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The manufacturing sector has been a zealous user of up-to-date industrial sciences like robotics and mechanization for years. Therefore, Artificial Intelligence (AI) is the next tread for the industry to enhance engineering design, productivity, speed up shop floor operations and reduce production cost.
The Manufacturer's Annual Manufacturing Report 2018 says 92% of senior manufacturing executives believed technologies like AI will empower them to scale up their productivity levels and enable staff to work smarter. Many of the manufacturing units' processes and machine-to-machine interactions have become complex and AI is here to bring simplicity and efficiency to it.
Below is a rundown that describes how AI is being used within Industry 4.0 – a name was given to the current trend of automation.
Designers and engineers, by giving a clearly defined design as an input, can use AI algorithms (predominantly known as generative design software) to consider conceivable versions of a solution. The solutions obtained can be used to determine which idea worked and which did not, thus advising an optimal solution.
Supply Chain is the pillar of the manufacturing unit. Many manufacturers have composite supply chains having various components and specialty tools. Any delay, breakdown or fault can cause a product assembly point to come to a halt. To determine the estimations of market demand, an algorithm can be used to take into account distinguishing demand patterns. Manufacturers can use this data to optimize inventory control, staffing, energy consumption, and make preferable financial decisions.
According to a report by McKinsey & Company, AI-enhanced supply chain management can help companies reduce forecasting errors by 20-50% to optimize stock replenishment.
Development in the field of AI is paving way for shared environments where man and machine work alongside. Thanks to AI and machine learning, robots no longer perform monotonous and repetitive tasks. Collaborative and context-aware robots will improve production throughput in labor-intensive settings, and will increase productivity to 20% for certain tasks, says McKinsey.
AI-enabled tools can foretell when the equipment is expected for maintenance. By means of embedding sensors in manufacturing equipment, AI can capture data like energy consumption of individual machines, study maintenance cycles, scan for the possibility of a defect and then optimize them in the following stage. As the volume of data increases, the system improves at perfecting itself and makes more precise predictions. McKinsey reports that AI-driven predictive maintenance can increase asset productivity by up to 20% and reduce maintenance charges up to 10%.
AI can help increase safety by automating risky activities. Facilitating robots with AI, high-risk jobs can be taken care of with minimum impact. These bots can work in unsafe environments while collecting, tracking, transmitting, and analyzing data at an accuracy level not done by the existing systems.
Manufacturers can now use AI systems to decrease scrap rates from faulty products and increase value out of the resources involved in the assembly process.
While AI will metamorphose factories in incredible ways, it will also bring forth challenges. According to an Infosys survey, 37% of manufacturers believe that training will be a notable issue when it comes to deploying AI. Some 32% of manufacturers admitted to a need for knowledge about how AI can help the business.
The use of AI appears as an innovative leap forward for many in the manufacturing sector. At length, this means implanting "learnability" in employees through a methodical process of established learning.
The long-term contribution of AI in the sector is inevitable. While the positives of AI keep on outweighing the negatives, the successful mantra of using AI is to balance: significant automation but with comparable importance on people participation and skill expansion.
