Business advantages of technological advancements in relation to PdM

Industrial organizations that have introduced industry 4.0 and its related innovative technologies into their operations and structures, have witnessed tremendous changes and considerable advantages mainly when it comes to the appliance of the predictive maintenance techniques. From improved productivity and enhanced worker security to streamline inventory control and quality management, the benefits are increasing by time. Next generation manufacturing equipment utilizes built-in sophisticated sensors and advanced programming tools in order to ameliorate the performed predictive analytics and anticipate possible failures before they occur. This won’t just reduce downtime, but further data-based predictive assessment will eliminate the guesswork from any existing preventative maintenance strategy giving by that engineers enough time to schedule, plan and initiate the needed repairs when the machinery if dormant or offline. Introducing IoT into the predictive maintenance practices allows predictive analytics to concentrate on equipment or devices that need continuous attention leading operators and engineers to make the required adjustments on their stocks of tools and spare parts as seems convenient. This will eventually make significant savings in money, space and above all in time on the industrial facility. Besides, some machines will be capable of executing self-maintenance. Consequently, their effectiveness would be enhanced by eliminating the need for additional technicians. Smart predictive maintenance strengthens as well the safety of the workforce. It enables the establishment of an accurate follow up of the related machinery so it could be maintained properly if needed avoiding by that any sudden malfunction that could pose a serious risk to the security and health of the operators and engineers. According to recent studies, the smart factory is expected to exceed $200 billion by 2022. Its huge expansion is stimulated mainly by PdM innovations such as the IoT, big data and advanced predictive analytics which are awaited to raise productivity and profitability levels to brand new heights. (Matthews, 2018).

This breakthrough of innovations would bring benefits to all parties involved from manufacturers to consumers.

With the development of industrial artificial intelligence (AI) and IoT, all industrial businesses are being reformed with software; huge amounts of data aren’t only managed to analyze the past or monitor the present but to predict the future. As connectivity and data accessibility become nowadays cheaper and more outspread in multiple industries, organizations are more looking into predictive maintenance or condition based maintenance empowered by machine learning and AI. Indeed, companies have greater capabilities with AI and machine learning algorithms to accurately process massive quantities of sensor information better and faster than ever before. Predictive maintenance employs data from

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multiple sources such as historical maintenance records, sensor information from machinery and weather data so as to indicate when a machine will require to be serviced. Utilizing real time asset data in accordance with historical data will allow operators making more informed decisions on when exactly a certain machine would necessitate an intervention or a repair.

Smart predictive maintenance takes significant amounts of data and by employing artificial intelligence and PdM software, transforms that data into useful insights and data points helping the company overcome the data overload issues. Typically, sensor data and machine learning programs make it feasible for the firm to rapidly to bring added value into huge volumes of unorganized data. Therefore, the smart PdM tools enhance the existing maintenance policies and strategies by introducing AI to make sure that the workers have the right knowledge and gain the sufficient expertise to maintain the mission-critical assets operating at their maximum productivity and performance. (Uptake Website, 2018).

Mobility Work is accessible from any location and any smart instrument. Any operator can simple log in to use or modify data or scan radio frequency identification (RFID) code on a piece of equipment so as to acquire all the needed information. By combining integration of the IoT with a constantly growing list of aspects, Mobility work will grant the operators and engineers a full access of all relevant data gathered by real time sensors so as to take the necessary actions and react automatically and immediately. Furthermore, CMMS and IoT allow upgrading the spare parts management. In other quarters, CMMS and IoT procure all company’s divisions with a dataflow on when and how physical assets are utilized. This permits furthermore the included departments to follow closely the usage of spare parts and determine the purchase of novel ones. The sensors installed on physical assets seize and process data to the CMMS where in the information is stocked and assessed in the context of asset’s records. Such an approach develops game-changing opportunities. For a start, operators can actually plan their interferences depending on predictive maintenance analytics and practically perform the required repair of the component or the machine before it breaks down and even the break down actually takes place, the maintenance team will be informed immediately through the implemented CMMS. This signifies a significant improvement of the production cycle thanks to use of advanced predictive analytics and innovative proactive maintenance. As we have explained above, the gathered data is used primarily to forecast when the machinery is wearing down or needs repair. As an obvious consequence, the unplanned downtime can be easily reduced and automatically the maintenance costs would be cut down as well. (Peycheva, 2017).

From a general point of view, O&M have a positive perspective on IoT predictive maintenance. In terms of change, the majority of industrials and PdM specialists don’t assume that employment levels will be modified due to the introduction of industry 4.0 in PdM practices, however, they do foresee a transition in the job roles. Enhancements in Operational Equipment Efficiency (OEE) are greatly awaited. In fact, as we have mentioned previously, multiple companies believe that using, processing and assessing the data in real-time will lead to better decision making procedures. It is also believed that the appliance of innovative PdM systems will allow improving the financial results of the industrial facilities as the upfront costs of implementation will be justified by the benefits of these advanced programs and tools

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on the medium and long terms in terms of reduction of workforce and limitation of downtime and spare parts as well as the costly maintenance interventions. Furthermore as the capture, storage and use of real time operational data will become a high priority for the related company, the assets useful lives would be meaningfully extended. Smart PdM will widely upgrade the operational safety and health. It will contribute to the change of the roles and responsibilities of maintenance and reliability specialist and professionals and will eventually cause the convergence of operational technology and information technology. (Jalan, 2018).

