To make an analogy with one of my favorite sports—Formula 1 motor racing—oftentimes success and a top-three podium finish hinge on critical decisions that are made in real time. These decisions could be about engine performance settings, tire changes and the like, based on continuous telemetry and data analysis by the pit crews and engineers. This type of information and analysis allows drivers to take corrective actions (e.g., reducing braking or “coasting” going into a corner) and get the best performance out of their cars.
In the steelmaking production process, the situation is similar. Here, the “car” is the production process and product being produced, and the “pit crew” is composed of production operators, planners and business operators. The crew leverages the manufacturing execution and enterprise resource management systems to help them analyze, diagnose and plan.
In addition to investing in core standardization of business processes and supporting IT systems, steel companies are continually evaluating where to make focused investment decisions to improve their business operations and achieve sustainable competitive advantage. (For more information, see my previous blog: A strategy for steel company survival).
Focus on integrated process automation
One of the key areas for leveraging technology as a way to drive increased competitive advantage (e.g., superior product quality) is through integrated process automation. Due to the relatively short-lived nature of new product differentiation in today’s markets, better integration of process automation systems with traditional back-office systems can help drive faster innovation and product quality improvements.
For instance, consider a steel slab that is being processed through hot rolling, pickling, cold rolling and annealing operations. At each stage, a multitude of product quality data (e.g., the tensile strength parameters of the coil) is being captured. Moving through the different production operations, tensile strength measurements will typically vary (hopefully within pre-defined, allowed tolerances) as a result of operational variability.
Based on such real-time quality feedback, some steel companies are already using the data and analysis at a given step of production to take dynamic production control decisions at the next step downstream. For example, a company could slightly adjust cold rolling pressure to move the quality parameters (established during hot rolling) back towards the target set-point values and away from the upper (or lower) tolerance limits. Such decisions can be made based on running data-driven, real-time analytics and rules engines that model the production processes.
These corrective actions can have a two-fold benefit. First, they help ensure product quality remains within the sweet spot of the set-point target area, thereby avoid quality downgrades. Second, in some cases the actions can reduce operating costs (e.g., by needing to use less energy). This concept of dynamically changing downstream process unit manufacturing instructions based on actual upstream results provides a “feed-forward” capability.
This approach can also be taken to the next level to achieve even tighter integration and move towards cognitive analytics and analysis. Such operating and product data can be used to drive updates to core master data that is used within enterprise management (or ERP) systems—and thereby close the loop with sales order management processes as part of a continuous “self-learning” system.
For example, by evaluating operational and quality data, it is possible to dynamically refine the mathematical rules for each steel grade and material properties that are being used as part of the product definition (or “order dressing”) process and models in the enterprise management system. As a result, the order dressing process is being continually optimized based on the latest plant and production conditions as appropriate. Here is a scenario: As operating improvements are achieved on a given annealing furnace (due to improved engineering), these improvements are automatically and progressively reflected in the product definition business rules that drive the sales order configuration and subsequent production planning and process control processes.
A future on the podium
Given today’s advances in IT, enabled by enterprise management systems—such as the SAP S/4HANA and related analytical platform capabilities—these integrated “smart manufacturing,” closed-loop control systems can now be realized as part of a steel company’s digital strategy roadmap. In turn, this will help drive further innovation and provide a basis for sustainable, competitive market differentiation. The result? It could be a podium finish for your steel organization.