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Mahindra & Mahindra Drives Profitability via Dynamic Segmentation
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Automotive
Applicable Functions
- Logistics & Transportation
Use Cases
- Inventory Management
- Supply Chain Visibility
Services
- Data Science Services
The Challenge
Mahindra & Mahindra Farm Equipment, part of the $20 billion Mahindra Group, is the world’s number-one tractor company by volume. Its automotive business competes in almost every segment of the industry. The Spares Business Unit (SBU) provides genuine vehicle and tractor spare parts via advanced capabilities in sourcing, assembling, warehousing and distribution. To maximize supply chain efficiencies and service, Mahindra & Mahindra constantly evaluates scientific methods to tweak demand forecasting, inventory management and replenishment planning strategies to ensure that the right parts are available at the right place and time. However, their traditional, manually driven segmentation processes and tools often resulted in inefficient allocation, high safety inventory levels and less-than-optimal service levels.
About The Customer
Mahindra & Mahindra Farm Equipment is part of the $20 billion Mahindra Group. It is the world’s number-one tractor company by volume, and its automotive business competes in almost every segment of the industry. The Spares Business Unit (SBU) provides genuine vehicle and tractor spare parts via advanced capabilities in sourcing, assembling, warehousing and distribution. The company constantly evaluates scientific methods to tweak demand forecasting, inventory management and replenishment planning strategies to ensure that the right parts are available at the right place and time.
The Solution
Mahindra & Mahindra SBU leverages Blue Yonder Luminate Platform, which applies machine learning (ML) to both systematically classify spare parts into unique demand clusters and establish the significance of demand attributes. The result is built-in visibility, intelligence, context and collaboration tools that enable increased allocation efficiency and profitability. Via optimization techniques, powered by ML, service levels are assigned to demand clusters, with the goal of minimizing safety inventory costs. In addition, ML and artificial intelligence algorithms accurately estimate lead times and further optimize the cost-to-serve from both a customer service and inventory cost perspective. Instead of making manual decisions, the SBU can analyze data at scale, identify critical demand variables and autonomously optimize the supply chain for specified profitability goals.
Operational Impact
Quantitative Benefit
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