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Driving Innovation in Industry 4.0 Through Business Model Simulation
Abstract
In the dynamic Industry 4.0 landscape, organizations aim to enhance economic performance and sustainability. Business Model Innovation (BMI) plays a vital role, enabling firms to integrate disruptive technologies and maintain competitiveness. However, current BMI research mostly focuses on static Business Models (BMs), neglecting the dynamic interactions between BMs components. However, dynamic BM analysis is critical in the era of Industry 4.0 as it is a valuable decision-making tool supporting strategic planning in complex BMs. In this work, we propose a novel approach to conceptualize dynamic BM scenarios through a metamodel. Then, we present a model to demonstrate the use of our approach in the context of Industry 4.0. Finally, we discuss the practical implications that our proposal has on equipping firms with mechanisms to foster innovation and adaptability and respond to market volatility and competition.
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With the changing conditions of business conduct, Industry 4.0 means a rapid development of new technologies, increased intensity of competition and evolving globalization, thus presenting enterprises with new challenges. There is a need to transform business models into open models that make extensive use of open innovations. A company’s aspiration to achieve success on the market requires monitoring the degree of strategy implementation. The aim of this article is to present a tool for assessing the competitiveness and effectiveness of the business model of an enterprise in the Industry 4.0 era. The tool for assessing the business model is based on the balanced scorecard. It was constructed on the basis of literature and expert research. The article presents its practical application in an enterprise.
Citation: Yuana, R.; Prasetio, E.A.; Syarief, R.; Arkeman, Y.; Suroso, A.I. System Dynamic and Simulation of Business Model Innovation in Digital Companies: An Open Innovation Approach. J. Open Innov. Technol. Mark. Complex. 2021, 7, 219. https:// Abstract: Digital companies must improve their business models evolutionarily and innovatively. Therefore, IT investment, especially for revitalizing digital capabilities for operational changes, is important. Most companies are looking to source innovation from outside organizations. Partnership assessment is a crucial problem since it is not easy to integrate internal and external capabilities. The study aims to define business model innovation based on system dynamics with partnership scenarios. Open innovation is needed to evolve to meet market expansion. Partnership and IP strategies are discussed. System Dynamics modeling is utilized to map a system structure to capture its behavior and the relationships between elements, creating a simulation over time. The study develops a BMI to show how OI variables significantly contribute to the engine of growth for digital companies. The simulation reveals that OI has a significant effect on the company's performance, indicated by significantly growing revenue after two years. At the early stage, patenting IP is not practical since the companies involved are unclear about the detailed IP. The IP success or the partner failure rate does not affect revenue significantly. After two years, companies sharing IP find the best way to benefit from it and lead to sustainable growth.
Purpose
System dynamics (SD) is emerging as a powerful approach to understand, analyse, simulate and predict complex and dynamic business processes. In particular, it is true in the process of business model innovation (BMI) and not only as a computational approach. Due to the visual and diagrammatic tools employed by SD, innovation managers overcome humans' mental limitations and improve knowledge management in the BMI. This paper discloses the value of SD's visual tools, i.e. causal loop diagrams and stock and flow diagrams, to contribute to the “Knowldege Visualization” research line, and promote the application of SD as a powerful approach to support decisions in BMI.
Design/methodology/approach
Given the explorative nature and the “how” question driving the research, the methodology involved is a single, holistic case study. In particular, the case is about a business model (BM) diversification, in which an information and communication technology service provider has added to its existing business model a new BM based on a digital multi-sided platform, for passengers transportation.
Findings
The diagrammatic tools of SD, that is causal loop diagrams and stock and flow diagrams, allowed entrepreneurs to overcome the complexity of the business parameters concurring in the design of the BM.
Originality/value
The value of this research is in the exploratory approach and in the originality of the perspective by which SD is analysed: the visual knowledge perspective.
