I just got off the phone with a consulting firm that was studying the decision of whether to outsource manufacturing or develop in-house production. The conversation reminded me of the importance that sales forecasting has to the efficient operations of a manufacturing company. Many resource planning software packages use a combination of current orders and statistical forecasting algorithms to predict what their future product requirements will be and this may be a valid method if your industry is mature, the seasonal fluctuations are stable and there is no expectation of growth. But what of cases where growth is expected? Suppose you have just launched a new product addition to your product portfolio. You have no orders and no history on which to extrapolate. What then?
One solution is to use Sales Forecasts. As I define it, a sales forecast is a single opportunity with an identified customer who is investigating a single existing product. The forecast is created by the sales staff after the initial contact with a customer and provides the monthly requirement in each of the next 12 months, the price and a probabilbity of closing. After the initial contact, the probability may only be 2% but as the sales staff work with the customer and support them through the sales cycle, the probability increases. It hits 100% when the order is received and the opportunity is closed.
For a single forecast, the estimation of the probability is quite imperfect. But overall, across a number of sales staff and across all the opportunities out there, the aggregate weighted unit volume and revenue has significant statistical accuracy. Add to that the orders already on the books and a resonable picture of future volume and revenue can be predicted on a month by month basis. The expected volume can then be used to justify purchasing the required inventory according to the leadtimes.
The basis for the accuracy of the forecast comes from the experience of the sales staff and the principle that collective wisdom is better than the best guess of the smartest person in the group. In his book The Wisdom of Crowds, James Surowiecki argues that a crowd, where the members act independently, use their own albeit limited information and best judgement, share information and have a method to aggregate their efforts, can make decisions that outperform the best individual in the group.
In the case of the sales forecast, the software provides an ideal way for the group to aggregate the decision and arrive at the expected unit volume. Feedback and performance monitoring also help hone the group’s ability to “hit” the numbers.
Obviously, this approach is best suited to products with long sales cycles where customers are in contact with the sales staff for extended periods prior to ordering and to companies which can identify hundreds or thousands of opportunities: technical OEM sales, for example. It works equally well with a distribution channel as it would with direct sales structures. I’m not sure it would work well with web-based e-commerce model since that would require the customer to do the work of tracking the opportunity when all they really want is some product information. Perhaps there is a way to provide incentives and train customers to do that but I’d rather rely on experienced sales staff who can evaluate the opportunity and can tell if the customer is blowing smoke.