SUBJECTIVE PROBABILITIES ELICITATION AND COMBINATION IN RISK ASSESSMENTS PROBLEMS

Very many areas of human activity are associated with greater or lesser risks. In order to make reasonable decisions, these risks must be properly assessed. The consequences of any risk can be characterized on the basis of two fundamental dimensions (metrics): (1) losses associated with the outcomes of implementation an unfavourable event; (2) probabilities that quantify the uncertainties in the occurrence of these outcomes. This article in a concise form presents and analyses approaches to subjective probabilities elicitation and combining the obtained individual estimates in group subjective probabilities estimation.

Modelling project complexity driven risk paths in new product development

Project complexity has been extensively explored in the literature because of its major contribution towards the failure of major projects in terms of cost and time overruns. Researchers have identified important factors that contribute to the project complexity and validated their findings through case studies. Few studies have even focused on developing tools for evaluating the project complexity. However, existing research has not explored an important aspect of linking project complexity to different types of project and supply chain risks. We propose a framework for establishing risk paths across project complexity elements, project and supply chain risks, and resulting consequences. Project complexity elements are the knowns at the commencement stage of a project whereas project and supply chain risks are the uncertainties that might realize within the life cycle of the project. We demonstrate application of our proposed framework through a simple simulation example using Bayesian Belief Network. The method can be an important contribution to the literature and beneficial to the practitioners in terms of introducing a new perspective of investigating causal paths of interacting project complexity elements and risks

The Use of Intelligent Systems for Planning and Scheduling of Product Development Projects

AbstractThe paper investigates the use of intelligent systems to identify the factors that significantly influence the duration of new product development. These factors are identified on the basis of an internal database of a production enterprise and further used to estimate the duration of phases in product development projects. In the paper, some models and methodologies of the knowledge discovery process are compared and a method of knowledge acquisition from an internal database is proposed. The presented approach is dedicated to industrial enterprises that develop modifications of previous products and are interested in obtaining more precise estimates for project planning and scheduling. The example contains four stages of the knowledge discovery process including data selection, data transformation, data mining, and interpretation of patterns. The example also presents a performance comparison of intelligent systems in the context of variable reduction and preprocessing. Among data mining techniques, artificial neural networks and the fuzzy neural system are chosen to seek relationships between the duration of project phase and other data stored in the information system of an enterprise

Pendekatan Bayesian untuk Analisis Survival pada Kasus Demam Berdarah Dengue Pasien RSUD Dr. Soetomo Surabaya

Demam Berdarah Dengue (DBD) merupakan penyakit yang disebabkan oleh virus Dengue yang ditularkan melalui gigitan nyamuk Aedes aegypti dan Aedes albopictus. Indonesia menjadi negara Asia Tenggara tertinggi dengan kasus DBD. Provinsi Jawa Timur merupakan provinsi dengan jumlah kasus DBD tertinggi kedua pada 2017 dan 2018. Salah satu analisis statistika yang digunakan untuk mengetahui ketahanan hidup adalah analisis survival, sehingga akan dianalisis model survival faktor karakteristik pasien yang mempengaruhi laju kesembuhan (lama rawat inap) pasien DBD di RSUD Dr. Soetomo. Analisis Bayesian memperlakukan semua parameter yang tidak diketahui sebagai variabel random dan memiliki distribusi. Estimasi parameter dengan pendekatan bayesian untuk mengatasi kasus jumlah data terbatas karena mempertimbangkan distribusi prior (informasi sebelumnya). Model survival parametrik yang digunakan mengikuti pola distribusi Weibull 3 dan 2 parameter. Model terbaik dengan WAIC terkecil adalah model survival Weibull 2 parameter dengan faktor yang berpengaruh signifikan adalah usia pasien, pendidikan terakhir (SMA), pekerjaan (tidak bekerja), diagnosis masuk rumah sakit (II), suhu tubuh, denyut nadi, dan kadar sel darah putih

