Effective Design And Use Of Computer Decision Models.

This is the published version. Copyright 1980 MIS Quarterly.Computer decision models often provide useful results as management planning tools. However, these tools are frequently limited to firms with staffs of specialists who can assimilate the technical nature of the models. For other firms, the success of decision models such as simulation have not been demonstrated. This paper looks at recent literature regarding decision model deficiencies, evaluates selected financial simulation model packages, and suggests design needs for expanding the use of decision models to a broader range of firms. [ABSTRACT FROM AUTHOR] Copyright of MIS Quarterly is the property of MIS Quarterly & The Society for Information Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.

Cognitive Feedback in GDSS: Improving Control and Convergence

Cognitive feedback in group decision making is information that provides decision makers with a better understanding of their own decision processes and that of the other group members. It appears to be an effective aid in group decision making. Although it has been suggested as a potential feature of group decision support systems (GDSS), little research has examined its use and impact. This article investigates the effect of computer generated cognitive feedback in computer-supported group decision processes. It views group decision making as a combination of individual and collective activity. The article tests whether cognitive feedback can enhance control over the individual and collective decision making processes and can facilitate the process of convergence among group members. In a laboratory experiment with groups of three decision makers. 15 groups received online cognitive feedback and 15 groups did not. Users receiving cognitive feedback maintained a higher level of control over the decision-making process as their decision strategies converged. This research indicates that (1) developers should include cognitive feedback as an integral part of the GDSS at every level, and (2) they should design the human-computer interaction so there is an intuitive and effective transition across the components of feedback at all levels. Researchers should extend the concepts explored here to other models of conflict that deal with ill-structured decisions, as well as study the impact of cognitive feedback over time. Finally, researchers trying to enhance the capabilities of GDSS should continue examining how to take advantage of the differences between individual, interpersonal, and collective decision making

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Design for Six Sigma Digital Model for Manufacturing Process Design

The transition to digital manufacturing has become more important as the quantity and quality of the use of computer systems in manufacturing companies has increased. It has become necessary to model, simulate and analyse all machines, tools, and raw materials to optimise the manufacturing process. It is even better to determine the best possible solution at the stage of defining the manufacturing process by using technologies that analyse data from simulations to calculate an optimal design before it is even built. In this paper, Design for Six Sigma (DFSS) principles are applied to analyse different scenarios using digital twin models for simulation to determine the best configuration for the manufacturing system. The simulation results were combined with multi-criteria decision-making (MCDM) methods to define a model with the best possible overall equipment effectiveness (OEE). The OEE parameter reliability was identified as the most influential factor in the final determination of the most effective and economical manufacturing process configuration

Five dimensions in the communication of design intent

Industries which utilize Computer Aided Design, (CAD), are in a similar situation to the film industry, where the use of Computer Graphics, (CG), has reached such a level of reality that audiences often do not spot where CG has been used. This has resulted in a general attitude among critics of: “CG is what you expect in a film, but what we often lack is a decent plot”. Over a similar period, CAD software has become a powerful tool with proficient users, whilst the marketplace for such services now takes such facilities for granted. The ‘wow factor’ has faded. The special effects used in films has contributed to this dulling of presentation impact, which leads us to question where we stand in relation to a competitive edge, with the realization that: “CAD is what you expect from a firm, but what we often lack is clear intent.” The questioning of competitive edge draws us into some complex issues, concerning the reduction of compromise for design intent, where priorities fight for first place. There is no disputing the importance of time to market, yet the time compression technologies may no longer be providing a sufficient cutting edge. Even if new technologies facilitate even shorter lead-times we will always face the threat of a time management trap and potential loss of design quality. As a high-risk strategy for competitive advantage, contractual agreements for specified short lead-time deliveries, in some cases with penalty clauses written in, have established an expectation among the client base. Such a strategy leads us to effectively burn our bridges, in sacrificing margins for schedule 3 slippage and error compensation, leaving us nowhere to go but back. With such a lean approach to product development we have to improve our focus on the plot and its intent for design quality. The more investment we make at the front end, to enable the decision making process, the more likely we are to avoid pain at the back-end. Presently, decisions are made on a resource of available quality and quantity of data, using a perspective which is based on the experience, tacit knowledge and intuition of those involved. Whilst intuition is a good starting point or fall-back, as with tacit knowledge, it often proves difficult to substantiate. Background experience is the most valuable asset here but proves ineffectual when faced with low quality data, either through ambiguity, error or lack of substance. The improvement of quality standards require that we look closely at the production and presentation of data in the context of decision making and establish a process by which quality decisions can be made quickly and efficiently. This paper focuses on the process of communication between designers and their colleagues and clients, concerning the presentation of CAD models, from a cognitive perspective. It first establishes a context for individual differences in the management of auditory and visual information for decision making. This is followed by a discussion of five approaches to the communication of design intent and concludes with a checklist, to aid selection of an effective approach to communication

