Problem Definition
SteelForge is a U.S.-based specialty steel manufacturer with $1.8 billion in annual revenue and four production plants spread across the industrial Midwest. The company produces high-strength alloys used in automotive, aerospace, and heavy machinery applications. SteelForge operates its own blast furnaces and rolling mills, giving it full vertical integration from raw steel to finished product. The company employs 6,200 workers across its four facilities and has long been considered a reliable, mid-cost producer in the specialty steel segment.
Over the past three years, SteelForge's gross margins have deteriorated sharply --- falling from 28% to 20%. This 8-percentage-point decline has eroded approximately $144 million in annual profitability. Management initially attributed the squeeze to rising raw material prices and competitive pressure from imported steel, but recent benchmarking suggests that SteelForge's costs are diverging from domestic peers in ways that commodity pricing alone cannot explain. The company's stock price has fallen 30% over the period, and two activist investors have taken board seats.
The CEO has engaged your consulting team with a clear mandate: identify a credible path to $140 million in annual cost savings that would restore gross margins to at least 25% within two years, without reducing output volume. The board has signaled willingness to invest up to $250 million in capital expenditures if the payback is under five years. Your team must determine: where are the largest cost reduction opportunities, what is the root cause of the margin decline, and what investment program should SteelForge pursue?
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If asked, please share that:
- SteelForge's total COGS is approximately $1.44 billion (80% of $1.8B revenue at 20% gross margin)
- Three years ago, COGS was $1.296 billion (72% of $1.8B at 28% margin) --- revenue has been flat
- The company's four plants range in age from 12 to 47 years old
- Plant 3, the oldest facility, was built in 1979 and has received only incremental maintenance upgrades
- Management has explored raw material hedging strategies but found limited savings potential since input costs are commodity-driven
- The industry average gross margin for specialty steel producers is 26%
Question 1Structuring
How would you approach identifying $140 million in annual cost savings across SteelForge's operations?
Hint · Structuring
Build 3–4 branches that are specific to this client and question, not a generic framework. Check they don't overlap and together cover the problem.
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- SteelForge's COGS breaks down into five categories: raw materials, energy, labor, maintenance, and overhead
- The company operates four plants with different ages, product mixes, and equipment generations
- Management has already renegotiated supplier contracts and implemented a hiring freeze --- both yielded limited results
- The CEO specifically stated that headcount reductions alone will not close the gap
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A rigorous cost reduction framework for a multi-plant manufacturer should cover four dimensions:
1. Cost Category Decomposition
- a) Break COGS into the five major categories: raw materials, energy, labor, maintenance, overhead
- b) Identify which categories have grown fastest over the three-year period (absolute and percentage increase)
- c) Benchmark each category against industry averages and best-in-class producers
- d) Separate controllable costs (energy efficiency, maintenance practices, overhead allocation) from uncontrollable costs (commodity input prices)
2. Plant-Level Disaggregation
- a) Compare unit costs (cost per ton) across all four plants for each cost category
- b) Identify statistical outliers --- plants where specific costs deviate significantly from the internal average
- c) Assess plant age, equipment vintage, and technology generation as potential drivers of cost differences
- d) Evaluate capacity utilization at each plant (underutilized assets inflate per-unit fixed costs)
3. Operational Efficiency Assessment
- a) Measure energy efficiency (energy consumed per ton of output) across plants and against industry benchmarks
- b) Evaluate yield rates (percentage of raw material that becomes salable product vs. scrap)
- c) Assess equipment uptime and unplanned downtime frequency
- d) Review maintenance spend patterns (preventive vs. reactive)
4. Investment and Implementation
- a) Size the savings opportunity for each lever (quick wins vs. capital-intensive projects)
- b) Prioritize by payback period against the board's five-year threshold
- c) Sequence implementation to deliver early wins that fund larger investments
- d) Identify risks (production disruption during upgrades, implementation timeline)
What the interviewer is looking forShow guidanceHide guidance
This is an interviewer-led case. After the candidate presents their structure, guide them toward a plant-level cost decomposition. Push back if the candidate proposes only top-down cost benchmarking without disaggregating by plant. The hidden insight is that company-wide averages mask a severe outlier at a single plant.
