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7 symptoms forecasting illness“According to a recent Business Finance survey, two out of three finance executives expected their 2009 budget targets to be obsolete within the first six months of the year.”

In a 2010 white paper from IBM the authors, Steve Player and Steve Morlidge, blame antiquated processes and tools combined with misconceptions about forecasting accuracy for the “illness” of most forecasting systems in business today.  In their paper Seven Symptoms of Forecasting Illness (© IBM 2010) they point to 7 symptoms of potential forecasting trouble, which we’ll highlight below.

  1. Semantic Confusion: Does your firm find it uncomfortable to cope with unexpected or unwelcomed forecasts? Like the manager who is asked for a “best estimate” and then is held accountable for it, or criticized for “making changes” in a forecast update, or one which management doesn’t like.  The bottom line is the blurring between “forecasts” and “targets” (or goals).  Resolution involves more honest, open and direct communication from the top down.

 

  1. Visual Impairment: Are you obsessed with the year-end forecast number to the exclusion of everything else? Or surprised by early-year unexpected developments?  The root cause here tends to be inflexibility or lack of adaptability to changing conditions, and is best resolved via use of a rolling (i.e., cumulative/adjusted monthly or quarterly) type of forecast.

 

  1. Delusions of Accuracy: Are you obsessed with accuracy, pouring too much quality managerial time and talent agonizing over a forecast’s development, trying to hit it on the nose? The price paid for error here may include bonuses, promotional expense, or losing sight of what’s possible “as internal views obscure external learning.”  Here again, forecasting more periodically and being quickly adaptive to outside changes will help.

 

  1. Systemic Overload: Are forecasts too detailed?  Is there too much pressure to provide greater detail and analysis?  These cause the system to become bloated and unwieldy in a downward spiral of frustration.  The fallacy is in believing that more data is always better.  Instead, the authors opine, limit your forecasting to a few key critical drivers that truly affect company performance.  Use the 80/20 rule, and emphasize analysis over data gathering in extremis.

 

  1. Prosperity Syndrome: Do your forecasts tend to trend up over-optimistically regardless of industry or conditions? Are they too biased toward growth?  Ignoring the reality of industry or economic cycles exposes any firm to strategic missteps.  Don’t mistake a happy event for a trend.  And above all, don’t neglect your key customer-satisfying strategic differentiators.  Instead, recognizes your biases, focus on current, key market and economic realities, and competitive realities.

 

  1. Lack of Coordination: Are forecasting views internally characterized by chaos and conflict? Do different departments see the future differently?  Are managers’ biases reflected in their (conflicting) forecasts?  Lack of integration by management of key forecasting projections is at the root.  A system is required company-wide that users can believe and have faith in – one that does not discourage differing views, and inspires collaboration.

 

  1. Asocial Behavior: Does the firm routinely manipulate or distort forecasts even when not in the company’s long-term best interests? Is knowledge withheld or manipulated?  You could end up rewarding sand-baggers and masking problems (or opportunities) in the marketplace and obscure the firm’s true potential.  If so, take a look at bonuses, comp and reward systems for undesirable links between forecasting and performance, or incentives not well aligned with overall company goals.  Reward employees for the value they create rather than the targets they negotiate.

 

The full IBM White Paper can be found at the website of proformative.com.  You should be able to find the link here.

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forecastingOur friends at APICS recently pointed out some thoughtful considerations for those companies who find that their forecasting performance for inventory, sales or production are not as accurate or stable as they would like.  In the Mar/Apr 2014 issue of APICS Magazine, research director Jonathan Thatcher writes about the key issues companies must consider.  We’ll reprise a few of his key suggestions today.

First, notes Thatcher, “Don’t schedule production based on the greater of the forecast or sales orders.  Instead, make sure orders consume the forecast as they come in.  Ideally, your ERP system should display figures for forecast, customer orders and requirements summary.”

So for example, when the forecast calls for 100 units and customer orders equal 25 for a given period, this leaves a remainder of 75 forecast units.  Production does not care whether the units are ordered or forecast – they’re just “units” to them.  Through Sales & Operation Planning then, your team can consider the ideal forecast to project, based on sales trends as well as how well production is meeting demand while avoiding adding unnecessary inventory.

A second issue Thatcher notes is what’s called the MRP demand time fence (DTF).  Set the DTF equal to production lead time, and make sure your MRP system shows forecasts to zero within the demand time fence.  If it takes one week to manufacture a unit, then the DTF would be one week.  As the article posits: “Forecasts made inside the DTF should be ignored as it is too late to produce them, and those forecasts will overstate demand.”

And of course, don’t forget to flag extraordinary or non-repeating (“odd”) orders.  Don’t make these outliers part of history on which regular forecasting is based.

Finally, make sure that your weekly forecasting period matches the periods used by sales.  Sales rarely occur evenly week over week.  Aggregate numbers, Thatcher points out, “are your friends” insofar as “data based on long histories is easier to forecast than daily or weekly data” with less variability.

By definition, APICS notes, “no forecast is completely correct.  But we can get a little closer to perfectin with a stronger forecasting practice.”

For more information on this topic, try starting at the APICS Magazine site here.  (Note: there is generally a lag-time between the appearance of an article in print form and its appearance at their site.  The article excerpted above came from the “Ask APICS” section of the Mar/Apr issue.)

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