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Risk-Based Decision Making

In the modern world, e-commerce interactions are increasingly being carried out in virtual environments, thereby increasing the possibility of fraudulent transactions due to non-compliance by either communicating party. As such, there is an increased need for developing tools which help in making informed interaction-based decisions. Risk-based Decision Support System (RDSS) is a complete and comprehensive methodology developed at DEBII for assessing, expressing and managing risk in e-commerce interactions. RDSS is the first and only one of its type for risk assessment, measurement and management in e-commerce interactions. The objective of the project is to assess, analyse the level of risk as a function of its constituents in collaborative environments, and then manage it while decision-making. In order to achieve this, RDSS develops an architectural framework to ascertain and model the level of performance risk and financial risk according to the specific characteristics of the interaction, and according to their uncertainty when determining each of them at a certain point in time in the future. The risk in the interaction is then analysed and quantified according to its constituents by using two mathematical measures; the possibility theory and fuzzy logic. The output is then presented to the user in graphical format for better understanding. Based on the analysed risk, RDSS then carries out the steps of risk management and utilises it while making informed interaction-based decisions. For risk management, RDSS utilises a mathematical approach which determines the impact of the interaction initiating the agent’s risk propensity on the determined level of risk in an interaction, based on which it recommends an interaction-based decision. It provides the user with an effective decision-making methodology in an open interaction environment. The output of the project, when applied to interactions in virtual environments, would help the interacting agent to maximise its interaction experience and expected benefits.

Risk Quantification

Business interactions are the engine which drives the economy of the modern world. By business interactions, we mean the multi-disciplinary areas in that domain and which might have financial implications to either agent involved. The interactions in these domains are carried out with the aim of achieving certain specific outcomes which are consequential for the progression, advancement and sustenance for the particular business or individual. Failure to achieve those specific outcomes might have far reaching consequences to the business or individual. One of the important outcomes of the result of failure might be the risk of experiencing financial loss in such interactions. In this project, we analyse and assess this risk as an important constituent of making informed decisions within a broader perspective of business interactions. One of the areas in that domain in this project for risk quantification is the probabilistic assessment of loss in revenue generation in Demand-Driven Production. In today’s competitive world, manufacturers are constantly subjected to massive pressure to reduce their operational costs and at the same time improve or increase their production efficiency. Cost reduction relating to production is not a bad thing for the manufacturers, but doing this implies that they have to shift or to adopt a new process for producing or manufacturing their goods. One such process that the manufacturers have to adopt is to produce and deliver the consumers’ orders within a fixed timeframe in order to obtain the required revenue from the transactions. In this project, we consider processes from the manufacturers’ perspective and assess the probabilistic level of risk incurred by not fulfilling the required demand within the given period. We also look at the level of financial consequences to the manufacturer as a result of not fulfilling the required demand. The output of this project will have far-reaching consequences when applied to the manufacturing industry.

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