Ever since the Industrial Revolution, manufacturing industry has focused on reducing cost by increasing operational efficiency, creating a safer work environment, and recently focused on improving the customer experience, making supply chains management even more complex in ways never imagined. Manufacturing, as an industry, has matured practices for decades, however, the success of these has always been depended on people and their experience at various levels of the organization. Availability and efficient use of the human brain have been one of those last frontiers that industry has tried to solve for years.
What is AI?
AI is the intelligence shown by computer systems to perform tasks that normally require human knowledge, intelligence, skills, learning, such as speech recognition, decision making, visualizing, etc. AI encompasses a range of technologies that are learned over time, as they are exposed to more data. Machine learning algorithm is the ability of AI-enabled systems to learn without being explicitly programmed. It uses algorithms to analyze data, learn from it, and then make decisions or provide output. Machine learning algorithms allow machines to perform a specific task by training the system using available data. Deep learning is a critical component of machine learning methods which are based on learning data representations, and not to task-specific algorithms. Learning can be supervised, semisupervised, or unsupervised.
For better understanding, AI is an umbrella term, machine learning (ML) is a subset of AI, and deep learning is a subset of ML.
Importance of Artificial Intelligence in Manufacturing
Generative Design: Engineering and R&D face major challenges – productivity, efficiency, budget constraints, and the number of designs that can be considered. To overcome these, design goals, parameters for materials, manufacturing methods, and other details can be incorporated into the software which works on all possible combination of these input variables and suggested designs. These are further refined and improved by each iteration since ML comes into play. AI-powered technologies can deliver better designs and eliminate waste.
Digital Twin: Existing fault detection process and tools are less accurate as compared to AI-powered model, resulting in billions of dollars wasted on product recall and loss of brand value. To overcome this a virtual model of a process, product, and physical assembly or supply chain can be created. This is called as the digital twin, which is a combination of data and intelligence that represents the framework and behavior of a physical system. This will help simulate and identify problems and suggest solutions.
Parts embedded with sensors in physical assembly provide a real-time steady stream of data, which can be used in simulation by creating a real-time image or model. Virtual model will improvise based on the physical model and both can help each other to be more effective.
The digital twin can also increase the efficiency of data usage by processing only critical information and discarding other information. This model can help identify future anomalies. Digital twin gives an insight of machines, process, and entire supply chain management which will help design better products, more efficient processes and innovation, and reduced time to market.
Predictive Maintenance: The current way of maintenance rests on the traditional approach of manual monitoring of standard shelf life of components to perform predictive maintenance. This can change significantly if sensors are embedded in manufacturing equipment and alerts can be generated on a real-time basis for required maintenance. AI-enabled tools will predict when the equipment is due for maintenance on more actual condition basis. The next phase or level of predictive maintenance is predictive intelligence that enables manufacturers to maximize efficiency in a way that equipment rarely fails.
Asset Management: ML has an edge in visual pattern recognition, helping in applications for physical inspection of assets in the entire supply chain management. Using algorithms and data sets, applications can differentiate between shipments or assets with damage and wear & tear. It can prepare the log of events and suggest corrective action for repairing assets.
Supply Chain Management: Supply Chain is the backbone of the manufacturing industry. AI is helping reduce cost by reducing transportation cost, warehouse management, reducing pilferage, and efficient supply chain administration. ML is capable of processing large, diverse data set on a real-time basis for demand forecasting. In a collaborative supply chain model, it’s helping in the reduction in freight costs and improving supplier delivery.
Inventory Management: Most of the supply chains struggle to face modern challenges of globalization, volatile demand, and increasingly differentiated, personalized, and complex products. There is a huge challenge of balancing customer service levels while keeping inventories in check. Assumptions, approximations, and one-size-fits-all logic are falling far short of effective inventory management, leading to stock-outs, customer service issues, and missed opportunities when they fail to work for slow-moving items. The pain areas in inventory management have always been overstocking of inventory and inability to track important items. This is where AI is helping in real-time monitoring and optimization of assets. AI can help with better supply and demand planning. The primary purpose of an inventory optimization solution is to provide customers with the correct product, at the correct location, at the correct time with minimum inventory management.
There should be an intelligent inventory optimization solution that automatically adjusts inventory levels to accurately meet changing customer demands, thereby freeing up capital, improving service levels, decreasing costs, and increasing inventory turnover. AI and machine learning algorithms are helping in inventory optimization across the entire supply chain management.
Increased Safety: AI can help increase safety by automating risky activities. Powering robots with AI and processing real-time data, risky jobs can be handled with minimum impact. These bots can navigate/work in unsafe environments like boilers, furnaces, hazardous material, welding, die-castings, etc. while collecting, tracking, transmitting, and analyzing data at a precision level not done by existing systems.
Hybrid Workforce: Advancements in the field of AI is opening avenues for a hybrid workforce where man and machine work together. The two will not only work together to boost productivity but also create new jobs that are enabled by AI. Today’s robots are working in a manner as depicted in science fiction stories earlier. Thanks to AI and ML, they no longer perform monotonous, repetitive, and mechanical tasks. They are playing a pivotal role in the manufacturing sector.
Although there are tremendous scopes and benefits of using AI in manufacturing, there are key challenges in its implementation to its full potential:
- Difficult to integrate cognitive projects with existing processes and systems
- The fear factor of lack of jobs due to AI
- Cybersecurity and data privacy concerns
- Shortage of skills and talent.
- Legal challenges
- The budget for the cost of the system
- Managing product workflow
However, if we look at the bigger picture, the advantages of AI far exceed the challenges in its implementation. The scope and potential this technology promises will make the next phase of industrial revolution worth watching out for.