Traditional vs. Innovative methods of forecasting demand.
The utilization of historical data for predictions
Demand is, unarguably, not fixed, and their fluctuations are unpredictable. Therefore, demand planners have to employ several methods and techniques to get the approximate figures. As mentioned above, the most popular practice among organizations is statistical forecasting—the traditional reliance on historical sales data to forecast the future demand. This is also the traditional approach in legacy demand planning software.
History alone, however, is not a good indicator of future demand. Market needs don’t simply follow a linear upward or downward line, and looking in the rearview mirror doesn’t help you drive forward.
Solely utilization of historical data results in imprecise forecasts and inaccurate decision-making. This method can only reach maximal 70% efficiency in terms of accuracy, which is still insufficient. Eventually, businesses have to bear the brunt: redundant inventory, lowered and rigid serviceability, skyrocketing costs and expedites—fees paid to secure the remedial goods deliveries in case of incorrect shipments.
Back to the above example, suppose the commercial department used only data from two previous school opening seasons to formulate the predictions. The production managed to reach 1000 pairs of shoes to meet the commercial’s original intent. But then the unprecedented pandemic kicks in; schools are closed, demand plummets by two-thirds. The company now has 600 pairs of shoes in excess. Historical data didn’t predict the incidence, and the company now faces an overproduction crisis.
If companies keep clinging to the status quo, the business is more vulnerable to risks. So how can they get rid of the problem? The answer is simple: Get off the beaten path and employ a new approach for higher forecast accuracy.
How driver-based forecasting can drive planning efficiency up
Instead of basing predictions solely on throwback data, companies can now use real-time indicators for driver-based forecasting. Driver-based forecasting allows businesses to learn more about the effects of market changes on demand and adjust forecasts simultaneously. They can get the latest updates and understand what impacts drive demands right when they fluctuate: price changes impact, promotion activities, natural event impacts, etc., both internal and external. With demand drivers, companies can discover the uncharted territory that historical data alone fails to reveal.
Once companies have learned how useful market drivers are, a new question arises: How can they process a huge amount of data to gain timely insight?
This is what a next-gen demand planning software or an Integrated Business Planning (IBP) software with built-in demand planning solution can do.
Leverage automated advanced analytics for effortless decisions
The leverage of artificial intelligence and machine learning (AI/ML) makes next-gen demand planning special. Companies can now harness and process data from external and internal sources quickly and automatically. The process output is more meaningful and improves visibility and rationality for decision-making. Companies can achieve up to 90% accuracy in forecasts while making decisions faster with more confidence. The risk of false predictions drops down to just one-third compared to sole historical data usage. Further benefits in productivity, efficiency, and bottom-line returns are also foreseeable along the way.
Maximize planners’ productivity and effectiveness.
Effective demand planning is vital to a company’s success. It is necessary to upgrade capabilities to match the pace of data and market changes. And to do this, it’s imperative to choose the correct implementing solutions for all stakeholders’ best interest.
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