Industrial products forecasting needs sales input

This is an excerpt of the original article. It was written for the May-June 2018 edition of Supply Chain Management Review. The full article is available to current subscribers.

May-June 2018

Last month, I was in Atlanta at the Modex trade show. In one sense, it is a tribute to the automation technologies managing today’s distribution networks. And, I’m not only talking about automated materials handling systems, but also the software and NextGen technologies such as robotics, wearable technologies, including smart glasses and augmented reality solutions and sensors enabling the Internet of Things. In another sense, all of these solutions are coming together to drive fulfillment. With the increase in e-commerce, getting the right product to the right customer at the right time has never been more important.

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The lion’s share of demand forecasting publications deal with consumer products and therefore focus more on forecasting demand impacts from the promotional and new product activities of marketing organizations, with less focus on those of the sales organization. In contrast, sales organizations of industrial product companies typically play the most important role in creating and shaping demand—because customers are largely businesses to which they directly sell. I must admit that most of my forecasting publications have been consumer products oriented. Yet, my first major project at a product company, Data General (DG), led to the development of a model to forecast computer revenues based on sales force size and other characteristics.

Sales forecasting at a computer manufacturer

I joined DG, a Fortune 500 industrial products manufacturer of minicomputers, in the early 1980s as a management science analyst in an internal consulting group. The group did in-house analytical projects for various departments within the company. I looked to do my first project with the commercial side of the company, in one of the sales or marketing organizations. A sales support director was my first client at DG, and he was interested in analyzing sales force productivity.

We started the project by collecting data on sales reps and how much they sold each fiscal quarter, as well as other information about them. After much data crunching, we uncovered a correlation between the amount of time a sales rep was with the company and his/her sales performance—a “learning curve” per se. For example, we found that newly hired sales reps sold very little in their first six months at the company. It turned out there was a steep learning curve to selling computers. As a rapidly growing high-tech company with significant sales rep turnover, many of our reps in our “hot” sales markets were fairly new to the company.

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