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Weekly UpdatesHowever, the deterioration pattern of each component in all systems other than the structural system was assumed to be linear (Equation 2). The linear pattern of deterioration assumes standard service conditions that yield time-dependent linear deterioration of building components, based on previous research findings that linear patterns of deterioration are appropriate and valid for interior components and exterior claddings (Shohet and Paciuk, 2004; Moubray, 1997).
P[P.sub.i,j,k] = A[P.sub.i,j,k] = -40/dl[c.sub.j,k], (2)
where: P[P.sub.i,j,k] = Projected performance score for component k of system j in building i; A[P.sub.i,j,k] = Actual performance score for component k of system j in building i; and dl[c.sub.j,k] = Designed life cycle for component k of system j.
Although this was not proven for all building components, and since this research does not investigate the exact pattern of deterioration for all components, the linearity assumption was made in order to simplify the calculation process. However, the equations used in this research are parametric, so future studies could replace these equations with others, if they are found to be more accurate and precise.
The weight of each system in the building's performance indicator is calculated as the ratio between the system's Life Cycle Costs (LCC) and the building's LCC. Since this ratio weighs the systems based on their LCC relative to the total LCC of the building, it represents a physical performance score weighted on the basis of the LCC criterion. This criterion sets a service life planning means for the allocation of maintenance resources.
The prediction of a building's performance indicator projects future functioning level based on actual monitoring of its performance and on other assumptions, as detailed above. In this research, patterns of performance projection were developed for all 51 main hospital building components. Based on this, future performance can be projected for each system in the building, for the building as a whole, and for the entire facility that is composed of several buildings (Lavy and Shohet, 2007b).
The process of performance projection includes two patterns of deterioration: non-linear and linear deterioration. Although the concept of different patterns of deterioration is well documented in the literature, this research advances one step further: it proposes the use of these patterns of deterioration to not only project the performance of a single element or system in a building, but to project it for the entire building and even of the entire facility, using Life Cycle Costs as the weighting principle for the various building systems. Moreover, it allows decision-makers to break each building down into its separate systems, and to analyze it in great detail, down to its components.
Maintenance Efficiency Indicator Procedure
This procedure computes the Maintenance Efficiency Indicator (MEI), which indicates the actual efficiency with which maintenance activities are implemented. The MEI range of values for healthcare facilities is defined as: (1) lower than 0.37, representing a high level of efficiency; (2) between 0.37 and 0.52, representing a standard level of efficiency, with 0.45 being the middle of this range; and (3) higher than 0.52, representing a low level of efficiency. These values are based on a desired performance level of 90 points, 25 years as an average age of Israeli healthcare facilities, and Annual Maintenance Expenditure that assumes implementation of a preventive maintenance policy (Shohet et al., 2003). In order to calculate the Maintenance Efficiency Indicator, this procedure uses the following two indicators: (1) the Normalized Annual Maintenance Expenditure (NAME) which is the adjusted Annual Maintenance Expenditure--this value expresses the maintenance expenditure, weighing the effects of the building's age and level of occupancy (Shohet et al., 2003); and (2) the actual Building Performance Indicator (BPI) for the entire facility, measured on a 100-point scale. The MEI calculation is shown in Equation 3. The NAME itself is composed of two parameters: (1) the Annual Maintenance Expenditure, which is the annual amount of resources per sq-m spent on maintenance activities in the facility; and (2) the facility coefficient, representing the maintenance resources for implementing preventive maintenance policy. This calculation is shown in Equation 4.
MEI = NAME/BPI, (3)
NAME = AME/FAC(y), (4)
where: MEI = Maintenance Efficiency Indicator; NAME = Normalized Annual Maintenance Expenditure ($US per sq-m); BPI = Building Performance Indicator for the facility; AME = Annual Maintenance Expenditure ($US per sqm); and FAC(y) = Facility coefficient for year y.
Figure 4 delineates the Maintenance Efficiency Indicator on a two dimensional graph where the vertical axis represents the BPI scores of the buildings, while the horizontal axis represents the NAME. The diagonal dashed line represents the normative standard level of MEI (0.45) in hospital facilities in Israel, and the other two lines provide the upper and lower boundaries of this range as derived from the standard deviation of the sample population. This diagram provides a strategic tool for long-range facility management in healthcare. On the vertical axis, the diagram allows setting of performance benchmarks and short as well as long-term objectives. The horizontal axis provides a clear means for economic evaluation of the annual expenditure on maintenance. The normative lines set the criteria for efficient maintenance (MEI = [+ or -]0.37), standard efficiency (MEI = [+ or -]0.45), and poor efficiency (MEI > 0.52).
