Client Organization:

India's largest private steel manufacturer

Project Owner:

VP Operations

The Problem:

Client is a market leader but had concerns that they should substantially improve in terms of Availability (Machine up & running), Performance (Production at optimum efficiency), and Quality (Every pipe produced meets quality standards). Client asked us to identify specific factors that can decrease productivity losses by 15%.

The Solution:

We blended different data sets collected by PLC machines, plant operators and then identified data collection issues by crosschecking the information. We performed detailed analytics on the blended data up to fine granularities and identified Key Performance Statistics (KPS) that readily indicated the efficiency of a plant. We quantified substantial losses unknown to the users until then. We built various descriptive and predictive analytical models and gained a no. of insights from the data. While the objective was to reduce productivity losses by up to 15%, we were able to make specific recommendations, which could increase productivity by 60-65 percent.

Tools & Technologies:

R & Python