Technology

IoT Applications – The Rise Of The Intelligent Factory Operations

We’ve all seen how the Internet of Things (IoT) has transformed life at home – but how can it transform the way enterprises conduct business? As more physical objects become digitized through IoT technologies, its use potential and adoption grow as well. Today, IoT is an omni-sector technology being used to better real world outcomes, with applications ranging from agriculture to law enforcement.

IoT Applications IoT technologies
Internet of Things Image source: Pixabay

Indeed, the influence of IoT technologies is on the rise, having saturated the consumer electronics market and become a big player in the commercial world, it’s increasingly being used in the industrial sector.

By bringing equipment, warehouse space and everything in between into the digital realm, manufacturers are provided an inside look into every nook and cranny of their operations. Data collected from these nooks and crannies exposes the hidden faults, inefficiencies and even impending machine failures that’d halt operations for hours, days or weeks. By shining light on these problems, manufacturers can remain several steps ahead – and leave their competition in the dust.

How IoT technologies enables smarter, more efficient manufacturing

The number of IoT devices in manufacturing is predicted to grow from 237 million in 2015 to 923 million in 2020. Manufacturers are expected to invest $70 billion annually in IoT solutions by 2020, according to a TATA Consultancy survey. Judging by volume alone, these solutions are no longer a luxury: as connected devices and facilities proliferate, those who fail to adopt will inevitably be left behind.

For a manufacturing facility, remaining competitive means production in the most cost effective, quickest manner. One the keys to achieving this in a 21st-century operation is data mining from each component of the entire manufacturing ecosystem. Instead of making decisions based on previous experience, accumulated knowledge or simply making guesses, IoT-enabled equipment can automatically collect and present the intelligence needed to make data-driven decisions. This machine derived data, made available in real time, supports the full visibility needed to make changes to increase efficiency and productivity, decrease downtime and bring flexibility to the manufacturing process. The most obvious IoT technologies of this type is for prescriptive maintenance.

Malfunctioning or broken manufacturing equipment can trigger periods of downtime, when machines fail to perform at all. Downtime isn’t just an inconvenience – it causes shortcomings in both quality and quantity while costing you precious time on already-tight delivery schedules. It bears mentioning that downtime is almost always more expensive than perceived: according to one comprehensive economic calculation, downtime in an auto manufacturing facility has a true cost of $22,000 per minute, or $1.3 million an hour.

Enter prescriptive maintenance. Instead of fixing equipment after it breaks (reactive maintenance) or following a preventive maintenance schedule that performs (expensive and potentially needless) routine tune-ups in order to avoid a breakdown, prescriptive maintenance intervenes surgically and intelligently to correct specific machine issues before they impact the production line . Prescriptive maintenance analyzes the data produced by each smart system and its myriad components (in the context of well-defined and well-monitored operational thresholds) to make predictions on when an likely to malfunction. As a result, problems are caught (and hopefully tended to) before a breakdown – preserving tight deadlines and staying on track for order fulfillment.

IoT technologies can deliver significant insight into problems on the opposite end of the manufacturing process too: getting the product out the door and to its final destination.

Consider the fact that up to 10% of a warehouse’s inventory ends up getting lost – sometimes in transport, and sometimes in plain sight. No matter where these lost products may or may not be, one thing is certain: the financial impact of lost cargo exceeds $50 billion each year. It’s no wonder that locating tracked objects is one of the most desired applications manufacturers want to see from IoT.

Thanks to IoT technologies, manufacturing facilities can minimize the amount of lost objects when transporting product from facility to facility. Shipments outfitted with radio frequency identification (RFID) tags wirelessly communicate information from a sensor to a tag reader, updating its location without any human prompting. This real-time, up-to-date information keeps better tabs on the process, before a shipment leaves a manufacturing facility incomplete.

Even though these two examples occur on opposite ends of the manufacturing, they have one important thing in common: both understand the importance of effectively communicating the data generated by a single object to inform on the entirety of the operation. But that doesn’t mean that the Facility Manager should lose sight of the forest for the trees.

Device-level insights inform plant-wide optimization

From experience, a manager knows that each element of the manufacturing process is important: delays in the factory due to a broken conveyor belt, for example, mean delays in production which impact shipping, warehousing and customers. However, how that chain reaction occurs – and how to prevent it from happening again – is not a problem identified or solved with the naked eye.

This is the importance of data generated on the device level: to show exactly what went wrong, when it went wrong and what could be done to prevent it in the future. This is also exactly what IoT technology does in interfacing physical objects with the digital world – on the device level.

Still, the wise manager will note that simply taking in data on the device level can score you points but it won’t – on its own – win you the game. Even with a centralized analytics and management portal, and a deft human hand at its helm, each segment of the manufacturing process can only refine its own “island” of efficiency. These “islands” are important, yes, but managers need to join these “islands” together in order to obtain plant-wide optimization.

Data sharing is what connects these islands plant-wide. The manufacturing process isn’t restricted to any one place: supplier operations, raw material processorssubcontractors and others are part of the equation. Delays in one mean delays in the others, but that can’t be confirmed for sure unless these different components of the manufacturing process are willing to communicate with each other. Bridges are built between these islands by enabling data sharing from one part of the supply chain to another. For this too, IoT is an ideal solution, owing to its distributed network structure.

Although there is some hesitation to share data with other organizations out of concern for competition, in most cases, the benefits outweigh the risks. A recent MIT study found that two-thirds of businesses who use IoT in their operations share data with their suppliers and even their competitors. Businesses find that sharing data actually increases their value as an organization. Not only does data sharing show different organizations what’s going wrong, it gives them the tools needed to improve, therefore improving operations across the board.

The parts of the manufacturing operation inform the success of the whole. Connecting each link of the entire chain through actionable data permits managers and executives to see the bigger picture and make better decisions for the good of the entire process, not just one narrow silo. By valuing each part of the puzzle for its own importance while simultaneously viewing it as part of a greater whole, the manufacturing process can gain new insights which equal to improvements and improved efficiency across the board. And ultimately, that makes for a much more intelligent factory operation.

Author Bio:

Yaniv Vardi is the CEO of Panoramic Power, a leader in device level energy monitoring and performance optimization