Tata Steel, Jamshedpur

The pioneer of steel production in India, Tata Steel is one of the top ten steel makers in the world. It is also the world’s second most geographically diversified steel producer.

Simulation & Optimization Modelling for Future Capacity Analysis

We identified the bottlenecks in the current plant setup and developed an optimization model for handling the increasing capacities in the plant. We carried out a simulation study in the facility to develop the process.

Key Benefits:

  • Identified key bottlenecks in the logistics layout that can hinder expansion plans.
  • Resource utilisation keeping in mind further expansions
  • Queuing time and turn around time analysis
  • Significant savings by identifying key bottlenecks
  • Significant savings by identifying key bottlenecks

Tata Steel, Kalinganagar

Tata Steel wanted to study the potential capacity of the rail tracks designed for (Tata Steel Kalinganagar). At present, the layout has been developed for handling rakes for low MTPA. TSK has planned to augment the capacity to three times the current MTPA. Tata Steel wanted to conduct a simulation study to understand whether the proposed tracks and layout will prove adequate to handle rake movements for the enhanced capacity plan.

Key Benefits:

  • Quantification of current state layout and performance levels
  • Resource utilisation keeping in mind further expansions
  • Identified key bottlenecks in the logistics layout that can hinder expansion plans.
  • Comparison of predictions from the Baseline model vs Future State simulation model
  • Queuing time and turn around time analysis

Koppers (Major US Checmical Manufacturer), Rail Car Simulation

Koppers is a global producer of chemicals, carbon compounds, and treated wood products. It is based out of Pittsburgh, Pennsylvania. It has a global manufacturing and distribution network across continents. Koppers wanted to right size their rail car and plan future purchase based on simulation modelling.

Key Benefits:

  • Right sizing of railcars in order to fulfil the desired service levels
  • Efficient future purchase & acquistion of new rail cars
  • Reduction in loading & unloading times
  • Optimization of their entire rail car fleet