Bridgestone Corporation is a manufacturing giant in the transportation industry, making tires for the global car, motorcycle, agricultural, truck, and bus markets. In order to streamline its manufacturing operations across eight production sites in Europe, the company’s Bridgestone EMEA subsidiary sought a way to integrate artificial intelligence (AI) to bring efficiency to their overall production.
Manufacturing tires to many different specifications, for many different assets, in several different locations, smartly and efficiently – can be a daunting task. It requires the integration of thousands of machines, devices, sensors, and employees working with smart data that has been structured and learned to provide useful analytics.
Optimization through AI
For this process, real-time data from different operations are compounded into a single platform, with optimal production planning provided though the power of artificial intelligence. They used several software applications powered by Dassault Systèmes 3DEXPERIENCE to achieve this. It’s all part of Bridgestone’s overarching manufacturing mission and theme that runs through all of its manufacturing strategy, to be “Dan-Totsu,” Japanese for “the absolute and clear leader.” “Our smart factory program plays a key role in our enterprise-wide Dan-Totsu objective to lead in everything we do,” said Adolfo Llorens, vice president, manufacturing, Bridgestone EMEA. “Dassault Systèmes’ digital technology will support this mission. By optimizing manufacturing processes at our plants, we can improve decision-making and productivity, and ultimately reduce costs.”
AI in manufacturing has gained momentum and will continue to accelerate on the factory floor. According to Deloitte’s survey of AI adoption in manufacturing, the manufacturing sector is long on AI. Ninety-three percent of companies surveyed believe AI will be “a pivotal technology to drive growth and innovation in the sector.” The survey also found that 83 percent of companies think AI has made, or will make, a notable impact. Within the 83 percent, about one third believe AI projects have already brought value. Fifty-six percent think these projects will bring value within two to five years.
“Looking at technology trends, more companies will invest in hybrid technology systems to optimize production, costs, inventory, or quality control, to predict sales and prices, or perform predictive maintenance,” a report on their survey results said. “Companies are less enthusiastic about investing in technology used for a single purpose, such as visual surveillance, robot localization and expert systems.”
The survey brings optimistic findings and is seemingly representative of globally minded, competitively prepped manufacturing companies. AI isn’t just good technology to have. Today it’s a necessary ingredient to successfully compete.
One of the many challenges today’s high performing manufacturing operations managers face is knowing when. When will things fail. When they need to schedule downtime. When they should order material and have people and resources ready. That’s where AI and machine learning, using discrete data, become quite useful.
“The area where AI is going to have the biggest impact in manufacturing in the short term is in predictive maintenance and predictive quality,” explains Kavita Ganesan, Ph.D., founder of Opinosis Analytics, an AI and machine learning consulting firm in Sandy, Utah. “Why? Because of data. Data is being generated daily when it comes to machine parameters before and at the time of failure as well as from all the manual repetitive work that goes into quality assurance.”
Ganesan says if this data is correctly collected and stored, it can be leveraged to “predict” potential downtime before it happens, allowing maintenance to be planned accordingly. “While unplanned maintenance can be costly, planned maintenance at times when machines don’t need to be working can limit revenue loss,” he adds.
Data is the secret sauce of smart manufacturing. Data, when gathered carefully, analyzed fully, and put to meaningful use through advanced algorithms and machine learning, isn’t just data anymore. It becomes lightning in a bottle. It’s what plant managers and other industrial leaders can use to make informed decisions based on fact. Yet making decisions based on real-time data has been challenging.
With new technology that not only computes, but predicts, and integrates well, there’s a whole new tribe to follow in manufacturing: the one that’s engaged AI to get results. Getting results means reducing costs, boosting quality control metrics, improving cycle times, reducing downtime, increasing uptimes, and getting your quality product out the door and to market, ahead of your competition.
As for the Bridgestone efforts to improve tire manufacturing with AI and smart systems: they’ve been able to reduce their planning cycle from weeks to days. Plus, they’ve expanded the use of plant assets, and are now able to respond quickly to disruption through a central platform that monitors production and act according. Artificial intelligence, combined with a few good tools, solutions, and smart thinkers, is doing the trick.
Editor: To learn more about data science and AI for innovation, watch 3DEXPERIENCE: A Virtual Journey Episode 2, our Dassault Systèmes North America virtual event series, now available on demand.