The manufacturing industry has moved incrementally for the last 40 years, businesses of all types have been moving the needle year after year without seeing radical change. A driving force is the culture throughout manufacturing is risk adverse and more traditional. This week we sat down with Remi Duquette from Maya HTT to uncover the missing links with regards to AI adoption in manufacturing and steps every company can take to streamline their digital journey.
Background:
Remi Duquette is a driven innovator with over 20 years of building practical and effective solutions for a variety of industry applications. Mr. Duquette is the Vice-President of Industrial AI at Maya HTT, a company that provides computer-aided engineering software to industrial engineering companies and is an expert in industrial AI, IIoT, engineering, and software development. Remi Duquette has a deep understanding of the maturity of the industry and challenges that manufactures face when trying to undergo a digital adoption.
Breaking down Barriers:
Canadian manufacturers are aware that AI adoption is critical as a large portion of the workforce starts to prepare for retirement, many SME will be struggling to sustain their competitive advantage in the coming years. Additionally, the productivity per shop floor worker has stalled over the last 10 years so the effect of automation and robotization have reached a plateau. Human augmentation through AI-tech is becoming a critical need and no longer a nice to have. Here in Canada, manufacturers are risk adverse and nudge the innovation needle only slightly year after year. Mr. Duquette highlighted that the maturity of the industry, lack of variance in the data and the traditional culture within manufacturing are slowing down the digital transition. Canadian manufacturers need to strategically disrupt their current systems to understand the true benefit of digital transformation of their processes.
Enabled by the slower innovation processes within the industry, manufacturers have been known to implement incremental changes to technology over a long period of time. In doing so, the systems accumulated an enormous amount of data, but this data has low level of variance due to the slow incremental nature of the process enhancements over a long period of time. For AI and ML algorithms to successfully learn, they need variance in the data.
In many cases, manufacturers don’t have the data they think they have, and that’s ok! Understanding the type of data needed for advanced technology implementation and adoption can be difficult, which is why turning to experts in the industry can save you time and money. Remi and his team at Maya HTT have created systems to aid manufacturers navigating their data through AI Readiness Services. Maya HTT will collaborate with your team to understand your process and the data you currently have. This process often entails going back to the shop floor and creating minor disruptions to generate variance in the data. In changing the parameters (whether it be pressure, temperature, inputs, humidity) for a set duration of time, 20 minutes a day, for a certain period, 2 weeks or up to 2 months will generate clean data with variance you need to proceed. Remi reminds us that you don’t need big data to start a project, but you do need good data, which is what lead the Maya HTT team to this solution as well as their synthetic data generation solution.
Expert’s Advice:
Artificial intelligence allows companies to augment people to promote maximum productivity and efficiency. Preparing your company for a digital transformation can take many forms, Remi advises companies to establish strong AI projects roadmap, data pipelines in support of the business use cases, and focus on change management to promote success throughout the AI adoption process.
Ensuring that you establish data pipelines that encompasses all aspects, whether it be data acquisition to storing at the right frequency and variance, will allow you to test and understand your data more accurately. Developing pipelines and collecting data for 2-3 months before kicking off an AI project with external stakeholders will propel your journey in the right direction.
In Mr. Duquette’s experience, with over 150 AI for manufacturing projects completed, he has yet to see any layoffs due to technology adoption. There is a preconceived notion that AI and new technology will make many roles in manufacturing obsolete, where in fact it has been shown to create jobs for manufacturers by increasing productivity and efficiency by augmenting the existing worker’s productivity. For wide span adoption of AI in manufacturing there needs to be a culture shift throughout the industry. Communicating effectively with all employees, especially the technicians and other shop floor attendants will aid in dismaying the idea that robots and computers will take their job’s. In some cases, re-skilling or up-skilling is needed, but if and when communicated effectively, employees understand the technology and the value it provides, and will embrace change as it will augment their value on the shop floor
The Canadian manufacturing industry accounts for 10% of Canada’s total GDP, new technologies have the opportunity to disrupt the industry and positively impact Canada’s economic future. Ensuring your team is well prepared by establishing data pipelines and communicating effectively will be key drivers of success. If you’re interested in learning more, or taking the next step in your digital journey, reach out the Maya HTT team. ![[untitled]](_uploads/6402384cc6d2e.png)