The Program Manager's Support System (PMSS) : an executive overview and description of functional modules.

Shipping list no.: 92-370-P.

Vector Error-Correction Models in a consumer packaged goods category forecasting decision support system

A framework for developing marketing category management decision support systems (DSS) based upon the Bayesian Vector Autoregressive (BVAR) model is extended. Since the BVAR model is vulnerable to permanent and temporary shifts in purchasing patterns over time, a form that can correct for the shifts and still provide the other advantages of the BVAR is a Bayesian Vector Error-Correction Model (BVECM). We present the mechanics of extending the DSS to move from a BVAR model to the BVECM model for the category management problem. Several additional iterative steps are required in the DSS to allow the decision maker to arrive at the best forecast possible. The revised marketing DSS framework and model fitting procedures are described. Validation is conducted on a sample problem.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.

Clinical Decision Support System Sonares

A decision support system SonaRes destined to guide and help the ultrasound operators is proposed and compared with the existing ones. The system is based on rules and images and can be used as a second opinion in the process of ultrasound examination.

Case-based Reasoning Method for Real-time Expert Diagnostics Systems

The method of case-based reasoning for a solution of problems of real-time diagnostics and forecasting in intelligent decision support systems (IDSS) is considered. Special attention is drawn to case library structure for real-time IDSS (RT IDSS) and algorithm of k-nearest neighbors type. This work was supported by RFBR.

MultiDecision-2: A Multicriteria Decision Support System

The paper presents a multicriteria decision support system, called MultiDecision-2, which consists of two independent parts - MKA-2 subsystem and MKO-2 subsystem. MultiDecision-2 software system supports the decision makers (DMs) in the solving process of different problems of multicriteria analysis and linear (continues and integer) problems of multicriteria optimization. The two subsystems MKA-2 and MKO-2 of of MultiDecision-2 are briefly described in the paper in the terms of the class of the problems being solved, the system structure, the operation with the interface modules for input data entry and the information about DM’s local preferences, as well as the operation with the interface modules for visualization of the current and final solutions.

A Multi-agent Framework for Distributed Decision-making Systems

An approach of building distributed decision support systems is proposed. There is defined a framework of a distributed DSS and examined questions of problem formulation and solving using artificial intellectual agents in system core.

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.

Support tools for paediatric inpatient prescribers:a review

Prescribing support for paediatrics is diverse and includes both standard texts and electronic tools. Evidence concerning who should be supported and by what method is limited. This review aims to collate the current information available on prescribing support in paediatrics. Many tools designed to support prescribers are technology based. For example, electronic prescribing and smart phone applications. There is a focus on prescriber education both at undergraduate and postgraduate level. In the UK, the majority of inpatient prescribing is done by junior medical staff. It is important to ensure they are competent on qualification and supported in this role. A UK national prescribing assessment is being trialled to test for competence on graduation and there are also tools available to test paediatric prescribing after qualification. No information is available on the tools and resources UK prescribers currently use to support their decision making. One US study reported a decrease in the availability of paediatric prescribing information in a popular reference text. There is limited evidence to show that decisionsupport tools improve patient outcomes, however, there is growing confirmation that electronic prescribing reduces medication errors. There have been reports of new error types, such as selection errors, occurring with the use of electronic prescribing. Another concern with computerised decision-support systems is deciding what alerts should be presented to the prescriber and when/how often in order to avoid alert fatigue. There is little published concerning paediatric alerts perhaps as a consequence of commercial systems often not including paediatric specific support.

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.

Support for paediatric prescribers:what is discussed at hospital board level?

Prescribing support tools range from traditional printed texts to state-of-the-art computerised decision support systems. Comparison between available literature is difficult due to country-specific resources often being the focus of the research. In the UK, it is widely accepted that hospitals take their own individualised approaches to reducing prescribing errors. Objective - This study focused on specialist paediatric hospitals. It aimed to identify the localised approaches taken by paediatric hospitals to reduce prescribing errors. Method - Applied thematic analysis was used to explore the publically published board meeting minutes from the four specialist stand-alone paediatric hospitals in England. Three years of data was collected from each hospital. Codes were collected into groups to identify themes from the data. Results - The main themes identified were clinician involvement in prescribing support is important; credit card-sized reminder tools are used to provide prescribing guidance; electronic prescribing is considered important for reducing prescribing errors; feedback from clinical pharmacists on prescribing errors is widely used; junior doctors require extra support when prescribing; medical records may be incomplete and specific prescribing support (eg, antibiotic prescribing support) is widely in use. Conclusions - There is no single collaborative approach taken to paediatric prescribing support in English paediatric hospitals. Success of electronic prescribing in English paediatric hospitals is considerably behind leaders such as the USA. Use of clinical pharmacists to support prescribers is important as supported by previous studies in Spain and the USA.

Decision support system for production control: Multiple criteria decision making in practice

This paper presents a development of decision support systems for solving scheduling problems. It consists of two parts — the first describing the production processes which can be handled by the system and the second describing how the system works.

Towards a model for exploring the relationship between managerial decision problems and decision support opportunities

The organisational decision making environment is complex, and decision makers must deal with uncertainty and ambiguity on a continuous basis. Managing and handling decision problems and implementing a solution, requires an understanding of the complexity of the decision domain to the point where the problem and its complexity, as well as the requirements for supporting decision makers, can be described. Research in the Decision Support Systems domain has been extensive over the last thirty years with an emphasis on the development of further technology and better applications on the one hand, and on the other hand, a social approach focusing on understanding what decision making is about and how developers and users should interact. This research project considers a combined approach that endeavours to understand the thinking behind managers’ decision making, as well as their informational and decisional guidance and decision support requirements. This research utilises a cognitive framework, developed in 1985 by Humphreys and Berkeley that juxtaposes the mental processes and ideas of decision problem definition and problem solution that are developed in tandem through cognitive refinement of the problem, based on the analysis and judgement of the decision maker. The framework facilitates the separation of what is essentially a continuous process, into five distinct levels of abstraction of manager’s thinking, and suggests a structure for the underlying cognitive activities. Alter (2004) argues that decision support provides a richer basis than decision support systems, in both practice and research. The constituent literature on decision support, especially in regard to modern high profile systems, including Business Intelligence and Business analytics, can give the impression that all ‘smart’ organisations utilise decision support and data analytics capabilities for all of their key decision making activities. However this empirical investigation indicates a very different reality.