Bullwhip or Forrester effect is result of uncertainty and changes in demand that magnify as we move upstream in the supply chain. The farther upstream the supplier is, in the supply chain, the more variations in inventory levels. Unfortunately this behavior is taken for granted for most industries. Some advocates of Kanban and JIT believe that using these techniques would eliminate such behavior and makes the supply chain more predictable to the extent that large variations are avoided. This is not a true assumption for the following reason. Kanban and JIT are not planning tools, they are execution methods. Hence they cannot be used to dynamically plan ahead of time when there are inevitable variations in demand. When you design your supply chains with a certain demand in mind, then as the demand goes down, Kanban would react accordingly unless your buffers are too large such that much of the inventory will remain unused between stages resulting in excess inventory. If the buffers are NOT big enough to avoid the excess inventory problem, then it is likely that shortages will occur when there is a surge in demand? The buffers are all used up and the pipeline will sit empty resulting in shortages and loss in revenue etc.
Here are some observations and reasons why we no longer have to run our supply chain under the assumption of bullwhip phenomena. We all know that plans are not perfect however re-planning is the key and doing it fast and in parallel is the reason why we can avoid BW effect. This is explained in more detail below.
From Serial to Parallel
Bullwhip happens because of the serial behavior of the supply chains. In other words each downstream stage tells the stage before it until it gets to the first stage. This delay is one of the reasons for the rise in the amplitude of the inventory. However this behavior can be changed by providing multiple levels of visibility upstream using collaboration tools. Such tools can be set up to send signals to suppliers as far back as needed in order to share with them the trends in demand that are observed in the consumer behavior. Using point of sale information as well as demand signaling and demand planning technologies, the information shared can save suppliers much cost as well as make them a better and more reliable supplier.
Whole vs Segments
Another notion related to parallel analysis of the supply chain has to do with how the buffers are set up at various stages of the supply chain. In contrast to the traditional techniques of each stage deciding on their own inventory levels before, during and after that stage, Multi Echelon Inventory Optimization (MEIO) technology looks at the entire supply chain and each layer thereof in parallel, not in an isolated and serial manner. Using probability and queuing theory it can make fairly accurate predictions as to how much inventory of each item should be at every stage of the supply chain to avoid shortages and/or excesses yielding unprecedented delivery performance while minimizing cost. Such a parallel treatment of the supply chain would eliminate the BW effect and change it to a “stick effect.” MEIO takes into account both the cost and service levels at every stage given the lead-times and interactions between stages to produce a holistic solution not an isolated serial solution.
Responsive vs Predictive
The more responsive we are the less predictive we need to be. Widespread use of cell phones have made all of us a lot more responsive. As a result we do a lot less planning. How many times have you heard someone saying “I will call you when I get there.” In the past you had to specify exact time and location to meet up with someone! Today’s S&OP technology allows real-time planning to be more responsive. In other words within hours a new plan can be generated if and when there is a change in demand or supply. Obviously faster planning does not eliminate the time it take to physically build and transfer goods, however it does significantly shorten the cycle time to delivery. Hence it can reduce the potential amount of inventory quite considerably resulting in a more stable supply chain rather than a BW supply chain. This is more of a responsive planning in contrast to predictive planning.
The value of an item at the most downstream point in the supply chain is several times higher than the cost of an item at the most upstream location! So if you look at the weighted variation of inventory taking into account cost factors, then the variation in value is fairly constant and not as variable as the quantity depicted in the BW. This is a key issue in balancing the supply chain and risk management. In order to ensure the availability of parts, the upstream locations can take higher risks than the downstream locations. However, the way the supply chains are set up today, the reward/risk ratio is a lot higher for the downstream companies than the upstream suppliers. By making this ratio more equitable, much better and more efficient supply chains can result in terms of adaptability and responsiveness. One way to do this is a commitment to buy a minimum amount within a defined window of time. With this level of confidence, suppliers can assess their own risk and not only ensure delivery of what is needed but take additional risk knowing that they have some level of downside protection.
Although BW effect may not be completely eliminated however the size of the waves can be significantly reduced resulting in a much more stable and predictable supply chain.