Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Agent based mobile negotiation for personalized pricing of last minute theatre tickets

This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.This paper proposes an agent based mobile negotiation framework for personalized pricing of last minutes theatre tickets whose values are dependent on the time remaining to the performance and the locations of potential customers. In particular, case based reasoning and fuzzy cognitive map techniques are adopted in the negotiation framework to identify the best initial offer zone and adopt multi criteria decision in the scoring function to evaluate offers. The proposed framework is tested via a computer simulation in which personalized pricing policy shows higher market performance than other policies therefore the validity of the proposed negotiation framework.The Ministry of Education, Science and Technology (Korea

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

The integrated use of enterprise and system dynamics modelling techniques in support of business decisions

Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink

Modeling Individuals' Behavior: Evaluation of a Policymaker's Tool

With a continuous decline in the cost of manipulating data and a continuous increase in the richness of data banks, policymakers have increasing opportunities to build and apply so-called micro-simulation models--modelsthat attempt to simulate the behavior of the individuals in a large population under a specified program. The efforts of the Department of Labor to use a model in evaluating proposed changes in the unemployment insurance system point up both the power and the weaknesses of such models. Any user who applies these models without attempting to understand which of their strengths and weaknesses are most important for analyzing the problem at hand is asking for trouble. Easy to use or not,these models are not user friendly.

Some results from a system dynamics model of construction sector competitiveness

Despite government-led good practice initiatives aimed to improve competitiveness in the U.K. construction sector, fluctuations in growth-driven demand, investment and constant regulatory revisions make it very difficult for an enterprise to plan strategically and remain competitive over a timescale exceeding 2 to 3 years. Research has been carried out to understand the historical evolution and changing face of the construction sector and the dynamic capabilities needed for an enterprise to secure a more sustainable competitive future. A dynamic model of a typical contracting firm has been created based upon extensive knowledge capture arising from fieldwork in collaborating firms together with a detailed review of the literature. A construct called the competitive index is used to model contract allocation in a stylised market. The simulations presented enable contracting enterprises to reflect strategically with a view to remaining competitive over a much longer time horizon of between 15 and 20 years. The rehearsal of strategy through simulated scenarios helps to minimise unexpected behaviour and offers insights about how endogenous behaviour can shape the future of the enterprise. To date, work on construction competitiveness has been either of a static nature or set predominantly at the level of the project. This study offers a new perspective by providing a dynamic tool to analyse competitiveness. It creates a new paradigm to support enhanced construction sector performance

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Human Resource Practices, Knowledge-Creation Capability And Performance In High Technology Firms

This study examines the relationship among key HR practices (i.e., effective acquisition, employee-development, commitment-building, and networking practices), three dimensions of knowledge-creation capability (human capital, employee motivation, and information combination and exchange), and firm performance. Results from a sample of 78 high technology firms showed that the three dimensions of knowledge creation interact to positively affect sales growth. Further, the HR practices were found to affect sales growth through their affect on the dimensions of knowledge-creation capability