How do you forecast lumpy demand?

2 min read

Pencil crayons lined up at different heights how to forecast lumpy demand

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What is demand forecasting?

Inventory demand forecasting is how companies predict customer demand for an inventory item over a defined period. Accurate inventory demand forecasting enables a company to hold the right amount of stock, without over- or under-stocking, for optimum inventory control. Historical data trends and market knowledge of how demand can fluctuate are often used to forecast inventory demand.

Forecasting accuracy usually relates to the sophistication of your inventory forecasting techniques. The more accurate your demand forecasts are, the more efficiently you’ll be able to serve customers’ needs without investing capital in large amounts in stock. In turn, this will help you lower your overall operational costs.

Red binoculars looking out into the horizon forecasting demand

Different demand types or classifications

If you analyse the historical sales data of every product in your warehouse, you’ll find that the demand for different items varies considerably. Some will have consistently high demand over time; others will have sporadic or low demand.

In addition, as products move through their product lifecycle, their demand types will keep changing.

It’s important to understand an item’s demand type, as this is what will determine the kind of calculation (or algorithm) you use for forecasting. Using the same analysis for a product with an erratic demand type for one with slow demand will result in inaccurate forecasts.

This article focuses on demand forecasts for items with lumpy or intermittent demand.

What is intermittent or lumpy demand?

Intermittent or lumpy demand is where your stock items will see periods of high demand followed by periods of zero demand. While some people think it’s impossible to forecast demand for items with lumpy demand accurately, this article will prove them wrong.

As it’s estimated that 80% of revenue comes from just 20% of products, this means 80% of your SKUs will have fairly low demand that is intermittent or lumpy.

If you’re operating in aviation, automotive, defence and manufacturing industries, you’re more likely to experience lumpy demand.

As lumpy demand is so common across companies, it’s essential to get accurate demand forecasts to maintain the right stock levels to keep your customers happy.

How do you forecast lumpy demand?

Forecasting lumpy demand using statistical methods

Most statistical forecasting methods struggle to provide accurate demand forecasts for items with lumpy demand, and are often very complicated. Unless statistical modelling is your forte, it’s unlikely that you’ll find them helpful.

Using statistical modelling also has limitations, including bias, the lack of independent smoothing parameters for demand size and interval size, the assumption that demand size and demand interval are independent, and no way to deal with product obsolescence.

Using software to forecast lumpy demand

As statistical modelling is so complex, it’s better to use software to forecast where you have lumpy demand. Enterprise resource planning systems (ERP), warehouse management systems (WMS) and eCommerce platforms can offer a certain level of forecasting. However, investing in inventory optimisation software, such as EazyStock, supports more complex demand forecasting requirements – including lumpy demand. It also reduces the need for you to be a statistical whizz.

EazyStock connects to your ERP, WMS or inventory management system and calculates each SKU’s product life cycle stage and demand type. It also considers seasonality, trends and sales promotions when forecasting demand and optimum inventory levels.

You can quickly and accurately forecast demand for all your items, including those with lumpy or complex requirements to reduce stock outs, reduce tied-up capital and, most importantly, meet customer requirements.

Book a demo to learn more about how EazyStock can help overcome your lumpy demand forecasting issues.

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Kate Granger

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2 min read

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