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A Systematic Review Approach of Continuous Improvement Pillar Management Framework’s
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Today’s market is constantly changing, so companies are required to continuously evolve their processes so that they can meet the increasingly complex requirements of stakeholders, from customer needs to sustainable policies. The application of Business Process Management improves its processes, bringing together all the activities carried out by the company that aim to generate value for the client. As a consequence, one of the biggest problems for companies is the absence of information management and communication with internal and external stakeholders. Duplication and deprivation are very common in these companies and generate a huge amount of waste.
To solve this problem the developments done in this research work aims to generate a new way of looking to Business Management, focusing on companies that adopt Continuous Improvement, using a structured flow of tools and techniques. In structured approaches to Continuous Improvement management frameworks recurrently uses modelling support pillars. This study is based on a company that has implemented eight-pillar Total Productive Maintenance (TPM) and uses performance indicators to monitor the state of systems and processes. The first step involved the application of the Cluster, Discriminant and Principal Component Analysis, so that it was possible to aggregate the indicators of each pillar. The processes of strategic management of the company and management of the pillars have been taken into account.
Through the proposed approach a reduction of around 86% in the total number of pillar of the company was obtained, avoiding diverse informational wastes through redundancies of information, increased reliability, coherence and ease of access to it as well as greater visibilities of their interactions and information responsibilities necessary to the management of systems management of continuous improvement, based on pillars of action.
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Performance Indicators Business Process Management Cluster and Discriminant Analysis Principal Component Analysis