It has been seen, in every few years, industrial work get reshaped mainly due to technological forces or economic forces. But this time with the COVID-19 pandemic, work in every Industry is now reshaping and the new kinds of roles are growing with three trends i.e.
Remote work, Digitalization, and Automation.
The biggest challenge is only 20-25% of the workforce could actually work remotely in the long term. Many managers who are very worried that they don’t know how to manage their employees remotely because sometimes, they have all these persons sitting in front of him and that’s the sign of working. And now when workers are far away from them, they don’t really know what each person is doing. And especially when if their objectives are not clear or the outputs are not clear, then, it’s very difficult to say whether the person is actually doing the work.
As there were spikes in demand for various kinds of things during COVID-19, and the only way companies could respond is through Automation with challenges of reskilling people. So it’s a tougher future. But if we manage to help workers make it through this, it could be a brighter future for our businesses as well as for our workers.
These changes in industry taking a different shape as we saw during recessions over the past several decades and in the few years following the recession, we actually saw automation levels and automation adoption rise very rapidly, because that’s the time when businesses are very keen to capture efficiencies, stabilize the cost base, and, in many other ways, respond to the new economic realities post the recession
So this time also we expect something similar, in the case of this particular phase as well—to see automation levels rise, and we are seeing things like Industry 4.0, Industrial IoT , robotic automations around the world are starting to rise, and we’re seeing the stock prices of companies that produce automation-related products actually start rising. So there’s an anticipation that this will be a greater shift or a trend going forward more in Industrial Automation. It is the time requirement.
IIoTexpert strongly advice their clients to use this pandemic time for implementing Industry 4.0 and other important changes in your factory without doing any further delay so that you can help your business as well as your workers. Our expert team globally are always with you to support
Faced with the COVID-19 crisis, industrial leaders have one business imperative: maintaining their operations. IIoT, implemented in a plug-and-play mode, can be instrumental in ensuring business continuity and minimizing economic damage by ensuring employee safety and security, improving liquidity, and lowering short-term costs.
Cost Saving & Safety:
Companies are suddenly dealing with work from home on a large scale, as well as new concerns about protecting their remaining on-site employees, and have adapted their workforce organization in consequence. IIoT tools can play an important role in ensuring a seamless transition through these changes in our manufacturing environment.
As the crisis unfolds, industrials can benefit from short-term cost reductions with the the help of several IIoT-enabled tools.
Remote employee collaboration:
In general, the more digitized a company’s processes are, the simpler it is to collaborate remotely. Off-the-shelf IIoT tools support the continuation of operations with fewer employees on site, since they facilitate remote work in direct and indirect functions. Measure production levels, and improve performance gaps across shifts. Other IIoT tools, such as digital heat maps, can support root-cause analyses for various problems. With machine breakdowns, for instance, IIoT tools can receive input from sensors that help pinpoint problems, such as broken components or oil leakage that could interfere with production. Teams can then review the tool outputs and discuss the potential sources of error over videoconference.
Digital performance management:
IIoT-based software solutions can provide a real-time dashboard of key performance indicators to support shop-floor performance dialogs, increasing transparency. These tools also allow the tracking of improvement actions and send alerts to operators via mobile devices. The software evaluates machine data, such as information on overall equipment effectiveness, part production, and quality through IIoT connectivity. Improved performance management can help companies boost labor productivity by 20 to 40 percent.
In-line process optimization:
IIoT can increase production efficiency of single machines or entire production lines by using advanced analytics to optimize process parameters. The algorithm analyzes information on all available variables, including production, scheduling, asset condition, and input goods. Data from individual machines get combined with information about the overall production program, allowing companies to optimize machine settings based on previous and subsequent production steps. This allows companies to adjust production schedules quickly to account for changes in demand or unexpected supply-chain disruptions.
IIOT-enabled asset optimization:
This use case involves using advanced analytics to identify the root causes and countermeasures related to the three drivers of overall equipment effectiveness (OEE): availability, performance, and quality. For instance, an aerospace supplier had a low OEE when producing an important airplane component. It then used IIoT solutions to monitor and detect certain problems, such as tool wear and missing materials. Based on this sensor information, the company was able to optimize job sequences in a central control room. With these improvements, the company achieved 80 percent OEE.
Supply-chain integration across the value chain:
IIoT facilitates real-time data exchange between all supply-chain participants, creating an integrated view of production programs, scheduling, inventories, quality, and anticipated delivery times. In addition to building transparency and trust, such tools can also reduce supply-chain costs and risks—for instance, by receiving signals from connected machines when they are running out of raw materials, or by tracking the flow of materials along the supply chain using geolocation tags. With these insights, companies can optimize inventory levels, production planning, and transport utilization through a more holistic approach. (The information on inventory is used to improve planning across the supply chain, including decisions about producing materials.) Companies will also learn about supply-chain problems more rapidly, allowing them to act before they escalate.
