What is Bullwhip Effect?

Bullwhip Effect is an important phenomenon in supply chain and was first noted by MIT systems scientist Jay Forrester. It represents the instabilities and fluctuations in product and supplier orders throughout various stages of supply chain. Manufacturers order supplies and raw materials based on their demand forecast; however, variations can often be amplified when moving up the supply chain, leading to excessive inventory investment, lost revenues, missed productions plans and more.

The easiest illustration of the Bullwhip Effect can be understood from the “Beer Distribution Game” developed by Jay Forrester. In this game, participants play roles of customers, retailers, wholesalers and suppliers of a popular brand of beer. The participants take order decisions based only on orders from the next downstream player in the form of passing a paper. There is no communication allowed among the participants. Studies from simulations of the game show that small variations in initial demand could lead to order amplitudes of 900% only four steps down the supply chain. These fluctuations are not only related to the behavior of people involved but also related to the characteristics of supply chain. Studies show that inventory swings in larger and larger “waves” in response to customer demand (the handle of the whip), with the largest “wave” of the whip hitting the supplier of raw materials.

For e.g. a retailer might see a drop in sales by X% due to an external event. So, the retailer cuts orders o wholesaler by 2X% (based on expectation of lower sales in the future and desire to reduce its current inventory). The wholesaler will cut the orders to be manufactured by 4X% to prepare for future low sales and try to decrease inventory levels. Lastly, the wholesaler will further cut orders to suppliers of raw materials to prepare the “low cycle”. Thus, volatility across the supply chain increases and this phenomenon has been observed in consumer-packaged goods, food, semi-conductor, and other industries.

There are 4 main causes of Bullwhip Effect, creating distortions in the supply chain leading to tremendous inefficiencies.

1. Demand Forecasting is done individually by all members in supply chain. Each member updates its demand based on orders received from “downstream” partner. As demand forecast is frequently updated across supply chain, the variation or difference in the demand forecast from one end to another end increases significantly.

2. Order Batching takes place when companies order at certain times of month or year in a large infrequent order vs. frequent smaller orders.

3. Price Fluctuation occurs when customers buy more quantity than required due to price incentives.

4. Rationing and shortchanging happens when a seller attempts to limit order quantities by delivering a percentage of the order placed by the buyer.

Lessons from the past

Macroeconomic data from 2008 Financial Crisis shows the Bullwhip Effect operating at a much broader scale. For e.g. US retail sales (i.e. consumer demand) declined by 12%; US manufacturers pulled down inventories by 15% and manufacturing sales declined almost 30%; while imports plunged over 30%. Many countries experienced declines of more than 10% in import and export. When the economy started bouncing back, bullwhip pattern reverses as each echelon boosts ordering both to cover expected higher sales and to replenish quickly depleting inventories.

Strategies to reduce Bullwhip Effect:

Traditional Solutions: Negotiation and collaboration among different levels of supply-chain, along with visibility to customer demand information reduces Bullwhip Effect. It is advisable to use Point of Sales (POS) data and not the orders data from downstream partners to estimate future demand. However, POS data is not free and those who facilitate it require compensation.

Advanced Solutions: Invest in technology to improve accuracy of demand forecast and inventory management to meet the consumption volatility. Artificial Intelligence models can be used to simulate order behaviors of down streams partners to get better idea of the demand.

About Live Insights

We are working with a leading CPG brand in Toronto to improve demand forecasting and inventory management using advanced analytics and machine learning. If you are interested to learn about our product offering or request a demo, please get in touch at liveinsights.ai.

Sources:

MIT beer game: http://news.mit.edu/2012/manufacturing-beer-game-0503

The Bull Whip Effect in Supply Chains case study, Sloan Management Review

https://hbr.org/2015/09/chinas-slowdown-the-first-stage-of-the-bullwhip-effect

Multiagent Model to reduce the Bullwhip Effect — Borja Ponte Blanco and David de la Fuente García