A Simulation Technique for Optimising Maintenance Teams for a Service Company

Abstract

The maintenance of some sophisticated and complex equipment -for example, diagnostic equipment used in hospitals -is frequently outsourced to the original equipment supplier or another maintenance service provider. Companies that own such assets can either enter into a service agreement, or they can use service providers on a call-out basis. A fundamental problem for the provider of maintenance services is to determine the optimum number of maintenance technicians or artisans. An investigation was done for a service company that provides planned and breakdown maintenance services to hospitals and clinics that use specialised medical imaging equipment. Data on the duration of maintenance tasks and traveling costs were used to obtain an input distribution for a simulation model. The model that was developed can now be used to determine the number of technicians that would optimise the service company's profit. This paper discusses the features of the simulation model, as well as some results obtained with the basic simulation model. OPSOMMINGDie instandhouding van sommige gesofistikeerde en komplekse toerusting, soos byvoorbeeld diagnostiese toerusting wat in hospitale gebruik word, word dikwels uitgekontrakteer na die oorspronklike vervaardigers van die toerusting. Ondernemings wat sulke toerusting besit kan dan 'n diensleweringskontrak met die diensverskaffer aangaan, of gebruik maak van 'n uitroepstelsel. 'n Fundamentale probleem vir die diensverskaffer is om die optimum aantal tegnici te bepaal. 'n Ondersoek is gedoen vir 'n tipiese diensverskaffer wat beplande sowel as onbeplande instandhouding doen vir hospitale en klinieke wat gesofistikeerde optiese toerusting bedryf. Data vir die tydsduur van take sowel as reistyd is gebruik om insetverdelings te verkry vir die ontwikkeling van 'n simulasiemodel. Die model wat ontwikkel is kan gebruik word om die aantal tegnici te bepaal wat die wins vir die diensmaatskappy sal optimeer. Hierdie artikel bespreek die eienskappe van die simulasiemodel wat ontwikkel is, sowel as van die resultate wat met die basiese simulasiemodel verkry is.

A simulation-based decision support system for industrial field service network planning

“…Daily call pattern, service types, product types, regional travel speeds, breaks, regions, round and sequential trip, dispatching alternatives Visser 29 Staffing and contracting Weekly call patterns, service contract, location-based travel times, product types, single service, spare parts delivery Rapaccini et al 15 Districting, dispatching, cross-training Daily call patterns, contracts, service types, product types, travel and work time distribution, geographic locations, spare parts demand, spare parts quantities Control variables: capacity, dispatching policy, scheduling policy, districting policy, cross-training policy, spare parts management policy Colen and Lambrecht 7 Cross-training Service types, contract, skills (product), call priorities, interdependency maintenance/breakdown, travel time approximation, round trip rules are changed, this might change the distribution of travel times significantly. In addition, state-of-the-art commercial simulation packages do not offer special functionality for modeling field services and require a high degree of customization to adequately model FSNs.…”

“…Watson et al, 9 Dear and Joseph, 17 Visser, 29 and Duffuaa and Raouf present simulation models based on DES and the Monte Carlo method, 30 which are used to support staffing and response time planning. Hill uses a similar approach to compare the responsiveness of different dispatching rules for field service support.…”

Integrated planning of spare parts and service engineers with partial backlogging

“…The integration of spare parts and manpower planning was, however, rarely considered. The few papers that do study this integration mainly use simulation as a methodology for the performance analysis (Hertz et al 2014;Visser and Howes 2007). Hertz et al (2014) review the literature on simulation models in after-sales service logistics.…”

Service Supply Chain Planning for Industrial Services – Design and Application of a Decision Support Tool

“…The most relevant contributions based on the use of simulation to support decision making in this context can be clustered in different parts. On a tactical level support staffing, process design, or more specifically dispatching policies, are investigated based on response time and utilization indicators by Hill [15], Watson et al [7], Dear [16], Visser [23] and Duffuaa and Raouf [24]. Another important class of contributions focuses on the optimal qualification and dispatching strategies in manpower planning for different scenarios [25][26][27].…”