Alok K. Choudhary

Risks in Enterprise Cloud Computing: The Perspective of it Experts

Cloud computing has become an increasingly prevalent topic in recent years. However, migrating hitherto internal IT data and applications to the cloud is associated with a wide range of risks and challenges. The study reported in this paper aims to explore potential risks that organisations may encounter during cloud computing adoption, as well as to assess and prioritise these risks, from the perspective of IT practitioners and consultants. A questionnaire was designed and distributed to a group of 295 highly experienced IT professionals involved in developing and implementing cloud based solutions, of which 39 (13.2%) responses were collected and analysed. The findings identified a set of 39 cloud computing risks, which concentrated around diverse operational, organisational, technical, and legal areas. The most critical top 10 risks perceived by IT experts were found to be caused by current legal and technical complexity and deficiencies associated with cloud computing, as well as by a lack of preparation and planning of user companies.

Kernel distance-based robust support vector methods and its application in developing a robust K-chart

Traditional statistical process control (SPC) techniques are not applicable in many process industries due to autocorrelation among data. In addition, most conventional charts are based on the assumption that quality characteristics follow a multivariate normality assumption. Therefore, the reduction in process variability obtained through the use of SPC techniques has not been realized in the industries. However, this may not be reasonable in many real-world problems and its extension poses serious limitations. Hence, it is not only desirable, but also inevitable to have some techniques that can serve the same purpose as SPC control charts used for correlated parameters. In this paper, a robust support vector method drawn from statistical learning theory was applied to develop a multivariate control chart based on kernel distance, which is a measure of the distance between the centre of a class and the sample to be monitored. The proposed robust chart takes advantage of information extracted from in-control preliminary samples. A robust support vector method-based chart aims to solve the over fitting problems when outliers exist in the training data set. The robust support vector method makes the decision function less sensitive towards the noise and outliers. The performance of the robust chart is tested on the problem taken from the literature and the results verify the effectiveness of the chart and validate that the robust chart is better than the conventional charts when the distribution of the quality characteristics is not multivariate normal. Experiments for the problem undertaken confirm the reduction in the number of support vectors and there is significant improvement in performance when compared with the standard support vector methods.

The needs and benefits of Text Mining applications on Post-Project Reviews

Post-Project Reviews (PPRs) are a rich source of knowledge and data for organisations - if organisations have the time and resources to analyse them. Too often these reports are stored, unread by many who could benefit from them. PPR reports attempt to document the project experience - both good and bad. If these reports were analysed collectively, they may expose important detail, e.g. recurring problems or examples of good practice, perhaps repeated across a number of projects. However, because most companies do not have the resources to thoroughly examine PPR reports, either individually or collectively, important insights and opportunities to learn from previous projects, are missed. This research explores the application of knowledge discovery techniques and Text Mining to uncover patterns, associations, and trends from PPR reports. The results might then be used to address problem areas, enhance processes, and improve customer relationships. A case study related to two construction companies is presented in this paper and knowledge discovery techniques are used to analyse 50 PPR reports collected during the last three years. The case study has been examined in six contexts and the results show that Text Mining has a good potential to improve overall knowledge reuse and exploitation.

A framework for collaboration moderator services to support knowledge based collaboration

Knowledge sharing is a major challenge for collaborative networks and is essential to improve the productivity and quality of decisions taken by both collaborative networks and their member organisations. A critical aspect of effective knowledge sharing within virtual organizations (VOs) is the identification of the most appropriate knowledge for reuse or exploitation in a particular context, as this requires efficient tools and mechanisms for its identification, sharing or transfer. Additionally, partners need to be aware of when knowledge needs to be shared, the implications of doing so and when their decisions are likely to affect other partners within the collaboration. Therefore, tools and methods are needed for identification, acquisition, maintenance and evolution of knowledge and to support effective knowledge sharing which includes awareness of possible consequences of actions and increased awareness of other partner’s needs during the collaboration. The Collaboration Moderator Services (CMS) are designed to address these issues relating to knowledge based collaboration by providing a set of functionalities to raise users’ awareness of opportunities, problem areas and lessons learnt from and during collaborations. This paper presents the system architecture and specifications of the CMS within the context of the SYNERGY system, whose purpose is to offer interoperable service utilities to help enterprises plan, setup and run complex knowledge collaborations. The CMS are designed to support both individual organizations and collaborations as a whole throughout the VO lifecycle and the different functionalities provided by CMS to achieve this are discussed in this paper.

