Altair > Case Studies > Mahindra Rise: Enabling Smart Engineering with IoT

Mahindra Rise: Enabling Smart Engineering with IoT

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Technology Category
  • Application Infrastructure & Middleware - Data Visualization
  • Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
  • Aerospace
  • Automotive
Applicable Functions
  • Product Research & Development
  • Warehouse & Inventory Management
Use Cases
  • Inventory Management
  • Vehicle Performance Monitoring
Services
  • Data Science Services
  • System Integration
About The Customer
Mahindra & Mahindra is a global leader in the transportation industry, with operations spanning automotive, aerospace, and agribusiness sectors. The company relies on 50 to 100 systems for each automotive project it undertakes, including systems for product cost, inventory, and billing. These systems need to be interconnected for a properly functioning parent system, necessitating collaboration between product and manufacturing engineering, cost, and legacy systems. The company's various systems, such as ERP, CRM, PLM, SAP, and TDM, generate specific data throughout the product life cycle, which needs to be collected from various departments and systems to facilitate comprehensive analysis and key decision-making.
The Challenge
Mahindra & Mahindra, a global pioneer in the transportation business, faced a significant challenge in managing and analyzing the vast amounts of data generated by multiple IT systems. These systems, which include Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Product Lifecycle Management (PLM), Systems, Applications and Products (SAP) and Tool Data Management (TDM), are integral to the company's automotive, aerospace, and agribusiness operations. Each system generates specific data throughout the product life cycle, requiring collection and analysis to facilitate key decision-making. The challenge was to create a standardized decision support system that could consolidate data from these multiple sources and present the right information at the right time to the right person. The company needed a solution that could interlink all these systems for a properly functioning parent system, enabling collaboration between product and manufacturing engineering, cost and legacy systems.
The Solution
Altair provided a solution in the form of a web business intelligence platform, SmartWorks™, which layered analytics on top of Mahindra & Mahindra's systems. A key component of this platform was Altair SmartSight™, a data analytics platform that accelerates data visualization, exploration, and discovery. It collects and aggregates data from various systems and presents it in interactive dashboards. Mahindra & Mahindra used the SmartSight platform as an integral part of the vehicle engineering product development process to automate production planning and engineering. This enabled them to easily visualize the data and analyze projects at all stages of development for improved decision-making. The platform also allowed users to formulate custom Key Performance Indicators (KPIs) and parameters using an intuitive drag and drop interface, which could then be shared and managed for their own analyses.
Operational Impact
  • The implementation of Altair's SmartWorks™ and SmartSight™ platforms has transformed Mahindra & Mahindra's data management and decision-making processes. The platforms have enabled the company to visualize and analyze data from multiple systems in a consolidated, interactive dashboard, significantly improving the efficiency of their product development process. The ability to formulate custom KPIs and parameters has provided the teams with a dynamic, up-to-date interface for data analysis, facilitating faster and more informed decision-making. The platform's role-based access control has also enhanced data security. Overall, the solution has not only streamlined the company's data management but also improved transparency and collaboration across different teams.
Quantitative Benefit
  • Drastically reduced data consolidation from many sources
  • Faster decision-making capabilities
  • Improved efficiency

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