Demand forecasting solution with 10+ market disruption indicators, delivers prediction accuracy of 92%

Sigmoid provided a customized demand forecasting solution utilizing advanced ML algorithms, incorporating market indicators and macroeconomic parameters. The solution led to 80% faster time to insights, reduced man-hours in demand calculations, improved forecast accuracy, and enabled swift responses to changing market dynamics.

Business Challenge

The client is a leading multinational beverage company with a diverse product portfolio serving 65+ markets globally. The business strategy team was looking for ways to drive category growth and efficient inventory management by accurately forecasting industry wide demand for the distilled spirits category. The traditional forecasting methodologies were not capturing weather information or macro-economic parameters. To withstand external market forces and maintain steady sales for the product category, the client needed a centralized demand forecasting model that would provide them a consolidated view of the global market.

Sigmoid Solution

We created a customized demand forecasting solution using advanced ML algorithms to estimate future demand for the global distilled spirit segment. The estimates were predicted across monthly, quarterly, half-yearly and yearly intervals for 3 external market scenarios– optimistic, moderate, and pessimistic. The forecast model accounted for more than 10 market disruption indicators and macro-economic parameters for calculating demand for different markets. The forecasting model was built to generate values for over 10k combinations of country, category, sector, and price tiers.

Business Impact

Sigmoid’s demand forecasting solution enhanced planning and inventory control across global markets. The automated data pipelines reduced manual efforts, leading to higher efficiency and focus on value-add activities. The client was able to capitalize on industry trends for expanding overall market share.

80% faster

model processing

92% accuracy

in aggregate forecasts


monthly data refresh cycle

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