Hakan Tarakci

Learning effects on maintenance outsourcing

The objective of this paper is to study learning effects on maintenance outsourcing. We consider a situation in which a manufacturer offers a short-term outsourcing contract to an external contractor who is responsible for scheduling and performing preventive maintenance and carrying out minimal repairs when the process fails. The manufacturers payment to the contractor consists of a fixed amount along with cost subsidization for each maintenance operation performed. We assume learning occurs when the contractor performs preventive maintenance that reduces both time and cost. Two types of learning are considered: natural learning and learning by costly efforts. We demonstrate that a well-designed payment scheme can induce the contractor to adopt the maintenance schedule that maximizes the manufacturers profit.

Incentive maintenance outsourcing contracts for channel coordination and improvement

Consider a manufacturer who has a process with an increasing failure rate over time. In order to improve the process performance, the following two types of maintenance activity are outsourced to an external contractor: (i) preventive maintenance is performed periodically to improve the reliability of the process when the process is functional; and (ii) corrective maintenance is used to restore the process to a specified condition when it fails. We consider the use of incentive contracts to induce the contractor to select the maintenance policy that optimizes the total profit of the manufacturer and contractor. It is demonstrated that an incentive contract based on a combination of a target uptime level and a bonus always leads to the desired win-win coordination, and provides flexibility in allocating the extra profit generated from coordination and, importantly, an incentive to the contractor to improve the efficiency of the maintenance operations. The incentive contract can also be used to select the most economically efficient contractor from multiple contractors with different maintenance capabilities.

Maintenance outsourcing of a multi-process manufacturing system with multiple contractors

Consider a manufacturer with a manufacturing system that consists of multiple processes. The manufacturers revenue is determined by the minimum of the uptimes among the processes. The maintenance functions of the processes are outsourced to independent contractors so that each contractor is responsible only for one process. A performance-based incentive contract is offered to each contractor, consisting of an uptime target level and a bonus rate for exceeding the uptime target. Under the incentive contract, a contractor receives a bonus only when the achieved uptime exceeds the target level specified in the contract. We develop a model for jointly determining the uptime target levels and bonus rates for the contractors which maximize system profit. We also demonstrate the financial benefits of coordination and added flexibility in allocating the additional profit to the contractors. In addition we show the impact of the variation in individual process maintenance times and costs on channel coordination and profits.

On the staffing policy and technology investment in a specialty hospital offering telemedicine

We study a specialty hospital providing traditional face-to-face consultations by experts and telemedicine services by tele-specialists. As accuracy of diagnosis and treatment by tele-specialists are paramount in such a setting (unlike call center management), our main focus is to determine the optimal investment level in telemedicine technology with the trade off being between accuracy/quality and cost. Using a heuristic proposed in queuing theory, we provide the optimal investment in telemedicine technology together with the staffing policy, considering the various cost components, including staffing, technology investment, incorrect treatment, and waiting. The model also incorporates buy-in by the patients in the form of the arrival (show-up) rate dependent on the technology level established. We find that under certain conditions the hospital should not invest in telemedicine. Finally, we provide the optimal tele-specialist policy of the ratio of patients to treat via telemedicine and to refer to the face-to-face consultation. Our model also suggests that a policy of treating all patients via telemedicine is never optimal.

Maintenance-outsourcing contracts for a system with backup machines

In this paper, we consider a system with multiple components, each prone to failure, during which production is halted. Minimal repair is performed by an external contractor whenever a component breaks down. The contractor also conducts a general preventive maintenance (PM) for the whole system at pre-determined times. The contractor’s goal is to minimise maintenance-related costs; however, the system (made up of the contractor and the manufacturer, who gains revenue whenever the system is up) profit would be maximised if the revenue is also considered. Since these goals usually require different PM schedules, we propose a cost subsidisation scheme which coordinates the system. We then extend this basic model by considering the existence of a backup machine which will allow the system to continue running (albeit, generating a lower revenue) whenever a component fails. We show that the existence of such a machine reduces the profit difference between uncoordinated and coordinated systems.

