ARTICLE
Inter Cars: Data driven nie oznacza „bez kierowcy”
Improved Forecast Accuracy for 500,000 Products – Inter Car’s Road to Success with Data Analysis
Inter Cars achieved this using • SAS® Viya
Inter Cars is the largest importer and distributor of automotive spare parts for passenger cars and commercial vehicles in the Middle and Eastern Europe. Their extensive presence is evident through their network of over 600 branches.
The organization comprises a workforce exceeding 14,000 individuals, spread across various entities, including their headquarters, subsidiary companies in Poland, foreign subsidiaries, and regional warehouses throughout Europe.
Inter Cars – Facts & Figures
500,000
forecasted products
600
branches
6 months
for return on investment
Challenge
Inter Cars sought to develop a sales forecasting solution for the retail market to meet increasing customer demands and maintain a competitive edge. The main challenge was to ensure timely product availability in both stores and among wholesalers, all while minimizing costs. The objective was to enhance forecast accuracy for around 500,000 products, encompassing 12 million time series across 19 countries.
Solution
Based on SAS® Viya and Microsoft Azure, Inter Cars has replaced prior methodologies with an advanced solution that incorporates data cleansing and augmentation processes. This includes the detection of inventory anomalies and outliers. A unique approach was implemented to estimate seasonal patterns within the hierarchical structure and their inheritance for low-frequency sales and across multiple countries. Machine learning algorithms were then utilized to approximate long-term trends and incorporate weather-related effects into the forecasts.
- Seasonality patterns: Determining seasonality patterns for individual products and each branch within the hierarchical structure, within across multiple countries, using a 24-month sales history as the basis.
- Sales potential: Assessing the sales potential of a specific product in a given location, based on sales data from the previous 12 months.
- Forecast: Allocating sales potential according to the identified seasonal pattern.
Results
Thanks to the cooperation between SAS and Algomine, 68% of the products now benefit from more accurate forecasts, leading to improved product availability at retail points and, consequently, increased revenue. The expected time for a return on investments is six months.
The forecasts generated by the SAS system are integrated into the business application, enabling users to plan product deliveries more swiftly and with greater precision. The implementation of these advanced analytical solutions has provided employees with valuable time that can be redirected towards addressing other strategic tasks critical to the company’s objectives.
”Inter Cars has transformed its sales forecasting with the support of SAS and Algomine. The advanced solution, integrated with AI analytics and algorithms, significantly improved product availability. This partnership, extending for years, has not only met our objectives but also freed up time for strategic tasks. We appreciate the ongoing positive impact on our business.”
Retail Experts
Grzegorz Mikulski
Principal Industry Consultant,
SAS
Marcin Ramotowski
Senior Account Executive,
SAS
Patryk Iwanek
Data Scientist,
Algomine
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