Product Innovation Risk Management based on Bayesian Decision Theory

Abstract Innovation is an inexhaustible force for the prosperity of one nation, and also the life source of enterprises. Product innovation is an important aspect of innovation. However, the product innovation activities has high-risk characteristics. Enterprises have to perform scientific and effective product innovation risk management. Based on a general introduction of Bayestian Decision Theory principle, the author studied the practices of product innovation in enterprises. The paper discussed how to use Bayesian Decision Theory to achieve quantitative innovation-risk management in product innovation: based on the description of three elements for product innovation risk management, the author discussed the process of bayesian risk decision-making in product innovation. Thus to providing references for scientific decision of innovation activities in enterprises

Credit Risk Evaluation as a Service (CREaaS) based on ANN and Machine Learning

Credit risk evaluation is the major concern of the banks and financial institutions since there is a huge competition between them to find the minimum risk and maximum amount of credits supplied. Comparing with the other services of the banks like credit cards, value added financial services, account management and money transfers, the majority of their capitals has been used for various types of credits. Even there is a competition among them for finding and serving the low risk customers, these institution shares limited information about the risk and risk related information for the common usage. The purpose of this paper is to explain the service oriented architecture and the decision model for those banks which shares the information about their customers and makes potential customer analysis. Credit Risk Evaluation as a Service system, provides a novel service based information retrieval system submitted by the banks and institutions. The system itself has a sustainable, supervised learning with continuous improvement with the new data submitted. As a main concern of conflict of interest between the institutions trade and privacy information secured for internal usage and full encrypted data gathering and as well as storing architecture with encryption. Proposed system architecture and model is designed mainly for the commercial credits for SME’s due to the complexity and variety of other credits

Research on framework of risk management of uncertain innovation

Abstract The uncertainty of innovation determines its characteristic of high risks. The management of innovation risks is significant. The h igh risks of innovation activities require managers to imp le ment scientific and effective innovation risk management. On the basis of a general rev iew o f the risk management of uncertain innovation, by combining the case of innovation project, this paper discussed the framework of uncertain innovation risk management wh ich combination the qualitative and quantitative management methods, in the hope of providing scientific references for managers making innovation risk management decisions

An integrated Bayesian-Markovian framework for ascertaining cost of executing quality improvement programs in manufacturing industry

Purpose Typically, the budgetary requirements for executing a supplier’s process quality improvement program are often done in unstructured ways in that quality improvement managers purely use their previous experiences and pertinent historical information. In this backdrop, the purpose of this paper is to ascertain the expected cost of carrying out suppliers’ process quality improvement programs that are driven by original equipment manufacturers (OEMs). Design/methodology/approach Using inputs from experts who had prior experience executing suppliers’ quality improvement programs and employing the Bayesian theory, transition probabilities to various quality levels from an initial quality level are ascertained. Thereafter, the Markov chain concept enables the authors to determine steady-state probabilities. These steady-state probabilities in conjunction with quality level cost coefficients yield the expected cost of quality improvement programs. Findings The novel method devised in this research is a key contribution of the work. Furthermore, various implications related to experts’ inputs, dynamics related to Markov chain, etc., are discussed. The method is illustrated using a real life of automotive industry in India. Originality/value The research contributes to the extant literature in that a new method of determining the expected cost of quality improvement is proposed. Furthermore, the method would be of value to OEMs and suppliers wherein the quality levels at a given time are the function of quality levels in preceding period(s)

Product risk assessment: a Bayesian network approach

Product risk assessment is the overall process of determining whether a product, which could be anything from a type of washing machine to a type of teddy bear, is judged safe for consumers to use. There are several methods used for product risk assessment, including RAPEX, which is the primary method used by regulators in the UK and EU. However, despite its widespread use, we identify several limitations of RAPEX including a limited approach to handling uncertainty and the inability to incorporate causal explanations for using and interpreting test data. In contrast, Bayesian Networks (BNs) are a rigorous, normative method for modelling uncertainty and causality which are already used for risk assessment in domains such as medicine and finance, as well as critical systems generally. This article proposes a BN model that provides an improved systematic method for product risk assessment that resolves the identified limitations with RAPEX. We use our proposed method to demonstrate risk assessments for a teddy bear and a new uncertified kettle for which there is no testing data and the number of product instances is unknown. We show that, while we can replicate the results of the RAPEX method, the BN approach is more powerful and flexible