Can Connected And Smart Products Reduce Bullwhip Effect in the Supply Chain

Amazon’s collaboration with Brita on the Smart Pitcher is what busy parents have been waiting for a long time. It brings us closer to the Jetsons era and “The Autonomous House”. There is, however, one group that will be even more thrilled than busy parents with this development – Supply Chain Planners.

To understand why this is the case we revisit the Bullwhip effect. You can review the background in the seminal paper on The Bullwhip Effect in Supply Chains.

Some highlights from the paper by Dr. Lee, Dr. Padmanabhan and Dr. Whang below.

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.)
Perhaps the best illustration of the bullwhip effect is the well-known “beer game.”3 In the game, participants (students, managers, analysts, and so on) play the roles of customers, retailers, wholesalers, and suppliers of a popular brand of beer. The participants cannot communicate with each other and must make order decisions based only on orders from the next downstream player. The ordering patterns share a common, recurring theme: the variabilities of an upstream site are always greater than those of the downstream site, a simple, yet powerful illustration of the bullwhip effect. This amplified order variability may be attributed to the players’ irrational decision making. Indeed, Sterman’s experiments showed that human behavior, such as misconceptions about inventory and demand information, may cause the bullwhip effect.

In the paper the authors highlight that the Bull Whip effect is caused, in addition to supply chain participant’s irrational decision-making, by the following

  • Demand forecast updating
  • Order batching
  • Price fluctuation
  • Rationing and shortage gaming

We will focus on the demand forecast updating aspect as smart and connected products can dramatically improve the situation. We will not focus on the other 3 as they have more to do with reducing cycle times and strategies that are specific to certain situations and industries.

Many industries have adopted some type of demand information sharing to reduce amplification of forecast variability in the supply chain. You can see examples of demand information sharing, including CPFR and VMI,  in the paper and in related literature. The Amazon and Brita Smart Pitcher relationship is a major step change and  transformation with respect to demand information sharing. The smart and connected Pitcher in this case can instantaneously transmit demand information to the Retailer (Amazon) and Manufacturer (Brita) at the same time. Such a scenario can be replicated for many product categories in consumer and industrial markets. It is not entirely fictional to imagine that in the near future supply chain participants will be getting demand signals instantaneously from smart and connected products as opposed to Point-of-Sale data or forecasts.

Kris Gorrepati

My 2 cents on Supply Chain Management, Manufacturing, Design, New Product Development, Software Engineering, and related topics.

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