Optimizing Replenishment Strategies via Demand Forecasting

Optimizing Replenishment Strategies via Demand Forecasting
Solution Optimizing Replenishment Strategy
Solution Optimizing Replenishment Strategy
Industry Retail
Region US
Technology Microsoft Azure
Context
The process of predicting future consumer demand for a good or service over a given time frame is known as demand forecasting. It is a fundamental component of business planning and supply chain management, allowing organizations to predict consumer demands and adjust their operations accordingly. Historical sales data, market trends, seasonal patterns, and outside variables like the state of the economy or competitor activity are all used in demand forecasting. Businesses frequently use a mix of qualitative and quantitative methods. Depending on the business requirements and operational applications, forecasting solutions can be built at multiple levels of granularity. We worked closely with the client organization and the solution's target users to obtain a better grasp of their workflows and issues. Our solution catered to their needs and optimized replenishment strategies that enabled more precise decision-making for purchase orders.
Problem statement

Our client, a leading global hardware retailer, was facing several hurdles with managing high cardinality data and challenges with sales data. Moreover, their current forecasting plan required high volumes of manual intervention, thus increasing execution time. They approached us with this problem, looking for a demand forecast that would solve these issues that they were facing. MathCo suggested creating a reliable forecasting system that could produce precise demand projections at the DFU level. This would subsequently improve inventory management and product availability across states by optimizing replenishment strategies and facilitating more accurate decision-making for purchase orders. 

Impact

  • 66% reduction in demand planner overrides. 
  • $12.33MM monthly opportunistic costs uncovered due to BIAS improvements. 
  • 7 hours execution time, brought down from 20 hours. 
  • 90% of cases saw a reduction in severity of under-forecasting. 

Access the Case Study to Learn More about This Partnership

Demand Planner

Discover how our Demand Planner solution helped a CPG company reduce inventory holding costs by 15%, cut baseline planning time by 35%, and lower lost sales from stockouts by 20%. Through advanced AI/ML models and data integration, the client achieved precise demand forecasting and improved operational efficiency.

Read more

Demand Forecasting for Optimizing Truckloads

Optimize truckload efficiency with data-driven demand forecasting. Discover how our custom solution helped a leading battery distributor achieve a 44% load factor reduction and a projected 7x ROI through advanced analytics, improved load planning, and operational enhancements.

Read more

Manufacturing Demand Forecasting

We helped a leading plumbing giant discover an estimated $2 million savings in production costs using a manufacturing demand forecasting solution.

Read more