Table of Contents:
Chapter One: What is Industrial IoT?
What is IoT?
IoT stands for the Internet of Things. The Oxford English Dictionary defines the Internet of Things as “a proposed development of the Internet in which everyday objects have network connectivity, allowing them to send and receive data.”
Simply put, IoT is the network of internet-connected physical objects which can communicate with each other and with other systems.
IoT is common in our everyday lives, from wifi-controlled light bulbs and temperature controls (like Nest) to smart home systems (like Amazon’s Alexa and Google Home).
What is IIoT?
The Industrial Internet of Things, or IIoT, refers to IoT used in an industrial context. These concepts revolve around connecting machines and data management in “smart factories” to achieve improvements in productivity and quality.
Connected assets and edge devices send information to data communications infrastructures, which turn it into actionable information. Over time, engineers can use this data to find patterns that can help identify larger issues and their root causes. The information can also help drive business decisions and process improvements.
IoT vs. IIoT: What’s the difference?
While IoT applications tend to be consumer-centric, IIoT applications focus on improving efficiency in manufacturing, supply chain, and management contexts.
To handle critical machines in high-stakes industries, IIoT devices must be sophisticated. Sensors must be sensitive in order to provide the precision of data needed to enable the automation, visibility, and analysis they offer to manufacturers.
On the other hand, IoT products are used in lower-risk situations, often as consumer products. Their benefits usually result in convenience, and the consequences of a piece of equipment failing are less severe.
In industrial environments, production continuity, safety, and security are critical. ARC Advisory Group recommends that “extreme vigilance must be employed to ensure that the omnipresent connectivity and openness implied by the IIoT does not compromise any of the above or overwhelm users and/or applications with too much raw data.” These unique challenges require IIoT to have more robust features than regular IoT.
Chapter Two: Benefits of IIoT for Manufacturers
IIoT is changing the game for manufacturers. IIoT-connected machines capture and communicate real-time data more accurately and consistently than previously possible. IIoT allows organizations to break open data silos and gain access to information at every level.
The benefits of this actionable data are far-reaching. Operators, supervisors, and engineers can gain visibility into production. Engineers can take cues from the process, operator, and machine data to achieve continuous improvement and improve efficiency on the plant floor. Also, management can make informed business decisions backed by data. Overall, personnel at all levels can detect problems and inefficiencies sooner and optimize their operations. This data-driven decision-making takes the guesswork out of solving problems.
Benefits of IIoT:
1. Increased machine utilization
Industrial IoT enables manufacturers to connect their machines to the internet. Connected machines give manufacturers insight into machine health and important KPIs in real-time. These can include overall equipment effectiveness (OEE) and overall process effectiveness (OPE). This data helps manufacturers identify and fix causes of unplanned downtime. They can also increase machine utilization by highlighting need for preventive equipment maintenance.
2. Predictive maintenance
Real-time data from IIoT-connected systems can help predict defects in machinery. This allows manufacturers to take preventative measures against the issues before they occur, ultimately resulting in higher machine uptime and greater overall productivity. Preventing equipment failures reduce process time, rework, scrap, and unplanned downtime. These improvements help manufacturers save on associated costs.
3. Asset tracking
Manufacturers can track products throughout the supply chain and alert stakeholders of damage or possible damage to goods.
4. Facility management
IoT-connected environmental sensors can monitor conditions such as vibrations, temperature, humidity, and more. They can detect conditions that negatively impact operations or cause excessive wear and tear to equipment.
5. Just in Time Manufacturing
Real-time data reporting makes Just in Time manufacturing possible. Processes can be adjusted in real-time to eliminate waste and allow for production to finish on time and in sync with materials in process and raw materials. This helps bring planned production closer to actual production.
6. Connecting remote assets
Connecting devices means that data from remote assets are now accessible from a central location. These assets can be monitored and controlled remotely, allowing for a greater degree of control.
7. Easier-to-use interfaces
Connected software allows operators, engineers, and managers to monitor data through HMIs (human-machine interfaces). HMIs are much more intuitive, especially for personnel without a high level of IT proficiency. These interfaces also centralize data from different sources. As a result, personnel can master tools without extensive training or needing to rely on IT staff.
