A Day in the Life of an Industrial IoT-Enabled Smart Factory

The IIoT’s unique ability to connect to and collect data from factory equipment and processes, value-chain partners, and business applications helps companies to gain real-time, actionable insights and to yield outcomes that weren’t previously possible. With these insights, manufacturers can improve the performance of their assets, their stakeholder experiences and their ability to capture economic rents.

But what does the use of the IIoT in a smart factory really look like in practice? Let’s explore this by examining a day-in-the-life at a fictional smart factory that builds forklifts, run by a company called Forklift USA. We’ll see how Forklift USA uses the IIoT in a variety of ways to help optimize its operations as raw materials move into the factory, parts are fabricated and forklifts are assembled, tested and shipped out. 

9:00 a.m. - A Smoother Supply Chain

It’s a sunny Monday morning at Forklift USA, and Jimmy, who supervises raw materials intake and storage at the factory, has assembled his team at the loading dock to unload a truck pulling in with a new shipment of steel from one of the company’s suppliers. 

Jimmy previously planned to have his team unload the truck at 8:00 a.m. However, an Electronic Logging Device (ELD) installed in the truck transporting the steel shipment sent its location via a cellular wireless network to Forklift USA’s supply chain visibility system at 7:00 a.m., showing that the truck was behind schedule due to a sudden storm. The visibility system combined this location data with real-time traffic and other third-party data to update the truck’s estimated time of arrival to 9:00 a.m.. An alert on this new ETA was sent from the visibility system to Jimmy’s smart phone, informing him of the delay. 

So from 8:00 a.m. to 9:00 a.m. Jimmy had his team move raw materials to create new storage space for another shipment expected to arrive the next day, a task he previously scheduled for later that afternoon. As a result, instead of paying workers overtime because of time wasted waiting for a shipment to arrive and making Jimmy late for his son’s baseball game that night, Jimmy and his team were able to complete all of the day’s scheduled activities on time – reducing costs and ensuring Jimmy saw his son hit his first home run.

10:30 a.m. - Enhancing Worker Productivity

Later in the day, Mario, who was hired six months ago, uses a welding machine to create the chassis for a forklift. Mario was hired to replace Bill, who did this welding work for more than 20 years before he retired. 

The welding machine has an IIoT application that uses integrated sensors to track the machine’s activity, including the temperature and the time it takes to complete the welds. The application uses a Low Power Wide Area (LPWA) cellular wireless gateway to transmit this data to the factory’s data lake.

Soon after Mario took over for Bill, Mario’s supervisor, Jane, noticed when analyzing the data it was taking Mario 25 percent more time than Bill to complete the welds required in this step of the chassis production process. Jane met with Mario and realized that, lacking Bill’s long years of experience, Mario was completing the welds in a sequence that required him to move the chassis halfway through the process. 

Jane showed Mario a more efficient sequence for making the welds, and Mario’s productivity soon matched Bill’s. Thanks to this increase in productivity, the welding department at the factory is back on track to meet its quarterly production goals. And Mario’s improved productivity now qualifies him to secure a raise after his one-year anniversary at the company. 

1:00 p.m. - Reducing Downtime with Predictive Maintenance 

In the early afternoon, the factory is humming with activity. For example, Susan’s team is using a sheet metal cutting machine to manufacture body panels for the forklifts. In the past, unexpected dulling of knives in the sheet metal cutting machine forced Susan’s team to stop the production process to replace the knives, reducing the equipment’s availability and overall factory efficiency. 

However, the original equipment manufacturer (OEM) of the sheet metal cutting machine has integrated a new IIoT predictive maintenance application into their equipment. The application uses the Programmable Logic Controller (PLC) and sensors to collect data on the machine’s activities and the condition of the knives and then sends the data via a cellular wireless network to the OEM’s cloud. In the cloud, the IIoT application uses this data and predictive analytics to determine when the machine’s knives will need to be replaced.

This allows the OEM to send Susan new knives for the machine so that they arrive before the old knives have become too dull to work effectively. In addition, Susan can schedule replacement of the knives at a time when doing so will not slow the factory’s manufacturing processes, keeping production of body panels on track. 

Since the launch of the new IIoT predictive maintenance application, metal cutting machine downtime has been reduced, helping Susan’s team break their previous body panel production records, earning them congratulations from the factory manager at the last company-wide meeting. 

2:30 p.m. - Rapid 3D Printing of Custom Components

One of Forklift USA’s new customers, a global online retailer, has worked with Forklift USA to add their own IoT device to ten forklifts they are ordering. The device will be used with a new proof-of-concept IoT application the retailer is testing that integrates data on their forklifts’ location and status into their warehouse management application, helping them deliver products to customers faster and more efficiently. 

However, adding this device to forklifts requires production of a uniquely sized plastic container for the device, which is being installed under the forklift’s dashboard. When the online retailer receives the forklifts, they will put the IoT device into the container themselves. The factory has recently installed a new 3D printer that can produce these containers. Daniel, who manages it, uses the 3D printer’s IIoT-enabled connectivity to download the container’s design from the online retailer. 

While the 10 forklifts go through the usual production process, Daniel prints the 10 containers on the 3D printer so that they are ready in time for final assembly. An autonomous material handling vehicle then brings the 10 containers to the assembly cell where they are added to the forklifts that will be shipped to the retailer.

Forklift USA was able to win the initial order for 10 forklifts (and its first with this online retailer) thanks partially to their ability to provide this type of customization. Forklift USA is hopeful that this ability to customize their products for the online retailer could help them receive other orders from the online retailer in the future.

4:30 p.m. - Improving QA with Industrial IoT Generated Insights

Before they are packaged up and shipped to customers, completed forklifts are inspected for quality assurance (QA) by Meredith, a 20-year company veteran with a careful eye for detail. Meredith used to use a clipboard with a checklist. However, today, as she inspects the last forklift of the day before she ends her shift, she uses a rugged tablet with electronic checklists that are connected to the factory’s ERP, QA and other applications.

As each forklift comes to her for QA, she fills out the checklists as she visually inspects and tests each forklift. During this process she also reviews an IIoT QA application that collects and analyzes data on the manufacturing processes for this particular forklift’s chassis, body panels and other parts, as well as data during the assembly of this forklift. 

Using Artificial Intelligence (AI) and Machine Learning (ML) technologies, this IIoT application can determine if certain patterns in the manufacturing and assembly data indicate a higher probability of quality issues with a forklift. For example, last month the system indicated that Meredith should review a forklift’s seat, since the time spent assembling the seat for the forklift was much shorter than normal. Sure enough, the seat was not properly installed and had to be reinstalled before delivery to the customer. 

However, today the IIoT application does not flag any problems with the forklift Meredith is currently inspecting. She marks the QA inspection for the forklift complete on her tablet, waves it through to the packaging and shipping department and then punches out at 5:00 p.m. after a long day. 

Smart Manufacturing with the Industrial IoT

As this day-in-the-life of Forklift USA’s smart factory demonstrates, the IIoT’s ability to connect to and collect data from manufacturers’ supply chains, equipment, partners and customers enables manufacturers to dramatically improve factory performance. Together with data analytics, the IIoT can deliver manufacturers real-time insights they can act on to prevent delays, avoid mistakes, improve efficiency and deliver more value to their customers. By improving decision making not just in the boardroom, but on the shop floor itself, the IIoT can empower manufacturers to build smart factories that are more efficient, safer and better able to deliver value to customers. 

To learn more about how companies can use the IIoT to make manufacturing smarter, watch the IndustryWeek and Sierra Wireless webinar Transforming Manufacturing for Growth: Gaining a Competitive Advantage with IoT.

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