RELEX Solutions > Case Studies > AI-Based Demand Planning Boosts Forecast Accuracy and Sales for One Stop

AI-Based Demand Planning Boosts Forecast Accuracy and Sales for One Stop

Technology Category

Analytics & Modeling - Machine Learning
Sensors - Level Sensors

Applicable Industries

Equipment & Machinery
Retail

Applicable Functions

Logistics & Transportation
Sales & Marketing

Use Cases

Demand Planning & Forecasting
Inventory Management

Services

Data Science Services

About The Customer

One Stop is a leading convenience store chain in the UK with over 900 company and franchise neighborhood stores across Great Britain. The retailer offers local communities a range of everyday household essentials, fresh produce, quick and easy lunchtime favorites, and dinner deals. Many One Stop stores also offer additional services including free cash machines, Post Office, PayPoint, lottery, and more. A subsidiary of Tesco, One Stop has been a RELEX customer since 2010, using its space and assortment solutions. In 2019, the retailer expanded their use of RELEX to include forecasting and replenishment solution in stores and distribution centers.

The Challenge

One Stop, a leading UK convenience store chain and a subsidiary of Tesco, faced challenges in managing the complexity of their product assortment. Their broad product offering ranged from ultra-fresh products with short spoiling times to more ambient inventory with longer shelf life. Demand for many products was sensitive to external factors such as weather, and sales for some products were easily cannibalized by promotions on similar items. These complex forecasting scenarios, in which multiple drivers could overlap and interact to impact demand, required a sophisticated solution. One Stop aimed to increase day-level forecast accuracy for products with demand driven by weather and cannibalization, and improve fresh product availability without seeing a corresponding rise in spoilage.

The Solution

One Stop turned to RELEX’s fully machine learning-based demand forecasting solution to manage their forecasting challenges. The AI-driven supply chain implementation allowed One Stop to process large amounts of information and extract weather-related demand factors from their historical sales data. By automatically applying these insights with local weather predictions to their demand forecasts, they were able to improve availability for weather-sensitive products. RELEX’s machine learning also helped One Stop manage their product cannibalization, contributing to more accurate forecasts for products whose demand is significantly impacted when prices for similar products change. Despite the increased sophistication and complexity of their planning tool, it remained easy to use and reduced manual work.

Operational Impact

Within just four months of implementing AI-driven demand forecasting in their stores, One Stop saw major improvements. The retailer drove a significant increase in forecast accuracy on both the product-week level and the product-store-week level. This increased forecast accuracy improved replenishment outcomes as well. By automatically drawing more accurate store-level demand forecasts into replenishment planning, RELEX improved One Stop’s in-store availability across their entire assortment while contributing to a significant sales increase. The company saw even larger improvements to availability in weather-sensitive products such as ice cubes, which had once been among their most challenging categories to forecast. Despite complex forecasting scenarios, the results showed improvements in day-level forecast accuracy for products with weather-driven demands, cannibalization, and fresh products' availability without spoilage corresponding.

Quantitative Benefit

3.2 pp increase in forecast accuracy on product-week level
1.8 pp increase in forecast accuracy on product-store-week level
8.5% increase in ultra-fresh product availability without significant increase in spoilage