bluuebunny/crossref_metadata_2025_split huggingface dataset:
Formulation Tactics and the Success of Organizational Decision Making*
Authors: Nutt Paul C. 
Published: June 2007

ABSTRACTOne hundred and seventy‐seven cases profiling organizational decisions were analyzed to determine how managers carry out formulation as they initiate a decision‐making process. Analysis revealed that formulation was carried out by applying problem, idea, target, and reframing tactics. Decision adoption rates, decision value, and time to carry out the decision‐making process were used to determine the success of these tactics, controlling for situational effects of importance, urgency, resources, and management level. Reframing was found to be the most successful tactic under all conditions, but was the least frequently used by decision makers. Problem and idea tactics were the least successful and success did not improve with additional resources, but these tactics were used more often than the other tactics. Problem tactics were even less successful when applied to urgent and important decisions. Target tactics were surprisingly effective for crises and important decisions. Decision makers seem prone to use ineffective formulation tactics and give little consideration to the opportunities or constraints imposed by the situation as a tactic is selected.
Coordinated Replenishments from a Common Supplier
Authors: Russell Randolph M. , Krajewski Lee J. 
Published: June 2007

ABSTRACTCoordinated replenishment strategies may be implemented by jointly ordering multiple items from a common supplier. A major benefit of coordinated replenishment is that it increases the size of shipments, permitting the buyer to enjoy transportation economies without a major increase in average inventory levels. The coordinated replenishment problem is complex because side constraints govern the attainment of transportation rate breaks. The problem is further complicated by the presence of purchase quantity discount opportunities. Thus, the buyer must decide which items to order independently, which items to include in a group order, and the order quantities of each item, governed by the frequency of independent or group orders.We present a mathematical model and a heuristic solution procedure that provide analytical support to the buyer seeking to minimize total costs of replenishing multiple items from a common supplier. The relevant costs are purchase prices, ordering costs, holding costs, and transportation costs. Coordinated replenishment provides nearly a 30 percent reduction in controllable costs relative to independent control. Experimentation with the heuristic has yielded optimal solutions over 88 percent of the time. When optimality was not obtained, the mean penalty was much less than one percent. The average heuristic search was more than two orders of magnitude faster than branch and bound, even for small problems, and possessed a much tighter distribution around the mean search time.
Due Date Assignment, Job Order Release, and Sequencing Interaction in Job Shop Scheduling*
Authors: Ahmed Imtiaz , Fisher Warren W. 
Published: June 2007

ABSTRACTDepending on the techniques employed, the due date assignment, release, and sequencing procedures in job shop scheduling may depend on one another. This research investigates the effects of these interactions with a simulation model of a dynamic five‐machine job shop in which early shipments are prohibited. Performance of the system is measured primarily in terms of the total cost (work‐in‐process cost, finished goods holding cost, and late penalty) incurred by the shop, but a number of non‐cost performance measures are also reported. The results support existence of a three‐way interaction between the due date, release, and sequencing procedures as well as interaction between shop utilization and procedure combination. Statistical tests are used to identify those rules that perform best both overall and in combination with other rules.
Rule‐Based Expert Systems and Linear Models: An Empirical Comparison of Learning‐By‐Examples Methods*
Authors: Chung Hyung‐Min Michael , Silver Mark S. 
Published: June 2007

ABSTRACTBuilding models of expert decision‐making behavior from examples of experts’ decisions continues to receive considerable research attention. In the 1960's and 70's, linear models derived by statistical methods were studied extensively. More recently, rule‐based expert systems derived by induction algorithms have been the focus of attention. Few studies compare the two approaches. This paper reports on a study that compared linear models derived by logistic regression with rule‐based systems produced by two induction algorithms—ID3 and the genetic algorithm. The techniques performed comparably in modeling the experts at one task, graduate admissions, but differed significantly at a second task, bidder selection.
A Simple Minimum‐Bias Percentile Estimator of the Location Parameter for the Gamma, Weibull, and Log‐Normal Distributions
Authors: Muralidhar Krishnamurty , Zanakis Stelios H. 
Published: June 2007

