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This chapter proposes a contingency model that examines the most relevant contingent variables for understanding the factors that explain innovative costing techniques in a sample of firms investing in advanced manufacturing technologies (AMT firms). Furthermore, the aim of this study was also to ascertain whether AMT firms use significantly innovative managerial practices (IMPs).

The chapter uses material from a survey investigation and interviews with financial directors and the survey was conducted using a sample of AMT firms selected from an Italian industry. The method differs from previous studies in that it considers the relationship between a relevant contingency variable of AMT firms (i.e. the levels of integration, but also environmental uncertainty and size) and various innovative costing, a relationship that has not been previously explored. The research was developed from the relevant literature.

The results indicate that there is no association between innovative costing and AMT firms in general, however the findings show that activity-based costing (ABC) is positively associated with fully integrated AMT firms. Large AMT firms have the highest percentage of innovative costing usage, such as ABC and strategic costing (SC). The relationship between AMT firms that perceive a high degree of environmental uncertainty and innovative costing was supported by the data in case of strategic dimension (target costing (TC) and life cycle costing (LCC)). Expectations of a relationship between AMT firms and IMPs, such as just-in-time (JIT), total quality management (TQM) and activity-based management (ABM), were not supported by the data.

We recognise that specific research limitations might reduce their generalisation, especially the number of statistical observations.

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.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

Increasing competition within the global supply chain network has been pressuring managers to improve efficiencies of production systems while, at the same time, reduce manufacturing operation expenses. One well-known approach is to have better control of the manufacturing system through more accurate forecasting and efficient control. In other words, a production control paradigm with more reliable forward visibility should help in maintaining a cost-effective yet lean manufacturing environment. Hence, this study proposes a predictive decision support system for controlling and managing complex production environments and demonstrates a Visual Interactive Simulation (VIS) framework for forecasting system performances given a designated set of production control parameters. The VIS framework is applied to a real-world manufacturing system in which the primary objective is to minimize total production while maintaining consistently high throughput and controlling work-in-process level. Through this case study, we demonstrate the use and validate the effectiveness of VIS in optimization and prediction of the examined production system. Results show that the predictive VIS framework leads to better and more reliable decision making on selection of control parameters for the manufacturing system under study. Statistical analyses are incorporated to further strengthen the VIS decision-making process.