Automating Raw Material Ordering for Retail In-house Production
In the dynamic landscape of retail, optimizing supply chain processes is crucial for efficiency, cost reduction, and maintaining a competitive edge.
In-house production has become a necessity rather than just a trend in retail. Retailers now offer their own food products across various formats, including discount stores and gas stations. The assortment has significantly expanded, encompassing fast food, complex salads, health-conscious options, and premium segments. One critical aspect is managing raw material and ingredient procurement for in-house production. In this article, we explore an example of how a retailer automated its ordering process, leveraging machine learning and cloud computing.
In-house production has become a necessity rather than just a trend in retail. Retailers now offer their own food products across various formats, including discount stores and gas stations. The assortment has significantly expanded, encompassing fast food, complex salads, health-conscious options, and premium segments. One critical aspect is managing raw material and ingredient procurement for in-house production. In this article, we explore an example of how a retailer automated its ordering process, leveraging machine learning and cloud computing.
Challenge
The retailer faced several challenges:
- Diverse Assortment: With 965 different ready-to-eat food items, forecasting demand accurately across stores was complex.
- Store-Level Forecasting: Predicting demand at the store level for each day over a 6-week horizon required granular insights.
- Labor-Intensive Planning: Traditional methods of calculating ingredient requirements were manual and time-consuming.
- Waste and Write-Offs: Inaccurate forecasts led to excess inventory and losses.
Solution
Step 1: Demand Forecasting
Machine learning algorithms were employed to predict demand. Factors considered included sales history, seasonality, trends, days of the week, holidays, and even weather conditions. The model integrated weather forecasts, automating a previously labor-intensive process.
Step 2: Propagating Demand to Ingredients
Each production SKU had a corresponding recipe specifying ingredients and quantities. The forecasted demand for final SKUs was propagated to ingredient-level forecasts.
Step 3: Mapping Ingredients to Orders
Reference tables mapped ingredients to specific items for ordering. This streamlined the process, ensuring accurate procurement.
Step 4: Calculating Requirements
Using production recipes, the retailer calculated ingredient needs. Ingredients were translated into specific SKUs for ordering based on recipe consumption.
Step 5: Automatic Purchase Orders
The forecasted demand was transmitted via an API to the retailer’s accounting system. Centralized purchase orders were generated for suppliers.
Microsoft Azure’s cloud computing power accelerated calculations.
Machine learning algorithms were employed to predict demand. Factors considered included sales history, seasonality, trends, days of the week, holidays, and even weather conditions. The model integrated weather forecasts, automating a previously labor-intensive process.
Step 2: Propagating Demand to Ingredients
Each production SKU had a corresponding recipe specifying ingredients and quantities. The forecasted demand for final SKUs was propagated to ingredient-level forecasts.
Step 3: Mapping Ingredients to Orders
Reference tables mapped ingredients to specific items for ordering. This streamlined the process, ensuring accurate procurement.
Step 4: Calculating Requirements
Using production recipes, the retailer calculated ingredient needs. Ingredients were translated into specific SKUs for ordering based on recipe consumption.
Step 5: Automatic Purchase Orders
The forecasted demand was transmitted via an API to the retailer’s accounting system. Centralized purchase orders were generated for suppliers.
Microsoft Azure’s cloud computing power accelerated calculations.
Results and Benefits
The project achieved the following:
Labor Efficiency:
Labor costs decreased by 4x due to automation.
Accuracy:
Improved forecasts reduced waste and write-offs by 20%.
Competitive Advantage:
Leveraging advanced technologies allowed the retailer to stay ahead in a highly competitive industry.
In conclusion, embracing data-driven approaches, cloud computing, and automation can revolutionize supply chain management, enhance profitability, and position retailers for success in an ever-evolving market.
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