An interactive knowledge-based system for forecasting new product reliability

During the development phases of a new product, special attention is usually directed to the allocation of reliability improvement programs. This paper will present an interactive knowledge-based decision support system (DSS) for optimizing this allocation process. The DSS utilizes an extension to the modified exponential reliability growth mode, which was developed to quantify and monitor reliability during the product development phases.

This paper addresses the reliability of complex electronic systems. It determines the rate of growth, and the level of initial reliability required in order to meet the expected market requirement.     

An AHP model of DSS effectiveness

Empirical studies have reported equivocal, or even dysfunctional, results from the use of decision support systems (DSS). Recent examples are the Davis, Kottemann, and Remus production planning experiments. According to the researchers, these experiments demonstrate that DSS what-if analysis creates an ‘illusion of control’ that causes users to overestimate its effectiveness. Such experimental findings are contrary to case-supported DSS theory. This paper examines the discrepancy. It first overviews the decision-making process, presents a generic DSS, identifies the theoretical role of the DSS in improving decision making, develops a multiple criteria model of DSS effectiveness, and gives a DSS for delivering the model to users. Illustrating with recent empirical investigations and the Davis, Kottemann, and Remus studies, the DSS-delivered model is used to reconcile the incongruity between the experimental findings and the case-supported theory. The paper concludes with a discussion of the article's implications for information systems research and practice.     

Cognitive biases and decision support systems development: a design science approach

Abstract.  This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

Reliance,trust and heuristics in judgmental forecasting

Judgmental forecasting gives light to the use of computers in human decision making. This paper reviews studies in judgmental forecasting focusing on what has been learned from human judgment and human–computer interaction. Available information was analyzed in the framework of three dimensions: reliance and trust on computer suggestions and heuristics employed by forecasters to produce forecasts. Results show that computer’s advice disuse is pervasive in forecasting; and the disuse increases with higher task complexity and lower perceived system performance. Explanations and past performance are good candidates to increase trust in computer’s advice, but the appropriate format to deliver this information is still controversial. Forecasters usually overforecast but report to prefer underforecast, which can lead to a cognitive dissonance and in turn to conflicting goals in the task. Heuristics research in time series forecasting indicates that forecasters heavily assess their own judgment, which in turn tend to be grounded in last outcomes and an overall evaluation of features like mean, trend and autocorrelation. It appears that heuristics not always lead to harmful biases for the forecast.     

Design of input vector for day-ahead price forecasting of electricity markets

In new deregulated electricity market, price forecasts have become a fundamental input to an energy company’s decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques.     

Building Linear Programming Models from Structural Model Components

Model management research investigates the formulation, analysis and interpretation of models. This paper focuses on the formulation aspects of modeling so that the task can be supported by decision support systems (DSS) environments. Given the knowledge intensive nature of the formulation process, the development of a modeling tool requires explicating the knowledge pertaining to modeling. This involves comprehending not only the static knowledge about model components (e.g. decision variables, coefficients, associated indices and constants), but also the process knowledge required to construct models from model pieces. The proposed top-down approach configures equations by exploiting the structural modeling knowledge inherent in equation components. The possible representation of equations at various abstraction levels is introduced, the aim being to uncover the structural model components together with the process knowledge required for their appropriate configuration. As part of developing this conceptual model, the role of semantic and syntactic information in model building is investigated. The paper proposes an approach where the formulation semantics are captured as a simple 'action-resource' view which composes models by identifying and piecing together the equation components. The process of equation construction is illustrated with examples from the linear programming (LP) modeling domain. The proposed top-down approach is contrasted with a bottom-up method.     

Empirical evaluation of a decision support model for adopting software product line engineering

ContextThe software product line engineering (SPLE) community has provided several different approaches for assessing the feasibility of SPLE adoption and selecting transition strategies. These approaches usually include many rules and guidelines which are very often implicit or scattered over different publications. Hence, for the practitioners it is not always easy to select and use these rules to support the decision making process. Even in case the rules are known, the lack of automated support for storing and executing the rules seriously impedes the decision making process.ObjectiveWe aim to evaluate the impact of a decision support system (DSS) on decision-making in SPLE adoption. In alignment with this goal, we provide a decision support model (DSM) and the corresponding DSS.MethodFirst, we apply a systematic literature review (SLR) on the existing primary studies that discuss and present approaches for analyzing the feasibility of SPLE adoption and transition strategies. Second, based on the data extraction and synthesis activities of the SLR, the required questions and rules are derived and implemented in the DSS. Third, for validation of the approach we conduct multiple case studies.ResultsIn the course of the SLR, 31 primary studies were identified from which we could construct 25 aspects, 39 questions and 312 rules. We have developed the DSS tool Transit-PL that embodies these elements.ConclusionsThe multiple case study validation showed that the adoption of the developed DSS tool is justified to support the decision making process in SPLE adoption.     

