challenge
Our client operates a fast-growing retail supply chain with 50+ stores located across the East Coast of the US.
To plan for future supplies, the company manually collected and analyzed historical datafrom sales and production. As operations grew, statistical analysis alonecould no longer ensure accurate planning.
Limited scalability
The existing approach provided basic trends but couldn’t adapt to the company’s rapid growth and accommodate the growing number of stores and product categories.
Time-intensive processes
Collecting data, running forecasts, and adjusting predictions became increasingly resource-heavy.
Operational inefficiencies
Demand variability led to challenges in sales, logistics, and inventory management, requiring a more strategic approach to planning.
solution
Leveraging our experience in data enablement and supply chain, we started a step-by-step demand planning transformation.
Step 1
Expanded data parameters, incorporating factors such as promotional activities, regional demand fluctuations, and supply chain constraints.
Step 2
Developed a hybrid AI-powered forecasting model, blendingfeature engineering with machine learning to handle the growing complexity of data and parameters.
Step 3
Introduced iterative refinement, allowing the forecasting model to adapt by integrating new product lines, store locations, and customer behavior patterns as the company’s market presence expands.
impact
The implementation of an AI-powered forecasting solution developed by Trinetix transformed the company’s demand planning processes, delivering measurable improvements in operational efficiency and business performance.
Forecast accuracy improved by 10%
By integrating machine learning models with traditional methods, the company boosted forecast accuracy, significantly reducing the need for corrections.
Inventory costs reduced by 15%
Optimized stock levels and reduced overstocking minimized inventory holding costs, delivering savings within industry-expected ranges for AI-enabled demand planning.
Stockout events decreased by 13%
Accurate forecasts enabled better alignment between inventory and demand, cutting stockouts and ensuring consistent product availability across the store network.
Getting started with demand forecasting, most companies rely on time series analysis as a basis. In case data is enough and consistent, time series analysis is an efficient way to predict demand patterns for specific products grounding on historical data. However, as SKUs and the number of stores grow, traditional methods often fail to scale, prompting companies to explore machine learning for more accurate and adaptable forecasts.
