Benchmarking of the Maintenance Service in Health Care Organizations

Benchmarking of the Maintenance Service in Health Care Organizations

María Carmen Carnero (University of Castilla-La Mancha, Spain & University of Lisbon, Portugal)
DOI: 10.4018/978-1-5225-2515-8.ch001
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Abstract

The support services of health care organizations, such as maintenance, have not traditionally been considered important from the perspective of care quality. Nevertheless, the degree of excellence in maintenance significantly influences availability, maintenance costs and safety of facilities, medical equipment, patients and care staff. Thus, it would be of great importance for health care organizations to apply benchmarking to their maintenance processes, as do other processing companies, in order to determine the quality of maintenance provided, and compare it to other, similar, organizations. This would also allow all the continuous improvement processes to be controlled, and actions for radical improvement to be carried out by comparing performance with that of companies in other sectors. This chapter describes a multicriteria model integrating a fuzzy Analytic Hierarchy Process with utility theory to obtain a valuation for the Maintenance Service of a Health Care Organization over time.
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Background

There are four types of benchmarking (Kelessidis, 2000):

  • 1.

    Competitive Benchmarking: Benchmarking is carried out against competing companies and the data analysis is intended to determine the reasons behind the superior performance of the competition. This has the advantage that there is a series of exogenous variables, which affect the organization and its competitors equally if they all belong to the same economic sector. Nonetheless, it is unlikely that competing businesses will cooperate unless, for example, they compete in different markets.

  • 2.

    Internal Benchmarking: This is applied between units of departments belonging to multinational companies with branches, manufacturing operations or sales offices spread over different countries or geographical areas.

  • 3.

    Process Benchmarking: This compares processes with some degree of similarity but in companies from different sectors.

  • 4.

    Generic Benchmarking: This analyses technological aspects, comparing companies from the same or from different sectors.

The benchmarking application process has the following stages (Dunn, 1999):

  • Establish the scope.

  • Develop the project plan.

  • Select the key performance variables to benchmark.

  • Identify potential participant companies.

  • Measure performance of reference company.

  • Measure performance of benchmarking participants.

  • Communicate your results.

  • Compare current data.

  • Identify best practices and enablers.

  • Formulate the strategy.

  • Implement a plan.

  • Monitor results.

  • Plan for problem solving.

Key Terms in this Chapter

Fuzzy Analytic Hierarchy Process: The inability of AHP to deal with imprecision and subjectivity in the judgements given by the decision maker is solved using fuzzy AHP. It is, therefore, a multi-criteria technique, based on AHP, but using range of values to incorporate the uncertainty of the decision maker. A number of different methodologies have been developed for applying it, e.g.; the algorithm designed by Van Laarhoven and Pedrycz, which is a direct extension of the original AHP method; the Buckley method for incorporating fuzzy comparison ratios into the methodology; other methodologies used are Chang’s Extent Analysis Method and Cheng’s entropy-based Fuzzy AHP.

Benchmarking: A continuous improvement process consisting of seeking, comparing and adapting best practice in other organizations to one’s own, by using indicators or benchmarks to achieve the highest level of performance.

Fuzzy Logic: A technique of computational intelligence introduced by Lotfy A. Zadeh in 1965 which allows the imprecision, uncertainty, vagueness, etc., which characterize human judgements and thought to be included. It represents knowledge, which is primarily linguistic and qualitative, in mathematical language, by the use of fuzzy sets and associated characteristic functions.

Analytic Hierarchy Process: A multi-criteria technique developed by Thomas Saaty in 1980 by which a ranking of alternatives is calculated based on the principles of comparison by pairs, decomposition and synthesis. A hierarchy must be constructed to establish the relationship between the goal, criteria, sub-criteria and alternatives; to determine the relative importance of the alternatives with regard to each of the criteria or between criteria, linguistic terms are used that include the judgements of the decision maker.

Activity Sector: Classification of economic activity into groups which have common characteristics in the production processes carried out in each of them, and which distinguish them from other groupings.

Maintenance: A set of technical and management actions carried out to operate, preserve, improve and adapt machines and/or facilities in an organization over their lifetime, so as to support the aims established by the company at minimum cost.

Monte Carlo Simulation: A method for estimating uncertainty in a variable which is a complex function of one or more probability distributions; it uses random numbers to provide an estimate of the distribution and a random number generator to produce random samples from the probabilistic levels.

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