Cost Estimation Software – A Comparison
LineLab can be used to estimate production costs. But is LineLab a cost estimation tool?
Bottom-Up, Top-Down: The Basics
The fundamental goal of cost estimation is to apply the tools and metrics of cost accounting to production that has not yet occurred. Not only does cost estimation incorporate all of the complexities of cost accounting, it also introduces the difficulties of capturing production before it even happened. Fundamentally, cost estimation methods are classified as either of two main approaches: top-down methods and bottom-up methods. [1][2][3]
Top-down methods estimate costs based on the cost of similar, past projects. Bottom-up methods, on the other hand, estimate cost by considering all of the resources (material, labor) which will go into the final product.
Bottom-up methods arrive at a cost estimate for the final product by summing up the cost of the constituent parts. This has the subtle effect—especially when estimating manufacturing costs—that the impact of variation on production flow is ignored. Sometimes the user gets to make a blanket guess (e.g. “30% of total costs”), but depending on flow behavior, rate, and product mix, the real cost premium may be quite different. Also, bottom-up approaches require detailed system knowledge that may be difficult to obtain in complex organizations or early in the design phase.
Top-down approaches, on the other hand, rely on vast libraries of historical data. This makes top-down approaches problematic when you need to represent unprecedented system changes. Users often specify a “percent new design” (or “heritage”) variable, and an additional cost factor is then applied based on that percentage. Most top-down methods are weight-based: the average cost per pound is derived from past projects and applied to the expected weight of the new part.
The Association for the Advancement of Cost Engineering (AACE) recommends using top-down (parametric) methods when less than 15% of the project is defined (accuracy ±20–50%), and bottom-up for the final, detailed estimate when 50–100% of the project is defined (accuracy ±3–15%). [2]
Typical Cost Estimation Tools
Top-down (parametric) tools are designed for the early design phases and rely on libraries of historical data. Users supply subjective variables—e.g. perceived “complexity” (on a 5-point scale), approximate “new design (%)”, or expected part weight (often the key scaling factor). Many commercial products allow “least/likely/most” inputs for uncertainty and can perform sensitivity analyses.
Bottom-up tools provide separate costs for every operation and sum them up. Commercial packages often include detailed process-model libraries, but these aren’t extensible—you’re limited to the provider’s built-in models unless you pay for custom consulting. Because detailed inputs are required, bottom-up estimators often come as plugins to CAD software (e.g. SEER-3D, aPriori). After setup, they can deliver rapid, back-of-the-envelope cost estimates with design sensitivities. However, they still ignore production-flow effects (queues, variation, etc.), sometimes baking in a flat x% margin to “cover” these unknowns. Engineers also complain about being locked into the vendor’s process models.
Today, many cost estimation tools are based on correlations, yet rely on adding up the costs of separate parts, partly implementing a bottom-up approach (interactions like queueing effects are not considered). This way, software providers can combine the relative convenience of using historical data (vs. having to develop detailed models of the process physics) with the convenience of a CAD plugin (in the case of Facton, which extracts BOM trees from CAD data). On the flipside, of course, the accuracy of the method drops off significantly: neither are process physics captured, nor the effects of complex manufacturing systems. In particular, the key limitation of top-down approaches still exists: historical data can't predict the behavior of novel process chains. Unsurprisingly, combining the relative convenience of top-down cost estimation techniques with the simplifications and idea of linearity of bottom-up techniques, the downsides and error sources of both are also combined. This makes these tools suitable wherever +/- 20-50% error is negligible and no important investment decisions are made (e.g. limited runs).
LineLab
LineLab borrows some aspects from both top-down and bottom up cost estimation methods, but it is capable of modeling production systems with high accuracy, capturing complex production system dynamics, and precisely determining possible utilization and required inventory. This makes LineLab highly accurate in representing even unprecedented system changes.
With LineLab, engineers enter basic model inputs about the process chain and production system. In that respect, it differs from those "parametric" cost estimation tools, where users get a rough idea of costs based on historical correlations and estimated inputs for weight, complexity, and % new design. Some physics-based process models are provided for LineLab, similar to bottom-up cost estimation tools, but it's up to the user to use them or implement new, custom process models.
In addition, LineLab provides custom "parametric" inputs, and it can process uncertain (min, likely, max) inputs, making it usable long before bottom-up cost estimation tools can be used. At the same time, the production flow models make LineLab's results (and sensitivity analysis) much more exact than those of bottom-up tools. That said, LineLab does not currently plug into CAD the way some bottom-up cost estimation tools do.
LineLab also provides a number of features not typically offered in cost estimation tools: advanced trade study tools that draw relationships between any input and any output (not just cost), business model and lifecycle analysis, sustainability parameters, unlimited parametrization and optimization of any number of variables, and of course a detailed view of operations inside the factory.
- Inputs: Engineers define basic process-chain parameters. Unlike pure parametric tools, LineLab lets you use—or build—physics-based process models.
- Parametric & Uncertain Data: You can still feed in min/likely/max values long before a detailed bottom-up model is feasible.
- Flow Modeling: LineLab’s dynamic flow simulations yield far more precise cost and sensitivity insights than stand-alone bottom-up tools.
- Additional Features: Advanced trade-study tools (linking any input to any output), business-model & lifecycle analysis, sustainability metrics, unlimited parametrization/optimization, and an in-depth view of factory operations.
The following table summarizes the key differences:
| Mostly Top-Down e.g. SEER-Space, SEER-H, Price Systems | Mostly Bottom-Up e.g. SEER-MFG, aPriori | LineLab | |
|---|---|---|---|
| Core Elements | Datasets of similar products | Built-in process models | Dynamic models of production flow |
| Typical Inputs | Weight, complexity, %-new-design, BOM | Detailed dimensions / CAD file | Process chain, optional: parametric |
| Impact of Production Flow | Unable to capture expected changes | Not captured | Automatically calculated, high accuracy |
| Custom Process Models | Some allow custom correlation data (e.g. SEER-H) | Not usually possible | Yes |
| Built-In Process Models | Usually included (correlation data) | Yes | Examples provided, focus on custom |
| Sensitivity Analysis | May be only qualitative | Yes (design inputs) | Yes (all inputs incl. custom) |
| Design-to-Cost | Most tools | Yes | Yes |
Bottom Line
You can use LineLab to estimate production costs. However, LineLab is not a classic cost-estimation tool but a new, different methodology. It captures the interplay of operations—not just separate steps—and permits far more detailed modeling. Unlike parametric databases, LineLab doesn’t rely on historical correlation data; instead, it delivers higher accuracy and shines when pioneering new designs and processes.
- “NASA Cost Estimating Handbook,” 2020
- Christensen & Dysert, “Cost Estimate Classification System – As Applied in Engineering, Procurement, and Construction for the Process Industries,” 2005
- Boehm et al., “Software Development Cost Estimation Approaches – A Survey,” 2000