The hidden value of a simpler steel portfolio – the grade consolidation approach
The steel grade consolidation approach is under the umbrella of the Steel Production Excellence toolkit developed by CBMM in close collaboration with steelmaking companies, leaders in their segments. It combines deep metallurgical knowledge, AI-enabled portfolio analysis and disciplined implementation/value tracking. The objective is simple and practical: identify families of products that can be produced from fewer, more versatile steel chemistries, quantify holistically the operational savings from the SKU reductions and industrially implement the optimized portfolio within the mill’s actual process capabilities.
Steel plants are built for scale. Modern meltshops commonly operate with heats of more than 250 tonnes and continuous casting sequences of at least eight heats. In larger plants, heat sizes can exceed 350 tonnes. These economics favor long and stable campaigns. Customer demand, however, is increasingly fragmented across grades, dimensions, standards and delivery conditions. The result is a structural mismatch between the batch sizes optimal for productivity/costs and the volume the market requests in each planning cycle.
A single steel chemistry decision can generate 20 to 200 additional SKUs. Each SKU creates another demand signal for planning, another sequencing constraint for the meltshop and caster, another inventory position to manage, and another opportunity for material to miss its intended order.
Shorter sequences
More starts and stops can reduce caster utilization, increase tundish changeovers and energy demand, and generate more first- and last-of-sequence material.
Grade changes within a sequence
Chemical transitions may create semifinished material that matches neither programmed specification, increasing downgrades or discounted sales.
Early production to stock
Producing an annual or multi-month demand in one campaign can raise slab and billet inventory, weaken hot-charging opportunities. Wihile pushed to finished products, the strategy can cause ageing inventories of coils, beams or sections. All this with a penalty in working capital before customer offtake is secured.
Traditional product-costing systems do not always expose these effects. The cost of small batches and low-volume products can be spread across the larger portfolio. Capacity losses may sit in OEE, transition material in yield, excess semi-finished stock in working capital, and alloy or energy demand in separate accounts. Viewed independently, no single line tells the full story. Viewed as a system, the economics can be substantial.
Move niobium from a grade-level solution to a portfolio platform
Niobium is well established as a microalloying element for advanced steels. Its metallurgical effects can also support a broader operating philosophy and grade consolidation, especially in bulk commodity production due to three factors:
Niobium metallurgy can influence multiple processing stages. Through grain refinement, precipitation control and interaction with thermomechanical processing, niobium can help create fewer and more robust chemistries that deliver a wider range of property targets with controlled downstream processing. In structural steels, niobium is typically used as a true micro addition, often at levels around 100 ppm. At these low addition levels, it can enhance strength and consistency without changing the applicable standard requirements. This makes niobium especially suitable for versatile chemistries designed to serve multiple grades and processing routes.
Niobium can support tighter control of properties, reducing standard deviation of mechanical strength. This gives operators confidence to consolidate grades, including strength variability, through-thickness consistency, segregation, weldability and sensitivity to deleterious elements.
The analysis combines CBMM’s metallurgical expertise with a proprietary AI-enabled tool. Production and quality data are used to identify grades with compatible requirements, assess candidate clusters, estimate the effect of alternative alloy designs and check whether the proposed solution fits the plant’s actual capabilities. Expert judgment remains essential. The model accelerates the search and makes the trade-offs visible; metallurgists and mill teams determine which opportunities are technically sound and commercially acceptable.
This hybrid approach is particularly important due to niobium’s high adaptability to the manufacturing routes: reheating, deformation, rolling temperatures, cooling strategy and other process variables can amplify its metallurgical contribution. CBMM has developed metallurgical equations and proprietary models that describe these interactions. Combining these physics-based relationships with AI and plant production data allows the analysis to search a much broader solution space and identify the optimum chemistry-process window for each candidate grade
cluster.
The output is not simply a theoretical “future portfolio.” It is a prioritized set of grade-specific initiatives. Each initiative links a candidate chemistry and process route to a defined volume, implementation difficulty, expected KPI movement and economic impact. This creates a bridge between metallurgical design and executive decision making.
The engagement is structured to move quickly from a portfolio hypothesis to proven industrial results. The first four phases typically take three to four months, depending on portfolio size, data availability and trial cadence. Full implementation generally requires a further three to six months. Throughout the engagement, stage-gate reviews with senior sponsors create transparency on value, technical risk and the decisions required to proceed.
1. Define the scope and secure mutual commitment
CBMM and the steel producer define the target portfolio using high-level data on production volumes, number of grades, and major complexity drivers. The parties sign an NDA, establish project governance, nominate the core team, and secure the required resources and time Commitment. The outcome is a clearly defined scope, protected data access, and aligned sponsorship for the engagement.
2. Build and validate the customized hybrid model
The team gathers production, process, quality, and complexity data to establish the operational baselines. CBMM then adapts its hybrid model to the steel producer’s industrial big data, combining advanced analytics with metallurgical knowledge to predict whether grades can be consistently bundled while maintaining mechanical- property requirements. The model is calibrated and validated with the producer’s technical team before being used for the portfolio diagnostic.
3. Diagnose the portfolio and develop prioritized initiatives
The validated model is used to identify an optimized grade portfolio and quantify the full financial potential across capacity, yield, operating costs, and working capital. The top-down diagnostic is then translated into a bottom-up roadmap of specific grade-consolidation initiatives, each prioritized by economic impact, technical feasibility, implementation effort, and sequence. The project steering committee selects the initiatives that will proceed to industrial validation.
