A Framework for Data-Based Change Impact Analysis in Manufacturing

Fabian Sippl; Gunther Reinhart

doi:10.1016/j.procir.2021.11.042

A Framework for Data-Based Change Impact Analysis in Manufacturing

Fabian Sippl, Gunther Reinhart

Chair of Sustainable Production

Technical University of Munich

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Manufacturing companies recognize the efficient handling of technical changes as a critical success factor for the long-term competitiveness. At the same time, the increasing digitalization of the change management process leads to the availability of historical change data. This paper presents a holistic conceptual framework for the change impact analysis in manufacturing, starting with the initial description of changes and ranging to the presentation of results. It includes the use of advanced data analytics methods such as process mining for the characterization of changes and the identification of relevant stakeholders based on a digital representation of the organizational structure.

Original language	English
Pages (from-to)	247-252
Number of pages	6
Journal	Procedia CIRP
Volume	104
DOIs	https://doi.org/10.1016/j.procir.2021.11.042
State	Published - 2021
Event	54th CIRP Conference on Manufacturing Ssystems, CMS 2021 - Patras, Greece Duration: 22 Sep 2021 → 24 Sep 2021

Keywords

Change Impact Analysis
Manufacturing Change Management
Process Mining
Stakeholder Identification
Text Mining

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10.1016/j.procir.2021.11.042

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title = "A Framework for Data-Based Change Impact Analysis in Manufacturing",

abstract = "Manufacturing companies recognize the efficient handling of technical changes as a critical success factor for the long-term competitiveness. At the same time, the increasing digitalization of the change management process leads to the availability of historical change data. This paper presents a holistic conceptual framework for the change impact analysis in manufacturing, starting with the initial description of changes and ranging to the presentation of results. It includes the use of advanced data analytics methods such as process mining for the characterization of changes and the identification of relevant stakeholders based on a digital representation of the organizational structure.",

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AU - Reinhart, Gunther

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AB - Manufacturing companies recognize the efficient handling of technical changes as a critical success factor for the long-term competitiveness. At the same time, the increasing digitalization of the change management process leads to the availability of historical change data. This paper presents a holistic conceptual framework for the change impact analysis in manufacturing, starting with the initial description of changes and ranging to the presentation of results. It includes the use of advanced data analytics methods such as process mining for the characterization of changes and the identification of relevant stakeholders based on a digital representation of the organizational structure.

KW - Change Impact Analysis

KW - Manufacturing Change Management

KW - Process Mining

KW - Stakeholder Identification

KW - Text Mining

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U2 - 10.1016/j.procir.2021.11.042

DO - 10.1016/j.procir.2021.11.042

M3 - Conference article

AN - SCOPUS:85121609011

SN - 2212-8271

VL - 104

SP - 247

EP - 252

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 54th CIRP Conference on Manufacturing Ssystems, CMS 2021

Y2 - 22 September 2021 through 24 September 2021

ER -

A Framework for Data-Based Change Impact Analysis in Manufacturing

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Keywords

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