Effects Of Machine Breakdowns Research Articles

The machine breakdown paradox: How random shifts in the production rate may increase company profits

The causes and effects of machine breakdowns have frequently been investigated in the past. One popular stream of research studies technical errors in production and analyzes their impact on the inventory policy of the company. In this paper, we show that random shifts in the production rate of a machine, which may occur, for example, due to technical defects, may lead to a reduction in total cost and therewith to an increase in profit. This obvious paradox may lead to situations where it is economically rational for the company to sustain a technically inefficient situation, or even to take measures to intentionally induce a shift in the production rate, for example by damaging the machine on purpose. In this paper, we illustrate this paradox by referring to an existing inventory model, and trace it back to common assumptions made in the literature.

Approximations to production lot sizing with machine breakdowns

In general, the study of the convexity (concavity) of the total annual cost function (the annual net profit function) should be one of the main research topics about the inventory model. This paper first shows that the long-run average cost function per unit of time for the case of exponential failures is unimodal. However, it is neither convex nor concave. Second, the better lower bound Q ℓ ∗ and upper bound Q u ∗ can be obtained to improve some existing results. Finally, numerical examples reveal the lower bound Q ℓ ∗ for the optimal lot size is a rather good approximation to the optimal lot size. Scope and purpose In 1992, Groenevelt, Pintelon and Seidmann focused on the effects of machine breakdowns and corrective maintenance on the economic lot sizing decisions. They indicate that the economic manufacturing quantity (EMQ) is a good approximation to the optimal lot size. The main purpose of this paper is to provide a better approximation to the optimal lot size than EMQ.

A simple approximation of the EMQ model with Poisson machine failures

Consideration is given to the effect of machine breakdowns on the finite replenishment rate inventory model, which is also known as the Economic Manufacturing Quality model. It is assumed that the number of failures in a production run is Poisson distributed while the repair times are random and can assume any general distribution. Upon failure, the machine will receive immediate service, and the demand will be met from inventory. Long repair times can lead to stockout situations where no backordering is allowed. With the aforementioned assumptions, a simple approximate model is developed that describes the behaviour of the problem. Specific formulations are given for the cases where the repair times are exponential and constant. The model is based on results from Poisson and regenerative processes, and when tested against simulation, it provides very good solutions.

Simulation of JIT production to determine number of Kanbans

A major portion of the work done in modelling Just-In-Time production systems has used queueing theory, mathematical programming or simulation. In all analytical models, little, if any, consideration has been given to the effect of machine breakdowns, set-up times, quality problems and variability in process times. A new methodology is proposed to implement the simulation of the “pull” system and based on that methodology a database oriented simulator, CADOK (Computer Aided Determination Of Kanbans) is developed. This system models breakdowns, reworks, set-up times, variable processing times as well as variable market demand. This JIT simulator has the additional capability of simulating systems which have both convergence as well as divergence in the manufacturing flow. The performance measure of interest is the number of kanbans between any two workstations. While implementing the system, a brief study was made on the effect of set-up times, breakdown frequency, repair times, demand and the variance in processing times. The results are provided in the form of plots and verball description.

Production Lot Sizing with Machine Breakdowns

Economic lot sizing and batching models often assume reliable manufacturing facilities. In this research, we focus on the effects of machine breakdowns and corrective maintenance on the economic lot sizing decisions. Two production control policies are proposed for coping with these stochastic interferences. The first policy assumes that production of the interrupted lots is not resumed after a breakdown. Instead, the on-hand inventory is depleted before a new cycle is initiated. Under the second policy studied here, production is immediately resumed after a breakdown, if the current on-hand inventory is below a certain threshold level. It is shown that this control structure is optimal among all stationary policies. We show that under both policies the optimal lot sizes will always be bigger than the ones in a corresponding deterministic case, and that the optimal lot size increases with the failure rate. We also provide exact optimal and closed form approximate lot sizing formulas and derive tight bounds on the average cost per unit time for the approximations. In addition, we present various structural properties for these policies and operational insights relevant to such management decisions as machine replacement or maintenance schedules.

Simulation of a production line system with machine breakdowns using network modeling

In a manufacturing system, several factors can affect the reliability of the system in producing expected output levels, including resource input rates, labor rate variability, product quality, and machine failures. Although all of these factors will be reflected in the modeling process presented in this paper, the primary focus will be on the effect of machine breakdowns on system output. Network modeling and simulation with Q-GERT is the vehicle of analysis that is employed. This technique will be demonstrated via a realistic case example encompassing a complex production system consisting of several assembly lines, each containing several machines. The statistical results of the simulation of the example system are presented and discussed. In addition, examples of how the simulation model can be used to test changes in machine repair times and breakdown rates will be presented.