Misconceptions about cost calculation with CAD data

As manufacturing processes become increasingly digitised, the integration of CAD data into cost calculation workflows has emerged as a promising solution for improving accuracy and efficiency. The potential for CAD-based costing to “revolutionise” the way manufacturers calculate production costs has generated significant excitement in the industry.

The use of CAD data in cost calculation offers several compelling advantages:

• Precision: CAD models provide exact geometric information, allowing for more accurate material quantity estimations and helps to reduce waste.
• Speed: Automated analysis of CAD data can significantly reduce the time required for cost estimation, enabling faster quoting and decision-making processes.
• Consistency: By basing calculations on standardised CAD data, companies can achieve more consistent cost estimates across different projects and teams.
• Design optimisation: Early cost feedback based on CAD data can inform design decisions, potentially leading to more cost-effective products (Design-to-Cost).

In theory, the integration of CAD data into cost estimation processes creates a streamlined, efficient workflow:

• Automatic feature recognition: Advanced software can analyse CAD models to identify specific features, such as holes, pockets, or complex surfaces, which directly impact manufacturing costs.
• Material calculation: By accurately determining the volume and surface area of parts, CAD data enables precise calculation of material requirements and associated costs.
• Process planning: Based on the geometry and features identified in the CAD model, software can suggest appropriate manufacturing processes and estimate the time required for each operation.
• Tooling requirements: CAD data can be used to determine the complexity and amount of tooling required, helping to estimate related costs more accurately.
• Assembly analysis: The complexity of the assembly process for multi-part assemblies can be estimated using the CAD data, which facilitates a more precise estimation of labour costs.
• Design iteration: As changes are made to the CAD model, cost estimates are automatically updated, providing real-time feedback on the financial impact of design decisions.

This theoretical workflow promises a level of automation and accuracy that could significantly improve the cost estimation process. However, we show in the following sections that the reality of CAD-based costing is often much more complex and characterised by various misconceptions that can lead to inaccurate or incomplete cost calculations.

One of the most prevalent misconceptions about CAD-based cost calculation is that CAD data alone provides all the information required for accurate cost estimates. CAD models are undoubtedly a valuable source of information, but they have certain limitations. These lead to incomplete or inaccurate cost calculations when relying solely on them.

CAD data excels at providing precise geometric information about a part or assembly. However, geometry is just one piece of the costing puzzle. A CAD model can provide the exact dimensions, volume, and surface area of a component, but it does not contain information about:

• Manufacturing processes required
• Material properties and costs
• Labour rates and skill requirements
• Equipment capabilities and limitations
• Production volumes and economies of scale

In case of more complex assemblies, very often not all individual parts are detailed by the designer. This occurs, for example, with purchased parts that are modelled using simple representations.

Relying solely on geometric data for cost estimation can lead to oversimplified calculations that fail to account for the complexities of real-world manufacturing.

To generate more accurate cost estimates, several factors need to be considered alongside CAD data:

• Materials: While a CAD model may specify a material type, it doesn't provide any information about material costs, availability, or possible alternatives. These factors can significantly impact the overall cost and may vary based on market conditions and supplier relationships. A key factor here is also the information about the raw part geometry and the associated cutting or machining volume.
• Purchased parts, DIN standard parts: These are very often not included in the CAD data with relevant prices. An automated calculation leads to completely incorrect results.
• Processes: The optimal manufacturing process for a part isn't always evident from its geometry alone. Factors such as production volume, required tolerances, and available equipment all influence process selection and associated costs.
• Personnel: CAD data doesn't account for the skill level required for manufacturing, assembly, or finishing processes. Labour costs can vary widely based on complexity, required expertise, and geographic location.

As CAD-based costing tools become more sophisticated, there's a growing misconception that these automated systems can entirely replace human expertise in cost estimation. While these tools undoubtedly enhance efficiency and provide valuable insights, the role of human expertise remains crucial in ensuring accurate and contextually appropriate cost calculations.

Human experience brings a level of nuance and contextual understanding that automated systems often struggle to replicate:

• Process selection: Experienced estimators can quickly identify the most suitable manufacturing processes based on factors that may not be evident in the CAD data alone, such as production volume, material properties, and available equipment.
• Design for manufacturability (DFM): Skilled professionals can spot potential manufacturing challenges or opportunities for cost reduction that may not be apparent to an automated system.
• Historical knowledge: Human estimators draw upon past projects and outcomes, applying lessons learned to new situations and anticipating potential issues.
• Continuous improvement: Experienced professionals can identify trends and patterns across multiple projects, driving continuous improvement in cost estimation processes.

