Keywords
Industry 4.0, smart factory design, shop floor management, information and communication technology
This article is included in the Software and Hardware Engineering gateway.
Industry 4.0, smart factory design, shop floor management, information and communication technology
Smart factory design has become a buzzword in production engineering in the context of the Industry 4.0 debate: According to a study by PWC (PricewaterhouseCoopers) consultants, 91% of industrial companies are investing in digital factory technology in Europe and 90% of survey participants believe that opportunities of smart factory design outweigh its risks from a business perspective (PWC, 2022).
Digital data processing enables the automation of all production steps and their and autonomous flow integration (Mabkhot et al., 2018; Rejikumar et al., 2019). Intelligent value chains and product life cycles supporting the way of products from design to recycling have been called “Industry 4.0 technologies”. Industry 4.0 is characterized by on-time interoperability of virtual decentral and service oriented modular systems in the supply chain (Rejikumar et al., 2019). This extends to the usage phase of smart products, i.e. intelligent and networked products indicating producers the necessary life cycle data and retrieving operation data automatically through the Internet of Things (Brettel et al., 2014).
Smart factories comprise self-organizing virtually interlinked autonomous supply chain production and delivery environments which integrate production, machinery, and information technology based on decentral information and communication infrastructures (Rejikumar et al., 2019). These cyber-physical systems self-organize essential production flows but also provide interlinks for human intervention and human integration in the work process, which is essential for control and supervision. Social machines collaborate with human beings by retrieving or analyzing provided data and images according to man-defined requirements or supporting human activities physically or informationally (Zavadska & Zavadsky, 2018).
The integration of information and communication in smart factory environments offers important opportunities to realize economies of scale, save resources and use manpower more efficiently (Oesterreich & Teuteberg, 2016). To implement smart factory designs at the informational level, however, huge amounts of data have to be collected stored, retrieved and analyzed to develop and supervise production routines and order flows autonomously. This puts high requirements on information technology technology development and maintenance.
Smart factory design refers to factory layout, planning and construction aimed to integrate machines and production based on Industry 4.0 information technology (Kagermann et al., 2013). Machinery and equipment communicate via the Internet of Things a virtual networking platform (Osterrieder et al., 2020). Based on that technology, smart factories could span the whole supply and delivery chain: social machines, i.e. communicative self-organized IT technology could communicate across factories and digital production technologies. Different companies could interact locally and self-reliantly (Mabkhot et al., 2018).
This study evaluates opportunities, challenges and success factors of information and communication in smart factory design based on a systematic literature review of empirical studies to outline the status of existing literature, identify further empirical research fields and inform companies how to make smart factory environments succeed.
Although a series of topical (2018 and beyond) reviews in the context of Industry 4.0 and smart manufacturing are available, the research field of information and communication integration in smart factory design has not yet been explored: Rejikumar et al. (2019) retrieve distinguishing attributes of Industry 4.0 applications from earlier studies and analyze the content and timeline of publications, but do not explicitly discuss opportunities and challenges of the technologies. Fatorachian and Kazemi (2020) evaluate impacts of Industry 4.0 on supply chain performance, a part sector of smart manufacturing. Erro-Garcés (2019) conducts a meta-analysis of Industry 4.0 studies published between 2005 and 2018 and highlights managerial issues. With its Industry 4.0 focus, this approach is broader than the current study, does not explicitly mention challenges, and does not include the most recent papers. Calabrese et al. (2020) conduct a review of opportunities, difficulties and development goals of Industry 4.0 technologies and systematize nine technologies, among them smart worker and smart equipment technologies which are part of the smart manufacturing process, but does not conclusively describe them. The study lacks an analysis of success factors.
Five recent reviews in the context of smart manufacturing are available: Yang et al. (2018) systematize recent research trends in smart manufacturing based on a review. The review by Osterrieder et al. (2020) of smart factory studies systematizes technical solutions and identifies groups of technologies to outline a digital value stream. Hughes et al. (2020) discuss future potentials of Industry 4.0 applications to manufacturing but do not assess presently available technologies. None of these studies discusses smart factory design, its potentials, risks or success factors.