- Good candidates break costs into major categories (materials, energy, labor, maintenance, overhead) and propose benchmarking against industry averages
- Strong candidates add a plant-by-plant dimension to their framework, recognizing that facility-level variation is critical in manufacturing. They propose isolating which cost categories have grown fastest and where
- Excellent candidates structure their approach as a two-step drill-down: first identify which cost categories drove the $144M increase, then disaggregate the worst-performing category by plant to find the root cause. They recognize that some cost categories (raw materials) may be outside SteelForge's control and prioritize controllable levers
Question 2Numeracy
Using Exhibit 2, calculate the annual energy cost savings if SteelForge upgrades Plant 3's blast furnaces from 62% to 85% thermal efficiency. The upgrade would cost $180 million. What is the simple payback period, and should SteelForge proceed?
Hint · Numeracy
Write the formula before you plug in numbers, keep units and zeros explicit, and sanity-check the order of magnitude at the end.
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Share the following after the candidate has reviewed Exhibit 2:
- Upgrading to 85% efficiency means Plant 3 would consume less energy per ton in proportion to the efficiency gain
- The formula is: new energy cost per ton = current cost per ton x (current efficiency / target efficiency)
- The $180 million investment covers new furnace linings, heat recovery systems, automated combustion controls, and a 4-month installation period
- During the 4-month installation, Plant 3's output would be temporarily shifted to Plants 1 and 2, which have 15% spare capacity combined
- Assume energy prices remain constant for the payback calculation
Exhibit 2Plant-Level Unit Economics
| Metric | Plant 1 | Plant 2 | Plant 3 | Plant 4 | Company Avg |
|---|---|---|---|---|---|
| Location | Indiana | Ohio | Pennsylvania | Michigan | --- |
| Year built | 2012 | 2008 | 1979 | 2014 | --- |
| Annual output (M tons) | 1.08 | 0.90 | 1.08 | 0.54 | 3.60 total |
| % of total output | 30% | 25% | 30% | 15% | 100% |
| Total cost per ton | $380 | $390 | $480 | $375 | $400 |
| Energy cost per ton | $62 | $68 | $148 | $60 | $89 |
| Labor cost per ton | $68 | $72 | $75 | $70 | $72 |
| Maintenance cost per ton | $30 | $32 | $52 | $28 | $36 |
| Furnace thermal efficiency | 90% | 88% | 62% | 89% | 82% |
| Furnace age (years) | 14 | 18 | 47 | 12 | --- |
| Equipment uptime | 94% | 92% | 81% | 95% | 91% |
| Headcount | 1,350 | 1,200 | 1,800 | 850 | 6,200 total |
Energy cost per ton includes natural gas, electricity, and industrial oxygen. Thermal efficiency measures the percentage of fuel energy converted to useful heat in the blast furnace. Industry benchmark for modern furnaces is 88-92%.
Source: SteelForge case file
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Step 1: Identify Plant 3's current energy cost
From Exhibit 2:
- Plant 3 output: 1.08 million tons per year
- Plant 3 energy cost per ton: $148
- Plant 3 total energy cost: 1.08M x $148 = $159.8 million per year
Step 2: Calculate new energy cost at 85% efficiency
| Item | Value |
|---|---|
| Current thermal efficiency | 62% |
| Target thermal efficiency | 85% |
| Efficiency ratio (current / target) | 62 / 85 = 0.729 |
| New energy cost per ton | $148 x 0.729 = $107.9, round to $108/ton |
Step 3: Calculate annual savings
| Metric | Before Upgrade | After Upgrade | Difference |
|---|---|---|---|
| Energy cost per ton | $148 | $108 | $40 |
| Annual output (tons) | 1,080,000 | 1,080,000 | --- |
| Total energy cost | $159.8M | $116.6M | $43.2M |
Annual energy savings: approximately $43 million
Step 4: Payback analysis
| Metric | Value |
|---|---|
| Investment required | $180 million |
| Annual savings | $43 million |
| Simple payback period | $180M / $43M = 4.2 years |
| Board's threshold | 5 years |
| Within threshold? | Yes, with 0.8 years of margin |
Step 5: Context and recommendation
- The $43M in annual savings represents 31% of the $140M target from a single project
- After the upgrade, Plant 3's energy cost per ton ($108) would approach the average of Plants 1, 2, and 4 ($64), though not fully close the gap since the target is 85% vs. their 88-90% average
- Payback of 4.2 years is within the 5-year threshold but tight --- the candidate should flag that a 4-month production disruption during installation could delay savings realization, effectively stretching payback toward 4.5 years
- Reality check: $43M per year in energy savings for one plant is plausible given that Plant 3 currently spends $160M on energy annually --- the savings represent a 27% reduction, consistent with the efficiency gain
What the interviewer is looking forShow guidanceHide guidance
This question tests whether the candidate can extract the right data from Exhibit 2, set up the efficiency calculation correctly, and interpret the result. The most common mistake is inverting the efficiency ratio (dividing target by current instead of current by target). A second common error is applying the savings to total company energy rather than to Plant 3 only. Let the candidate work through each step. If they struggle with the efficiency formula, provide it and assess their ability to execute.
