SINTEF Manufacturing

Manufacturing companies have played an important role in improving standards of living worldwide. However, in a linear setting, they are also linked to unsustainable production and consumption patterns. The concept of circular economy has gained traction in recent years: it aims at eventually severing this link, through keeping resources ‘in the loop’. Through a systematic literature review, this paper seeks to revisit the concept of circular economy in the manufacturing industry in order to determine whether the body of research has moved beyond concept development and into verified implementation in industry. Furthermore, we aim to analyse the important link between circular economy in manufacturing and sustainable development. The review shows that the field has indeed moved from purely conceptual work into empirical studies and research into implementation tools. However, in empirical studies, the sustainability impact of CE practices is typically adressed only through the environmental dimension, neglecting the social and economic dimensions. Further, a key finding is that the prevalence of narrow approaches to sustainability in manufacturing leads to a risk that circular economy implementation efforts will fail to provide solutions that are socially, environmentally, and economically beneficial. Holistic approaches are needed to avoid the implementation of solutions that may be framed as circular, but neglect the sustainability component.

Abstract Purpose of Review This paper provides an overview of the role of humans and robots in smart factories, their connection to Industry 4.0, and which progress they make when it comes to related technologies. Recent Findings The current study shows that a decade was not enough to provide a reference implementation or application of Industry 4.0, like smart factories. In 2011, Industry 4.0 was mentioned for the first time in the scientific community. Industry 4.0 arrived with many new enabling technologies and buzzwords, e.g., Internet of Things (IoT), Cyber-Physical Systems (CPS), and Digital Twins (DT). Summary This paper first defines smart factories and smart manufacturing in relation to the role of humans and robots. Followed by an overview of selected technologies in smart factories. Concluded by future prospects and its’ relation to smart manufacturing.

Zero Defect Manufacturing is a disruptive concept that has the potential to entirely reshape the manufacturing ideology. Building on the same quality management philosophy that underpins both lean production and Six Sigma, the Zero Defect Manufacturing paradigm has in recent years developed significantly, given the onset of Industry 4.0 and the increasing maturity of its digital technologies. In this paper, we review contemporary advances in Zero Defect Manufacturing using structured literature review. We explore emergent themes and present important directions for future development in this continuously emerging field of research and practice. We highlight two specific Zero Defect Manufacturing strategy types: defect prevention, and defect compensation; as well as identify two important themes for future ZDM research, namely advancing ZDM research (particularly with a view to progressing from zero-defect processes to zero-waste value chain strategies) and overcoming the global application challenges of ZDM (with emphasis on cyber-security and the extension of defect prevention and compensation strategies to less explored manufacturing processes).

Purpose This purpose of this study is to provide an overview of the current state of research on Lean Six Sigma (LSS) and Industry 4.0 and the key aspects of the relationships between them. The research analyses LSS's evolution and discusses the future role of LSS 4.0 in an increasingly digitalized world. We present the benefits and motivations of integrating LSS and Industry 4.0 as well as the critical success factors and challenges within this emerging area of research. Design/methodology/approach A systematic literature review methodology was established to identify, select and evaluate published research. Findings There is a synergistic nature between LSS and Industry 4.0. Companies having a strong LSS culture can ease the transition to Industry 4.0 while Industry 4.0 technologies can provide superior performance for companies who are using LSS methodology. Research limitations/implications One limitation of this research was that as this area is a nascent area, the researchers were limited in their literature review and research. A more comprehensive longitudinal study would yield more data. There is an opportunity for further study and analysis. Practical implications This study reviews the evolution of LSS and its integration with Industry 4.0. Organisations can use this study to understand the benefits and motivating factors for integrating LSS and Industry 4.0, the Critical Success Factors and challenges to such integration. Originality/value This is the first systematic literature review on LSS 4.0 and can provide insight for practitioners, organisations and future research directions.

