Many-Objective Genetic Type-2 Fuzzy Logic Based Workforce Optimisation Strategies for Large Scale Organisational Design

Workforce optimisation aims to maximise the productivity of a workforce and is a crucial practice for large organisations. The more effective these workforce optimisation strategies are, the better placed the organisation is to meet their objectives. Usually, the focus of workforce optimisation is scheduling, routing and planning. These strategies are particularly relevant to organisations with large mobile workforces, such as utility companies. There has been much research focused on these areas. One aspect of workforce optimisation that gets overlooked is organisational design. Organisational design aims to maximise the potential utilisation of all resources while minimising costs. If done correctly, other systems (scheduling, routing and planning) will be more effective. This thesis looks at organisational design, from geographical structures and team structures to skilling and resource management. A many-objective optimisation system to tackle large-scale optimisation problems will be presented. The system will employ interval type-2 fuzzy logic to handle the uncertainties with the real-world data, such as travel times and task completion times. The proposed system was developed with data from British Telecom (BT) and was deployed within the organisation. The techniques presented at the end of this thesis led to a very significant improvement over the standard NSGA-II algorithm by 31.07% with a P-Value of 1.86-10. The system has delivered an increase in productivity in BT of 0.5%, saving an estimated £1million a year, cut fuel consumption by 2.9%, resulting in an additional saving of over £200k a year. Due to less fuel consumption Carbon Dioxide (CO2) emissions have been reduced by 2,500 metric tonnes. Furthermore, a report by the United Kingdom’s (UK’s) Department of Transport found that for every billion vehicle miles travelled, there were 15,409 serious injuries or deaths. The system saved an estimated 7.7 million miles, equating to preventing more than 115 serious casualties and fatalities

Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda

Workplace Artificial Intelligence (AI) helps organisations increase operational efficiency, enable faster-informed decisions, and innovate products and services. While there is a plethora of information about how AI may provide value to workplaces, research on how workers and AI can coexist in workplaces is evolving. It is critical to explore emerging themes and research agendas to understand the trajectory of scholarly research in this area. This study's overarching research question is how workers will coexist with AI in workplaces. A search protocol was employed to find relevant articles in Scopus, ProQuest, and Web of Science databases based on appropriate and specific keywords and article inclusion and exclusion criteria. We identified four themes: (1) Workers' distrust in workplace AI stems from perceiving it as a job threat, (2) Workplace AI entices worker-AI interactions by offering to augment worker abilities, (3) AI and worker coexistence require workers' technical, human, and conceptual skills, and (4) Workers need ongoing reskilling and upskilling to contribute to a symbiotic relationship with workplace AI. We then developed four propositions with relevant research questions for future research. This review makes four contributions: (1) it argues that an existential argument better explains workers' distrust in AI, (2) it gathers the required skills for worker and AI coexistence and groups them into technical, human, and conceptual skills, (3) it suggests that technical skills benefit coexistence but cannot outweigh human and conceptual skills, and (4) it offers 20 evidence-informed research questions to guide future scholarly inquiries

A shared framework for the common mental disorders and Non-Communicable Disease: key considerations for disease prevention and control.

BACKGROUND: Historically, the focus of Non Communicable Disease (NCD) prevention and control has been cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), cancer and chronic respiratory diseases. Collectively, these account for more deaths than any other NCDs. Despite recent calls to include the common mental disorders (CMDs) of depression and anxiety under the NCD umbrella, prevention and control of these CMDs remain largely separate and independent. DISCUSSION: In order to address this gap, we apply a framework recently proposed by the Centers for Disease Control with three overarching objectives: (1) to obtain better scientific information through surveillance, epidemiology, and prevention research; (2) to disseminate this information to appropriate audiences through communication and education; and (3) to translate this information into action through programs, policies, and systems. We conclude that a shared framework of this type is warranted, but also identify opportunities within each objective to advance this agenda and consider the potential benefits of this approach that may exist beyond the health care system

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PD

A Critical Evaluation of Business Improvement through Machine Learning: Challenges, Opportunities, and Best Practices

