What Could a Factory 4.0 Look Like?

Source: Christian Cavallo for ThomasNet

Technologies integral to the so-called “4th Industrial Revolution” — artificial intelligence, sensors collecting real-time data in a so-called “Internet of Things,” collaborative robotics, industrial robotic automation, among others — still feel like science fiction but are rapidly maturing as we enter the second decade of the 21st century.?

What could a factory 4.0 look like, especially compared to a 2023 factory? In this article, we will explore what these new technologies could look like in practice, using Josie, a fictional factory 4.0 owner, and her daily routine. Though speculative, this article will use current information and tools to build a portal into the potential of 4.0 manufacturing.?

Starting the Day: Digital Twin Check-in

Josie wakes up to notifications on her phone: her smart factory reports that 20 electronic chipset assemblies were manufactured throughout the night and are awaiting her personal approval.?

As she prepares for work, she logs into the facility’s digital twin portal on a tablet. She navigates to the descriptive view of this digital twin, which shows a visual representation of the location of all assets in the factory. On the screen, she sees a three-dimensional map of the three-story smart factory. Josie zooms in on Level 3, where the 20 chipsets sit on a circular conveyor within the quality control (QC) room.?

In this smart factory, the QC room has an array of cameras and sensors so that product inspection can be done without needing to enter the clean space. Josie taps the room in the digital twin, and jacks into a suite of visual inspection cameras. Positioned all around the assemblies, Josie now has a closeup view with multiple angles from her kitchen table.?

She inspects the quality of soldering and other important components on the first assembly and is satisfied with the work – she swipes it to the right, pushing it to packaging. She does the same procedure for the 19 other chipsets as she eats breakfast, halting only two for a further in-person inspection.?

The Commute: Mobile Logistics

As Josie moves into her car, she switches to the informative twin view that displays all the various operational and sensor-related data for each asset. Navigating to the main manufacturing floor on Level 2, she notices three mobile manufacturing cobot cells (MMCCs) are not fully charged, despite being plugged in overnight.?

Josie highlights them and examines their specifications – she sees that each is scheduled for preventative maintenance (PM) in the coming months. Being preemptive, Josie instructs them to the maintenance bay on Level 1 for early PM. She watches them through the digital twin as they autonomously undock from their stalls, form an orderly line, and board the elevator.?

As Josie sits comfortably, watching the car navigate itself through a busy highway, she switches over to the predictive view of the digital twin. The predictive view will help organize the workday by providing predictions about future events like inbound and outbound logistics, manufacturing quotas, and other scheduled events, which also are centered in Level 1 of the smart factory.?

Of the 10 loading bays on Level 1, Josie sees that four are currently occupied by trailers being loaded for distribution. She also sees that five incoming trailers are expected throughout the day, the first arriving at 8:30 a.m., a setup that would traditionally stress out even an experienced operations manager. Without fear, Josie pulls up the predictive scheduler tool.?

The predictive scheduler tool shows in real time the status, location, and load size of all inbound assets and provides estimated fill times for the four docked trailers. She prompts the transportation management system (TMS) and yard management system (YMS) to jointly generate 1,000 potential schedules, routing the trucks so that their movements cause no unnecessary pile-ups or stoppage.?

After a few minutes, the scheduler provides Josie with an optimized sequence complete with exact times and highlighted directions for each vehicle. Josie approves this schedule, sending the update to his gates and docks so distribution vehicles can be automatically notified with directions.?

Punch in: Cobots Roll-out

Josie arrives at her smart factory. She walks into the warehouse and greets some workers who arrived shortly before her. They are rolling out a suite of warehouse cobots and setting them up for the day’s work.?

Each warehouse cobot is composed of a mast and lift cylinder with forklift prongs that lower into a cart on top of an omnidirectional mobile platform. Think of a forklift without a driver’s seat, attached to a cart, with a large, industrial Roomba underneath it.?