Some specialized reviews partly address potentials and risks of smart manufacturing in certain contexts: Mabkhot et al. (2018) identify “perspectives of smart factory applications and technical support systems for smart factory implementation” in the form of proprieties of smart production and smart products but does not refer to smart factory planning and construction. The study also neglects potential risks of smart manufacturing. Lee et al. (2018) discuss literature in machine health management (i.e. maintenance and repair) in smart factories, but do not refer to smart factory design. Mittal et al. (2019) critically review the applicability of available smart manufacturing and Industry 4.0 maturity models to SME and diagnose low adaptiveness to small business manufacturing practices. All three studies focus on a part segment of smart manufacturing and are thus narrower in range than the intended study and problem rather than solution focused.
So far, no review systematically juxtaposes the opportunities and risks of information and communication integration in smart factory design based on a comprehensive analysis of presently available technologies and referring to the most recent studies. This review aims to close this research void.
This study conducts a systematic review based on a methodology suggested by Synder (2019). The purpose of the study is to identify and analyze topical empirical research in information and communication integration in smart factory design. Three key research questions are meant to be answered:
• What are opportunities of smart factory applications from the perspective of production company applications?
• Which problems of smart factory applications have been observed?
• What can be done to design smart factory applications so that opportunities are fulfilled while problems are avoided?
Figure 1 outlines the literature research process: To identify eligible studies the review uses a systematic research strategy. The review is limited to studies in English published in peer reviewed journals or at academic conferences in the period 2018 to 2022. To systematize the research process, the databases EbscoHost, Web of Knowledge, Science Direct and Scholar Google were consulted using the following homogenous keyword combination: [“smart factory design” OR “smart factory] AND [information OR communication] AND [review OR empirical]” The databases sort by relevance and studies are considered until a point of saturation is reached i.e. no further eligible studies are found or results repeated.
A secondary manual evaluation process deselects reviews and then discards non-empirical studies, studies of minor empirical quality and studies that do not fit content-wise, i.e. do not focus on smart factory applications but are more general, e.g. on Industry 4.0. From the initially identified 54 studies (based on the key word combination), 11 are identified as reviews (see above) and deselected from the core analysis, 27 are discarded due to lacking focus on “smart manufacturing” or lacking empirical evidence, 16 remain for the final review. For a graphical overview of the literature research process (see Figure 1).
First the studies are summarized in an author-centric table addressing sample, research method, identified opportunities, limitations and success factors and points of critique. This step corresponds to Webster & Watson’s (2002) content matrix. The study to catalogue the studies and extract relevant major and subcategories, a so-called concept matrix is drafted, which reorganizes the points made by arguments in major and sub-items and structures the textual evaluation (compare appendix). The textual evaluation of the studies follows the organization of the concept tables.
Based on a synthesis of the review results, opportunities and risks of smart factory technologies are juxtaposed. Drawing on the outlined success factors the potentials to resolve inherent risks are discussed. Further research requirements are given if adequate solutions to recognized risks of smart factory design are unavailable.
