Systematic Analysis of Engineering Change Request Data - Applying Data Mining Tools to Gain New Fact-Based Insights
Doctoral thesis, 2020
Large, complex system development projects take several years to execute. Such projects involve hundreds of engineers who develop thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases, but due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important in the product development process to enhance a product. The opportunity at hand is that vast amount of data on industrial changes are captured and stored, yet the present challenge is to systematically retrieve and use them in a purposeful way.
This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.
Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.
The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves.
These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products.
This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.
Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.
The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves.
These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products.
Markov chain
Machine learning.
Product Development
Design Structure Matrix
Design Analytics
Engineering Change Request
Author
Ívar Örn Arnarsson
Chalmers, Industrial and Materials Science
Engineering changes are common in industry as they are opportunities to improve, enhance, or adapt a product. They driver for a change can be e.g. related to quality, safety, changes in external circumstances or regulation. These engineering changes often referred as Engineering Change Requests (ECRs) are largely generated through product development projects and are often stored in database while worked and later for some form of knowledge management purpose.
Despite ECR being captured and stored it is often cumbersome for product developers to identify historical ECRs due to the vast amount of them. Historical ECRs might contain valuable knowledge relevant to a current design and it is often wondered if the ECR content might be analyzed in a new way insightful way. The content of ECR data must contain information permitting identification of the types of errors and changes made, including part title, part name, part number, problem description, root cause, solution and test results.
This thesis primarily focuses on ECR data in combination with three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge about ECR information needs, and applying mathematical tools for solution design and testing.
Results show a list of engineering information needs related to ECRs, examples of visualizations based on unstructured data, industrial case study where complex product development processes are modeled using the Markov chain Design Structure Matrix, and studies that investigate how advanced searches based on natural language processing techniques and clustering within engineering databases.
Despite ECR being captured and stored it is often cumbersome for product developers to identify historical ECRs due to the vast amount of them. Historical ECRs might contain valuable knowledge relevant to a current design and it is often wondered if the ECR content might be analyzed in a new way insightful way. The content of ECR data must contain information permitting identification of the types of errors and changes made, including part title, part name, part number, problem description, root cause, solution and test results.
This thesis primarily focuses on ECR data in combination with three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge about ECR information needs, and applying mathematical tools for solution design and testing.
Results show a list of engineering information needs related to ECRs, examples of visualizations based on unstructured data, industrial case study where complex product development processes are modeled using the Markov chain Design Structure Matrix, and studies that investigate how advanced searches based on natural language processing techniques and clustering within engineering databases.
Areas of Advance
Information and Communication Technology
Transport
Subject Categories
Other Engineering and Technologies
ISBN
978-91-7905-310-9
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4777
Publisher
Chalmers