Automating motor maintenance with AI and ML driven solutions built on Google Cloud Platform
The Client is the third largest two-wheeler manufacturer in India. The client’s business spans across industries like Automobile, Aviation, Education, Electronics, Energy, Finance, Housing, Insurance, Investment, Logistics, Service and Textiles. The client is a big advocate of innovative, easy-to-handle, and environment-friendly products, backed by reliable customer service. They aim at delivering total customer satisfaction by anticipating customer need and presenting quality vehicles at the right time and at the right price. They have proved time and again that this sense of responsiveness along with a penchant for quality is the winning formula. The client also has many firsts to its credit including the fact that they launched seven vehicles on the same day – a rare feat in Automotive history.
The client looked for a trusted partner in ML/AI who can successfully guide them in leveraging Google Cloud’s Cloud For Marketing (C4M), Artificial Intelligence, and Machine Learning capabilities to drive breakthrough innovation for their maintenance servicing business. The client looked to Pluto7 as a domain expert with a proven track record for support with their data warehousing, smart analytics, and machine learning initiatives.
A major division of the client’s business function is the Vehicle Servicing and Maintenance division. Running on a legacy data infrastructure, the process of identifying what part of each vehicle needs servicing and how long will non-serviced parts keep working efficiently was being handled in-person by the client’s servicing agents. This was both inefficient as well as more costly for the end customer, as some major parts which were on the verge of breaking down were often skipped – leading to much larger repair costs as well as personal inconvience.
To help them solve this existing problem , Pluto7 came up with an innovative strategy to predict the working lifetime of each individual part on their whole portfolio of vehicles and identify the most necessary servicing routines for each individual customer’s vehicle when they brought it to the shop. This allowed for pro-active maintenance – enabling the client to increase the average running life of all of their vehicles sold in the market.
For customer insights, the client’s marketing team relied on insights from data in various sources to guide their marketing strategy. Pluto7 and Google Cloud provided the client with a holistic view of their customers by connecting data across unintegrated data systems. The client’s goal is to was to leverage a cloud platform for scalable self-service analytics that will enable them to explore additional marketing channels and 3rd party data to gather granular intelligence on their customer’s background, needs, behavior, and accordingly tailor their business operations. Pluto7 worked with the client on a three-phase design and production pilot using machine learning methods to accelerate time to insight, provide scalability, and reduce IT overhead.
After creating realtime data pipelines and centralizing their legacy data into one cohesive data warehouse, Pluto7 trained an ML model that identifies defects in inviduals parts and predicts the life expectancy for them. These predictions were fed into the client’s existing servicing CRM , removing the need for any human intervention for suggesting the required servicing packages and defining a maintenance schedule for the customer’s vehicle. Pluto7 also built an expansive analytical platform for the Business intelligence team to analyze insights about part performance, operational costs and human resources.