BUSINESS TRANSFORMATION
How to use AI to control digital manufacturing?
Every process in a manufacturing company must be efficient for success. Each step must be optimised for minimum errors, hyper-productivity*, and throughput. Digital manufacturing makes this possible. By leveraging digital technologies, manufacturers can create a networked and completely integrated factory that enables them to optimise the manufacturing process using insights from real-time data analytics. The availability of sufficient data makes it possible to use artificial intelligence (AI) to optimise processes for maximum efficiency.
Digital manufacturing enables companies to improve quality, change production rates quickly as per demand, reduce inventory and time to market, eliminate bottlenecks and enhance record keeping. As per a 2019 Deloitte study, increased investment in digital manufacturing has led to an increase of 10% in manufacturing output, 12% in labour productivity and 11% in utilisation of capacity.
Digital manufacturing methods allow manufacturers to evaluate workflows and also the complete value chain before and during the manufacturing process. Process monitoring, traceability and simulation can all be connected by one digital thread. The setup allows AI to be implemented for improving processes and gaining real time insights. Efficient AI models can assess large streams of data and arrive at decisions to maximise quality and speed. In a nutshell, AI models integrate historical learnings procured from manufacturing successes and failures and show a way forward towards maximum efficiency. Both manufacturers and customers are satisfied with the results.
AI plays a vital role during the design phase
When product developers or engineers design a product or a part, AI systems study the 3D designs and create a virtual prototype or a digital twin. AI then virtually determines the most optimal way to manufacture the product. Several decisions are taken at this stage – the tools required, the orientation of the part in the mould, the tool pathing and many other critical technical decisions. This approach to designing and manufacturing ensures quality and speed to market.
AI can optimise the back end of a manufacturing process
AI helps in boosting collaboration between humans and automation. Moving to the network of machines, the digital thread travels through the orders that are queued and sequenced based on inputs from the front-end. Raw materials are loaded and necessary calibrations are set. Sensor readings from each machine can be recorded and analysed. That allows comparison between the product being manufactured and its digital twin. Any errors or changes required can be implemented on the fly. The lessons learned can be fed back to the AI model so that the process can be further refined.
AI can improve machine health
Machines are the core of all industrial manufacturing processes. Maintaining machine health is critical for industrial health. AI enables monitoring of machine health and provides manufacturers with real-time information and deep insights into the condition of the machinery. This makes it possible for manufacturers to practise predictive maintenance instead of reactive and preventive maintenance only. Downtime can be significantly reduced. Existing problems, if any, can be diagnosed and managed early allowing manufacturers to save time and money on expensive repairs and maintenance.
Advantages for the customer
When AI is used during manufacturing, risks reduce, and customer experience improves. Unlike traditional manufacturing methods where quality defects are determined only at the end of the process, digital manufacturing methods allow for mistakes to be caught in the digital space and can be immediately corrected, thus saving on manufacturing time and cost. By using AI in the process, the end products are better and more consistent in quality. Fewer parts are likely to be sent back for rework because of tighter tolerance levels in place.
Industrial AI – the way forward
Industrial AI is expected to make a bigger impact on the manufacturing sector and more and more companies will shift from generic AI models to more precise and fit-for-purpose AI applications. Most functioning AI models now are trained to work with large volumes of data, but new-age AI models leverage expertise in specific domains to interpret and predict outcomes based on machine learning and deep analytics.
Challenges
Clearly, implementing AI in manufacturing can drive the digital transformation of the sector into a more specialised, effective, and productive one. However, no amount of investing in digital technologies will spell success until the teams involved become skilled in the use of AI. Manufacturing leaders must ensure that their teams know how to use the information that AI-enabled tools and models generate.
Skill gaps in understanding and using AI, to a large extent, exist throughout the manufacturing sector – from managers to the frontline workers. These gaps must be recognised and addressed at the earliest for manufacturing leaders to successfully harness the power of AI. Only then can the digital transformation of the manufacturing sector be complete.
* For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.
