A survey on the readiness of Industry 4.0 adoption in the manufacturing sectors


 In developed countries, the majority of manufacturing companies are moving to Industry 4.0 to stay competitive. But as far as concerning the developing countries there should be need of proper knowledge about the Industry 4.0. The findings of the survey are presented in this article for evaluating the readiness of manufacturing industries in Indian perspective to adopt Industry 4.0 innovations. Also, readiness factors were validated based on the benefits realized by manufacturing industries as a result of implementing Industry 4.0 technologies through the development of hypotheses. Besides, this survey is the first to examine Industry 4.0 adoption status in Indian manufacturing industries. According to the findings, readiness factors have a significant relationship with the benefits achieved through the implementation of Industry 4.0, and research agenda for the future are discussed.


Introduction
Increasing uncertainties in today's global market have forced numerous manufacturing industries to introduce innovative production techniques to improve the productivity, pro tability, and competitiveness of industries. The Industry 4.0 concept has been considered by manufacturing industries as a new dimension and many of them have implemented it in several various ways and titles. A tradition of continual improvement effort is important for an organization's success and survival. Industries need to re-evaluate their processes and ensure that they are reliable and optimized and follow the voice of the consumer (Sharma and Gidwani, 2019). The Industry 4.0 concept seeks to boost intelligent factory effectiveness and productivity through the application of innovative manufacturing processes and strategies. Smart manufacturing helps products, machinery, and technology to be interconnected, optimizes facilities, eliminates costs, and maximizes pro ts. In general, Industry 4.0 offered the opportunity of exible mechanisms to dynamically modify production functions, offering high granularity and accuracy processes for self-decision and real-time operation and selection of equipment (Vaidya et al., 2018;Alcácer & Cruz-machado, 2019).
Industry 4.0 focuses mainly on creating a smart network for manufacturing focused on digitization and automation through which computers, machines, and goods communicate among one another without human intervention (Sokolov and Ivanov, 2015;Gilchrist, 2016). To grasp the consequences of the Industry 4.0 implementation, the literature lacks coordinated and closely oriented studies by practitioners and researchers (Hofmann and Rüsch, 2017;Almada-Lobo, 2016). Studies are required to establish a mock-up for the Industry 4.0 implementation and related new executive methods by analyzing various factors, bene ts, implementation challenges, and hurdles to the Industry 4.0 implementation (Hermann et al., 2016). These studies will help in enhancing the awareness of Industry 4.0. Additionally, the consequence of Industry 4.0 is the emergence of smart industrial frameworks which involve smart goods, intelligent transportation, intelligent production, intelligent production systems, smart equipments, smart devices, smart suppliers, and so on (Shrouf et al., 2014;Schmidt et al., 2015;Li, 2018;Kamble et al., 2018a).
To succeed in today's increasingly diverse and aggressive market climate, digitization is also a must for supply chain processes (Pereira and Romero, 2017;Wu et al., 2016). Although, Ghobakhloo (2018), Sarvari et al. (2018) performed a study on Industry 4.0 transition process and roadmaps. But still, Industry 4.0 is not implemented absolutely in manufacturing industries of developing nations such as India. Nimawat and Gidwani (2021) delineate the various barriers towards the implementation of Industry 4.0. A successful primary focus for this technology transition is to recognize success factors, potential drivers, and barriers (Kamble et al., 2018b). Nosalska et al. (2019) suggested that the necessity of maturity frameworks and checking the readiness of manufacturing industries to adopt plans for digital change. Nimawat and Gidwani (2020b), Abdirad and Krishnan (2020) indicated the limited quantity of researches has been carried out in the quantitative eld, indicating that there is insu cient research focused on empirical ndings. Wagire et al. (2019) suggested the need for examination of the consistency of theoretical frameworks through empirical analysis in various geographical regions. Nimawat and Gidwani (2020a), Nimawat and Gidwani (2020c) presented a lack of empirical research regarding veri cation of the status of Industry 4.0 adoption in developing nations. Hence there is a need to examine the level of awareness about the bene ts of Industry 4.0 implementation and readiness about the industry 4.0 in manufacturing industries of developing nations.
