Impacts of Industry 4.0 technologies on Lean principles

Industry 4.0 is increasingly being promoted as the key to improving productivity, promoting economic growth and ensuring the sustainability of manufacturing companies. On the other hand, many companies have already partially or fully implemented principles and tools from the Lean management approach, which is also aimed at improving productivity. While the two approaches use very different strategies, they share some common principles. The objective of this article is to highlight the links between the principles and tools proposed by Industry 4.0 and those proposed by the Lean management approach, with a particular focus on how some of Industry 4.0's technologies are improving the implementation of Lean principles, depending on the technologies’ capability levels. As such, this study aims to provide a characterisation of the impacts of Industry 4.0 technologies on Lean principles according to targeted capability levels. The results obtained show strong support for Industry 4.0 technologies for Just-in-time and Jidoka, but little or no support for waste reduction and People and Team work. There is, therefore, a clear need to pursue the deployment of Lean management while improving certain Lean principles using Industry 4.0 technologies.     

The relevance of Industry 4.0 and its relationship with moving manufacturing out,back and staying at home

In view of the fact that Industry 4.0 is becoming increasingly essential, the implementation of Industry 4.0 technologies is believed to be an essential strategic component in further increasing the efficiency of manufacturing processes and in determining decisions concerning globalisation strategies. Thus, this paper provides an empirical analysis of the essential constructs of Industry 4.0, and drivers and barriers for Industry 4.0. The paper is based on 270 valid answers to a questionnaire-survey distributed among Danish manufacturers. Regression analyses were used to test the proposed hypotheses. The results of the analyses reveal that the identified drivers and barriers for Industry 4.0 have a positive impact on the perceived relevance of Industry 4.0 among companies. Furthermore, the analyses show that the perceived relevance of Industry 4.0 among companies has a positive impact on companies that have moved manufacturing back and on companies that have moved manufacturing out and back. Conversely, the perceived relevance of Industry 4.0 among companies has a negative impact on companies that have remained domestic. The perceived relevance of Industry 4.0 has no impact on companies that have moved manufacturing out. This research, in general, offers to both researchers and practitioners an increased consciousness concerning the relevant drivers and barriers for Industry 4.0 in decision processes about where to locate manufacturing.     

A framework for assessing the use of lean production practices in manufacturing cells

This study introduces a framework for assessing the use of lean production (LP) practices in manufacturing cells (MCs). The development of the framework included four stages: (a) defining LP practices applicable to MC, based on criteria such as the inclusion of practices that workers could observe, interact with and use on a daily basis; (b) defining attributes for each practice, emphasising the dimensions which were typical of their implementation in LP environments; (c) defining a set of evidence and sources of evidence for assessing the existence of each attribute–the sources of evidence included direct observations, analysis of documents, interviews and a feedback meeting to validate the assessment results with company representatives; (d) drawing up a model of the relationships among the LP practices, based on a survey with LP experts. This model supports the identification of improvement opportunities in MC performance based on the analysis of their interfaces. A case study of an MC from an automobile parts supplier is presented to illustrate the application of the framework.     

Intelligent Manufacturing in the Context of Industry 4.0: A Review

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.     

Towards a lean automation interface for workstations

Methods and principles of Lean Production have become the major concepts to create highly efficient processes since the early 1990s. Due to its high effectiveness by reducing complexity and focusing on value-adding tasks, the Lean concept is still successful. Nevertheless, its changeability to produce highly customised products is limited. Industry 4.0 describes the vision of a smart production which can meet these future market requirements. Enablers are innovative information and communication technologies and the integration of all production entities into a common digital network. Lean Automation is the application of Industry 4.0 technologies to Lean Production methods in order to combine benefits from both domains. First proprietary Lean Automation solutions exist, but to enhance changeability in production, a common, unified communication interface is required. This paper presents the ongoing work towards an interface for digitising Lean Production methods using Cyber Physical Systems.     