Purpose This paper investigates developing a learning-to-learn capability as a critical success factor for sustainable lean transformation. Design/methodology/approach This research design is guided by our research question: how can suppliers learn to learn as part of a buyer-led collaborative lean transformation? The authors adopt action learning research to generate actionable knowledge from a lean supplier development initiative over a three-year period. Findings Drawing on emergent insights from the initiative, the authors find that developing a learning-to-learn capability is a core and critical success factor for lean transformation. The authors also find that network action learning has a significant enabling role in buyer-led collaborative lean transformations. Originality/value The authors contribute to lean theory and practice by making the distinction between learning about and implementing lean best practices and adopting a learning-to-learn perspective to build organisational capabilities, consistent with lean thinking and practice. Further, the authors contribute to methodology, adopting action learning research to explore learning-to-learn as a critical success factor for sustainable lean transformation.

For years, companies have tried to figure out how to consistently organize their businesses for improving quality and efficiency, and at the same time reduce costs and lead times. Lean principles and techniques have become a benchmark for Western manufacturing companies, founded on the success of the Toyota Production System. Despite its popularity, many companies still struggle to achieve a successful and lasting lean implementation. Existing research indicate that both organizational and technical barriers are important, such as lack of management support and commitment, poor involvement of employees, and excessive confidence in lean tools and practices. A gap in current research is the limited focus on identifying how different groups at different hierarchical levels in an organization perceive barriers to lean implementation. Unless the opinions of different groups are taken into account, efforts to overcome the barriers may be misguided. This paper reports on a two-year in-depth single case study of barriers to lean implementation, aiming at understanding why the company only to a certain extent has succeeded with their lean activities. Based on 28 interviews and observations, results indicate that the different groups at different hierarchical levels experience different types of barriers. The article will provide an important contribution to how companies can organize lean implementation processes better for future success, which should be relevant to both academia and industry.

Zero defect manufacturing (ZDM) aims at eliminating defects throughout the value stream as well as the cost of rework and scrap. The ambitious goal of zero defects requires the extensive utilization of emerging technologies. Amidst the major drive for technological advancement, humans are often kept out of the loop because they are perceived as the root cause of error. The report from the European Commission on Industry 5.0 emphasizes that human-centric is a key pillar in building a more resilient industry and is vital to incorporate the human component into the manufacturing sector. However, we did not find any publications that explain what human-centric ZDM is, nor what the roles of humans are in advancing ZDM. As a contribution to bridging this gap, a systematic literature review is conducted using different databases. We collected 36 publications and categorised them into 3 different human roles which are managers, engineers, and operators. From our search, we found out that managers play a vital role in cultivating ZDM in the entire organization to prevent errors despite the fact they often do not have direct contact with the production line as operators. Operators can help advance ZDM through knowledge capturing with feedback functions to the engineer to better design a corrective action to prevent errors. Assistive technologies such as extended reality are efficient tools used by operators to eliminate human errors in production environments. Human-centric is now a goal in the future manufacturing sector, but it could face barriers such as high technological investments and resistance to changes in their work tasks. This paper can contribute to paving the roadmap of human-centric ZDM to bring defects to zero and reposition the manufacturing sector to become more resilient.

Lean has evolved from an operational tool to a complete management concept that incorporates softer aspects such as participation, learning and leadership. This evolution challenges the traditional way in which large management consultancy firms intervene in the lean implementation process; an outside expert cannot easily install a soft, participation-oriented form of lean. To explore this challenge, we report a longitudinal, qualitative case study of how a large consultancy firm supported lean implementation in a public service organisation. Our findings show that although the consultants’ rhetoric had been adapted to the contemporary ideal of soft lean, their practice had not: implementation remained tool-centred and external consultants took the roles of experts. We posit that the business model of large consultancy firms and the nature of conventional client–consultant contracts may explain this mismatch between consultants’ talk and action. Hence, this challenge is difficult to overcome in practice, and managers are advised to consider critically what management consultants can and cannot effectively deliver in lean implementations.