ABSTRACTEstimating the unknown minimum (location) of a random variable has received some attention in the statistical literature, but not enough in the area of decision sciences. This is surprising, given that such estimation needs exist often in simulation and global optimization. This study explores the characteristics of two previously used simple percentile estimators of location. The study also identifies a new percentile estimator of the location parameter for the gamma, Weibull, and log‐normal distributions with a smaller bias than the other two estimators. The performance of the new estimator, the minimum‐bias percentile (MBP) estimator, and the other two percentile estimators are compared using Monte‐Carlo simulation. The results indicate that, of the three estimators, the MBP estimator developed in this study provides, in most cases, the estimate with the lowest bias and smallest mean square error of the location for populations drawn from log‐normal and gamma or Weibull (but not exponential) distributions. A decision diagram is provided for location estimator selection, based on the value of the coefficient of variation, when the statistical distribution is known or unknown.
The Positive Effects of Easy Goals on Decision Quality and Risk Propensity in an MCPLP Task
Authors: Taylor Lewis A. , Cosier Richard A. , Ganster Daniel C. 
Published: June 2007

ABSTRACTIn this study the potential performance benefits of easy goals were examined within the multiple cue probability learning paradigm (MCPLP). Specifically, the effects of varying levels of goal difficulty on performance and risk propensity (used to define a form of commitment) were investigated. With few exceptions, previous studies demonstrated support for difficult goals. In this study, contrary to the majority of past evidence, as goals became easier decision quality significantly improved. Moreover, risk propensity increased with easier goals and, as suggested by a post‐hoc analysis, had more direct impact on decision makers’ behaviors than goal levels. Goal‐related behavior in complex MCPLP tasks appears to significantly differ from the majority of goal evidence in other types of research due to the tendency for subjects to view complex task properties in the same context as goal levels.
Improving the Utilization of Front‐Line Service Delivery System Personnel*
Authors: Thompson Gary M. 
Published: June 2007

ABSTRACTThere are two types of work typically performed in services which differ in the degree of control management has over when the work must be done. Serving customers, an activity that can occur only when customers are in the system is, by its nature, uncontrollable work. In contrast, the execution of controllable work does not require the presence of customers, and is work over which management has some degree of temporal control. This paper presents two integer programming models for optimally scheduling controllable work simultaneously with shifts. One model explicitly defines variables for the times at which controllable work may be started, while the other uses implicit modeling to reduce the number of variables. In an initial experiment of 864 test problems, the latter model yielded optimal solutions in approximately 81 percent of the time required by the former model. To evaluate the impact on customer service of having front‐line employees perform controllable work, a second experiment was conducted simulating 5, 832 service delivery systems. The results show that controllable work offers a useful means of improving labor utilization. Perhaps more important, it was found that having front‐line employees perform controllable work did not degrade the desired level of customer service.
Sensitivity Analysis Revisted and Extended
Authors: Wendell Richard E. 
Published: June 2007

ABSTRACTThe tolerance approach to sensitivity analysis in linear programming provides a framework for dealing with simultaneous and independent variations of the coefficients and terms. Specifically, the approach yields a maximum tolerance percentage which in turn characterizes a region of variability for the coefficients or terms. This paper shows how to expand this region of variability yielding ordinary sensitivity analysis as a special case of the tolerance approach.
Integrating Neural Networks and Semi‐Markov Processes for Automated Knowledge Acquisition: An Application to Real‐time Scheduling*
Authors: Liang Ting‐Peng , Moskowitz Herbert , Yih Yuehwern 
Published: June 2007

ABSTRACTRecently, 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.
The Use of Stochastic Simulation in Knowledge‐Based Systems
Authors: Raghunathan Srinivasan , Tadikamalla Pandu 
Published: June 2007

