Enhancing Demand Forecasting Accuracy for an Industrial Manufacturing Giant 

Business Challenge

A leading industrial manufacturer headquartered in the USA, with over $4 billion in annual revenue, faced significant challenges stemming from inaccuracies in their demand forecasting processes. The company, known for its complex manufacturing operations, managed more than 20 manufacturing facilities and 70 warehouses worldwide. However, an ineffective forecasting system led to operational inefficiencies, with stockouts and inflated inventory holding costs (IHC) contributing to a 20% loss in overall business revenue.

Inaccurate Demand Forecasting

Operational Inefficiencies

One-Size-Fit-All Approach

Consequently, some SKUs experienced overstocking while others faced stockouts, collectively costing the company over $200 million annually. Addressing this challenge required a solution capable of improving forecast accuracy by at least 20%, enabling the company to reduce losses and enhance operational efficiency.

How Quantzig Helped

Quantzig initiated the engagement by analyzing the demand signals across various SKUs. This in-depth analysis enabled the development of an SKU classification framework, which served as a foundation for categorizing and managing inventory more effectively. The framework was designed to address the unique behaviors and demand patterns of individual SKUs, ensuring a more targeted approach to forecasting.

To enhance accuracy, Quantzig integrated advanced machine learning algorithms capable of interpreting the nuanced demand signals of each SKU. Multiple forecasting models were applied to each SKU, allowing the identification of the most suitable algorithm for predicting demand. This multi-model approach ensured that the forecasting process was tailored to the specific needs of each SKU, eliminating the inefficiencies of a one-size-fits-all methodology.

Quantzig further automated the calculation of the Mean Absolute Percentage Error (MAPE) for each forecasting model. This automation not only streamlined the process but also ensured the selection of the best-fit model for each SKU with minimal manual intervention.

Results and Impact

By leveraging this data-driven, automated approach, Quantzig delivered a solution that improved forecasting precision by 75–90%, which led to optimized stock levels, minimizing stockouts and overstocking.

Automating forecasting processes cut forecasting time by an impressive 80%, enabling quicker decisions and more efficient production planning with better resource allocation.