Table 1. Brief description of barriers and system requirement factors from the literature survey
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System requirement factors (Ai)/ Barriers (Ci) Acronyms
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Brief Description
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A1
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The efficient utilisation of resources, automation, data analytics and management faster reconfiguring manufacturing, the interconnection of various systems to make the decision and act at an instant, reducing lead time, and operating costs lead to the reduction of cost of manufacturing
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A2
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Continuous feedback from flexible market demand, the I4.0 systems, its database and components are interconnected with networks, leads to ramp up or down the production, and various customised products on a single manufacturing line as per the market requirement.
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A3
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The application of quick reconfiguring tools, components, hardware systems and software enhance the system to respond quickly to the fluctuating demand, identifying the error and new introduction of the product with the help of interconnected networks.
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A4
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Due to data sharing and transparency, and vertical and horizontal integration of systems in cyberspace, it improves the communication between systems without a human-machine interface and continuous monitoring on the production line for easier identification of faults and continuous improvements.
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A5
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As the systems are interconnected through networks, these can make their own decision and share the data to the right place at the right time without any human interference.
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A6
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The data collected from sensors, components, devices of the manufacturing systems to the user end through feedback are continuously monitored for improving and upgrading the existing production process and for the next launch. These multiple data and analysis in real-time assists the manufacturing systems to know the customer, their acquisition and predicting their type of demand and understanding the behaviour towards the products.
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A7
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The real-time data helps the human operators for more accessible interactions with the systems to minimise errors by predictive analysis features and make the systems pro-active.
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A8
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It allows the manufacturing systems to produce customised products as per the customer desire in the same production line without hampering the real productivity and operational efficiency.
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A9
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With the application of data analytics to the full range of data collected from the systems, various performance measure, productivity, bottleneck issues, delay, downtime, lead time are continuously optimised with various computing tools. This leads to improving productivity.
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A10
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With the application of advanced continuous monitoring process and data analytics, the chances of system breakdown, prediction for maintenance of tools and equipment, predictive analysis of the failure of the components, proper resource management leads to improving productivity.
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A11
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The data analytics and data mining applications on existing manufacturing industries and market demand are computed, analysed, and monitored, resulting in higher resource utilisation and reducing wastage.
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A12
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With the reduction in cost and high resource utilisation, high-quality products are produced along with customised products, as per customer, which is a prerequisite in aggressively competitive market survival and growth, leading to increased global business worldwide.
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A13
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Machine to machine and system to system interaction through networks and predictive analytics leads to reducing lead time in production
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C1
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Lack of robust empirical research of this new technology, unaware about new smart devices and technologies, digitalisation and humongous data industrialists are unsure about its values, and implication consequences in their existing manufacturing systems act as a barrier to adapt I4.0 technologies.
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C2
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Digitalisation refers to the interconnection of all the manufacturing systems, design and development, logistics, market prediction, customer demand and feedback through wired or wireless networks where data are kept transparent and hassle-free, which is challenging to achieve in existing manufacturing industries and hence acts as a barrier to adapt I4.0 technologies.
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C3
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As I4.0 is the latest technology and keeps upgrading daily to get robust and is entirely based on data analysis, big data, connectivity, and information technology applications, the employees need to upgrade their skills quickly to get acquainted and give their inputs efficient IoT solutions. Most of the employees find upgrading themselves to multi-skilled a challenging task and uncertain about its future implications, which creates disruptions and fear of losing jobs. Thus, it acts as a barrier to adapt to I4.0 technologies.
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C4
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Implementing I4.0 requires high investment in technical aspects and re-tooling aspects; moreover, industrialists mainly belonging to small and medium scale enterprises are unsure about its business model for its long-term survival, which acts as a barrier to adapt I4.0 technologies.
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C5
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It refers to the interconnection of end to end digital integration of the manufacturing process and complete transparency in data sharing. From these databases, virtual simulation and optimisation are processed continuously with real-time data, complex networks and tools to improve production efficiency and simultaneously reconfiguring the manufacturing systems as required. And implementing this in existing MSMEs acts as a barrier to adapt I4.0 technologies, as traditional methods are only used for optimisation.
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C6
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Many complexities generate to design and integrate the numerous traditional manufacturing systems and components via complex coding, software technologies and interconnecting it to cyberspace. This creates barriers in adapting I4.0 technologies in technological and implemental aspects.
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C7
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It refers to global industry standard and reference architecture as it is highly likely for the universal language of the production process to keep updated with the latest technology, customisation needed, demands, prerequisites of customers and sharing information. Due to a lack of standardisation, it acts as a barrier to adapt I4.0 technologies
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C8
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Lack of Uniform standards for tools & equipment, systems, languages, networks, data services and reference architecture like design and selection, supply chain services for more accessible communication and data transparency between organisation leads to a barrier to adapting I4.0 technologies.
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C9
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Due to a lack of Quick reconfiguration technologies in existing manufacturing systems and their sub-components and systems, especially in MSMEs, it acts as a potential barrier to adapt I4.0 technologies.
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C10
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Real-time full data sharing and monitoring among various units, customers, suppliers, logistics and a few selected data sharing with other industries is still a novel paradigm. Moreover, the complex programming and modelling of massive data cluster generate difficulties without accessing the actual data from the systems and components. Lack of transparency in systems acts as a potential barrier to adapt I4.0 technologies.
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C11
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The Government takes some initiatives in bringing the policies to implement and modernise existing MSMEs. However, it’s still insufficient to implement successfully due to uncertain business model and returns and reluctance to spend a fortune; hence, it acts as a barrier to adapting I4.0 technologies.
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C12
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There is a significant concern regarding data and information security of confidential industrial data on cyberspace due to fear of cyber-attack resulting in mass exposure of vulnerable data on the public domain. Thus, it acts as a barrier to adapt to I4.0 technologies.
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C13
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Data privacy is also crucial; managing massive data, protecting critical information of the individual, organisation like data related to bank accounts, cards, identity, address, contact number without any leaks and misuse, is the most challenging chore in cybersecurity in a secured server. Due to this threat, it acts as a barrier to adapt I4.0 technologies.
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C14
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As I4.0 is novel and its application is still in the development stage, there is uncertainty in benefits and financial profits from the business models after implementation, which requires robust empirical research with numerous real-time case studies. Thus its act as a barrier to adapt I4.0 technologies.
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C15
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Uncertainty in the interests of I4.0 due to its low vision and implementation strategy and many MSMEs are unaware and unsure about this technology and hence reluctant to adopt.
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C16
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Digitalisation creates legal issues on data privacy, security, artificial intelligence. Strict law regulations should be taken into account to prevent data and privacy infringements. This acts as a barrier to adapt I4.0 technologies.
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C17
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Lack of Integration of technology due to complexities in operational technologies, roadmaps, the physical system to a cyber-system creates a barrier to adapt I4.0 technologies.
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C18
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Data storage, maintenance, and assessing humongous data without compromising on its quality is challenging as these data collected from each component, sensors, products, manufacturing systems, inspecting equipment, market and sales, and customer feedback generates enormous data. This big data helps the systems predict and make decisions from the feedback, requiring high-quality data. This acts as a barrier to adapt I4.0 technologies.
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