8. Sharing knowledge across plants
Institutionalizing knowledge keeps critical knowledge within the workforce over time. Centralized knowledge can also help standardize processes. This is critical to continuous improvement efforts within an organization. Finally, having standardized, centralized knowledge allows experts to respond to issues no matter where they are.
Data silos and tribal knowledge, (knowledge that is gained over years of experience and passed down orally but not standardized or documented), are a significant cause of inefficiency for manufacturers. Sharing knowledge is more critical for manufacturers now than ever, as baby boomers are retiring from the workforce at a rate of 10,000 a day. If the retiring workforce’s knowledge is not preserved, it will need to be re-learned by later generations.
9. Process and behavior monitoring
The data collected from IoT-enabled devices and software allows managers to gain insight into employees’ performance. With this data, they can identify bottlenecks and areas for improvement. For example, they could learn that employees consistently make mistakes or produce defects during a given step. Using this information, process engineers can perform root cause analysis to determine what improvements can be made (and use this data as a benchmark to measure improvement).
Chapter Three: Components of IIoT
ARC Advisory Group identifies four key parts of the IIoT:
1. Intelligent assets
Intelligent assets comprise connected “things”–sensors, controllers, and edge devices–as well as application software and security components. This category includes modern assets designed with local intelligence and communications capabilities. These capabilities can also be added to legacy assets.
Each of these assets is capable of connectivity, built-in intelligence, and support for analytics. They generate data and share information across the value chain.
Examples of intelligent assets in IIoT include:
- Plant instrumentation
- Systems or other assets enabled with sensors, processors, memory, and communications capability
The following are some common types of equipment and devices:
Sensors provide new data from existing assets or incorporated into new or existing machines that are externally accessible using common protocols and communications technologies.
Edge devices are pieces of hardware that control data flow at the boundary between two networks. They essentially serve as network entry or exit points. Edge devices fulfill functions such as transmitting, routing, processing, monitoring, filtering, translating, and storing data passing between networks.
Edge devices collect, process, and store data closer to endpoints to make more efficient use of network resources.
IoT gateways are a common type of edge device in manufacturing settings. Sensors and other devices are also types of edge devices.
Embedded systems refer to computing that is dedicated to a single purpose (as opposed to general-purpose computing). Embedded devices have their own computing capabilities, including processor, memory, OS, and communications capability. In IIoT, there are many embedded computing devices that work in concert with other devices within the system.
An example of embedded computing in a manufacturing context is using a machine vision system for inspections in order to improve production quality and throughput. Embedded computer systems run on their own without human interaction through the use of sensors and other modes of communication.
2. A data communications infrastructure
Assets in an IIoT system need the Internet and other network technologies in order to communicate.
IIoT systems are often deployed on cloud infrastructures (such as Amazon Web Services). Cloud computing entails to storing, managing, and processing data using a network of remote servers rather than a local server.
IoT software analyzes the data collected by your equipment and devices. It also provides an interface for users to interact with the IIoT system. The software is what allows people to make better decisions and improve their performance.
Cloud-based software provides a number of benefits for manufacturers over on-premise systems. These include a lower total cost of ownership, greater reliability, greater speed, and flexibility.
An important but often overlooked part of any IIoT system is the people. People interact with the system by making decisions based on the data and analysis generated by the rest of the IIoT components. Better data and more powerful analytics tools allow people to become better connected to plant equipment, machines, systems, and other personnel. As a result, quantified decision-making will follow.
Chapter Four: Examples of IIoT in Manufacturing
Use cases for IIoT are myriad and ever-expanding. Here are a few examples.
Machine monitoring to track OEE/OPE
IIoT allows manufacturers to gain granular visibility into machine data by bringing their analog processes online. Manufacturers can monitor state data from their legacy machines by using sensors connected to an IoT gateway. Collecting data on machine uptime and unplanned downtime allows manufacturers to track overall equipment efficiency (OEE) and overall process effectiveness (OPE). These metrics are especially helpful as benchmarks to gauge process improvements.
Manufacturers can use connected sensors and devices such as machine vision cameras, scales, calipers, and temperature and humidity sensors at quality check stations. Intelligent sensors allow significantly more precision in the inspection process than manual inspections. Quality checkpoints can be integrated throughout the production process to catch defects early and resolve them before they travel down the line.
