Managing engineering changes in the engineer-to-order environment: challenges and research needs

Iakymenko, Natalia; Romsdal, Anita; Semini, Marco; Strandhagen, Jan Ola

doi:10.1016/j.ifacol.2018.08.249

Cited by 16 publications

(9 citation statements)

References 35 publications

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“…It is important to understand the effects of change propagation not only on engineering drawings, but also on the downstream activities of production, supply and procurement. The focus of previous research on EC propagation has mainly been on the engineering phase, without proper consideration of implications for the physical production phase or associated supply chain [8].…”

Section: Engineering Change Managementmentioning

confidence: 99%

Engineering Change Management in the Engineer-to-Order Production Environment: Insights from Two Case Studies

Iakymenko¹,

Romsdal²,

Semini³

et al. 2018

IFIP Advances in Information and Communication Technology

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Engineering changes (ECs) are part of any Engineer-to-order (ETO) project. The engineering change management (ECM) literature provides various tools, methods and best practices, and this study investigates ECM practices in the ETO production environment. Through two exploratory case studies, we identify five main ECM challenges; EC impact analysis, EC data management, internal and external collaboration and communication, and EC post-implementation review. Both companies have implemented the main ECM steps recommended in literature but there are considerable weaknesses in the execution of the post-implementation review process. More ETO cases are needed to confirm the findings and investigate how ECM tools and approaches vary by different dimensions.

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Section: Engineering Change Managementmentioning

confidence: 99%

Engineering Change Management in the Engineer-to-Order Production Environment: Insights from Two Case Studies

Iakymenko¹,

Romsdal²,

Semini³

et al. 2018

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Milestones can be considered as being points in time where local minima of uncertainty can be established [25]: for example, milestones provide checkpoints where planned budget and time (i.e., priors) can be compared to current best estimates of expenditure and duration (i.e., preferred posteriors). From these local minima of uncertainty, new predictions of cost and time can be made, which can be based on the creation of modifications to both path and destination [92]. Although cost and time can be far more than predicted at the outset or at previous milestones, all parties can be too deep into the project to quit [93].…”

Section: Generative Modelsmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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“…Der Management-Ansatz des Engineering Change Managements (ECM) umfasst unterstützende Methoden zur Organisation, Implementierung und Kontrolle von Änderungsbedarfen. Eine Herausforderung ist der unsystematische Umgang mit Änderungsbedarfen in der industriellen Praxis, obwohl Methoden und Hilfsmittel des ECM in der Literatur beschrieben werden [4]. Sofern ECM-Prozesse Anwendung finden, wird das Anwendungspotenzial nicht vollständig ausgeschöpft, da die unternehmensspezifische Anpassung (Tailoring) an die vorherrschenden Rahmenbedingungen in der Regel vernachlässigt wird.…”

Section: Motivationunclassified

“…Daraus resultiert eine unvollständige Informationsgrundlage für die Änderungsentscheidung. Darüber hinaus ist die Bewertung der Änderungsauswirkungen meistens ungenau und abhängig von Expertenwissen, ohne objektive Bewertungskriterien zugrundezulegen [4]. Vor allem die Vorhersage von Änderungspropagation stellt eine große Herausforderung dar [7].…”

Section: Motivationunclassified

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Systematische Bewertung von Auswirkungsanalysen des Engineering Change Managements

Gräßler¹,

Wiechel²

2021

DS 111: Proceedings of the 32nd Symposium Design for X

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Managing engineering changes in the engineer-to-order environment: challenges and research needs

Cited by 16 publications

References 35 publications

Engineering Change Management in the Engineer-to-Order Production Environment: Insights from Two Case Studies

Engineering Change Management in the Engineer-to-Order Production Environment: Insights from Two Case Studies

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Systematische Bewertung von Auswirkungsanalysen des Engineering Change Managements

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