Thank you for contacting us!
Your submission has been received and we'll be in contact with you shortly.
Return homeOops! Something went wrong while submitting the form.
Your submission has been received and we'll be in contact with you shortly.
Return home
Understand cost, navigate tariffs.
Drive deeper intelligence into product design cycles.
Scaled PCFs and reduction insights.
Determine your full supply chain and its cost.
Baseline your footprint and decarbonize.
Building Product Intelligence.
Find open roles at Muir.
Muir has plans for any scale.
Thank you for registering
Nov 2025
Most companies discover their products cost too much only after designs are finalized, tooling is ordered, and suppliers have submitted quotes, when fixing cost problems requires expensive redesigns and delays. By that point, 80% of product cost is already locked in by decisions engineers made weeks or months earlier without visibility into cost implications.This article explains how should cost modeling during design phases reveals cost impacts while you can still act on them, covering what early-stage cost analysis involves, why design decisions determine final costs, how to integrate cost modeling into design workflows, and which capabilities make cost intelligence accessible to engineering teams.
Looking to subscribe to our newsletter? Click here.
Should cost modeling calculates what a product or component theoretically costs to manufacture based on materials, labor, processes, and overhead; before you ever ask a supplier for a quote. This approach differs from traditional costing methods that wait for vendors to provide pricing after the design is complete.
Think of it this way: instead of designing a product and then hoping the cost works out, you see exactly how your design choices affect cost while you're still making those choices. When an engineer selects aluminum over steel or changes a part's process, the cost impact appears immediately rather than weeks later when it's expensive to make changes.
Should cost represents the theoretical optimal cost based purely on materials, manufacturing processes, labor rates, and reasonable overhead. Will cost reflects what you'll actually pay in the market including supplier margins, negotiation outcomes, and competitive dynamics.
The gap between these two numbers tells you something important. A large gap might mean you have room to negotiate with suppliers, while a small gap suggests you'll get better results by redesigning the product than by pushing suppliers on price.
A complete cost model during design pulls together several elements:
Each piece connects directly to design decisions. Material selection affects raw material costs, part complexity drives manufacturing time, and supplier location determines labor rates and shipping expenses.
The best time for should cost analysis spans three phases: concept development, preliminary design, and design iterations. During concept development, you're comparing fundamentally different approaches, cast versus machined, plastic versus metal, integrated versus modular. Cost visibility here prevents you from pursuing concepts that can never hit target costs.
Preliminary design is when you're defining material, manufacturing process and sub-components. Cost feedback at this stage guides optimization before detailed engineering begins. Throughout design iterations, continuous cost visibility shows whether changes improve or harm your cost position.
Design decisions determine final product cost, even though actual spending happens much later during manufacturing and procurement. When you select a material, you're not just choosing mechanical properties, you're setting raw material costs, available manufacturing processes, supplier options, and supply chain complexity.
Once designs move toward production, your cost reduction options narrow dramatically. At that point, you're limited to supplier negotiation and volume leverage rather than fundamental redesign. The earliest design phases, when you have maximum flexibility and minimum sunk cost, represent your greatest opportunity to influence product profitability.
When cost engineering happens after design freeze, organizations face expensive consequences. Design changes at this stage require tooling modifications that can cost tens or hundreds of thousands of dollars. Timeline pressure often forces acceptance of higher costs rather than proper redesign.
Missing target costs leaves only difficult options: compress margins and reduce profitability, increase prices and risk market competitiveness, or delay launch while redesigning for cost. Each path damages business outcomes.
Beyond direct financial impact, late cost discovery creates organizational problems. Engineering teams feel blindsided by cost targets they never designed to meet. Procurement teams scramble to negotiate savings that design decisions made impossible. Product managers face difficult tradeoffs between features, cost, and timeline that earlier visibility could have prevented.
Integrating cost analysis into design workflows doesn't require replacing existing systems. The goal is connecting cost intelligence to the decisions engineers already make every day, making cost modeling accessible to design engineers rather than just procurement specialists.
Start by defining your cost structure and identifying which cost drivers matter most for your product categories. A machined component requires detailed material and cycle time modeling, while an assembly focuses on labor content and part count.
Determine the level of detail appropriate for different design phases, rough order of magnitude during concept selection, detailed analysis during design optimization. Then establish data sources: material pricing databases, regional labor rate tables, manufacturing process time standards, and logistics cost models.
Connect ERP systems with cost modeling capabilities so design changes automatically trigger cost updates. This integration means engineers see cost implications immediately as they modify or change materials, turning cost from a periodic review checkpoint into continuous feedback.
Manufacturing process knowledge becomes accessible during concept development, not just during production planning. Engineers can understand how their design choices affect manufacturing feasibility, cycle time, and yield before committing to specific approaches.
Identify which design parameters most significantly impact cost for your products. Material type often dominates, but part complexity, production volume, surface finish requirements, and supplier location can each drive substantial cost variation.
Include supply chain considerations in cost models: tariffs that vary by country of origin, logistics costs that change with supplier location, and lead times that affect inventory carrying costs. A design decision that seems cost-neutral from a manufacturing perspective might have significant supply chain cost implications.
Build what-if models that let you compare design alternatives quickly: aluminum versus steel, domestic versus overseas manufacturing, single complex part versus multiple simple parts. The faster you can evaluate scenarios, the more options you'll explore.
Scenario modeling also helps you understand cost sensitivity and risk. If a design is highly sensitive to material pricing volatility, you might choose a more stable alternative even if current pricing favors the volatile option.
Compare model outputs against actual supplier quotes and production costs as they become available. This validation loop continuously improves model accuracy and builds confidence in using cost estimates for design decisions.