This article is concerning a survey of the implementation of values of Industry 4.0 in Indian industries of manufacturing sector and the overall readiness of Indian manufacturing industries for such emerging phenomenon. Also to examine the signi cant relationship of readiness factors of Industry 4.0 implementation with the bene ts achieved through the Industry 4.0 implementation.
The article's structure has been as follows: Sect. 2 offers readiness factors of Industry 4.0 implementation. Section 3 is concerned with the bene ts achieved through the implementation of Industry 4.0. Section 4 delineates the adapted methodology to execute the present survey. Section 5 presents the statistical data analysis and ndings obtained from the study's survey questionnaire. Section 6 deals with the conclusions of this survey. Finally, the future recommendations are proposed in Sect. 7.
(RF02) Awareness of expected bene ts Industry 4.0 needs large expenditures with unpredictable returns (Müller et al., 2018); accelerated technical change renders these expenditures much riskier in terms of unclear bene ts in the future (Schneider 2018;Kagermann et al. 2013). Industries remain confused and inexperienced with the phrase Industry 4.0 and are oblivious of the advantages of digital alteration because of this there is an unwillingness to implement it (Theorin et al., 2017;Müller et al., 2017). Industry 4.0 innovations are boosting pro tability (Ooi et al. 2018;Kiel et al., 2017). Shrouf et al. (2014), taking into account the accepted bene ts of Industry 4.0.
(RF03) Customized solutions Interdependence, interaction, and prompt sharing of information enable organizations to implement creative systems to ful ll consumers' diverse demands (Baldassarre et al., 2017). Currently, manufacturing industries are centered on making an incentive for the client, who is turning out to be increasingly mindful and requesting concerning lead time to the deliverance of manufactured goods and manufactured goods accessibility, availability, and trustworthiness quality (Witkowski, 2017). A profound paradigm transition of Industry 4.0 is known to be the customization of goods, services, and manufacturing (Gabriel and Pessl, 2016). Albers et al. (2016) analyzed how Industry 4.0 technologies could affect industry performance; approximately half of those polled believed that by eliminate defective products and providing better quality, Industry 4.0 could dramatically raise consumer trust.
(RF04) Competitive pressure Industries that implement Industry 4.0 technology boost their sector's competitiveness (Kiel et al., 2017). In order to remain competitive, rms have to deal with shortened launch time-to-market, reduced goods life -cycles, and the requirement of minimizing costs with an emerging modernization process (Hecklau et al., 2016). Modern customers have the exibility to select from a range of goods and services; still, they appear to be disappointed. For this reason, it is also essential to produce customized goods to create demand, to survive on the market, to maintain or raise consumers (Prahalad and Ramaswamy, 2004;Baldassarre et al., 2017).
(RF05) Different individuals are able to access single integrated structure of complete visibility Nimawat and Gidwani (2020c)  (RF07) Improvement in services and solution through consumer data inputs Industry 4.0 helps industries to properly identify the behaviors and desires of consumers (Shrouf et al., 2014), and "the production of personalized goods in a batch amount of one can be realized while preserving the cost-effective requirements of mass creation" (Lasi et al., 2014). This is pointed to as mass customization, which encourages the design to have customer requirements and makes last-minute modi cations (Shrouf et al., 2014;Heng, 2015). Seufert and Meier (2016) proposed that industries had to initially examine and de ne customer desires and expectations in order to effectively complete digital transformation. Those requirements can then be met by corresponding consumer-oriented improvements within the organization.
