Executive Overview
Artificial Intelligence (AI) will fundamentally disrupt not only the way we produce, but also the way supply chains look like. In 10 years, 30- 50 percent of manufacturing process will use AI, which will be increasingly embedded.
Now, we are in the middle of the transition and there are many use cases that show how to implement AI successfully. There are many lessons learned from these case studies. One of the key takeaways is that 90 percent of all work in an AI project is not actually about AI, but is done around it, this includes cleaning data, organizing teams, restructuring, bringing stakeholders together, and much more. Looking more at the technology, successful implementation often is based on a microservice architecture. This allows for faster deployment and project completion times as well as effective lifecycle management of AI — something that should always be incorporated right from the beginning.
The key challenges across the lifecycle include:
- Proof of Concept: Availability of quality data
- Prototype: Integrate multiple stakeholders (domain experts, finance, etc.) and their feedback/expectation
- In-use: Adapting to Change
This strategy report provides not only a detailed analysis of this key challenges, but also the key solutions and strategies to avoid them altogether. This strategy report summarizes the discussion and learnings from 2 events in early 2021, where ARC held a Forum workshop as well as an online discussion. Companies that supported these events include IBM, Microsoft, NNaisense, Pfizer, Philip Morris, Phoenix Contact, Siemens, and Voith.
AI Use Cases
Use cases enable us to see beyond theory and expand our thinking to include real-life problems. These may include mundane things such as differences in time zones, travel restrictions of experts, or even point us to fundamental challenges in AI.
Microsoft shared how they provide the backbone for many operations. The vision ranges from device to the ERP level, creating a seamlessly connected factory. KPIs improvement shared ranged up to 200 percent, averaging between 50-100 percent. It was also critical to see that these improvements addressed productivity, sustainability, agility, speed to market, as well as customization. The latter demonstrated to thing: Without AI, there will be no batch size 1 with the KPIs close to mass production.
IBM shared use cases for a cognitive supply chain, which adapts to disruptive events as well as changes in the company processes, and how they achieved it. Disruptive events include not only pandemics, but other economic and natural threats from the bursting of economic bubbles to flooding, volcanoes, and other extreme weather or climate-related events. A successful and resilient supply chain needs to be able to react quickly to these “black swan” events. AI is certainly not the weapon of choice for all supply chain events, and there are ways to determine if AI is right for you. IBM makes the decision using a 4-field matrix along the axis Frequency (exceptional to regular) and Complexity (rules based to situational). AI is most effective in regular events, which are rather complex, where there is a sufficient amount of data and AI can adapt to complex situations (see chart). This segmentation is based on IBM’s own experience, using AI in their supply chain. Use cases include:
- Incoming parts
- Finished products inspection
- Test operation …….