The establishment of predictive maintenance has been greatly enhanced thanks to the introduction of M2M communication which enables a more accurate storage and gathering of data in real time so as to extract the necessary information and allow the operators monitoring and maintaining the equipment adequately. M2M sensors mounted on a machine or a device allow the continuous monitoring of its parameters and make sure it is functioning within the recommended tolerances. Once, they record a deviation of these tolerances’ values, the technicians are informed to take immediate action. The huge advantage in the making of this process by M2M channels is the accuracy and rapidity in delivering the needed outputs.

Indeed, data is transmitted through solid and suitable channels. The information being collected by the M2M sensors is being communicated by adequate networks that could vary depending on the devices. Though the choice of the channel would depend on some factors such as the device mobility, security protocols etc. After analyzing the data, trends for successful operation and maintenance are determined so as to develop a set of rules and procedures to follow in order to ameliorate the overall performance of the industrial facility.

The stored information will furthermore permit to create effective maintenance schedules reducing by that the machinery downtime, the failures occurrence and the costs related to it.

The combination of M2M connectivity and predictive maintenance is essential for procuring a business with the necessary Intel concerning the health and performance of its equipment and averting the potential deficiencies that could lead to serious human, material and financial damages. (Fuchs, 2017).

The innovative PdM concepts present a variety of benefits to the industrial companies. The cloud platforms allow the systems to be closely connected and exchange and communicate across the Internet facilitating their integration. Hardware costs and investment in information technology infrastructure are significantly diminished which signifies that firms wouldn’t be forced to invest in their own data centers. Moreover, with a cloud based PdM solution, there’s practically no time for expensive or lengthy adjustments and updates. The cloud provider would be capable of updating and maintaining the server as well as the application software.

Cutting down the upfront costs would also encourage small businesses and start ups on implementing these advanced tools. Cloud-based computerized maintenance management systems (CMMS) could be evaluated by a web browser or an app. This signifies that maintenance staff could recapture data wherever they are placed. Maintenance can be monitored, planned, scheduled and automated from a web browser. Operators, engineers and managers can be sure enough that procedures and processes are traceable by examining casually up-to-date manuals or logging in real time. While cloud applications are able of cutting don the costs, increasing speed and enhancing the management of information and

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analysis, the key advantage is elevated equipment uptime. Data from shop floor can actually be transformed into meaningful information by the cloud application and utilized to progress from reactive to proactive maintenance. (Wilkins, 2019). Such improvements offer an accurate prediction of the future behavior of the machinery as well its Remaining Useful Life (RUL).

The RUL itself is employed in order to schedule properly the PdM operations so as to avoid the potential deficiencies, the breakdowns and the stop time and to optimize the operational and maintenance interventions costs.

We can conclude therefore that the technological trends including the Internet of Things, big data analytics and edge computing are practically transforming the current industrial facilities into smart factories. Industrial Internet of Things sensors gather a vast collection of data from manufacturing machinery or items in production and communicate it to devices that can stock it and analyze it. As for big data analytics software, it shifts through the industrial IoT Intel, giving managers the necessary insights into the rights approaches to enhance the quality, streamline the production and reduce the costs. On the other hand, edge computing enables much of that analysis to be executed right on the facility floor which will eventually remove the burden on networks and the need to put in place IT infrastructure and reduce by that the overall costs. As an obvious outcome of these advanced technologies, it is expected that managers will have a larger visibility into their production processes gaining by that a huge competitive advantage in their related markets. For this reason, a large number of companies have started heavily invested in these technologies. Analysts at McKinsey & Company predict that by 2025, the economic influence of IoT factories will reach up to 3.7 trillion dollars per year. Consequently, edge computing is considered today as a technique for making predictive maintenance more efficient and more reliable. Indeed, in edge computing, devices assess the sensor data and analyze it on the factory floor. The outputs of this analysis are subsequently transmitted to the cloud or data center in order to produce the required insights. Such approach will permit firms to make more effective use of their network resources. A typical IIoT sensor could gather information about machinery pressure or temperature every couple of seconds or several times per second and the IIoT network could involve dozens or hundreds of these sensors. If the solution had to send all of the individual pieces of information to a single data center or a cloud computing site, it would probably consume all of the existing bandwidth on the firm network. Oppositely, with edge computing, factory instruments perform the majority of the analytics cutting down by that the data amount that has to travel across the industrial network. Moreover, edge computing saves organizations considerable financial resources on their cloud computing costs. Usually, most of the IIoT data is meaningful for a specific amount of time and then it could be eliminated. Therefore, if a company wants to keep this data for an extra period of time, it must pay additional expenses that could be highly expensive. Although, with edge computing, the quantity of stocked data is kept to a certain limited level and by that the related costs can be cut down. Another advantage of edge computing is the insurance of high availability. In some cases of IIoT implementation, the internet connectivity isn’t always available due to the remote location of the relevant equipment or the unreliability of the mobile connection. With edge computing, the equipment and solutions in question don’t require per se an Internet connectivity to perform the needed analytics tasks. (DELL, 2017).

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We have tried to explain on this subsection the advantages of innovative technological tools so as to assess their impact on the PdM systems. It is easier therefore to sum up that industrial firms can get ahead of the competition by heavily investing in industrial IoT infrastructure nowadays. The key into making a full smart market today is by moving forward from the traditional and old-fashioned perspectives of the predictive maintenance concept and introducing fully innovation into its practices.

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PART 3: Interactions between innovation, CM