"So What?" Cascade:
- Level 1 (surface): The upgrade saves money on energy at Plant 3
- Level 2 (implication): The $43M annual savings represents 31% of the $140M target from a single investment, making it the cornerstone of any cost reduction program
- Level 3 (actionable insight): At a 4.2-year payback, this is within the board's 5-year threshold but leaves little margin for error --- the candidate should flag construction delays or energy price drops as risks that could stretch payback beyond 5 years
Question 3Judgement & Insights
Review Exhibit 1 and Exhibit 3. What is the root cause of SteelForge's margin decline, and why might management have missed it?
Hint · Judgement & Insights
Read the exhibit title, axes and units first. Lead with the ‘so what’, then back it with one or two numbers.
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- Hand the candidate Exhibit 1 (cost breakdown with 3-year trend) and Exhibit 3 (SteelForge vs. industry cost index)
- If asked: SteelForge's management reports costs at the company-wide level, not by individual plant
- If asked: the CFO's quarterly presentations to the board highlight raw materials as the primary cost concern because it is the largest single cost category
- If asked: Plant 3's furnaces were last upgraded in 2003 and are rated for 25-year useful life, which expired two years ago
Exhibit 1SteelForge COGS Breakdown and 3-Year Trend
| Cost Category | % of COGS | Current ($M) | 3 Years Ago ($M) | Growth (%) | $ Increase |
|---|---|---|---|---|---|
| Raw materials | 45% | $648 | $623 | 4% | $25 |
| Energy | 22% | $320 | $221 | 45% | $99 |
| Labor | 18% | $259 | $250 | 4% | $9 |
| Maintenance | 9% | $130 | $124 | 5% | $6 |
| Overhead | 6% | $83 | $78 | 6% | $5 |
| Total COGS | 100% | $1,440 | $1,296 | 11% | $144 |
Note: Revenue has been flat at $1.8B over the period. Gross margin fell from 28% ($1.8B - $1.296B) to 20% ($1.8B - $1.44B). Raw materials are commodity-priced inputs (iron ore, nickel, chromium) purchased on global markets. Energy includes natural gas, electricity, and industrial oxygen consumed in blast furnace and rolling mill operations.
Source: SteelForge case file
Exhibit 3Cost Index --- SteelForge vs. Industry Average (Indexed to 100)
| Year | SF Overall | Industry Overall | SF Energy | Industry Energy | SF Raw Materials | Industry Raw Materials |
|---|---|---|---|---|---|---|
| Year 0 (base) | 100 | 100 | 100 | 100 | 100 | 100 |
| Year 1 | 104 | 102 | 115 | 104 | 103 | 103 |
| Year 2 | 110 | 103 | 130 | 107 | 106 | 105 |
| Year 3 (current) | 116 | 105 | 145 | 110 | 108 | 108 |
Index base = 100 at Year 0 for both SteelForge and industry average. "Industry" represents the weighted average of the six largest U.S. specialty steel producers. SteelForge's raw materials index tracks industry closely, confirming that commodity prices affect all producers equally. SteelForge's energy index diverges sharply from industry, indicating a company-specific problem rather than a market-wide trend.
