USING PLANT SIMULATION IOT DATA ANALYTICS FOR ROOT CAUSE ANALYSIS

MG MOTOR INDIA, THE AUTOMOTIVE MANUFACTURER INCREASES THROUGHPUT BY 15 PER CENT IN PAINT SHOP.

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THE CASE STUDY IS ABOUT MG MOTOR INDIA using MindSphere and Plant Simulation loT data analytics to identify the root cause of production loss. Using the MindSphere visualisation application the automotive manufacturer increased throughput by 15 per cent in paint shop.

ADDRESSING THE BOTTLENECKS IN PAINT SHOP OPERATIONS

In 1924, Morris Garages vehicles was founded in the United Kingdom (UK). MG Motor India (MGI) is a subsidiary of SAIC Motor Corporation Limited and specializes in automotive manufacturing.

Most operations are located primarily in India at their plant in Halol. The company manufactures automotive parts and vehicles. There are many moving parts in an automotive assembly line, including a paint shop that focuses on surface treatments to the body of the vehicle and pre-treatment and electrolytic deposition (PTED) stations. Streamlining PTED paint shop procedures has been a significant bottleneck for the MGI plant team over the past few years.

With legacy automation systems in operation for over 20 years, the team couldn't use existing system such as programmable logic controllers (PLCs) and Andon Systems to disclose or suggest information on productivity development prospects.

They couldn't study processes aside from standard key performance indicators (KPIs) such as overall equipment effectiveness (OEE) or downtime data. The systems in the paint shop did not provide reliable and accurate real-time performance data.