Slow and Intermittent Product Demand Forecasting Facts & Myths

Slow and Intermittent Product Demand Forecasting Myths

Test the Slow and Intermittent Product Demand Forecast Myth

After reviewing several retail software provider websites, a visitor will see that many software companies highlight their ability to forecast slow or intermittent product demand.  Following that line of reason, the measure of success is found by testing the forecast accuracy for a given period of time and repeating the test regularly. Taking the results and calculating the mean and standard deviation will tell us if the forecast is accurate.  The problem is that forecast accuracy is not the ‘smoking gun’ solution to slow or intermittent demand products.

To manage slow and intermittent product demand, we recommend you avoid:

• Reviewing forecast accuracy within a single company traditional demand period
• Measuring in-stock
• Using incorrect forecast methods like Time Series, Exponential Smoothing, and This Year/Last Year. These fail to deliver the correct stock model. Some of these smooth or ignore zeros, which is a bad idea. Croston’s method may be used, but, without thoughtful implementation, then it, too, will deliver poor results.

[QuickLinks]When managing slow and intermittent product demand, you should:

• Review forecast accuracy over a window of time that includes multiple periods.
• Measure service attained – NOT in-stock. The focus needs to be on the inventory stocking requirements necessary to meet service level.
• Use inventory management software that correctly identifies slow and intermittent demand.  This software should not smooth or ignore a zero in sales history when calculating a forecast.  The software should deliver an inventory management solution that is based on service level and cost-effective inventory management models.

Service Attained = [Sales / (Sales + Lost Sales)]

How to Achieve Service: Optimize Facings and Ignore Demand Forecast

The pick size and store facing (presentation model) often have a significant impact in the end results. If a product has a forecast of 4 units a quarter and a pick size of 4, then the forecast accuracy only really matters across 3 months (1 quarter). If the stock model or store facing is one unit, when the on hand is one unit, then the system will buy more. In this store facing example, the forecast accuracy again only matters across 2-3 months. The critical metric to look at here is service attained and not in-stock. To put it differently, inventory to meet customer demand and sales results is what’s important.

Overstock, Shelf-Life and Smoothing the Zeros

There will always be overstock without a good strategy for slow and intermittent product demand.  The resulting overstock will force you to commit more inventory dollars to these products that could be used elsewhere.  Shelf-life issues will also be created by smoothing through the zeroes in the sales history. The pains of overstock and inventory lost due to shelf life expiration can co-occur without a strategy for slow and intermittent product demand

Focus on a Combination of Service and Demand Forecasting

Successfully managing slow and intermittent product demand requires a comprehensive approach that combines service and demand forecast accuracy across a broader range of periods. By avoiding the common pitfalls and identifying the slow and intermittent demand products in your assortment, you can take control and see the difference in just 45 days. This approach will help you ‘Tighten the Links in Your Chain™’ and feel more informed and prepared for the challenges ahead.

Are your ready to ‘Tighten the Links in Your Supply Chain?™’

Remember, you’re not alone in this. We’re here to help you manage your forecast accuracy and inventory management opportunities. Whether you need a free consultation or want to see how your business can improve with a demo, we’re ready to help you feel more confident in your retail strategy.

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Stuart D

Stuart Dunkin is a strategic visionary who leads by calling on his broad range of experience as a former retail executive as well as a consultant for top retailers and for the E3/JDA software company. He founded Data Profits to solve a real-world problem plaguing retailers today – the disjointed relationship between supply chain data, people and business goals. He has experienced first-hand the challenges and frustrations that occur when decisions are made based on wrong data, which often leads to the wrong actions being taken with crippling results like overstock, out-of-stock, and increased working capital.
Stuart graduated from Auburn University, with bachelor of science in business administration.

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