Sustainability challenges and how Industry 4.0 technologies can address them: a case study of a shipbuilding supply chain

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.acceptedVersio

Capability Maturity Model for Organizational Competence in Production Scheduling and Control

Literature on production planning and control has focused almost exclusively on technical aspects, addressing the development and implementation of new or existing analytical models for different types of production environments. Recent studies have however shown that organizational factors have a significant impact on the production planning and control performance. The aim of this study has been to develop a capability maturity model that evaluates an organization s ability to take into account the organizational factors in performing the production scheduling and control process. This study advances the understanding of how organizational factors influences production scheduling and control. This study is motivated by two research questions: (1) How to evaluate the organizational competence in production scheduling and control? (2) What is the current level of organizational competence for production scheduling and control in a selected case company? To answer these questions, the study utilized a qualitative literature study to examine organizational factors discussed in literature. The capability maturity model is based on these findings and on the organizational maturity levels defined in ISO/IEC 15504-7:2008. In order to test out the model, a case study at the pipe manufacturer Pipelife Surnadal was conducted. The findings from this research show that organizational factors have a significant impact on production scheduling and control performance. Organizational factors should therefore not be overlooked. Nine organizational factors of production scheduling and control were identified in this study: 1) decision autonomy between shop floor and schedulers, 2) department structure and scheduler location, 3) scheduler training, 4) knowledge and communication facilitation of scheduling interconnections, 5) collaboration during rescheduling, 6) common understanding of problems and constraints, 7) fit between context and scheduling and control systems, 8) synchronization of performance indicators, and 9) reduction of compensation tasks. The capability maturity model for organizational competence in production scheduling and control evaluates scheduling environments on a five-level maturity scale. The model specifies characteristics and criteria for each level, which make it easy for an environment to evaluate itself and get suggestions for improvement. By using the developed model, the organizational competence of Pipelife Surnadal s scheduling and control function was evaluated. The theory-testing and evaluating case study showed that the model is well fitted for real life situations, and identified some organizational aspects that they should focus on in order to improve their scheduling and control performance. The findings in this report support the idea that organizational factors have an influence on production scheduling and control performance. This study presents a number of relevant organizational factors, and introduces a model that helps scheduling and control environments to evaluate themselves and get improvement suggestions regarding handling organizational factors

