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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.3 pp.551-560
DOI : https://doi.org/10.7232/iems.2020.19.3.551

Developing a Conceptual Framework Model of Industry 4.0 for Industrial Management

M. Di Nardo*
Department of Chemical, Materials and Production Engineering of the University of Naples Federico II
*Corresponding Author, E-mail: btmtictac@yahoo.com
May 6, 2020 June 22, 2020 June 30, 2020

ABSTRACT


In this paper, the paradigms of industry 4.0 and its leading technologies enabling their development and dissemination, are introduced. A first outlined overview of this context, in which the Western Industry and its problems to be solved, is presented. This preliminary discussion will schematize a flow model that uses the previously mentioned and described technologies, applicable to production realities confined within a single plant or distribution. Human resource management, as a supervisory role of the entire organization, will receive great importance. News aspects relating to maintenance and safety will be focused in the future.



초록


    1. INTRODUCTION

    The new paradigm of industry 4.0 is a revolution, which allows communication between humans and machines throughout large networks or better the net. This revolution arises up from the need to reduce as much as possible production costs, thanks to the scale economy. It prompted the industry to distribute its production processes on multiple sites, or better to decentralize them. This organizational structure has made the world of the industry particularly sensitive to tools applications such as the Internet and cloud computing, which generated a robust reality push virtualization: its effective performance monitoring and remote management. Decentralization and virtualization are paradigms moving to the fourth industrial revolution, whose new structure created are known as Industry 4.0 (Hermann et al., 2015).

    Industry 4.0 arises and develops thanks to even more uncertainty through a market where competition requires new concepts of manufacturing based on continuous flexibility and reconfigurability (Koren, 2006). Simulation tools, already used in different contexts (Gallo et al., 2013), help this new paradigm of Industry. In fact, for the new manufacturing context, Worn et al. (2000) identify the processes of new approaches where simulation plays an important role (Worn et al., 2000). Kosari et al. (2018) analyse the impact of reducing cycle time in manufacturing. However, in regards to plant safety, they limit an analysis of ergonomics while a systemic analysis is needed.

    The industrial revolution we are talking about is “in progress” since it consists of organizational changes currently being implemented started from a relatively short time and which there are high expectations on Western Governments as well as on the entire industrial world. A new organizational model, based on new instruments, is necessary to guide industries in crisis in competitions with the new one in emerging countries; Western Asia finds space in a social context where the working population, its workforce, decreases, and its average age is gradually growing older (Qin et al., 2016).

    In a context where the need of scale economy requires the use of a boost decentralization too, an urgent need for a collaborative and integrated production envi-ronment arises: the product is designed as a system made in turn by subsystems, each of them produced within one of its plants where the production of the entire product is then coated. It is necessary to design a whole management system taking care of all aspects. Furthermore, managing complex production companies can make profitable using sensors installed on various tools and production machines that collect information on processes, either machine or production state as a whole.

    In this work, new paradigms of Industry 4.0 are presented:

    In the second paragraph, there will be new architectural technologies which make possible the fourth industrial revolution; the third paragraph will propose a manufacturing scheme model. The approach applied to process models was of systemic-holistic one.

    2. METHODOLOGY

    The new paradigm of Industry is already available for production companies, but its complete implementation is still under development.

    There is a need for advanced management and compelling exchange of data, whose technology is built on existing devices and networks as localization sensors or optical reading, the Internet, and infrared devices.

    In this section, the technological and physical architecture will be shown and:

    • 1. how it has been passed from the Internet to the Internet of Things;

    • 2. how increasing the availability of smaller and more sophisticated sensors is rewriting paradigms of the numerical control machine tool;

    • 3. how the use of new and powerful processors can create a duplicate of cybernetic production, a cyber- physical system, thanks to which manage production.

    Innovations introduced are all connected so that full use of the net is an essential tool for a contemporary design, although distributed over different locations in the plant. The use of sensors and the Internet of Things will allow Cloud Manufacturing and the construction of a cybernetic physical system.

