Determinants of information and digital technology implementation for smart manufacturing

This study aims to identify and analyse factors that determine the implementation of Information and Digital Technologies (IDT) of smart manufacturing. By performing a state-of-the-art and content-driven review of literature, consulting a group of experts from academia and industry, and implementing interpretive structural modelling methodology, the study identified eleven enabling factors and mapped the contextual interrelationships among them. The study further explained the complex precedence relationships that exist among determinants of smart manufacturing IDT adoption. Results showed that perceived benefits and management support are the two driver determinants that act as stepping-stones in the implementation of smart manufacturing IDT. Operations technology maturity and cybersecurity maturity were found to be the dependent determinants of smart manufacturing IDT implementation and highly driven by the linkage and driver determinates. The findings are expected to assist academicians, industrialists, and the policymakers with achieving a detailed understanding of smart manufacturing transformation processes, and conditions that facilitate the manufacturing digitalisation in the Industry 4.0 era.     

A circularity measurement toolkit for manufacturing SMEs

The development and adoption of the concept of circular economy in the last two decades have been remarkable. However, despite its widespread adoption, little progress has been made regarding its measurement, especially in manufacturing SMEs. This paper, therefore, proposes a Circularity Measurement Toolkit (CMT) which enables the assessment of the degree of circularity in manufacturing SMEs. A conceptual CMT framework, which provided the basis for the proposed tool and that defined the different types of circular practices and a classification or levels of circularity was developed from an extensive literature reviewed. To ensure the structure's accuracy of the proposed CMT in terms of requirements to be measured, the monitoring process and actions involved, the tool was verified through a Delphi-study. Furthermore, its practicality was validated through a case study approach in a manufacturing SME. This paper contributes by filling a gap in the CE measurement field through the proposal of the CMT. Besides providing an evaluation of the degree of circularity in the practices of manufacturing SMEs, companies can also employ the proposed CMT to identify corrective actions or future efforts for the adoption of CE practices.     

An empirical study for smart production for TFT-LCD to empower Industry 3.5

Abstract

With increasing technological advancements, manufacturing intelligence has become a crucial issue for maintaining competitive advantages. Industry 4.0, proposed by Germany, is one of the large-scale projects to achieve manufacturing intelligence and smart production. Others include the Advanced Manufacturing Partnership 2.0 (AMP2.0) from the United States, Industry 4.1J of Japan and Made in China 2025. On the other hand, most of the emerging countries may not be ready for the migration of Industry 4.0 directly since their industrial infrastructures are different with the leading countries. This study aims to propose Industry 3.5 as a hybrid strategy between existing Industry 3.0 and to-be Industry 4.0, in which digital decision-making, big data analytics, and manufacturing intelligence are integrated to empower smart production with disruptive innovations that can be realized in existing industrial infrastructure. To estimate the validity of the proposed Industry 3.5, an empirical study was conducted in a thin film transistor liquid crystal display (TFT-LCD) manufacturing factory in Taiwan. A smart daily planning and scheduling (DPS) system is developed to enhance manufacturing intelligence for smart production without a fully automation facility as the Cyber-Physical System proposed in Industry 4.0. This study concludes with discussions of development directions for industrial revolution.     

A framework for operator – workstation interaction in Industry 4.0

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. We name the proposed framework ‘Operator – Workstation Interaction 4.0’. The latter’s capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory’s production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented.     

The industrial management of SMEs in the era of Industry 4.0

Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional enterprise information systems such as ERP and MES. However, SMEs find themselves ill-equipped to face these new possibilities regarding their production planning and control functions. This paper presents a literature review of existing applied research covering different Industry 4.0 issues with regard to SMEs. Papers are classified according to a new framework which allows identification of the targeted performance objectives, the required managerial capacities and the selected group of technologies for each selected case. Our results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things. Likewise, SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still absence of real applications in the field of production planning. Finally, our literature review shows that reported Industry 4.0 projects in SMEs remained cost-driven initiatives and there in still no evidence of real business model transformation at this time.     

Design of smart connected manufacturing resources to enable changeability,reconfigurability and total-cost-of-ownership models in the factory-of-the-future

The fourth industrial revolution requires higher capabilities of changeability and reconfigurability (C–R) of the future factories (FoF), as well as a higher focus on business models that are based on total-cost-of-ownership (TCO) paradigm. Up to date, there are little scientific contributions to deploy C–R into TCO models, as well as to systematic plan and design manufacturing resources such as to facilitate FoF ecosystem. In order to address this issue, this paper introduces research results that show how to deploy C–R, connectivity, smartness and TCO requirements into the technical solutions of manufacturing resources of FoF. Contributions emerging from this research include an index to measure C–R capability of manufacturing resources, a model to assess economic feasibility of a FoF over its lifecycle, as well as a methodology and related tools to design smart connected manufacturing resources with embedded features to facilitate changeability and reconfigurability in a FoF. Theoretical contributions are explained through a case study of a fast reconfigurable robotic manufacturing cell. Preliminary results demonstrate that it is possible to rapid design smart connected manufacturing resources and integrate them into FoF architectures that support convertibility, integrability, modifiability, adaptability, serviceability, scalability, integration of resources from various producers, service clustering and cloud-based services.     