The appendix provides an overview of the retrieved studies in the form of a content matrix (Table 1). Further classification is seen in concept matrices of opportunities, risks and success factors of smart factory design (Table 2, Table 3 and Table 4 respectively) (Webster & Watson, 2002). The arguments for opportunities, risks and success factors are each classified into technical informational, economic and sustainability (social or environmental) aspects. Technical aspects dominate the discussion and refer to the planning phase and the operation phase of smart factories. This structure guides the following sections:
Empirical research in smart manufacturing – 2018 to 2022 | |||||
---|---|---|---|---|---|
Study | Industry/application | Potentials | limitations | Success factors | Point of critique |
Baek, 2021 | Adaption and monitoring of vibration sensor signals in automated storage | Real time fault analysis Digital failure detection | High function complexity | Digital failure management Facilitate automatic prognosis | Limited risk assessment |
Braccini & Margherita, 2018 | Sustainability of smart manufacturing, case study | Improved productivity & product quality Continuity of energy consumption Safer work environment, lower human work load Job enrichment | No critical reflections | ||
Büchi et al., 2020 | Analysis of success factors of Industry 4.0 application in manufacturing units | High investment costs Risk of obsolescence | Openness to technical innovation Depth, breadth of Industry 4.0 technology Modularity of Industry 4.0 local units | ||
Guo et al., 2019 | Usage of digital twin to model smart factory design | Quick analysis of design options Early identification of design errors Time saving Reduce costs early in planning phase | Complexity at planning stage | High real-world fidelity of digital twin Accurate parameter validation Control strategies Virtual model correspondence to physical data structures | Study limited to design stage |
Häckel et al., 2019 | Analysis of IT security risks in smart factory networks using graph theory and value at risk | Real time information synchronization Automated communication | IT security risks IT component non-availability Complex network structures High investment Rapid development cycles | Tight IT security measures Appropriate support required Access accuracy and accountability solutions |
1st Author | Industry/application | Potentials | limitations | Success factors | Point of critique |
---|---|---|---|---|---|
Jin & Lee, 2018 | Smart factory construction Korean metal working firms | Complex planning process | CEOs progressive intentions Innovative lending 6 investment Framing industrial characteristics Firm growth and high R&D activity | Limited focus on I&C technology | |
Ko et al., 2020 | 113 smart factories in Korea | Improved final product quality Process optimization | Securing and managing facility data Product demand forecast Low job creation | Integrate modular systems Targeted yield management Supervising defect rate | Limited to (early) Korean smart factories |
Lee, 2021 | Survey among workers in smart factories in Korean SME | Production line supervision Improved efficiency & productivity Flexible production | High investment costs Limited supply chain abilities | ERP system Targetted quality management Ethical management: CEO support Productivity management systems Work process standardization | Limited to Korea (only partly comparable to more industrialized countries) |
Li et al., 2019 | Embedding big data solutions in smart factories Semistructured interviews | Top management commitment Business-IT strategic alignment Appropriate hardware (cloud, communication technology, big data) Supply chain integration | |||
Mantravadi et al., 2022 | Industrial Internet of Things in manufacturing Case study | Interoperability Quality improvement Responsiveness Flexibility to different IoT devices | Develop standardized interfaces Ensure modularity Use open-source components | Single case only University industrial lab | |
Micheler et al., 2019 | Smart technologies in Industry 4.0 Online survey & workshop | Higher sustainability Informational networking Self-organizing shop-floors | Resource constraints Connection between modular systems Dynamic & extreme environmental conditions disturb connectivity Compatibility problems, complexity Lacking strategic guidance | Ensure system compatibility particularly hard & software Utilization of cloud technologies | |
Suebsook et al., 2020 | Smart manufacturing in Thailand | Increase firm performance i.e. market share, cost reduction by interoperability technological advances and service orientation | |||
Schaupp & Diab, 2020 | Interviews with smart factory managers in Germany | Automate the self-organization of industrial capital | Shift of personal control from management to cybernetic control Replacement of direct labour by autonomous systems | Pure economic viewpoint, no success factors | |
Vestin et al., 2018 | Smart factories for wood house builders Single case study | Improved competitiveness due to economies of scale | Maturity of culture & organization to modern work methods | ||
Wang & Lee, 2021 | 5G communications in smart factory scenarios | Limited available measurement data | Indoor path loss prediction analysis Training of path loss models based on neural networks | ||
Xia et al., 2021 | Digital twin system for smart factory construction application in hydraulic cylinder production | Reduction of work-in process inventory Save delivery time Virtual reality simulation, mapping & control | Definition of digital twin on basis of product lifecycle requirements Refinement of digital twin in planning &construction phase Reliance on Building information management database Analyze user requirements |
Only two studies (Guo et al., 2019; Xia et al., 2021) refer to technical opportunities in the planning stage of smart factories, i.e. the actual design phase, and suggest to develop and refer to a digital twin of the planned factory to simulate the production environment first (Guo et al., 2019). Digital twins are electronic usually 3-dimensional models which are developed and refined in the planning process. They comprise building-related information, machinery equipment data and are extended to simulate production flows and interconnections in the supply chain (Xia et al., 2021). Digital twins allow the flexible analysis of design options and realistic simulation of production conditions. Simulations in the planning stage avoid erroneous designs and avoid ill-designed physical plants (Guo et al., 2019; Xia et al., 2021).
