In recent years, Industry 4.0 has emerged as one of the most discussed concepts and has gained significant popularity in both academia and the industrial sector. Both Industry 4.0 and lean manufacturing utilise decentralised control and aim to increase productivity and flexibility. However, there have been few studies investigating the link between these two domains. This article explores this novel area and maps the current literature. This is achieved through a systematic literature review methodology, investigating literature published up to and including August 2017. This article identifies four main research streams concerning the link between Industry 4.0 and lean manufacturing, and a research agenda for future studies is proposed.

Manufacturing flexibility improves a firm’s ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

The most recent trend manufacturers have embraced to seek operational performance improvements is the use of a wide range of digital technologies typically associated with Industry 4.0. However, few studies have investigated the relationship between such technologies and the long-established lean manufacturing domain, and how they, together, influence operational performance. Based on data from a cross-sectional survey of manufacturing companies, this study investigates the relationships between the use of lean manufacturing, factory digitalisation, and operational performance using hierarchical multiple regression analysis. While simultaneously controlling for the effects of production repetitiveness, company size, and length of lean manufacturing implementation, the findings show that both lean manufacturing and factory digitalisation individually contribute to improved operational performance. Furthermore, it is found that when used together, they have a complementary (or synergistic) effect that is greater than their individual effects combined. These research findings provide both theoretical and practical insights into how lean manufacturing and factory digitalisation affect the operational performance of manufacturing firms. In light of the upcoming fourth industrial revolution, these findings suggest that lean manufacturing is not obsolete but rather is more important than ever in order to reap the benefits from emerging technologies and translate them into improved operational performance.

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

The shipbuilding industry is under significant economic pressure and in need of more efficient solutions to secure economically sustainable operations. It is also challenged by social issues and the need for a greener maritime industry is critical. Accordingly, the shipbuilding industry is pressured across all three dimensions of sustainability. This paper aims to identify the sustainability challenges in shipbuilding supply chains and explore how Industry 4.0 technologies can impact the sustainability of shipbuilding. This is achieved through a case study of a shipbuilding supply chain, which results in the identification of its primary sustainability challenges. Further, this work proposes a set of nine digital solutions to support sustainable operations in shipbuilding as the paper’s primary contribution. This lays the foundation for further empirical research on sustainability and digitalization in shipbuilding, while for practice the paper provides enhanced insight into how Industry 4.0 technologies can be adopted in shipbuilding supply chains.

Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of their adoption and use have been much more challenging than anticipated. In this paper, we explore these challenges and present a conceptual model, a use-case matrix and a product–process framework for a smart production planning and control (smart PPC) system and illustrate the use of these artefacts through four case companies. The presented model adopts an incremental approach that companies with limited resources could employ in improving their PPC process in the context of industry 4.0 and sustainability. The results reveal that while make-to-order companies are more likely to derive greater benefits from a smart product strategy, make-to-stock companies are more likely to derive the most benefit from pursuing a smart process strategy, and consequently a smart PPC solution.