The business is an abstraction of the way in which value is created and delivered. The concrete representation is the business model, expressed by a group of artifacts built with different languages. It serves to describe, explain, analyze, design, and evaluate the business. The set of concepts, construction rules, artifacts, and languages required to express it, are defined by a Meta-Business Model (MBM). Multiple authors have proposed different MBMs, each one with a specific motivation and objective. Some of these MBMs are widely recognized and have been applied in contexts like innovation and entrepreneurship. Due to new challenges, such as sustainability, being faced by businesses and given new ways of producing and delivering value, like the sharing economy, Novel Complex Businesses (NCBs) are emerging. NCBs are businesses characterized by circular structures made out of numerous inter-related components, and by creating value out of the product/service schema. While existing MBMs fulfill certain purposes, they do not have the expressiveness required to describe NCBs precisely enough to describe and analyze them. This paper introduces an MBM with the concepts, construction rules, and graphical notation needed to represent NCBs. We also illustrate an NCB and present the results of the validation for our MBM.
Industry 4.0 (I4.0), also known as the fourth industrial revolution, describes the digitalization of manufacturing industries. The transition to I4.0 is crucial for manufacturing firms to sustain competitive advantage and seize new opportunities. Most research has focused on the technological aspects of I4.0 in the form of product and process innovations. Despite I4.0's rising attention from both researchers and practitioners, little research exists about I4.0 business model (BM) innovation, even though BM innovations can be more successful than product or process innovations. To address this research gap, we analyze 32 case studies of I4.0 BM innovators. We develop a taxonomy to characterize I4.0 BMs and derive 13 patterns of I4.0 BMs by applying the taxonomy to the case studies. Three super-patterns are identified: integration, servitization, and expertization. Integration innovates a BM with new processes and integrates parts of the supply chain. New combined products and services are the basis for servitization. Expertization is a hybrid of product- and process-focused BMs, which includes consulting services and multi-sided platforms. This study contributes to research with a framework for describing, analyzing, and classifying BMs for I4.0. The findings deepen the understanding of how I4.0 impacts ecosystem roles, BMs, and service systems. Archetypal patterns show how firms can leverage I4.0 concepts and build a conceptual basis for future research. The taxonomy supports practitioners in evaluating the I4.0-readiness of their existing BM. The patterns additionally illustrate opportunities for becoming an I4.0 firm.
Sustainable business model innovation (SBMI) in large multinational corporations is increasingly perceived as a key driver for competitive advantage and corporate sustainability. While the SBMI literature acknowledges that corporations require dynamic capabilities to innovate their business model for sustainability, the role of organization design to nurture dynamic capabilities for this purpose has been scantly addressed. By taking a qualitative research approach, we address how organization design affects dynamic capabilities needed for SBMI. Accordingly, from an organization design perspective, we identified barriers and drivers on three levels: the institutional, the strategic, and the operational. The contributions of our study are threefold. First, we contribute to a recent discussion on how organizational design affects dynamic capabilities needed for business model innovation. Second, we present a multi-level framework to show how interconnected barriers and drivers obstruct or enable SBMI. Third, our study answers a call to advance theoretical perspectives on SBMI.
Industry 4.0 integrates cyber-physical systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 (Wortmann et al., Conference on model-driven engineering languages and systems (MODELS’17), IEEE, pp 281–291, 2017) to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for Industry 4.0 that originates from different perspectives on modeling and related standards. From the vantage point of modeling, Industry 4.0 is the combination of systems engineering, with cyber-physical systems, and knowledge engineering. Research currently is splintered along topics and communities and accelerating progress demands for multi-disciplinary, integrated research efforts.
As the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensively than ever. The Industry 4.0 era supplied manufacturing systems with new technologies aiming to digitalise the modern factory and as such plenty of alternatives, relevant to its design, are generated. In order to evaluate the impact of a suggested solution, realistic validation is required before its implementation in the production. Production system simulation software is commonly utilised for this purpose. As the complexity of the production system rises and new technologies are integrated, academia can assist the manufacturers to deal with these problems, taking care of the complex modelling. The theoretical backbone offered by universities, paired with the experience of the production engineers can be proven a powerful tool for the evolution of today’s manufacturing systems. The Teaching Factory paradigm constitutes the missing link that aims to bridge the gap between academia and industry. In the current study, the design and evaluation of a real manufacturing system, using discrete event simulation and based on real data obtained from the metal industry, are presented in the context of a Teaching Factory paradigm. The current production system is modelled under the guidance of the metal industry’s expert engineers and real production data are utilised in the created model. A channel of remote communication has been established supported by ICT infrastructure, so as to facilitate data and knowledge exchange. The impact identification of the individual parameters on the response of the system is accomplished towards the selection of the proper configurations for enhanced throughput. A real-life pilot case from a copper industry is presented where the conclusions indicate the good prospects that arise out of this two-way communication.
