirements for customer service, it is necessary to consider the complete scope of supply chain management, from supplier of raw materials, through factories and warehouses, to demand in a store for a finished product. Hewlett-Packard has developed a framework for addressing the uncertainty that plagues the performance of suppliers, the reliability of manufacturing and transportation processes, and the changing desires of customers. The author describes several cases in which entire product families have been reevaluated in a supply chain context. The methodology he presents should help others to manage their own supply chains more successfully.
1. Even JIT fanatics carry inventory of finished goods close to the customer and with suppliers. These stockpiles are small, though, because the manufacturing process has been tuned (1) to reduce uncertainty in the first place and (2) to recover quickly when something does happen. Paul Zipkin describes this as “pragmatic JIT.” See:
P. Zipkin, “Does Manufacturing Need a JIT Revolution?” Harvard Business Review, January–February 1991, pp. 40–50.
2. Lee and Billington describe a number of ways in which a firm’s supply chain can break down. This example corresponds to their Pitfall 11: Organizational Barriers. See:
H.L. Lee and C. Billington, “Managing Supply Chain Inventory: Pitfalls and Opportunities,” Sloan Management Review, Spring 1992, pp. 65–73.
3. IBM reduced its U.S. spare parts inventory investment by half a billion dollars — a 20 to 25 percent reduction — by introducing an analytical tool to set stocking levels. See:
M. Cohen et al., “Optimizer: IBM’s Multi-Echelon Inventory System for Managing Service Logistics,” Interfaces 20 (1990): 65–82.
4. I depict uncertainty in the system with the familiar “bell” curve of the normal distribution. Raw data, shown in the underlying histogram for the suppliers in Figure 2, can be readily summarized in statistical form as the mean and standard deviation. These quantities determine the shape of the curve. While I’ve shown each distribution in the example to be the same shape, in practice some will be wider (more variation, or a higher standard deviation), and some will be narrower.
5. The vulnerability of upstream suppliers to variability in customer orders is wonderfully captured in the “beer game.” This game casts players in the various roles of a beer distribution supply chain: retailer, distributor, wholesaler, and factory. The game is used at MIT to sensitize students to the importance of systems thinking. An ability to focus on system problems is critical to improving supply chain performance. See:
P. Senge, The Fifth Discipline (New York: Doubleday/Currency, 1990).
6. Makridakis and Wheelwright provide a good introduction to the field of forecasting. See:
S. Makridis and S. Wheelwright, “Forecasting: Framework and Overview,” TIMS Studies in the Management Sciences, ed. S. Makridakis and S. Wheelwright (Amsterdam: North Holland, 1979), pp. 1–15.
7. There should be more to negotiations with suppliers than price. While important, there are other measures of supplier performance that can have a greater impact on overall profitability than just a few pennies on the unit price. Hewlett-Packard’s Corporate Procurement team posts, in public areas, the names of the company’s best and worst suppliers as measured by delivery performance. Poor performers try to remove their names from that public record, while the top suppliers strive to maintain — or improve — their place on the list. For such a simple idea, this has had a remarkable impact.
8. Myriad examples of tactical planning tools appear in the literature. For example, see:
S. Graves, “A Tactical Planning Model for a Job Shop,” Operations Research, July–August 1986, pp. 522–533.
9. Silver and Peterson describe the basics of modern inventory theory. See:
E. Silver and R. Peterson, Decision Systems for Inventory Management and Production Planning (New York: John Wiley, 1979).
Nahmias provides another useful text. See:
S. Nahmias, Production and Operations Analysis (Homewood, Illinois: Richard D. Irwin, 1989).
Magee’s timeless piece is a good introduction to the field. See:
J. Magee, “Guides to Inventory Policy,” Harvard Business Review, January–February 1956, pp. 49–60.
10. The extension of the manufacturing process into the distribution center initially challenged managers accustomed to centralized control. They looked at the new approach with caution out of concern for issues like process control, quality, and operational efficiency. Because of different reporting lines for the factory and the distribution center, high-level intervention was required to execute the change, which was widely acknowledged to benefit the company on the whole. Organizational obstacles such as this represent one of the major pitfalls of supply chain change management. Modeling, which facilitates data-driven decision making, is one of the tools that can be used to overcome these barriers.
The author is grateful to his coworkers on HP’s Strategic Planning and Modeling team for their contributions: Corey Billington, Rob Hall, and Steve Rockhold; to Brent Carter from HP’s Vancouver Division; and to Hau Lee of Stanford University.