Consulting

An Indian steel and power major was facing financial pressures and operational challenges. Frequent changes in raw material source resulted in variations in quality along with additional impacts on operating parameters. The company was looking to leverage AI/ML to address these challenges and transform its operations to enhance output at lower cost while maintaining consistent quality.

In addition to financial and operational challenges, the company was also looking to address other internal challenges through digitally led transformation. The company was reliant on relatively older operational technology (OT) and the disconnected nature of its various OT and Information Technology (IT) platforms prevented a 360-degree visibility into existing processes and with desired granularity. Operational decision-making was largely driven by empirical rules and was not data driven. These empirical rules were not revised regularly. Inadequate exposure to global standards and inconsistency in operating philosophies contributed further to the inefficiencies.

The company managed multi-plant operations comprising of different operating philosophies, non-standard production process and technology, skill variability and limited visibility on the performance drivers which resulted in sub-optimal costs, throughput and quality.

Apart from internal challenges, there were sectoral and structural challenges as well. The Indian steel industry was facing both demand and supply side challenges. While consumption was muted, producers were experiencing frequent fluctuations in the availability and prices of raw materials. These challenges were further accentuated by sub-optimal logistics infrastructure and ballooning debt servicing costs, resulting in significant balance sheet stress for all companies in the industry.

The client was looking to address these internal and external challenges and leverage digital to turnaround its business operations through:

• Effective sourcing of input raw materials 
• Optimizing consumption through right material blends
• Maximizing throughput and capacity utilization
• Maintaining consistent quality

EY built tailormade digital solutions for the company’s integrated production set up, including sourcing models and assisted decision-making tools for the procurement function, and blending/ recipe mix models for coke ovens, sinter plants, blast furnaces, and steel melting shops. EY undertook a rigorous approach, incorporating multiple aspects for diagnostics and solutioning:

Our solutions at different production units considered the dynamic material properties and changing plant conditions and were integrated for end-to-end process visibility, process control along with consumption optimization and productivity enhancement. The integrated tool covered the entire gamut of steel production value chain with features for descriptive cockpits, root cause analysis for key drivers, predictive insights and prescriptive recommendations.

For sourcing optimization, a total cost of ownership approach was imperative to determining the optimal mix of input sources which would help the client achieve least cost without compromising quality or productivity. EY developed and deployed the following solutions to help address the client’s sourcing needs:

Once sourcing needs were addressed, it was important to optimize consumption without compromising quality and productivity to drive maximum value realization. For this, the EY team developed a digital twin solution for different aspects of the production value chain, leveraging AI/ML techniques of genetic algorithms, neural networks and feature selection algorithms for relationship establishment and prediction and linear as well as non-linear programming concepts for optimization and prescriptive insights.

EY’s digital solutions were designed considering interplay of operational parameters with the chemical and physical properties of the materials and reduced consumption costs through optimized blends and optimized operating parameters while maintaining requisite quality and productivity. EY’s data driven analytical approach to solutioning supported by robust teaming and governance enabled development of contextual solutions to address the relevant challenges, transform operations and deliver tangible benefit through levers of consumption optimization, parameter optimization and productivity enhancement. The benefit delivered through consumption optimization was closely tracked for the major raw materials during the implementation of the respective solutions and are illustrated below:

Digital twin for blast furnace – consumption optimization of burden mix

Digital twin for coke oven – consumption optimization of coking coal

Digital twin for ladle refining furnace – consumption optimization of ferro-alloys

The deployment of digital solutions spanning the value chain transformed operations across the production line in an integrated manner to deliver intangible and tangible business benefits. The individual solutions delivered cost savings at their respective deployment sites and were integrated into the “Digital Twin” tool to leverage the synergies across solutions and to develop end-to-end process visibility and control along with process and parameter optimization.

The digital transformation was rooted in strong business and technical understanding and leveraged advanced AI/ML to establish relationships between technical parameters and optimize consumption and enhance productivity to deliver tangible cost savings at various production units without compromising productivity or quality.

In addition to the tangible benefits, the deployment of digital solutions spanning the value chain transformed operations and delivered additional intangible benefits through enhancement of people’s skillsets and productivity, KPI driven performance management, focus on root cause analysis and outcome driven daily meetings and stronger governance.