4. Pilot the concepts and verify the value
Selected initiatives are implemented through controlled industrial trials. The team evaluates specification compliance, mechanical-property
consistency, process stability, and operational feasibility while measuring the impact on complexity and cost KPIs. Successful trials provide proven
initiatives, validated benefits in terms of product costs and margins, and the evidence required to approve broader implementation.
5. Roll out and embed the optimized portfolio
The steel producer leads the full implementation of the approved portfolio changes, with CBMM providing targeted support as required. Grade
databases, production standards, process recipes, planning rules, procurement practices, costing routines, and performance-management systems are updated to reflect the simplified portfolio. The objective is to institutionalize the new operating model and sustain the identified benefits over time.
A practical governance cadence uses steering committees at each phase: kickoff and scope alignment; review of the top-down size of prize; approval of the bottom-up initiative portfolio; review of pilot results and quick wins; and confirmation of full impact after rollout. This gives leaders a clear line of sight from technical decisions to financial results.
Contact CBMM to explore a confidential, data-based assessment of grade consolidation tailored to your process, and evaluate potential financial gains prior to any process change
Optimize alloy chemistry to achieve performance with the right elements in the right balance (Mn, V, Nb).
• Lower CO2 footprint
• Better performance at lower cost
• Right alloy for every grade
Total Cost of Operation (TCO)
Optimize operations to reduce costs across the steelmaking value chain (BOF and EAF).
• Improved yield and performance
• Higher equipment reliability
• Lower raw material and energy cost
Optimize hot rolling of flat steel to maximize quality, yield and productivity.
• Reduced defects and rework
• Higher productivity and throughput
• Improved mechanical properties
Complexity Reduction
Consolidation of steel grades and longer production campaigns to reduce capital tie-up (lower # of SKU’s).
• Greater capacity utilization
• Lower inventory and working capital
• More stable operations
PREPARETION — Data Cleaning and Exploratory Data Analysis
ENRICHMENT — Run MicroSim for each coil or plate
MODELING — Train and test the hybrid model, combining Machine Learning and MicroSim
OPTIMIZATION — Balance cost, coverage and constraints
OUTPUT: Grade Consolidation proposals with quantified trade-offs and metallurgical safeguards
Data Cleaning and Exploratory Data Analysis
Production data is cleaned and explored to build a reliable modeling dataset and understand the operating conditions it represents.
• Remove incomplete and invalid records
• Identify and assess outliers
• Analyze distributions of key variables
• Check coverage across operating conditions
Hybrid Model Development
Original production data and MicroSim-derived metallurgical variables are combined to train a machine learning model that captures plant-specific patterns while incorporating physical knowledge.
• Combine production data with MicroSim-derived features for each coil or plate
• Split the enriched dataset into training and test sets
• Use the selected model to predict mechanical properties and support grade consolidation
Mn/S Envelope Definition
A curve is generated following the lower limits observed in the actual plant data, defining the minimum Mn/S ratio required for hot-shortness
protection and keeping proposed compositions in a metallurgically safe region.
• Compute the Mn/S ratio for every coil
• Generate a curve that follows the observed lower limits in the plant data
• The curve reflects plant-specific characteristics of the steelmaking and casting processes
• The curve is compared with typical values reported in the literature
MicroSim Simulation for Data Enrichment
MicroSim is run for each coil or plate to generate additional metallurgical variables, enriching the production dataset with physics-based information for modeling.
• Run a simulation for each coil or plate
• Extract relevant metallurgical variables
• Add simulation-derived features to the production dataset
• Use the enriched dataset to support hybrid modeling
Grade Consolidation Optimization
The selected hybrid model evaluates alternative compositions to identify grade consolidation opportunities that reduce cost while meeting
mechanical-property requirements and metallurgical constraints.
• Generate candidate compositions for the selected grades
• Predict mechanical properties for each candidate
• Screen proposals against quality and metallurgical constraints
• Compare feasible options by cost savings and grade coverage
Steel Grade Clustering
Grades with similar yield-strength characteristics are grouped to identify families with potential for consolidation. The groups are then examined to select candidates for optimization.
• Group grades based on similarities in yield strength
• Review the distribution of grades and coils within each group
• Compare chemical composition and mechanical properties
• Identify candidate grade families for consolidation
• Generate candidate Mn and Nb compositions for the selected grades
• Predict yield strength and check the acceptance window
• Compare proposed compositions with the Mn/S curves and other applicable constraints
• Evaluate cost impact and the number of grades covered
• Present the selected composition, eligible grades, and estimated savings for technical review
Strategic Advisor
Economist, PhD, Former Sr. Partner at McKinsey & Company with over 31 years of experience in leading operational excellence, capital productivity and corporate strategy programs at Steelmaking players worldwide.
Chief Technology Officer
Metallurgist, with over 25 years experience in application of technology on complex materials, former scientist at MIT and Technology Director at VoestAlpine Brazil.
Head of Str. Steel Technology Program
Metallurgist, with 20 years of experience, responsible for development of Nb technology for structural steel applications, former technology manager VoestAlpine Brazil.
Head of Growth
Metallurgist, with 20 years of experience in steelmaking, former Asc. Partner at McKinsey & Company, specialized in performance improvement programs at large steelmaking sites.