University of Twente Student Theses


Unlocking operational efficiency through reliable asset data : a case study of maintenance strategies in Company X

Elsawaf, Ahmed (2023) Unlocking operational efficiency through reliable asset data : a case study of maintenance strategies in Company X.

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Abstract:This thesis delves into the critical aspect of asset data quality within Company X, a global manufacturing organization facing challenges in maintaining accurate and reliable asset information. The main focus of this research is to address the problem of poor data quality, particularly in asset criticality classification, and its impact on maintenance operations and decision-making, which led us to the formulation of the research question: How can Company X attain a sufficient baseline of data quality to facilitate the transition from a budget-based maintenance strategy to a data-driven one? The first phase of the study involved an in-depth context analysis to understand how asset data is generated, used, and stored within Company X. It became evident that poor data quality, specifically mismatched asset criticality between SAP and the plant list, had adverse effects on maintenance strategies, resulting in additional costs and inefficiencies. To resolve this issue, the research developed a methodology for data analytics to address the asset data mismatches. Using data analytics, integrating software, and physical verification, a significant a selected pilot Plant Z was selected for application of our findings. Plant Z was first operating with a 63% asset criticality match. The successful implementation of this methodology resulted in Plant Z increasing its match by 23%, becoming a well-performing plant, with 86% of assets correctly criticalized across all data sets. These results exemplify the potential of data-driven maintenance to optimize operations and resource allocation. Furthermore, the research introduces a tool, the Incoming Asset Standardized Flowchart Tool, which acts as a standardized guide for employees when handling incoming assets. By utilizing this flowchart as a guide when dealing with new incoming assets, Company X can reduce human data entry errors, ensuring accurate asset registration and setup, ultimately improving data quality from the outset. The research then goes to outline the significance of data governance policies. By establishing clear roles, responsibilities, and data quality standards, Company X can foster a culture of data consciousness and accountability, driving employees to actively contribute to data accuracy and consistency to maintain reliable asset data withing the organization. The management summary concludes by emphasizing the importance of asset data to effective decision-making and proactive maintenance strategies. By empowering employees with the knowledge of data's impact on operations, Company X can foster a data-driven culture, driving continual improvement and cost savings. In summary, this thesis offers the practical solutions of data cleansing and the standardization of processes for Company X to enhance asset data quality and aid the transition towards data-driven maintenance. After the implementation of the asset quality improvement process, an operating plant saw a significant improvement, of which the rest of the plants will soon follow. The future implementation of the Incoming Asset Standardized Flowchart Tool would then come to prevent the same issue from reoccurring in the future and provide a reliable data quality baseline for Company X to transition to a data-driven maintenance policy.
Item Type:Essay (Bachelor)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:70 social sciences in general
Programme:Industrial Engineering and Management BSc (56994)
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