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Data mining as a technique for knowledge management in business process redesign
31
Citations
10
References
2005
Year
EngineeringBusiness IntelligenceBusiness Process ModelingBusiness AnalyticsLarge VolumesMining MethodsDecision AnalyticsBusiness Process RedesignKnowledge Discovery In DatabasesIndustrial Data MiningData ScienceData MiningLarge-scale DataBusiness Process AnalysisManagementProcess MiningBusiness Process Re-engineeringBusiness Information SystemsKnowledge DiscoveryBusiness Data MiningStrategic ManagementProcess DiscoveryBusiness Process ManagementBusiness OperationsBusiness ProcessBusinessKnowledge ManagementData Modeling
Practitioners have developed many methodologies to support competitive business process redesign, yet most have failed to deliver significant improvements. This study proposes using data mining to extract hidden knowledge from large organizational data to facilitate business process redesign and achieve order‑of‑magnitude improvements. The authors present a DM/BPR framework that applies data‑mining models to historical process data, extracting previously unknown knowledge and trends that are transferred to redesign efforts. Applying the framework successfully extracts hidden knowledge, yielding competitive advantage and transforming the organization into a prospect‑oriented, decision‑supporting entity across multiple business functions.
Abstract Purpose – Business process redesign (BPR) is undertaken to achieve order‐of‐magnitude improvements over "old" forms of the organization. Practitioners in academia and the business world have developed a number of methodologies to support this competitive restructuring that forms the current focus of concern, many of which have not been successful. The purpose of this paper is to suggest the use of data mining (DM) as a technique to support the process of redesigning a business by extracting the much needed knowledge hidden in large volumes of data maintained by the organization through the DM models. Design/methodology/approach – The paper explains how the DM/BPR tool will extract and transfer the much‐needed knowledge necessary for implementing the new business. Findings – The process of extracting knowledge hidden from large volumes of data (DM) has proved very successful in solving many business or scientific problems to achieve competitive advantage. As suggested in the DM/BPR framework, the DM model can be deployed on the massive data collected from past business processes of the organization which then yields the previously unknown knowledge and trends needed by top managers or decision makers in the organization for effective business process redesigning. Originality/value – The proposed DM/BPR framework transforms the old business into a new prospect‐oriented business organization by carefully re‐engineering the old system incorporating the new discovered knowledge which helps the manager to make wise and informed business decisions in the area of accountability, business change management expertise, business process analysis, business model design, business model implementation and others.
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