(RF08) Real-time data available for decision-making Extra worth is made by optimization and decision-making among items and all manufacturing equipments and facilities in real-time for the better implementation of Industry 4.0 (Oesterreich and Teuteberg, 2016;Hossain and Muhammad, 2016). Industry 4.0 profoundly impacts the manufacturing situations with crucial alteration in the carrying out of activities by the means of allowing for real-time production planning and control, as contrasted with traditional predictive-based production planning (Sanders et al., 2016). The sharing and utilization of information in real-time by the components on a shop oor empowers a certain level of self-su ciency, and additionally, progress towards autonomous production control and planning, which is an additional feature of Industry 4.0 (Liu and Xu, 2017).

(RF09) Execution cost
The execution of the Industry 4.0 concept has nancial constraints concerning its high implementation cost for associated technologies and facilities (Rennung et al., 2016;Erol et al., 2016;Porter and Heppelmann 2014). In the implementation of Industry 4.0, nancial restrictions are a major challenge with regard to the growth of advanced modern technology and the innovation of a sustainable process (Nicoletti, 2018;Theorin et al., 2017). The main focus that determines the amount of expenditure is the technological expertise of the target organization. The economic viewpoint, however, remains at a preliminary stage; this de ciency of clari cation on the study of cost-bene t and nancial returns on digital projects is a crucial problem for Industry 4.0 implementation (Arnold et al., 2016).
(RF10) Training expenses and availability Sackey et al. (2017) want to deal with the effect of Industry 4.0 on an industrial engineering training educational plan by presenting a viable instructing and knowledge framework. In order to adapt CPS incorporation in an Industry 4.0 based intelligent factory, the employee needs to adapt to learning new IT skills and logical thinking ability. Employees need to adapt to emerging technology in a smart factory and to emerging ways of working (Sony and Naik, 2019).
(RF11) Availability of machines and technologies as per Industry 4.0 Sevinc et al. (2018), Nimawat and Gidwani (2020c), and Türkes et al. (2019) recommended the availability of technology and machines as per Industry 4.0 innovation is the most important enabling factor for the implementation.
(RF12) Digital vision on the basis of emerging demands of the market Integration of digital facilities in items relies on IoT technology, smart processors, sensors, and programming-based software empowering novel abilities (Porter and Heppelmann, 2014). This is the current need to compete with the new market. Industry 4.0 incorporates high procedure digitalization and smart creation (Gorecky et al., 2014).
(RF13) Digital transformation leverages towards innovative product developments Some of the Industry 4.0 pillars are digital computing assistance systems, digital manufacturing, human-robot cooperation, virtual training, and control and oversight support systems (Park and Huh, 2018). In the conditions of extremely exible and modular manufacturing, advanced simulation tools for virtual prototyping, Internet of Services (IoS), and the IoT are seen as allowing customization (Brettel et al., 2017;Sanders et al., 2016;Monostori, 2014;Weyer et al., 2015). The digitization of manufacturing and the removal of distinctions between the real environment and the digital environment is another signi cant driver of support for Industry 4.0 (Frank et al., 2019a).
(RF14) Utilization and collection of consumer information regularly with manufacturing industries Industry 4.0 gives industries the ability to learn directly through their clients (Kiel et al., 2017;Yin et al., 2018). Customer information and data are conveyed smoothly and effectively to the production department and processes in the era of Industry 4.0 (Foidl and Felderer, 2015;Chiarini et al., 2020;Sader et al., 2019) regarding speci cations, basic dimensions, scheduling, and demands. CPS should support the notion that usefulness is not implicit in the quality of the manufactured goods but is perceived and measured by the user on the basis of bene t in operation and the su cient smart goods to a broader concept of service advancement (Frank et al., 2019b;Lightfoot et al., 2013). In the same context, many researchers reported (Morrar et al., 2017) how Customer Relationship Management (CRM) would optimize technology parts such as machine learning, competitive intelligence, and social media, using customer relationships and expertise.
