A Classic Revisited
Hau Lee’s ‘The Bullwhip Effect in Supply Chains’ is one of the best known supply chain theories to have emerged from academia. It even has a dedicated Wikipedia page.
The basic idea is that most supply chains have internal dynamics that enforce demand volatility. In essence these demand distortions come from three distinct sources:
- Purchasing quantities related to supply chain costs or discount levels
- Supplying before demand peaks for seasonal goods and promotions
- Rational / irrational
- Shortage gaming
- Purchasing discount levels
So the important thing to remember is that you do not have to beat your suppliers with the bullwhip, you have to beat the bullwhip!
The Supplier Dilemma – Take It Cool or React Quickly
The normal dilemma for the supplier is that it can see a distinct shift in customer ordering behavior. The quantity of goods ordered for a given period quickly grows significantly smaller or larger. Especially if a large customer order clears out supplier stock, the normal reaction is to order significantly more to one’s own warehouse to prevent such a ‘catastrophe’ – and that results in the demand signal to the next supplier being intensified.
The other alternative would be to judge the change in demand as normal variation and take no special action. But what if demand patterns had actually now changed?
Beat the Whip with Integrated Forecasting
We have found that the simplest solution to the problem is to segregate end demand forecasting, i.e. how the product is sold at POS, from the dynamics caused by the supply chain. Read more on the subject in our whitepaper: Sales and operations planning: From data to information, from information to decision making.
In integrated forecasting consumer level sales are normally forecast at each retail store, and through direct consumer sales channels such as online or mobile. The forecast contains demand shift estimations for promotions and seasons.
This end-level SKU-Store and SKU-Chain –level demand forecast is then run through a supply chain planning solution to calculate likely replenishment quantities for each forthcoming day. The replenishment forecast takes into account, for example, the building of promotion presentations before the promotion’s launch and other supply chain dynamics caused by retail requirements. Store replenishment needs are forecast, as are delivery requirements for the DC, and combined with direct sales forecasts and DC planning practices these can all be used to calculate order forecasts for suppliers.
The order forecast and end-demand forecast can be shared with suppliers to help them prepare for future orders as well as to distinguish end demand changes from pure ordering quantity changes.
The idea is simple and its spread to a mainstream solution has probably been hindered, more than anything, by the high calculation requirements it puts on supply chain planning solutions. If you want, for example, to give suppliers 1 ½ years data to help them optimize material purchases and capacity planning, it means calculating SKU-store-level forecasts for 540 days on a daily level. For a larger retailer it amounts to billions of forecast records.
If you have had problems with supplier performance, or want to help your sourcing efforts by buying smarter, collaboration with suppliers is a key development area. We have found that providing the supplier with visibility over your future order forecasts as well as end-demand forecasts helps them to plan better and improve their service level.