Source: SteelForge case file
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Identifying the root cause requires connecting three data points across exhibits:
1. Exhibit 1 reveals energy as the outlier cost category
While raw materials are the largest category at 45% of COGS ($648M), they grew only 4% over three years --- roughly in line with commodity inflation. Energy, at 22% of COGS ($320M), grew 45%. This 45% energy cost increase accounts for approximately $100 million of the $144 million total COGS increase. No other cost category comes close.
2. Exhibit 3 confirms the problem is internal, not market-wide
SteelForge's overall cost index has diverged from the industry average by 11 points over three years (from 100 to 116 vs. the industry's 100 to 105). Critically, the energy sub-index diverged by 30 points (100 to 145 vs. 110 for the industry). If rising energy prices were the cause, all producers would show similar increases. SteelForge's divergence proves the issue is company-specific.
3. Exhibit 2 pinpoints Plant 3 as the source
Plant 3's energy cost per ton is $148, compared to an average of $64 across the other three plants --- a ratio of 2.3x. Plant 3 produces 30% of total output but consumes 50% of total energy spend. The reason is clear: Plant 3's blast furnaces operate at 62% thermal efficiency, compared to 88-90% at the other plants. This is a direct consequence of Plant 3's age (47 years) and its furnaces exceeding their rated useful life.
Why management missed it:
- Averaging effect: Company-wide energy cost per ton blends Plant 3's $148 with the other plants' $64, producing a company average of approximately $89/ton. This average looks only modestly above the industry benchmark of $73/ton --- not alarming enough to trigger investigation
- Category bias: The CFO's quarterly reporting highlighted raw materials because it is the largest absolute cost category. But the relevant metric is rate of change, not absolute size. A 45% increase in a 22% category has more margin impact than a 4% increase in a 45% category
- Gradual degradation: Plant 3's efficiency did not decline overnight. Thermal efficiency likely degraded 1-2 percentage points per year as furnace linings deteriorated, making the trend invisible in quarterly reviews
What the interviewer is looking forShow guidanceHide guidance
This is the pivotal question where the candidate must synthesize data from multiple exhibits to identify the hidden root cause. The trap in Exhibit 1 is that raw materials are the largest cost category (45% of COGS), which tempts candidates to focus there. However, raw materials grew only 4% --- in line with commodity inflation and largely outside SteelForge's control. Energy, at 22% of COGS, grew 45% --- a massive outlier that accounts for the majority of the $144M margin erosion.
"So What?" Cascade:
- Level 1 (surface): Energy costs grew 45% while other categories grew 4-6%
- Level 2 (implication): When you combine Exhibit 1's energy spike with Exhibit 2's plant-level data showing Plant 3's energy cost per ton is 2.3x the other plants' average, the root cause becomes clear: Plant 3's aging furnaces are the single largest driver of margin erosion. But company-wide cost reporting averaged the problem across four plants, diluting Plant 3's outlier status
- Level 3 (actionable insight): Management's focus on raw materials was a category error --- they were looking at the biggest slice of the pie instead of the fastest-growing one. This is a common trap in multi-facility operations where plant-level reporting is not granular enough
Push back if the candidate says "energy costs went up" without identifying Plant 3 as the specific source. A general observation about rising energy prices would apply to the entire industry, but Exhibit 3 shows SteelForge diverging from industry peers specifically on energy --- meaning the problem is internal, not market-wide.
Question 4Creativity
The CEO asks for your final recommendation: should SteelForge upgrade Plant 3, close it and redistribute volume, or outsource Plant 3's production? Present a complete cost reduction program to reach the $140M target.
Hint · Creativity
Brainstorm in buckets (e.g. internal vs external, short vs long term) so ideas stay structured and you can see gaps.
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- Closing Plant 3 would trigger $85 million in one-time costs (severance for 1,800 workers, environmental remediation, asset write-downs)
- Redistributing Plant 3's volume to Plants 1, 2, and 4 is not fully feasible --- they have only 15% combined spare capacity, covering roughly half of Plant 3's output
- Outsourcing Plant 3's specialty alloys to contract manufacturers would cost approximately $520/ton vs. Plant 3's current $480/ton fully loaded cost --- but at market-competitive quality
- The remaining $97M in savings (beyond Plant 3's $43M energy savings) must come from other operational improvements
- If asked: procurement optimization across all plants could yield $25-30M; predictive maintenance rollout could save $15-20M; overhead consolidation (shared services, IT) could save $20-25M; yield improvement programs could save $15-20M
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Recommendation: Upgrade Plant 3 and execute a four-lever cost reduction program.
