How to Deal with the Industrial Internet of Things (and Why You Should)

The Internet of Things (IoT) is part of everyday life today. But the adoption of IoT in industry is just at its beginning stage. Below I describe 3 inspiring case studies, and what you can learn from them.

The embrace of the Internet of Things by the manufacturing industry and other industries is one of the major business trends of the moment. This is called the “Industrial Internet of Things” (IIoT). By adding sensors and data communication to hardware, production processes will become “smart.”

IIoT is applicable in 3 main areas. Below I will describe them and add an inspiring case study in each instance. 

1. Predictive Maintenance

Data generated by machines can predict when preventive maintenance is required. The outcome is based on factors such as the number of operating hours and the parameters of the device. This is very cost efficient because fewer machines will break down.

Case Study: Imbema Group

The Imbema Group’s Flamingo app identifies and validates vital hose connections that are used in port operations. This results in a more efficient process. But that’s not all. The collected data also predicts when a hose should be replaced.

2. Asset Management

IIoT helps companies keep an eye on their capital goods which can save both time and money. Though there is some overlap with predictive maintenance, there are plenty of other aspects of asset management as will be demonstrated by the case study below.

Case Study: Fingrid

Fingrid is the national electricity transmission company in Finland. It owns the country’s high-voltage grid and is responsible for transmitting the electricity from the producers and generators to distribution companies and heavy industry. To accomplish this, Fingrid has to manage a variety of assets, from the power lines and towers to the substations across the country.

To help accomplish this, IBM supplied Fingrid an IIoT solution that let them know where assets are, combining the maintenance history of those assets with cost elements. By deploying IBM Maximo Asset Management, Fingrid can tie costs to its assets and apply analytics for deep, real-time analysis. Efficiency is increased, and risk is reduced. 

3. Logistics

When coupled with historical and real-time IIoT data, artificial intelligence can advise optimized routes. This can be the shortest route a truck can take to its destination, but it can also be the best walking route at an industrial plant. In both instances, the application of this knowledge will result in lower costs. Moreover, the example of the truck (fleet management) will reduce CO₂ emissions.

Case Study: H & M

H&M run a fleet of 30 vehicles in the United Kingdom. They make over 1,000 pallet deliveries and collections each day. To optimize this process, the multinational company uses the TomTom Telematics’ WEBFLEET. This is a fleet management solution that provides real-time information on the location of vehicles.

Additionally, WEBFLEET lets companies communicate directly with their drivers, helps to improve driving behavior, saves on fuel, and connects to a whole ecosystem of 3rd-party solutions. Adopting WEBFLEET resulted in a 30% increase on collections and more accurate predictions of times of arrival.

As I described above, the Industrial Internet of Things makes processes more efficient, safe, and reliable. It also provides manufacturing companies with a platform to provide additional services based on the data generated by their devices. These can increase customer loyalty, but ultimately also lead to new revenue models in which their customers pay for the services provided.

The possibilities are almost endless; when do you step in?