Industrial companies will take different approaches to leveraging the power of IIoT in challenging times, but reforming the operating model with IIoT solutions is always helpful.
As a matter of fact, our solution is not like any traditional, expensive and a lengthy way of doing industrial IoT, usually done by other automation companies, but here we use a different tactics than our competitors, which is low cost, No more programming, easy integration, unlimited users, free software, free updates and 90-days free trial for qualified customer to know the plant and also to help building a trust on us. If you are not satisfied even after 3 months of trial, you can return it. But, this is also a fact that 9 out of 10 clients have never returned back their XL. Most importantly, the ROI for XL is less than six months.
The industrial internet of things (IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits. The IIoT is an evolution of a distributed control system (DCS) that allows for a higher degree of automation by using cloud computing to refine and optimize the process controls.
The IIoT is enabled by technologies such as cyber security, cloud computing, edge computing, mobile technologies, machine-to-machine, 3D printing, advanced robotics, big data, internet of things, RFID technology, and cognitive computing.
Five of the most important ones are described below:
Cyber-physical systems (CPS): the basic technology platform for IoT and IIoT and therefore the main enabler to connect physical machines that were previously disconnected. CPS integrates the dynamics of the physical process with those of software and communication, providing abstractions and modeling, design, and analysis techniques.
Cloud computing: With cloud computing IT services and resources can be uploaded to and retrieved from the Internet as opposed to direct connection to a server. Files can be kept on cloud-based storage systems rather than on local storage devices.
Edge computing: A distributed computing paradigm which brings computer data storage closer to the location where it is needed. In contrast to cloud computing, edge computing refers to decentralized data processing at the edge of the network. The industrial internet requires more of an edge-plus-cloud architecture rather than one based on purely centralized cloud; in order to transform productivity, products and services in the industrial world.
Big data analytics:Big data analytics is the process of examining large and varied data sets, or big data.
Artificial intelligence and machine learning: Artificial intelligence (AI) is a field within computer science in which intelligent machines are created that work and react like humans. Machine learning is a core part of AI, allowing software to more accurately predict outcomes without explicitly being programmed.
IIoT systems are usually conceived as a layered modular architecture of digital technology. The device layer refers to the physical components: CPS, sensors or machines. The network layer consists of physical network buses, cloud computing and communication protocols that aggregate and transport the data to the service layer, which consists of applications that manipulate and combine data into information that can be displayed on the driver dashboard. The top-most stratum of the stack is the content layer or the user interface.
Layered modular architecture :
IIoTContent layer User interface devices (e.g. computer screens, PoS stations, tablets, smart glassessmart surfaces
Service layer Applications, software to analyze data and transform it into actionable information
Network layer Communications protocols, Wi-Fi, Bluetooth, LoRa, cellular
Device layer Hardware: CPS, machines, sensors
No one knows exactly how much the Internet of Things will change the future. Cities, like Barcelona, Spain, have used Industrial IoT to completely liberate themselves from debt and skyrocket into massive profitability
Supervisory Control and Data acquisition (SCADA) systems uses a network of computers, PLCs, controllers, sensors, and user interfaces to create a high level supervisory control for operators controlling a large process plant or machinery. The PLCs and embedded controllers in the network perform real time control of individual subsystems of the SCADA system, while the operator provides high level mode and set-point changes.
Manufacturing Execution Systems (MES) on the other hand helps plan and execute process commands for the machines, therefore helping in maintaining proper quality of the products through monitoring and maintenance of the inputs.
Enterprise Resource Planning (ERP) systems, as the name suggests, helps plan resources in an organization. Modern ERP systems may include material purchase and inventory management, production and operations planning, and logistics management. Some ERP systems also include accounting, sales planning, and engineering tools.
The Real Challenge
Over the last half a decade, ERP, MES, and SCADA have tried complementing each other in industries, but haven’t been able to gain the expected success levels. The new developments in them over the years have also failed to garner success. This has left the gap wide open for IoT, analytics, and cloud based technologies to fill in the gap between ERP, MES, and SCADA
There is no doubt about the utility of ERP, MES, and SCADA systems; they have been in existence for several decades now in the factories. The real challenge is to get these systems to work together to ensure that the right person has the right information available in the right format at the right time.
