Technology—more specifically AI (artificial intelligence)—promises so many things: better quality, heightened productivity, greater safety, streamlined supply-chain management, improved product development and design, and greater resilience and sustainability, just to name a few.
We are seeing case study after case study of success stories of AI in manufacturing and the supply chain. For example, let’s consider Fedrigoni, which is a global reference manufacturer of specialty papers for packaging and other creative applications, self-adhesive labels, graphic supports for visual communication, and RFID (radio frequency identification).
The manufacturer recognized it needed to accelerate digital transformation by leveraging advanced AI, and it started by focusing on stock optimization and demand forecasting. Then, it expanded to more comprehensive digital transformation objectives. Ultimately, Fedrigoni enhances operation efficiency through collaboration with Palantir Technologies. Using AI technology, Fedrigoni can improve responsiveness and enhance its ability to anticipate and meet customer demands.
Let’s consider another case study: L’Oréal. During my time at IFS Connect in Nashville in early June, I had an opportunity to learn about many manufacturing companies using AI in unique and exciting ways. L’Oréal was one example. Andrea Masterton, vice president, marketing, Poka, an IFS Co., shared with me how the company moved to digitized training delivery and tracking, which empowered workers and team leads to keep skills matrix up to date. It also improved training completion and increased volume of skills.
As we all know, this skills gap is critical to address in the manufacturing industry. Labor is a challenge for industries like manufacturing. The data suggests there are way too many jobs to fill—something to the tune of nearly half a million open manufacturing jobs now, and with the rise in manufacturing, there will be millions of new manufacturing jobs expected to open up by 2033.
But the skills gap problem is bigger than just a lack of workers. It is a mismatch of workers and their skillsets. As technology and business evolves, the workers need to evolve as well. We need to reskill and upskill so workers can do a new or different job, as AI helps with some of the more manual or tedious tasks. As technology evolves, workers will need to evolve too—and here’s the key part, companies will need to be responsible for the education and training that is needed to take the business to the next level.
AI can certainly help solve this skills gap. It can bridge the gap that exists in manufacturing today, but it is going to require some strategy and effort.
And let’s be honest here. This only works if we have good data. You can’t do anything if you don’t have clean data. It all stems from good data. Resilience comes from good data. Productivity comes from good data. Quality comes from good data. Safety comes from good data. A streamlined supply chain comes from good data. Improved product development and design comes from good data. A better bottomline comes from good data. That is the secret sauce here. Not just data, but rather good, clean data.
That is the question you need to be asking in your organizations today. Is our data clean? This is a very critical question that you need to be sure you have the right answer to. It’s not a guessing game or one we are making assumptions about. If yes, then optimize all that clean data.
If not, then figure out how to clean it up. And clean it up quickly. Because without clean data we are all just sitting ducks. What steps are you taking to ensure you have clean data?
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