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Asset information management : suggestions for asset owner to overcome handover challenge

Rhamino, Fahad (2018) Asset information management : suggestions for asset owner to overcome handover challenge.

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Abstract:This research provides new insights into the challenge of Asset Information (AI) handover to the Asset Owner. Additionally, this research provides suggestions for the Asset Owner to overcome this challenge. AI handover occurs when the Service Provider has completed the construction of an asset on behalf of the Asset Owner and hands over the ownership of the asset, including the asset’s AI, to the Asset Owner. The AI handover process involves the following activities – related to Asset Owner and Manager - : (1) specifying the Employer’s Information Requirements (EIR) which is a contractual document that sets out all requirements concerning AI to be delivered by the Service Provider; (2) the moment when the Asset Owner acquires the handed-over AI from the Service Provider; and (3) the way the acquired AI is stored in the Asset Information Model (AIM) [3]. The AIM’s purpose is to be the single source of approved and validated information related to assets. The AIM’s aim is to provide AI to help Asset Management (AM) functions make better decisions [4]. Within the AIM, the inventory module registers which assets an organisation has and the corresponding locations. This module contains AI that is mostly static and describes the assets’ physical elements, such as name, location, length and width. This AI is often acquired during the AI handover. However, previous research has demonstrated that many Asset Owners are challenged during the AI handover to acquire the AI they require for AM decision making [1]. Unfortunately, when Asset Owners want to retrieve AI to inform AM decision making they often discover too late that crucial AI is missing because it was not acquired during the AI handover [2]. In sum, the bottlenecks within EIR specification have a negative chain reaction on the follow-up AI handover activities – AI acquisition and AI storage. The bottlenecks within EIR specification might be seen as the primary bottlenecks that constrain the AI handover process in providing the AI used for AM decision making. To improve the AI handover process there must be an emphasis on the importance of specifying accurate EIR within contracts [8].
Item Type:Essay (Master)
Clients:
Arcadis, Qatar
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/76142
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