    At the end of this section will be clear logic DAMA and how it can assert itself.

    2.1 Cyber-Physical Systems (CPS)

    A CPS is defined as a transformative technology designed to manage integrated systems with two components: the first one real and then another virtual, which manages data gathered from the real.

    A transformative technology consists of an abstraction that creates, based on data collected from the real, a virtual reality, its twin, the functional control of the first, and the prediction of phenomena related to it. The growth of the availability and the subsequent installation of sensors on the machine tools makes it possible to collect data relating to them and to send them through the network connection (Lee et al., 2015b). All this leads to the generation of high volumes of data; the phenomenon is also known as Big Data. Cyber-Physical Systems can be developed to manage big data in order to achieve the goal of intelligent, resilient, and self-adjusting production (Shafiq et al., 2015).

    The CPS is in initial development, so it is essential to define it in its essential structure that is based on an architecture with five levels, where each level is an integration phase and interrelationship between the physical part of the production plant and the cyber-computational. The first level consists of an intelligent connection that is the accurate and reliable acquisition of data from components of machine tools and process through measures obtained by sensors or controllers installed on them. The choice of appropriate sensors and the use of a wireless connection is crucial.

    The second layer of the structure of the CPS is the conversion of data into information, which takes place through specific algorithms and carries out an evaluation and monitoring of the state of the machine tool components. All data collected are channelled towards cybernetics, which is the third level of structure which manages this large amount of data which must be interpreted with an analytical model allowing to figure out additional information in order to have a complete state of special machine tools and, at the same time, a fleet that they make up.

    The layer that is concerned with the representation of these data put forward is the fourth: named cognitive one. Thorough knowledge of a monitored system passes through a correct representation that allows an informed decision to be taken regarding the setting of parameters regulating the production. The cognitive process allows configurations of machine settings. This level, like the first part, is a cybernetic reference point with the physical part of the system. It serves as feedback concretely realized oversight over the process. The type of check is then performed, so defined resilient.

    An implementation of the CPS in the industry today allows several advantages that can affect production in different fields: components, machine tools, and the entire production. This implementation may be a creation of a digital twin of each machine, obtained thanks to a selection of critical data for processing, which will then be converted into critical information for machining processes. In this way, you can create a digital twin entire production system that will oversee the functions of the real one.

    This impact on the management of maintenance and safety (Monostori et al., 2016) opens entirely new scenarios. In fact, from a classic preventive approach, based on an organization as accurate as possible concerning faults that may occur invariably, we have moved to a predictive maintenance policy, a disclosure of which has been catalyzed by the availability of sensors that have been widely discussed. The use of physical-cybernetic systems allows preventing many hidden faults and thus avoiding losses from non-functioning of machinery. In this perspective, an application of CPS, through the acquisition of data, transformed into information from which can be deduced analytically other information, has a potential to secure a “self-consciousness” with prognostic capabilities, Prognostic Health Management (PHM), and consequent automaintenance capabilities (Monostori et al., 2016). Our collection, systematic management, and data synchronization are possible thanks to the Internet of Things, able nowadays to process significant data, stronger than adequate platforms (Platform as a Service, PaaS). Monitoring the health status of machines used in production so allows maintenance work “on condition” the so-called Condition Based Maintenance (CBM), value creation, through continuous monitoring of machine performance and, therefore, our production quality, and in ultimately creating value.

    Using similar production management, based on a physical-cybernetic system, it may be possible programming and remote control. In other words, the use of physical- cybernetic systems opens the way to Cloud Manufacturing. The CPS is the arrival point, the integration of the Physical and digital world. The digital world has to be always on, connected more and more than a simple connection. It has to provide [9] data to be analyzed. According to Xu et al. (2014), the new challenges are to implement and improve the CPS and Physic infrastructure. In this regard, the Pandemic emergency has highlighted the lack of reliable physical connections due to the overuse of smart working in all the Countries.

