PADDME—Process Analysis for Digital Development in Mechanical Engineering
Abstract
:1. Introduction
- 1.
- How can a method for process analysis in product development be designed to facilitate cost-effective process optimisation for digital engineering?
- 2.
- In what way can a technology’s readiness level for digital engineering methods be measured based on a process analysis?
2. Materials and Methods
2.1. Digital Engineering
2.2. Design Process Management
Product Development Characteristics
- Design processes are highly dynamic and creative [63].
- Changing product requirements or boundary conditions [64].
- Every design process differs, since a unique product, not existing at the beginning, is designed [66].
- The process is highly problem-driven and generates new knowledge [67].
- Shared information is not taken into account [3].
3. Purpose and Scope
4. Methodological Approach
4.1. Use Case: Integrating Data-Driven Methods into Product Development Processes
4.2. Requirements for Process Analysis in Design Departments of Small and Medium Enterprises
4.3. Analysis Method—PADDME
- 0.
- Preparation;
- 1.
- Process capturing;
- 2.
- Process evaluation;
- 3.
- Potential analysis;
- 4.
- Process redesign and integration of data-driven methods.
4.3.1. Phase 0: Preparation
Goal and Results
Methods
4.3.2. Phase 1: Process Capturing
Goal and Results
Methods
Tools
- In communication tasks, the medium of the messages can be set (e.g., e-mail, paper, or by voice).
- In normal tasks, the used programs and tools are added to the context menu.
- Approvals have also been realised by adapting the existing task template using custom fields for sender, receiver, and approval information.
- The data and information fragments are extended by the data format and version. If a task has the same input and output file, the version is incremented.
Intermediate Summary
4.3.3. Phase 2: Process Evaluation
Goal and Results
Methods
- accrual of services, e.g., changing responsibility;
- defined output of a sub-process;
- distinct requirement profile or client–contractor relationship;
- defined individual-provided resources;
- autonomy with respect to subsequent units;
- performance goals for specified sub-processes.
Intermediate Summary
4.3.4. Phase 3: Potential Analysis
Goal and Results
Methods
- 1.
- Prediction of different values;
- 2.
- Identification of interrelationships and contexts;
- 3.
- Use of old data as a basis for new product generations;
- 4.
- Support for decisions.
Intermediate Summary
4.3.5. Phase 4: Process Redesign and Integration of Data-Driven Methods
Goal and Results
Methods
Intermediate Summary
4.4. Technology-Readiness Framework
4.4.1. Technology
Technology Basis
Tools
System Integration
Media Discontinuity
4.4.2. Data
Data Acquisition
Data Transfer
Data Provision
Data Usage
4.4.3. Quality
Operation
Traceability
Transfer Time
Security
4.4.4. Organisation
Responsibility
Qualification
Gateways
Knowledge-Based Work
5. Case Study
5.1. Phase 0: Preparation
5.2. Phase 1: Process Capturing
- Many iterations and change requests in design processes. This leads to long development times and many repetitive work steps.
- High coordination requirement between departments, which results in many reconciliation meetings and a high number of iterations as well.
- Short timeframes combined with long waiting times, for example, for simulation or test results, during the design.
- Data retrieval from simulation to design is subject to media discontinuities, since simulations needs different data formats to design.
- Elaborate evaluation of simulation results requires a high level of staff expertise and time, which is not always available and results in a high workload in the department.
- Correction iterations with simulation service providers are necessary if there are errors in the simulation setup. The data check as well as the iterations cost time.
- Testing is the bottleneck in the approval process due to long timeframes. Therefore, test results are available not until two iteration loops ahead. This results in additional iteration loops being required to fix potential errors.
5.3. Phase 3: Potential Analysis
5.4. Phase 4: Process Redesign
5.4.1. IT System Training
5.4.2. IT System User
5.4.3. Data Management System
5.4.4. Data Input
5.4.5. Data Output
5.4.6. Process
6. Discussion
6.1. Case Study
6.1.1. Economy
6.1.2. Quality
6.1.3. Quantifiability
6.1.4. Consistency
6.1.5. Representation of Data, Information, and Knowledge
6.2. Method
7. Summary and Outlook
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ARIS | Architecture of Integrated Information System |
BPM | Business process management |
BPMN | Business process model and notation |
BPR | Business process re-engineering |
CAD | Computer-aided design |
CAE | Computer-aided engineering |
CRISP-DM | Cross-industry standard for data mining |
DSM | Design structure matrix |
EDM | Enterprise data management |
EFQM | European Foundation for Quality Management |
EPC | Event-driven process chain |
IDEF | Integration definition for function modelling |
KBE | Knowledge-based engineering |
KDD | Knowledge discovery in databases |
PADDME | Process analysis for digital development in mechanical engineering |
PDCA | Plan–do–check–act |
PDM | Product data management |
SME | Small and medium enterprises |
TCT | Total cycle time |
VSM | Value-stream mapping |
Appendix A. Approaches for Integrating Data-Driven Methods
Appendix A.1. KDD Process
- 1.