(RF15) Digital collaboration of all departments Nimawat and Gidwani (2020c) suggested one of the enabling factors for Industry 4.0 adoption is cross-departmental digital cooperation.. Methods such as sophisticated scheduling methods, mass customization, and micro-segmentation enable industries to provide consumers with multi-choice products, e ciently accomplish the last-mile challenges with strong importance, deliver consumer orders at a quicker pace by implementing creative, digitized logistics and delivery procedures such as delivery by drones and beyond consumer requirements (Hofmann and Rüsch, 2017;Ghobakhloo, 2018;Zawadzki and Żywicki, 2016). This can be achieved through the digital collaboration of all departments.
(RF16) Entire production data remotely accessed IIoT assists in remotely accessed overall production information (Hossain and Muhammad 2016). Through Industry 4.0 implementation provides the possibility to access all production information remotely with the help of internet services; this can minimize time delay in process execution (Jeschke et al., 2017;Türkes et. al., 2019).
Bene ts Achieved Through The Implementation Of Industry 4.0 Industry 4.0 concepts enhance product quality (Oesterreich and Teuteberg, 2016;Sommer, 2015;Kagermann et al., 2013). Industry 4.0 re ects a "modern paradigm" of manufacturing, leading to "quicker and also more e cient decision-making" (Kang et al., 2016) and an "entirely novel manufacturing strategy" (Veza et al., 2015). Smart goods are provided with algorithms capable of optimizing their processes, usage, and servicing (Oesterreich and Teuteberg, 2016;Nunes et al., 2017;Zhou et al., 2017). By utilization of high-grade digital technology for production information helps to optimize demand, reduce failure and improve productivity (Schlechtendahl et al., 2015;Flammini et al., 2009). Industry 4.0 innovations increase productivity (Liao et al. 2017;Fatorachian & Kazemi 2018). The exibility provided by digital operations management provides workers with better workplace environments and a secure and safe manufacturing environmental condition (Kamble et al., 2018a). In the future, Industry 4.0's commitment to highly sustainable industrial value generation would be outstanding (Stock and Seliger, 2016). Not only between machines, but also between individuals, and between machines and humans, the endless collaboration and sharing of information (Wan et al., 2016). Industry 4.0 innovations aim to shorten product launch time (Oesterreich and Teuteberg 2016). "Digital technology allows for the manufacturing and services of the automatic and self-optimized products as well as distribution exclusive of manual involvement" (Hofmann and Rüsch, 2017).
The self-optimization of manufacturing networks including a number of parameters along with availability, costs (Weyer et al., 2015;Liu and Xu, 2017), and utilization of assets (Liu and Xu, 2017) is another reference point, which will support the economic component of sustainability and also the environmental component, potentially but less clearly. "Industry 4.0 provides a high degree of exibility in the production, maintenance, and operation of automated processes" (Jazdi, 2014). The emergence of applications for Industry 4.0 aims to minimize costs, increase productivity, e ciency, and adaptability, and increase customization of goods (Ghadge et al., 2020).
Making a reference to the Industry 4.0 economic viewpoint, transparency and process interconnection make it possible to optimize processes (Oesterreich and Teuteberg, 2016), rising quality, e ciency, productivity, exibility, and customization (Peukert et al., 2015;Hossain and Muhammad, 2016;Kagermann et al., 2013). Improved demand orientation, new value proposals (Rehage et al., 2013), and smart manufacturing technologies (Lasi et al., 2014;Oettmeier and Hofmann, 2017) allowing for load balancing to enable them. Smart products (Stock and Seliger, 2016) are needed for this, which improves the competitiveness of a company (Porter and Heppelmann, 2014). Table 2 presents identi ed eleven bene ts achieved through the adoption of Industry 4.0.

Methodology
The survey questions related to readiness factors and bene ts of Industry 4.0 in this research are extracted from the literature review. The query for readiness and bene ts arising from the literature review is the extent of readiness factors are ful lled by manufacturing industries and according to which how much bene ts they achieved. The survey problems are nally translated into hypotheses at this signi cant point. Initially, a reliability and validity test has been conducted. After that statistically descriptive data analysis has been carried out. Subsequently, the status of normality of survey data has been examined and found not normally distributed. Therefore, non-parametric tests as spearman's correlation test and chi-square test have been employed for analyzing the signi cant relationship of each readiness factor with all the Industry 4.0 bene ts. The methodology of this research work is presented in Fig. 1.