[FIGURE 4 OMITTED]
Actual Risk Procedure
This procedure aims to categorize the actual level of risk for each system in each building. The risk scales were defined as ordinal scales with five categories of risk: Very Low, Low, Standard, High, and Dangerous. This procedure provides an indication for the level of risk associated with each of the 51 main components in a building (also discussed in the Building Performance Indicator (BPI) procedure). The assumption is that the following two parameters characterize a risk level: (1) actual Building Performance Indicator and (2) actual maintenance policy and design parameters. Table 2 presents the calculation method for the actual risk for one building component--the control panels, which are part of the elevator system. The values presented in this table are parametric, and were developed as an average of the responses received from a survey of five Israeli healthcare facility managers in public acute-care hospitals; therefore, these are the model's default values. However, since these are parametric figures, they may be changed and adapted according to the specific requirements of each type of building and for each user's needs.
Based on this table, the actual risk of any specific component may be deduced according to the higher option, i.e., if the BPI shows an actual risk category of Low, but the maintenance policy fits the actual risk category of Standard, then the final actual risk of that component will be Standard (the higher value of Low and Standard). If the BPI is lower than 30 points, then the maintenance policy has no affect on the actual risk, which means that it remains Dangerous, regardless of the maintenance policy.
5. CONCLUSIONS
The research proposes deductive hierarchical reasoning for strategic healthcare facility management. The reasoning mechanism implements integrated analyses of Key Performance Indicators that shed light on organizational effectiveness and efficiency of healthcare FM, and on performance and maintenance policy setting.
When making a decision, a facility manager must consider many factors in FM decision-making. Yet, existing models supporting decision-making processes are quite limited, particularly at the strategic level of facilities management. This may be attributed to the fact that the integration between the different parameters of the facility has not yet been researched thoroughly, particularly with reference to the effects of these parameters on the facility's service life planning. As a result, this research focused on the identification of principal variables affecting the performance and maintenance of facilities throughout their service life. These parameters were drawn together into an analytical Integrated Healthcare Facility Management Model, which proposes simultaneous diagnosis and analysis of the complexities involved in this intricate area. Almost all facility managers and owners of public and private facilities face these complexities. Managing these complexities is, however, more critical in healthcare facilities that operate 24 hours a day, 7 days a week, provide emergency intensive and life-saving care and treatment services, and support critical infrastructure of healthcare, such as power supply for operating theaters, and medical gas in intensive care units.
The development of the IHFMM enhances the existing body of knowledge about the management of built facilities and provides generic parameters, as well as methods, for the complicated decision-making processes in healthcare facility management. It enables the facility management discipline to become more structured and quantitative by offering simultaneous hierarchical analysis of healthcare FM core parameters, as seen by the structure of the model. The IHFMM may provide a means for coping with complexities, such as insufficient data, that the facility management discipline often faces. In addition, the developed IHFMM may provide new means and concepts for measuring the effectiveness and efficiency of performance and operations of facilities.
The proposed IHFMM could assist healthcare facilities managers in their FM-related decision-making process, as it creates the basis for strategic decision-making in facility management. The facility coefficient procedure, for example, shows significant evidence that the maintenance expenditure in a building significantly depends on a combination of factors that have not been taken into consideration in previous research, such as the age of the building, its level of occupancy, and even the type of environment in which the building is located. The projected performance procedure can be used as an indicator for the projection of the physical condition of a building and its various systems and components, by using linear and non-linear patterns of deterioration for each specific component. Based on these two parameters, strategic decision-making, such as determining the best investment in terms of resource allocation and even broader aspects of facility management which were not discussed in this paper, such as space planning and workplace design, can be undertaken. The third procedure this paper deals with is the Maintenance Efficiency Indicator that expresses the efficiency with which resources are utilized. Using this indicator provides strategic decision-makers with a powerful tool in terms of identifying required changes in order to improve efficiency and productivity in implementing maintenance work. The fourth procedure is the actual risk procedure that combines the physical performance and maintenance policy into a 5-point scale representing the risk associated with a building and its various systems and components. Using each of these procedures by itself can add an important component to strategic facility management; however, using the IHFMM as an inclusive model for healthcare facility management has the capacity to make considerable changes in this process.
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