    2.2 From the Internet of Things and Cloud Computing to Cloud Manufacturing

    Internet of Things (IoT) is the recent phenomenon by which devices, mostly of daily use, can exchange data using the Internet protocol (IP). This trend developed first among the most technological products such as TVs, home automation devices, automobiles, etc. It has also affected the industrial world as it is becoming common practice to make a revamping of countless industrial devices, old or new, you will also become able to collect and exchange data using sensors. The usefulness of this technology has been discovered with the tendency to design machines and devices by the use of position detection using radio waves; with the progress and the lowering in the costs of this technology, there has been the IoT dissemination.

    The “Cloud-based Manufacturing” can be defined as a production network model, taking advantage of access to a variety of diversified and shared productive resources and then to production lines reconfigurable CPS allows optimal allocation of production with obvious increases on efficiency and effectiveness according to demand (Lee et al., 2015a). By the Internet of Things Cloud Computing, the step was short, and the junction ring was the Internet of Services Application to smart devices.

    Cloud Computing is a technology that is intended to allow IT services, through easy access to the network, from any location and on-demand, with data management as efficient as possible (Nardo et al., 2020). As part of this technology, it is possible to identify three levels:

    • •the infrastructure that deals with process data, archive, creates connections between related data, and perform all the basic IT processes that typically are also standardized;

    • •the virtual platform, deals with providing an environment in which to develop, test, maintain, and generally manage software or, more generally, a computer application;

    • • the third level, user interface, is the software provided.

    Everything is made available for users is seen as a service in Cloud Computing, so even the three levels making up the technology are seen as services, identified as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).

    The application of Cloud Computing to the world of industrial processes for the control of machine tools and more in general of the production line is called Cloud Manufacturing and is one of the key industry 4.0 technologies, one that allows an integrated control and in reality remote productive complex and distributed.

    Cloud Manufacturing, therefore, looks to be a multidisciplinary domain that synthesizes the Internet of Things, networked manufacturing, and cloud computing.

    In any case, cloud manufacturing operates through a precise procedure that involves the virtualization of productive resources, their subsequent encapsulation in the cloud service, and their centralized management also remote.

    Resources can be divided into physical resources for production and manufacturing expertise. The first form can exist in hardware and a software form and include raw material, the means of production, computers, etc.; the latter include the “know-how”, simulation software, analysis tools, the workforce, and standard indoor use.

    Production skills rather are dynamic and intangible resources, which consist of the ability of an organization to comply with duty by appropriate expertise.

    After identifying the resources, it’s necessary to bring them “virtual”, create “virtual classes” to associate processes made available by resources.

    It is, therefore, as if they were created domains, which are encapsulated the machining, at this point, available on demand. These domains are called STRL, i.e., Standard Resource Locator, and is conceptually very similar to what is the URL for the Web.

    Sending to the cloud takes place through commands SRL that always contain four pieces of information that constitute a kind of vector: [processing, resource, performer, necessary] skills.

    It is clear that the network transmission of data such as sensitive and strategic to achieve the enterprise's goal, in which you configure the production, raises the sensitive issue of the confidentiality of such data. Some security measures are necessary, such as:

    • • the compression and the encryption of data to the storage layer;

    • •the use of virtual LANs, which provide higher levels of security;

    An adequate level of security is essential since Cloud Manufacturing is set up as a service, and the end-user must be able to completely trust. Another very important issue, investigated by Brecher et al. (2009) is interoperability and modularity of the platform offered by the cloud service (PaaS). This topic develops the need to use standard languages and modular platforms, to employ the cloud service to the whole of the product development cycle so that the CAD-CAM-NC chain is continuous between the different phases and likewise between the different software (Valilai and Houshmand, 2012). Such a refinement of the tools would increase the orientation of cloud manufacturing services, broadening the range, so they would be to lay the concrete foundations for the DAMA. In the next section, we will define the state on the standardization of data models and their platforms on which the data are exchanged and processed.

