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Proactive Supply Chain Performance Management with Predictive Analytics

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measu...

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Detalles Bibliográficos
Autor principal: Stefanovic, Nenad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214046/
https://www.ncbi.nlm.nih.gov/pubmed/25386605
http://dx.doi.org/10.1155/2014/528917
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author Stefanovic, Nenad
author_facet Stefanovic, Nenad
author_sort Stefanovic, Nenad
collection PubMed
description Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.
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spelling pubmed-42140462014-11-10 Proactive Supply Chain Performance Management with Predictive Analytics Stefanovic, Nenad ScientificWorldJournal Research Article Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. Hindawi Publishing Corporation 2014 2014-10-15 /pmc/articles/PMC4214046/ /pubmed/25386605 http://dx.doi.org/10.1155/2014/528917 Text en Copyright © 2014 Nenad Stefanovic. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Stefanovic, Nenad
Proactive Supply Chain Performance Management with Predictive Analytics
title Proactive Supply Chain Performance Management with Predictive Analytics
title_full Proactive Supply Chain Performance Management with Predictive Analytics
title_fullStr Proactive Supply Chain Performance Management with Predictive Analytics
title_full_unstemmed Proactive Supply Chain Performance Management with Predictive Analytics
title_short Proactive Supply Chain Performance Management with Predictive Analytics
title_sort proactive supply chain performance management with predictive analytics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214046/
https://www.ncbi.nlm.nih.gov/pubmed/25386605
http://dx.doi.org/10.1155/2014/528917
work_keys_str_mv AT stefanovicnenad proactivesupplychainperformancemanagementwithpredictiveanalytics