Timothy S. Vaughan

Failure replacement and preventive maintenance spare parts ordering policy

Abstract This paper addresses inventory policy for spare parts, when demand for the spare parts arises due to regularly scheduled preventive maintenance, as well as random failure of units in service. A stochastic dynamic programming model is used to characterize an ordering policy which addresses both sources of demand in a unified manner. The optimal policy has the form ( s ( k ), S ( k )), where k is the number of periods until the next scheduled preventive maintenance operation. The nature of the ( s ( k ), S ( k )) policy is characterized through numeric evaluation. The efficiency of the optimal policy is evaluated, relative to a simpler policy which addresses the failure replacement and preventive maintenance demands with separate ordering policies.

A Cross‐Functionally Integrated Undergraduate Business Core Curriculum

In the summer of 1993, a faculty team from the College of Business at Northern Illinois University began working to develop a cross‐functionally integrated undergraduate business principles core curriculum. This paper describes the integrated curriculum, which is comprised of a nine‐hour integrated lecture covering business principles and a three‐hour applications seminar. Both of these courses,which are team taught, are described in the paper. The aper also discusses numerous lessons and issues that should be taken into consideration throughout comprehensive integration efforts.

The effect of correlated demand on the cyclical scheduling system

This paper investigates the effect of correlated demand on the cyclical scheduling system. The paper first analytically demonstrates that a blend-to-order production environment will generally realize correlated demand for blending components, even if the demands for finished blends are mutually independent. The degree of component demand correlation is shown to depend on the nature of the recipes used to blend finished products. A simulation model is then used to investigate the effects of this correlation, for the case of blending components produced on a single process under a cyclical scheduling policy. The results demonstrate that component demand correlation increases the variance of the production cycle length, and induces correlation between the cycle length and demand per period within the cycle. Both effects contribute to an increase in the variability of total demand during the production cycle, thus increasing the requirement of safety stock for the blending components. In the environment under concern, an analysis of the cyclical scheduling system--which assumes independent random demand--will underestimate the true safety stock requirement.

In search of the memoryless property

This paper describes a distribution fitting exercise that has been used in an undergraduate introductory simulation class. The intent is for students to collect data describing a customer arrival process, with the goal of determining whether the exponential distribution is a good fit. The paper briefly reviews the memoryless property, and presents some teaching tips directed toward students¿ understanding of the concept. The data collection exercise is then presented, with attention to common pitfalls students have encountered in the past. A presentation of the variety of distributions that emerge as ¿best fitting¿, when samples are actually drawn from an exponential distribution, serves as a warning against over-reliance on goodness of fit measures.

The effect of process adjustment error on?? chart design

The existing literature on economic design of X process control charts generally assumes perfect process adjustment, such that the process mean is returned to an exactly centered “in control” state following any real or false alarm control chart signal. This paper presents a model which demonstrates the effects of imperfect process adjustment on the economically designed control chart parameters. The model demonstrates that the optimal control limit width depends fundamentally on the precision with which the process can be adjusted. The greater the process adjustment error, all else constant, the wider will be the optimal control limits, in order to alleviate the potential for process overcontrol and tampering effects. By endogenously modeling these effects, the new model helps to rectify the problem of poor statistical properties for which the economic design approach has been criticized.

Alternative control mechanisms for cyclical scheduling systems

This paper investigates the relative performance of alternative mechanisms for controlling the cycle length in a cyclical scheduling system. While all methods investigated are able to maintain a target long run average cycle length, some provide greater inventory efficiency than others, with more informed application of idle time. Simulation results demonstrate that a fixed idle time policy provides worst results, while an aggregate workload heuristic based on earlier dynamic policy characterisation provides superior performance. The importance of the control mechanism is shown to be greatest when the process is subject to high demand intensity, particularly when the demand process is sporadic.

Variables inspection for SPC-quarantined production

When a control chart signals an out-of-control condition for a production process, it may be desirable to “quarantine” all units produced since the last in-control SPC sample. This paper presents an efficient procedure for variables inspection of such “SPC quarantined” product. A Bayesian sequential inspection procedure is developed which determines whether the out of control production is of acceptable quality. By inspecting the units in reverse of the order in which they were produced, the procedure is also capable of detecting the point at which the process went out of control, thus eliminating the need to inspect units produced prior to the onset of the out of control condition. Numerical examples are presented, and the performance characteristics of the procedure are demonstrated using Monte Carlo simulation.