10.  Key Performance Medics:  Specially trained analysts who come to the rescue to turn around poor performance indicators

9.   Strategery:  a plan, approach, line of attack (very similar to strategy)

8.   Regression Forecasting:  Reverting to an earlier, more accurate forecast to give the impression of better forecasting

7.   Imperial Forecasting:  The ultimate Top Down forecast, dictated by the ruler of the kingdom

6.   Reactive demand shaping:  coming up with a marketing promotion during the month in which additional sales are needed rather than planning ahead as part of an overall strategy (or Strategery)

5.   Tough Luck Capacity Planning:  Similar to Rough Cut Capacity Planning; however, with Tough Luck, capacity is fixed so the supply plan drives the demand plan

4.   New Product Insanity:  The attempt to forecast new product launch dates

3.   Higher Keys:  Rooted in the data structure concept of hierarchies, this mutation is the idea that key groups are formed through aggregation

2.   Moving Adage Forecast:  A twist on moving average, continually changing forecast caused by management mood swings

And the number 1 term…

1.    Disaggravation:  The Steelwedge disaggregation functionality which enables users to enter forecasts at higher levels of aggregation, reducing the number entries and thus, reducing user aggravation.

A multi-billion dollar global manufacturer is struggling. Two divisions of the company are at odds on how best to achieve world class forecast accuracy. Regional sales account representatives provide forecasts well above historical sales levels. Why? Because inventories made available to each country are insufficient to meet market demand. The result: predict more sales to try to influence supply decisions and receive a greater portion of supply for your region. One division has decided that a centralized approach is best and is no longer considering regional sales input. The other division is moving to a collaborative S&OP approach where regional input is requested, evaluated and incorporated in the overall plan.

Which method do you think will produce a better plan?
Which method will distribute limited resources better?
Which method will yield higher profitability?

Time will tell for this organization. Yet, we can make a prediction today. Experience would suggest that a well-designed, collaborative S&OP process will produce better results. Here’s how we look at how Top-Down and Bottom-Up S&OP drives better results.
1. Bottom-Up Inputs: Bottom-up forecasts are accumulated from many contributors. A distributed sales force may have hundreds or thousands of contributors. Each contributor has a specific area of expertise such as a specific customer, product or geographic area. The contributor enters her forecasts for her specific area of responsibility. Forecasts from all contributors are summed to capture an overall bottom-up forecast.
2. Top-Down Inputs: Top-down projections apply a more centralized view. A small number of forecasters will look at various inputs and generate forecasts. Influencing factors may include market data, economic indicators, and general product and customer trends.
3. Balancing Top-Down and Bottom-Up Forecasts: The beauty of top-down and bottom-up planning is their ability to look at the world from differing vantage points. The folks in the “ivory tower” know important information, but they don’t know everything. The folks in the field have keen insights into their unique areas, but they only see their small piece.

Recommendations:
1. Gather Objective Inputs: The collaboration challenge is to capture the small pieces without tainting the field forecaster’s view. In other words, don’t tell the field forecasters the top-down targets. When field forecasters are told what their forecasts are expected to be, they tend to send back values right in line with the top-down values. Such tainted bottom-up forecasts miss the point of gathering field intelligence.
2. Balance Inputs: An effective marriage will capture top-down and bottom-up forecasts separately.
3. Manage by Exception: Look for forecasts with the most significant (unit and/or revenue focused) difference between top-down and bottom-up forecasts. Is there an opportunity the field sees that the top-down approach did not capture? A management by exception S&OP tool will make comparisons quickly to enable users to analyze critical differences and refine the ultimate consensus driven forecast.
4. Provide Feedback: Tell forecasters how they’re doing. Measure forecast accuracy and bias. Track performance at various levels, including individuals. Forecasters who consistently over or under forecast (bias) should know that the organization knows. Such bias may be intentional or unintentional. Either way, behavior needs to change to produce reliable projections to which the organization can deliver.
S&OP really does lead to improved bottom-line results. Break down the walls of distrust and embrace collaboration.

