ISSUES WHICH CAN BE SOLVED WITH AI
After steam, electricity, and computers, the manufacturing industry is looking at another developing trend that aims to change its dynamics completely – Information.
WIDENING SKILL GAP TACKLED BY MACHINE LEARNING
The ever-growing domains of knowledge and technology require the skill set of your workforce to stay on pace with global developments. Alternatively, dynamic algorithms can be built with machine learning applications to perform complicated tasks that would otherwise require a specifically trained workforce. E-commerce giant Amazon has tied up with Kiva Robotics to employ about 30000 fulfillment robots in its gigantic warehouses.
DISASTROUS PRODUCT RECALLS EVADED USING AI AND SIMULATION
Product Recalls occur due to the inflexible nature of manufacturing processes that discourages improvisations in products. With the aid of simulation software and artificial intelligence applications, we can flex manufacturing processes to correct possible product flaws dynamically. Digital Twins are now predicting product failures during prototyping.
DISRUPTING EQUIPMENT FAILURE FIXED USING ADVANCED ANALYTICS
Technical failures in machinery can completely disrupt delivery schedules, thereby damaging reputation. These can also inflate the maintenance budgets. However, by analyzing historical data of technical snags using statistical methods employed by big data tools, we can predict future failures and schedule precautionary maintenance without hurting delivery schedules. Tata Consultancy Services was hired by an automotive OEM to increase their overall equipment effectiveness which subsequently rose from 65% to 85% using advanced analytics.
MISLEADING EXPECTATIONS CORRECTED USING MODERN TECHNOLOGY IN MANUFACTURING
Industries often fail to meet the stakeholder expectations which are usually based on historical data extrapolated to present market conditions. It happens because conventional statistics do not take into account many other factors such as natural calamities, logistic disruptions, and political scenarios. Advanced analytics and big data tools can take into consideration a wide range of factors to provide more reliable and accurate expectations.
Benefits of AI in Manufacturing
Manufacturers are facing yet another challenge in the 21st century marketplace, customers are moving at an incredibly fast pace and expect manufacturers to be able to keep up with their requests. Technology has drastically changed the way customers communicate with suppliers, and the flexibility technology offers should allow manufacturers to react to orders in real-time. Artificial intelligence (AI) is just one of the technologies that give manufacturers the ability to keep up with the new pace of business.
In a manufacturing setup, several minute details are not visible to the human eye or often go unnoticed. Advanced technologies like machine learning and artificial intelligence help to find microscopic defects in products such as circuit boards at resolutions well beyond human vision. Also, the use of collaborative robots by manufacturing companies is becoming increasingly popular. These robots can work productively with human colleagues and can take instructions from humans including new instructions that are not anticipated in the robot’s original programming. Hence, better machine senses will result in a safer workplace in the long run.
Supply chain efficiency
Artificial intelligence is also expected to have a great deal of impact on areas of manufacturing that do not have any connection with robotics. The use of AI technology in the supply chain of manufacturing companies can forecast patterns of demand for products across time, geographic markets, and socioeconomic segments while accounting for macroeconomic cycles, political developments, and even weather patterns using different algorithms. Artificial intelligence is also highly beneficial in carrying out predictive maintenance for equipment, with sensors tracking operating conditions and performance of factory tooling, learning to predict breakdowns and malfunctions, and taking or recommending corrective actions.
Automated quality control
Artificial intelligence can aid in faster feedback loops, helping manufacturing companies to tackle unplanned downtimes, low yield (percent of units that pass quality control), and low productivity (time it takes to make a product). AI can help to speed up processes and ensure accuracy rather than relying on humans for in-process inspection and quality control which is time-consuming and also there are chances of failure to detect errors.
While humans are forced to work in 3 shifts for ensuring continuous production, while robots are capable to work for 24/7 in the production line. Businesses can be witnessed to expand in terms of production capabilities and meet the high demand of customers worldwide.
Interesting Projects & Applications of AI in Manufacturing
- Improving semiconductor manufacturing yields
- Asset Management, Supply Chain Management, and Inventory Management
- Predictive maintenance
- Supply chain forecasting
Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations are is achievable with machine learning
Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimization are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.
Asset Management, Supply Chain Management, and Inventory Management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today.
The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.
Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC.
Analytics and MI-driven process and quality optimization are predicted to grow 35% and process visualization and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing(48 pp., PDF, no opt-in) PriceWaterhouseCoopers
McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.
Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.