The objective of this paper is to critically review currently available Smart Manufacturing (SM) and Industry 4.0 maturity models, and analyze their fit recognizing the specific requirements of Small and Medium-sized Enterprises (SMEs). To this end, this paper presents features that are characteristic for SMEs and identify research gaps needed to be addressed to successfully support manufacturing SMEs in their progress towards Industry 4.0. The results of this study show that only a limited number of the SM and Industry 4.0 roadmaps, maturity models, frameworks and readiness assessments that are available today reflect the specific requirements and challenges of SMEs. The main findings include: (1) the current standard starting "level 1″ (base level) of most maturity models appears to be disconnected from the real digitization and smart manufacturing maturity level of many SMEs. Therefore, we propose a "level 0″ specifically designed to reflect the 'real-base level' for SMEs; (2) the transition from this new base level, "level 0″, to the current standard "level 1", requires significant effort including a mind-set change; (3) maturity models and readiness assessments can be associated with an SM toolkit, and (4) SMEs need to develop their own, unique SM or Industry 4.0 vision and roadmap. This study provides insights that help towards developing a realistic SM (Industry 4.0) maturity model for SMEs that reflects their industrial realities more accurately. With the help of SM maturity models that are more customized to the SME specific requirements, the SMEs' stakeholders will be able to better define their SM (Industry 4.0) vision, roadmap, and strategic projects. It will ultimately lower the entry barrier and reduce the risk of the transition process towards SM and Industry 4.0 and support the critical change in culture. Summarizing, we identified manufacturing SMEs' specific requirements, conducted a literature review of current SM maturity models, and discussed how these maturity models reflect the SME specific requirements.
The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R&D cases involving academia and industry.
: We introduce the Industry 4.0 paradigm, presents its background, current state of development and its influence on the development of the simulation modelling paradigm. Further, we present the multiple case study methodology and examine several research and development projects involving automated industrial process modelling, presented in recent scientific publications and conclude with lessons learned.
We present the research problems and main results from five individual cases of adoption of the new simulation modelling paradigm. Main lesson learned is that while the new simulation modelling paradigm is being adopted by big companies and SMEs, there are significant differences depending on company size in problems that they face, and the methodologies and technologies they use to overcome the issues.
While the examined cases indicate the acceptance of the new simulation modelling paradigm in the industrial and scientific communities, its adoption in academic environment requires close cooperation with industry partners and diversification of knowledge of researchers in order to build integrated, multi-level models of cyber-physical systems. As shown by the presented cases, lack of tools is not a problem, as the current generation of general purpose simulation modelling tools offers adequate integration options.
Simulation is an established tool for predicting and evaluating the performance of complex stochastic systems that are analytically intractable. Recent research in simulation optimization and explosive growth in computing power have made it feasible to use simulations to optimize the design and operations of systems directly. Concurrently, ubiquitous sensing, pervasive computing, and unprecedented systems interconnectivity have ushered in a new era of industrialization (the so-called Industrial 4.0/Industrial Internet). In this article, we argue that simulation optimization is a decision-making tool that can be applied to many scenarios to tremendous effect. By capitalizing on an unprecedented integration of sensing, computing, and control, simulation optimization provides the “smart brain” required to drastically improve the efficiency of industrial systems. We explore the potential of simulation optimization and discuss how simulation optimization can be applied, with an emphasis on the recent development of multi-fidelity/multi-scale simulation optimization.
The business model canvas is a well-known tool used to understand the way in which an organization creates and delivers value. Though it is mostly recognized as a 9 block template with sticky notes, it is much more complex than what it seems. Unfortunately, the static representation of the model has contributed to the loss of its dynamics and the information that comes with it. Enterprises do not contemplate the flow network involved in their business model, and miss the opportunity to identify problems and test changes. This situation invites to enrich the canvas model and show more benefits that can be obtained with its application. This paper presents an enriched design of the business model canvas, based on system dynamics and the results obtained after simulating it.
Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model patterns. However, the various understandings of the business model pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model pattern database allows for navigation to the relevant set of patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.
Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.
Today's Enterprises exist in highly dynamic environment. Simulation could be used to reveal complex dynamic behavior of enterprise, especially for playing out dynamic what-if scenarios, in determining enterprise's response to a change. Instead of relying on guidelines for simulating prescriptive models of enterprise as in other approaches including our own in which we simulated intentional models of enterprise, we propose a comprehensive metamodel of system dynamics and provide relation-based mapping to intentional metamodel. Ongoing explorations suggest that while several challenges of simulating enterprise aspects for what-if analyses remain unaddressed, in the least we take a step toward making simulation of intentional models more structured.
A successful system first begins with an understanding of the business processes of an organisation. Assuch, business process modelling (BPM) represents a collection of related, structured activities or set of tasks that produce a specific service or product to stakeholders. It graphically represents how a businessorganisation conducts their business processes conceptually. Throughout the literature, some challengeswith BPM have emerged, such as standardisation of process modelling, identification of the value of processmodelling, and model-driven process execution. However, one of the most challenging issues in business process management is that organisations are traditionally considered to be static networks of transaction processes rather than dynamic. There is therefore a need to aide analysts and practitioners alike by providingmethods that can guide and capture the dynamic aspects of an organisation. This paper aims to present twoBPM methods, and discusses extending them using the norm analysis method (NAM) to enable the analysts to model the dynamics of business processes and to accommodate exceptions that have not been dealt with by other conventional methods
The business model concept generally refers to the articulation between different areas of a firm's activity designed to produce a proposition of value to customers. Two different uses of the term can be noted. The first is the static approach - as a blueprint for the coherence between core business model components. The second refers to a more transformational approach, using the concept as a tool to address change and innovation in the organization, or in the model itself. We build on the RCOV framework - itself inspired by a Penrosian view of the firm – to try to reconcile these two approaches to consider business model evolution, looking particularly at the dynamic created by interactions between its business model's components. We illustrate our framework with the case of the English football club Arsenal FC over the last decade. We view business model evolution as a fine tuning process involving voluntary and emergent changes in and between permanently linked core components, and find that firm sustainability depends on anticipating and reacting to sequences of voluntary and emerging change, giving the label ‘dynamic consistency’ to this firm capability to build and sustain its performance while changing its business model.
Industry 4.0 (I4.0) literature has been concerned with how to implement digital technologies in manufacturing processes. However, the I4.0 concept also comprises broader changes in the company's business model (BM) through digital transformation, which deserves more attention, especially from the technology providers' perspective. As they are essential to advancing the digital transformation diffusion and creating I4.0 ecosystems, their business model innovation (BMI) in this context is a keystone for I4.0. Thus, we consider how changing
factors in the I4.0 context affect the business model dimensions of technology providers. We analyze changing factors in the three main BM dimensions: front-end, back-end, and value proposition. We study ten I4.0 technology providers and explain the mechanisms of change involved in their BMI, explaining how and why such changes happen. The findings provide practitioners with insights to anticipate the necessary actions to manage and incorporate such changes, whether they rely more on data and IoT devices, new BMs such as training and
consulting, dynamic pricing forms, new software/hardware, or service competencies. We also provide a detailed analysis of elements of change in the BM that can be used in theory to understand better the different digital transformation impacts in the Industry 4.0 context.
Industry 4.0 is a central strategy to strengthen the competitiveness of the manufacturing sector over the next years. Nevertheless, there is a lack of common understanding of Industry 4.0 and tools to help companies’ transformation to Industry 4.0, especially for small and medium-sized enterprises (SMEs). To address these research gaps, this study proposes a framework to characterise and evaluate Industry 4.0 scenarios to aid companies’ transition towards Industry 4.0. For this, a design science (multi-methodological) approach is adopted, including an Industry 4.0 use case survey, modelling and simulation and two proof-of-concept cases developed in collaboration with a Canadian college centre for technology transfer. The results indicate that the proposed framework can help companies identify Industry 4.0 scenarios more intuitively to assist project conception, portfolio selection and planning during Industry 4.0 roadmap development. Finally, the results of this study suggest that Industry 4.0 can be implemented incrementally while companies increase their digital capabilities and maturity.