    2.3 DAMA and the Need for New Platforms and Languages for a Collaborative and Integrated Environment for the Design and Product Development

    Productive contexts in different plants require careful design phase of the product and its production. Since the project of the same product could be done by operators working simultaneously at several locations at the same time, an urgent need for a collaborative and integrated project environment and a production planning arises up; Therefore it seems essential to adopt a platform for sharing data and information necessary to the design of the product and its implementation.

    In order to achieve this goal, it was adopted a standard that provides integration of the data for the various phases of the product assisted by the development computer. The standard acronym “STEP”, Standard for Exchange of Product data model, expected to be applied protocols indicated by the abbreviation APs that define the data type for some application domains; thus, for example, for the 3D representation of parts or mechanical assemblies I have the AP203 protocol, to indicate a boundary type representation, using the AP204, for the design of processes for the automotive industry using the AP214 (Hecklau et al., 2016;Witherup and Verrecchia, 2019).

    However, a similar standard is not enough. A multiplant industrial company also needs a platform to take care of data exchange.

    In addition to undeniable advantages, the classical application of STEP involves some criticality as the large number of APs, the high cost, in terms of processed data, duplication and repetition of the design files and also the conversion of old files related to projects in archives.

    About the development of a suitable architecture for the exchange and integration of data, there was to identify a problem that would allow at the same time:

    • • to overcome all the difficulties and the limits placed by STEP;

    • • to combine the conflicting demands of allowing different designers (possibly distributed on several sites) intervening to collaborate on the same project and the integration of different data all in the STEP standard.

    Valilai and Houshmand (2012), researchers in the academic world, propose a platform to satisfy all the above requirements using a modular and layered approach instead of a classic.

    The platform, named “XMLAYMOD”, derives from the LAYMOD platform. This one can avoid duplication of files related to the same data items in different APs, thereby overcoming the limitations of a classical application of STEP.

    Furthermore, the modular structure, represented in the following figure, allows the use of different software packages for the production aided by the computer such that the data structure is the same.

    The afore mentioned researchers have applied this platform to the design and the production of a detail, verifying the real validity.

    In a production context distributed for the machineto- machine communication, as well as for collaborative design, it was considered appropriate to adopt an XML language (which stands for extensible Markup Language) as this language allows smooth communication between devices on the network and is also compatible with the STEP standard. A service-oriented approach of this type is based on computing paradigms that make use of remote servers, the “cloud computing,” of which we have extensively discussed above.

    3. RESULTS

    A framework model for the news industry

    After describing IOT of the industry in the last years, it schematizes a model that takes into account their use in the control system and the organization of the productive reality.

    The proposed organizational model is compatible with the production hypothesis distributed on more productive cells within the same factory or on more plants and involves the use of sensors installed on the machines, a network provided with protection and sufficient devices to an appropriate data management, which is sensitive, an ERP (Enterprise Resource Planner) for computerized and optimal management of resources involved in the production and human control, supervision of the entire system in place for the management of critical issues.

    In detail, it is a physical-cybernetic system, because each of the involved production machines is provided with an adequate number of sensors that make it possible to collect an adequate number of data to virtualization of the real system as faithful as possible to true.

    The data collected by sensors are sent to a processor and converted into information. The network user may be a LAN or a cloud system to their circumstances, for production realities with a single plant, a LAN presents greater guarantees of security compared to the possibility of the discharge of “sensitive” data.

    Always relating to the security of the acquired data, there are two data storage units through periodical backups so that the processed data is available; the two databases redundantly choice is dictated by a desire to ensure a higher level of security.

    The following step is the interpretation of data collected in order to obtain information to be processed analytically, thus obtaining more information in turn. Therefore it is possible to monitor a physical system constituted by production units by means of one of its virtual twins.

