Artificial Intelligence(AI) has already proven to solve some of the complex problems across the wide array of industries like automobile, education, healthcare, e-commerce, agriculture etc. and yield greater productivity, smart solutions, improved security and care, business intelligence with the aid of predictive, prescriptive and descriptive analytics. So what can AI do for Manufacturing Industry?
AI sometimes misleads many manufacturers, suggesting a large end-to-end system. In reality AI is more often a collection technologies like Computer Vision, NLP(Natural Language Processing), Speech Recognition, Conversational AI (chatbots), Analytics and Automation – each with its own strengths and applications.
The Manufacturing industry has always been available to embrace the innovative technologies. Drones and industrial robots have been a part of the manufacturing industry since 1960’s. From machinery inspection and diagnostics to production planning, AI-powered analytics enables manufacturers with the improvements in efficiency, product quality and safety of the employees.
Predictive maintenance eliminates the guess work and promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. It allows convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. A report by McKinsey and Company notes that AI-driven predictive maintenance can increase asset productivity by up to 20% and reduce maintenance cost up to by 10%.
This solution works like a diagnostic tool-kit. By acting upon high-volume data, it can provide actionable insights on daily basis – including root cause analysis – there by increasing the yield, reducing the expenditure and improving the overall ROI. In cases where maintenance is unavoidable, field technicians and operating teams will be provided with specific instructions on what should be fixed and when, resulting in very focused repairs.
If the machine is prone to wide range of un-understood failure modes(say some of them are very rare), it can be impractical to create sufficient models of high quality to predict them all.
Digital twin is a ‘digital replica’/’virtual model’ of an entity(a product, process or service). AI technology can build the accurate models using the CAD and AI tools to include theoretical and real-world data. This combination of data is enabling us to build digital twins more accurately. Digital twin leverages Internet of Things (IoT), but requires the skill of machine learning and AI. Having a digital model helps us predict the wear, movement and interactions with other devices. It also enables us to find limitations, bottlenecks, mistakes or better features to accelerate time to market.
Monitoring the employees and work-place/job-site helps to reduce the risk of accidents, there by yielding the increased work-force productivity. AI-powered computer vision solutions like safety gear detection and recognition in conjunction with human tracking can be used to ensure safety gear compliance. HAR(Human Activity Recognition), through spatial and temporal analysis (video analytics) can be used to monitor employees to prevent unauthorised access and avoid accidents, thereby providing the safety.
In this fast-paced technology driven world, it’s highly necessary for manufacturers to maintain high levels of quality and to comply with quality regulations and standards to meet short time-to-market deadlines. AI can be used to optimise time-consuming product validation process in the design of new products such as processors. This application of AI helps discover hidden bugs earlier in the design process. AI can be used to find microscopic defects in products such as circuit boards at resolutions well beyond human-vision, using a machine learning algorithm. AI can be helpful in notifying manufacturing teams of emerging production faults that are likely to cause product quality issues.
This method involves inputting of parameters defined by designers and engineers into an AI algorithm called “Generative Design Software”.Parameters include cost and time constraints, material types and available manufacturing methods etc. The Algorithms explores all the possible configurations and generates design alternatives. The proposed alternatives can be tested using Machine Learning, the process will be iterated through all alternatives till the optimal and best solution is found. Good thing about this approach is there’s no human bias factor which means no assumptions are taken into consideration and there’s no logical starting point.
At a given time supervisor can only watch over few workers. AI with the help pf video analytics can be used to monitor and track employees, flag their failures and enable their learning from their better colleagues, there by leading to increased productivity and efficiency.
Track the breaks(smoking, lunch, washroom etc.) taken by employees and report productive working hours.In a manufacturing plant, at the assembly line track the assemblers’ work which includes timing each step and checking for mistakes.Provide supervisors and workers with feedback so that they can avoid repeating an error.Activity analysis of employees within the manufacturing plant or warehouse can enable intelligent assignment of work/task.
Apart from the use cases discussed, AI can still bring lot more to the table and offer the competitive edge. AI-powered self driving vehicles can be used within the manufacturing plants to automate movement of materials. Voice-driven solutions may lead the charge. AI in conjunction with RPA(Robotic Process Automation) can provide much more advanced solutions to the smart warehouses. We can gather enough data from humans in manufacturing plant to train machines to mimic them. A report from London Business School shows that companies save little with robotics alone, but those that retool business processes with AI and robotics combined have seen cost savings as high as 30-70%.
AI may be efficient at creating the things, improving the things, automating the things and making the things cheaper, but there’s no replacement for human ingenuity in dealing with unanticipated changes in tastes and demands. AI-powered extreme monitoring in a manufacturing plant (low-wage work environment) can be inhumane.