Purpose
: The rising interest in the integration of digital advanced manufacturing and production systems in the Industry 4.0 context is one of the main factors in the introduction of Additive Manufacturing (AM). The novel technology might change the way firms operate, and the way they interact with consumers, opening new horizons for an improved profit margin and more sustainable business models. The research presented a comprehensive review on the potent role of AM in adhering to customers’ complicated and unique needs using various technologies and techniques, as it discussed the role of AM in introducing new business models that increase the business competitiveness and profitability through an optimisation of production processes. In addition, AM is grasping with innovative solutions varying from waste reduction to shorter supply chains to longer products lifecycle, incentivising firms to adopt it for the economies realised on materials, energy, and costs. Notwithstanding, AM implementation is still in its infancy and faces technical challenges of capability, IT integration, and outcomes.
Design/Methodology/approach
: This research is based on a quantitative approach that was administered online by means of a highly structured online survey, which aim was to collect primary data that fills the gaps of the research hypotheses due to the lacking nature of research papers exploring them. Therefore, the literature review was a paramount phase in acquiring empirical knowledge about the problem background and concept boundaries that shaped the topic's core objectives and research questions from the lacking nature of explored areas.
Findings
: This research investigated the role of AM in industry 4.0 by exploring its impact and intersection with AM firms’ business models while exposing the limitations and challenges to its adoption within industrial contexts. The study highlighted that the AM positive impacts on companies’ business models on the value chain and turnover. This study also revealed the eco-design prospect of AM that will be helpful for different firms to rethink their business models shaping them to be more cost-efficient.
Originality
: This research gave insight on AM technology through a quantitative survey that mainly aimed to classify knowledge and to investigate the role of AM as a lever in improving firms’ value chains through an exploration of possible intersections with business models and impacts of implementation, possible sustainability scenarios and challenges it may face within Industry 4.0 context.
Being successfully applied in business-to-consumer industries for quite some time, digital platforms only recently have found their way into the industrial business-to-business world. However, rapid developments, particularly in the context of Industry 4.0, have indicated enormous potentials from digital platforms for business models and inter-company relationships – fields of particular importance in industrial marketing. Motivated by a lack of empirical insights and calls from academia and practice, this paper sheds light on how digital platforms change industrial firms’ business models and inter-company relationships. We applied an exploratory case study design and analyzed a unique sample of 11 platform cases from the United States and Germany, differentiating between platform types and roles. The study reveals recurring themes in business model innovation and inter-company relationships. The results were elicited using a quasi-statistical approach, discussed and interpreted against the backdrop of the current state of research on platforms, business models, and industrial marketing.
Simulation is a key technology for developing planning and exploratory models to optimize decision making as well as the design and operations of complex and smart production systems. It could also aid companies to evaluate the risks, costs, implementation barriers, impact on operational performance, and roadmap toward Industry 4.0. Although several advances have been made in this domain, studies that systematically characterize and analyze the development of simulation-based research in Industry 4.0 are scarce. Therefore, this study aims to investigate the state-of-the-art research performed on the intersecting area of simulation and the field of Industry 4.0. Initially, a conceptual framework describing Industry 4.0 in terms of enabling technologies and design principles for modeling and simulation of Industry 4.0 scenarios is proposed. Thereafter, literature on simulation technologies and Industry 4.0 design principles is systematically reviewed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. This study reveals an increasing trend in the number of publications on simulation in Industry 4.0 within the last four years. In total, 10 simulation-based approaches and 17 Industry 4.0 design principles were identified. A cross-analysis of concepts and evaluation of models' development suggest that simulation can capture the design principles of Industry 4.0 and support the investigation of the Industry 4.0 phenomenon from different perspectives. Finally, the results of this study indicate hybrid simulation and digital twin as the primary simulation-based approaches in the context of Industry 4.0.
