There’s no denying that competition on a global market is fierce, which makes planning and production for your business difficult and increases the likelihood of forecasting errors. Imagine ending up with a full warehouse of high-end couture clothes and no one to buy them to because they’re out of fashion. What a nightmare!
But it doesn’t have to be that way. All you need to do is adopt a business management solution centered on accurate response, an approach that helps suppliers and retailers alike improve forecasting, accelerate the supply chain, and reduce costs. Don’t treat the world as if it were predictable: your forecasts may be different from reality. Instead, accurate response helps you incorporate uncertainty in your planning process and match your forecasts to what’s really happening.
Case study. How Sport Obermeyer used accurate response to increase its profits
In the skiwear business, it’s a complicated to predict demand, as it depends on variables such as weather, fashion trends, economy, not to mention that the selling season is only two months long. However, by implementing accurate response in the early nineties, leading supplier Sport Obermeyer managed to eliminate the cost of producing unwanted skiwear almost entirely and also increase its profits by between 50% and 100%. And here’s how they did it:
- Sport Obermeyer abandoned the outdated approach of basing production commitments on firm orders, an approach that had worked for the previous 30 years but it was proving obsolete as the sales volumes were growing and the production was unable to keep up with the orders. Instead, they implemented a series of improvements: they began to book production earlier, based on speculation about what the retails would order, developed a more complex supply chain, shorten lead times by more than one month, and they convinced their largest retail customers to place their order sooner.
- As these efforts were still not good enough to help them predict what people would buy, Sport Obermeyer assembled a panel of company experts and asked them to make independent forecasts. This led to an interesting discovery: the forecasts tended to be more accurate in cases where the different experts had separately come up with similar projections. Matching this insight with early buying patterns helped them improve accuracy in forecasting.
- Sport Obermeyer devised a production-planning approach that made use of the new finding, called risk-based production sequencing, which allowed them to become more responsive to the market in areas that deliver the highest payoffs. This model helped Sports Obermeyer reduce costs and increase profits by two-thirds. It also guided them to make numerous adjustments to their supply chain and product redesign process, which delivered additional impact.
- Be resourceful in using demand indicators to improve forecasts. Don’t just rely on sales data early in the season, be more creative! For example, you can use custom orders to predict mass-production trends as well.
- Institute a system for tracking forecast errors. Instead of placing the blame, try a more systematic approach and document the incorrect forecast. When was it made? What was it based on? All this information can be used to predict better estimates.
