Jwm Will Bertrand

Operations management research methodologies using quantitative modeling

Gives an overview of quantitative model‐based research in operations management, focusing on research methodology. Distinguishes between empirical and axiomatic research, and furthermore between descriptive and normative research. Presents guidelines for doing quantitative model‐based research in operations management. In constructing arguments, builds on learnings from operations research and operations management research from the past decades and on research from a selected number of other academic disciplines. Concludes that the methodology of quantitative model‐driven empirical research offers a great opportunity for operations management researchers to further advance theory.

A dynamic priority rule for scheduling against due-dates

Abstract We simulate the performance of a simple production system in which due-dates are set internally. We investigate priority rules aimed at minimizing tardiness, emphasizing a “modified duedate rule” which functions effectively in conjunction with internally-set deadlines and which adapts to both tight and loose conditions in the due-dates. This rule is simple and logical, and shows considerable promise for application in complex production systems.

A Comparison of Due-Date Selection Rules

Abstract In sequencing and scheduling models it is usually assumed that due dates represent exogeneous information. In many practical settings, however, due dates can be discretionary, or at least negotiable. Relatively few studies have incorporated discretionary due dates, and even then the rules proposed for due-date selection have seldom been developed from normative, analytic results. In this research we return to very basic scheduling models in search of fundamental insights and relationships that suggest guidelines for due-date selection in more complicated situations. We exploit some fundamental results from scheduling theory involving the single-machine model in order to compare three basic strategies for due-date selection.

An investigation of due-date assignment rules with constrained tightness

Abstract We describe an experimental study of a single-machine scheduling model for a system that assigns due-dates to arriving jobs. The average lightness of the due-dates is assumed to be governed by a policy constraint, which we link analytically to the parameters of the decision rules for due-date assignment. We examine the use of different kinds of information in setting due-dates, and we investigate the relationship between the due-date assignment rule and the priority dispatching rule. On the basis of our results we identify situations in which the dispatching rule is critical to effective scheduling, others in which the due-date assignment rule is critical, and still others in which the combination of the two rules is a critical design issue.

The use of workload information to control job lateness in controlled and uncontrolled release production systems

Abstract The performance of a workload dependent scheduling and due date assignment rule is investigated. The rule uses time-phased workload information and time-phased capacity information. Performance comparisons are made with a rule that uses only time-aggregated workload information. The performance for two methods of releasing jobs to the shop is investigated: an uncontrolled method where the jobs are released at random, and a controlled method where jobs are released to maintain a specific workload norm. Computer simulation is used as the research tool. The mean and standard deviation of lateness are used as performance measures. The results indicate that the use of time-phased workload information may decrease the variance of the lateness, as compared with the use of time-aggregated workload information only. However, the magnitude of the effect depends on the type of sequencing rule used. Furthermore, if time-phased information is used, control of the mean lateness requires that the amount of capacity available for loading used in the loading procedure differs from the amount of capacity actually available for production. It is shown that by selecting adequate parameter values, both a constant mean lateness and a small variance of lateness can be obtained with this type of assignment rule. These results are valid for both types of release situations. So even in the case where the workload of the shop is under strict control, and the mean operation flow time does not, therefore, vary, the use of time-phased workload information can decrease the variance of the lateness.

Workload based order release and productivity : a missing link

In this paper we propose an extension of the job shop models that are used in the theoretical studies, that can explain the differences between the results of theoretical studies and empirical studies into the effects of workload control on performance. The extension assumes that the workload level affects the effective processing times in a shop and is based on the results of empirical research on the relationship between workload and shop performance. Using computer simulation we investigate the effects of workload based work order release on the shop performance under the new job shop model. The simulation results confirm the effect of workload control on shop performance found in empirical studies, and show that, under certain conditions, without workload control the shop performance can become unstable

A framework for production control in health care organizations

The paper presents a hierarchical framework for production control of hospitals which deals with the balance between service and efficiency, at all levels of planning and control. The framework is based on an analysis of the design requirements for hospital production control systems. These design requirements are translated into the control functions at different levels of planning required for hospital production control. The framework consists of five levels of planning and control: patient planning and control, patient group planning and control, resources planning and control, patient volumes planning and control and strategic planning, though this last level does not make part of production control as such. Each of the levels of the framework is further elaborated in terms of the decisions made regarding patient flows and resources, and the co-ordination of the different planning levels. Implications of the framework are discussed by describing some points where current practice deviates from assumptions made in our approach. Recommendations for future research and development of the planning framework are formulated.

A study of simple rules for subcontracting in make-to-order manufacturing

We study a low volume component manufacturing operation facing order arrival rate greater than the service rate, thus necessitating subcontracting of some of the orders, for the case where order lead times are exogenous and highly variable. The major objective of the firm is to maximize capacity utilization and minimize tardiness (so as to minimize cost and maximize delivery reliability). Limiting the focus to operational decisions, we develop four heuristic decision rules with varying informational needs and complexity to determine when and which orders should be subcontracted. The performance of the decision rules over a range of environments is examined first under the assumption of perfect information. Then, we investigate the robustness of the policies for up to ±50% error in parameter estimates. The results show that, compared to simpler rules that do not seek to use the shop-workload information in making the subcontracting decision, the performance of the shop-workload feedback based decision rules (1) produce a superior performance, especially when the target utilization is high, and (2) are robust with up to ±50% error in parameter estimates.

Design requirements for health care production control systems

This paper addresses the issue of determining design requirements for production control in health care organizations, with a restriction to the internal production control of hospitals. Hospital management has limited possibilities to control hospital production, as hospital production processes are driven by medical specialists who, however, do not manage that process. We consider therefore the hospital as a virtual organization, consisting of a number of relatively independent businesses in a common framework. Each business unit functions as a focused factory for a range of more or less homogeneous products. Production control principles can be applied to each of these businesses, but not to the system as a whole. A number of elements from classical production control theory can be also applied to health care, i.e. the use of decoupling points, the bottleneck-oriented approach, and the operational control between production and market. However, important factors that need to be considered in health pro...

Supply chain design : flexibility considerations

Publisher Summary Flexibility has emerged recently as an important performance indicator for operational systems. This chapter discusses three dimensions of flexibility for supply chain design: volume flexibility, mix flexibility, and new product flexibility. Manufacturing flexibility has many dimensions among which hierarchical relationships can be identified. In particular, volume, mix, and new product flexibility depends on machine, labor, and materials handling flexibility. Each flexibility dimension is further characterized by its range and the uniformity within the range. The chapter also reviews the resource investment problem for the single-plant product-family resource investment problem under demand level uncertainty. This research reveals the intricate relationships between investments in dedicated and flexible resource and its dependence on the level of demand uncertainty, on the correlation between demands, and on the sales margin differences between products. The research also provides basic insights for solving of the single-plant resource investment problem under specific conditions such as linear investment costs.