ABSTRACTKnowledge‐based systems support the decision‐making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge‐based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge‐based systems.
Single and Multiple Period Decision Models for Analysis of Quality and Quantity of Validation*
Authors: O'Leary Daniel E. 
Published: June 2007

ABSTRACTThis paper investigates a single period model for the analysis of the impact of the quality of the validation effort. The single period model uses a Bayesian approach to find that validation is a critical point process. That model is then extended to allow for the uncertainty of the validation process to determine the quality of the underlying model. Some monotonicity results are developed for the model and investigated in light of the process being a critical point process. The model indicates that, consistent with comments from real world settings, the impact of the quality of the validation effort can be substantial.The paper also presents two multiperiod models of the impact of the quantity of the validation effort. In practice, the development of an expert system may follow a recurring multiperiod life cycle, where a prototype is built, the system is validated to determine how well it performs, and based on that performance, is either funded or not funded. The first multiple period model assumes that validation and funding occurs at each point in the PVF budget cycle. The model employs Bayesian revision of probabilities to update the prior probability of obtaining a model with an appropriate level of success. It is found that the critical point for multiperiod problems is different than that for single period problems. This model forms the basis of the second model. The second multiple period model extends the first by assuming that the quantity of validation can be varied. The more validation, the more likely that flaws in the model will be found. Thus, the more validation, the better the understanding of the level of performance of the model.
Automated Discovery in Managerial Problem Formulation: Formation of Causal Hypotheses for Cognitive Mapping
Authors: Billman Beth , Courtney James F. 
Published: June 2007

ABSTRACTDevelopment of knowledge acquisition techniques known as automated discovery systems has occurred in deep and narrow domains of knowledge. Automated discovery is the generation of new knowledge by a computer system on its own, without the help of another knowledge source. This paper describes research and validation of an automated discovery system for a wide and shallow domain—business management. The system continues recent advances in expert systems research which have enhanced cognitive mapping, a problem formulation tool. The system perceives the behavior of distal variables in the environment through probabilistic cues‐to‐causality, and generates previously unknown hypotheses by aggregating the probabilities into a single criterion of causal relatedness. The system is validated against the source code of a simulated managerial environment, and causal relationships posited by decision makers experienced in the play of the gaming simulator.
A Model‐Based Approach to Investigate Performance Improvements in Rule‐Based Expert Systems
Authors: Gulati D. , Tanniru Mohan R. 
Published: June 2007

ABSTRACTOne of the major issues in the development of large, rule‐based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.
Evaluating Consumer Preferences for Existing Multiattribute Products: A Non‐Metric Approach
Authors: Lau Kin‐nam , Post Gerald V. 
Published: June 2007

ABSTRACTA model evaluating consumer preferences for multiattribute products is derived. The model possesses the following features: (1) the method works for existing products; (2) the input data require only overall product rankings and attribute rankings; (3) the distribution of part worths (utility) for particular attributes is derived and used to judge the trade‐offs among different attributes; (4) the procedure can be implemented with existing software; and (5) the attributes do not need to be quantifiable.
The Information Problems in Organizations: A Research Model for the Value of Information and Information Systems*
Authors: West Lawrence A. , Courtney James F. 
Published: June 2007

ABSTRACTGrowing recognition of the need for better management information system (MIS) theory and practice centers around problems with identifying the value of information and information systems in organizations. This paper addresses this issue by showing that existing theory in organizational economics and industrial organization directly relates to information problems in organizations (their nature, and their effects in terms of organization structure and performance). The paper categorizes these problems and associated theories into four distinct classes and shows that MIS research and practice can be mapped to information problems of each type. Because the economic theory pertinent to specific information problems includes the nature of the organizational response to information constraints, it also provides insight into the nature of the improvement to be realized if information constraints can be overcome. These improvements can then be interpreted as the value of the associated information systems which suggests using the resultant classification scheme to map between information problems and system value. The paper shows that this methodology provides a more appropriate level of focus than either traditional normative or business value methods, especially because many improvements have structural as well as performance consequences.
Testing Prospect Theory in a Deterministic Multiple Criteria Decision‐Making Environment*
Authors: Salminen Pekka , Wallenius Jyrki 
Published: June 2007