Toward the design of an intelligent DSS for forecasting

The emergence of decision support systems (DSS), artificial intelligence (AI), and microcomputers in the information systems arena provides managers with some new insights to the planning and control of a productive system.

This paper illustrates how an intelligent microcomputer-based decision support system (MDSS) was developed to support demand forecasting.     

Decision Analytics mit Heatmap-Visualisierung von mehrschrittigen Ensembledaten

Today’s forecasting techniques, which are integrated into several information systems, often use ensembles that represent different scenarios. Aggregating these forecasts is a challenging task: when using the mean or median (common practice), important information is lost, especially if the underlying distribution at every step is multimodal. To avoid this, the authors present a heatmap visualization approach. It is easy to visually distinguish regions of high activity (high probability of realization) from regions of low activity. This form of visualization allows to identify splitting paths in the forecast ensemble and adds a “third alternative” to the decision space. Most forecast systems only offer “up” or “down”: the presented heatmap visualization additionally introduces “don’t know”. Looking at the heatmap, regions can be identified in which the underlying forecast model cannot predict the outcome. The authors present a software prototype with interactive visualization to support decision makers and discuss the information gained by its use. The prototype has already been presented to and discussed with researchers and practitioners.     

Sequential revision of belief: an application to complex decisionmaking situations

This research addresses the process of sequential revision of beliefs or judgments in complex situations. The task domain, military command and control, provides decision makers with opportunities to revise their tactical judgments as streams of information flow in for their consideration. A contrast-inertia model is proposed that describes subject's sequential revision of beliefs exhibited by subjects and a resulting order-effect that is observed when subjects attempt to integrate pieces of confirming and disconfirming evidence. Two experiments were conducted to test the predictions of the contrast-inertia model and to investigate various aspects of the order effect. The experiments manipulated the initial starting position or anchor against which subjects contrast new evidence to revise their beliefs. Results form both experiments showed strong recency and order effects when subjects integrated inconsistent pieces of evidence sequentially, regardless of the initial anchor. Moreover, the contrast-inertia model fit the experimental data very well and confirmed the basic assumptions predicting an order effect     

Corporate dashboards for integrated business and engineering decisions in oil refineries: An agent-based approach

It is generally very challenging for an oil refinery to make integrated decisions encompassing multiple functions based on a traditional Decision Support System (DSS), given the complexity and interactions of various decisions. To overcome this limitation, we propose an integrated DSS framework by combining both business and engineering systems with a dashboard. The dashboard serves as a human-computer interface and allows a decision maker to adjust decision variables and exchange information with the DSS. The proposed framework provides a two-stage decision making mechanism based on optimization and agent-based models. Under the proposed DSS, the decision maker decides on the values of a subset of decision variables. These values, or the first-stage decision, are forwarded through the dashboard to the DSS. For the given set of first-stage decision variables, a multi-objective robust optimization problem, based on an integrated business and engineering simulation model, is solved to obtain the values for a set of second-stage decision variables. The two-stage decision making process iterates until a convergence is achieved. A simple oil refinery case study with an example dashboard demonstrates the applicability of the integrated DSS.     

Management of imprecision and uncertainty for production activity control

The operational levels of production management, often called production activity control (PAC) or manufacturing process control, require increasing reaction capabilities in order to adapt the workshop management to the changes of its environment. It often implies giving more responsibilities to the low decision levels. However, the management of the corresponding degrees of freedom is generally unusual. In such a situation, decision support systems (DSSs) provide a way to reconcile the satisfaction of mid-level objectives and the reaction requirements. A conceptual model is described that provides a design framework for a PAC DSS. Since the available knowledge lies mainly in expertise, a DSS has been implemented using a knowledge-based system. The uncertainty and imprecision of the managed information led to the use of fuzzy logic as a modeling tool. Moreover, various inference semantics have been implemented in the expert rules because different kinds of reasoning have been identified. Two versions of the DSS are described and several examples of implemented reasoning processes are developed.     

The roles of user motivation to perform a task and decision support system (DSS) effectiveness and efficiency in DSS use

The purposes of this study are to: (1) obtain measures of actual decision support system (DSS) use that include the three elements of DSS use proposed by Burton-Jones and Straub (Burton-Jones, A., & Straub, D.W., Jr., (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246), and (2) identify an important psychological construct – a user’s motivation to perform a task – and examine how it interacts with two DSS characteristics – effectiveness and efficiency – to affect actual DSS use. As predicted, the findings indicated that individuals who used a more effective DSS to work on a task that they were motivated in increased usage of the DSS, while DSS use did not differ between individuals who used either a more or less effective DSS to complete a task that they were less motivated in. The results also showed significant difference for two measures of DSS use (i.e., STEP and TIME) and no significant difference for one measure of DSS use (i.e., USE) between individuals who used either a more or less efficient DSS to perform a task that they were more motivated in. As expected, significant differences were found for individuals who used either a more or less efficient DSS to complete a task that they were less motivated in. Finally, the results showed that DSS use increased when perceived ease of use and perceived usefulness of the DSS were high; therefore, these results corroborate the findings of prior research in the context of actual DSS use.     