    The huge amount of information such as production progress or immediate availability of stocks in warehouses, the status of different machines, flows of semifinished and an occurrence of faults are sent to the ERP, a type of computer system, already widely used nowadays in industrial reality, act at optimum production management. The ERP, which has the task of making the production conscious and resilient automatically, has to manage operations research problems where boundary conditions change in real-time and limit the scheme, puts in evidence the presence or absence of criticality which may be the necks of bottle or faults that make some jobs not available.

    Processing of information takes place by means of pre-loaded algorithms, which constitute the core of ERP programming and has the aim of identifying commands to be sent to machines because production is always optimal net of all eventualities that may occur. Another of ERP expertise is the management of maintenance, ordinary and extraordinary, so that based on information collected and processed is always identified a maintenance scheduling such that minimal in number and at the same time, less expensive times and several downtimes.

    Maintenance will then be hybrid, which is programmed but flexible with the occurrence of a fault: they should become possible affordable interventions of appropriate maintenance on machines directly connected to those on which any failure may occur; the convenience of proper maintenance will be evaluated and reported in real-time.

    To oversee the entire production process, as shown graphically, is human control. The concept of industry 4.0 consists of no artificial intelligence algorithm, and no one can ever replace the ultimate control of man. For this reason, if the ERP error signals in the production line that cannot be solved by default pre-set procedures, is that the human operator must intervene by adopting choices that ensure greater continuity of production and hence its resilience. In an organizational model thus described, the role of the human operator is of super visioning and problem solving.

    In Industrial Plants, the problem of Safety is very important. One of the most studied problem regards the vulnerability of plant. At this regard Reason stated the probability of overcoming protective barriers. He com-pared the barriers with slices of Gruyere cheese [x]. The sliding slices has a certain vulnerability to be overcome when the holes of the barriers are aligned. In order to solve this problem in industrial safety several techniques have been proposed. However most of them have dif-ferent Independent Protection Layers (IPL). The correct design of this layers allows to protect the entire System.

    In the Industry 4.0, as shown, all the fields are connected to the Net. The machines communicate through the net. Therefore in addiction the Backup and the communication of the ERP from different places or to different industrial plants move through the ne that has to be guaranteed, reliable and safe. A safe plant passes through the protection of communication data. The security of the net may be proposed designing a particular system with the Internet Protocol Layers (Figure 5). Each Layer would communicate with the other using a different asymmetric cryptography with differ-ent security code.

    3.1 Oversight of the Entire Organizational Model and Human Resource Management

    The operator will oversee the entire system, so the management of human resources will appear crucial. In this regard, in this paragraph, we also intend to provide some guidance. First of all, it should be noted that when it comes to the human operator, it does not necessarily refer to a single individual, but also fall within a defini-tion of team people. Like all administrations, including that of human resources, it can be defined as “the set of strategic approaches to problems and consequent use of labor force, more or less qualified, aimed at corporate objected pursuit”. It is not limited to recruiting, selection, custody of tasks, and dismissal, as was the case until the 80s, but also invests aptitude of classification fields, training, and updating of skills [9]. A conceptual model, according to the organization Ford's model, as an opera-tor with constant personal skills and attitudes over time and in a homogeneous population of employees with a fixed level of education, is outdated.

    Stewart et al. (2011) highlight how working reality becomes more and more a place for personal growth that integrates with growth and pursuit of corporate objectives.

    For this one, winning human resource management must necessarily propose to pursue the following objec-tives:

    • • improvement of dynamics within individuals be-longing to the same work team;

    • • continuous improvement of the organization of work between different teams;

    • • development of a whole person;

    • • development by each operator of their increas-ingly qualifying skills and knowledge.

    In order to achieve these aims, in a context of clas-sification of operator’s aptitude profile, Leinweber iden-tified and grouped homogeneous groups in preparatory skills to work in an industrial company (Di Nardo et al., 2016):

    • • Technical skills,

    • • Methodological skills,

    • • Attitude to teamwork,

    • • Individual attitudes.

