While retailers are in a better position to adapt to demand swings quickly and efficiently, forecasting is a lot harder for distributors and manufacturers higher up the supply chain. The result of being too far removed from consumer demand signals is inefficient, inaccurate forecasting decisions that leads to product shortages and very expensive inventory padding.
A Costly Game of Best Guesses
Manufacturers and distributors suffer from the same problem of being too far away from retail (consumer-level) demand signals. They don’t own the demand streams. Being once or twice removed from all the factors that affect demand – such as promotions, seasons, etc. – causes a costly, yet preventable, “Bullwhip Effect”.
The Bullwhip Effect puts manufacturers and distributors in a no-win position.
Firms at each level of the supply chain generally lack sufficient visibility to resolve various trade-offs in decision making. They forecast in a narrow scope that prevents products from flowing properly to end-customers.
It is fixable. But we’ll save that for tomorrow’s post. Today we will look at what causes the Bullwhip Effect in supply chain and set the stage for some solutions tomorrow.
What is the Bullwhip Effect?
The Bullwhip Effect in supply chain exists because of constant fluctuation or oscillations in demand. Incongruent information in forecasting leads to distortion, thereby creating more order variability (a bullwhip) upstream in the supply chain.
It Works Like This
Companies get together (with much agony) to cobble together a best guess about sales demand based on last year’s retail sell-in data. But there’s a fundamental problem with that… what happened last year is NOT indicative of what’s to happen this year.
Planning production is a manual process. A company may sell through multiple channels (ship direct) and distributors who sell through to retail locations. The manufacturer must go to each retailer or distributor to find out what sold, or sometimes they’ll use historical shipping information. It’s not a clean process. Data comes from disparate sources, often through a manual collaboration process. They just cross their fingers and hope the forecast is correct from each one.
Forecasting inaccuracy begets forecast inaccuracy, and it just keeps getting worse as you go up the chain to the raw materials supplier. By then, over-ordering and under-ordering is completely out of whack. And expensive.
That’s the Bullwhip Effect.
This video from TechTarget sums it up nicely:
There is Good News
Supply chain planning vendors like Blue Ridge are partnering with companies that automate retail data for use at the manufacturing level (and in between) to improve collaboration and accelerate forecasting/production decisions based on real-time demand signals at the consumer level.
Tomorrow we will look at how you can do this to shut down the Bullwhip Effect, including an example from a leading organic raw pet food manufacturer that connected retail sales data to upstream production decisions to drive forecasting accuracy, operational efficiencies and profitable inventory alignment: