Electrolux is one of the largest appliance manufacturers in the world. In North America alone, Electrolux sells more than 2,000 different products to consumers through 9,000 business customers. A leader in product innovations that shape the appliance market, Electrolux was the first company to introduce a household washing machine, manufacture absorption refrigerators, put lights inside household laundry machines, introduce totally CFC-free refrigerators and freezers, manufacture the first robotic vacuum cleaner, and much more.

Increased competition from globalization has put great pressure on the appliance market, especially in the United States. Supply chains have become increasingly more complex. The biggest challenge for Electrolux was getting the lower level product mix forecast accurate – the overall accuracy was between 38%-50%. They needed to improve their forecast accuracy (>70%), reduce inventory (potential for 15% improvement), improve customer service levels (from 80% to >95%), and reduce costs for expediting and shipping products.

Electrolux was primarily using spreadsheets for sales and operations planning (S&OP). To create a forecast, the sales team delivered input with manual intermediate steps. This was time consuming and error prone as the analysis was not consistent across the sales team. Spreadsheets are not designed to handle large volumes of data, which makes it difficult to analyze years of historical data on a granular level. This spreadsheet approach also could not calculate forecast variance, which is necessary in the safety stock calculation for inventory optimization. There were also two different approaches for inventory optimization for single and multi-echelon networks.

Electrolux envisioned a standardized and automated approach for forecasting and inventory optimization that could be scaled as customer demand grew. The new system would also be able to adapt to constantly changing supply chain requirements.

The advanced analytics in SAS DDPO would allow Electrolux to gain valuable insights from their big data sets and enable them to make better data-driven decisions, improve demand forecast modeling and accuracy, provide for more efficient, collaborative planning, optimize inventory processes, and improve service levels to its customers. Supply chain process improvement was identified as one of the key areas where advanced analytics could add the greatest value.

For Electrolux, SAS Demand Driven Planning and Optimization delivers an enhanced, integrated platform for demand planning and inventory optimization, eliminating the reliance on multiple Excel-based processes.

Electrolux’s DDPO solution includes Demand Signal Analytics, Forecast Analyst Workbench, New Product Forecasting, Collaborative Planning Workbench, and Inventory Optimization Workbench. SAS Forecast Analyst Workbench (FAW) creates a statistical forecast based on the historical data. The statistical forecast is fed into the SAS Collaborative Planning Workbench (CPW) where Electrolux analysts can add value to the forecast based on market insights and real time promotional information. This enhanced forecast is utilized by SAS Inventory Optimization Workbench (IOW) to calculate inventory targets in the multi-echelon network to accurately predict inventory and satisfy the desired customer service level. A tuning & validation process is run automatically every quarter to calibrate inventory optimization parameters and quantify the benefits that could be expected from inventory optimization. Visual reports are also available for Electrolux to monitor the performance of the supply chain and to identify areas of improvement.

  • SAS® Forecast Analyst Workbench creates a statistical, automated baseline forecast for demand by product, location, customer, and month.
  • SAS® Collaborative Planning Workbench allows end-users (Demand Planners) to adjust the baseline statistical forecast on a monthly basis.
  • SAS® Inventory Optimization Workbench computes weekly inventory targets at the hubs and spokes of Electrolux’s multi-echelon network.
  • SAS® Visual Analytics generates reports to meet business requirements.

Results:

SAS conducted a proof of concept with Electrolux data and this conservatively increased customer service levels >95%,

Reduced inventory 15%

Improved forecast accuracy more than 90%

NPV in excess of $20m and payback within one year.