In a recent post on the GovDesignHub, we shared five challenges that are facing manufacturers and people that make things on behalf of the government courtesy of Phil Steiger of KETIV Technologies. According to Phil, today’s manufacturers are facing mounting supply chain challenges, they’re experiencing labor shortages while also experiencing increased demand, and they’re facing more international competition – which is impacting profit margins and bottom lines.
However, thanks to artificial intelligence (AI) and machine learning (ML) technologies, manufacturers could have a new solution to increase efficiency, productivity, and profitability, and to help them overcome these challenges.
Snigdha Sarkar, a Senior CFD Application Engineer and one of Phil’s associates at KETIV Technologies, recently published an article explaining where AI can have the most significant impact on the manufacturing industry. Here are eight ways that Snigdha thinks AI and ML can benefit manufacturers:
1. Increased productivity among engineers
AI simplifies calculations and coding to remove the burden of the most challenging mathematical problems. It performs these functions automatically or bundles them up into user-friendly, sometimes no-code tools that engineers with varying degrees of experience can leverage to accelerate their workflow.
Manufacturers leveraging AI can calculate with near-100% certainty when orders can be shipped and when they will arrive at their customers’ warehouses.
In fact, AI application increases employee productivity across the board by providing critical insights and automating repetitive processes. Because of AI automation, employees can spend less time on mundane work and double down on the more creative elements of their job, increasing their job satisfaction and empowering them to achieve their potential.
2. A more efficient and innovative design process (generative design)
AI drives software that can independently deliver production-level designs. It’s a game-changer. It does so based on a company’s existing and historical product catalog as well as goals and parameters (spatial, materials, costs, etc.) inputted by a designer or engineer. In a process known as generative design, the software creates multiple permutations for the operator to choose from and learns from each iteration to improve its future performance.
3. An enhanced customer experience
In many industries, it’s hard to differentiate on product (multiple manufacturers are making more or less the same things) or price (margins are already razor-thin with escalating costs and global competition.) The next logical step is to differentiate by providing a superior customer experience.
Because of AI automation, employees can spend less time on mundane work and double down on the more creative elements of their job, increasing their job satisfaction and empowering them to achieve their potential.
AI can help improve CX at multiple points along the customer journey.
AI can help improve sales rep performance. It can guide reps through the sales process to ensure even low-performers and new hires provide outstanding service. And it can give reps intelligent product and pricing recommendations in real-time to maximize margins and customer satisfaction.
AI can also improve shipping and delivery. There’s no better way to get customers bent out of shape than to promise a specific delivery or lead time and miss the mark. The downstream financial consequences can be severe. But thanks to AI, it doesn’t have to be that way.
Manufacturers leveraging AI can calculate with near-100% certainty when orders can be shipped and when they will arrive at their customers’ warehouses. They can also use AI to keep customers informed along the way, meeting and exceeding expectations.
4. Better inventory management and demand forecasting
Most manufacturers have experienced the pain of being over- or under-stocked at crucial moments, leaving money on the table and/or indirectly pushing customers into the arms of competitors. Inventory management has so many moving parts (shifting demand, omnichannel sales, material availability, production capacity, etc.) that humans can’t get right all the time. But, AI can.
Not only does this reduce costs for the seller, but it dramatically improves CX for most buyers who prefer self-serve over human interaction.
AI’s near-limitless computational potential makes maintaining appropriate stock levels achievable. Manufacturers can use AI to forecast demand, dynamically shift stock levels between multiple locations, and manage inventory movement through a bafflingly complex global supply chain.
According to Mckinsey Digital, AI-powered forecasting reduces errors by up to 50% in supply chain networks. It reduces lost sales due to out-of-stocks by 65% and warehouse costs by 10 to 40%. The estimated impact of AI within the supply chain is between $1.2T and $2T in manufacturing and supply chain planning. That’s a huge deal.
5. Improved quality control
The accuracy, infallibility, and speed of AI compared with humans can make the quality control process cheaper and much faster than in the past. AI can pick up microscopic errors and irregularities that humans would miss, improving productivity and defect detection by 90%.
Using AI in the manufacturing process often obviates the need for quality control. AI can either correct faults as it goes or (because it’s not fallible like human beings) create products that are essentially guaranteed to be error-free for better product quality.
6. Predictive maintenance
Predictive maintenance monitors the condition of manufacturing plant machinery and estimates when maintenance should be performed (hint: before faults occur). Predictive analytics reduces downtime, and routine maintenance costs, which is often carried out unnecessarily.
AI-powered forecasting reduces errors by up to 50% in supply chain networks.
AI and machine learning increase the effectiveness of predictive maintenance. The technology combines vast quantities of data captured from sensors in machinery (detecting heat, vibration, movement, noise, etc.), computer vision, and even external data like the weather and the health of other connected machines, leading to significant savings.
According to the U.S. Department of Energy data, predictive maintenance can provide savings of 8% to 12% over preventive care and reduce downtime by 35% to 45%. Extending the life of machinery and limiting unwanted shut-downs has a positive environmental–as well as financial–impact.
7. 24/7 manufacturing operation
As a human being myself, I’m ashamed to say we’re not the best workers. We need regular maintenance, fuel, and downtime; even then, we can only operate for about 8 hours daily.
Conversely, AI can work round the clock performing tasks with a higher degree of accuracy. It doesn’t get tired or distracted, it doesn’t make mistakes or get injured, and it can work in conditions (such as in the dark or cold) that we humans would balk at.
The ability to operate a factory at peak performance 24/7 without the need to pay human operators has a massive impact on a manufacturer’s bottom line. Meanwhile, reducing the workload that needs to be carried out by employees is an effective way to stave off the labor shortage.
8. Streamlined factory layouts
Determining the optimal factory layout is a skill that sounds relatively straightforward. In reality, however, designing the shop floor for maximum efficiency in the production process is incredibly complicated, with thousands of variables that must be considered. This is where AI steps in.
According to the U.S. Department of Energy data, predictive maintenance can provide savings of 8% to 12% over preventive care and reduce downtime by 35% to 45%.
With the lifecycles of products constantly changing, factory floor layouts should be fluid too. Manufacturers can use an AI solution to identify inefficiencies in factory layout, remove bottlenecks, and improve throughput. Once the changes are in place, AI can provide managers with a real-time view of site traffic, enabling rapid experimentation with minimal disruption.
RIICO is an AI system used to simulate and optimize factory floor layouts in industries where the lifecycles of products are constantly changing. It’s a bit like Sims with a virtual factory floor and a drag-and-drop interface.