Centre for Decision Engineering

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https://dspace.lib.cranfield.ac.uk/handle/1826/9

'Decision Engineering' is an emerging discipline that focuses on developing tools and techniques for informed operational and business decision-making within industry by utilising data and information available at the time (facts) and distributed organisational knowledge.

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Browsing Centre for Decision Engineering by Subject "DEG report"

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    Cost engineering: why, what and how?
    (2003-09-18T00:00:00Z) Roy, Rajkumar; Editor
    Cost has become a major business driver in many industries. It is observed that there is a lack of understanding about the process to estimate, manage and control costs across the lifecycle of a product. This report presents a business case to understand the principles of ‘Cost Engineering’ within the manufacturing industries. The main focus of the report is in the techniques and tools used in cost estimating – one of the major activities in cost engineering. Five different methods of cost estimating are discussed in the report along with cost management issues including risk analysis. The report also presents research findings on ‘industry practice’ in hardware and software development cost estimating. The study shows the lack of research in hardware cost estimating and highlights the lack of communication within different groups of people involved in cost engineering. The report then focuses on the research trends in cost engineering and presents two case studies from recent research projects at Cranfield University. The case studies clearly show the progress in formalising the cost engineering process and the improvements in the current understanding about the domain. Two major areas of research as identified in the report are: i) integrating the cost engineering capability with the ERP (enterprise resource planning) environment so that data can be shared effectively, and ii) capture and reuse of human expertise in cost engineering for performance improvement. Finally, the report also identifies the need for simpler and cheaper cost engineering software for Small and Medium scale Enterpris
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    Optimising customer support in contact centres using soft computing approach
    (2006-10-31T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh; Editor
    This paper describes the research and development of a methodology for optimising the customer support in contact centres (CC) using a soft computing approach. The methodology provides the categorisation of customer and customer service advisor (CSA) within CC. Within the current contact centre environment there is a problem of high staff turnover and lack of trained staff at the right place for the right kind of customer. Business needs to assign any available advisor to a customer and provide consistent and good quality of service. There is a need to identify the right amount of information to be displayed on the screen considering both the customer and the assigned advisor background. On the basis of data collected through case studies carried out within five customer contact centres, two step clustering analysis was used to derive the categories for customers and advisors based on demographic, experience, business value and behavioural attributes. We provide the methodology to develop a fuzzy expert system which assigns a new customer or advisor to the pre-defined categories. The authors have explained the steps which were followed for the development of the fuzzy expert system. A prototype system has been designed and developed to identify the type of customer and CSA based on the demographic, experience and behavioural attributes. The authors illustrate analysis with real data, based on the work with large scale customer contact centres. The CSA’s can play different roles and have different level of autonomy, but at the end they are humans with heart and voice. While product purchases, lifestyle information and billing data provide important information about customers, it is call detail records that describe a customer’s behavior and define their satisfaction with the services offered. Call detail records describe the transactions between customer and the company. This study describes the research and development of methodology for categorizing customer and customer service advisor within contact centre environment. On the basis of the categories derived for customer and service advisor; the minimum amount of information required by the CSA to serve the customer is analysed and discussed within the paper. The information requirement framework provides the amount of information which is required by the CSA on the basis of {customer, advisor} relationship. A promising area for future work is that of data mining the records within contact centres. The methodology for proposed fuzzy expert system and its application to CC setting should be of interest to many industry sectors including telecommunications and contact centre environme
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    Technology selection for human behaviour modelling in contact centres
    (2006-01-01T00:00:00Z) Shah, Satya Ramesh; Roy, Rajkumar; Tiwari, Ashutosh; Editor
    Customer service advisors can play different roles and have different level of autonomy, but at the end they are humans with heart and voice. While product purchases, lifestyle information and billing data provide important information about customers, it is call detail records that describe a customer’s behaviour and define their satisfaction with the services offered. Call detail records describe the transactions between customer and the company. This study looks on different techniques that can be used to model customer and CSA (customer service advisor) behaviour within a contact centre environment. A brief overview of the contact centre environment is discussed focusing on issues of customer and service advisor and the need to categorise customer and advisor within contact centre environment. The findings from the case study analysis within the current contact centres, provides the authors with understanding of different behaviour observed for customer and CSA’s within contact centres. The study also examines different human behaviour modelling techniques which the authors are interested in using to develop a model which can categorise the human with respect to demographic, experience and behavioural attributes within the context. Through the study it can be seen that soft computing techniques provide a major role in modelling of human behaviour and thus providing better results where this technique can be applied. The authors have also carried out a comparative analysis of all the techniques discussed within the paper and as seen from the analysis that soft computing techniques are widely used to model the user/human behaviour and thus can provide a platform for future research. Soft computing represents a significant paradigm shift in the aim of computing, a shift that reflects the fact that the human mind, unlike state of the art computers, possesses a remarkable ability to store and process information, which is pervasively imprecise, uncertain, and lacking in categoric