Most evaluated contributions however assess the technical advantages of smart factories in operation as compared to conventional production (Baek, 2021; Ko et al., 2020; Micheler et al., 2019; Braccini & Margherita, 2018; Mantravadi et al., 2020; Lee, 2021; Suebsook et al., 2020). The transition to smart manufacturing by designing a smart factory offers diverse advantages for businesses:
Smart factories contribute to an optimization of production process flows (Ko et al., 2020) and partly enable fully self-organizing shop floors (Micheler et al., 2019), which saves manpower on the shop floor and frees human resources for responsible control and supervision tasks. Production in automated supply and processing chains is adapted to demand at short notice, i.e. is on-time responsive to order flows (Lee, 2021).
Smart manufacturing realizes product quality improvements due to high automation quotas and digital control and planning solutions (Braccini & Margherita, 2018; Ko et al., 2020) Smart factories usually dispose of real time digital failure analysis, which facilitates error detection and avoidance (Baek, 2021). Human workers are discharged of responsibility.
The advantages of smart factory design at the informational level refer to the informational model backing technical implementation at the manufacturing machines and in the logistics of the production process. Digital media synchronize information flows across workshops, storages and machines on time (Häckel et al., 2019). Machines interact and communicate in a self-organized manner without necessary human intervention (Schaupp & Diab, 2020). Standardized production processes are run through the value chain automatically (Häckel et al., 2019). An extensive informational network systematizes the production process based on earlier flow data (Micheler et al., 2019).
Friction less order flows presuppose the interoperability of the IT systems of production machines and IT manufacturing planning systems (Mantravadi et al., 2022; Suebsook et al., 2020). Information and communication architectures are designed flexible to adapt to different Internet of Things, devices which allows a flexible composition of the production chain (Mantravadi et al., 2022). Work process inventories can be reduced on that basis (Xia et al., 2021) and manpower is saved for responsible extraordinary information management tasks (Micheler et al., 2019).
Technical and informational opportunities of smart factory design produce economic advantages. As compared to conventional production smart manufacturing sites frequently realize productivity increases (Braccini & Margherita, 2018; Lee, 2021). Demand based production reduces redundancies and allows efficiency gains (Lee, 2021).
Smart manufacturing saves time in the inner organizational order flow (Guo et al., 2019) and equally reduces delivery time due to just-in-time planning (Xia et al., 2021). Realized economies of scale reduce costs and increase business competitiveness (Vestin et al., 2018).
Social and environmental sustainability of smart manufacturing sites can be increased as compared to conventional production (Micheler et al., 2019) due to higher energy consumption continuity (Braccini & Margherita, 2018). Information technology-supported production machines are worker friendly and service oriented, which improves work conditions and satisfaction on the job (Suebsook et al., 2020).