Option Assessment for Plant 3:
| Option | Annual Savings | Investment | Key Risk | Verdict |
|---|---|---|---|---|
| Upgrade furnaces to 85% efficiency | $43M | $180M (4.2-yr payback) | 4-month production disruption | Recommended |
| Close and redistribute | ~$43M (energy) minus lost volume | $85M one-time | Can only absorb 50% of Plant 3's output; lose 15% of total company volume | Rejected |
| Outsource production | Negative ($520 vs. $480/ton) | Minimal | Higher cost, quality risk, loss of control | Rejected |
Closure fails because Plants 1, 2, and 4 cannot absorb Plant 3's full volume. Losing 15% of output directly contradicts the CEO's mandate. Outsourcing is more expensive per ton and introduces supply chain risk for specialty alloys where metallurgical consistency is critical.
Complete Cost Reduction Program ($140M target):
| Lever | Annual Savings | Investment | Payback | Timeline |
|---|---|---|---|---|
| Plant 3 furnace upgrade | $43M | $180M | 4.2 years | Months 6-18 |
| Procurement optimization (all plants) | $28M | $5M (analytics tools) | 2 months | Months 1-6 |
| Overhead consolidation (shared services) | $22M | $8M (IT integration) | 4 months | Months 3-12 |
| Predictive maintenance rollout | $18M | $15M (sensors, software) | 10 months | Months 6-18 |
| Yield improvement (scrap reduction) | $16M | $12M (process controls) | 9 months | Months 9-24 |
| Contingency / stretch targets | $13M | --- | --- | Ongoing |
| Total | $140M | $220M | --- | 24 months |
Implementation Sequencing:
-
Phase 1 (Months 1-6): Quick wins --- $50M in annualized savings. Procurement renegotiation and overhead consolidation require minimal capital and can begin immediately. These generate early cash flow to build organizational momentum and fund Phase 2.
-
Phase 2 (Months 6-18): Plant 3 upgrade and maintenance --- $61M in annualized savings. The furnace upgrade is the anchor project. During the 4-month installation window, shift Plant 3's volume to spare capacity at Plants 1 and 2; defer the remaining volume through customer scheduling agreements. Simultaneously roll out predictive maintenance sensors across all four plants.
-
Phase 3 (Months 9-24): Yield improvement and optimization --- $29M in annualized savings. Process control upgrades to reduce scrap rates from the current 6% to a target of 3.5%. This requires operational data from the predictive maintenance sensors installed in Phase 2.
Financial Summary:
| Metric | Value |
|---|---|
| Total annual savings (at full run-rate) | $140M |
| Total investment required | $220M |
| Board-approved capex budget | $250M |
| Blended payback | 1.6 years (weighted average) |
| Gross margin impact | 20% restored to 27.8% ($140M / $1.8B = 7.8pp) |
| Target margin (25%) achieved? | Yes, exceeded by 2.8pp |
Key risks: Plant 3 installation delays could push $43M of savings into Year 3. Mitigation: negotiate liquidated damages in the EPC contract and pre-stage critical equipment. Procurement savings assume supplier willingness to renegotiate --- lock in multi-year contracts during the current steel demand cycle.
Contributed by CaseDrill practice community
What the interviewer is looking forShow guidanceHide guidance
This is the synthesis question. The candidate must weigh three options for Plant 3, then assemble a broader cost reduction program that adds up to $140M. A strong candidate will do rough math to eliminate infeasible options quickly and present a structured program with a timeline. Push back on any recommendation that lacks quantification.
- Good candidates recommend the upgrade based on the payback analysis from Q2 and list additional savings levers without quantifying them
- Strong candidates explicitly rule out closure (insufficient capacity at other plants, high one-time costs) and outsourcing (higher per-ton cost, quality risk), then build a quantified savings bridge from $43M to $140M
- Excellent candidates propose a phased program with the Plant 3 upgrade as the anchor, sequence quick wins (procurement, overhead) before capital-intensive projects (predictive maintenance), and address the $180M investment within the board's $250M capex budget. They acknowledge that the $140M target requires executing on multiple fronts, not just Plant 3