Materials and structures made through additive manufacturing (AM) have received a lot of attention lately due to their flexibility and ability to customize structural components of complex geometry. One range of application not exploited so far is the use of additive manufactured metal plates for ballistic protection. In this study, plates of AlSi10Mg with dimensions 100 mm × 80 mm × 5 mm were manufactured in a powder-bed fusion machine. From the printed plates, material specimens were extracted and strained to fracture in uniaxial tension to reveal the mechanical response of the AM material. Metallurgical investigations were also conducted to study the microstructure of the as-built alloy both before and after testing. Next, the perforation resistance of the AM plates was disclosed in a ballistic range. During testing, the plates were impacted by 7.62 mm APM2 bullets at various velocities. In an additional test series, only the hard core of the same bullet inserted in a sabot was fired towards the plates. Based on high-speed camera images, the initial and residual velocities of the different bullets were measured, and the ballistic limit curves and velocities were determined. For comparison, the studies described above were repeated on a traditionally die-cast block of AlSi10Mg having the same chemical composition as the powder used in the 3D printing. Finally, based on the conducted material tests a standard constitutive relation and failure criterion, frequently used in ballistic impact simulations, were calibrated based on inverse modelling. Finite element models of the ballistic impact problems were established in ABAQUS/Explicit, and the numerical results were compared to the experimental data. Good agreement between predicted and experimental results was in general obtained, even though no special measures were undertaken concerning the fact that the target material was additively manufactured.

Purpose The purpose of this study is to investigate the perceived effects between soft management practices, employee behaviours and the implementation of digital technologies in manufacturing plants, as well as how these relate to the emergence of digital waste. Design/methodology/approach This paper uses case-based research. Data was collected in two large manufacturing companies based in Norway and Sweden through semi-structured interviews with two management representatives and four shop-floor employees. The data was used to evaluate 29 variables describing lean- and total quality management (TQM)-associated employee behaviours and soft management practices, in light of digital transformation. Findings The results suggest that several variables were positively influenced by the digital transformation process. These were top management leadership, middle management involvement, employee education, corporate social responsibility focus, innovation, knowledge sharing, work-family balance, psychological capital, job satisfaction and career commitment. Training employees, creativity, discretionary effort, turnover intention and proactivity appear to be negatively influenced by digital transformation The findings also indicate that several soft management practices and employee behaviours were not only influenced by manufacturing digitalization but also themselves influenced the process. The potential for digital waste creation was also detected in several variables, including reward and recognition and training employees. Practical implications Managers, practitioners and academics may learn about the importance of certain managerial practices and employees’ behavioural needs during the digital transformation process. The findings may help in prioritizing TQM and soft lean management practices and certain employee behaviours during the digital transformation and in creating awareness of digital waste. Originality/value This study builds on several existing studies discussing the impact of digital transformation on soft management practices and employee behaviours. It provides insights from a lean and TQM angle and offers a means of prioritizing certain practices and behaviours during a digital transformation. This study also highlights the significance of digital waste.

Additive manufactured conformal cooling channels has proved beneficial for molds used for casting. These casting tools often endure cyclic thermal influences that lead to thermal fatigue. To increase the structural resistance to thermal fatigue, the mold should be designed with some compliance for thermal expansion. This can be achieved by applying lattice structures in some sections of the mold. These structures may also be used as crack stoppers to prevent cooling fluid leakage. By use of complex internal structures, enhanced thermal management is possible. The manufacturing of molds is also faster, with less use of material.

Driven by the digital transformation currently pursued by organisations, artificial intelligence (AI) applications have become more frequent. Nevertheless, its impact on employees’ behaviors and attitudes is still poorly known. As employees’ engagement (EE) is a key element for a successful Lean Production (LP) implementation, there is the need to understand such AI’s implications on EE in this scenario. This paper aims to investigate the impact of AI on EE in lean organisations. We performed a qualitative-empirical approach in which we first interviewed twelve academic experts to grasp the investigated problem. Then, we conducted a multi-case study in manufacturing organisations undergoing a LP implementation to refine such understanding based on the observation of real-world evidence. Identifying commonalities between these stages allowed the formulation of propositions for future theory testing and validation. Findings indicate that AI may positively impact EE dimensions (physical, cognitive, and emotional) in human-centred work environments, such as lean organisations, although not at the same extent. Results also suggest that employees’ psychological conditions (safety, meaningfulness, and availability) are positively affected by the relationship between AI and EE. The demystification of AI’s effect on EE helps practitioners anticipate potential issues that can impair the LP implementation in the Fourth Industrial Revolution era.

