Tools and methods supporting the engineering change process can roughly be divided into two categories. Firstly, there are the tools that support in the management of the workflow and documentation of the process. The other category of support tools aids engineers at making decisions at a certain point within the change process (Jarratt, et al., 2010).
Work Flow and Documentation Support
In the past, some firms have relied on a paper-based ECM system, which only allowed one person or group to access the data and document at a time. With the increased volume of changes arising for a product and multiple changes being carried out simultaneously, such systems have become inefficient (Kidd & Thompson, 2000). Thus, firms are now utilizing computer-based systems generating change requests and change notice forms, which are then processed further electronically.
Apart from decision support systems, which will be discussed in a following section, Huang and Mak distinguish between three categories of computer based tools (Huang & Mak, 1998):
1. Dedicated Engineering Change Management systems contain the databases of engineering change activities and generate the electronic workflow documents, such as the change request, the change proposal, as well as the engineering change order. Such systems were often developed in- house and have a very limited flexibility.
2. Computer aided Configuration Management (CM) systems incorporate the functionality of dedicated systems, but additionally allow for product structuring and versioning.
3. Product Data Management (PDM) or Product Life-cycle Management (PLM) systems have become increasingly commonplace within organizations and are used throughout the entire design and product life-cycle. In addition to Configuration Management functionality, these systems support all stages of the product life-cycle. There are a number of large software companies providing such tools, including Siemens with its „Teamcenter“ suite, Dassault Systems with „Enovia“, Oracle with „Oracle Agile PLM“, Parametric Technology Corporation with „Windchill“ and SAP with „SAP PLM“. It is at this point important to note that, due to the complexity of such systems they are all developed by large corporations. Nonetheless, in the field of ECM, the „processes that have been implemented through workflow in PDM systems seem to be a copy of the old paper-based systems“ (Riviere, et al., 2002).
The many existing capabilities of PLM systems are a promising indicator that, once a feasible approach is developed, decision support can be integrated and greatly improve the handling of engineering changes within organizations. This is due to the fact that, only within the context of the entire life-cycle, one can get an overview on time, stakeholders, components and resources used (Softic, et al., 2014). The deployment of enterprise-wide management and executive information systems can thus ensure organizational agility and competitiveness (Noran, 2009).
Soft Technologies for Decision Support
One of the most effective ways of avoiding costly changes within the life-cycle is to engage engineers into discussions about the key aspects of the products, such as customer requirements, manufacturability and potential failure modes, as early as possible. In doing so, many firms rely upon one of the following soft measures (Huang & Mak, kein Datum):
Design for Manufacture and Assembly (DFMA) is used to help design teams fulfill the requirements on a product with the simplest means of product design and production. By employing this approach, firms aim at identifying and eliminating the over-engineering in product and production design.
Failure Mode and Effects Analysis (FMEA) is a measure in preventative quality management and has the goal of identifying potential defects in a product as early in the design process as possible.
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Quality Function Deployment (QFD) is a quality assurance method representing a framework for implementing quality requirements at every stage of a product design process. This approach aims at just fulfilling customer requirements without over-engineering.
The mentioned methodologies are helpful in guiding a design team toward increased engineering efficiency and effectiveness. Nonetheless, extensive expertise is required for the proper application and many aspects within the engineering process are not considered. That is why numerous organizations have adapted Decision Support tools to assist in the Engineering Change process.
Decision Support Tools
Utilizing software tools to support the decision-making process within engineering is very common for companies in the industrial sector. Yet, there are great variations in functionality and the context in which they operate. Most commonly used are Computer Aided Design (CAD) packages which can assist designers in the recognition of errors. It is important to point out though, that these tools, including Siemens NX and CATIA, are only able to detect the first stage of change and are unable to predict the propagation of a change to further components.
In trying to fill the gap of decision support in engineering change, many academic tools and prototypes have been developed. Yet, none of the tools seem to have been thoroughly tested or even implemented on a large scale. The following table will introduce some of the approaches and point out the individual shortcomings, which have impaired a widespread adoption.
Tool Approach Shortcomings
CAD - Computer Aided Design Analysis of geometry and indication of mismatch between design changes and geometrical constraints
Change propagation not
regarded; functional
relationships between
components not assessed C-FAR (Change Favourable
Representation) (Cohen, et al., 2000)
Examination of attributes and interactions between the core elements of entities in product models to predict the propagation effect
Suitable only for simple or small products, as limited interactions can be represented
Redesign IT (Ollinger & Stahovich, 2001)
Generation and evaluation of proposals for redesign plans by using a product model with physical quantities and causal relationships
Only specification of direction of change; no quantitative suggestions (Ollinger & Stahovich, 2001)
CPM - Change Prediction Method (Clarkson, et al., 2001)
Utilization of Design Structure Matrix (DSM) to estimate risk of change propagation within a product
Complex adjustment for varying cases required
CECM - Collaborative
Environment for Engineering Change Management (Lee, et al., 2006)
Case-based reasoning retrieval of previous change initiatives based on different ontologies; Basis for collaboration with structured online workflows to capture and integrate informal and unstructured knowledge
No adaptation of retrieved cases to match current change initiative
ADVICE (Kocar & Akgunduz, 2010)
Virtual environment for ECM; combines parametric and
graphical information;
integration of existing data for predicting change propagation
and making change
prioritizations
Only single criterion used for prioritization; very basic algorithms used for propagation analysis
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Information Structure
Framework (ISF) and Change Management Support Approach (CMSA) (Ahmad, et al., 2012)
Creation and capture of knowledge related to change propagation through structured process; prediction of propagation impact based on use of dynamic checklists
Previous knowledge not included; high effort for knowledge elicitation
FBS Linkage Method (Hamraz, 2013)
Integration of functional reasoning and change prediction based on a network of functional, behavioral and structural attributes
Regards only impact of change and change propagation on cost measures
Ontology based tool for task tracking and decision support (Softic, et al., 2014)
Acquisition, editing and querying of information based on ontological representation of product data; Visualization of task interdependencies
Prediction of change impact with only rudimentary tools; knowledge from past projects not sufficiently integrated
Table 8 – Tools for Engineering Change Management Decision Support
Despite the many attempts at providing a decision support tool for the engineering change process, all of the candidate approaches have their respective shortcomings. One of the results of the preceding analysis is the fact that the mentioned tools are either too general to add concrete support in the decision making process or too narrowly focused on issues like the change propagation analysis. Of course, there is a trade- off between the accuracy of a model and its complexity, but the proposed systems lack the flexibility to adjust to any specific environment. Most importantly, the candidate approaches do not sufficiently include the tacit knowledge already generated in previous cases.