Quality modeling and improvement of university facilities services using six-sigma: A case study on wayne state university FPM services

Literature survey shows that there is no published information concerning the investigation and/or evaluation (by the customer) of the services provided by universities facilities management units, and no previous research was done to measure and evaluate such services to address, identify, and model the critical factors affecting quality. This research work proposed a service quality model relating factors affecting quality of services provided by facility management units at higher educational institutions to the customer perception of service quality. It also examined the use of the Six-Sigma DMAIC methodology as an improvement strategy for services provided by facility management units at higher education institutions. Based on the service quality model developed and using a tool box of Six-Sigma methods, a case study at Wayne State University (WSU) was performed to examine and improve the facilities services for. A large scale survey was used as an instrument to measure customer satisfaction with the services delivered. The customer ratings for services showed that some service categories needed improvement. The initial service quality model was devised by surveying the literature, as well as conducting in depth interviews with people in the FM field at different levels of management hierarchy. The model was reviewed, refined, modified, and validated by conducting a Nominal Group Technique session, which led to a final proposed service quality model for higher education institutions. A set of Six-Sigma tools and techniques were utilized through different phases of the service process improvement, and to conduct an improvement process for the selected service category of General Improvement Request Form (GIRF). These tools and techniques included process map, Pareto charts, cause and effect matrix, and Failure Mode and Effect Analysis (FMEA). A modified process map was developed to avoid bottlenecks, and eliminate non-value adding activities. Critical tasks affecting process outputs were identified through Cause and Effect Matrix, and all Key Process Input Variables (KPIVs) were rank ordered with respect to the importance of the output variable. Potential failure modes, failure effects, and causes of failure were identified through FMEA. A risk Priority Number (RPN) was assigned for each potential failure mode, and recommended actions to eliminate and control failure modes were developed in this process.