AI and predictive data analytics are helping all kinds of businesses improve performance—and the manufacturing industry is taking notice. The performance gap between digital leaders and laggards in manufacturing is hard to ignore: manufacturers who efficiently leverage AI achieve approximately 45 percent higher earnings, according to the Harvard Business Review (HBR).
However, one critical problem with AI lies in its explosive popularity. Wanting to gain an edge on competitors, companies are quick to invest in a solution without properly vetting their data to determine whether it can work with such a technology. The reality is businesses need to have a far more extensive process in place to integrate their data and determine a proper AI solution.
In fact, it’s likely a mere 5-10 percent of businesses have taken the necessary steps to enable their data to maximize the value an AI platform can offer, according to data from a Frost & Sullivan digital manufacturing event. To enable the full benefits of AI and predictive analytics, certain preparations must be made to ensure a business’ data can seamlessly function in an AI environment.
There are three steps companies should take before leveraging AI in manufacturing:
Manufacturers shouldn’t adopt technology solutions simply for the sake of doing so—there must be a specific, tangible goal to improve business performance across the value chain. By the time a company commits to embarking on the digital transformation “journey,” it should have already identified what part of the business it wants to see optimized, whether it’s safety, quality, delivery or cost.
Put Together the
Once a manufacturing firm focuses its priorities on which areas of the value chain it wants to see improved, it should then decide how its relevant data will be consumed, and by whom. Start with the end user of the data and ask: What does the end-user of the data need to know? How will certain information drive increased performance? How will the predictive analytics provided by AI be used to prevent variances in the value stream? Understanding what data to emphasize and how it will be received is crucial to the successful adoption of an AI solution. Ultimately, adoption means the predictive analytics and information presented via AI can be easily understood and used to improve performance and prevent variances from ever happening.
Once a manufacturer establishes both a goal and a plan for how its data will be consumed, it must devote strong leadership focus to integrate this data across value streams into a common platform. These streams can range in complexity, from something as simple as the four walls of the plant itself, to more complex, including the supply chain and the end customer. Businesses that successfully integrate their data across the relevant value streams enjoy more value from AI and predictive analytics solutions than those that don't, according to HBR.
Don't get caught in the AI hype with your Industry 4.0 initiatives. If the data is not integrated and ready for AI it will have no impact on performance outside of small point solutions. The real value in AI is to look across all value stream data in big data sets to predict the patterns and misses humans can't. By doing the proper data visualization and integration across the relevant value streams, manufacturers can both ensure an optimal ROI on the predictive analytics solutions they invest in as well as remain one step ahead of less vigilant competitors.