Pluto7 helped the third largest two-wheeler manufacturer to improve their Demand Forecast Accuracy

August 18 , 2020

About the Customer

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.

Use Case Description

At TASPL processes from tracking the orders, forecasting the orders, getting inputs from the sales team, placing orders with suppliers and shipping from mother warehouse to satellite warehouse is done manually. They want the automation of tracking orders, forecasting the orders, sales inputs, placing orders with suppliers and shipping from mother warehouse to satellite warehouse

Challenges

1. 1. TASPL wants to reduce their lead time so that they don’t have to maintain stocks for a long duration. For instance, if they are maintaining a 30-day inventory currently, they want it to come down to 10 days or less.
   2. Improve Demand Forecast Accuracy
   3. Achieve tighter integration between planning, procurement, and shipping phases
   4. Drive innovation to connect the Vehicles to their connected Supply Chain
   5. Telemetry aligned to supply chain operations vision to be on par with Auto industry disruption
   6. Machine Learning at every stage of supply chain for decision to drive inventory and labor optimization
   7. TASPL wants this entire process to be automated so their team can focus on planning-related tasks

Solution

1. 1. A cost optimised production system with measures for the optimal usage of the Cloud Composer Eg.Create and Delete Cloud Composer jobs with REST API invoked using Cloud Functions
   2. A near real time or business time workload management system by configuring Dataflow and Cloud Composer with the right machine type and storage
   3. Adopting  the best practices in GCP Eg. creating only Regional Storage class for Storage, provision every resource in asia-south1 (Mumbai) region
   4. Monitoring, Application Logging & Audit loggingin the production system

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