Manual Cost Modeling Is Slowing You Down. Here’s How to Eliminate It.

If your team is spending weeks building product cost models or worse, skipping products entirely because modeling is too complex, you’re not alone. Many sourcing, procurement, and cost engineering teams still rely on manual spreadsheets and siloed data across multiple ERPs to answer one critical question: What should this product cost? It’s a time-consuming process that’s rarely scalable. And in a global market where every sourcing decision counts, this inefficiency is costing you more than time.

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Manual cost modeling persists because traditional tools weren’t built for today’s complexity. You might be pulling data from legacy PLMs, disparate ERP systems, or supplier quotes, each with its own limitations. Even with a robust BoM, building an accurate cost model means estimating regional labor, materials, energy, and overhead costs. Multiply that by hundreds of SKUs, and you’re looking at analysis paralysis or worse… no analysis at all.

This results in slow decision-making, skipped cost-outs, and missed opportunities during supplier negotiations. Teams are forced to choose between speed and accuracy and often settle for neither.

The Shift to Muir's Product Intelligence Platform:
Muir's Product Intelligence Platform changes the equation. Instead of building cost models manually, platforms like Muir automate the entire process. Muir uses AI to generate synthetic bills of materials (BoMs) and map the complete cost structure of a product, from raw material sourcing to final assembly, without the need for deep manual input.

Unlike static spreadsheets, Muir’s models are dynamic, contextual, and scalable across entire portfolios. They don’t just give you a number: they tell you why the cost is what it is, and what you can do about it.

How Muir Solves This Step-by-Step:

Synthetic Bill of Materials (BoM) Generation:
Muir turns even a basic product description into a detailed BoM. The platform uses procurement data to reverse-engineer the product structure, identifying raw materials, subcomponents, and manufacturing processes involved. No engineering specs required.
Cost Driver Analysis:
Each synthetic BoM includes a full breakdown of labor, energy, raw materials, and processing costs. You can see exactly which components drive cost and where the opportunities for cost-out are hidden.
Trade Flow Prediction:
Muir predicts global sourcing flows where materials come from, where they’re processed, and how they're transported based on the product’s manufacturing location. This allows you to factor in regional cost differences, tariffs, and supply chain risk automatically.
Scenario Modeling:
Want to compare the cost of producing a product in Mexico vs. China? Muir lets you simulate “what-if” scenarios across geographies, commodities, and suppliers in minutes, not weeks.
Reduction Engine:
Muir’s intelligent reduction engine surfaces actionable cost-saving strategies. It scores opportunities based on implementation difficulty and impact, helping you prioritize changes that are both feasible and valuable.
Portfolio-Level Insights:
With Muir, you can analyze hundreds or thousands of products at once. It identifies the 15% of SKUs responsible for 80% of your cost volatility so your team knows exactly where to focus.

Actionable Takeaway:
If you’re manually building cost models today, start by identifying the top 10 SKUs you can’t model quickly. Ask yourself: Do you have full visibility into their cost drivers? Can you simulate alternatives easily? If not, it’s time to consider a smarter approach. Request a demo of Muir’s synthetic BoM engine and cost intelligence platform.

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Manual Cost Modeling Is Slowing You Down. Here’s How to Eliminate It.

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