ABSTRACTProspect theory by Kahneman and Tversky [7] is tested in a deterministic multiple criteria decision‐making context. In two experiments conducted in classroom settings subjects made pairwise preference comparisons of condominiums for sale. The results of the experiments indicate that the traditional value model did not explain the subjects' revealed preferences as well as the prospect model. We conclude that prospect theory is a reasonable model of choice for many individuals in such a context.
Production Competence and Business Strategy: Do They Affect Business Performance?
Authors: Vickery Shawnee K. , Droge Cornelia , Markland Robert E. 
Published: June 2007

ABSTRACTThe purpose of this article is threefold. First, a comprehensive measure of production competence that assesses the level of support that manufacturing provides for the strategic objectives of a firm is developed. Second, hypotheses relating production competence to several financial measures of business performance are tested using data from a large sample of firms (n=65) in the furniture industry. Third, the impact of business strategy both directly on performance and as a moderating variable in relation to production competence is analyzed. The results of the study suggest that production competence may have more of an effect on business performance for certain strategies than for others.
Generalizing the Announced Price Increase Problem*
Authors: Arcelus Francisco J. , Srinivasan Gopalan 
Published: June 2007

ABSTRACTThis paper generalizes the announced price increase problem to consider a variety of practical concerns arising out of applications ranging from foreign exchange fluctuations to upper limits on the purchase of discounted supermarket items. These include limitations on the amount that can be purchased at the old price and/or on the length of the grace period, within which buyers may take advantage of this lower price. These constraints give rise to 16 possible situations. For each situation purchasing and inventory decision rules are developed. A numerical example is discussed to highlight the main features of the models.
Developing Measures for Assessing the Organizational Impact of Information Technology: A Comment on Mahmood and Soon's Paper
Authors: Sethi Vijay , Carraher Shawn 
Published: June 2007

ABSTRACTIn a recent study, Mahmood and Soon [26] identified and operationalized a set of variables that can be potentially impacted by information technology. The lack of factor analysis to verify the dimensionality of different variables is a notable omission in the study. This paper describes and employs the limited‐information factor analysis approach and the minimum average partial criterion for testing the unidimensionality of different variables in the Mahmood and Soon study. While reconfirming the construct validity of eight variables, our results question the unidimensionality of two original variables. Further testimony is provided in favor of our results by citing previous research studies.
Sensitivity of Linear Programs to Related Changes in Multiple Inputs*
Authors: Schenkerman Stan 
Published: June 2007

ABSTRACTIn a 1991 paper in Decision Sciences, Camm and Burwell presented smart modeling techniques for obtaining the sensitivity of linear programs to simultaneous common changes in common inputs. This note addresses sensitivity analysis of simultaneous related changes in multiple inputs, and shows how related multi‐parametric analysis can be performed with software that has no or only limited parametric programming capability.
The Development Manager's Advisory System: A Knowledge‐Based DSS Tool for Project Assessment*
Authors: Liberatore Matthew J. , Stylianou Anthony C. 
Published: June 2007

ABSTRACTThis research investigates whether the knowledge‐based decision support system (KBDSS) paradigm provides the necessary supporting structure and developmental framework for product development evaluation. To address the research questions posed in this study, it is necessary to develop and implement KBDSS's at specific decision points along the product development cycle. This paper describes the design, development, and implementation of a KBDSS to support a product development manager's decision concerning full‐scale development of a new product. From the systems design perspective, this paper addresses the integration and innovative use of a variety of techniques for knowledge acquisition, modeling, and processing. The approach utilized obtains the benefits of normative modeling as well as the flexibility and developmental advantages of knowledge‐based systems. Since its implementation, the system has been successfully used by a development manager to support his recommendation for an ongoing project. His complete satisfaction with this system served as the impetus for the design and development of a multi‐expert system which was implemented at the strategic level.