A decision support system for the selection of a casting process using analytic hierarchy process

This paper presents an Analytic Hierarchy Process (AHP) based decision support system to select the most suitable casting process for a given product. The hierarchical structure of the proposed method allows the decision maker to compare the different casting processes using the material suitability and flexibility, geometrical complexity, dimensional tolerance and surface finish of the casting, and the cost as the criteria for selection. Judgemental inconsistency of the decision maker in selecting the casting process is taken care by ensuring that the value of consistency ratio is below (0.1). A numerical example is presented to illustrate the effectiveness of the proposed methodology for selecting the suitable casting process.     

Sign Restrictions,Structural Vector Autoregressions,and Useful Prior Information

This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.     

An Examination of How Supervisors Influence Their Subordinates to Engage in Environmental Behaviors

Employee engagement in environmental behaviors is an important topic in operations management. Drawing upon stakeholder, commitment, and organizational support theories, this study creates and tests an empirical model of how store managers (i.e., supervisors) influence their direct reports (i.e., subordinates) to become engaged in environmental behaviors. Based on a dataset derived from supervisors and their subordinates who are employed at the same grocery store location across a large retail grocery chain, we test our study's nomological model and find support for the linkages proposed. Key research and managerial implications are discussed.     

A Learning Curve Analysis of Expert System Use*

In this article, we study how an expert system affects novice problem solving in a financial risk analysis domain. We demonstrate that novice performance is improved after exposure to an expert system. Further, we show that novice performance continues to improve when the system is withdrawn. By comparing learning curves for people with exposure to those without, we can assess how much the system has benefitted its users. We demonstrate a quantitative methodology to measure the increment of learning due to the use of an information technology. We also explore the issue of how expertise is transferred from the system to the user.     

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.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.     

Determining Differences in Expert Judgment: Implications for Knowledge Acquisition and Validation*

In knowledge acquisition, it is often desirable to aggregate the judgments of multiple experts into a single system. In some cases this takes the form of averaging the judgments of those experts. In these situations it is desirable to determine if the experts have different views of the world before their individual judgments are aggregated. In validation, multiple experts often are employed to compare the performance of expert systems and other human actors. Often those judgments are then averaged to establish performance quality of the expert system. An important part of the comparison process should be determining if the experts have a similar view of the world. If the experts do not have similar views, their evaluations of performance may differ, resulting in a meaningless average performance measure. Alternatively, if all the validating experts do have similar views of the world then the validation process may result in paradigm myopia.     

Breastfeeding experiences and perspectives among women with postnatal depression: A qualitative evidence synthesis

BackgroundStudies show that postnatal depression affects around 10–16% of women globally. It is associated with earlier cessation of breast feeding, which can negatively impact infants’ long-term development. Mechanisms underpinning associations between mental health and women’s decision to commence and continue to breastfeed are complex and poorly understood.AimThe aim of this review was to investigate breastfeeding experiences, perspectives, and support needs of women with postnatal depression. No previous reviews were identified which had addressed this aim.MethodA systematic search was conducted of six databases to identify relevant qualitative studies. Six included studies were critically appraised and synthesised using thematic synthesis.FindingsFive themes were identified: (1) desire to breastfeed and be a ‘good mother’, (2) struggles with breastfeeding, (3) mixed experiences of support from healthcare professionals, (4) importance of practical and social support, (5) support for mental health and breastfeeding. Most women with postnatal depression expressed strong intentions to breastfeed, although some perceived ‘failure’ to breastfeed triggered their mental health problems. Practical and non-judgemental support for their mental health needs and for successful breastfeeding from healthcare professionals, family and friends are needed.ConclusionMost women with postnatal depression desired to breastfeed but experienced breastfeeding difficulties that could impact on their mental health. By offering women with postnatal depression tailored and timely support, healthcare professionals could help women minimize breastfeeding problems which could consequently impact on their mental well-being and ensure they and their infants have opportunity to benefit from the advantages that breastfeeding offers.     

The Impact of a Structured-Argument Approach on Group Problem Formulation*

Despite advances in decision analysis and decision support systems, few formulaic procedures exist for undertaking problem formulation, particularly in group settings. This leaves managers with little procedural support for the important task of carefully structuring problems. In a laboratory experiment of 29 intact student teams, we contrasted two problem formulation methodologies: a structured argument approach (based on application of formal reasoning) and a group process approach (based on private idea generation prior to public sharing and evaluation of ideas). The structured argument approach took more time to use and failed to bring about more information search and equivocality reduction in group discussions. On the positive side, however, the structured argument approach led to a greater combination of both coverage of critical issues and consensus on those issues. Use of the structured argument approach also resulted in higher satisfaction with the problem definition and commitment to implementing results of the group meeting. Overall, the results reveal cost/benefit tradeoffs associated with developing argumentation-based procedures for problem formulation.     

Cognitive Fit: A Theory-Based Analysis of the Graphs Versus Tables Literature*

A considerable amount of research has been conducted over a long period of time into the effects of graphical and tabular representations on decision-making performance. To date, however, the literature appears to have arrived at few conclusions with regard to the performance of the two representations. This paper addresses these issues by presenting a theory, based on information processing theory, to explain under what circumstances one representation outperforms the other. The fundamental aspects of the theory are: (1) although graphical and tabular representations may contain the same information, they present that information in fundamentally different ways; graphical representations emphasize spatial information, while tables emphasize symbolic information; (2) tasks can be divided into two types, spatial and symbolic, based on the type of information that facilitates their solution; (3) performance on a task will be enhanced when there is a cognitive fit (match) between the information emphasized in the representation type and that required by the task type; that is, when graphs support spatial tasks and when tables support symbolic tasks; (4) the processes or strategies problem solvers use are the crucial elements of cognitive fit since they provide the link between representation and task; the processes identified here are perceptual and analytical; (5) so long as there is a complete fit of representation, processes, and task type, each representation will lead to both quicker and more accurate problem solving. The theory is validated by its success in explaining the results of published studies that examine the performance of graphical and tabular representations in decision making.