Information structure and the relative efficacy of tables and graphs

Users and system designers often prefer to display information with graphs rather than with tables. However, empirical studies that compared task performance with the two display types frequently revealed either an advantage of tables over graphs or no differences between the displays. This apparent contradiction may result from previous studies in which the importance of the structure that usually exists in displayed information is overlooked. We predict that graphic displays will have an advantage over tables when the displayed information has structure and when this structure is relevant for the task. These conditions generally exist in the actual use of information displays, but have seldom been assessed in experiments. In the present study participants in an experiment performed an information extraction task and a prediction task with unstructured or structured data and with different levels of prior information about the structure. The results showed that the information structure and prior knowledge about the existence of structure affected the advantage of graphic displays over tables when task performance depended on the use of structure. Existing approaches to the study of displays were analyzed in view of these findings. Actual or potential applications of this research include the development of better displays for process control and decision support and better operator training programs.     

Directed forgetting in organisations: the positive effects of decision support systems on mental resources and well-being

Decision makers in organisations are often overtaxed by huge amounts of information in daily business processes. As a potential support strategy, this study examined ‘directed forgetting’ (Bjork, 1970) in a simulated sales planning scenario. We assumed that the availability of a computer-based decision support system (DSS) triggers the forgetting of decision-related background information. Such directed forgetting should not only release memory capacities for additional tasks but also enhance decision quality and decrease strain of decision makers. Assumptions were tested in an experimental study with N?=?90 participants. Consistent with our assumptions, results revealed a higher recall of decision-unrelated information, higher decision quality and higher well-being when participants could use a DSS as compared to two Control conditions without a DSS. Moreover, directed forgetting effects were qualified by participants’ trust in the DSS. This study provides the first evidence for directed forgetting effects cued by information systems in a business context.

Practitioner summary: Information overload is an increasing challenge in modern business organisations. Extending findings from basic memory research, this study shows that availability of a computer-based decision support system triggers forgetting of decision-related background information, which in turn increases users’ mental resources for additional tasks, decision quality, and well-being.     

Intermediating user-DSS interaction with autonomous agents

The use of advanced decision support system (DSS) capabilities is hampered by the inadequacy of a "toolbox" organization of DSS from the user's perspective. In such a setup, the user is assumed to have all the knowledge and skills necessary to appropriately use the tools provided by the system in the decision-making process. This paper proposes a model for the use of autonomous agents as intermediaries between the users and the system. The model is organized around the human problem-solving process. The paper elaborates on the types of intermediary agents and the architecture for a DSS. The approach is illustrated using the prototype for an investment DSS.     

Assessing the quality of institutional DSS

Effectively assessing decision support systems (DSS) quality has long been a difficult challenge to developers and users. Despite the difficulty, the need to justify substantial investments in DSS projects motivates academics and practitioners alike to attempt continuously to improve quality assessment procedures and methods. Institutional DSS are likely to exacerbate the need for quality assessment because they tend to be larger and more expensive than personal DSS. Institutional DSS, by definition, are used by many people throughout the organization. It is very likely, in such cases, that users will have different perspectives, objectives and expectations from the system. Discussion and comparison of existing methods to assess DSS quality are presented, followed by a proposal for the use of the analytical hierarchy process (AHP) method as a more viable alternative for institutional DSS quality assessment. A case study is used to demonstrate the use of AHP for institutional DSS quality assessment in practice.     

Beyond spreadsheets: tools for building decision support systems

The complexity and long development time inherent in building decision support systems has thus far prevented their wide use. A new class of tools, DSS generators, seeks to cut the lead time between development and deployment. DSS generators provide tools that make it easier and faster to develop models, data, and user interfaces that are customized to the application's requirements. Using a DSS generator reduces DSS development to a decision analysis task-which requires expertise in decision analysis and mathematical modeling-rather than a programming task. DSS generators are crucial to the success of DSSs in practice. We describe the state of the art in DSS generator software, specifically in the realm of decision analysis methods. Decision analysis techniques account for the uncertain, dynamic, and multicriteria aspects of decisions. Essentially, they aid the evaluation of alternatives in the face of trade-offs. Well known decision analysis methods include decision trees and influence diagrams. We briefly describe the features of 11 commercially available DSS generators that specialize in decision analysis. Although not a comprehensive, complete analysis of these tools, the article clarifies the idea of DSS generators as DSS development environments and presents an overview of the progress in this area