Three reasons why AI is the future for manufacturing

Opinions expressed in this article are those of the author.

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Ramprakash Ramamoorthy, product manager at ManageEngine and Zoho Labs. Image Provided.
Article by Ramprakash Ramamoorthy, Product Manager at ManageEngine and Zoho Labs

Artificial Intelligence (AI) has impacted a substantial number of processes in the manufacturing industry with computer vision, a field of AI that trains computers to interpret and understand the visual world, being the most used technique. Deploying AI-based computer vision on the production line makes it possible to see things in microscopic detail, bringing superhuman capabilities to quality control. For instance, using AI to spot small but costly paint job imperfections in car manufacturing can dramatically improve quality control – adding significant value.

As the use cases for AI within manufacturing grow beyond computer vision and AI’s value is more widely recognised, global adoption of AI in the manufacturing market is projected to grow from $513.6 million in 2017 to $15,273.7 million in 2025. As businesses prepare to enter a post-pandemic marketplace, adopting a forward-thinking, AI-first approach will be key to future success. Here are the three areas in which AI will have the most impact:

Health and safety

AI can deliver real value by monitoring health and safety, in relation to both the people working on the factory floor and the machines they are working with. In this situation, AI can monitor movement and help to avoid accidents and dangerous interactions between humans and machines. Computer vision techniques can be deployed to count the number of people available on the manufacturing floor; and in a post-Covid world where workplace social distancing is essential, AI will be vital in ensuring adherence to new and existing health and safety regulations.

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Powerful optical character recognition (OCR) engines can also be deployed within the manufacturing line to convert typed or handwritten content into machine readable, editable formats. This allows the machines to read and understand the text so that track and trace tools can be deployed to post-production.

Additionally, computer vision is helping with the physical inspection of machines as well as controlling their calibration and tuning, and monitoring their health. Monitoring the health of machines means looking at many parameters and finding any anomalous combinations that could potentially cause downtime.

Impact of machine downtime can inflate costs to manufacturers, including loss of working days for the entire workforce. Therefore, the ability to predict which pieces of equipment require maintenance before the machine becomes unusable will help to reduce or completely avoid machine downtime; improving productivity and saving significant amounts of money.

Sustainability

A further benefit AI can bring the manufacturing industry is increased sustainability as it enables better utilisation of resources, which helps manufacturers become more efficient. As production slowly starts to pick up again after a three-month hiatus, manufacturers are navigating the unprecedented situation of having to manufacture to demand for the foreseeable future, and efficiency will be critical. By determining which units are underutilised and which are consuming large amounts of power, AI can then recommend the optimised way to run those machines to help reduce energy consumption; environmental impact and costs.

Probability over determinism

Traditionally, the manufacturing industry has relied heavily on deterministic processes. As AI becomes more prevalent, however, it requires them to shift their approach and rely, at least partly, on probability. That shift could prove challenging.

AI brings in an element of probability to the workflow and the industry must embrace this change to see its value. For example, a production line could have a standard operating procedure for machine downtime, but its new AI system may predict a 60 percent chance of downtime. This prediction, therefore, requires processes and hierarchies to be reworked to accommodate the 60 percent downtime probability.

While this necessitates a change, manufacturers can make this more manageable by applying a probabilistic approach to one workflow at a time and ensure it seeps through to their employees to fully embed this new way of working – weathering any initial difficulties to focus on the long-term value.

While it’s clear that AI will disrupt the manufacturing industry, it will do so for the better. It will help manufacturers improve their ability to avoid failures and downtime, optimise processes and machines, increase sustainability, improve quality control, and monitor the health and safety of employees who are navigating the strange, post-pandemic workplace of the future.

As AI is used to automate more processes, it will help to avoid accidents and reduce human error. However, it’s important manufacturers see it as a way to augment human workers, rather than replace them. After all, with many organisations using black-box AI, which can inform them of decisions, but not the reasons behind them, having a human in the loop is vital to ensure the decisions are founded.

Even as enterprises adopt explainable AI—AI that can explain its decisions to humans and thereby improve its trustworthiness—humans still form a vital part of the process as these decisions can then be worked on by a team, rather than an individual. Newer AI trends circle around bringing in causation and counterfactual inference to the table, thereby enhancing the quality of predictions. Ultimately, AI is going to disrupt not only the processes involved with manufacturing, but it will also disrupt the mindsets and approaches adopted by those within the industry.

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