Cost transparency for drawing-based manufactured parts is essential for industrial companies. It determines how early potential savings become apparent, how reliably quotes can be evaluated, and how well-founded purchasing and design decisions are. In practice, there are two fundamentally different approaches to this: traditional bottom-up costing and data-driven regression analysis.

In everyday life, this often becomes apparent in a very specific situation. A buyer has received three quotes for a machined housing, with prices that vary significantly. Without a reliable benchmark, it remains unclear which quote is fair and where there is room for negotiation. This is exactly where both methods come into play - albeit in different ways. Both methods pursue the same goal. For companies with large inventories of parts, the question is therefore not so much which method is fundamentally “better,” but rather which one is suitable for which purpose.

Bottom-up and regression analysis follow two different logics

Bottom-up costing has been considered the classic standard in cost engineering for years. It breaks down a component into its individual cost elements and models the manufacturing process as realistically as possible. Factors taken into account include materials, setup times, processing times, machine costs, labor costs, and individual process steps. The major advantage: Each cost element can be derived and explained in detail. In contrast, regression analysis is a data-driven approach. It uses historical purchasing data, technical characteristics from 2D and 3D data, and contextual information such as quantity, supplier, and timing to identify correlations between component properties and actual prices paid. The result is not a manually derived costing tree, but a statistically sound reference price. This is precisely where the fundamental difference lies: the bottom-up approach explains costs based on manufacturing logic, while regression analysis does so based on the data. One method thinks in terms of the process; the other, in terms of patterns.

Why bottom-up is strong in depth but reaches its limits in breadth

A cost engineer calculating the cost of a turned part requiring special machining proceeds step by step: setup time, cycle time, machine hourly rate, tool wear. Depending on the complexity, this can take anywhere from several hours to several days. Bottom-up costing really shines wherever individual complex parts need to be analyzed in great detail. It is particularly valuable when cost engineers need to understand manufacturing logic, verify process assumptions, or provide detailed justifications for target costs of specific components. In practice, however, this approach is time-consuming. Each cost estimate ties up time and highly qualified cost engineering resources. Furthermore, the bottom-up approach scales only to a limited extent across large parts inventories. Often, only a small portion of the inventory is ever costed in detail. When thousands of drawing parts need to be evaluated, quotes need to be reviewed quickly, or anomalies in the inventory need to be identified, manual or semi-manual detailed cost estimation reaches its operational limits. There is another factor to consider: The quality of the results depends heavily on how up-to-date process, time, and cost rates are maintained and to what extent experiential knowledge is incorporated into the valuation. As a result, consistency between individual cost estimates can suffer. Bottom-up costing is therefore technically thorough but only scalable to a limited extent.

How regression analysis makes a difference with large parts inventories

The strength of regression analysis lies in situations where companies need to systematically analyze entire inventories rather than simply evaluating individual parts. Instead of manually recalculating the cost of each component one by one, the model learns from existing data. It identifies similarities between parts, reveals price correlations, and quickly provides a reference price based on quantity. A typical application looks like this: An inventory containing several thousand drawing parts is imported. The model groups similar parts - such as flanges made of the same material with similar geometries - and identifies which parts have prices that vary widely. It is precisely these parts that are at the top of the list for renegotiation.

This results in significant efficiency gains, particularly for complex, drawing-based parts. Information that often has to be compiled manually in a bottom-up approach - such as geometry, dimensions, tolerances, machining operations, or semi-finished products - can be automatically derived from CAD and drawing data and linked to historical purchasing data. In practice, this opens up several use cases simultaneously: Regression analysis can evaluate large parts inventories in a short amount of time, check incoming quotes against a reliable target price, identify price outliers, and prepare for targeted renegotiations. As a result, cost analysis shifts from an ad hoc task for experts to a scalable basis for decision-making in procurement and engineering.

The data set determines reliability

Data-driven models are only as good as their data set. Reliable regression analyses are not derived from individual price points, but from a sufficient number of comparable data points within homogeneous component classes. It is crucial to clearly link price, quantity, supplier, and timing to technical characteristics from 2D and 3D data. Data quality is equally important. Often, the challenge lies less in the model than in the data. The same part appears multiple times in inventory, drawings and 3D models contradict each other, a material does not comply with the specified standard, or prices are stored without an expiration date. For PartSpace, this is part of the analysis: Such cases are identified and filtered out before a model learns from them. The reliability of a forecast can be quantified using error metrics such as MAPE. The standard for industry-ready AI is therefore not superficial accuracy, but rather verifiable validity.

Bottom-up is not being replaced, but rather redefined

In practice, comparing bottom-up and regression analysis rarely results in a simple either/or choice. Both approaches serve different purposes. Regression analysis is suitable wherever speed, scalability, and transparency are required across large sets of parts. The bottom-up approach remains relevant when individual components require in-depth physical evaluation or when the data set is too sparse for statistical models. A typical workflow might look like this: Regression analysis checks a inventory for anomalies. For the three parts with the largest deviations, the cost engineer then prepares a detailed cost estimate. Purchasing subsequently negotiates using a verified target price rather than an estimate.

PartSpace bridges the gap between these two approaches. A component’s manufacturing processes are derived from CAD and drawing data and then statistically evaluated - based on historical purchasing data. This allows for the automated evaluation of large parts inventories and the identification of anomalies. PartSpace is intended to complement - not replace - traditional bottom-up approaches. At the same time, identified features and manufacturing processes can be transferred to external bottom-up solutions, allowing cost engineers to move more quickly into detailed costing. This division of labor transforms the role of cost engineering. The focus shifts from manual, individual costing to control, validation, and strategic evaluation. AI handles preparatory work such as feature recognition, classification, and reference price determination. Cost engineers focus on complex or critical cases, while the procurement team enters negotiations with a more robust data foundation.

Outlook: How cost engineering is evolving

The future of cost analysis in the engineering field is neither purely manual nor purely statistical. It lies in data-driven methods that are scalable and, at the same time, become increasingly physically explainable step by step. This is precisely where the next level of maturity lies: models combine historical procurement data, 2D and 3D information, and manufacturing logic to form a robust basis for decision-making.

For industrial companies, this represents a strategic shift. Cost insights are no longer generated only during downstream cost estimation but are available early on in the design and procurement phases. Design, in particular, benefits from this: If you can see during the design phase which tolerance or material triggers a price spike, you can take corrective action early on. This also changes the role of cost engineering: away from manual, individual evaluation, and toward validation, classification, and control where technical depth has the greatest impact.  Bottom-up and regression analysis thus represent not so much a competition between methods as two complementary perspectives. Companies that effectively combine both lay the foundation for faster decisions, greater transparency, and more robust cost management across large parts inventories.