Investigating the Relationship between Lean Manufacturing and Industry 4.0

Lean manufacturing has for more than two decades been the most prominent methodology for improving the operational performance in manufacturing companies. Originating from the Toyota Production System (TPS) (Ohno, 1988), lean manufacturing is built on the idea of eliminating waste in all forms by focusing on the activities that create value for the customer (Womack and Jones, 1996). It is a low-tech continuous improvement approach that focuses on employee empowerment and the streamlining of manufacturing activities. In parallel with the development of the TPS, computers also slowly started to emerge in manufacturing systems more than 50 years ago, both in processing and in planning and control of the operations (Klingenberg and Antunes Jr., 2017). Recently, the technology-oriented Industry 4.0 concept is being branded as the next enabler of performance improvement in manufacturing. The Industry 4.0 vision refers to networks of autonomous manufacturing resources that are sensor-equipped and selfconfiguring, and is enabled by the integration of a large number of different digital technologies (Kagermann et al., 2013). In general, this increased use of digital data and digital technologies is typically referred to as digitalization. Lean manufacturing can work independently of information technology (IT) which by some has been viewed as a source of waste. Lean manufacturing utilizes decentralized control by giving local autonomy to the employees and emphasizes simplicity and transparency, whereas IT focuses on creating a centralized database, and IT systems are rigid, complex, and difficult to change and continuously improve (Åhlström et al., 2016). Although lean manufacturing and Industry 4.0 share the same objective of performance improvement, these underlying contradictory aspects might complicate a concurrent use. As Industry 4.0 seems destined to overtake lean manufacturing’s position as the most prominent approach for performance improvement in manufacturing companies, several relevant issues should be investigated. The theoretical foundation of this thesis is positioned within the operations management research field and investigates the relationship between lean manufacturing and Industry 4.0. We will look further into whether lean manufacturing is an enabler or inhibitor for moving toward Industry 4.0 and if and how these two domains can complement each other. In addition to focusing on lean manufacturing and Industry 4.0, we also take a contingency research approach by investigating in which environments these two domains are appropriate. The first research activity of this study was to conduct a systematic review of the existing literature on the relationship between lean manufacturing and Industry 4.0. After mapping the current research on this topic and identifying relevant research gaps, we defined three research questions. These are as follows: 1. What are the implementation patterns of both lean manufacturing and digital technologies across different production environments? The first research question aimed at uncovering possible differences in the implementation level of lean manufacturing and that of digital technologies among different production environments. Additionally, we investigated whether there are any significant differences in implementation levels between different company sizes. To answer this research question, we conducted a survey to investigate the relationships between environmental factors (i.e., production environment and company size) and the implementation levels of both lean manufacturing and digital technologies. 2. What are the performance implications of a concurrent use of lean manufacturing and digital technologies? The second research question sought to investigate the impacts on operational performance from using lean manufacturing and digital technologies. In addition to investigating the main (i.e., individual) effects of lean manufacturing and digital technologies on operational performance, their interaction effect was also investigated. The presence of a positive interaction effect suggests a synergistic effect greater than the main effects combined (Khanchanapong et al., 2014). To address this research question, we used survey data to analyze the relationship between the implementation levels of lean manufacturing and factory digitalization (i.e., the use of digital technologies for internal operations) and the corresponding operational performance. 3. How can digital technologies be used to support lean manufacturing? While most lean manufacturing practices can work independently of IT, part of this research study focused on how the emergence of the digital technologies associated with Industry 4.0 may support and further develop existing lean manufacturing practices. Companies that have already implemented lean manufacturing need guidelines on how to react to the impacts of Industry 4.0 (Meudt et al., 2017) and directions on how emerging technologies can be integrated into existing lean manufacturing systems (Wagner et al., 2017). This research question aimed at investigating the potential of such emerging digital technologies, outlining their possibilities, and presenting different concepts and cases of how they can be integrated with established lean manufacturing practices. To address this research question, we used existing literature, conceptual development, and a case study to highlight examples where digital technologies associated with Industry 4.0 can be or have been used to support existing lean manufacturing practices. Lean manufacturing and Industry 4.0 have been investigated individually, as well as together. This thesis presents contributions to both theory and practice, which can be summarized as follows: • An integrated framework for mapping different production environments. This framework differs from earlier mapping frameworks in the way that it considers more variables, and the defined values for each variable make it more accessible and easy to use. We suggest that this is, among others, an excellent tool for comparison in multiple case studies where it is expected that environmental factors may influence the results and should be controlled for. • New knowledge on the implementation patterns of lean manufacturing practices and of digital technologies across different production environments and company sizes. These results provide updated findings that can help us understand which parts of lean manufacturing and which digitalization aspects are universal, and which are contextdependent. Knowing the nature of these patterns is important to guide the development of implementation frameworks that take into account the characteristics of different production environments. • Providing empirical results showing that both lean manufacturing and factory digitalization individually are related to improved operational performance. Investigating both simultaneously adds the additional methodological benefit of controlling for potential confounding effects. The findings also provide support for a complementary effect of lean manufacturing and factory digitalization on operational performance. Our results suggest that the operational performance benefits of implementing either lean manufacturing or digital technologies in isolation are relatively modest. The true operational performance advantage comes when both domains are implemented; in other words, their concurrent use produces a synergistic effect that is larger than the sum of their individual contributions. • The presentation of concepts and cases of how digital technologies can support lean manufacturing practices. This contributes to research by illustrating how these two domains can complement each other. Further, we provide assessments on the benefits and drawbacks of such solutions, how digital technologies can address known limitations in existing lean manufacturing practices, and how it can contribute to improved operational performance. • The data-driven process improvement cycle for mapping current digitalization levels, as well as planning and guiding improvement processes. In addition to clarifying some definitions surrounding the term digitalization, the data-driven process improvement cycle provides a structured method to map existing processes and identify possibilities for further digitalization. The findings of this thesis also have several implications for practitioners, which can be summarized into the following recommendations: • Our findings indicate that the implementation level of lean manufacturing is quadratically related to production repetitiveness, which means that the implementation level tends to be lower in production environments with very low or very high repetitiveness. Although implementation level does not equal applicability, these insights should be used by managers to adjust their targets, expectations, and approaches when implementing lean manufacturing instead of forcing through a standardized implementation program. • The results of this study challenge the opinion that lean manufacturing and IT are incompatible. In fact, the results show that they tend to co-exist and mutually reinforce each other. That the greatest performance benefits are obtained when using lean manufacturing and digital technologies concurrently provides valuable insights when developing roadmaps for production improvement initiatives. With the promise of substantial performance improvements following an Industry 4.0 implementation, there might be the temptation to focus all attention on Industry 4.0 at the expense of lean manufacturing. However, our findings indicate that existing lean manufacturing systems should not be neglected but should rather be used as a basis for deploying new technologies into the manufacturing system. • The presented concepts and cases of how lean manufacturing and digital technologies can be integrated can be used as inspiration for how to approach the fourth industrial revolution. We recommend using digital technologies to address known problems and limitations in the manufacturing system, rather than digitalizing simply for the sake of it. • This thesis also presents several frameworks which should be useful for managers. Managers can use the framework for mapping production environments as a starting point for designing appropriate production planning and control solutions, comparing their operations with other companies, and identifying possible improvement areas. The data-driven process improvement cycle can be used to map the digitalization degree of current processes and provide guidance for how a higher degree of digitalization can be reached. Overall, this thesis should provide a better understanding and knowledge of the relationship between lean manufacturing and Industry 4.0. This thesis aspires to support those who either manage or study these two domains, individually or in combination

