Jabil creates the factory of the future with predictive analytics
By Indranil Sircar, Director, WW Industry Technology Strategy, Discrete Manufacturing Industry on July 27, 2016
Filed under Discrete Manufacturing
2016 has marked a breakthrough for what we are calling an intelligence revolution. With Industry 4.0, Microsoft believes it is intelligence that will most significantly disrupt and create the next revolution in business.
Driven by fundamental technology advances like connectivity and the cloud, intelligent implementations in manufacturing are transforming how businesses operate. Microsoft is at the forefront of this business transformation by empowering our customers and partners to create ecosystems of intelligence: bringing together intelligent systems of services, people and things into a digital feedback loop that helps companies draw better insight out of data and convert it to intelligent action.
From a business perspective, the focus is on providing the insights needed to engage with customers in new more meaningful ways, empowering employees with key insights, optimizing operations, and finally opening up opportunities to transform products and go after the new disruptive market opportunities. At Hannover Messe, we showcased a number of real-world examples where our customers and partners are taking advantage of systems of intelligence to do just this.
Jabil: optimizing operations for real-time, predictive analytics
One striking example of this transformation is Jabil, one of the leading design and manufacturing solution providers in the world. As a flagship showcase in our booth at Hannover Messe, Jabil showed the world how, by integrating predictive analytics with real-time manufacturing, the company was able to extend the factory floor to the cloud, gaining better agility to meet customers’ demands for increasingly faster, more customized solutions.
Using the Microsoft Azure Cortana Intelligence Suite, Jabil was able to advance digital manufacturing with real-time predictive analytics by connecting equipment, sensors, and people, and then optimizing their factory operations in a way that enabled them to be more competitive and differentiated.
Many manufacturers now use software to analyze real-time problems and defects in manufacturing for taking corrective action. But Jabil wanted to create a solution that would identify errors or failures early in the process – before they even occurred.
Jabil’s solution, which uses Microsoft Azure services, does just that, analyzing millions of data points from machines running dozens of steps through the manufacturing process, predicting failures earlier in the process, for example, at step 2 in a 32-step process instead of realizing the failure at step 15.
Clint Belinsky, Vice President of Global Quality at Jabil, says that as the company has deployed the predictive analytics solutions, Jabil has seen at least an 80 percent accuracy rate in the prediction of machine processes that will slow down or fail. These are pretty impressive results.
During Hannover Messe, Jabil’s demonstration continuously drew a steady stream of visitors and we received very positive feedback from the industry about the solution. Andrew Hughes, Principal Analyst from LNS Research, said this was the most impressive demo he saw at the show, and one of the world’s largest automotive manufacturers decidedly told us: “This is the solution we want for all of our plants, globally.”
This story is not only an exciting example of how a company is reshaping the way it does business in the era of an intelligence revolution, but it is truly groundbreaking. Jabil is not only predicting that a potential issue or failure could occur, but by having visual insight into all levels of production and operations, the company also now knows why the failure was predicted. That it is actionable information Jabil can use to avoid a costly loss, while shortening product lead times and delivering superior quality. It all translates to building a stronger competitive advantage while saving time and money.
Twitter: Indranil Sircar