When Chemical Engineering magazine published “Strategic Cost Engineering for Chemical Manufacturers” by RCM Thermal Kinetics’ David Loschiavo and Kelly Carmina, it addressed an issue that reaches far beyond theory: the widening gap between project expectations and actual outcomes in the CPI.
If you manage a plant, lead engineering teams, or evaluate capital projects, this topic isn’t academic—it’s your daily reality. Cost overruns, unclear scope, and unpredictable timelines can make even well-designed projects feel risky.
This review breaks down the most important insights from the article and explains how they apply directly to real-world decision-making in chemical processing.
Cost Engineering Is No Longer Optional. It’s a Risk-Control Tool.
Loschiavo and Carmina make a clear point: in the Chemical Process Industries, accuracy in cost estimation is inseparable from reliability in plant performance.
Chemical facilities deal with:
- Extreme conditions
- High-value or hazardous materials
- Tight regulatory boundaries
- Complex unit operations that don’t tolerate guesswork
Missing the mark early—even by a small percentage—can spiral into:
- Cost overruns
- Delays in commissioning
- Supply-chain complications
- Negative ROI or stranded assets
This is why cost engineering has evolved into a risk-management discipline, not just a budgeting exercise.
Why Chemical Projects Go Over Budget (And How To Prevent It)
The authors cited an industry statistic that should get any plant manager’s attention:
60–70% of large chemical projects exceed budget.
A few root causes:
- Unclear scope definition
- Lack of early-stage validation
- Inflationary swings in equipment and materials
- Underestimated regulatory or permitting complexity
The solution?
Structured estimating practices tied directly to process engineering outputs.
This may sound straightforward, but many organizations still rely on:
- Back-of-the-envelope equipment pricing
- Uncalibrated scaling factors
- Vendor budget quotes without supporting process deliverables
The article argues—and correctly—that true predictability requires technical definition that matches the estimate class, using AACE guidelines. ze.
The Role of Process Expertise: Why Not All Cost Estimates Are Equal
One of the most valuable takeaways from the article is this:
A cost estimate is only as good as the process engineering behind it.
For plants considering solvent recovery systems, MVR evaporation, distillation revamps, or waste-conversion processes, this is especially relevant.
Why?
Because the cost drivers in CPI projects come from technical behavior—feedstock variability, fouling potential, heat integration strategy, solvent loss, utilities balance—not from generic multipliers.
The article reinforces that estimates must reflect:
- Actual separation loads
- Modeled energy consumption
- Realistic equipment sizing
- Scaleup behavior validated through physical testing
This is where RCM Thermal Kinetics’ pilot testing and simulation-driven engineering approach adds practical value: you remove guesswork before committing capital.
BNB and ROM: Turning Qualitative Judgment Into Quantitative Confidence
A standout portion of the article highlights two internal tools:
- Bid-No-Bid (BNB)
- Risk and Opportunity Matrix (ROM)
Rather than being sales tools, both are actually risk translation frameworks.
BNB
This stage filters opportunities using four questions:
- Can we add value?
- Should we pursue?
- Can we compete?
- Is the engagement aligned?
For plant managers evaluating vendors, this is useful context. When suppliers use structured screening methods, it ensures:
- Transparent communication
- Clear technical alignment
- More accurate early estimates
ROM
This matrix quantifies risks related to:
- Technical performance
- Schedule
- Scope clarity
- Commercial terms
- Geographic constraints
What matters for operators is this:
the ROM forces risk into measurable dollars, not vague discussion.
That’s critical for budgeting and contingency planning.
Pilot Testing as Project Insurance
The article strongly advocates for physical validation—pilot testing, bench-scale work, or testing centers.
For chemical manufacturers, especially those operating in:
- Solvent recovery
- VOC reduction
- Crystallization
- Complex distillation
- Waste-to-value processes
Pilot testing can prevent multi-million-dollar surprises.
Key benefits:
- Verifies separation efficiency
- Identifies fouling or foaming behavior
- Optimizes energy profile
- Validates equipment sizing
- Derisks handoff from simulation to reality
In other words:
Pilot testing often pays for itself before the quote is even approved.
When to Use Parametric Estimating (And When Not To)
For early-stage screening, parametric methods are an efficient tool—especially when:
- Process definition is <10%
- You’re comparing alternatives
- You need feasibility-level accuracy (Class 5 or 4)
But the article makes an important distinction:
Parametric models should never replace detailed engineering when moving to a Class 3–2 estimate.
This is a common error in the CPI, where early estimates are incorrectly carried into funding requests without corresponding technical definition.
A Practical Example: Cost Engineering Done Right
The article’s case study of a specialty chemicals producer demonstrates the value of a phased approach:
- Class 4 feasibility
- Class 3 budget
- Class 2 control estimate
The result?
Final installed cost landed within 3% of the Class 2 estimate, with major OPEX and emissions benefits.
For our audience, this reinforces an important truth:
Predictability is engineered, not guessed.
Key Takeaways for Plant Managers and Engineering Leaders
If you’re evaluating a capital project, debottlenecking an existing system, or planning future expansions, here’s what matters most:
1. Tie estimating accuracy to the correct level of process definition.
Estimate Class ≠ formality. It dictates accuracy.
2. Demand process models, not just equipment lists.
Simulations, mass/energy balances, and validated assumptions drive credible numbers.
3. Use structured risk tools.
BNB and ROM (or equivalents) keep surprises out of the budget.
4. Validate assumptions through testing when feasible.
Pilot data reduces contingency and improves confidence.
5. Integrate cost engineering throughout the entire project lifecycle.
It’s not a “front-end” task, it’s part of every stage.
Final Thoughts: Why This Article Matters Now
Chemical manufacturers face continuous pressure:
- Lower emissions
- Higher recovery
- Reduced waste
- Tighter budgets
- Faster project timelines
Loschiavo and Carmina’s article provides a roadmap for achieving these goals through methodical, technically informed cost engineering. For any plant leader who needs both financial predictability and operational performance, the practices outlined are not merely recommendations—they’re competitive advantages.
By pairing rigorous cost engineering with proven process expertise and validation testing, we help chemical manufacturers plan with confidence and execute projects without financial surprises. Connect with our engineering team to discuss your next project.