Since 2011 when it was announced, Industry 4.0 has inspired a series of governmental and private programs worldwide. Nevertheless, it is an emerging research field and the academia calls for further explorative research, including on the enablers and inhibitors of Industry 4.0 implementation at national level. This paper addresses this topic based on a cross-sectional analysis of data collected through a two-step survey of 49 companies in Norway, 13 suppliers to the Oil and Gas industry and 36 from different manufacturing industries. The findings contribute primarily to the Training and Continuing Professional Development priority area in Kagermann et al. (2013).

Direct Metal Deposition (DMD) is an additive manufacturing (AM) process capable of producing large components using a layer by layer deposition of molten powder. DMD is increasingly investigated due to its higher deposition rate and the possibility to produce large structural components specifically for the aerospace industry. During fabrication, a complex thermal history is experienced in different regions of the workpiece, depending on the process parameters and part geometry. The thermal history induces residual stress accumulation in the buildup, which is the main cause of distortions. In order to control the process and enhance the product quality, the understanding of the workpiece temperature is substantial. In this study, two methods to predict temperature evolution during the DMD process are introduced based on analytical and finite element methods. The objective is to compare these methods to experimental results and to provide more insights about their capabilities to predict accurately the temperature gradient, the cooling rate, and the melt pool geometry. A comparison of the computational time is also provided. Based on the results of the investigation, It appears that the analytical method provides an effective and accurate method to understand the influence of the process on the workpiece temperature.

Industrial demand response (IDR) will play a crucial role in shaping future electricity systems, as it is a key element of a just energy transition and industrial development. The aim of this work is to provide an overview of the current status of IDR in a holistic perspective. First, the main benefits and potential of IDR are reviewed, together with the motivations and challenges for the industrial sector. Most recent advances in European markets and regulations with specific focus on IDR applications are explored. Then, the different resources which are currently available to help industries participate and implement IDR programmes are reviewed. In particular: 1) the (possible) tools for defining energy-aware scheduling and planning of the manufacturing systems are analysed; 2) The role of aggregators (i.e. intermediaries between industries and power markets) for facilitating explicit IDR is examined; 3) the importance of digitalisation to provide better IDR services from the manufacturing industry is highlighted, pointing out that digital twins, cyber-physical systems, Internet of Things sensors, robots, edge computing, artificial intelligence, and big data are promising technologies; and 4) most recent related research projects are reviewed. Finally, it is analysed and discussed how each of those resources can address the different challenges that are still preventing industries to apply IDR programmes.

We present a flexible system that can scan and localize workpieces in 3D for assembly and pick and place operations. The system contains a vision robot that acquires 3D point clouds by performing sweeps with a laser triangulation sensor. Because the sensor is mounted on a robot, we can choose a viewpoint and a scanner trajectory that is optimal for a given task. For example, we can sweep along a semicircular trajectory in order to recognize and grasp parts from a pallet. With the same vision robot, we can perform a sweep of the packaging container in order to recognize and manipulate elements of cardboard. The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose verification and pose refinement that significantly increase the accuracy and robustness of the system. As an application of the system, an industrial prototype workcell is presented. The task is to recognize, grasp and transfer parts of office chairs, such as seats, seat backs and armrests, from a pallet to a cardboard container. It demonstrates how the vision system is easily set up to recognize three different components by using CAD models. The prototype workcell further demonstrates how the pose estimation results can be fed directly to a separate handling robot in order to grasp a chair seat. A series of ten experiments was performed where the chair seat was placed in arbitrary poses in the pallet. Pose estimations were performed in just over one second per experiment, and the obtained accuracy was well within the tolerances for the grasp operation in all ten cases.

