Mahindra & Mahindra: Embedding AI in shop floor workflows to raise manufacturing productivity
Mahindra & Mahindra, a global industrial group, needed a smarter way to improve shop floor productivity and do more with existing factories, equipment, and people. Industrial engineers relied on motion and time studies, but captured observations by hand and reentered data into SAP systems, creating delays, manual effort, and risk of errors. Powered by the SAP Business AI Platform, Mahindra’s custom AI application turns spoken observations into trusted operational data in real time, helping engineers improve processes faster while keeping human judgment in control.
| Industry | Company size | Headquarters |
| Automotive | 324,000+ | Mumbai, India |
40%
higher efficiency when capturing motion-study data.
0
manual reentry of motion-study data, reducing errors.
40+
manufacturing facilities positioned to scale AI workflows.
The opportunity
Increasing output with existing factories, equipment, and people
Mahindra & Mahindra began as a steel trading company in 1945 before assembling Willys Jeeps after World War II, helping shape India’s automotive industry. Today, Mahindra operates as a federated industrial group spanning automotive, farm equipment, finance, construction, hospitality, agriculture, and power solutions. Across automotive and industrial manufacturing, rising customer expectations, tighter competition, and faster product cycles are increasing pressure to do more with existing factories, equipment, and people. For Mahindra, that challenge plays out at scale: it has 40-plus manufacturing facilities, 100-plus active models, and about 1,800 business processes for each model. At that scale, even small inefficiencies in how it observes, measures, and documents work can multiply quickly.
At this scale, even small data errors can have real-world consequences, delaying payments, disrupting suppliers, and ultimately impacting when medicines reach patients. Sanofi needed to improve speed and data accuracy without introducing additional risk in a highly regulated environment.
Turning manual shop floor studies into trusted operational data
Mahindra built the MOST Custom AI use case to modernize how industrial engineers capture motion and time studies. MOST refers to Maynard’s Operation Sequence Technique, a method for analyzing work steps, motion, and timing. The work on the shop floor stays the same: engineers still observe operations and apply their expertise. What changes is the capture experience. Instead of writing movements and timestamps by hand, the engineer speaks the action out loud as it happens. Powered by the SAP Business AI Platform, Mahindra’s custom AI application converts spoken observations into text and structures the associated task and timestamp for review. The SAP Fiori user experience lets engineers check and correct the output before approval, keeping human oversight central.
The solution
Turning spoken shop floor observations into trusted operational data
Mahindra built the MOST Custom AI use case to modernize how industrial engineers capture motion and time studies. MOST refers to Maynard’s Operation Sequence Technique, a method for analyzing work steps, motion, and timing. The work on the shop floor stays the same: engineers still observe operations and apply their expertise. What changes is the capture experience. Instead of writing movements and timestamps by hand, the engineer speaks the action out loud as it happens. Powered by the SAP Business AI Platform, Mahindra’s custom AI application converts spoken observations into text and structures the associated task and timestamp for review. The SAP Fiori user experience lets engineers check and correct the output before approval, keeping human oversight central.
Embedding AI in Mahindra’s SAP digital spine with trusted governance
Mahindra built on SAP because many of its core processes and data already reside in a single global SAP instance across 85-plus entities, making integration, scale, and governance essential. The company did not want AI to sit outside the enterprise landscape as a disconnected experiment. Building on earlier process harmonization and cloud modernization work, SAP Business Technology Platform capabilities, now a core part of SAP Business AI Platform, help orchestrate audio upload, AI processing, data handling, and integration with back-end systems. Process automation capabilities in the SAP Build solution support review and approval steps, while SAP Cloud ERP Private solutions store approved motion-time data as the system of record. The result is a governed workflow that turns spoken shop floor observations into trusted operational data inside Mahindra’s digital spine.
Building practical AI use cases with enterprise architecture guidance from SAP
Working with SAP enterprise architects helped Mahindra move from broad AI exploration to use cases with measurable business impact. Through the engagement, SAP helped assess where standard Joule capabilities could apply and where a custom AI approach would create more value. For MOST Custom AI, that guidance helped shape a governed, production-ready architecture aligned to Mahindra’s existing SAP investment and broader AI road map. The collaboration gave Mahindra a practical path to scale AI from a promising shop floor idea into an integrated enterprise workflow, keeping the focus on measurable outcomes rather than technology for its own sake.
The result
Increasing data-capture efficiency by 40% while eliminating manual reentry
At the first manufacturing facility, MOST Custom AI increased the efficiency of motion-study data capture by 40%. It also eliminated manual reentry of motion-study data, replacing handwritten observations and duplicate entry into SAP systems with voice-based capture that syncs structured data in real time to SAP Cloud ERP Private. This directly addresses the earlier pain points of duplicate effort, slow documentation, and error-prone entry, creating a cleaner path from observation to system of record.
Giving engineers more time for analysis and improvement
The result changes the day-to-day experience for industrial engineers. Instead of balancing observation with note-taking and subsequent data entry, engineers can focus on what requires their expertise: analyzing work patterns, validating the data, and identifying opportunities to improve the line. That matters in a manufacturing environment where AI adoption depends on trust. Because engineers remain in control of review and approval, MOST Custom AI shows how AI can augment their expertise, making work easier and strengthening confidence in the data.
Scaling a governed AI workflow across manufacturing facilities and use cases
The larger opportunity comes from scale. A focused voice-to-text capability becomes far more powerful when applied across Mahindra’s manufacturing footprint: 40-plus manufacturing facilities, 100-plus active models, and thousands of process touchpoints.
Looking ahead, Mahindra plans to explore Joule’s analytical capabilities for MOST Custom AI. Separately, it is working with SAP on a factory dispatch use case designed to increase vehicle throughput, while also identifying additional opportunities in finance and supply chain.
The team is also exploring how Joule Studio and the SAP LeanIX solution could support future agent-based scenarios across SAP and non-SAP systems. Together, these efforts are helping Mahindra establish a foundation for future Autonomous Enterprise capabilities, where trusted data, AI, applications, and governed business processes work together to support more connected and intelligent execution while people remain in control.