19th October 2021

What Best Practices Should I Follow In Demand Forecasting?

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There’s no such thing as perfect forecasting, but there are steps that can be taken to significantly improve forecasting accuracy. Ever-evolving technological advances continue to expand the options available to businesses, and these need to be embedded in best practices for full efficacy. Here’s what you need to know.



1) Avoid An Over-Reliance on Statistical Modelling

Reliable, predictable, and following an established methodology, statistical modelling has long been the go-to option for forecasters. However, the approach has several limitations. For instance, statistical accuracy improves as the data set grows, with patterns becoming verifiable only after 5 years of uninterrupted stability. As any supply chain planner knows, this is at odds with reality, which is why statistical demand forecasting is rarely completely accurate.

Statistical modelling remains a vital starting point for accurate forecasting, but for full effectiveness it needs to be used in tandem with other approaches.

2) Embrace Collaborative Forecasting

With a focus on increasing the efficiency of inventory management, collaborative forecasting involves real-time collaboration among supply chain partners. The highly agile approach draws the relevant information from the various ERPs that suppliers use, creating a coherent picture of the situation. When used in addition to traditional statistical methods, the results include fewer inventory stockouts, less requirement for stocking surplus inventory, and the capability to meet demand spikes.

Real-time collaboration keeps supply chains agile and active, radically improving the ability to predict and respond to demand.

3) Sense and Shape

When statistical modelling and collaborative forecasting are used together, companies have the information that they need to take control of demand. Demand sensing involves using real-time data pattern analysis to offer planners actionable signals such as demand spikes. Demand shaping involves responding to that information with actions such as real-time pricing adjustments. When managed correctly, the combination of sensing and shaping can influence demand to match supply.

When companies have all the information at their fingertips, they can shape demand patterns to ensure complete agility.

4) Measure Demand Accuracy At Multiple Levels

When it comes to demand forecasting, information is vital, but it means nothing without comprehensive analysis capabilities. If a projection is accurate at the item and location levels but fails at the customer level, this represents an actionable issue that can be addressed. As such, planners who focus exclusively on one or two areas of analysis introduce an immediate disadvantage to visualising and predicting demand.

To improve accuracy and responsiveness, test accuracy at as many levels as possible, and confirm the accuracy with comprehensive analysis.

5) Select The Right Demand Planning Platform

The four points discussed above form a cohesive strategy for successfully projecting and managing demand. However, unless the approach is both integrated and accessible, errors are likely to occur. Data is currently growing exponentially as organisations embrace in-depth ERPs, increasing the risk of errors unless formidable and real-time analysis is applied. As such, selecting the right demand planning platform can make all the difference.

To ensure that your analysis is working with rather than against the data, aim to integrate as many forecasting approaches as possible within one hub.

What Next?

Excellent demand forecasting is about improving accuracy, improving responsiveness, and increasing clarity. For more information about how Reflex Planning can help, get in touch today to see our innovative software in action, using live data from your business.

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