Data-driven engineering design research: Opportunities using open data

Engineering Design research relies on quantitative and qualitative data to describe design-related phenomena and prescribe improvements for design practice. Given data availability, privacy requirements and other constraints, most empirical data used in Engineering Design research can bedescribed as “closed”. Keeping such data closed is in many cases necessary and justifiable. However, this closedness also hinders replicability, and thus, may limit our possibilities to test the validity and reliability of research results in the field. This paper discusses implications and applications of using the already available and continuously growing body of open data sources to create opportunities for research in Engineering Design. Insights are illustrated by an examination of two examples: a study of open source software repositories and an analysis of open business registries in the cleantech industry. We conclude with a discussion about the limitations, challenges and risks of using open data in Engineering Design research and practice

Improving engineering change management by introducing a standardised description for engineering changes for the automotive wiring harness

Engineering change management is a key part in the development of products that requires a lot of resources and time. A key problem is the lack of a shared ontology to describe engineering changes. This creates problems, additional effort and hinders the digitalisation of the engineering change management. This is especially true for the development of the automotive wiring harness where a low degree of automation together with the occurrence of many changes in a multi-variant system poses a big challenge. A description that is unambiguous, comprehensive and coherent is needed. The research presented in this paper tackles this problem. A standardised description for the engineering change management for the automotive wiring harness is introduced in this publication. The authors outline the approach that has been used to create a systematic description. The validation of the standardised description is based on two approaches: a case study of a development project and an ongoing development project. The validation shows that 94% of all engineering changes can be described in the proposed standardised way. Concepts where the standardised descriptions can be used to improve the engineering change process are outlined at the end of the paper. The paper thereby presents a way that directly improves the engineering change process and the product development process. It enables the further improvement of the engineering change management by providing a basis for an automatic processing, evaluation and implementation of engineering changes

Assessing the Impact of Changes and their Knock-on Effects in Manufacturing Systems

Manufacturing systems are subject to frequent changes caused by technology and product innovation, varying demand, shifted product mix, continuous improvement initiatives, or regular substitutions of outworn equipment and machines. Elements within a manufacturing system are connected by a complex network of relations such as material flow, technological dependencies, infrastructure, and intangible cause-and-effect-chains. Depending on the scale of changes they may also interfere with engineering, procurement, logistics, or even manufacturing strategy. Thus, the total impact in terms of expected costs and required time for planning and implementation of those “manufacturing changes” is hard to predict. The objective of this paper is to provide a decision support for manufacturing change management and to enable a thorough analysis of changes in manufacturing systems. Although the topic of change propagation received considerable attention in product development in order to quantify the knock-on effects of engineering changes, comparable endeavors have not yet been made in the field of manufacturing science. Following a review of prevailing approaches from product development and manufacturing literature, a model-based approach for the prediction and assessment of change propagation in manufacturing systems is presented. Applied structural modeling techniques, the derived graph algorithm, and the proposed procedure of the approach are outlined. Finally, an industrial case study is presented to demonstrate the potential but also the limitations in practice.Deutsche Forschungsgemeinschaft (collaborative research center SFB 768 “Managing cycles in innovation processes: Integrated development of product-service systems based on technical products"

Future-proofing the through-life engineering service systems

Future-proofing through-life engineering service systems (TESS) is crucial for ensuring their reliable, long and economical whole lives. The TESS are typically composed of high value industrial products and engineering services organised around them. Future-proofing can broadly be achieved by enabling disruption and change management capabilities. However, understanding of TESS future-proofing is limited, which is also important due to the recent industry 4.0 advancements. This paper contributes by presenting (1) a concept of TESS future-proofing, (2) a framework of TESS future-proofing, and (3) examples of the framework application at: (i) management level via change prediction method (CPM), and (ii) operational level via industrial augmented reality (AR)

Opportunity discovery in initiated and emergent change requests

When a change request is raised in an engineering project an ad hoc team often forms to manage the request. Prior research shows that practitioners often view engineering changes in a risk-averse manner. As a project progresses the cost of changes increases. Therefore, avoiding changes is reasonable. However, a risk-averse perspective fails to recognize that changes might harbor discoverable and exploitable opportunities. In this research, we investigated how practitioners of ad hoc teams used practices and praxes aimed at discovering and exploiting opportunities in engineering change requests. A single case study design was employed using change request records and practitioner interviews from an engineering project. 87 engineering change requests were analyzed with regards to change triggers, time-to-decision and rejection rate. In total, 25 opportunities were discovered and then 17 exploited. Three practices and six praxes were identified, used by practitioners to discover and exploit opportunities. Our findings emphasize the importance of the informal structure of ad hoc teams, to aid in opportunity discovery. The informal structure enables cross-hierarchal discussions and draws on the proven experience of the team members. Thus, this research guides project managers and presumptive ad hoc teams in turning engineering changes into successful opportunities

Connectivity as the capacity to improve an organization’s decision-making

This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences

Connectivity as the capacity to improve an organization’s decision-making

This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences

Characteristics of changeable systems across value chains

Engineering changes (ECs) are inevitable for businesses due to increasing innovation, shorter lifecycles, technology and process improvements and cost reduction initiatives. The ECs could propagate and cause further changes due to existing system dependencies, which can be challenging. Hence, change management (CM) is a relevant discipline, which aims to reduce the impact of changes. EC assessment methods form the basis of CM that support in assessing system dependencies and the impact of changes. However, there is limited understanding of which factors influence the change-ability across value chains (VCs). This research adopted a VC approach to EC assessment. Dependencies in products and processes were captured, followed by the risk (i.e. likelihood x impact) assessment of ECs using change prediction method (CPM). Four case studies were conducted from two industries (automotive, furniture) to identify design (product) and manufacturing (process) elements with high risk to be affected by ECs. Based on the case results, characteristics were identified that influence change-ability across VC. This contributed to the CM domain while businesses could also use the results to assess ECs across VC, and improve the design of products and processes by increasing their changeability across VC e.g. by proactive decoupling or reactive handling of system dependencies.Department for Business, Energy and Industrial Strategy (BEIS), UK under Advanced Manufacturing Supply Chain Initiative (AMSCI