    Through the pursuit of four above objectives, it is possible to integrate every individual involved in the organization, personal growth with a pursuit of business objectives; this proposal for human resources manage-ment becomes crucial with the progress of innovation technology (and even more so in an organizational model such as the one proposed), where a human oper-ator plays the delicate role of supervision (Di Nardo et al., 2020). This is certainly the most suitable proposal for the best holistic approach to the problem of human resource management in the flow actions in the follow-ing figure according to the CPS production scheme (Figure 6).

    The synchronization model will be developed more and more in the new paradigm of the industry. The machines have to be properly synchronized.

    3.2 Compatibility of the Model with the Principles of Lean Production

    Born in Toyota, Lean Production is the organiza-tional philosophy that, after the “Fordism” (or even Taylorism) was born in the early last century, in the 80s and 90s has rewritten the paradigm of industrial organi-zation. Lean production gives the name to the organiza-tional model, based on close human operator’s integra-tion in the production chain, it provides synchronization procedures of times processes slaughter of process cycle time (also called lead time), the discard of production and the increase of quality and customer satisfaction, regardless of the technology used in the production pro-cess. Precisely because of the close integration of hu-man activity with the entire production chain, the Toyo-ta model seems to be antithetical in contrast to the push to the automation of Industry 4.0. However, with care-ful analysis, Kolberg and Zuhkle highlight the scope for improvement of this organizational model that can adapt to current technologies and concepts relive it through the pursuit of those who are its original objec-tives (Kolberg and Zühkle, 2015;Nimsai Siriyod, 2019). The Kanban is a system that aims to manage batches of workpieces and semi flows from a processing station to the other using tabs that ensure tracking. A scan would certainly make it more streamlined. Another prin-ciple of Lean Production is a continuous improvement, summarized in the Japanese word “kaizen”. Even this latter can be improved through new technologies intro-duced thanks to the cybernetic real production system. A collection of data relating to the operation of the ma-chines involved in the production and study of a trend of their sensitive parameters (such as quality of the fin-ish, processing time, consumption of wear parts of the tool) concerning different settings imposed by ERP for the effect of changing the boundary conditions men-tioned above, enables us to implement with the power-ful ability of computer analysis calculation and evalua-tions that were left to the human operator's sensitivity, certainly less accurate. In the future, great importance will be given to the study of human error and how it can be reduced. At this regard, a first study has been already proposed. However is quite true that new technologies can be instrumental for a renewal of concepts and prac-tices introduced now three decades ago by the philoso-phy of “just-in-time”, without which, however, change the original objectives, all linked to the reduction of lead times and increased customer satisfaction.

    4. CONCLUSIONS

    After presenting the current industrial scenario and described new technologies suitable to the organization-al renewal tool, as part of this work, it was presented a general organizational model. This model adopts the paradigms industry 4.0, that is, the DAMA (Design An-ywhere, Manufacture Anywhere) and control using Cyber-Physical Systems, in a push automation envi-ronment where the human operator’s role is not margin-al, but as opposed to oversight and problem solving, and a crucial role. The control on the machines in real-time allows the optimum setting of production settings so that the production is resilient and the economic damage to the machine fault or maintenance was min-imized. They were also considerations about compati-bility with other production philosophies like Lean Pro-duction and has concluded that not only the new organ-izational paradigms are compatible with the principles to which the Lean production is inspired, but new tech-nologies are inspired to the improvement of some long-established practices, such as e-kanban and on the method. In future it will be analyzed a case study and more important studies will develop about the human role, safety, and maintenance.

    Figure

    IEMS-19-3-551_F1.gif

    Model of a CPS architecture.

    IEMS-19-3-551_F2.gif

    A service business “cloud” model.

    IEMS-19-3-551_F3.gif

    Diagram representing the LAYMOD platform.

    IEMS-19-3-551_F4.gif

    Flow chart of the proposed explanatory model.

    IEMS-19-3-551_F5.gif

    The new design security internet protocol.

    IEMS-19-3-551_F6.gif

    CPS production Human model.

    Table

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