Although various alloy systems have been explored for additive manufacturing (AM) during the past decade, introducing a new alloy remains a challenging task. Most of the materials require iterative builds, for investigating numerous parameters and determining a viable and repeatable process window. Among the challenging yet highly demanded materials, Haynes 282 superalloy was chosen. It was initially processed through conventional density cube approach, by varying the process parameters for each processed cube. Although the relative densities of the initial builds were not dramatically low, micro-cracks were present in all of them, mostly evolved on a selective number of grain boundaries and spanning only across a single laser path. Detailed modelling and advanced characterization techniques were employed to understand the root cause and cracking mechanism. It was found that the grain boundary precipitates are responsible for crack initiation, amid stress gradient across the grain boundary due to the adjacent grain orientations. Therefore, the failure mechanism is determined as ductility-dip cracking. Based on the findings, a new process window was defined using elevated temperature and novel scanning strategy. No cracks were observed under the modified processing window, meaning that the material can reliably be processed by laser beam powder bed fusion (PBF-LB).

Modern industry requires different advanced metallic alloys with specific properties since conventional steels cannot cover all requirements. Aluminium alloys are becoming more popular, due to their low weight, high corrosion resistance, and relatively high strength. They possess respectable electrical conductivity, and their application extends to the energy sector. There is a high demand in joining aluminium alloys with other metals, such as steels, copper, and titanium. The joining of two or more metals is challenging, due to formation of the intermetallic compound (IMC) layer with excessive brittleness. High differences in the thermophysical properties cause distortions, cracking, improper dilution, and numerous weld imperfections, having an adverse effect on strength. Laser beam as a high concentration energy source is an alternative welding method for highly conductive metals, with significant improvement in productivity, compared to conventional joining processes. It may provide lower heat input and reduce the thickness of the IMC layer. The laser beam can be combined with arc-forming hybrid processes for wider control over thermal cycle. Apart from the IMC layer thickness, there are many other factors that have a strong effect on the weld integrity; their optimisation and innovation is a key to successfully delivering high-quality joints.

Purpose Companies with routine operations often pursue team‐based continuous improvement in the context of standardized work. Continuous improvement requires that work standards are periodically “unlocked”, i.e. made objects of reflection and improvement. This paper aims to theorize and empirically explore a method for unlocking standards which has received little attention in the literature: systematic work observation. It identifies which factors constitute and promote a work observation practice that supports continuous improvement. Design/methodology/approach The paper presents an explorative, qualitative case study of an industrial company in which systematic work observation is practiced. Empirical material was collected from two principal sources: company documentation and teaching material; and interviews with workers, managers and work design experts from three of the company's major plants. Findings Systematic work observation supports continuous improvement when there is genuine two‐way communication between the worker being observed and the supervisor acting as observer. Through dialogue, the appropriateness of the standard procedure is reflected on. Systematic work observation is supported by frequent day‐to‐day interaction between supervisors and workers. Frequent interaction builds relationships of trust and a shared purpose. A necessary requirement is that supervisors are technically competent and know the details of the operating procedures. The results also indicate that supervisors, not fellow workers, should preferably take the role as observers. Originality/value Systematic work observation as an instrument for continuous improvement has not yet been explored in a serious scholarly manner. The findings of this paper have practical implications for companies that wish to implement systematic work observation.

To be competitive in dynamic and global markets, manufacturing companies are continuously seeking to apply innovative production strategies and methods combined with advanced digital technologies to improve their flexibility, productivity, quality, environmental impact, and cost performance. Zero Defect Manufacturing is a disruptive concept providing production strategies and methods with underlying advanced digital technologies to fill the gap. While scientific knowledge within this area has increased exponentially, the current practices and impact of Zero Defect Manufacturing on companies over time are still unknown. Therefore, this survey aims to map the current state of practice in Zero Defect Manufacturing and identify its impact on production performance. The results show that although Zero Defect Manufacturing strategies and methods are widely applied and can have a strong positive impact on production performance, this has not always been the case. The findings also indicate that digital technologies are increasingly used, however, the potential of artificial intelligence and extended reality is still less exploited. We contribute to theory by detailing the research needs of Zero Defect Manufacturing from the practitioner’s perspective and suggesting actions to enhance Zero Defect Manufacturing strategies and methods. Further, we provide practical and managerial suggestions to improve production performances and move towards sustainable development and zero waste.