Information and communication integration in smart factory design

Opportunities of smart factory design

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).

Risks of smart factory design

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 highly sophisticate solutions at the core company redundant (Häckel et al., 2019). Studies further discuss low strategic guidance and orientation from in-house management with regard to the conclusive implementation of smart factory designs (Micheler et al., 2019). Leaders feel a loss of personal control of 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).

Success factors of smart factory design

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).

Implications for practitioners

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 frictionless 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.

Implications for academia and call for further research

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.

Study limitations

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.

Underlying data

All data are available as part of the article.

Reporting guidelines

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).