The Impact of Nonagility on Service Level and Project Duration*

“Why projects are late” is a fundamental issue in theory and in practice. One well‐established explanation is that a project's network structure can be a significant factor—the “fatter” or more parallel the project, the later the project completion. Herein we show that a lack of agility, which may frequently arise in projects when there are external resources, can also be a significant factor in project delays—both in more serial‐like projects as well as more parallel ones. Specifically, this article formally characterizes the concept of agility in projects, shows how a lack of agility can have a significant impact on a project, and gives some general properties on agility with respect to a project's structure.     

Limits to Concurrency*

In the global race to bring new products to market, many firms have adopted concurrent engineering as a technique to shrink development lead time. Due to the many concurrent engineering success stories in the business and engineering literature, a common misconception has grown that more concurrency is always better. The major contribution of this paper is a rigorous demonstration that limits to concurrency exist even in the simplified situation in which concurrency is modeled as the number of design modules to be executed in parallel. As complexities such as communication linkages between modules are layered onto our basic model, we show that the expected project completion time is minimized at a finite number of modules, a number that decreases with increasing problem complexity. In general, the more complex the project, the stricter the limits to concurrency. This strongly suggests that project managers should be cognizant of the potentially adverse effects of pushing concurrency too far.     

On Inventory Abatement via Manufacturing Randomness Reductions*

Under a continuous improvement framework, the policy of abating inventory via reductions in manufacturing randomness is considered. To explore this policy, a model of a real-world production-inventory system is developed, tested, and studied. The results suggest that manufacturing randomness reductions, even substantial ones, may not necessarily lead to inventory abatement, and paradoxically may lead sometimes to an inventory increase. In these cases, however, manufacturing randomness reductions will translate into higher customer service levels.     

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.     

Distinguishing Features of Just-in-Time Systems in the Make-to-Order/Assemble-to-Order Environment*

Few studies of just-in-time (JIT) implementation examine their significance in make-to-order manufacturing environments. This study examines the relative importance of several operating variables that are characteristic features of JIT systems within such environments in North America. The results suggest that the most salient features of JIT for make-to-order and assemble-to-order firms are the elimination of waste (in the form of time and defects), reduced setup time, reduced lotsize, and a smaller pool of suppliers. In addition, the results suggest that the use of non-domestic suppliers can possibly hinder efforts at JIT implementation of materials procurement.     

Optimal Cash Tender Offers*

This paper develops a contingent claims analysis, a simple decision model for determining the optimal price in a cash tender offer. The implied behavior of the optimal tender offer premium over the market price of the target common stock is also investigated. Those readers interested in applying the model may obtain an interactive computer program written in FORTRAN and implementable on a personal computer from the authors on request.     

TWO DESIGN PRINCIPLES FOR KNOWLEDGE-BASED SYSTEMS*

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem-solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role.     

Process Mean Determination with Quantity Discounts in Raw Material Cost*

Setting the mean (target value) for a container-filling process is an important decision for a producer when the material cost is a significant portion of the production cost. Because the process mean determines the process conforming rate, it affects other production decisions, including, in particular, the production setup and raw material procurement policies. In this paper, we consider the situation in which quantity discounts exist in the raw material acquisition cost, and incorporate the quantity-discount issue into an existing model that was developed for simultaneously determining the process mean, production setup, and raw material procurement policies for a container-filling process. The product of interest is assumed to have a lower specification limit, and the items that do not conform to the specification limit are scrapped with no salvage value. The production cost of an item is proportional to the amount of the raw material used in producing the item. A two-echelon model is formulated for a single-product production process, and an algorithm is developed for finding the optimal solution. A sensitivity analysis is performed to study the effects of the model parameters on the optimal solution.     