Two types of learning effects on maintenance activities

This paper studies a manufacturer with a system prone to failure. The manufacturer performs two types of maintenance activities: preventive maintenance (PM), performed periodically, resets the system, and Minimal Repair (MR), performed after breakdowns, restores the system to working condition. It is assumed that two different types of learning take place: (i) repetition learning: due to the repetitive nature of PM, the manufacturer gains experience and learns to perform the PM activities faster and at a lower cost and (ii) failure learning: each failure gives the manufacturer the opportunity to find the root causes, to learn from mistakes and to improve the system. This paper, the first one to quantify failure learning in maintenance literature, assumes that such learning can then be applied during the next PM activity, which brings down the failure rate for the next PM cycle. For the increasing failure rate case, repetition learning increases the PM frequency, whereas failure learning causes the manufacturer to reduce the optimal number of PM activities. However, for the constant failure rate, repetition learning has no effect on the PM frequency, whereas failure learning may actually increase it.

Learning and forgetting effects on maintenance outsourcing

This article studies the effects of learning and forgetting on the design of maintenance outsourcing contracts. Consider a situation in which a manufacturer offers an outsourcing contract to an external contractor to maintain a manufacturing process. Under the contract, the contractor schedules and performs preventive maintenance and repairs the process whenever a breakdown occurs. Two types of learning effects on the cost and time of performing preventive maintenance are considered: learning from experience (natural) and learning by a costly effort/investment. It is assumed that forgetting occurs under each learning type. A model is developed for designing an optimal outsourcing contract to maximize the manufacturers profit. An extensive numerical analysis is carried out to empirically demonstrate the effects of learning and forgetting on the optimal maintenance contract and the manufacturers profit.

Steady-state skill levels of workers in learning and forgetting environments: A dynamical system analysis

This article presents a study on the long-term (i.e., steady-state, convergence) characteristics of workers’ skill levels under learning and forgetting in processing units in a manufacturing environment, in which products are produced in batches. Assuming that all workers already have the basic knowledge to execute the jobs, workers learn (accumulate their skill) while producing units within a batch, forget during interruptions in production, and relearn when production resumes. The convergence properties in the paper are examined under assumptions of an infinite time horizon, a constant demand rate, and a fixed lot size. Our work extends the steady-state results of Teyarachakul, Chand, and Ward (2008) to the learning and forgetting functions that belong to a large class of functions possessing some differentiability conditions. We also discuss circumstances of manufacturing environments where our results would provide useful managerial information and other potential applications.

Optimal ordering decisions under two returns policies

To avoid stockouts and maintain product availability, retailers typically carry excess units and subsequently incur higher cost. In case of style/fashion goods, demand forecasting is extremely difficult due to short selling cycles. The purpose of this study was to minimise the cost of excess stocking without compromising product availability. To achieve these conflicting objectives, our study includes two ordering instances and two returns policies. The time between orders subsequently helps resolve demand uncertainty. Existing studies consider only one type of returns policy, that is, returns on the entire purchase quantity; whereas our study considers two types of returns policies: returns on the first order size and returns on the entire purchase quantity. This study also includes models for the retailer and the supply chain system. Analytical and numerical insights into our study enable the retailer to select his appropriate returns policies to maximise his as well as system’s expected profits. We also show that perfect coordination of partners will help them improve their profits considerably.

Facility location with adjacent units: a simple approximation scheme

In this paper, we provide a simple approximation scheme for the optimal objective value for a particular type of location problem. Typically, such problems are solved using the classic set covering formulation. Such a formulation automatically requires data for the constraint matrix and can get too large to implement or too difficult to solve to optimality. The scheme presented in this paper has minimal need for such data. Based on a simple count and with some basic and realistic assumptions about the geometry of the problem, we provide an algebraic formula that gives a close approximation to the optimal objective function value. Our formula can be easily implemented in a spreadsheet or hand-held calculator making it an effective planning tool for practice and also a good pedagogical aid. We illustrate by applying it to a location problem involving individual states in the continental US and collectively to the entire country.