Who Needs A Forecast? 

Every business, large or small, needs to forecast demand. For businesses, forecasts anticipate how much product or service a company can expect to sell in different markets, at different times, given various conditions. Economists and the financial community use forecasts to guide policy and investment decisions. And the meteorologist, well, everyone knows about the weatherman.

It’s the weatherman that first gave forecasting a bad name. How often did you prepare for warm weather, only to walk out the door and find out it was “unexpectedly cold”? Or it rained on a day that was supposed to be sunny. That’s the story of the weatherman. "The forecast is always wrong" is often the first reaction to any suggestion that forecasting can make things better.

But even the weatherman is getting better, almost to the point that in recent years, people seem to take weather forecasts for granted. Have you ever wondered how the weatherman improved the forecasting process? We think information has a lot to do with it.

Warm fronts, cold fronts, jet streams – insight into all of these conditions give meteorologists the information, or leading indicators, to determine that a new weather pattern is on the horizon. But don’t be surprised if they continue to speak in probabilities (e.g. 30% chance of rain), because like everything else in life, the weather is not guaranteed.

For entrepreneurs and Fortune 500 executives alike, forecasts are also critical. When will your product launch? How many will you sell? What results do you expect from entering a new market? Forecasts are fundamental to any business, but unfortunately, the average company struggles to create a reasonable forecast.

One supply chain executive recently recounted the forecast performance of his former employer in the fashion industry… “We were 60% accurate two weeks out on current products, and 30% accurate on new products in the same timeframe”. Think about it. Compared to the new product introduction forecast, there’s a better chance of winning a coin toss (50 percent probability).

But it seems misery does deserve company. According to a recent GMA study, forecast accuracy in the consumer goods industry has slipped in recent years from 77% in 1996 to 66% in 2002 at aggregate levels of these businesses (1).

They’ve done better before. So what is it going to take to get these companies, large and small, back to their previous performance levels or to new levels of achievement? We think a little bit of process and a better approach to information analysis.

First, focus on true demand. If you are guessing what someone else already knows, you’re missing out on an opportunity. Collaborate. In supply chains for example, leading manufacturing companies work closely with customers, such as Wal-Mart, to anticipate consumer demand. Then they offset this “true demand” picture using lead times and other supply chain policies to plan replenishment of supply. Focusing on true demand does not mean you should stop forecasting. Use forecasting to anticipate the ultimate demand picture, and collaboration to share forecasts (and assumptions) and synchronize plans.

Second, understand the assumptions. Know which patterns your forecasting techniques are tuned to predict. If an important condition is not considered, you will have to build in additional processes to account for such events. Also remember that in any planning process, it’s critical to document assumptions so that you can review these anticipated behaviors as plans evolve.

Speaking of evolving – keep up with dynamics. The market and business environment can change quickly. Monitor trends and leading indicators to ensure that you have your finger on the pulse of your business patterns. Modern forecasting techniques can automatically account for dynamics in trend and seasonal patterns, so it has never been easier to keep pace.

So forget about the age old excuse, “the forecast is never right”. If the weatherman can get better, so can you.

Register to access On Demand forecasting services through the Digital Tempus BI Network and start generating accurate forecasts today.

 

 (1) Source: 2003 GMA Logistics Study