Opportunities | subcategory | source |
---|---|---|
Technical in planning | Digital twin: design options analysis Virtual reality simulation, mapping & control | Guo et al., 2019 Xia et al., 2021 |
Technical in operation | Real time, digital fault analysis Error avoidance | Baek, 2021 |
Process optimization | Ko et al., 2020 | |
Self-organizing shop floors | Micheler et al., 2019 | |
Product quality improvement Improved final product quality Quality improvement | Braccini & Margherita, 2018 Ko et al., 2020 Mantravadi et al., 2022 | |
Production flexibility, responsibleness | Lee, 2021 | |
Technological advance | Suebsook et al., 2020 | |
Informational | Real time information synchronization | Häckel et al., 2019 |
Automated communication Self-organization | Häckel et al., 2019 Schaupp & Diab, 2020 | |
Informational networking | Micheler et al., 2019 | |
Cost reduction by digital twin in planning phase | Guo et al., 2019 | |
interoperability | Mantravadi et al., 2022 Suebsook et al., 2020 | |
Flexibility to different IoT devices | Mantravadi et al., 2022 | |
Reduced work in process inventories | Xia et al., 2021 | |
Economic | Productivity increase | Braccini & Margherita, 2018 |
Improved efficiency & productivity | Lee, 2021 | |
Time saving Reduced delivery time | Guo et al., 2019 Xia et al., 2021 | |
Increased firm performance | Suebsook et al., 2020 | |
Economies of scale - competitiveness | Vestin et al., 2018 | |
Sustainability/social | Energy consumption continuity | Braccini & Margherita, 2018 |
Higher sustainability | Micheler et al., 2019 | |
Service orientation | Suebsook et al., 2020 |
Technical risks of smart factory design at the planning stage are often concerned with the excessive complexity of site and equipment layout (Jin & Lee, 2018). Guo et al. (2019) are concerned about the potentially lacking adequacy or over-sophistication of the “digital twin” i.e. building information management model, which impairs its operability and puts the reproducibility of simulation results at risk. Due to rapid planning cycles and dynamic technological development (Häckel et al., 2019) smart manufacturing equipment is threatened by obsolescence (Büchi et al., 2020). Häckel et al. (2019) fear compatibility problems among IT and production machines and incompatibility between the diverse modular units of the plant. Limited availability of measurement data could impair the prognosis and early identification of compatibility issues (Wang & Lee, 2021).
At the stage of operation, technical problems could emerge due to high function complexity (Häckel et al., 2019), which entails a high number of interactions between modular production devices and the corporate enterprise resource planning architecture (Baek, 2021). Incorrect demand forecasts resulting from technical malalignment could mean a major threat to the implementation of efficient smart manufacturing systems (Ko et al., 2020). Häckel et al. (2019) fear lacking operationality of smart manufacturing equipment due to the failure and temporary unavailability of essential components. The limited operability of individual Industry 4.0 components could endanger the flow of the whole production process if all units are interdependent and automatized (Micheler et al., 2019).
Informational risks of smart factory design are frequently connected to IT security (Häckel et al., 2019). Autonomous and interdependent systems and complex network architectures relying on the web 2.0 as a communication channel have been exposed to hacker attacks and data abuse (Ko et al., 2020). In smart manufacturing, complex network architectures intermesh the whole supply chain. Limited capabilities of supply chain partners (Lee, 2021), can impair the functioning of the whole logistic process and make high sophisticated solution at the core company redundant (Häckel et al., 2019). Studies further discuss low strategic guidance and orientation from inhouse management with regard to the conclusive implementation of smart factory designs (Micheler et al., 2019). Leaders feel a loss of personal control if information and manufacturing technologies interact self-reliantly and are reluctant to admit further digitalization steps (Schaupp & Diab, 2020). According to Vestin et al. (2018), lacking organizational adaptiveness to modern technologies is a major reason for the failure or inadequate implementation of smart factory technologies.
Economic restrictions to smart factory design are repeatedly mentioned in the retrieved studies (Büchi et al., 2020; Häckel et al., 2019; Lee, 2021). Companies fear that the investment in smart factory architectures will not amortize due to lower-than-expected efficiency gains (Häckel et al., 2019). High investment costs in digital solutions are a major reason to stick to established analogous production systems. Businesses facing resource constraints are partly unable to gain investment partners for innovative Industry 4.0 solutions if the profitability is uncertain (Micheler et al., 2019).