In today’s extremely competitive world, corporates are trying to find better ways to improve efficiency, productivity, and enterprise wide collaboration. Some of these corporates are using process improvement mechanisms such as lean six sigma, Kaizen, and Kanban to discover and implement lean, efficient methods of doing the same tasks, while others are using technology to gain a competitive edge.
Here are three scenarios that show the gaps that can be filled to positively impact productivity:
1. System scenario: “I would like to get access to my plant data, but it’s too expensive with my current system.” —Discrete Manufacturer
2. People scenario: “I am getting data from all my equipment. I like how it’s presented, but it’s stale and I don’t trust it. Data seems to be manipulated before it’s reported up.” —Beverage Packager
3. Process scenario: “For every 10 process parameters, only one equipment parameter is logged.” —Process Automation Manager
The gaps in these scenarios exist in varying degrees across MES installations, and this is precisely where IIoT comes into play to expand the capabilities of MES rather than replace it.
Technological progress enabling IIoT ranges anywhere from smarter sensors and actuators to more reliable cloud infrastructures. IIoT in this sense is less of a disruptor and more of a sign of progress along the continuum of technology.
IIoT is disrupting manufacturing, starting with existing systems, and this is spurring initiatives, pilots and studies around the world. Though IIoT is a step toward the future, it does beg the question for many manufacturers: “What about the manufacturing execution system (MES) that I have today?”
It’s important to note that the MES is one part of the process, people and systems triangle of productivity. IIoT is a net productivity enabler and a complement, rather than a substitute, to MES. In fact, MES has been notoriously costly to implement with long execution schedules. However, we have seen where smart devices and cloud-based systems allow manufacturers to stand up line downtime and overall equipment effectiveness (OEE) within days without substantial investments.
These IIoT smart devices can even enable machines that are not network-connected or do not include a programmable logic controller (PLC).To answer the initial question (“What about the MES that I have today?”), it is important to realize that it is less about substituting and more about complementing the MES with IIoT.
A properly implemented MES can bridge the world of corporate IT and connect it to the near real-time world of automated operational technologies called IT-OT integration.
It’s the ultimate vector of development for the manufacturing industry.
This new confluence of emerging technologies can completely reshape the manufacturing act — from product design and engineering to distribution and after-service.
The change is much needed since the manufacturing industry is experiencing great pressure on several fronts:
The growing need for higher productivity and leaner processes.The consumers’ demand for hyper-personalized products and experiences.Plus, the overall digital disruption upending markets at a fast pace.Industry 4.0 solutions are emerging as a response to all these challenges.
By 2025, Industry 4.0 could generate manufacturers and suppliers an estimated $3.7 trillion in value creation potential. Today, however, a mere 30% of manufacturers are already realizing value from their investments. For most, the race to digitization has just begun.
If your organization is currently at the evaluation stage, too, we advise you to take a close look at the following technological areas of innovation.
Predictive MaintenanceUnplanned equipment downtime and sudden failures are well-familiar adversaries to lean manufacturing. The new generation of predictive maintenance solutions, powered by state-of-the-art machine learning algorithms, can identify the early signs of failure and even anticipate malfunctions before those occur.Such solutions can be plugged to your central control panel and generate alerts whenever the slightest deviations in performance are recorded. Furthermore, they can estimate the optimal maintenance schedule for critical equipment, so that you can order all the spare parts in advance, dispatch the on-site technician and minimize the downtime window to a bare minimum.
Per Deloitte, predictive maintenance solutions can help manufacturers:Increase manufacturing equipment availability and uptime by 10–20%.Subdue maintenance planning time by 20-50%.Reduce the overall maintenance costs by 5–10%.
Industrial IoT deployments are growing at a significant speed, and there’s a good reason for that: connectivity adds more clarity to the manufacturing process.
The latest array of sensors can capture a variety of data points ranging from temperature to sound and vibrations — all of these parameters can tell a lot about the equipment operating conditions or the state of produced/transported goods.
According to a recent McKinsey survey, manufacturers are actively exploring the following industrial IoT use cases:
Service level optimizationEnhanced operational visibilityNew servicised offeringsConnected productsManufacturing process optimizationImproved sales enablement
Example:Rolls-Royce was one of the first companies to deploy a full-scale servitization offering to cement their spot as a leading engine supplier. With the TotalCare® service package, the company rents its engines to airlines for a monthly fee that also covers all maintenance work.To maximize their profit margins, Rolls-Royce collects sensor data from engines and uses predictive algorithms to estimate the optimal maintenance schedule. This way, they deliver added-value to the customer without stretching their operational costs.
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