In the dynamic landscape of Consumer Packaged Goods (CPG), strategic forecasting is paramount. Accurate predictions pave the way for efficient operations, improved long-term capacity planning, and harmonious relationships with suppliers. However, achieving accurate forecasting remains a challenge for many businesses. This article presents a three-pronged approach, leveraging data and machine learning, to reduce forecasting errors by up to 35%.
The Cornerstone of Effective Forecasting: Data Collection
Data is the linchpin of any forecasting endeavor. An overlooked but indispensable part of improving forecasting accuracy is the gathering and tidying of relevant data.
Unconstrained demand data, which records historical demand unhampered by limitations such as stock shortages, should be your primary focus. Most businesses track constrained sales, but unconstrained demand data can offer richer insights, especially when forecasting is conducted on a daily or weekly basis.
It's also important to understand the key factors affecting your demand. These demand drivers can range from promotional activities and pricing adjustments to weather patterns and store operation times. Identifying these factors can guide your next steps toward improved forecasting.
Leveraging Machine Learning for Accurate Predictions
With comprehensive data at your disposal, the next move is to create machine learning models to draw insights from this data. When correctly applied, these models can provide a substantial 20% reduction in forecasting errors compared to traditional statistical methods.
You can enhance the performance of these models by adding more variables into the mix, such as prices, shortages, and promotional activities. This could result in an overall boost in forecasting accuracy of up to 30-35%. However, achieving such results often necessitates the use of custom-designed models, which can outperform their standard counterparts.
Refining Forecasts with FVA and Strategic ABC XYZ Segmentation
The final piece of the puzzle involves refining your forecasts with the Forecast Value Added (FVA) methodology and smart ABC XYZ segmentation. This process could potentially reduce overall forecasting errors by an additional 5-15%.
Start by training your planning team to spot judgmental biases, both intentional and unintentional. Then, implement the FVA methodology, a key to achieving excellence in demand planning. Finally, use strategic ABC-XYZ segmentation to amplify your planners' efficiency, leading to more focused and effective planning.
Summary
Adopting this three-tiered approach can lead to substantial improvements in your CPG business's forecasting accuracy. The use of machine learning can decrease errors by 20%, while integrating demand drivers like promotions and shortages can each contribute a further 5% reduction. Finally, employing FVA and ABC XYZ strategies can shrink errors by an additional 5-15%.
Ultimately, this approach can greatly enhance operational efficiency in your CPG business. It can not only slash forecasting errors by up to 35%, but it could also lead to a 10-14% reduction in inventory and a 15-20% decrease in shortages, enabling your CPG business to thrive in today's competitive marketplace.
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