PurposeThe purpose of this paper is to make a performance model of a shoe upper manufacturing unit of a shoe manufacturing industry by computing both the availabilities, i.e. time dependent system availability (TDSA) and the long‐term availability.Design/methodology/approachThe present work is carried out by developing performance model based on Markov birth‐death process. The unit consists of four subsystems. The first order governing differential equations are derived using the mnemonic rule and further solved by adaptive step‐size control Runge‐Kutta method to calculate the TDSA, while the long‐term availability is calculated using normalizing condition, initial boundary conditions and recursive method. Both the availabilities are considered for system's performance criterion.FindingsThe subsystem A, i.e. sewing machine is the most critical from maintenance point of view, which has more impact on the system's performance as compare to other subsystems. The repair priorities of other subsystems have also been proposed.Practical implicationsThese methods can also be used to find out the performance of other manufacturing industries.Originality/valueThe results of the present work are very useful for finding the critical subsystem and its effect on the system performance in terms of availability. Further, based on findings the maintenance priorities of various subsystems can be decided.
Purpose
The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.
Design/methodology/approach
The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.
Findings
The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.
Originality/value
A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.
Objective: The current economic and political climate demands a focus on efficiency and productivity, whilst delivering quality, across all aspects of the National Health Service (NHS). Operative theatres act as a critical, yet costly resource. The Audit Commission employs the use of percentage theatre utilisation as a principal measure of NHS operating theatre service and efficiency performance. We analysed theatre utilisation data in a five-consultant, high turnover, urology department within a NHS University Teaching Hospital. Our aim was to examine the relationship between theatre utilisation data, cost effectiveness and income generated. Patients and methods: Data on the usage of a dedicated urology theatre was collected over 251 hours for a full calendar month. A total of 176 consecutive procedures were performed. Linear regression analysis was performed to assess the correlation between number of operating hours, cases per hour, utilisation percentages and income generated. Results: There was no correlation between percentage theatre utilisation and income (R2=0.0191, p=0.82). No relationship was identified between percentage theatre utilisation and total number of cases performed (R2=0.0001, p=0.99). Although there appeared to be a positive correlation between the number of cases performed and income generated, this was not statistically significant (R2=0.725, p=0.067). Furthermore, there was no association between the number of cases performed per hour and income generated (R2=0.3184, p=0.32). Conclusion: Our data identifies no correlation between percentage theatre utilisation, income generated and number of cases performed. Utilisation percentages are not a reliable performance indicator when used in isolation, and therefore should be used as part of a more global picture when assessing cost effectiveness and efficiency performance.
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