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.