The Data-Driven Process Improvement Cycle: Using Digitalization for Continuous Improvement

Industry 4.0 is the first industrial revolution to be announced a priori, and there is thus a significant ambiguity surrounding the term and what it actually entails. This paper aims to clearly define digitalization, a key enabler of Industry 4.0, and illustrate how it can be used for improvement through proposing an improvement cycle and an associated digitalization typology. These tools can be used by organizations to guide improvement processes, focusing on the new possibilities introduced by the enormous amounts of data currently available. The usage of the tools is illustrated by presenting four scenarios from Kanban control, where each scenario is mapped according to their digitalization level

RFId technology in the manufacture of customized drainage and piping systems: a case study

While Radio Frequency Identification (or RFId) technology has gained significant traction in the downstream operations and industries like retail, adoption upstream of the value-chain has been much slower. Few reported cases of implementations in job-shops exists today for several reasons, key among which is the relative cost of the technology and uncertainties regarding the expected results. In this paper, we present the insights from the evaluation and pre-implementation stage of a project to implement RFId technology in the customized products’ department of a large process manufacturing company in Europe. The case company is an innovation leader in the European pipe and drainage systems’ manufacturing industry. Preliminary findings indicate the need to align RFId implementation with strategic goals to minimize the risk associated with the implementation and increase the chance of success

RFId technology in the manufacture of customized drainage and piping systems: a case study

While Radio Frequency Identification (or RFId) technology has gained significant traction in the downstream operations and industries like retail, adoption upstream of the value-chain has been much slower. Few reported cases of implementations in job-shops exists today for several reasons, key among which is the relative cost of the technology and uncertainties regarding the expected results. In this paper, we present the insights from the evaluation and pre-implementation stage of a project to implement RFId technology in the customized products’ department of a large process manufacturing company in Europe. The case company is an innovation leader in the European pipe and drainage systems’ manufacturing industry. Preliminary findings indicate the need to align RFId implementation with strategic goals to minimize the risk associated with the implementation and increase the chance of success

RFId technology in the manufacture of customized drainage and piping systems: a case study

While Radio Frequency Identification (or RFId) technology has gained significant traction in the downstream operations and industries like retail, adoption upstream of the value-chain has been much slower. Few reported cases of implementations in job-shops exists today for several reasons, key among which is the relative cost of the technology and uncertainties regarding the expected results. In this paper, we present the insights from the evaluation and pre-implementation stage of a project to implement RFId technology in the customized products’ department of a large process manufacturing company in Europe. The case company is an innovation leader in the European pipe and drainage systems’ manufacturing industry. Preliminary findings indicate the need to align RFId implementation with strategic goals to minimize the risk associated with the implementation and increase the chance of success.publishedVersion© 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd

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

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.acceptedVersionLocked until 2.3.2019 due to copyright restrictionsThis is an [Accepted Manuscript] of an article published by Taylor & Francis in [JOURNAL] on [date], available at https://doi.org/10.1080/00207543.2018.144294

The complementary effect of lean manufacturing and digitalisation on operational performance

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