Bullwhip Effect
INTRODUCTION In today’s business world, supply chain management has never been more important. Operational efficiency hinges on the ability to properly forecast and meet demand from customers, wholesalers, distributors, and many other key players. Any distortion of this information can be very costly and may require permanent changes to a firm’s supply chain infrastructure. Perhaps the most infamous source of this demand data variability is the bullwhip effect.
DEFINITION & CAUSES In 1990, executives at Proctor & Gamble noticed something odd about the ordering patterns for one of its most successful and well known products, Pampers diapers. Retail sales would always vary slightly from period to period, but this variability was not unusual or extreme in nature. However, upon moving up the supply chain and examining orders from distributors, Proctor & Gamble observed greater variability than what was seen at the retail level. Furthermore, executives found that the company’s own orders of materials from suppliers exhibited even larger swings. None of this made any sense to Proctor & Gamble; babies consumed diapers at a relatively predictable rate, yet demand order variabilities were more and more amplified as they moved up the supply chain. The company named this phenomenon the bullwhip effect, from the uncanny likeness of a cracking whip to the growing oscillations in demand observed when moving up the supply chain. Intuitively, one can view the bullwhip effect as behavioral in nature. This is perhaps best illustrated through playing the well-known “beer game.” In this exercise, participants take on the roles of customers, retailers, wholesalers, and suppliers of beer but are not allowed to freely communicate with each other. Instead, they must make ordering decisions based solely on orders from the next downstream player. Academic application of the beer game has consistently yielded common results: variability upstream is much greater than
DEFINITION & CAUSES In 1990, executives at Proctor & Gamble noticed something odd about the ordering patterns for one of its most successful and well known products, Pampers diapers. Retail sales would always vary slightly from period to period, but this variability was not unusual or extreme in nature. However, upon moving up the supply chain and examining orders from distributors, Proctor & Gamble observed greater variability than what was seen at the retail level. Furthermore, executives found that the company’s own orders of materials from suppliers exhibited even larger swings. None of this made any sense to Proctor & Gamble; babies consumed diapers at a relatively predictable rate, yet demand order variabilities were more and more amplified as they moved up the supply chain. The company named this phenomenon the bullwhip effect, from the uncanny likeness of a cracking whip to the growing oscillations in demand observed when moving up the supply chain. Intuitively, one can view the bullwhip effect as behavioral in nature. This is perhaps best illustrated through playing the well-known “beer game.” In this exercise, participants take on the roles of customers, retailers, wholesalers, and suppliers of beer but are not allowed to freely communicate with each other. Instead, they must make ordering decisions based solely on orders from the next downstream player. Academic application of the beer game has consistently yielded common results: variability upstream is much greater than