The Issue


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Are forecasting errors causing costly stock-outs and excessive inventory?



The Issue

Because of several non-IT issues, even the best forecasting/planning software cannot improve accuracy without addressing issues such as:

·         Countering the “bullwhip effect” which amplifies forecast errors upstream along the supply chain

·         Factors other than demand that influence sales, e.g. stocks, prices, promos, batch ordering, target deadline spikes etc.

·         Reducing data load of forecasting & planning (large systems become unwieldy; forecasting/planning systems must be lean)


Expert Panel


The Dubai Supply Chain and Logistics Group (www.sclgme.org) has set up a panel of forecasting experts, with 2 of our experts for a series of webinars and seminars to address the non-IT issues of forecasting & supply chain planning – without which forecasting errors will not reduce.   The panel has experience in improving forecasts and service levels in large corporations (Lipton/Unilever, Reckitt-Benckiser, Panasonic, HP, GSK).


The webinars are FREE OF COST.  If you cannot attend, the minutes can be received and queries can be mailed to the panel via SCLG after the sign up.



Most companies and software rely on time series methods for forecasting.  However, if average sales variation is above ~40%, as often, time series algorithms are useless. Only establishing the cause of variation and incorporating them into forecasting, can improve accuracy.  Sales variations are almost always excess of demand variation.  A major factor for this is the bullwhip effect, peculiar to supply chains.  Frequent changes in sales strategy and supply/stocks also cause big variations, even while demand remains stable.  The solution lies in:


1.     Making demand/inventory information available upstream (retailer to wholesaler to manufacturer), and make estimates where this is not possible.

2.     Establishing key forecasting parameters in a one-off exercise:

a.     Impact of price, promotion, sales effort, stocks, ordering and sales-target systems

a.     Tolerance levels, safety stocks, seasonality indexes... (by SKU/category)


Since the system must be lean (large planning systems are unwieldy), attendant IT requirement is small. Most in-house software should lend itself for this.




1.     Understanding where time series forecasts work (& where not)                       3.     Improving accuracy via incorporating price, promotion, stocks...

2.     Reducing forecast errors by countering the bullwhip effect                              4.     How to manage inaccuracy with safety stocks, escalation...




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