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Modern Manufacturing

Artificial Intelligence in Manufacturing Processes

John Klinke
Director, Oracle Industry Strategy Group

Guest Author: Aniello Pepe, Director, Industry Solutions Group, Oracle

Artificial Intelligence (AI) has tremendous potential in manufacturing processes. However, there is still a lot of hype which can lead to unrealistic expectations.

In this post, I’d like to clarify some doubts and provide some hints for an effective AI adoption path for manufacturing processes. 

AI Use Cases in Manufacturing

The main goal of AI technology in manufacturing is to support the decision making process, either making smarter decisions or making faster decisions. Smarter decisions can be achieved by having AI-powered applications take into account a much larger data set than a human being can easily process. Faster decisions are possible by AI-powered applications automating decision making processes to increase efficiency and speed compared to manual or human-driven decision making. 

AI can also unleash further evolutionary step in manufacturing processes. Indeed, it is a self-limiting approach to apply AI techniques just to make existing processes more efficient, when AI could be used to make new products that were not possible before or make products differently to gain a competitive advantage. 

One example is moving from prescriptive planning on the shop floor – determining the exact sequence of work orders to be executed on each production resource – towards a self-adaptive shop floor environment allowing more degrees of freedom. For instance, providing production goals and main constraints and allowing the shop floor to make real-time decisions about what to do. 

AI can be applied in both cases. However, in the first case, it is used to manage exceptions – that is, trying to reduce the impact of unplanned disruptions. While in the second case, AI can be used to equip the shop floor with the intelligence to constantly adapt to the current situation to achieve target production goals and KPIs.

AI Depends on Digital Threads

AI is not magic. It needs the availability of relevant data from multiple business sources. The more information AI applications can consume, the more intelligent the decisions they can derive. AI-based capabilities need to be founded on free-flowing data threads connecting a broad range of business processes. For example, smarter decisions on the shop floor depend not only on data about the situation at plant level, but also on relevant information of what is happening in finance, marketing, sales, service and even in the social media space for the company.

Oracle’s Approach to AI-Enhanced Manufacturing

Oracle’s goal to improve manufacturing with AI involves equipping every single business process with the capabilities for contributing to and exploiting digital threads of information, and then reshaping business processes with game-changing capabilities through embedded AI functionality that leverages these digital threads. For more information about Oracle’s AI approach, check out our AI Applications for Supply Chain and Manufacturing.  For more information about digital threads, check out Adapting Manufacturing for the Digital Age.