Modeling the planning and scheduling across the outsourcing supply chain: a Chaos-based fast Tabu–SA approach

Planning and Scheduling are the interrelated manufacturing functions and should be solved simultaneously to achieve the real motives of integration in manufacturing. In this paper, we have addressed the advanced integrated planning and scheduling problem in a rapidly changing environment, where the selection of outsourcing machine/operation, meeting the customers (single or multiple) due date, minimizing the makespan are the main objectives while satisfying several technological constraints. We developed a mixed integer programming model for integrated planning and scheduling across the outsourcing supply chain and showed how such models can be used to make strategic decisions. It is a computationally complex and mathematically intractable problem to solve. In this paper, a Chaos-based fast Tabu-simulated annealing (CFTSA) incorporating the features of SA, Tabu and Chaos theory is proposed and applied to solve a large number of problems with increased complexity. In CFTSA algorithm, five types of perturbation schemes are developed and Cauchy probability function is used to escape from local minima and achieve the optimal/near optimal solution in a lesser number of iterations. An intensive comparative study shows the robustness of proposed algorithm. Percentage Heuristic gap is used to show the effectiveness and two ANOVA analyses are carried out to show the consistency and accuracy of the proposed approach.

A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in CBM is handling of data-sets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such data-sets. The framework proposed in this research uses a hybrid approach to deal with big data-set for smarter decisions. Furthermore, we compare the performance of radial basis function-based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in CBM is to predict the effect of data errors on quality due to highly imbalance unstructured data-set. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones.

Semantic web in manufacturing

Abstract Advances in manufacturing systems include attempts to create collaborative networks for enterprise integration and information interoperability. To achieve collaboration and sharing effectively, various networking technologies have been proposed in the literature. The web has emerged as a basic entity for interconnecting man and machine and almost all parts of the enterprise community are being reshaped to exploit the opportunities that it offers. Apart from web technology, there are various other tools and techniques that have attracted research communities for representing data in ways that both machines and humans can understand. Semantic web, the second-generation web technology, is enriched by machine-processable information to support the users in their tasks. This paper presents the vision of the semantic web and describes ontologies and associated metadata as the building blocks of the semantic web. It reviews the literature dealing with the application of the semantic web and ontology in the broad domain of manufacturing. First, brief details about key enablers, i.e. web services, semantic web, semantic services, and ontology, are presented. Then the implementation of these approaches in different sectors of manufacturing is discussed. A knowledge base for all the information resources concerned with the manufacturing domain is also built up in this paper. An ontology model for a knowledge base of information resources is designed in Protégé software, which can be used for storing and searching information about authors, journals, blogs, newspapers, and many other sources of information.

Knowledge management and supporting tools for collaborative networks

Knowledge management and supporting tools for collaborative networks Alok K. Choudhary a , Jenny Harding b , Luis M. Camarinha-Matos c , S.C. Lenny Koh d & Manoj K. Tiwari e a Logistics and Supply Chain Management Research Centre, Management School, The University of Sheffield, United Kingdom b Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, United Kingdom c Faculty of Sciences and Technology, New University of Lisbon, Lisbon, Portugal d Logistics and Supply Chain Management Research Centre, Management School, The University of Sheffield, United Kingdom e Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, West Bengal, India Published online: 01 Feb 2013.

Knowledge discovery from post-project reviews

Many construction companies conduct reviews on project completion to enhance learning and to fulfil quality management procedures. Often these reports are filed away never to be seen again. This means that potentially important knowledge that may assist other project teams is not exploited. In order to ascertain whether useful knowledge can be gleaned from such reports, Knowledge Discovery from Text (KDT) and text mining (TM) are applied. Text mining avoids the need for a manual search through a vast number of reports, potentially of different formats and foci, to seek trends that may be useful for current and future projects. Pilot tests were used to analyse 48 post-project review reports. The reports were first reviewed manually to identify key themes. They were then analysed using text mining software to investigate whether text mining could identify trends and uncover useful knowledge from the reports. Pilot tests succeeded in finding common occurrences across different projects that were previously unknown. Text mining could provide a potential solution and would aid project teams to learn from previous projects. However, a lot of work is currently required before the text mining tests are conducted and the results need to be examined carefully by those with domain knowledge to validate the results obtained.

Part selection and operation-machine assignment in a flexible manufacturing system environment: a genetic algorithm with chromosome differentiation-based methodology

Abstract Production planning of a flexible manufacturing system (FMS) is plagued by two interrelated problems, i.e. part type selection and operation allocation on machines. The combination of these problems is termed the machine-loading problem, which is a well-known complex puzzle and treated as a strongly NP-hard problem. In this research, a machine-loading problem has been modelled, taking into consideration several technological constraints related to the flexibility of machines, availability of machining time, tool slots, etc., while aiming to satisfy the objectives of minimizing the system unbalance, maximizing throughput, and achieving very good overall FMS utilization. The solution of such problems, even for moderate numbers of part types and machines, is marked by excessive computation complexities and therefore advanced random search and optimization techniques are needed to resolve them. In this paper, a new kind of genetic algorithm, termed a genetic algorithm with chromosome differentiation, has been used to address a well-known machine-loading problem. The proposed algorithm overcomes the drawbacks of the simple genetic algorithm and the methodology reported here is capable of achieving a better balance between exploration and exploitation and of escaping from local minima. The proposed algorithm has been tested on ten standard test problems adopted from literature and extensive computational experiments have revealed its superiority over earlier approaches.