Finally, smart factory design is assumed to be little responsible from a social perspective: Smart manufacturing hardly creates new jobs but makes workers with low qualification redundant (Ko et al., 2020). Human labor is replaced by a network of self-reliant machines and information and communication technology (Schaupp & Diab, 2020).
Risks | subcategory | source |
---|---|---|
Technical in planning stage | Complexity at planning stage | Guo et al., 2019 Jin & Lee, 2018 |
Risk of obsolescence | Büchi et al., 2020 | |
Rapid planning cycles | Häckel et al., 2019 | |
Compatibility problems | ||
Limited measurement data | Wang & Lee, 2021 | |
Technical in operation stage | High function complexity | Baek, 2021 Häckel et al., 2019 |
Component non-availability | Häckel et al., 2019 | |
Correct demand forecast | Ko et al., 2020 | |
Unconditional operability | Micheler et al., 2019 | |
Informational | IT security risks | Häckel et al., 2019 |
Securing and managing data | Ko et al., 2020 | |
Complex network structures | Häckel et al., 2019 | |
Limited supply chain capabilities | Lee, 2021 | |
Modular system interconnections | Micheler et al., 2019 | |
Low strategic guidance/orientation | Micheler et al., 2019 | |
Loss of personal control | Schaupp & Diab, 2020 | |
Organizational adaptiveness to modern technologies | Vestin et al., 2018 | |
Economic | High investment costs | Büchi et al., 2020 Häckel et al., 2019 Lee, 2021 |
Resource constraints | Micheler et al., 2019 | |
Sustainable/social | Low job creation | Ko et al., 2020 |
Replacement of human labour | Schaupp & Diab, 2020 |
Success factors of smart factory design targeted at controlling technical, informational and economic risks and caveats. At the technical planning stage of smart factories, the real-world fidelity of the digital factory model (digital twin) is essential. It is gradually adapted to real data structures and production flows as planning progresses (Guo et al., 2019). Xia et al. (2021) explained that the development of a digital twin requires a detailed analysis of the product life cycle and extensive data bases of the building information model, which is continuously updated and fed with the most recent production data.
Modular systems are resilient to disruptions in the value chain or temporary information lacks since they can accomplish their tasks self-reliantly, even if part of the production network breaks down (Ko et al., 2020). On the other hand, strict modularity based on common technical standards is essential to fit the value creation chain together and interconnect it in virtual space (Büchi et al., 2020). Mantravadi et al. (2022) recommended the application of standardized modular interfaces to ensure adaptiveness when the line’s process flow has to be changed or new equipment is integrated. Hardware and software, e.g. the digital databases, should be fully integrated (Li et al., 2019; Mantravadi et al., 2020), which requires electronic system compatibility across all levels of the value chain (Micheler et al., 2019). Büchi et al. (2020) advise that planning adequate breath, and depth of Industry 4.0 technology is essential to ensure sustainable evolution of the smart factory when novel technologies emerge in future or a redesign of the production process is required.
In technical operation, smart factories should be equipped with a detailed productivity management system to direct order flows through the system effectively. Baek (2021) emphasizes the relevance of reliable automated prognostic tools to schedule production planning based on a data base (Guo et al., 2019). To keep automated production systems running, accurate parameter validation and control is indispensable which again is based on a gapless information management system (Ko et al., 2020). To ensure high production quality of automated manufacturing systems these should dispose of an equally digitalized quality management concept and rely on standardized work processes as much as possible (Lee, 2021). The detailed supervision of defect rates through that system allows to recognize deviances early and human intervention should be possible without delay in that case (Ko et al., 2020).
