Purpose This paper aims to explore how big data analytics (BDA) collected and stored through specific data software [Construction Operations Building Information Exchange [COBie], integrated workplace management systems [IWMS], computer aided facilities management (CAFM), etc.] can play an essential role in improving the performance management system in the facility management (FM) industry. It defines the big data components and explores the benefit of BDA in any business through an extensive literature review and a pilot case study in the UAE. Design/methodology/approach The research was carried out based on a qualitative approach. It attempts to identify through a case study how the data collected and the technologies that go along with will increase the functionality and the efficiency of the FM services. The research studies the implementation of a big FM organization, hereafter referred as “M” of software that exports the data collected from COBie and the computer aided facilities management (CAFM) system and shapes them into input to improve the performance of the FM service providers. The study includes two components in anticipation of providing a complete picture: first, five semi-structured interviews with industry experts and company employees representing the hierarchy of the staff, i.e. top, middle and operational levels; one director, two managers and two operational-level employees were interviewed to determine the current situation of the company in terms of BDA; and second, detailed documents and archives records review for the data collected on a randomly chosen sample of facilities for the period 2013-2015. The interviews were designed to achieve two specific objectives. Primarily, they were aimed at collecting empirical evidence on the existing status of big data within the UAE FM context and at investigating the importance of the data collected for performance measurement in the industry as supported in the literature. Second, these interviews sought to identify any critical issues that need to be addressed within the data collection process when devising the big data platform for FM. Findings The paper seeks to provide a guideline to the service providers in the FM market to understand the importance of big data to be shared from the design and construction to the operational phase as it improves their operational performance. Originality/value This paper studies the impact of big data on the FM performance management, a very recent topic where only few researches were conducted earlier.

The Building Maintenance Cost Information Service (BMCIS) offers a comprehensive and rigorous framework for collecting data about the running costs of buildings. Nevertheless, it is pitched at such a level of detail that the amount of data collected and analysed may be constrained. This paper describes the deveopment and testing of a novel technique which reduces the amount of data to be collected without any unacceptable reduction in utility. It draws on the principle of cost-significance to create a simple model of maintenance and operating costs (together called running costs) from a rare and consistent set of data for 20 buildings at York University. The model contains only 11 elements, yet can predict the total running costs of each of four categories of building to an accuracy of about 21 2%. It can also predict annual costs to about 7%, despite variations in the periodicity of costs such as painting and insurance. The model was tested using the jacknife method and on virgin data. It proved to be extremely robust, predicting the running costs of 12 new buildings to within 5%. The model offers a simple framework for collecting and analysing reliable and consistent data on running costs.Life Cycle Costs, Maintenance Costs, Operating Costs, Cost-significance,

TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractThis paper explores the application of LCC (Life Cycle Costing) concepts in the oil and gas industry. The paper details research into the development of a LCC model for using in SAP (System, Application and Products in Data Processing). Information held in the existing system in the oil and gas industry has been investigated in order to determine whether or not it is adequate to support LCC application for assets. The conceptual framework will develop the LCC technique as a tool to carry out costing analysis for new and existing systems. It will ensure that existing systems data are optimized for use in LCC applications and will investigate the feasibility of integrating the LCC model with existing systems. Based on this conceptual framework a LCC model will be developed.The proposed system model provides a structural breakdown of cost (SBC) that can be applied to any asset at any level, such as super system, system, sub-system and equipment level in its lifecycle. The purpose of this SBC is to act as an aide memoir, as the starting point for developing a project/asset specific SBC that is tailored to the needs of a particular LCC requirement. The overview of SBC is provided to identify the data requirement for estimated cost element and provides a definition for cost element.Consequently, if SAP-LCC is used for analysis at every, it is possible to identify the level that is the most significant in order to develop or reduce the cost in LCC at that level.

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A number of recently developed algorithms to handle uncertain information in whole life costing (WLC) are explained and validated in the context of two example applications. In the first example application, the proposed methodology is compared to the sensitivity analysis technique. The break-even point has been correctly identified in almost all cases. Furthermore, it has been shown how the employed fuzzy methodology may be seen as a generalised sensitivity approach to which has been added a measure of the precision with which input variables are known to the decision-maker. In the second example application, the proposed methodology is compared to two probabilistic techniques: the confidence index method and the Monte Carlo simulation (MCS) technique. The proposed methodology correctly identified the most uncertain cost items and portrayed well the confidence in ranking. Besides, all predicted net present values were in close agreement with those obtained by the MCS technique. This showed once more the robustness of various measures and concepts employed in the developed algorithms.