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CASE STUDY
Demand Forecasting and Inventory Optimization for Leading Automotive Company
Industry | Automotive
Technology | Machine Learning
Location | USA
Our client is a leader in the American automotive parts industry, combining over fifty years of engineering expertise with advanced technology. Founded in 1967 and based in Detroit, Michigan, this company has built its reputation on innovation and a commitment to sustainable and high-performance automotive components. It has grown into a key supplier in the U.S. auto industry, known for its comprehensive range of parts that support the production of fuel-efficient sedans, SUVs, and electric vehicles.
Challenges
The client relied on traditional data science methods for demand forecasting.
These traditional methods failed to capture complex data patterns.
The client struggled to balance inventory levels effectively.
Inefficient inventory management led to increased costs and lost revenue.
Key Outcomes
40%
Increase in Operational Efficiency
10-20%
Increase in Customer Satisfaction
15-30%
Decrease in Inventory Costs
Solutions
Created feature stores by extracting the data from the data lakehouse.
Leveraged statistical models like ARIMA, SARIMA and Deep Learning Time Series models.
Created an optimization layer for inventory management.
Developed intuitive dashboards and visualization tools for stakeholders
What Customers Say about Royal Cyber
Congratulations and a big thank you to everyone that successfully implemented our demand forecasting solution. The team did a great job ensuring that our upgrades did not compromise operations or result in excess costs.