The informational basis is key to operate smart factories without friction, which comprises an effective failure management (Baek, 2021). Wang & Lee (2021) exemplify this by a digital path loss training algorithm based on 5G technology which intervenes in case of erroneous production flows. Maximum IT security standards are required to keep self-reliant smart manufacturing systems safe and running. Access limitations and clear accountability regulations are fundamental to the informational safety of the production line. This includes adequate (human) IT support in case of extraordinary events (Häckel et al., 2019). As Li et al. (2019) observe, smart factory effectiveness and sustainability depend on a conclusive strategic business-IT alignment scheme, which includes supply chain interaction. Micheler et al. (2019) suggest relying on cloud technologies for the storage and sharing of huge data volumes in that inter-business network.
To make smart factory design an economic success, businesses should dispose of the necessary managerial and cultural preconditions: Business culture should be open to innovation (Büchi et al., 2021), which as Jin & Lee (2018) explain depends on the progressive attitude of the top management. Leaders should be involved and committed to Industry 4.0 technologies to guide businesses on the long way to autonomous production and accept the necessary investments in sustainable technology (Li et al., 2019). An environment of high research and development activity and strongly growing companies is advantageous to the frictionless implementation of smart production systems since companies usually have to rely on innovative lending and investment partners to implement their strategy. A stringent yield management is essential to monitor the efficiency of smart production sites (Ko et al., 2020).
Success factors | subcategory | source |
---|---|---|
Technical in planning | Real world fidelity of digital twin Virtual model correspondence to physical data structures | Guo et al., 2019 |
Analysis of product livecycle requirements (digital twin) Reliance on BMI data | Xia et al., 2021 | |
Modularity of technology Modular systems Modular standardized interfaces | Büchi et al., 2020 Ko et al., 2020 Mantravadi et al., 2022 | |
Appropriate hardware & data integration System compatibility | Li et al., 2019 Mantravadi et al., 2020 Micheler et al., 2019 | |
Breadth & depth of Industry 4.0 technology | Büchi et al., 2020 | |
Technical in operation | Reliable automated prognostic tools | Baek, 2021 |
Accurate parameter validation and control | Guo et al., 2019 | |
Supervision of defect rates | Ko et al., 2020 | |
Productivity management system | Ko et al., 2020 | |
Work process standardization | ||
Effective quality management | Lee, 2021 | |
Informational | Effective failure management | Baek, 2021 |
Path loss training in production flows | Wang & Lee, 2021 | |
Tight IT security | Häckel et al., 2019 | |
Adequate IT support | Häckel et al., 2019 | |
Access limitation and accountability | Häckel et al., 2019 | |
Strategic business-IT alignment | Li et al., 2019 | |
Supply chain integration | Li et al., 2019 | |
Cloud technology usage | Micheler et al., 2019 | |
Economic | Openness to technical innovation Progressive CEO intentions Top management commitment | Büchi et al., 2021 Jin & Lee, 2018 Li et al., 2019 |
High R&D activity, firm growth | Jin & Lee, 2018 | |
Innovative lending & investment | Jin & Lee, 2018 | |
Targeted yield management | Ko et al., 2020 |
Summarizing the review results, smart factory design opportunities, risks, and success factors are conclusively classified into five corresponding categories technical aspects in planning and operation, informational aspects, economic aspects and social/ecological aspects. Businesses benefit of some fundamental advice as to the planning and operation of smart factories.
To utilize design opportunities in technical planning proactively, businesses are required to control technical complexity at the planning stage, avoid compatibility issues and risks of rapid obsolescence. Digital twin simulation are useful to predict future physical performance and ensure the friction less interaction of all plant components in a modular design.
In technical operation smart manufacturing technology excels due to real time digital fault analysis, self-organizing shop floor environments and can realize higher quality standards at improved flexibility than conventional technologies. These benefits are threatened by low operability due to high system complexity and interdependency. To avoid these difficulties businesses should standardize operation and quality management routines and establish interlinks for early human intervention in case of difficulties.
At the informational level, smart factory design allows the automation of communication via self-organizing informational networks. In a real world application, however, IT security risks threaten plant operation and private data could be abused. Businesses risk losing control of production processes, and intervening late in case of failure, which can result in the costly failure of the entire production line. Low in-house competency to monitor and repair the plant, makes businesses dependent on expensive external experts. Businesses can reduce this dependence by developing in-house knowledge on their IT system and by applying tight IT security standards.
