From trains to sports, here are three real-world case studies of businesses leveraging smart, connected devices and advanced analytics to create competitive advantage, happier customers and new sources of revenue.
You can’t help but notice that the world around us has become far more connected. Information, entertainment, food and drink, banking, retail, travel and accommodation, all and more are now available at the click of a button, anyplace, anytime.
A similar change is happening across manufacturing and there is certainly no shortage of articles, features, videos and interviews eulogising the power and benefits of data and digital technologies.
Increasingly, however, manufacturers are searching for real-world case studies – from their industry and beyond – that demonstrate the significant returns available and get them thinking about possible applications in their own organisations.
Below, you’ll find exactly that.
Transforming the world of football with artificial intelligence
Dutch analytics startup, SciSports, takes real-time data and applies machine learning, deep learning and artificial intelligence to capture and analyse this information.
According to SciSports, traditional football data companies generate data only on players who have the ball, leaving everything else undocumented. Spotting a gap in the market, SciSports developed a real-time tracking camera system dubbed ‘BallJames’.
BallJames automatically generates 3D data from video, with 14 cameras placed around the stadium recording every movement on the field. BallJames then generates data such as the precision, direction and speed of passing, sprinting strength and jumping height.
“This forms a complete picture of the game,” explains SciSports founder and CEO, Giels Brouwer. “The data can be used in lots of cool ways, from allowing fans to experience the game from any angle using virtual reality.”
The data can also help coaches on the bench better identify when a player is tiring, and the company’s SciSkill Index evaluates every professional football player in the world in one universal index.
“[We] use machine learning algorithms to calculate the quality, talent and value of more than 200,000 players. This helps clubs find talent, look for players that fit a certain profile and analyse their opponents,” Brouwer says.
Since its inception in 2012, SciSports have quickly become one of the world’s fastest-growing sports analytics companies. Brouwer says the versatility of the SAS Platform has been a major factor in its success.
“With SAS, we’ve got the ability to scale processing power up or down as needed, put models into production in real time, develop everything in one platform and integrate with open source.
“Our ambition is to bring real-time data analytics to billions of football fans all over the world. By partnering with SAS, we can make that happen,” Brouwer concludes.
Click here to go deeper into the SciSports story, including a video explaining how the company is helping transform player selection and talent scouting by leveraging SAS Viya, SAS Visual Data Mining and Machine Learning, SAS visual Analytics, and SAS Visual Statistics.
Keeping customers happy through predictive maintenance
When it comes to transportation, nothing frustrates passengers more than delays – especially unexpected ones. That’s why rail operates take every advantage possible to maximise their operations and keep customers happy.
In constant operation in all kinds of weather and conditions, it’s no surprise that a large portion of rail company’s operational costs go towards maintenance.
In constant operation in all kinds of weather and conditions, it’s no surprise that a large portion of rail company’s operational costs go towards maintenance.
By leveraging analytics and the Internet of Things (IoT), state-owned Finnish railway operator, VR Group, has successfully moved from a traditional maintenance approach that focused on replacing parts as needed to a predictive regime.
Traditionally, VR Group approached maintenance in two ways. Major systems, like wheels and bogies, were covered in scheduled maintenance. Often, parts were being replaced when they still had a lot of life left.
The other method was to fix things, like doors, when they broke down. These were hard to forecast and could lead to missed routes and unhappy customers.
In recent years, VR Group has begun fitting sensors on various systems and subsystems to monitor symptoms of wear and potential other failures. However, the sensors themselves only collect the raw data. The real benefit comes from analysing that data, often in real-time, to allow engineers to take faster, more appropriate responses.
To add this level of intelligence to their operations, VR Group turned to SAS Analytics.
VR Group developed a predictive maintenance program that focuses on continuously monitoring the condition of parts. In this program, mathematical models predict when parts are likely to fail so that they can be replaced before they cause unplanned downtime.
By looking at sensor data, SAS Analytics gives VR Group a real-time overview of its 1,500-strong fleet.
By looking at new and historical data, SAS Analytics helps VR Group plan the maximum interval between certain maintenance events, such as turning wheels (on a lathe) or replacing the more than 30,000 wheel-and-axle sets found on every train.
Optimising work in this way has the potential to reduce the amount of maintenance by one-third, according to Kimmo Soini, senior vice president for maintenance at VR Group.
SAS Analytics also helps VR Group identify the root causes of failures, which can increase savings and improve the reliability of trains. Additionally, effective insight into IoT enables the railway company to minimise stock levels of spare parts and materials, keeping only what it needs on hand.
Are your perceptions of Industry 4.0 wrong?
Industry 4.0 is commonly understood as the trend towards automation and data exchange in manufacturing technologies, including cyber-physical systems, IoT and cloud computing.
However, this is often misinterpreted to just mean the automation of basic physical and digital processes. <any manufacturers have already achieved this level automation and so, believing they’ve already cracked Industry 4.0, they stop innovating.
In reality, Industry 4.0 encompasses so much more. We define it as using data across your entire value chain and using that data to generate actionable insights which unlock cost savings or revenue growth.
Download our exclusive free eBook to learn how your business can use advanced analytics to expose the monetary value trapped in your data.
Improving healthcare outcomes thanks to IoT insights
In health care, analysing IoT data can result in increased uptime for cancer treatment machines, which means that more patients are treated when they are scheduled. If a treatment is missed, it can be up to 40% less effective, so reducing potential service interruptions is critical.
Elekta is a Swedish medical device manufacturer that provides equipment and clinical management to help treat people with cancer and brain disorders. Operating in the highly competitive medical device industry, Elekta has leveraged connectivity and smart devices to generate greater value for their customers, as well as new sources of revenue.
Over the past decade, Elekta has been reportedly using IoT to help differentiate its service business and optimise its field service.
By monitoring hundreds of sensors, identifying issues early and proactively correcting them, service personnel arrive on site armed with both the necessary information and parts needed. Elekta says that such monitoring has led to a 30% reduction in site visits.