Inventory Control and Scheduling of Multiple Products in a Common Facility*

Consider a set of chemical products to be produced in a single facility. Each product has its own unique reaction time (which is assumed to be independent of its batch size), as well as other cost and demand values. In this paper, we address the problem of determining the optimal number of batches, batch sizes, and an accompanying production schedule for these products in the single facility that will minimize the total cost. Two different algorithms have been developed for this problem, the performances of which are contrasted with classical cyclic production schedules. Finally, some guidelines for the application of these methods to real-life problems are outlined.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Models and Methods to Support a New Type of Inventory Performance Measure: The ESWSO

When analyzing a reorder point, order quantity (r, Q) inventory systems, one important question that often gets very little, if any, attention is: When a stockout occurs, how large is it? This paper is directed at researchers and practicing inventory planners with two objectives. First, we provide several models and algorithms to compute the Expected Shortages When a Stockout Occurs (ESWSO) for a variety of stochastic environments. We show that when ESWSO, is used in conjunction with the traditional fill rate measures it greatly enhances a planners ability to plan for shortages. Second, we develop two cost‐minimizing inventory models—one addressing the backorder and the other the shortage scenario—to show how the ESWSO can be seamlessly integrated into an inventory‐cost framework to specify lot sizes and safety stocks.     

Application of the Net Present Value Criterion in Random and Flow Shop Scheduling*

While the majority of the literature on shop scheduling has emphasized time-based performance criteria such as mean flow time, lateness, and tardiness, the primary goal of management should be the maximization of shop profitability. In this research the net present value (NPV) criterion is introduced to measure shop profitability. This measure combines aspects of job flow time and inventory holding costs into a single measure. A simulation model of a job shop is used to examine the performance of a variety of time- and value-based scheduling rules. These rules are evaluated with respect to the NPV criterion in both random and flow shop environments. The results suggest that priority rules that utilize monetary information about jobs yield a higher NPV than many time-based rules in most situations, with little sacrifice in job tardiness. A well-researched time-based rule, critical ratio, also provides excellent performance when the shop is heavily loaded.     

Customer Order Scheduling in a General Job Shop Environment*

The primary objective of this study is to examine the performance of order-based dispatching rules in a general job shop, where the environmental factors are shop utilization and due date tightness. An order is defined as a collection of jobs that are shipped as a group—an order—to the customer, only on completion of the last job of the order. We specifically compare dispatching rules from past job-based studies to some rules adapted to encompass order characteristics. Standard flow time and tardiness measures are used, but in addition, we introduce measures that combine average performance with variation in an attempt to capture the performance of a majority of the orders processed in the shop. Of the 16 dispatching rules tested, our results show that four of the simple rules dominate the others. We also found that order-based rules perform better than their job-based counterparts. The study makes use of multivariate statistical analysis, in addition to the usual univariate tests, which can provide additional insight to managers using multiple criteria in their decision process.     

The Effect of Lead Time Uncertainty on Safety Stocks

The pressure to reduce inventory investments in supply chains has increased as competition expands and product variety grows. Managers are looking for areas they can improve to reduce inventories without hurting the level of service provided. Two areas that managers focus on are the reduction of the replenishment lead time from suppliers and the variability of this lead time. The normal approximation of lead time demand distribution indicates that both actions reduce inventories for cycle service levels above 50%. The normal approximation also indicates that reducing lead time variability tends to have a greater impact than reducing lead times, especially when lead time variability is large. We build on the work of Eppen and Martin (1988) to show that the conclusions from the normal approximation are flawed, especially in the range of service levels where most companies operate. We show the existence of a service‐level threshold greater than 50% below which reorder points increase with a decrease in lead time variability. Thus, for a firm operating just below this threshold, reducing lead times decreases reorder points, whereas reducing lead time variability increases reorder points. For firms operating at these service levels, decreasing lead time is the right lever if they want to cut inventories, not reducing lead time variability.     