At the economic level, smart factories promise productivity increases, higher quality standards and in effect improved competitiveness. The investment costs to build smart factories, however, are significant. To amortize these expenses, smart production lines should be designed flexible to adapt to different production jobs and volumes. Investment or financing partners should be provided a reliable calculation of expected benefits of the smart factory.
If planned to requirements, smart factories can save energy, however, threaten unqualified jobs which are substituted by automated processes. Businesses should plan digitalization and automation early to develop their work force so that responsible jobs in machine and computer operation can be taken over by long-standing employees, while job cuts are avoided.
The evaluation of recent (published 2018 to 2022) studies in smart factory design has provided some general insights in the opportunities, risks and success factors of smart factory design from a business perspective. Essential categories for classifying these issues have been developed, which can be used as a foundation to further empirical research in smart factory design. The literature analysis has found 11 reviews and 16 empirical studies fitting with the research objective, which suggests that available research in Industry 4.0 and smart factory design tends to be theoretical and literature focussed. In available empirical research, practice applications are frequently based on single case studies i.e. smart factory applications in individual companies (Braccini & Margherita, 2018; Lee, 2021; Mantravadi et al., 2022; Vestin et al., 2018; Xia et al., 2021), which impairs the representativeness of these studies. Empirical studies differ in focus and range: Some focus on particular technologies (e.g. Wang & Lee, 2021: 5G communications; Guo et al., 2019: digital twin; Häckel et al., 2019: IT security risks; Baek, 2021: vibration sensor signals in automated storage). Their results apply to specific conditions but are not generally applicable to smart factory design in other contexts. Other studies are very broad in range (e.g. Büchi et al., 2020; Industry 4.0 application in manufacturing units; Jin & Lee, 2018: Smart factory construction in Korea; Micheler et al., 2019: Smart technologies in Industry 4.0). The results of these studies are broadly applicable but little concrete concerning concrete smart factory implementations. Businesses planning smart factory solutions, thus obtain little valuable information from current academic research.
Further empirical research in smart factory design is required, to systematize available smart manufacturing technologies and empirically analyse implementations of smart factory solutions, ideally in the form of a comparative analysis including several businesses. the issues of smart supply chain integration and man–machine interaction planning have hardly been addressed in recent empirical studies and further research in these fields of smart factory design is desirable.
This study has provided an overview of recent empirical and review-based publications in smart factory design, has derived advice for businesses investing in the field and has outlined further academic research requirements. However, the insights gained here are limited in range. Only 27 studies (11 reviews and 16 empirical studies) have been referred to due to limitations in range. Publications before 2018 have not been considered. The provided overview on smart factory design research thus is not comprehensive and the inclusion of further studies would be useful to obtain a representative overview of available smart factory technologies and their potential integration.
Collection and analysis were performed by Christian Fauska. The first draft of the manuscript was written by Christian Fauska, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
FIGSHARE: PRISMA checklist and flowchart for ‘Information and communication integration in smart factory design: a systematic review’, https://doi.org/10.6084/m9.figshare.20279223.v1 (Fauska & Kniežová, 2022).
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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Are the rationale for, and objectives of, the Systematic Review clearly stated?
Partly
Are sufficient details of the methods and analysis provided to allow replication by others?
Partly
Is the statistical analysis and its interpretation appropriate?
Partly
Are the conclusions drawn adequately supported by the results presented in the review?
No
Competing Interests: No competing interests were disclosed.
Are the rationale for, and objectives of, the Systematic Review clearly stated?
Yes
Are sufficient details of the methods and analysis provided to allow replication by others?
Yes
Is the statistical analysis and its interpretation appropriate?
Partly
Are the conclusions drawn adequately supported by the results presented in the review?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: human resources, organizational behavior, entreprenership
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | |||
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