Machine Vision and IIoT Drive Next Wave of Logistics Efficiency

Industry 4.0 banner image

The Fourth Industrial Revolution, or Industry 4.0, is bringing waves of innovation to the logistics sector. Machine vision technologies are central to riding these waves.

Industry 4.0, often referred to as the Industrial Internet of Things (IIoT), looks like this: Sophisticated networks of sensors feed immense volumes of digital data into learning algorithms that improve productivity, flexibility, and profitability throughout production environments. In the logistics chain, machine vision systems illustrate the potential of networked sensors to transform industrial processes.

Logistics operators are bearing the brunt of the surge in digital commerce, which gives the industry a strong incentive to use technologies that streamline business processes and improve customer satisfaction. Thus, nobody in logistics can afford to overlook the potential of Industry 4.0.

The Evolution of Industry and Technology: A Brief Review 

Logistics Industry 40  Evolution

Before we dive into the promise of machine vision in Industry 4.0, let’s take a quick look at the first three phases:

1.0: Steam power. Between about 1760 and 1840, the First Industrial Revolution used steam power to standardize production processes, kicking off a shift from agrarian to industrial societies.

2.0: Mass production. Late in the 1800s, assembly lines enabled manufacturers to sell complex products in massive quantities. Henry Ford’s automotive factories exemplified Industry 2.0.

3.0: Digitization. From the 1950s through the 1970s, integrated circuits, networks, and data processing applications ushered in Industry 3.0, laying the groundwork for the Fourth Industrial Revolution.

Now, let’s look at the promise of Industry 4.0.

How Machine Vision Enables Industry 4.0 in Logistics

Sensors, networks, and learning algorithms make it easier to understand and analyze the workings of industrial production in any setting, from an automotive factory to an oil refinery. Logistics systems, which store and deliver products, illustrate the potential of Industry 4.0. 

A logistic chain’s hub is the distribution center, which has four primary functions:

  • Inbound — receiving finished products from manufacturers.
  • Order fulfillment — retrieving products from storage and preparing them for delivery
  • Sortation — dividing products into categories and storing them.
  • Outbound — loading products onto trucks for shipping to customers.

Industrial cameras make it all possible because they:

  • Guide robots for product picking
  • Identify products packages
  • Track packages throughout the distribution center
  • Inspect packages for quality control
  • Gauge items for intelligent sortation and truck load optimization

As the logistics industry enters a new phase, these trusted imaging tools become even more important.

Doing the Work: Machine Vision Data Drives Value in Industry 4.0

Barcode scanners have been a critical part of logistics for decades but were predominately laser-based technology and lacked the rich data from an image-based camera. With the recent adoption of machine vision-based barcode readers, data rich images are captured and used across the supply chain, but we are just now seeing companies investigate vision inspection for quality control in logistics. In Industry 4.0, image-based barcode readers help logistics companies envision their entire operation in a holistic fashion, using data and software to make everything work better. Here’s how:

Data science and deep learning. Industrial vision systems generate digital images that reveal key characteristics about everything in the distribution center in real time. Learning algorithms analyze training images to tell the distribution center’s automated systems what an optimum product looks like.

Logistics Industry 40  Deep Learning

As products move through the distribution center, machine vision cameras feed digital images into a deep learning application that flags deviations from the training images. The algorithms are optimized to become more accurate over time as systems are retrained with new image sets.

Quality control. Deep learning applications help distribution centers sort products and fulfill orders more accurately because the cameras keep a constant watch for items with damaged labels, missing labels, damaged packaging, and other issues that degrade the customer experience.

Logistics Industry 40  Tunnel with Filmstrip

Predictive maintenance and troubleshooting. Deep learning-enabled vision systems also can be programmed to inspect production line components for evidence of wear and tear. Over time, these algorithms learn to provide accurate forecasts of when parts are likely to fail. Shutting down conveyor lines for repair during scheduled maintenance times is far less costly than enduring unexpected breakdowns that grind everything to a halt.

As more commerce moves online and orders roll in 24/7/365 in the years to come, distribution centers will have far slimmer margins for error. This places even more pressure on logistics operators to conduct in-depth failure analyses to identify problems, optimize maintenance, and limit the potential for time-intensive breakdowns.

Automated DCs - IoT and analytics

Advanced robotics. Industrial robots allow manufacturers to automate delicate processes like semiconductor fabrication. These robots depend on machine vision cameras to align components with exacting precision. Likewise, in logistics, barcode readers and machine vision cameras help robots move finished boxes onto a pallet to remove the reliance on humans for outbound logistics. Learning algorithms help them constantly improve accuracy and throughput.

Automated DCs - Robotics

One domain still requires human input: picking products and placing them in delivery cartons during order fulfillment. To date, a robot cannot improve on human eyes, brains, and fingers, but that’s likely to change in the next few years. Startups are working on replicating the movements of the human hand and the learning functions of the brain and its visual centers. It’s only a matter of time before they figure out how to automate the picking process.

Cognex Clears a Path to Industry 4.0 in Logistics

Empowered by deep learning software, machine vision cameras and sensors will be integral to the evolution of next-generation robotics in the distribution center. Moreover, advances in 5G wireless networking and edge computing devices will reduce network latency and allow distribution center operators to squeeze even more inefficiencies out of their automated systems.

None of this works, however, without the trusted eye of proven industrial automation vision systems like the ones Cognex has been perfecting for decades. Our combination of easy-to-use deep learning software and rugged, reliable industrial vision systems, and image-based barcode readers combined with Edge Intelligence software  is unmatched in the industry.

Mike Poe

Global Content Marketing Manager, Cognex

A technology marketer for over two decades, Mike enjoys communicating how technology can improve business outcomes. Over his career, he’s helped businesses in many industries understand the value of technology through work at Dell/EMC, Sun Microsystems, a few technology startups, and, since January 2020, at Cognex. When not extolling the virtues of using machine vision across various industries, he can be found exploring local Massachusetts and Cape Cod waterways with his kayak, taking in local craft breweries, or working on home improvement projects.

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