An Empirical Test of the Strategic-Grid Model of Information Systems Planning*

Contingency models of information systems planning predict that no single planning approach will suit all organizations' needs. Little empirical research has been undertaken, however, to evaluate this prediction. Accordingly, we used McFarlan, McKenney, and Pyburn's (1983) strategic-grid model to study the information systems planning problems encountered by 49 governmental agencies. Twenty-seven agencies were required to follow a planning approach best suited to organizations that had a high level of dependence on both their existing and proposed systems. We predicted that agencies not having these characteristics would encounter the most problems with the approach. The remaining 22 agencies could choose their own planning approach. We studied this latter group to determine whether the problems encountered by the first group could be attributed to the mandated approach. Overall, the empirical results obtained were equivocal. Some results indicated that more planning problems were encountered by agencies in which the mandated approach was not appropriate to their position in the strategic grid. Other results were not supportive of this proposition. More work needs to be undertaken, therefore, to evaluate the predictive and explanatory power of contingency models of information systems planning. In addition, our research indicates a need to develop more rigorous theories of information systems planning behaviors, to improve the instruments needed to measure these behaviors, to explore the relationship between information systems planning behaviours and organizational effectiveness, to investigate how organizational learning impacts planning behaviors, and to determine the types of information systems planning problems that diffuse through organizations and those that remain localized.     

A Comparative Analysis of Master Production Scheduling Techniques for Assemble-To-Order Products

This paper studies the master production scheduling (MPS) activity of manufacturing firms that produce assemble-to-order (ATO) products. It describes four techniques for master scheduling ATO products: end-product bills, modular bills, super bills, and percentage bills. These procedures are compared in terms of the percentage of customer orders delivered late, the mean tardiness of customer order deliveries, and the total cost of inventory using simulation analysis. The results indicate that the performance of an MPS technique is affected by the level of uncertainty of the end products' demands and the degree of component commonality in the product structure. In particular, modular bills produce the highest customer service level and super bills produce the lowest total inventory cost under most operating conditions. The conclusions also suggest that the choice of a particular MPS technique is often a compromise between the benefits of improved MPS performance and the costs of implementing and executing the MPS system.     

Solution Procedures for the Lot-Streaming Problem

Lot streaming is the process of splitting a job into sublots so its operations can be overlapped and its progress accelerated. We present a computationally efficient procedure for solving the m-machine, two-sublot problem, and we discuss the bottleneck insights that emerge from the analysis. We also examine heuristic approaches for more than two sublots and discuss computational results for these procedures.     

Integrative Cycle Scheduling Approach for a Capacitated Flexible Assembly System*

This study revisits the traditional single stage, multi-item, capacitated lot-sizing problem (CLSP) with a new integrative focus on problem structuring. Unlike past research, we develop integrative cycle scheduling approaches which simultaneously address lot-sizing, capacity, and sequencing issues. Our purposes are to (1) explore the effect of sequencing on inventory levels, (2) examine the problem of infeasibility in the economic lot scheduling problem (ELSP), and (3) provide a simple methodology of generating low-cost cycle schedules in an environment with discrete shipping, dynamic demands, limited capacity, zero setup cost, and sequence-independent setup times. Our procedures are compared to benchmark cycle scheduling approaches in terms of both inventory cost and computation time under different demand scenarios, using the operating data from a flexible assembly system (FAS) at the Ford Motor Company's Sandusky, Ohio plant.     

A Theory of Production Competence Revisited*

A recent article [3] proposed a definable relationship between production competence and business performance and presented empirical evidence to support the relationship. The purpose of this note is four-fold. First, it corrects the authors' numerical measure of production competence. The correction changes the nature of the relationship between competence and performance. Second, this note suggests an improved numerical measure of business performance (the dependent variable in the study). The authors of [3] defined performance in a manner which inadvertently captures elements used to measure production competence (the independent variable). The result is a deceptively close fit of the authors' model with the data. The third purpose of the note is to introduce a more appropriate theoretical framework for the production competence construct. It is shown that production competence is closely related to the formulation and implementation of manufacturing strategy and can best be understood within that context. Last, an alternative conceptual model of the relationship between business strategy, production competence, and business performance is presented. The new model includes a construct which measures the “fit” of a firm's business strategy to its external, competitive environment.     

Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.