Predictive performance model in collaborative supply chain using decision tree and clustering technique

Derrouiche, Ridha; Holimchayachotikul, Pongsak; Leksakul, Komgrit

doi:10.1109/logistiqua.2011.5939435

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Seifert and Eschenbaecher introduced a predictive performance measurement approach as a planning tool for virtual organizations to anticipate the performance of a planned virtual team [16]. Derrouiche et al proposed an integrated framework for supply chain performance evaluation based on data mining techniques [17].This framework enables the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. Stefanovic [18] presented the proactive supply chain performance management model which unifies business intelligence tools like data warehousing and data mining to integrate data and to perform predictions of the key performance indicators (KPI), and finally to deliver derived knowledge to decision makers via collaborative web portal.…”

Section: Background Researchmentioning

confidence: 99%

Collaborative predictive business intelligence model for spare parts inventory replenishment

Stefanovic

2015

COMSIS J

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In today?s volatile and turbulent business environment, supply chains face great challenges when making supply and demand decisions. Making optimal inventory replenishment decision became critical for successful supply chain management. Existing traditional inventory management approaches and technologies showed as inadequate for these tasks. Current business environment requires new methods that incorporate more intelligent technologies and tools capable to make fast, accurate and reliable predictions. This paper deals with data mining applications for the supply chain inventory management. It describes the unified business intelligence semantic model, coupled with a data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions and to deliver this information to relevant decision makers in a user-friendly manner. Experiments carried out with the real data set, from the automotive industry, showed very good accuracy and performance of the model which makes it suitable for collaborative and more informed inventory decision making. [Projekat Ministarstva nauke Republike Srbije, br. III-44010: Intelligent Systems for Software Product Development and Business Support based on Models]

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Section: Background Researchmentioning

confidence: 99%

Collaborative predictive business intelligence model for spare parts inventory replenishment

Stefanovic

2015

COMSIS J

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Section: Background Researchmentioning

confidence: 99%

Proactive Supply Chain Performance Management with Predictive Analytics

Stefanovic

2014

The Scientific World Journal

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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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