Information and communication integration in smart factory design [version 2; peer review: 1 approved with reservations, 1 not approved]

Strategic smart factory design is essential to utilize Industry 4.0 technologies in production environments effectively. Although a series of earlier reviews in the context of smart manufacturing have been published, so far none addresses smart factory design, i. e. the planning and operation of smart factories. This review provides an overview of recent research in the field by systematizing opportunities, risks and success factors of smart factory design as available from recent empirical studies (2018-2022). Businesses are informed how smart factory design should be approached and implemented to realize cost advantages and increase competitiveness. Academic research benefits of a classification of relevant issues and open research fields are outlined.


Introduction
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).But is this assumption justified or positivistic?Introduction Earlier related reviews Methods Results Discussion In addition, the underlining tables in the Results chapter have been placed at the end.The continuous text is now more coherent to read and the tables give detailed knowledge.
Any further responses from the reviewers can be found at the end of the article technologies.Fatorachian and Kazemi (2020) evaluate the 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 do not conclusively describe them.The study lacks an analysis of success factors.(2020) discuss the 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 the 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 neglected potential risks of smart manufacturing.Lee et al. (2018) discuss literature on 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.

Methods
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 the 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, and 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 catalog 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, the 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.

Results: I&C integration in smart factory design
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: 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 selforganizing 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 oversophistication 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 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).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) 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).

Success factors of smart factory design
The informational basis is key to operate smart factories without friction, which comprises an effective failure management (Baek, 2021).Wang & Lee (2021)  ).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.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.Therefore I tried to mediate: 1.In the abstract, the authors should clarify the present research work's objective and describe how the study could benefit researchers.The authors should also explain the statement given in the abstract "So far none addresses smart factory design, i.e. the planning and operation of smart factories".The abstract must be modified systematically.
--> The abstract is based on the possibilities of the journal.It gives only a first overview.In my opinion, all the points raised will only be clarified with the scope of the introduction.
2. There are several discrepancies in the introduction section, as follows: -->The reviews were even summarized and compared in tabular form.For the sake of simplicity, I have placed the tables at the end of the article.This way, they are a supplement to the body text.
5.The research outcomes should be described.
-->I discuss the findings in the Results chapter and discuss them in detail in the Discussion chapter based on the tabular analyses of the studies.
6. Which data collection and analysis tools/methods have been used?--> according to Snyder (2019), the method is shown in detail and also graphically in the methods chapter.
7. The authors should explain the reason for the focus on selecting research studies for 2018-2022.
--> the study was written in 2021 and 2022.This is the actual status of research.This u forced also in your point 3.
8. According to the journal standards, the references cited need to be more systematic.
--> I have provided detailed tabular lists of all results and their methodological evaluation.This is all that can be systematized.We use a Proven Methodology according to Snyder (2019) and already present the results in detail also in tabular form and derive the whole evaluation text in detail.
9. The authors must add the description of tables 2, 3, and 4.
--> The tables are the summary of the chapter.U ca see it as underlining the content.
10. How and why the opportunities, risks, and success factors are same?--> Because they are the key enabler for success.
11.The authors should define the results obtained by analyzing opportunities and risks in the discussion section.The authors should describe how the researchers could consider the different categories in smart factory design.Which types of factors and limitations should consider for achieving desired outcomes?--> They are combined.U can not unique them.U have to understand the whole technical topic with the factettes-opportunities, risks, and success factors -to achieve outcomes.

Competing Interests: non
Reviewer Report 07 November 2022 https://doi.org/10.5256/f1000research.134334.r153717 1. the summary must be revised by explicitly stating the need and the goal of the study.The current summary only gives an overview of smart factories.
--> It is described in detail in the introduction.I would also not elaborate on the possibilities of the descent owed here.
2. the literature review is very brief and needs to be elaborated.
--> In principle you want a longer overview.I don't want that, because other articles investigate the topics and it is shown that the scientific contribution is finished for me.I will include wieter anlehnungen in other articles 3. what methods of LR are used in this study?Please specify.
--> According to Snyder (2019), the method is shown in detail and also graphically in the methods chapter.
4. The research questions must be stated very clearly and should be consistent with the research objectives.
--> They are listed under the methods as follows: -What are the opportunities of smart factory applications from the perspective of applications in manufacturing companies?-What problems have been observed in smart factory applications?-What can be done to design smart factory applications to take advantage of the opportunities and avoid the problems?
5. there is a lack of insights.The authors need to mention these as they have studied various literature reviews, so they should state how these studies help to discuss the findings.
-->I discuss the findings in the Results chapter and discuss them in detail in the Discussion chapter based on the tabular analyses of the studies.
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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.
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.
a) What is the Research objective?--> It's here in the Introduction section in the last paragraph b) What are the needs and current scenario?--> As described in the objective: "This study assesses opportunities, challenges, and success factors of information and communication in designing smart factories based on a systematic literature review of empirical studies to outline the state of the existing literature, identify further empirical research areas, and inform companies on how to successfully design smart factories."c) Describe the backgrounds.--> This issue is highlighted in a separate chapter, "Earlier related reviews."d) Which types of problems have been faced by researchers in previous research works?--> It's here in the Introduction section in the last paragraph where it belong e) What is the meaning of the statement given "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".-->The sentence is correct.Rewritten: "To date, there is no review work that systematically compares the opportunities and risks of information and communication integration in the design of smart factories based on a comprehensive analysis of the currently available technologies and with reference to the latest studies.This review aims to fill this research gap." 3. The authors must include the latest research works on smart manufacturing.--> The article was written in 2021 and beginning 2022.Published in August 22.It was actually.I will develop to the newest development in other articles.4. The authors must review the literature extensively to understand the present condition of smart factory design.

Identification of studies via databases and registers Identification Screening Included Figure 1. Outline of review process.
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 (Guo et al., 2019;Xia et al.,y designOnly two studies(Guo et al., 2019;Xia et al., (Schaupp & Diab, 2020)er 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 toVestin et al. (2018), lacking organizational adaptiveness to modern technologies is a major reason for the failure or inadequate implementation of smart factory technologies.
(Büchi et al., 2020;s to smart factory desLee, 2021)peatedly 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 lowerthan-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).
Büchi et al. (2020)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.
(Li et al., 2019021)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 The literature analysis has fLee, 20Guo et al., 2019empirical studies fitting with theXia et al., 2021)ve, 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.

Table 1 .
Overview on reviewed studies.

Table 2 .
Concept matrix of opportunities of smart factory design.

Table 3 .
Concept matrix of risks of smart factory design.

Table 4 .
Concept matrix of success factors of smart factory design.