Search results

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

Industry 4.0 has been identified as a key cornerstone to modernise economies where man and machines complement each other seamlessly to achieve synergies in decision-making and productivity for contributing to SDG 8: Decent Work and Economic Growth and SDG 9: Industry, Innovation and Infrastructure. The integration of Industry 4.0 remains a challenge for the developing world, depending on their current status in the industrial revolution journey from its predecessors 1.0, 2.0 and 3.0. This chapter reviews reported findings in literature to highlight how robotics and automated systems can pave the way to implementing and applying the principles of Industry 4.0 for developing countries like Mauritius, where data collection, processing and analysis for decision-making and prediction are key components to be integrated or designed into industrial processes centred heavily on the use of artificial intelligence (AI) and machine learning techniques. Robotics has not yet found its way into the various industrial sectors in Mauritius, although it has been an important driver for Industry 4.0 across the world. The inherent barriers and transformations needed as well as the potential application scenarios are discussed.

The unpredictable distribution of globalization has directed the world economy and revealed the need to establish cooperation in all business processes. This global structure also highlights the necessity of designing an agile supply chain that is capable of continuous information sharing during end-to-end transportation for the purpose of creating sustainable connections. In this process, various strategies enhanced with contemporary information and communication technologies have been developed to create a fast and accurate data-sharing network between logistics service providers and supply chain parties. However, unlike the short-term interruptions experienced in the past, COVID-19 has caused unprecedented problems in the dynamics of most economies. In eliminating these problems, it has been seen that information communication technologies, which provide the advantage of digital visibility in normal operations, need to be redesigned with the technology of the autonomous age. To meet this requirement, Industry 4.0, a revolutionary conceptual trend that started roughly a decade ago but that has become considerably more widespread during COVID-19, has been integrated into business models as a key concept that governs intelligent transformation in the chain. This process, which is today considered a compulsory intelligent transformation rather than an alternative method, has however led to some conflicts, especially in adaptation, expertise, and security subjects. This chapter of the book evaluates, within the scope of cybersecurity and workforce in logistics services, the conflicts created by the digital solution methods that have been integrated into business models to reduce the negative effects of COVID-19.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

To integrate environmental management systems into daily operations, the environmental aspects of management control systems (MCS) are enhanced. Although different approaches and concepts for Environmental Management Control Systems (EMCS) have been developed, two main problems appear: First, insights into how to implement EMCS are rare. Second, concepts are constructed mainly for large companies rather than for SMEs.

To close these research gaps, an implementation framework for SMEs is developed based on Epstein’s corporate sustainability framework. By using an action-oriented research approach, the implementation framework is analysed and tested on three Logistics Service Providers (LSPs).

The framework worked well with two of the firms analysed and failed with the third firm. The case study results enable a first evaluation of the implementation requirements that are essential for implementing EMCS in SMEs.