Sensing,smart and sustainable product development (S3 product) reference framework

Enterprises must become ‘sensing, smart and sustainable (S³)’ to face global challenges related to local, national and global market dynamics. Therefore, reconceptualisation and redesign in these enterprises must accommodate emergent technologies, new practices and strategies. In this sense, enterprises have used new product development as a strategy for remaining competitive in the marketplace; thus, they can provide a new generation of products offering solutions to contemporary social problems and responding to changing consumer demands. These new-generation products are mostly technology-based and consider sustainable objectives. In this context, concepts such as sensing, smart and sustainable products (S³ products) have emerged to satisfy different social requirements. Therefore, this work focuses on providing a reference framework that presents a systematic process for the development of S³ products. This reference framework is based on the integrated product, process and manufacturing system development reference model. The main objective of this work is to fill the gap vis-à-vis the current lack of design roadmaps that permit the development of this new generation of products in S³ enterprises. The development of a reconfigurable micro-machine tool is presented as that of an S³ product.     

The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda

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.     

Exploring adoption of augmented reality smart glasses: Applications in the medical industry

This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The results indicate that there were a number of factors to be considered in order to understand ARSG adoption by physicians. Usefulness was influenced by ease of use, compatibility, ease of reminding, and speech recognition, while ease of use was affected by ease of learning, ease of medical education, external influence, and privacy. Privacy was the only negative factor that reduced the perceived ease of use, and was found to indirectly create a negative attitude. Compatibility emerged as the most significant external factor for usefulness. Developers of ARSGs should pay attention to healthcare-specific requirements for improved utilization and more extensive adoption of ARSGs in healthcare settings. In particular, they should focus on how to increase the compatibility of ARSGs. Further research needs to be conducted to explain the adoption intention of physicians.     

A decision framework for maximising lean manufacturing performance

Since the development of the original value stream mapping (VSM) by Taichi Ohno at Toyota, a number of authors have suggested several additional VSM tools to understand and improve the value stream through waste reduction. A single best VSM tool, though effective in dealing with a certain waste type, becomes redundant as other wastes take centre stage and/or organisational priorities change. To overcome this, a decision framework based on a novel formulation of the integrated analytical hierarchy process (AHP) – pre-emptive goal programming (PGP) has been proposed. This framework not only guarantees accurate selection of an ideal VSM tool, based on the current organisation's priorities, but also aids the decision maker to arrive at the optimum implementation sequence of a chosen set of VSM tools to identify and reduce all wastes present in the system, thereby maximising organisational performance in the shortest timeframe.     

Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies

The adoption of Industry 4.0 technologies has been deemed as a strategy to increase product quality and make manufacturing processes more efficient. However, the way that these technologies are integrated into existing production systems and which processes they can support is still under investigation. Thus, this paper aims to examine the relationship between lean production (LP) practices and the implementation of Industry 4.0 in Brazilian manufacturing companies. To achieve that we use data from a survey carried out with 110 companies of different sizes and sectors, at different stages of LP implementation. Data collected were analysed by means of multivariate analysis. Our findings indicate that LP practices are positively associated with Industry 4.0 technologies and their concurrent implementation leads to larger performance improvements. Further, the contextual variables investigated do matter to this association, although not all aspects matter to the same extent and effect.     

Design and application of Internet of things-based warehouse management system for smart logistics

Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability.     

Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies

The current literature claims the direct effects of industry 4.0 technologies (I4?T) on lean manufacturing practices (LMP) and sustainable organisational performance (SOP). LMP are also found to have a positive influence on SOP. However, the integrated effect of I4?T and LMP on SOP has not been empirically investigated. To address this gap, this research study investigates the indirect effects of I4?T on SOP with LMP as the mediating variable; furthermore, it aims to confirm or not the direct effects of I4?T on LMP and SOP. The study is based on data collected from 205 managers, working in 115 manufacturing firms. The findings suggest significant direct and indirect effects of I4?T on SOP and confirm the presence of LMP as a strong mediating variable. The results of the study extend the literature on I4?T by identifying I4?T as an enabler of LMP, leading to enhancement of the SOP. Implications and future research directions for academicians, practitioners, and consultants are provided.     

An investigation into the development of the absorptive capacity of manufacturing SMEs

To sustain their competitive advantage in a highly competitive landscape, SMEs, in spite of their limited resources, need to effectively learn. The ability to access and successfully use knowledge is at the heart of absorptive capacity. The main purpose of this empirical study is to investigate the importance of awareness as an antecedent dimension of the concept of absorptive capacity of manufacturing SMEs from aerospace and advanced engineering industry in the UK. The study will also attempt to explore the impact of awareness on the other dimensions of absorptive capacity. Awareness is related to the importance, motivation and interest of learning and innovating. Our study, which focuses on internal processes adopts a mixed method approach and is based on the data collected from 43 SMEs. Our finding suggests that awareness as an antecedent dimension can play a vital role in helping SMEs to improve their knowledge-based resources through changes within their internal organisational and cognitive processes.     