The manufacturing industry is constantly evolving and is influenced by many external factors that automated systems may struggle to account for:

• Material price volatility: Commodity prices can fluctuate rapidly due to global economic conditions, trade policies, or supply chain disruptions. Human experts can interpret these changes and adjust estimates accordingly.
• New emerging technologies: As new manufacturing technologies emerge, human expertise is crucial in evaluating their potential impact on costs and determining when and how to incorporate them into estimates.
• Regulatory changes: Industry regulations can affect manufacturing processes and costs. Human experts stay informed about these changes and their potential impact on production expenses.

A common misconception in the manufacturing industry is that all CAD data is uniform and equally suitable for cost calculation purposes. However, the reality is far more complex. The quality, format, and integrity of CAD data can vary significantly, impacting the accuracy and reliability of cost estimates derived from this information.

The CAD landscape is diverse, with numerous software packages and file formats in use across the industry:

• Software diversity: Popular CAD software includes Creo, Siemens Teamcenter, AutoCAD, SolidWorks, CATIA, Fusion 360, and many others. Each has its own strengths, limitations, and ways of representing data.
• File format proliferation: Common formats include STEP, IGES, STL, and native formats like .dwg or .prt. Each format has different capabilities in terms of preserving design intent and metadata.
• Interoperability challenges: Translating between different CAD systems and file formats can lead to data loss or interpretation errors, potentially affecting cost calculations.
• Feature recognition variability: Cost estimation software may interpret features differently depending on how they were created in the original CAD system, leading to inconsistencies in automated analysis.

The quality of CAD data can significantly impact the accuracy of cost estimates:

• Incomplete models: CAD files may lack critical information such as material specifications, tolerances, or surface finish requirements.
• Overly simplified designs: Highly constrained models can be difficult for automated systems to interpret, leading to errors in feature recognition and cost estimation.
• Simplified representations: CAD models created for visualisation purposes may lack the detail required for accurate manufacturing cost estimation.
• Legacy data: Older CAD files may not conform to current standards or may have been created using outdated practices, complicating their use in modern cost estimation tools.
• Human error: Mistakes in modelling, such as gaps between surfaces or incorrect feature definitions, can lead to erroneous cost calculations.

Efforts to standardise CAD data face several obstacles:

• Industry-specific requirements: Different industries may have unique CAD data needs, making universal standardisation challenging.
• Proprietary interests: CAD software vendors often prioritise their proprietary formats, resisting full standardisation that might reduce their competitive advantage.
• Evolving technology: Rapid advancements in CAD technology can outpace standardisation efforts, leading to a constant game of catch-up.
• Implementation costs: Adopting new standards can be expensive and time-consuming for companies, particularly those with large libraries of legacy CAD data.

As production processes become increasingly digital, confidence in automated costing systems is growing. While these tools offer significant advantages in terms of speed and consistency, it is a misconception to believe that they are always accurate. In fact, automated systems face numerous challenges when it comes to capturing the complexity of real-world manufacturing scenarios.

Manufacturing processes are usually much more complex than what can be captured in an automated system:

• Process variations: Even within a single manufacturing process, there can be numerous variations based on specific equipment, materials, or quality requirements.
• Setup and refitting times: These can vary significantly based on factors like batch size, operator experience, and equipment condition, which are difficult for automated systems to predict accurately.
• Secondary operations: Many parts require additional processes like deburring, heat treatment, or surface finishing, which may not be evident from the CAD data alone.
• Production environment: Factors such as factory layout, material handling systems, and efficiency of the work processes can influence costs but are difficult to take into account in automated calculations.
• Quality control: The extent and complexity of quality assurance processes can significantly affect overall costs and may vary based on customer requirements or industry standards.

Automated cost calculation systems often struggle to account for the wide range of variables related to equipment, tools, and human factors:

• Performance of equipment: The efficiency, accuracy, and operating costs of the various machines vary, which can significantly impact production costs.
• Tooling considerations: The lifespan of tools, the time required for retooling and the requirements for special tools can vary greatly and are often based on experience rather than easily quantifiable data.
• Operator skill levels: The expertise and efficiency of machine operators can have a substantial impact on production times and quality. These factors are difficult to standardise in automated systems.
• Learning curves: As operators become more familiar with a particular workpiece or process, efficiency typically improves. Automated systems may struggle to account for these improvements over time.
• Maintenance and downtime: Equipment reliability and maintenance schedules can affect overall production costs but are challenging to predict accurately in automated calculations.