“Why should I put any effort into creating a forecast when I know he will change it anyway?” This quote came from a client with whom I worked. The clear message was that their company’s collaborative forecasting process was not supported by the senior executive who had the final say in what the “consensus” plan would be. His actions unintentionally conveyed a message that the Sales and Operations Planning process which led to his desk had little merit in his eyes. The prevailing sentiment shared by S&OP participants was that their work was unimportant.

Opportunity lost! Executives must understand that S&OP can be successful only when they are engaged and supportive. Trumping the collaborative output sends a demoralizing signal. Empowered participants deliver superior results.

An effective S&OP process leverages inputs from across functional disciplines and at various levels of responsibility. Most decisions should be made prior to the Executive S&OP meeting. Executives should focus on setting policy, reviewing lower level decisions, breaking ties and making critical, strategic decisions.

Don’t kill collaboration. Empower participants and achieve better results.

By the way…the executive who applied his own final forecast values achieved forecast accuracy levels worse than if they had used the statistical forecast alone. Lots of analysis and meetings with nothing to show for it!

An effective S&OP program depends on solid, accurate demand forecasts. Best practice companies do three things well: statistical, top-down and bottom-up forecasting. Many companies are doing one or two of these, but few are doing all three well. Of course, some companies do none. Let’s just say these companies have a huge upside improvement potential.

A statistically generated forecast should use a “best fit” approach to select the mathematical algorithm that minimizes error (such as mean absolute percentage error (MAPE)). The statistical engine should select the best algorithm for each time-phased data series or set of regression data. The resulting forecast should serve as a starting point for bottom-up and top-down forecasts.

Bottom-up forecasts are accumulated from many contributors. A distributed sales force may have hundreds or thousands of contributors. Each contributor has a specific area of expertise such as a specific customer, product or geographic area. The contributor enters her forecasts for her specific area of responsibility. Forecasts from all contributors are summed to capture an overall bottom-up forecast.

Conversely, a top-down approach applies a more centralized view. A small number of forecasters will look at various inputs and generate forecasts. Influencing factors may include market data, economic indicators, and general product and customer trends. Here, too, the statistically generated forecast is a good number from which to start.

The beauty of top-down and bottom-up forecasts is their ability to look at the world from differing vantage points. The folks in the “ivory tower” know important information, but they don’t know everything. The folks in the field have keen insights into their unique areas, but they only see their small piece. The challenge is to capture the small pieces without tainting the field forecaster’s view. In other words, don’t tell the field forecasters the top-down targets. When field forecasters are told what their forecasts are expected to be, they tend to send back values right in line with the top-down values. Such tainted bottom-up forecasts miss the point of gathering field intelligence.

An effective marriage will capture top-down and bottom-up forecasts separately. A management by exception S&OP tool will make comparisons quickly to enable users to analyze critical differences and refine the ultimate consensus driven forecast.

A statistically generated forecast should use a “best fit” approach to select the mathematical algorithm that minimizes error (using mean absolute percentage error (MAPE)). The statistical engine should select the best algorithm for each time-phased data series or set of regression data. The resulting forecast should serve as a starting point for bottom-up and top-down forecasts.

Bottom-up forecasts are accumulated from many contributors. A distributed sales force may have hundreds or thousands of contributors. Each contributor has a specific area of expertise such as a specific customer, product or geographic area. The contributor enters her forecasts for her specific area of responsibility. Forecasts from all contributors are summed to capture an overall bottom-up forecast.

Conversely, a top-down approach applies a more centralized view. A small number of forecasters will look at various inputs and generate forecasts. Influencing factors may include market data, economic indicators, and general product and customer trends. Here, too, the statistically generated forecast is a good number from which to start.

The beauty of top-down and bottom-up forecasts is their ability to look at the world from differing vantage points. The folks in the “ivory tower” know important information, but they don’t know everything. The folks in the field have keen insights into their unique areas, but they only see their small piece. The challenge is to capture the small pieces without tainting the field forecaster’s view. In other words, don’t tell the field forecasters the top-down targets. When field forecasters are told what their forecasts are expected to be, they tend to send back values right in line with the top-down values. Such tainted bottom-up forecasts miss the point of gathering field intelligence.

An effective marriage will capture top-down and bottom-up forecasts separately. A management by exception S&OP tool will make comparisons quickly to enable users to analyze critical differences and refine the ultimate consensus driven forecast.