Null and alternative hypotheses:
Ha13: Digital transformation leverages towards innovative product developments has a signi cant association with Industry 4.0 bene ts.
Ho14: Utilization and collection of consumer information regularly with manufacturing industries has no signi cant association with Industry 4.0 bene ts.
Ha14: Utilization and collection of consumer information regularly with manufacturing industries has a signi cant association with Industry 4.0 bene ts.
Ho15: The digital collaboration of all departments has no signi cant association with Industry 4.0 bene ts.
Ha15: The digital collaboration of all departments has a signi cant association with Industry 4.0 bene ts.
Ho16: Entire production data remotely accessed has no signi cant association with Industry 4.0 bene ts.
Ha16: Entire production data remotely accessed has a signi cant association with Industry 4.0 bene ts.

Statistical Data Analysis And Results
The research tool used was a questionnaire for the survey. The questionnaire is comprised of three elements. The rst section inquired about the context of the respondents. The second section examined the understanding of their level of adoption of Industry 4.0 in their manufacturing industries and if they are dealing with the implementation of Industry 4.0, also asked about the bene ts they achieved through the implementation of Industry 4.0 in the third section. The participants from different manufacturing industries that deal with Industry 4.0 concept were asked to evaluate two aspects (readiness factors and bene ts) using a 5-point Likert scale. The readiness factors and bene ts rating scales vary from: 1 -strongly disagree to 5 -strongly agree.
All queries were formed in a close-ended form in the survey questionnaire. On the basis of an exhaustive literature review, the survey questionnaire was formed.
The actual number of questionnaires collected was 175. Out of which 89 questionnaires collected were from the manufacturing industries (3-Micro Scale Industry, 3-Small Scale Industry, 20-Medium Scale Industry, and 63-Large Scale Industry) dealing with Industry 4.0 implementation. Hence, the total response rate was 16.18% (89/550).

Reliability and validity:
In the analysis, reliability and validity are crucial because it is necessary to ensure that the evidence obtained, evaluated, and interpreted is accurate and consistent in order to accomplish trustable outcomes (Saunders et al., 2009). By using a small number of respondents as a pilot sample before the delivery of the questionnaires to participants, Robson (2016) proposes a strategy for con rmation. The authors followed this approach. In this scenario, in line with the guidelines of Robson (2016), a target of 6 respondents was used for the pilot study, so the questionnaire was sent to 6 manufacturing industry professionals. Four risks to reliability are claimed by Robson (2016); participant bias, participant error, observer bias, and observer error.
This pilot study aimed to assure that, when interpreting and responding to the questions, the rst two risks were resolved by avoiding inappropriate questions and contradictions. An opportunity had also been offered to provide suggestions on whether any extra questions were necessary to resolve the problem and to provide suggestions on the questionnaire's arrangement and linguistic aspects. As the questionnaire used speci c optional questions that didn't need interpretation, the last two risks were not applicable. Some questions were corrected as a result of input from the pilot study to assure that participants had a similar understanding of the questions.
Reliability implies the consistency of the measure of the investigated survey (Field, 2011). Cronbach's alpha (α) is that the most often used to evaluate reliableness (Field, 2011). Cronbach's alpha (α) for both readiness factors and bene ts is 0.972 which is very close to one, which cruels that the estimation (survey) is more reliable.