“Poka-yoke” is a lean manufacturing technique that translates to “mistake-proofing” from Japanese. Manufacturers can also use IIoT devices to prevent errors from occurring in the first place. For example, manufacturers can deploy pick-to-light systems to guide operators to specific bins for assembly processes. They can use scales to detect when a product’s weight is out of spec, signaling a mistake.
Chapter Five: Success Factors for IIoT Implementations
Implementing IIoT at your plant is a worthwhile but complex undertaking. Companies should consider IIoT implementations company-wide digital transformation rather than a one-off project. Successfully pulling off an IIoT implementation requires tight alignment among your management, engineering, IT, and OT teams and company-wide buy-in.
Before you dive in, it’s important to understand what it will entail and be aware of common pitfalls. It’s not uncommon for manufacturers to find that an IIoT implementation was far more complex than they expected. According to a 2017 Cisco report, about 60% of IoT initiatives don’t make it past the proof of concept (PoC) stage.
ARC Advisory recommends the following success factors for an IIoT implementation:
- Appropriate domain knowledge in both OT and IT
- Clear understanding of operational requirements, including the need for flexibility and expandability
- Close integration between OT and IT
- Close cooperation between OT and IT suppliers; between suppliers and end users; and between plant engineers, process engineers, and data scientists when developing and implementing solutions
- Careful consideration of how the solution will be maintained and finetuned over time
- Ability to adapt to new business models
- “Bulletproof” cybersecurity
- Secure and robust networks (wired and wireless)
- Close internal cooperation between internal IT and OT groups as well as between plant engineers, process engineers, and data scientists
Having these factors in place can set your organization up for long-term success and get you on your way to reaping the benefits of a digital transformation.
Chapter Six: How to Implement an IIoT Project
1. Define goals for your IIoT project
The most important part of your IIoT project is defining clear, measurable goals. What is the business case for your IIoT project?
IIoT projects should address specific business problems, which can include (but are not limited to) improving quality, increasing machine utilization, and driving faster improvement cycles.
You should know what questions you want your data to answer before you set out to collect it. Determining the answer to this question should involve a conversation with your production staff and engineers. Analyze, categorize, and summarize information on areas that can be improved. Chances are you already have an idea of areas for improvement–now you can use data to pinpoint it and make informed decisions.
2. Identify the measures of success
Your goals should be measurable by KPIs and pre-defined measures of success. Without a foundation in key business metrics, the new technologies a company implements will fail to live up to their promise.
3. Define a plan.
Ask yourself, how can I collect the data I need to meet my goals? What technologies will help me collect this data?
In order to answer these questions, you must evaluate the state of your equipment’s connectivity. Most modern machines are designed to give you a suite of information and come equipped with connectivity features such as OPC or Ethernet connectivity. Legacy machines require a more complex process, and can usually be brought online using sensors (such as current sensors or GPIO sensing) and an IoT gateway.
You’ll also need to consider any changes you need to make to your facility or network infrastructures, such as installing Ethernet drops and running cables. Keep in mind that any changes that need to be made will take extra time to implement. It’s also crucial to gain the cooperation of those in your company with IT expertise, as you’ll likely need their approval to make any changes.
4. Prove ROI with a proof of concept (POC).
A proof of concept, also known as a proof of value (PoV), is an experiment that should answer the following questions:
What value does the technology create for your company?
What is your return on investment?
You should be strategic when planning a proof of value. Find an area where you can demonstrate a quick return on investment (ROI) on a small scale. Start small and have a concrete time frame, and manage expectations with defined success metrics and a specific data set to measure.
The more complex your initial setup is, the longer it will take to deploy and the lower the likelihood it will be successful. People have a tendency to add complexity to solve problems. Resist!
5. Get organizational buy-in.
Use the data gained from your IIoT connections to demonstrate the ROI of your POC. Once you have the ROI of a successful proof of concept in hand, it’s time to pitch the project to management.
Any successful business transformation requires a cultural change in order to support and sustain the initiative. Support and commitment from upper-level management are critical to guide the project.
6. Scale the implementation.
As you prepare to scale into full implementation, be sure to incorporate feedback from stakeholders about the PoC.
As you move from the PoC to the full implementation, it’s helpful to have a high-level roadmap. This provides clarity for the project, links actions to vision, and provides a reference for timeline and cost.
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