Warehouse cobots help load and unload the heavy boxes and barrels stored on the warehouse’s shelves and transport them to their intended destination. Each worker takes a data-link from their assigned cobot – a sort of short-range transponder that looks like a walkie-talkie – and straps it to their belt so that the cobot will follow them from a safe distance. Though less autonomous than other cobots in Josie’s factory, warehouse cobots are more flexible for the dynamic nature of inventory management and so are directed by their human managers.?

Josie boards the lift elevator to Level 2, where one of the MMCCs she sent for maintenance joins her to regroup with the assembly line once more. She regards the impressive piece of machinery; the mobile cobot cells are much larger and more complex than the warehouse cobots. Being roughly the size of a full-sized bed, each cell consists of a cobot assistance arm, a workstation for human workers, and a modular equipment piece specific to each cell’s prescribed duty. Josie notices this MMCC is a soldering cobot, so it carries large spools of wire solder, and the cobot has a soldering gun at the end of its 7-axis arm.?

As the liftgate opens to Level 2, Josie watches as the cobot returns to its place in the manufacturing line. Once it parks itself in the right space, designated by a box drawn on the floor, its arm begins to solder components coming down the manufacturing line. Without a human supervisor, the MMCC can provide autonomous manufacturing for simple, single-step? procedures.

As Josie walks to her office, she sees a technician approach the cobot and board it, standing behind the driving console. Putting it into manual driving mode, the technician parks it farther down the line and takes hold of the soldering gun attached to the robotic arm. The technician sits at the workstation to start work on a complex electronic assembly, using the cobot’s haptic feedback arm to stay within soldering bounds.?

Josie walks into her office and begins tackling the day’s work with relative ease.?

End of the Day: Lights Out

After a long day of emails, communications, planning, and process optimizations, Josie goes to leave her smart factory; but before she does, she checks in to the digital twin one last time.

She reviews the day’s work and the status of manufacturing lines. In the workday, 152 electronic assemblies were produced, verified, and packaged, and 10% of the warehouse inventory was taken to distribution. She also sees that their in-house levels of indium, a metal necessary for their products, must be restocked. She sets a notification for procurement specialists, so they know first thing tomorrow how much to order and where to send it.

There are still events happening on the manufacturing line – simple soldering, assembling, packaging, and many other tasks that do not require a human are being performed by cobots as workers hang up their hardhats Josie makes her way to the lift and schedules all the cobots to park themselves in their charging ports at 2 AM. She waits for all personnel to exit the manufacturing floor and then turns off the lights. Cobots need no lights while they work, but it is still strange to hear whirring servo motors and see the occasional flash of metal through the darkness.?

Josie descends to Level 1 and walks through the warehouse one last time – digital twins are nice, but nothing beats the human eye. She notices a concentration of product on one side of the warehouse, which could make unshelving a pain for workers; she sends a notification on her digital twin for other users to distribute these materials across shelves starting tomorrow. Josie leaves the warehouse and steps into her car, and remotely arms the factory’s security system for the night.

After-Work Firefighting

As the car powers on and begins to navigate home, Josie gets a late email – one of her prime contractors requests a design change, effective immediately. Revisions typically involve a complete reworking of assembly lines, halting production, and headaches for all involved – but not for Josie.?

Taking the updated schematics, she uploads them into the twin and requests a report on how this will change the manufacturing schedule. After a few minutes, an updated assembly line, list of materials, and cobot organization layout are generated. The digital twin dashboard then provides a simple, actional list of tasks to retool the manufacturing line to this new revision, which Josie can start implementing tomorrow.?

Satisfied that this fire has been quickly managed, she finally puts the tablet away. It is almost unthinkable that operations managers of the past were so disconnected from their factories, and Josie couldn’t imagine how difficult this job must have been without the help of this technology. As she pulls up to her house, she is glad she can leave work at her smart factory and feels safe knowing that an immense array of sensors, robotics, automation, and control systems are providing her value even as she sleeps.?