- Data selection: In this initial phase, relevant data are identified and selected for analysis. The dataset is chosen based on the project’s objectives, domain knowledge, and data availability. The selected data should align with the specific problem or research question at hand.
- 2.
- Data preprocessing: Once the data are selected, they undergo preprocessing to prepare them for analysis. This phase involves cleaning the data by handling missing values, correcting errors, and resolving inconsistencies. Data integration may also be performed to combine multiple datasets into a unified format. Transformation techniques, such as normalisation or aggregation, can be applied to make the data suitable for further analysis.
- 3.
- Data transformation: In this phase, the preprocessed data are transformed into a suitable representation for analysis. This typically involves converting the data into a format that can be effectively processed using data mining algorithms. Feature selection or extraction techniques may be applied to reduce the dimensionality of the dataset and capture the most relevant information.
- 4.
- Data mining: The core of the KDD process lies in the data mining phase. Here, advanced algorithms and techniques are applied to extract patterns, relationships, and insights from the transformed data. Data mining algorithms can be categorised into various types, including classification, clustering, regression, association rule mining, and more. The choice of algorithm depends on the nature of the problem and the knowledge that is desired to be extracted.
- 5.
- Pattern evaluation: Once patterns and relationships have been discovered through data mining, they need to be evaluated for their quality, significance, and usefulness. This evaluation is performed based on domain expertise, statistical measures, and evaluation metrics specific to the problem domain. Patterns that meet the desired criteria are considered valuable and can be further analysed.
Appendix A.2. CRISP-DM Process
- 1.
- Business understanding: This initial phase focuses on understanding the project objectives, requirements, and constraints from a business perspective. It involves identifying the goals of the project, defining the problem statement, and forming a clear understanding of how the project outcomes will benefit the organisation.
- 2.
- Data understanding: In this phase, data sources are identified and collected. The data are then explored to gain a comprehensive understanding of their structure, quality, and potential limitations. Data issues and challenges are addressed, and initial insights are derived to determine the feasibility of the project.
- 3.
- Data preparation: This phase involves preparing the data for analysis. It includes data cleaning, transformation, and integration to ensure data quality and consistency. Data preprocessing techniques, such as handling missing values or outliers, are applied to create a clean and reliable dataset.
- 4.
- Modelling: In this phase, various data mining and machine learning techniques are applied to build and validate models. The appropriate modelling techniques are selected based on the project objectives and the nature of the data. Iterative experimentation and model refinement are performed to achieve the desired level of accuracy and performance.
- 5.
- Evaluation: The models developed in the previous phase are evaluated against the business objectives and criteria established in the first phase. Model performance and effectiveness are assessed using appropriate evaluation metrics. This phase helps determine if the models meet the project requirements and if further improvements are needed.
- 6.
- Deployment: The final phase focuses on deploying the data mining results into the operational environment. This involves integrating the models into existing systems or processes, creating user interfaces or reports for end-users, and providing documentation and training to ensure the successful implementation and adoption of the results.
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Scale | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |
---|---|---|---|---|---|
Likert | Not digital | Mostly not digital | Partly digital | Mostly digital | Fully digital |
Percentage | Less than | 20– | 40– | 60– | More than |
Consent | Not applicable | Mostly not applicable | Partly applicable | Mostly applicable | Applicable |
Aspect | Likert | Percentage | Consent |
---|---|---|---|
Technology | |||
Technology basis | x | ||
Tools | x | ||
System integration | x | ||
Media discontinuity | x | ||
Data | |||
Data acquisition | x | ||
Data transfer | x | ||
Data provision | x | ||
Data usage | x | ||
Quality | |||
Operation | x | ||
Traceability | x | ||
Transfer time | x | ||
Security | x | ||
Organisation | |||
Responsibility | x | ||
Qualification | x | ||
Gateways | x | ||
Knowledge-based work | x |
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Gerschütz, B.; Consten, Y.; Goetz, S.; Wartzack, S. PADDME—Process Analysis for Digital Development in Mechanical Engineering. Processes 2024, 12, 173. https://doi.org/10.3390/pr12010173
Gerschütz B, Consten Y, Goetz S, Wartzack S. PADDME—Process Analysis for Digital Development in Mechanical Engineering. Processes. 2024; 12(1):173. https://doi.org/10.3390/pr12010173
Chicago/Turabian StyleGerschütz, Benjamin, Yvonne Consten, Stefan Goetz, and Sandro Wartzack. 2024. "PADDME—Process Analysis for Digital Development in Mechanical Engineering" Processes 12, no. 1: 173. https://doi.org/10.3390/pr12010173