An integrated framework for supporting fuzzy decision-making in networked manufacturing environments

In this paper we propose an integrated framework, based on smart objects to support fuzzy decision-making processes applied to manufacturing environments. The processes involved range from factory-production level up to higher decision-making levels, either in the context of traditional single enterprises, up to the one of supply chains and distributed and ubiquitous manufacturing environments. Therefore, the proposed framework promotes contributions for solving different kind of problems, including, among others: networked supply chain management; production planning and control; factory supervision and productivity management; real-time monitoring; data acquisition and processing. The web access via different middleware devices and tools at different process levels, along with the use of integrated algorithms and smart objects, which is possible and will promote an optimized use of knowledge and resources for supporting better decision-making. Moreover, the proposed framework also aims at promoting a wider collaboration process among various groups of stakeholders.This work was supported by FCT “Fundação para a Ciência e a Tecnologia” under the program: PEst20152020.info:eu-repo/semantics/publishedVersio

PREdictive model for DISaster response configuration (PREDIS decision platform)

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe extraordinary conditions of a disaster, require the mobilisation of all available resources, inducing the rush of humanitarian partners into the affected area. This phenomenon called the proliferation of actors, causes serious problems during the disaster response phase including the oversupply, duplicated efforts, lack of planning. The aim of this research is to provide a solution to reduce the partner proliferation problem. To that end the main research question is put forward as “How to reduce the proliferation of partners in a disaster response”? Panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2013 via regression analysis, MA and AHP gives rise to the formation of a predictive decision-making platform called PREDIS. It is capable of predicting the human impact of the disaster (fatality, injured, homeless) of up to 3% of errors and enables the decision makers to estimate the required needs for each disaster and prioritises them based on the disaster type and socio-economics of the affected country. It further renders it possible to rank and optimise the desired partners based on the decision maker’s preferences. Verification of the PREDIS through a simulation game design using a sample group of decision makers, show that this technique enables the user to decide within one hour after the disaster strike using the widely available data at the time of the disaster. It also enables non-experts to decide almost identically to experts in terms of the similarity of the choices and the speed of the decision.The lack of an extensive database for the potential humanitarian partners from which to choose, is the limitation of this research in addition to the lack of standardised set of minimum requirements for the suitable partners.The model is also as strong as its data feed which is inconsistent in various humanitarian sources

Corporate Firm-Level Knowledge Accumulation and Engineering Manpower Outsourcing

Firm-level knowledge is a key resource providing a competitive advantage in innovation for enterprises. Outsourcing strategies reveal trends in strategic business administration. However, internal knowledge accumulation (KA) and engineering manpower outsourcing (EMO) produce opposing effects on firm-level knowledge. This study analyzes the relationship between KA and EMO among enterprises in Taiwan by means of expert interviews, an analytic hierarchy process (AHP), and a fuzzy logic inference system (FLIS). The results show that, compared to EMO, firm-level KA affords a greater degree of influence on the effectiveness of firm-level knowledge. Based on the literature and expert interviews, the three sub-variables of knowledge integration ability (KIA), knowledge absorption ability (KAA), and knowledge sharing ability (KSA) are extracted from the KA variable, and the three sub-variables of cost, resources, and strategy are extracted from the EMO variable

Supply chain risk assessment approach for process quality risks

Purpose- The purpose of the paper is to proactively analyse and mitigate root causes of the process quality risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process related failure modes within global supply chain context. Design/Methodology/approach- The case study of a printed circuit board company in China is used as a platform for conducting the research. Using data triangulation, the data is collected and analysed through interviews, questionnaires, expert opinions and quantitative modelling for drawing useful insights. Findings- The fuzzy logic approach to FMEA provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Supply Chain Managers should conduct robust risk assessment during the design stage to avoid product safety and security risks. Research Limitations/implications- The research is based on a single case study. Multiple cases from different industry sectors may support in generalising the findings. Originality/Value- The study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network

Tools for enterprises collaboration in virtual enterprises

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle

“Story of a Bank” Basel II accreditation through university-industry collaboration-case study

This paper deals with a case study of credit risk scoring models at Industrial Bank. The aim of this research is to investigate how a Malaysian financial institution developed and integrated credit risk scoring models with current organisational needs and evaluation of best practices for university-industry collaboration on this initiative. Attempts were made to categorise the credit risk scoring models initiative according to a variety of statistical techniques from modeling. This is an exploratory study which uses qualitative research methodology. Analysis of document from company annual reports as well as articles from journal, Bank Negara Malaysia, (BNM) regulatory reports as well as working papers and semistructured interviews were conducted to identify the organisational needs as a result of context and task. A company-wide development system for credit risk scoring model was effectively integrated to provide a direct support to competence management endeavor. The company’s credit risk scoring models initiatives have also resulted in managerial implications such as increased effectiveness of risk management through measuring the riskiness of each customer and automated the whole process, thereby leading to significant efficiency improvements. Thus, scoring models help banks to control credit risks. Going forward, credit risk scoring model is to become the best practice approach of the receivables management process and is essential to effective credit risk management

Secure data sharing and processing in heterogeneous clouds

The extensive cloud adoption among the European Public Sector Players empowered them to own and operate a range of cloud infrastructures. These deployments vary both in the size and capabilities, as well as in the range of employed technologies and processes. The public sector, however, lacks the necessary technology to enable effective, interoperable and secure integration of a multitude of its computing clouds and services. In this work we focus on the federation of private clouds and the approaches that enable secure data sharing and processing among the collaborating infrastructures and services of public entities. We investigate the aspects of access control, data and security policy languages, as well as cryptographic approaches that enable fine-grained security and data processing in semi-trusted environments. We identify the main challenges and frame the future work that serve as an enabler of interoperability among heterogeneous infrastructures and services. Our goal is to enable both security and legal conformance as well as to facilitate transparency, privacy and effectivity of private cloud federations for the public sector needs. © 2015 The Authors