To thrive in today’s global markets, manufacturers need to optimize everything under their command — from supply chain, materials and order management to engineering design, manufacturing engineering, shop floor operations, order fulfillment, logistics and services.
With these objectives in mind, manufacturers are reinventing their world with technologies that connect products, smart factories and supply chains in new ways. This transformation is fueled in part by massive amounts of data and the use of artificial intelligence across the manufacturing environment.
As is the case in many industries, big data now goes hand-in-hand with AI for manufacturers. Modern manufacturers are producing enormous volumes of data, thanks to a steep drop in sensor costs that has allowed data collection at every stage of production, and now they need AI to make use of it all.
There’s good news on this front: Manufacturers are seizing the day. Many are already capitalizing on substantial investments in the technologies for AI — while planning to continue investing in the years to come. In fact, the market for AI in manufacturing is projected to grow by nearly 50 percent per year, to hit $17.2 billion by 2025.1
Common use cases
Let’s look at some of the more common use cases for AI in manufacturing, as called out by McKinsey & Company in a widely cited report on AI in the industrial sector.1
- Predictive maintenance — Smart manufacturers are using AI systems in conjunction with IoT data to predict and avoid machine failure. The goal is to use predictive maintenance to minimize disruption and inconvenience, prevent issues and resolve problems quickly. The payback for manufacturers can be huge. McKinsey reports that AI-driven predictive maintenance can increase asset productivity by up to 20 percent and reduce maintenance costs by up to 10 percent.
- Yield enhancement — Manufacturers can now use AI systems to decrease scrap rates from defective products and get more value out of the materials that go into the production process. These gains are made possible by using AI systems to identify causes of yield losses that can be avoided by changes to production processes or product designs. The payoff can be huge. For example, McKinsey says that in the semiconductor industry, decreasing scrap rates and testing costs can lead to a reduction in yield detraction of up to 30 percent of the total production cost.
- Quality testing — AI opens the door to new quality testing procedures. For example, manufacturers can now use machine learning and advanced image recognition systems to automate the visual inspection and fault detection of products, and to trigger the automatic ejection of defective products from a production line. These capabilities can yield significant savings. McKinsey says AI-driven quality testing can increase productivity by up to 50 percent and increase defect detection rates by up to 90 percent in comparison to processes based on human inspection.
- Supply chain management — Many manufacturers have complex supply chains that encompass thousands of diverse components and specialty tools. Any delays, breakdowns or mistakes can shut down a product assembly point. AI can help here. With AI, manufacturers can better predict the complex interactions between each production unit and automate requests for parts, labor, tools and repairs. McKinsey says AI-enhanced supply chain management can help companies reduce forecasting errors by 20 to 50 percent to optimize stock replenishment. Better still, AI can help manufacturers reduce lost sales due to stock‑outs by up to 65 percent and reduce inventory by as much as 20 to 50 percent in some settings.2
- Research and development (R&D) — In the R&D realm, AI systems can help design and engineering teams collaborate more closely, choose the best materials for a product, identify designs that may be prone to failure, and more. With AI, for example, designers can define a problem via goals and constraints, and then let the system come up with dozens or even hundreds of different solutions, some of which might be starkly different from conventional human approaches. “The application of machine learning to enable high-performance R&D projects has large potential,” McKinsey notes. “R&D cost reductions of 10 to 15 percent and time-to-market improvements of up to 10 percent are expected.”
- Business support functions — AI can help manufacturers automate key aspects of labor-intensive support functions, such as those in IT, human resources and finance operations. For example, AI and robotic process automation (RPA) can work together to automate routine functions like reviews of contracts and responses to IT help-deck calls. McKinsey predicts that this automation of support functions will drive improvements in both process quality and efficiency. The firm says automation rates of 30 percent are possible across functions and up to 90 percent for some routine service desk tasks.
- Collaborative and context‑aware robots — Unlike their counterparts that carry out fixed tasks in limited spaces, context-aware robots use computer vision and AI to operate alongside humans in shared environments. They have the ability to sense and avoid people and obstructions in their paths as they carry out their assigned tasks, such as finding, picking and moving parts on a manufacturing floor. McKinsey says that collaborative and context-aware robots will improve production throughput in labor-intensive settings, and will increase productivity by to 20 percent for certain tasks.
A case study
OTTO® Motors manufactures flexible and intelligent self-driving vehicles for material handling for enterprises across many industries, including aerospace, automotive, e-commerce and healthcare. Drawing on the power of AI, OTTO automates material movement jobs, such as bringing raw materials to the line, cross docking pallets and moving parts between processes. AI is an essential workload for OTTO Motors. It allows an OTTO vehicle to analyze its environment, internalize that information and then render a decision quickly as it moves across the industrial floor.
The use cases presented here offer a glimpse at what is happening now and what lies ahead with AI in manufacturing. Through its ability to help manufacturers put data to work to optimize everything under their command — from the supply chain to product quality and factory throughput — AI will serve as one of the keys to manufacturing success in the new, digitally driven industrial revolution.
Ready to learn more?
For a closer look at OTTO Motors successes with AI, read the Dell EMC case study “Building a Safer Workplace with Self-Driving Vehicles” or watch the video “OTTO Motors Builds a Safer Workplace with AI and Dell EMC.”
1 MarketsandMarkets, “Artificial Intelligence in Manufacturing Market worth $17.2 billion by 2025.”
2 McKinsey & Company, “Smartening up with Artificial Intelligence (AI) — What’s in it for Germany and its Industrial Sector?” April 2017.