A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

The development of an intelligent decision support framework in the contact centre environment

In a time of fast growing technology and communication systems, it is very important for the industry and the corporations to develop new contact centre environment technologies for better customer contact requirements. The integration of contact centre (CC) into day-to-day organisational operations represents one of the most promising trends in the 21 st century economy. Whatever the nature or point of contact, customers want a seamless interaction throughout their experience with the company. Customers receive more personalised experience, while the company itself can now provide a consistent message across all customer interactions. Based on the literature studies and the research carried out within the contact centre industry through the case studies, the author identified the customer and advisor behavioural attributes along with demographic, experience and others that later are used to derive the categories. Clustering technique identified the categories for customers and advisors. From the initial set of categories, fuzzy expert system framework was derived which assigned a customer or advisor with the pre-defined set of categories. The thesis has proposed two novel frameworks for categorisation of customer and advisor within contact centres and development of intelligent decision support framework that displays the right amount of information to the advisor at the right time. Furthermore, the frameworks were validated with qualitative expert judgement from the experts at the contact centres and through a simulation approach. The research has developed a novel Soft Computing based fuzzy logic categorisation framework that categorises customer and advisor on the basis of their demographic, experience and behavioural attributes. The study also identifies the behavioural aspects of customer and advisor within CC environment and on the basis of categorisation framework, assigns each customer and advisor to that of a pre-defined category. The research has also proposed an intelligent decision support framework to identify and display the minimum amount of information required by an advisor to serve the customer in CC environment. The performance of the proposed frameworks is analysed through four case studies. In this way this research proposes a fully tested and validated set of categorisation and information requirement frameworks for dealing with customer and advisor and its challenges. The research also identifies future research directions in the relevant areas.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design rules and guidelines for generic condition-based maintenance software's Graphic User Interface

The task of selecting and developing a method of Human Computer Interaction (HCI) for a Condition Based Maintenance (CBM) system, is investigated in this thesis. Efficiently and accurately communicating machinery health information extracted from Condition Monitoring (CM) equipment, to aid and assist plant and machinery maintenance decisions, is the crux of the problem being researched. Challenges facing this research include: the multitude of different CM techniques, developed for measuring different component and machinery condition parameters; the multitude of different methods of HCI; and the multitude of different ways of communicating machinery health conditions to CBM practitioners. Each challenge will be considered whilst pursuing the objective of identifying a generic set of design and development principles, applicable to the design and development of a CBM system's Human Machine Interface (HMI). [Continues.

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

A critical analysis of the relationship between business information system technology and supply chain management with special reference to optimum efficiency within large enterprises in the food and drug retail sector in KwaZulu-Natal.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.The purpose of this research is to explore the relationship between supply chain management and business information system technology within the food and drug retail sector with special reference to optimum efficiency. Business information system technology leverages information and knowledge sharing throughout the supply chain which enables them to respond more effectively to an ever-changing and volatile marketplace. The relationship between supply chain management and business information system technology is multi-faceted and complex in nature, and consequently, has the ability to penetrate every element of an organisation’s functionality. Furthermore, it has the ability to penetrate the functionality of an entire chain or network of suppliers and markets irrespective of their position around the globe. Supply Chain management literature teaches that optimisation within functional areas is not as effective as cross-optimisation across functions and supply chain networks. As supply chains rely on business information technology for crossoptimisation, optimum efficiency will always be a moving target for as long as business technology and supply chain management continue to break through new ground. In this research large food and drug supply chain networks and their business information system requirements, trends, influence, effect and constraints were reviewed, with special reference to creating optimum efficiency in their supply chain networks. This research was motivated by discussions with Pick ‘n Pay’s business information system’s service provider and their desire to optimise Pick ‘n Pay’s supply chain network efficiency in a highly price-competitive environment, resulting in an in-depth case study being carried out on Pick ‘n Pay (Pty) Ltd stores in KwaZulu-Natal. The review of literature suggests that cross-optimisation is only mutually beneficial within trust relationships that exhibit seamless flows of information throughout a supply chain. Data suggests that despite Pick ‘n Pay’s use of advanced business information system technology, Pick ‘n Pay together with the food and drug retail stores in South Africa have a long way to go on this issue

