Artificial intelligence has the potential to transform the manufacturing industry. From product development to demand planning to process control, AI provides real-time information to make the entire manufacturing process more efficient. The possibilities seem endless, but manufacturers have been slow to embrace the changes.
According to our recent research report, Artificial intelligence in manufacturing operations, maintenance and quality are the two areas of the greatest activity, with production and supply chain management lagging behind. The report found that companies from across different manufacturing segments are choosing to implement a small set of use cases in their manufacturing operations rather than acting on several across the value chain.
In fact, just three use cases account for the majority of AI implementations at top manufacturers:
- Intelligent maintenance
- Product quality inspection
- Advanced simulation and digital twins.
The good news is the industry has made significant progress with AI in these three areas. For example, a company produces memory technologies on silicon wafers, which is a highly complex and precise process with defects that are invisible to the human eye. Now, computer vision spots any problems and has greatly improved manufacturing efficiency and effectiveness.
Another company is using historic and current data to predict parts failure. It developed an AI algorithm to predict potential problems and help identify when parts might fail, so it can make proactive maintenance decisions for its customers.
Machine learning also uses data to drive efficiency. One telecommunications company is using a video application to monitor an assembly line at one of its factories. Now, machine learning alerts the operator of inconsistencies in the process, so they can be corrected in real time.
Even with the significant potential of AI, only 34% of manufacturing companies in the US are deploying it widely. Frontrunners will enjoy an advantage but there is still time to catch up. If you want to kickstart your AI efforts, you need:
- To identify clear business value and benefits: Easily identified and quantifiable benefits will make it easier to build a strong business case. Look for opportunities to reduce downtime, improve OEE, reduce product defects, or reduce inventory.
- Ease of implementation: Focus on less-complex projects with shorter payback periods and a higher return on investment to further strengthen the business case.
- To activate your data: Information must be from a trusted source and contain enough data to be useful for a project.
- AI knowledge: There are existing packages and frameworks available to help you accelerate your AI project.
- Visibility: Allow employees to understand how decisions are reached to ease adoption by operational teams.
Scaling AI implementations beyond proof of concept (PoC) remains one of the biggest challenges for manufacturers, but the opportunities for AI to streamline the manufacturing process are significant and every manufacturer needs a plan to scale.
Lalit Khandelwal is an EVP, Manufacturing & Industrial Sector Head at Capgemini and he can be reached at here.