TY - JOUR

T1 - Data based root cause analysis for improving logistic key performance indicators of a company’s internal supply chain

AU - Schmidt, Matthias

AU - Maier, Janine Tatjana

AU - Härtel, Lasse

N1 - Conference code: 7

PY - 2020

Y1 - 2020

N2 - The manufacturing industry faces an increasingly complex and dynamic environment due to shorter product life cycles, advanced production structures and expanding customer services. It is imperative that logistic key performance indicators (KPIs) be considered along with product costs and product quality to obtain a competitive advantage. Numerous companies possess an internal supply chain that fails to meet logistic performance goals set by the management. The measurables for logistic performance include logistic KPIs such as delivery time as well as cost relevant figures including work-in-process or the utilization of employees. In a case of unsatisfactory logistic KPIs, it is pertinent to identify the root causes before attempting to rectify the situation. Increasing digitalization within industry means a substantial volume of confirmation data is available regarding the core processes of a company's internal supply chain. This study discloses a model-based analysis of confirmation data to identify the root causes of unsatisfactory logistic KPIs. A framework for the analysis is constructed by defining generic cause-and-effect relationships between the relevant logistic KPIs and influencing as well as disturbing factors. The results produced by the model-based analysis and the interpretation of the confirmation data show the occurring cause-and-effect relationships for particular use cases and deduce the root causes for insufficient logistic KPIs. From there, companies can develop and implement suitable steps to increase the logistic KPIs by focusing on the newly-identified root causes instead of non-related, but recurring, complications. A case study is included to show the practicality of the presented method. The root cause analysis provides the basis for advanced logistics controlling systems to automatically identify weak-points and propose counteractive measures and therefore continuously improve and adapt the supply chain to changing conditions.

AB - The manufacturing industry faces an increasingly complex and dynamic environment due to shorter product life cycles, advanced production structures and expanding customer services. It is imperative that logistic key performance indicators (KPIs) be considered along with product costs and product quality to obtain a competitive advantage. Numerous companies possess an internal supply chain that fails to meet logistic performance goals set by the management. The measurables for logistic performance include logistic KPIs such as delivery time as well as cost relevant figures including work-in-process or the utilization of employees. In a case of unsatisfactory logistic KPIs, it is pertinent to identify the root causes before attempting to rectify the situation. Increasing digitalization within industry means a substantial volume of confirmation data is available regarding the core processes of a company's internal supply chain. This study discloses a model-based analysis of confirmation data to identify the root causes of unsatisfactory logistic KPIs. A framework for the analysis is constructed by defining generic cause-and-effect relationships between the relevant logistic KPIs and influencing as well as disturbing factors. The results produced by the model-based analysis and the interpretation of the confirmation data show the occurring cause-and-effect relationships for particular use cases and deduce the root causes for insufficient logistic KPIs. From there, companies can develop and implement suitable steps to increase the logistic KPIs by focusing on the newly-identified root causes instead of non-related, but recurring, complications. A case study is included to show the practicality of the presented method. The root cause analysis provides the basis for advanced logistics controlling systems to automatically identify weak-points and propose counteractive measures and therefore continuously improve and adapt the supply chain to changing conditions.

KW - Engineering

KW - Cause-Effect-Relationships

KW - Data Analysis

KW - Logistic Key Performance Indicators

UR - http://www.scopus.com/inward/record.url?scp=85081535643&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2020.01.023

DO - 10.1016/j.procir.2020.01.023

M3 - Conference article in journal

VL - 86

SP - 276

EP - 281

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - 7th International Academy for Production Engineering Global Web Conference - 2019

Y2 - 16 October 2019 through 18 October 2019

ER -