In times of crisis, manufacturers turn to AI

The volatility of artificial intelligence estimates in the manufacturing market – $5 billion or $10 billion or $16 billion between 2025 and 2027 – corresponds to the volatility of the manufacturing industry when Covid-19 broke the chains of supplies and disrupted logistics networks, all over the world.

But all point to a surge in AI adoption as manufacturers rapidly digitize to adapt to a post-pandemic reality. According to the Infosys Digital Radar 2020 report, manufacturing is among the industries with the greatest improvement in digital maturity over the previous year.

Another separate survey conducted in October-November 2020 of more than 1,100 senior manufacturing executives worldwide found that nearly two-thirds of manufacturers use AI in their day-to-day operations, while a quarter do. spent at least half of their total IT budget.

While there are limitless possibilities for AI in manufacturing, most companies prioritize use cases that solve their problems in the areas of quality assurance, risk mitigation, and inventory/supply chain management.

AI, from quality assurance to disaster prevention

Unlimited data and the availability of affordable computing and storage to process it is the wind under AI’s wings. The beauty of AI technologies is that they are able to understand unstructured information – video, sound, photographs, gestures – which make up around 90% of a company’s data.

Unstructured data is a rich source of information, which sadly goes largely unused as companies stick to neatly ordered information in their internal databases.

But that is slowly changing as manufacturers realize that AI, applied to unstructured data, can provide unprecedented visibility into unknown unknowns to deliver real value.

Some of the most compelling use cases are in quality assurance, where AI can proactively identify issues to prevent a later crisis: take product recall, for example.

Most incidents are due to design or manufacturing faults, resulting from a combination of human, machine and process factors. AI-powered pattern-matching solutions can monitor video streams and operational parameters to detect anomalies early, long before they turn into a serious problem.

Unsurprisingly, the auto industry could be one of the biggest beneficiaries – Ford is using image recognition to spot wrinkles in its seats.

Recently, General Motors recalled its Chevrolet Bolt EV when it detected a manufacturing defect from a supplier related to a fire hazard; the company could have avoided this problem by proactively using AI in manufacturing.

AI, monitoring the workshop to mitigate risk

AI can monitor sensor data and flag anomalies that could lead to equipment failure or downtime. Machine learning and predictive analytics tools can perform what-if analysis on equipment data, including vibration, temperature, and speed, to simulate various scenarios if any of these parameters change and trigger an alarm if necessary.

The Port Authority of Singapore, for example, is using AI to create a digital twin of its container shipping hub and with it, simulating multiple scenarios to decide on the optimal location for a vessel based on its manpower, space and asset requirements.

AI, to manage inventory in times of supply crisis

The chip shortage problem has forced automakers to change the way they manage inventory. Instead of ordering chips just in time, automakers plan their inventory in advance.

Proving more accurate than traditional methods for forecasting demand and planning supply, AI can play a valuable role here, listening to various inputs to make inventory decisions autonomously, in real time.

Based on demand at any given time and available stock, the solution can calculate safety stock, reorder levels, and order quantity.

Stung by the chip crisis, some automakers are turning to a make-to-order business model, producing vehicles only against confirmed orders.

Ford requires customers to order vehicles online instead of choosing a vehicle from dealer inventory; there is a place here to use AI to decide the final configuration based on the choice of features and the availability of chips and parts, in order to maximize the profit on each sale.

In e-commerce, Amazon uses AI to estimate demand and store items in warehouses as close to the customer as possible.

AI, to manage complex supply chains

With global ties, expansive product portfolios and now Covid-fueled disruptions, supply chains are a complex thing.

As manufacturers also grapple with changing regional alignments and environmental regulations, they need agility and flexibility in managing their supply chains.

AI-powered supply chain solutions can help the manufacturing industry meet these challenges by making sense of huge volumes of data, providing end-to-end visibility, and enabling businesses to take better decisions, faster and independently.

The manufacturing sector is no stranger to upheaval, but the disruption caused by the pandemic could be its biggest challenge yet.

The good news is that the industry has embraced digital technologies, including artificial intelligence, to overcome its problems and focus on sustainable modes of operation.

Manufacturers are also embracing new trends like Tiny AI that help them automate manufacturing processes in a sustainable way.

While the list of use cases is virtually endless, most companies deploy AI to control quality, mitigate risk, and also to deal with uncertainties in the supply chain.

Vijay Narayan has over 25 years of experience developing strategies and delivering technology-driven business performance improvements for Fortune 1000 companies. He is Senior Vice President and Industrial Leader of Manufacturing at Infosys, where he is responsible for managing sales and delivery professionals as well as developing and executing strategic roadmaps for Infosys manufacturing.

Comments are closed.