This paper presents a critical evaluation of the impact of machine learning (ML) on business improvement, focusing on the challenges, opportunities, and best practices associated with its implementation. The study examines the hurdles faced by businesses while integrating ML, such as data quality, talent acquisition, algorithm bias, interpretability, and privacy concerns. On the other hand, it highlights the advantages of ML, including data-driven decision-making, enhanced customer experience, process optimization, cost reduction, and the potential for new revenue streams. Furthermore, the paper offers best practices to guide businesses in successfully adopting ML solutions, covering data management, talent development, model evaluation, ethics, and regulatory compliance. Through real-world case studies, the study illustrates successful ML applications in different industries. It also addresses the ethical and social implications of ML adoption and discusses emerging trends for future directions. Ultimately, this evaluation provides valuable insights to enable informed decisions and sustainable growth for businesses leveraging machine learning

A machine learning-based job forecasting and trend analysis system to predict future job markets using historical data.

Over the last two decades, technological advancements have created more job markets and job opportunities than ever. With the ever-increasing demand, it has become vital for academic institutions and businesses to keep up with employment requirements. The problem is more severe for modern and rapidly evolving industries such as software development. This study implements a prediction system for job trends to enable job seekers, organizations, and academic institutions to understand and align their endeavors to match the market requirement. Employers can benefit from the system by using it to identify potential talent shortages and proactively address them. Policymakers can also use the system to understand the potential impacts of changes in the job market on the economy, enabling them to make informed decisions about supporting employment growth. The prediction is made possible by creating a rich dataset based on more than 522,180 job listings from the past 24 months in the software industry. The dataset is fed to a Bidirectional LSTM model to predict the future trends of the job market for various roles and technologies. Auto-aggressive prediction is implemented using the bi-directional LSTM model as this combination proves to produce the most accurate results after multiple quantitative analyses and evaluations. This study evaluated the proposed solution against known real-world data and it was concluded that the system can predict the job trend for at least the next 12 months with a relatively high accuracy of 95.71%

Readiness of IT organisations to implement Artificial Intelligence to support business processes in Gauteng Province, South Africa.

Artificial Intelligence (AI) has emerged as a research field and more particularly studies pertaining to the readiness of organisations to implement AI. Although AI implementation has proliferated across industries, many organisations still struggle to successfully achieve their business goals, associated with AI and Fourth Industrial Revolution (4IR). This study attempts to close this gap, by conducting a deductive case study, and thematic analysis into the readiness of a Gauteng-based IT organisation to implement AI towards achieving their business goals, in line with the benefits associated with 4IR. To achieve this, the researcher draws on the Technology-Organisation-Environment (TOE) framework to reflect on the dimensions and group them into strategy, perception and awareness, challenges, and organisational culture, related to contextual factors. This paper reports on the outcomes of open-ended interviews and focus group discussions involving 31 participants across IT management, senior-, and junior technical staff, about the enabling and hindering factors of AI readiness. The study further offers insights and a research agenda to support IT managers and staff to make informed decisions towards increasing their readiness to implement A

The Inscrutable New Actor: An Employee Perspective on the Flipside of AI

An in-depth understanding of employees’ threat perceptions towards AI or other IT-related transformations could inform and elevate existing innovation processes, leading to higher adoption rates. Existing IS and management research mostly refers to organizational performance measures and customer perceptions, neglecting the critical role of employees. This paper argues that effective transformation and integration of this new actor AI predominantly depends on employees–acting as intermediaries between the technology and customers. Noting the largely neglected flipside of AI transformation from an employee perspective, the current article conducts a qualitative investigation among 103 healthcare professionals to derive important AI-adoption barriers. Drawing on self-determination and social impact theory, data among five AI-application categories were analyzed, leading to three important job-related threat dimensions: Professional Development & Leadership, Workforce Empowerment & Collaboration, Workforce Resilience & Risk Management. The resulting conceptual framework offers valuable cross-industrial insights, contributing to the broader understanding of adoption resistance