Immersive Computing Technology to Investigate Tradeoffs Under Uncertainty in Disassembly Sequence Planning

The scientific and industrial communities have begun investigating the possibility of making product recovery economically viable. Disassembly sequence planning may be used to make end-of-life product take-back processes more cost effective. Much of the research involving disassembly sequence planning relies on mathematical optimization models. These models often require input data that is unavailable or can only be approximated with high uncertainty. In addition, there are few mathematical models that include consideration of the potential of product damage during disassembly operations. The emergence of Immersive Computing Technologies (ICT) enables designers to evaluate products without the need for physical prototypes. Utilizing unique 3D user interfaces, designers can investigate a multitude of potential disassembly operations without resorting to disassembly of actual products. The information obtained through immersive simulation can be used to determine the optimum disassembly sequence. The aim of this work is to apply a decision analytical approach in combination with immersive computing technology to optimize the disassembly sequence while considering trade-offs between two conflicting attributes: disassembly cost and damage estimation during disassembly operations. A wooden Burr puzzle is used as an example product test case. Immersive human computer interaction is used to determine input values for key variables in the mathematical model. The results demonstrate that the use of dynamic programming algorithms coupled with virtual disassembly simulation is an effective method for evaluating multiple attributes in disassembly sequence planning. This paper presents a decision analytical approach, combined with immersive computing techniques, to optimize the disassembly sequence. Future work will concentrate on creating better methods of estimating damage in virtual disassembly environments and using the immersive technology to further explore the feasible design space

CAGER: classification analysis of gene expression regulation using multiple information sources

BACKGROUND: Many classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene's expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features. RESULTS: In this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChlP-chip data, or their combinations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Classification Analysis of Gene Expression Regulation) that integrates several software components for automated analysis of transcriptional regulation using decision trees. Finally, we use CAGER to study the transcriptional regulation of Arabidopsis genes in response to abscisic acid, and report some interesting new results. CONCLUSION: Models built with ChlP-chip data suffer from low accuracies when the condition under which gene expressions are measured is significantly different from the condition under which the ChIP experiment is conducted. Models built with automatically identified motifs can sometimes discover new features, but their modeling accuracies may have been over-estimated in previous studies. Furthermore, models built with automatically identified motifs are not stable with respect to noises. A combination of ChlP-chip data and predefined motifs can substantially improve modeling accuracies, and is effective in identifying true regulons. The CAGER web server, which is freely available at , allows the user to select combinations of different feature types for building decision trees, and interact with the models graphically. We believe that it will be a useful tool to facilitate the discovery of gene transcriptional regulatory networks

The design co-ordination framework : key elements for effective product development

This paper proposes a Design Co-ordination Framework (DCF) i.e. a concept for an ideal DC system with the abilities to support co-ordination of various complex aspects of product development. A set of frames, modelling key elements of co-ordination, which reflect the states of design, plans, organisation, allocations, tasks etc. during the design process, has been identified. Each frame is explained and the co-ordination, i.e. the management of the links between these frames, is presented, based upon characteristic DC situations in industry. It is concluded that while the DCF provides a basis for our research efforts into enhancing the product development process there is still considerable work and development required before it can adequately reflect and support Design Co-ordination

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t