Learning organisation and lean production: an empirical research on their relationship

This study aims at identifying the pairwise relationship between specific sets of Lean Production (LP) practices and Learning Organisation (LO) dimensions. More specifically, the association between 10 LP operational constructs and seven LO dimensions, based upon previous literature, is empirically examined. A survey-based study was carried out with 135 companies undergoing a LP implementation. The study sample was categorised according to two control variables: type of organisation and company’s LP implementation experience. Collected data was analysed through multivariate techniques. Results indicate that LP implementation and LO development are highly correlated. Additionally, findings also show that the extent of relationships varies according to the context, allowing the identification of synergistic associations and conflicting ones. The identification of these relationships provides means to better comprehend how the implementation of specific LP practices corroborates to enhancing LO capabilities within the organisation and vice-versa.     

Lean manufacturing as a high-performance work system: the case of Cochlear

This paper addresses the Special Issue call for Australian examples of innovative management systems that enable the production of successful products by drawing on a single case study: medical device manufacturer Cochlear. Through qualitative case study methodology, we examine the human resource management practices that complemented the implementation of lean manufacturing principles. We argue that in their implementation, Cochlear’s management team enriched the traditional understanding of lean and its focus on waste reduction, low cost and quality assurance by adopting people management practices as an integrated component of the overall management capability which allowed their people to grow and develop. The combination of lean and HR practices transformed Cochlear to a high-performance work system and positively impacted production processes and output. By examining a medical device manufacturer, an under-researched sector, our paper expands existing literature on lean manufacturing and provides implications for practitioners.     

Industry 4.0: Current practice and challenges in Malaysian manufacturing firms

This research employed a qualitative approach to discuss the current practice and challenges of Malaysian manufacturing firms in the implementation of Industry 4.0. The study examined data from seven manufacturing companies pursuing Industry 4.0 initiatives to identify various options for their strategies. The study found that the implementation of Industry 4.0 in the manufacturing firms is still in the exploratory stage. The companies involved in this study were discovered to conduct exploration using an adaptive-like framework. That is, throughout the process, the majority of the subjects are 'trying and adding' Industry 4.0 to their operations. Their trial-and-error approach is based on what is feasible and effective in their manufacturing environment. Overall, the investigation determined that data management and integration, as well as personnel re-education, were the respondents' primary operational challenges.     

Lean manufacturing in Brazilian small and medium enterprises: implementation and effect on performance

The main objective of this paper is to investigate the degree to which lean manufacturing (LM) practices are being implemented within Brazilian small and medium enterprises (SMEs), by defining what LM means, in the view of SMEs in Brazil. In addition, the paper investigates the relationship between LM implementation and performance of such companies. The research method used in this paper comprised of an exploratory survey using the partial least-square method with the structural equation modelling technique (PLS-SEM). The results show that the companies studied used practices in a fragmented manner, without a holistic view of LM, which is considered to be one of the most important parts of this approach. Only the constructs of statistical process control, total productive maintenance and employee involvement are being implemented as an integrated approach for Brazilian SMEs. These companies do not take other important constructs such as customer involvement, continuous flow, pull production, set-up time reduction, supplier development and supplier feedback into consideration as part of LM systems, despite using some practices in a dispersed manner. Concerning performance, the present study shows that even in a fragmented way, the implementation of these LM practices help these companies to achieve improvement in operational performance.     

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.     

Designing lean value streams in the fourth industrial revolution era: proposition of technology-integrated guidelines

Despite the envisioned interrelations, the way Industry 4.0 (I4.0) technologies can influence the design and implementation of lean value streams is still unknown and little empirical evidence is found in the literature. This article aims at proposing guidelines integrated with I4.0 technologies for designing lean value streams. We gathered experts’ opinions regarding the relationship between guidelines for designing a lean value stream and I4.0 technologies. The identification of the most important relationships provided arguments for the proposition of enhanced guidelines for designing lean value streams within the Fourth Industrial Revolution context. The integration of I4.0 technologies into the guidelines for designing a lean value stream raises a distinct approach that benefits from the simplicity and efficiency of Lean Production with ease and agility of the technologies typical of the Fourth Industrial Revolution. Such technology-integrated guidelines may allow overcoming existing barriers while lead companies to superior performance results.     