Smart production systems: automating decision-making in manufacturing environment

Smart production systems (SPS) are manufacturing systems capable of autonomously diagnosing their health and autonomously designing continuous improvement projects, leading to the desired productivity improvement. The main component of SPS, developed in this paper, is the Programmable Manufacturing Advisor (PMA), which evaluates the system's health and calculates optimal steps for continuous improvement. The analytics of PMA are based on the theory of Production Systems Engineering (PSE); the numerics of PMA are based on PSE Toolbox, which implements the PSE methods. In this paper, the PMA-based SPS architecture with manager-in-the-loop is described, theoretical/analytical foundations of PMA are outlined, its software/hardware implementations are commented upon, and demonstrations of PMA-based SPS operation are provided using two production systems: automotive underbody assembly (large volume manufacturing) and hot-dip galvanisation plant (small manufacturing organisation).     

Industry 4.0: Architecture and Equipment Revolution

The development of science and technology has led to the era of Industry 4.0. The core concept is the combination of “material and informationization”. In the supply chain and manufacturing process, the “material” of the physical entity world is realized by data, identity, intelligence, and information. Industry 4.0 is a disruptive transformation and upgrade of intelligent industrialization based on the Internet-of-Things and Big Data in traditional industrialization. The goal is “maximizing production efficiency, minimizing production costs, and maximizing the individual needs of human beings for products and services.” Achieving this goal will surely bring about a major leap in the history of the industry, which will lead to the “Fourth Industrial Revolution.” This paper presents a detailed discussion of industrial big data, strategic roles, architectures, characteristics, and four types of innovative business models that can generate profits for enterprises. The key revolutionary aspect of Industry 4.0 is explained, which is the equipment revolution. Six important attributes of equipment are explained under the Industry 4.0 perspective.     

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch, short-cycle, and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments, which poses great challenges to manufacturing enterprises. Fortunately, recent advances in the Industrial Internet of Things (IIoT) and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart, flexible, and resilient manufacturing systems. In this context, this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes. Specifically, a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels. Moreover, the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology, which can be added to or removed from the networks in a plug-and-play manner. Materials, information, and financial assets are passed through interactive links across the networks. Subsequently, analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices. Consequently, an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions. The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method, reducing manufacturing cost, manufacturing time, waiting time, and energy consumption, with reasonable computational time. This work potentially enables managers and practitioners to implement active perception, active response, self-organization, and self-adaption solutions in discrete manufacturing enterprises.     

Sustainable manufacturing in Industry 4.0: an emerging research agenda

This systematic review intends to identify how sustainable manufacturing research is contributing to the development of the Industry 4.0 agenda and for a broader understanding about the links between the Industry 4.0 and Sustainable Manufacturing by mapping and summarising existing research efforts, identifying research agendas, as well as gaps and opportunities for research development. A conceptual framework formed by the principles and technological pillars of Industry 4.0, sustainable manufacturing scope, opportunities previously identified, and sustainability dimensions, guided analysis of 35 papers from 2008–2018, selected by a systematic approach. Bibliometrics data and social network analysis complement results identifying how research is being organised and its respective research agendas, relevant publications, and status of the research lifecycle. Results point to that the current research is aligned with the goals defined by different national industrial programs. There are, however, research gaps and opportunities for field development, becoming more mature and having a significant contribution to fully developing the agenda of Industry 4.0.     

An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.     

Scopus scientific mapping production in industry 4.0 (2011–2018): a bibliometric analysis

Research in industry 4.0 is growing, driven by the innovations in production systems on a continuous basis. In this study, we identified the evolution of themes inherent in the industry 4.0 using a bibliometric software, namely SciMAT (Science Mapping Analysis Software Tool). The analyses included 1882 documents, 4231 keywords, and the relevant information was extracted based on frequency of co-occurrence of keywords. The clusters were plotted in two-dimensional strategic diagrams and analysed using the bibliometric indicators such as the number of publications, number of associated documents, and h-index. The results revealed that 2017 had the largest number of publications. Expert authors in the field and the periodicals that published the most were identified. The science mapping presented 31 clusters in which the most representative motor themes were CPS (Cyber-Physical System), IoT (Internet of Things), and Big Data. In addition, it was possible to identify fields with high investment of efforts by the scientific community such as the union between lean production and industry 4.0, production-centered CPS (CPPS), IoT (Industrial Internet of Things - IIoT), among others. The overlapping map showed an increase in the number of keywords from 338 to 1231 over the period of data. The map of scientific developments supported by an exhaustive research, it was possible to show the state of the art, the main challenges and perspectives for future research in the field of industry 4.0 such as Technology, Collaboration/Integration, Management and Implementation.