"Supply Chain Management" John T. Mentzer

"Sales Forecasting Management"
John T. Mentzer and Carol C. Bienstock

The Impact of Forecasting Improvement on Return on Shareholder Value
John T. Mentzer; Journal of Business Forecasting

Improving Salesforce Forecasting
Mark A. Moon and John T. Mentzer; Journal of Business Forecasting

Benchmarking Sales Forecasting Management
John T. Mentzer, Carol C. Bienstock, and Kenneth B. Kahn; Business Horizons

Seven Keys to Better Forecasting - Webinar with Dr. Mentzer,
March 16, 2005

5th Annual World Class Sales and Forecasting Management Conference,
May 10-12, 2005

Technology Ventures 2005,
May 18-20, 2005

This is about one of the fastest moving targets in industry today. Configured products, those with bundled components e.g. PC, monitor, keyboard, mouse, etc., are difficult to manage because they’re always on the move as components tend to change when new versions emerge, and as customer preferences change.

Managing the product mix, or as some call it product configuration planning, is the process of creating demand plans for product combinations and options that customers will buy. It includes demand for configured product overall, and demand for the underlying Components. This is critical because: (1) Substantial working capital is tied up in components; (2) A single component bottleneck can severely reduce the ability to deliver on customer commitments; and (3) Subtle changes in mix can have a big impact on the bottom line.

The following diagram illustrates the complexity of product mix forecasting for one configured product with two simple components. In reality, configured products can have hundreds of components, and – worse yet – multiple configured products often compete about the same components.



One can argue that product mix planning is the most challenging task in today's marketplace. Bottom-up Planning is a daunting, time consuming task because of (1) component mixes changing over time; (2) complex component relationships where one component can be used in many configured products or sold as separate products; (3) market intelligence is often available in aggregate terms and must be dis-aggregated down to be useful. If the forecast for the Configured Product changes, how will the component forecast change? How quickly can the forecast be updated and communicated with supply planning and other stake-holders?

Many organizations work around this difficulty by forecasting components directly. This approach is particularly error-prone when demand for the configured product changes: What components will be affected? If the component is shared with other products, how much is associated with the changing product?

At the other end of the planning spectrum, Top-down Planning overlooks important subtleties hidden in the data: (1)bottlenecks; (2) component mix changes over time; and (3) the impact of configured products mix changes on inventory and capacity.

One approach to solving this multi-dimensional, moving challenge is to use a Statistical Bill Of Materials (sBOM) which are statistically based aggregations of configured products, which mask the complexity of the nearly limitless detailed manufacturing BOMs, but expose insights locked into the detailed data. The sBOM solution enables planners to change demand forecasts in a few key-strokes, and the complexity of handling component demand changes or rolling low-level data up to higher levels of aggregation takes place automatically - and in seconds.

Companies like Juniper Networks, Tellabs and Fed-Ex have found that the sBOM solution significantly reduces their planning cycle-times, provides better forecasts, and greatly improves visibility into their business performance.

“The sBOM technology enables us to have a big picture view of our business while understanding nuances only attainable through a small picture view”, said one early adopter of the sBOM solution. He had a good grasp on forecasting what customers were buying (configured products), but had long struggled with translating this information down to the ever-shifting component level. Armed with templates like the one below, he would make forecast overrides at the Configured Product level (Baseline Forecast), and in seconds he would be able to asses the impact at the component level (Dependent Baseline Forecast).



In another user example, a Supply Planner was struggling to forecast how much a component would be used in the various configured products. What would otherwise have been a complex and lengthy project was done in minutes using the sBOM solution.

In yet another example, a VP of Finance needed to better understand how changes in attach rates would impact costs and margins. One of their key (configured) products was transitioning from old component technology to newer technology, but no-one had a good grasp on the rate of configuration change, nor of the resulting impact on product margin. Using templates like the one below, this VP was able to improve her financial predictions by leaps and bounds. Moreover, by better understanding how attach rates impact costs and margins, she was much better enabled to intelligently drive pricing and promotion strategies.