Wide-spread AI in manufacturing is inevitable. With the costs associated with integration, data collection and staff training, not to mention all of the AI start-ups and new products entering the market, you may not want to be the first kid to jump in the pool. I get it. But do you want to play catch up with your competitors? Can you afford it? There has to be a middle ground.
How do you get started with AI, Big Data and analytics and how it will help your manufacturing operations?
- Get smart. The proliferation of IoT has allowed machines – smart machines – to collect and transmit an ever-growing amount of data. The more data points collected, the more reliable the model and identify patterns. Companies are using the information to improve product quality, prevent production downtime, improve workflow, and maintain inventory and resources.
- Define the problem. What issue or problem do you want predictive analytics to solve? Do you want it identify machinery that may be under performing? Your maintenance team can pre-order parts, schedule maintenance before it becomes an issue, preventing downtime and loss revenue. Do you want it to keep track of inventory and supply and demand trends? How about materials? Knowing what and how much product you need to manufacture will enable your operation to run lean and efficient.
- Hire a data ninja. Collecting the data is meaningless if you don’t do anything with it. You need a team to analyze the data and figure out what it means for your business. Data analysts will build your models for meaningful results, and tweak when necessary.
Lessons Learned from Those that are Doing it
Join us at AI Manufacturing, September 12-13 in Oak Brook, Illinois as Alex West with IHS Markit delves into on Analytics in Manufacturing and discuss case studies that highlight lessons learned and provide some “best-practice” tips to the implementation of transformational technologies.
Learn where and how to start a project; how different industries are applying Industrial IoT technologies; common challenges in introducing analytics-based solutions; how to engage teams to utilize the technologies and how to get them to work together; and what are the potential returns on investments (ROI’s) that have been achieved.