Simulation application for resource allocation in facility management processes in hospitals

摘要

Purpose - The increasing percentage of aging population (longer life expectancy) and the rnchanging financial policies in the healthcare systems put governments under pressure to optimize rnits healthcare expenditures without compromising quality. One way to cut down the costs is rnthrough improving and optimizing the facility management processes. This paper aims to focus on rnthe issues surrounding this.Design/methodology/approach - To demonstrate the application of the research, service rnmanagement (SM) process which deals with the building services related requests from the rncustomer, one of the facility management (FM) processes, is taken as the focus of this paper. The study rnapplies the lean principles to the SM process to identify the value added and non-value added activities rnin the process. Process logistics flow is modified to comply with the lean theory. The collected datarnfrom six participating hospitals in Germany for the two months of the year 2002 are also used asrninputs for the simulation model.Findings - Simulation is used to quantify the impact of the lean principles proposed changes on thernsystem performance. The simulation analysis has proved to be an effective tool in the selection ofrnoptimum resources for the SM process in hospitals. The implementation of lean and simulation willrnassist the facility manager in the selection of the optimum crew size in various sub processes, thusrneliminating the trial and error approach.Research limitations/implications - To develop a generic model for all categories of hospitals,rnsubstantial data are needed for the simulation model. In this paper, the SM process results from onerncategory of hospitals are presented.Practical implications - The methodology can be extended to the other FM processes in differentrnhospitals, with proper modification.Originality/value - The simulated process model was useful to analyze "what if scenarios for therndecision-making regarding optimum resource allocation.