Article Top 10 IoT Trends in Manufacturing
By Jillian Sloan / 12 Mar 2018
By Jillian Sloan / 12 Mar 2018
These days, it feels like everything is becoming intelligent: smartwatches, smart homes, smart cars — and now, smart factories.
Like other smart things, smart factories use the Internet of Things (IoT) to create an information network that shares rich, real-time analytics and empowers autonomous tasks and decision-making capabilities.
With smart manufacturing, organizations can predictively meet business needs through intelligent and automated actions driven by previously inaccessible insights from the physical world. Smart manufacturing transforms businesses into proactive, autonomic organizations that predict and fix potentially disruptive issues, evolve operations and delight customers — all while increasing the bottom line.
Smart manufacturing comprises all aspects of business, blurring the boundaries among plant operations, supply chain, product design and demand management. Enabling virtual tracking of capital assets, processes, resources and products gives smart manufacturing enterprises full visibility — streamlining business processes and optimizing supply and demand.
When it comes to the IoT, almost anything is possible. This might be overwhelming if you’re just beginning to explore ideas for a transformation initiative. To help guide your journey, we’ve compiled a list of the top 10 IoT trends in manufacturing and how they could benefit your business.
Sensors have an incredible — and almost unlimited — role to play in the IoT. Use cases for sensors are endless because they can be as small as a pen tip, making it possible to embed them in virtually anything, such as name tags or bottle caps.
These sensors can be designed to read data such as temperature, vibrations and more, and feed real-time data into analytics systems. When applied to a manufacturing plant, sensors help minimize defects, improve yield, and reduce downtime and waste.
Additionally, sensors can create new revenue streams and enhance customer experience. For example, a farming equipment manufacturer embeds sensors in its machinery to collect data when the equipment is used. The company then takes that user-sourced reporting to inform farmers about soil health or provide real-time information about planting. In doing so, the customer gets more than a piece of farming equipment — he or she gets a smart farming tool that could help yield a better crop.
As the name suggests, a digital twin is a digital representation of a physical object or system. Digital twins are not necessarily new technology, but the inclusion of real-time data and advanced analytics in recent years have made digital twins more robust and responsive to the real world.
“By 2020, we estimate there will be more than 20 billion connected sensors and endpoints, and digital twins will exist for potentially billions of things,” reported Gartner in its “Top 10 Strategic Technology Trends for 2018” report.1
Case studies for digital twins will continue to expand. In the immediate future, digital twins will help manufacturing plants move from preventive to predictive or condition-based maintenance — effectively reducing downtime and lowering operational maintenance costs.
Swarm intelligence describes the process of collecting information from many individual intelligent things and centralizing the data to create actionable insights. For a manufacturing plant, swarm intelligence can be applied to the entire manufacturing floor and used to optimize scheduling and logistics for operations.
For example, if one station of the manufacturing plant experiences an increased workload or a breakdown in machinery, swarm intelligence will automatically optimize operations to have another station compensate and keep production schedules on track.
Ranked by Gartner as a top strategic technology trend for 2018, Artificial Intelligence (AI) is growing in adoption and capability. According to the Gartner report referenced above, 41% of businesses have already adopted or begun to pilot an IoT solution. The rest of those surveyed (59%) are still researching or developing their AI strategy.1
So far, AI has been largely used for predictive maintenance. With the ability to collect and interpet large quanitities of data and recognize patterns, AI can foresee when equipment may fail. As a result, it can autonomously schedule a qualified technician to address the issue before the problem occurs. Factories can also lean on AI and machine learning to perform hazardous jobs, improve factory agility and increase efficiency.
Assisted by AI and machine learning, autonomous things are self-governed and able to respond to their environment in real time. If you think of the smart factory as a brain, then autonomous systems are the cerebellum — able to interpret information received through sensory organs (or sensors) and control motor function (or the factory machine functions).
Autonomous systems help factories achieve greater agility and optimize operations by transmiting data to adjust machinery processes and connect with supplier networks for efficient workflow.
Edge computing is not new technology, but it’s growing in popularity and value — especially for manufacturers. Also tapped by Gartner as a top technology trend, edge computing is a technology method that can take vast data sets — such as information collected from various sensors within the factory — and output insightful and actionable data.
Additionally, edge computing combats high Wide Area Network (WAN) costs, traffic and latency by keeping data collection and information processing close to the source.
Radio Frequency Identification (RFID) tags have tremendous opportunity. With a tiny sensor and antenna, RFID can be attached to things as small and simple as a name badge and be virtually invisible. Manufacturers can use RFID tags to track assests and monitor product data, such as performance, for predictive maintenance.
RFID tags can also be applied to products to create engaging customer experiences. For example, a drinking fountain and reusable water bottle company installed RFID tags to its water bottles. When users refill their bottles at the company’s water fountain, a scanner reads the tag and provides personalized feedback, such as how much water the user has consumed and how many bottles the user has prevented from entering a landfill.
Unlike Virtual Reality (VR), which replaces a user’s entire vision with a virtual environment, Augmented Reality (AR) places a layer of digital content over the real world. Engineers and manufacturers are applying AR in both worker and consumer scenarios.
Luxury car manufacuters, for example, are using AR to improve safety and the end-user experience. Information such as speed and radio station is displayed with AR to appear as if hovering over the car hood, within the driver’s line of sight. Drivers never have to take their eyes off the road to see how fast they’re going or the name of the song they’re listening to.
For workers, AR is a game changer when it comes to repairs, maintenance and other complex fieldwork. In conjunction with the IoT, a worker can use AR to get real-time information to pinpoint problems and receive detailed instructions for repair while keeping both hands free.
Robotics made its manufacturing debut in 1962 when General Motors installed the Unimate robot in its New Jersy factory. Early robotics were highly supervised and could perform only one or two limited tasks.
Today, robotics not only perform multiple tasks autonomously, but they can also communicate and collaborate with other robots.
Factories with large production lines can build an entire network of collaborative robots, also called “cobots.” Manufacturers that produce various models of a product can connect tens of thousands of devices and robots to enable a single production line of cobots to adapt and assemble multiple product models. By eliminating setup time and reconfiguration, factories can take advtange of a 24/7/365 production schedule.
Additionally, collaborative robots can quickly produce highly customized products and handle dangerous tasks that pose safety risks to human workers.
In its early days, 3–D printing was a popular tool for prototypes and models. But now, 3–D printers can produce highly intricate, multimaterial components and final products. This has enormous implications on a manufacturing supply chain, enabling factories to create their own parts on site and by demand.
The U.S. 3–D printer manufacturing industry has been on a steady rise since 2014 and is projected to reach $17.2 billion in 2020. The ability to create virtually anything — quickly and without retooling — has solidified the value of 3–D printing in manufacturing.
Successful integration of the right IoT solutions can help your manufacturing plant gain a competitive advantage. For most organizations, the biggest hurdle is knowing where to start. Collaborating with a partner in the IoT space can help you plan, implement and launch an IoT solution with minimal disruption and long-term returns on your investment.