Employees and customers in call centres: confirmatory and exploratory study

Aquest estudi tracta de connectar les disciplines de RH i Màrqueting, i examina el model de service-profit chain (SPC) en el sector de Call Centre, caracteritzat per un servei remot i un negoci basat en la reducció de costos. Les dades s'han col·lectat del Projecte "Global Call Center Project". Hem realitzat dos estudis. En el primer estudi (confirmatori) s'ha emprat una mostra internacional (n = 937). En el segon estudi (exploratori) vam utilitzar una mostra espanyola (n = 109). Els resultats revelen que el model SPC té una aplicació diferent en els call centres. Encara que a nivell general podem confirmar la majoria de les relacions del model, els resultats indiquen que la satisfacció del client és un resultat separat, i no un precursor del rendiment de l'empresa. També trobem una alternativa de mesurar el constructe individual de la satisfacció de l'empleat amb dades disponibles de la organització. Addicionalment, vam descobrir certes discrepàncies en la relació entre la productivitat i la satisfacció dels empleats.El presente estudio es una conexión entre las disciplinas de RH y Marketing, y examina el modelo service-profit chain (SPC) en el sector de Call Centre, caracterizado por un servicio remoto y un negocio basado en la reducción de costes. Los datos se han colectado del Proyecto “Global Call Center Project”. Hemos realizado dos estudios. En el primer estudio (confirmatorio) se ha empleado una muestra internacional (n = 937). En el segundo estudio (exploratorio) utilizamos una muestra española (n = 109). Los resultados revelan que el modelo SPC tiene una aplicación diferente en los call centres. Aunque a nivel general podemos confirmar la mayoría de las relaciones del modelo, los resultados indican que la satisfacción del cliente es un resultado separado, y no un precursor de rendimiento de la empresa. También encontramos una alternativa de medir el constructo individual de satisfacción del empleado con los datos organizacionales disponibles. Adicionalmente, descubrimos ciertas discrepancias en la relación entre la productividad y la satisfacción de los empleados.This thesis is an interface between HR and Marketing discipline, by examining the Service-Profit Chain (SPC) model in the context of call centre, characterized by remote service and cost cutting business models. Data was gathered from the Global Call Centre Project. We carried out two studies. The first one is a confirmatory study, using an international sample (n=937). In the second study we use a Spanish sample (n=109) and carry out an exploratory study. Findings reveal that the SPC model behaves somewhat differently in call centres. Although there is general support for most of the links in the model, the results indicate that customer satisfaction in the call centre industry is a separate outcome, rather than a precursor to company performance. In addition, we found a way to measure the individual level of employee satisfaction construct with organizational available data. We also discovered some discrepancies in the relationship between employee satisfaction and employee productivity

Data quality assurance for strategic decision making in Abu Dhabi's public organisations

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Master of Philosophy”.Data quality is an important aspect of an organisation’s strategies for supporting decision makers in reaching the best decisions possible and consequently attaining the organisation’s objectives. In the case of public organisations, decisions ultimately concern the public and hence further diligence is required to make sure that these decisions do, for instance, preserve economic resources, maintain public health, and provide national security. The decision making process requires a wealth of information in order to achieve efficient results. Public organisations typically acquire great amounts of data generated by public services. However, the vast amount of data stored in public organisations’ databases may be one of the main reasons for inefficient decisions made by public organisations. Processing vast amounts of data and extracting accurate information are not easy tasks. Although technology helps in this respect, for example, the use of decision support systems, it is not sufficient for improving decisions to a significant level of assurance. The research proposed using data mining to improve results obtained by decision support systems. However, more considerations are needed than the mere technological aspects. The research argues that a complete data quality framework is needed in order to improve data quality and consequently the decision making process in public organisations. A series of surveys conducted in seven public organisations in Abu Dhabi Emirate of the United Arab Emirates contributed to the design of a data quality framework. The framework comprises elements found necessary to attain the quality of data reaching decision makers. The framework comprises seven elements ranging from technical to human-based found important to attain data quality in public organisations taking Abu Dhabi public organisations as the case. The interaction and integration of these elements contributes to the quality of data reaching decision makers and hence to the efficiency of decisions made by public organisations. The framework suggests that public organisations may need to adopt a methodological basis to support the decision making process. This includes more training courses and supportive bodies of the organisational units, such as decision support centres, information security and strategic management. The framework also underscores the importance of acknowledging human and cultural factors involved in the decision making process. Such factors have implications for how training and raising awareness are implemented to lead to effective methods of system development