A decision support system for selecting sanitation systems in developing countries

Due to the socio-economic inhomogeneity of communities in developing countries, the selection of sanitation systems is a complex task. To assist planners and communities in assessing the suitability of alternatives, the decision support system SANEX™ was developed. SANEX™ evaluates alternatives in two steps. First, Conjunctive Elimination, based on 20 mainly technical criteria, is used to screen feasible alternatives. Subsequently, a model derived from Multiattribute Utility Technique (MAUT) uses technical, socio-cultural and institutional criteria to compare the remaining alternatives with regard to their implementability and sustainability. This paper presents the SANEX™ algorithm, examples of its application in practice, and results obtained from field testing.

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.

Intelligent decision support for family financial planning

In today’s financial markets characterized by constantly changing tax laws and increasingly complex transactions, the demand for family financial planning (FFP) services is rising dramatically. However, the current trend to develop advisory systems that focus mainly on the financial or investment side fails to consider the whole picture of FFP. Separating financial or investment advice from legal and accounting advice may result in conflicting advice or important omissions that could lead to users suffering financial loss. In this paper, we propose a conceptual model for FFP decision-making process, followed by a novel architecture to support an aggregated FFP decision process by utilizing intelligentagents and Web-services technology. A prototype system for supporting FFP decision is presented to demonstrate the advances of the proposed Web-service multi-agentsbased system architecture and business value.

A framework to monitor sustainability in the grains industry

Community awareness of the sustainable use of land, water and vegetation resources is increasing. The sustainable use of these resources is pivotal to sustainable farming systems. However, techniques for monitoring the sustainable management of these resources are poorly understood and untested. We propose a framework to benchmark and monitor resources in the grains industry. Eight steps are listed below to achieve these objectives: (i) define industry issues; (ii) identify the issues through growers, stakeholder and community consultation; (iii) identify indicators (measurable attributes, properties or characteristics) of sustainability through consultation with growers, stakeholders, experts and community members, relating to: crop productivity; resource maintenance/enhancement; biodiversity; economic viability; community viability; and institutional structure; (iv) develop and use selection criteria to select indicators that consider: responsiveness to change; ease of capture; community acceptance and involvement; interpretation; measurement error; stability, frequency and cost of measurement; spatial scale issues; and mapping capability in space and through time. The appropriateness of indicators can be evaluated using a decision making system such as a multiobjective decision support system (MO-DSS, a method to assist in decision making from multiple and conflicting objectives); (v) involve stakeholders and the community in the definition of goals and setting benchmarking and monitoring targets for sustainable farming; (vi) take preventive and corrective/remedial action; (vii) evaluate effectiveness of actions taken; and (viii) revise indicators as part of a continual improvement principle designed to achieve best management practice for sustainable farming systems. The major recommendations are to: (i) implement the framework for resources (land, water and vegetation, economic, community and institution) benchmarking and monitoring, and integrate this process with current activities so that awareness, implementation and evolution of sustainable resource management practices become normal practice in the grains industry; (ii) empower the grains industry to take the lead by using relevant sustainability indicators to benchmark and monitor resources; (iii) adopt a collaborative approach by involving various industry, community, catchment management and government agency groups to minimise implementation time. Monitoring programs such as Waterwatch, Soilcheck, Grasscheck and Topcrop should be utilised; (iv) encourage the adoption of a decision making system by growers and industry representatives as a participatory decision and evaluation process. Widespread use of sustainability indicators would assist in validating and refining these indicators and evaluating sustainable farming systems. The indicators could also assist in evaluating best management practices for the grains industry.