Incorporate feedback from procurement and manufacturing teams who see where models diverge from reality. These gaps often reveal hidden cost drivers or market dynamics your model doesn't yet capture.
Effective early-stage cost analysis requires capabilities that make cost modeling fast, accurate, and accessible to engineers during design not just procurement specialists during sourcing.
Advanced cost modeling tools automatically extract material specifications from initial designs to generate cost estimates without manual data entry. This automation is critical because manual cost estimation takes too long to support iterative design.
Real-time estimation means engineers see cost impacts immediately as they modify designs, making cost a natural part of design iteration rather than a separate activity that happens later.
Supply chain intelligence, supplier locations, manufacturing capabilities, regional cost differences, lead times, directly informs cost models because supply chain decisions are design decisions. Choosing a manufacturing process or material grade determines supplier options, and supplier locations drive labor costs, logistics expenses, and tariff implications.
Multi-tier visibility matters because component costs depend on sub-supplier capabilities and locations, not just direct supplier relationships. Platforms like Muir AI connect cost modeling with supply chain mapping so you can see the full cost picture across your entire supply network.
The ability to model alternative scenarios quickly, different suppliers, manufacturing locations, material substitutions, or process changes, makes early cost analysis valuable for design decisions. You're not just calculating a single cost estimate; you're exploring the cost landscape to find optimal approaches.
Speed matters because design teams evaluate many options during development. If scenario analysis takes days, engineers will evaluate fewer alternatives and potentially miss better solutions.
Integration with product lifecycle management and enterprise resource planning systems ensures cost data flows throughout your organization without manual transfers. Cost estimates automatically populate into business cases, target cost tracking, and procurement planning.
This integration also means validated actual costs flow back into cost models, creating the feedback loop that continuously improves estimation accuracy.
Different calculation approaches suit different design phases and product types. Parametric modeling uses historical data and cost relationships to estimate based on product parameters like weight, size, or complexity. It's fast and works well for early concept comparison when detailed designs don't exist yet.
Bottom-up costing builds costs from individual components, manufacturing operations, and activities. It's more time-intensive but provides the accuracy needed for detailed design optimization and procurement planning.
Comparative analysis benchmarks against similar products or components, leveraging existing cost knowledge to estimate new designs. This approach works particularly well when you're developing variants of existing products or entering product categories where you have relevant experience.
Manufacturing simulation models production processes to estimate cycle times and resource requirements, accounting for setup time, run time, scrap rates, and equipment utilization. This method works best for products where manufacturing process time dominates cost.
Most effective cost modeling combines multiple methods, parametric analysis for early concepts, comparative analysis to validate reasonableness, and bottom-up or simulation-based approaches for detailed optimization.
Technology enables early cost analysis, but organizational culture determines whether teams actually use cost insights to make better design decisions. Shifting from reactive cost management to proactive cost optimization requires several changes.
First, empower designers with cost visibility and tools rather than keeping cost analysis centralized in procurement or finance. When engineers own cost outcomes, they naturally design to cost targets rather than designing first and hoping costs work out later.
Establish cross-functional collaboration between design, engineering, procurement, and manufacturing so cost discussions happen throughout development, not just at gate reviews. Regular touchpoints help teams understand tradeoffs and find creative solutions that balance cost with other requirements.
Create feedback loops so actual costs inform future cost models and design decisions. When teams see how their estimates compared to reality, they learn which assumptions were accurate and which need refinement.
Reward early cost optimization rather than only celebrating post-design cost reduction. Recognition systems that only value procurement savings after design completion inadvertently discourage the design decisions that make savings possible.
The future of product development lies in integrated intelligence that combines cost modeling with supply chain visibility and environmental impact analysis. When you can simultaneously evaluate cost, supply chain, and carbon footprint during design, you make fundamentally better decisions than optimizing any single dimension alone.
This holistic approach reveals opportunities that single-dimension analysis misses. A supplier that looks optimal from a pure cost perspective might introduce supply chain risk or carbon intensity that makes alternatives more attractive.
Muir AI provides this integrated approach, combining dynamic cost modeling, sourcing and carbon footprint analysis in a single platform. Design and procurement teams work from the same intelligence, evaluating cost, supply chain, and sustainability implications together as designs evolve.
Ready to bring cost intelligence into your design process? Book a demo to see how Muir AI helps you discover savings at the beginning of your product lifecycle.
Modern should cost modeling software connects directly to ERP platforms through APIs or plugins, automatically extracting design, materials and manufacturing processes to generate cost estimates without manual data entry or file conversions. This integration happens in the background while engineers work in their familiar engineering environment.
Accurate cost models require material pricing databases, regional labor rates, manufacturing process time standards, supply chain logistics costs, and tariff information. Organizations can source this data from commodity market indices, supplier data, internal historical records, and specialized cost intelligence platforms that aggregate and maintain current market data.
Should cost modeling calculates theoretical optimal costs based on materials, processes, and efficiency standards before receiving quotes, while traditional estimation relies on historical pricing or supplier quotations after design completion. Should cost gives you a fact-based target for what something ought to cost, while traditional estimation tells you what it has cost or what suppliers propose to charge.
Supply chain variability gets addressed by modeling multiple scenarios with different supplier locations, incorporating regional cost differences, tariff impacts, and logistics variables to understand the cost range across sourcing options. This scenario-based approach reveals which design decisions are sensitive to supply chain choices and which are robust across different sourcing strategies.
A should cost model in procurement is a detailed cost breakdown that calculates what a product or component theoretically costs based on materials, labor, overhead, and reasonable profit margins. This fact-based foundation for supplier negotiations helps procurement teams distinguish between fair pricing and excessive margins.
You've signed up!
Don't worry, we don't spam.