Having explored the common misconceptions surrounding cost calculation with CAD data, it's crucial to address how manufacturers can overcome these challenges. By adopting a more nuanced and comprehensive approach, companies can harness the power of CAD-based costing while avoiding its pitfalls.

To achieve accurate cost estimates, CAD data should be complemented with additional information sources:

• Integrate CAD-based costing tools with ERP systems to access up-to-date information on material costs, labour rates, and production capacities.
• Leverage real-time production data to refine cost estimates based on actual process times and resource utilisation.
• Incorporate data on supplier capabilities, lead times, and pricing to ensure cost estimates reflect current market conditions.
• Maintain and use a database of past projects, including actual costs and lessons learned, to inform future estimates.
• Utilise industry-specific cost databases and benchmarks to validate and refine internal cost estimates.
• Combine 2D drawings with the corresponding 3D models. The 2D data often contains further important information.

To improve the accuracy of automated cost calculation systems, consider implementing the following checks and balances:

• Multi-stage verification: Implement a tiered approval process where automated estimates are reviewed by experienced estimators before finalisation.
• Sensitivity analysis: Use tools that allow users to quickly assess how changes in key variables (e.g., material prices, batch sizes) affect overall cost estimates.
• Continuous learning algorithms: Implement machine learning techniques that can refine cost models based on feedback from actual production data.
• Error detection systems: Use algorithms to flag unusual or potentially erroneous cost estimates for manual review.
• Regular audits: Conduct periodic comparisons between estimated and actual costs to identify systematic deviations or errors in the automated system.

To maximise the value of CAD-based costing, companies should invest in developing their human capital:

• Cross-functional training: Provide opportunities for cost estimators to gain hands-on experience in design and manufacturing processes.
• CAD proficiency: Ensure that cost estimation teams are well-versed in the CAD software used by the design department to better understand model intricacies.
• Continuing education: Encourage team members to stay updated on the latest manufacturing technologies, materials, and industry trends.
• Knowledge sharing: Implement mentoring programs and regular knowledge-sharing sessions to disseminate expertise across the company.

For valid and reliable cost calculations, manufacturers should adopt a holistic approach to costing that considers all aspects of the production process:

• Design for manufacturability (DFM): Involve cost estimation experts early in the design process to identify potential cost-saving opportunities.
• Total cost of ownership: Consider not just production costs, but also factors like maintenance, logistics, and end-of-life disposal when calculating overall product costs.
• Scenario planning: Develop multiple cost scenarios that account for different production volumes, material options, and manufacturing processes.
• Risk assessment: Incorporate risk analysis into cost estimates, considering factors like supply chain disruptions, regulatory changes, and market fluctuations.
• Continuous improvement: Establish feedback loops that allow insights from production to inform future cost estimates and design decisions.
• Professional costing software: By using professional costing software, you can not only improve the accuracy and consistency of your calculations, but also gain valuable insights through advanced reporting and analysis capabilities. This allows for better decision making throughout the entire product lifecycle, from initial design concepts to end of life considerations, ultimately leading to more competitive pricing strategies and improved profitability.

The use of CAD data in costing offers a certain potential for improving efficiency and accuracy. However, it is crucial to recognise and address the misconceptions associated with this approach to ensure effective implementation. By being aware of these misconceptions, manufacturers can develop more robust and reliable cost estimating processes that utilise the strengths of both CAD-based automated calculation and human expertise.

The key takeaways from this article include:

1. CAD data is a valuable starting point, but it must be supplemented with additional information about materials, processes, labour, and market conditions.
2. Human expertise remains crucial in interpreting data, understanding complex manufacturing scenarios, and making informed decisions.
3. The quality and format of CAD data can significantly impact cost calculations, necessitating standardisation efforts and quality control measures.
4. Automated systems, while powerful, must be designed to account for the complexities of real-world manufacturing and should include checks and balances.
5. A holistic approach to cost estimation, integrating CAD data with other information sources and combining automation with human insight, yields the most accurate and reliable results.

The future of costing in manufacturing is not about choosing between automation and human expertise, but about finding the optimal balance between the two. By investing in both technological solutions and human capital, companies can develop costing processes that are both accurate and efficient.