In the contemporary complex and fast-changing markets, there is persistent pressure for Small-and-Medium Enterprises (SMEs) to engage in business model innovation to promptly meet customer expectations and successfully compete for survival. In the last decade, multiple approaches to business model development and innovation have been explored. However, they have been primarily designed for large-sized companies, while SMEs display distinctive organizational attributes. Thus, SMEs require a tailored approach to design, experiment and innovate their business models, in order to frame the specific complexity of their value creation processes. This paper aims to explore how a Dynamic Business Modelling approach complies with inherent SME characteristics and serves as a lean strategy design tool for innovating associated business models. A case-study of a real SME currently involved in innovating its business model illustrates and discusses the empirical evidence emerging from the use of this approach.
The following article presents a brief literature review conducted to extend our knowledge about how Industry 4.0 affects business models and to identify business model innovations derived in consequence. Based on the results, a set of features, issues and requirements have been identified and three different approaches has been suggested to make firms getting closer to the industry 4.0 phenomenon such as service orientation, networked ecosystems and customer orientation. Furthermore, the impacts on the creation, delivery, and capture of value through the reconfiguration of Business Models due to embracing the Industry 4.0 are depicted. As a result, four different ways to innovate the business models based on different degrees of innovation are proposed to embrace the digitalization. Those paths goes from optimizing internal and external processes or improving customer relationship to creating new value networks or smart products and services through disruptive business models.
Purpose - The purpose of this study is to systematically analyse and synthesise the existing research published on offsite manufacturing/construction. The study aims to highlight and associate the core elements for adopting the offsite concept in different construction contexts. This ultimately facilitates the enhancement of the offsite uptake.
Design/methodology/approach - The research study was carried out through a systematic literature review (SLR). The SLR was conducted to identify and understand the existing themes in the offsite research landscape, evaluate contributions and compile knowledge, thereby, identifying potential directions of future research. The grand electronic databases were explored to gather literature on the offsite concept, lean and agile principles, and simulation. A total of 61 related articles published between 1992 and 2015 have been included in this study. The relevant literature was systematically analysed and synthesised to present the emerging offsite themes.
Findings - The descriptive and thematic analyses presented in this paper have identified related offsite research studies that have contributed to setting a firm foundation of the offsite concept in different construction contexts. Each of the 62 articles was examined for achieving the aim and objectives of this study, the method of data collection, and coverage of offsite themes. The results of the analyses revealed that the articles mostly provide information on the offsite concept and its definitions (53%) and offsite barriers and/or drivers (27%). However, limited attention has been paid to the integration of lean and agile principles (13%) and simulation (7%) within the offsite concept, which are therefore more open to research within the offsite concept.
Research limitations/implications - The literature review highlights the main themes and components of the offsite construction concept. This forms a solid basis and motivation for researchers and practitioners to build on to enhance the uptake of the offsite concept in different contexts. This study also presents a research roadmap within the offsite concept, along with a recommendation for further research to be conducted using the research framework proposed in this study. The framework could lead to validation of using simulation to integrate lean and agile principles within the offsite concept.
Originality/value - This paper presents a systematic review of the literature related to offsite construction in different contexts. The emerging components, that is, offsite definitions, drivers and/or barriers, lean and agile principles and simulation have been highlighted and discussed thematically. A research framework that enables pursuit of the integration of lean and agile principles offsite through the lens of simulation has been proposed. The framework is expected to open up new opportunities on the effectiveness of offsite development in different contexts.
Keywords: Offsite Manufacturing, Lean and Agile principles, Offsite Construction, Future roadmap and framework, Modelling and Simulation, Systematic literature review
Digitization—the continuing convergence of the real and the virtual worlds will be the main driver of innovation and change in all sectors of our economy. The exponentially growing amount of data and the convergence of different affordable technologies that came along with the definite establishment of Information and Communication Technology are transforming all areas of the economy. In Germany, the Internet of Things, Data and Services plays a vital role in mastering the energy transformation, in developing a sustainable mobility and logistics sector, in providing enhanced health care and in securing a competitive position for the leading manufacturing industry. This article discusses the impact, challenges and opportunities of digitization and concludes with examples of recommended policy action. The two key instruments for enhanced value creation in the Age of Industrie 4.0 are platform-based cooperation and a dual innovation strategy.
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