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.     

Structural Equation Modelling of lean manufacturing practices

The purpose of this research study is to analyse lean manufacturing practices in different industries and to identify the critical factors for its success implementation. Despite the fact that lean manufacturing has been frequently promoted as a means of improving business competitiveness, little empirical evidence exists in the literature validating its positive link with organisational performance. Lean manufacturing practices not only help in reducing the number of defects but also reduces the cost of production. For this purpose, empirical data is collected to measure the lean manufacturing practices prevailing in different industries located in Tamil Nadu, India. A Structural Equation Modelling (SEM) technique is used to build the measurement and structural models. Later, statistical estimates are used to validate the model that has been built. The data analysis helps to determine whether to accept or reject the hypothesis that has been stated based on the structural model. The result shows how lean manufacturing practices are correlated and help in improving the organisational performance among the industries being surveyed.     

Empowering production workers with digitally facilitated knowledge processes – a conceptual framework

Recent digital advancements, including social software, mobile technologies and augmented reality, offer promising opportunities to empower knowledge workers in their production environment by leveraging their knowledge processes, decision-making skills and social interaction practices. This paper proposes a conceptual framework for empowering workers in industrial production environments with digitally facilitated knowledge management processes. The framework explores four concrete facets of digital advancements that apply to a wide range of knowledge processes and production strategies in manufacturing companies. Each of these advancements are capable of supporting one specific facet of the individual knowledge management processes of workers; knowledge transfer, discovery, acquisition and sharing. The study contributes to the production research community by aligning emerging digital technologies and current trends in advanced manufacturing environments to benefit workers and improve job satisfaction, efficiency and productivity. The paper also contains suggestions about developing innovative solutions for production environments that support workers with digital technologies for flexible production.     

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.     

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.     

Challenges in the transformation to lean production from different manufacturing-process choices: a path-dependent perspective

The implementation of lean production remains popular among industrial companies, and the requirement for individualised steps in its implementation is widely accepted; however, research has not yet considered the different process choices available to the companies. The conclusions drawn from the automotive industry's mass production environment may be misleading, given the different conditions of many other industries. We therefore compare case data from a craft producer with the rich, case-study based literature of mass producer companies that highlight the transformation from mass to lean production. We derive a list of specific challenges a craft producer must approach using a different strategy than a mass producer. This study adds to the lean production theory by demonstrating how different manufacturing process choices influence the lean transformation process and its successful implementation. It provides specific propositions concerning the transformation to lean by considering the different processes of craft and mass production companies. Furthermore, the case study gives an in-depth understanding of the challenges a craft-oriented company faces when becoming lean.     

A roadmap for Assembly 4.0: self-configuration of fixed-position assembly islands under Graduation Intelligent Manufacturing System

The layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (assembly layer, perception layer, interaction layer, cognition layer, and service layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods.     

Integrated and Intelligent Manufacturing: Perspectives and Enablers

With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership (AMP) in 2011 and the National Network for Manufacturing Innovation (NNMI) in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further “leverage existing resources... to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features: integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing (i²M) systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Internet of Things and Services (IoTS), cyber-physical systems (CPSs), and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.     

A participatory systems design methodology for changeable manufacturing systems

The ability to adapt to changes in products, processes and technologies is a key competitive factor. Changeable manufacturing paradigms have emerged to address this need, but the industrial implementation remains challenging. In this paper, a participatory design methodology for changeable manufacturing systems is proposed, including requirements specification, selection of appropriate manufacturing paradigm and suitable physical and logical enablers. The methodology supports companies in determining the potential for and mechanisms of transitioning towards changeable manufacturing systems, based on knowledge of products, production, technologies and facilities. The developed methodology is applicable to both new and existing manufacturing systems. It is demonstrated in two industrial cases which highlight its applicability and differences in the appropriate recommended manufacturing systems transition towards changeability as a result of differences in manufacturing characteristics, change requirements and enablers.