Descriptive statistics: Table 3 Table 4 provides the descriptive statistics on the bene ts of the Industry 4.0 adoption among Indian manufacturing industries. The results show that the most important bene ts achieved by the Indian manufacturing industries through Industry 4.0 concepts are: Increase in productivity, Improving sustainability, Improve decision-making process, Increase of processes visualization and control, Increase worker safety, and Optimize automation processes.   Table 5 and Table 6 delineates that responses do not align with the normal distribution. Therefore, for the investigation of the survey non-parametric tests (Spearman's correlation test and Chi-square test) have been utilized.   Hypothesis testing using chi-Square test and spearman's correlation test: Using SPSS, the null-hypothesis test was carried out. The summary is presented in Table 7 indicates the outcomes of each questionnaire answer. The chisquare test results are shown in Table 7 and the p-value will be taken into consideration in particular. The chi-square test provides the researcher the opportunity to determine whether the obtained sequence could be taken into consideration as a random sequence within the data. A relation is then inferred between the two variables if the sequence is not random. The null hypotheses have been reported in this way in the methodology section. For example, the rst is: Management willingness ( rst variable) has no signi cant relationship with the bene ts achieved by the adoption of Industry 4.0 (second variable). To deny the null hypothesis, a 'p' value of less than 0.05 (5 percent) has been established; in this situation, the alternate hypothesis implies valid (Plackett, 1983).
As a cut-off point, the test assumes a 5 percent value to reject the null hypothesis. This implies that there is a 5 percent chance of being no or wrong relationship rather than a real relationship between two variables. The sixteen hypotheses exhibit the relationship of readiness factors of Industry 4.0 with Industry 4.0 bene ts. The outcome of Table 7 demonstrates that there is a signi cant relationship among all readiness factors of Industry 4.0 with the Industry 4.0 bene ts.  The spearman's correlation coe cients presented in Table 8 indicate that all readiness factors and all bene ts are strongly positively correlated.
Furthermore, the outcome of the survey spearman's correlation test shows that all readiness factors of Industry 4.0 adoption have clear bene ts in respect to manufacturing industries. The amount of bene ts of manufacturing industries increases as the score of readiness factors of Industry 4.0 adoption increases.
The ndings thus support all results of the chi-square test and reject all sixteen null hypotheses.
Many industries claim that Industry 4.0 should be somewhat fresh, they don't have a strong idea about how they can execute this concept's adoption; however, they see the potential for signi cant change through emerging product implementation techniques and revolutionary technological developments.
As a result of the rejection of all null hypotheses using the chi-square test, the respondents acknowledge that in the manufacturing industries of developing countries, all readiness factors of Industry 4.0 have a signi cant association with the bene ts of Industry 4.0. As a result of the above hypothetical ndings, it was suggested that the industrial effectiveness related to all aspects of bene ts-improved signi cantly after the adoption of Industry 4.0. These hypothetical ndings demonstrate the utility for manufacturing industries that have not yet adopted Industry 4.0 but want to operate in a challenging manufacturing situation to maximize pro ts. In order to keep them viable in a challenging market companies should consider adopting Industry 4.0 technologies partially in the rst stages, and fully implementing them later for maximizing pro ts. Therefore, it has also been concluded that stakeholders and administrators of manufacturing industries can verify the status of the Industry 4.0 concept with the help of the above stated sixteen readiness factors. And validation of the results of the chi-square test has been done through spearman's correlation test. Spearman's correlation test also delineates that all readiness factors have a signi cant relationship with all bene ts of Industry 4.0.
This study indicates that the initiative to introduce this modern paradigm is in modest development, and in the future, the industries should see strong success and future promise of actual process and manufacturing results, and investment return, and market opportunities.

Future Scope
To advance this eld, research has to be done to help manufacturing industries properly manage execution efforts by developing a statistical model and testing it on manufacturing industries interested in implementing Industry 4.0. Policymakers may conduct research using several case studies in order to generalize the ndings. Future studies should look at speci ed sectors of manufacturing industries. This survey has been laid down in the Indian manufacturing environment (developing nation); the different environments such as from developed nations may provide some different results. Future research may also be conducted using more number of industries. Results may also be validated through the survey of other developing nations in the same context in future studies.

Declarations
Ethical Approval: Not applicable Consent to Participate: Not applicable Consent to Publish: Not applicable Authors Contributions: The authors con rm responsibility for the following: study conception, data collection, analysis and interpretation of results, and manuscript preparation.