Prasad Akella , Founder and Chairman, Drishti10.03.22
What’s a surefire way to compromise your company’s brand, reputation, contracts, and very existence? Assemble and sell products with subpar quality standards.
So how can manufacturers—particularly those who make products that have significant consequences on people’s health and wellbeing—use digital technologies to improve quality in their assembly operations? While the prevailing orthodoxy is to automate and to replace the worker, there is a new and growing technology that offers a rich alternative: digital assistants that empower the worker on (and off) the line to drive greater quality.
Increasingly critical to modern medical device manufacturing, digital assistants make it easy for workers to flawlessly and efficiently build products and ensure the efficacy of the process itself. These assistants simultaneously supplement and enhance the natural talents and capabilities of humans, and assist the augmented employee in making better and faster decisions. They do so by sharing key insights they constantly gather and analyze with the human. How these tools and platforms use AI to create the underlying insights about an assembly line’s productivity or defect rates and help address them are a shining example of collaboration between human and machine on the plant floor.
Think of the core concepts and processes of lean production as an existence proof—creating and training associates on work approaches and problem-solving methodologies to both empower the front line and to quickly solve issues. By digitally augmenting workers with analytics and AI-powered video, we can get the next significant boost in ability. Training times can be significantly reduced and more value can be captured from the workforce with minimal additional demands being placed on it. Simultaneously, and importantly, one can develop an engaged workforce that is helping you focus your attention on the most impactful issues with its deep insights on problems to be solved. Ultimately, this leads you to reducing churn, since job satisfaction is at a different level.
People have an infinite capacity to learn. For example, by viewing the most relevant videos served up by the digital assistant, line associates and team leaders get to view what exactly needs to be fixed. Similarly, aggregate findings across shifts and workers can surface issues that associates can debug and address. The more this iterative process is used, the more is learned, and the smoother the process becomes.
In this way, the organization turns into a continuously learning organization—employees across the board are able to identify, prioritize, act, and establish the efficacy of the assembly procedures themselves, a task that has never been done continuously, in real time, in an unbiased manner. Manufacturers can use these digital assistants to drive personalized on-the-job training by identifying mistakes as they occur and retraining associates in real time—a major boon to risk mitigation. The beginnings of a new culture on the plant floor emerges.
The fact is, whether from employee turnover or simply adjusting the workforce to changing demands, at least 20% of a whole team needs to be trained or retrained every year to become proficient on previously untrained tasks or as new products are added to the plant’s output. Cross-training improves the value of the associate to the manufacturer while cross-trained staff increases the manufacturer’s ability to be robust to the significant market changes we are witnessing today—a win-win scenario.
The current state of training and onboarding is as follows: an introduction to manufacturing processes, followed by a trainer-led employee shadowing period and more line specific a priori training, followed by intermittent verification and spot training. Typical training methods take 60 to 90 days to fully ramp up a line associate. With AI digital assistants, that time can be cut in half. Additionally, personalized verification, support, and retraining can occur as needed beyond that window—something never before possible without heavy human resources being deployed to manually monitor, track, and observe thousands of cycles.
Further, video allows for global operations to be unified in their standardization as it requires no translation, is agnostic to cultural differences, and communicates the nuances of the task almost impossible to photograph (much less describe in written instructions).
Also, people like learning through video. A 2021 survey by Wyzowl showed 69% of people say they’d most prefer to learn about a new product or service by watching a short video.2 (YouTube has transformed learning preferences forever.) Video is also much more stimulating than written training manuals, more memorable than spoken instructions, and can be performed in real time with the help of AI. If a photo is worth a thousand words, a video must be worth countless.
With the unique insights video and AI-based digital assistants generate, what if the right worker is used for the right task, where his or her contribution is maximized and is accomplished effortlessly? Put another way, the process starts with putting the right workers in the right stations, then helping them improve continuously while also improving the manufacturing process. It allows engineers to use their creativity and subject matter expertise along with workers to solve problems, without having to stand for hours on the line with a clipboard and stopwatch. The digital assistant points out where the engineer and the line associate might want to pay a bit more attention (like a spellchecker), then lets the humans do what they do best—problem solve.
For example, if two workers line up their station tools differently when performing their assembly tasks, within the limitations of the prescribed standardized work, and one worker consistently has shorter cycle times, the digital assistant will flag this difference for his or her supervisor to dig deeper into it. Perhaps there is a quality concern to address, or maybe it is a better way to perform the given task, and the supervisor will update the standard work accordingly. Positive outliers like these often get overlooked because of the day-to-day firefighting of manufacturing. And the opportunity to celebrate, tap into, and promote a creative employee is lost.
This ability is particularly valuable in medical device manufacturing. Oftentimes, due to the stringent nature of product design and process verification within the medical device industry, initial production processes are locked in whether or not they are the best and most efficient process for the job. Digital tools fundamentally transform this stagnant process, and permit the alleviation of risk to the patient.
References
Dr. Prasad Akella is founder and chairman of Drishti, an AI-powered video analytics provider that augments workers in assembly operations to improve quality and efficiency.
So how can manufacturers—particularly those who make products that have significant consequences on people’s health and wellbeing—use digital technologies to improve quality in their assembly operations? While the prevailing orthodoxy is to automate and to replace the worker, there is a new and growing technology that offers a rich alternative: digital assistants that empower the worker on (and off) the line to drive greater quality.
Increasingly critical to modern medical device manufacturing, digital assistants make it easy for workers to flawlessly and efficiently build products and ensure the efficacy of the process itself. These assistants simultaneously supplement and enhance the natural talents and capabilities of humans, and assist the augmented employee in making better and faster decisions. They do so by sharing key insights they constantly gather and analyze with the human. How these tools and platforms use AI to create the underlying insights about an assembly line’s productivity or defect rates and help address them are a shining example of collaboration between human and machine on the plant floor.
Faster Learning Curves
The unfortunate fact in the industry today is we face annual labor churn rates of over 20%.1 One is forced to build above average training programs and feedback mechanisms as well as a resilient and robust product process to compensate for this significant churn. Central to this compensatory mechanism is the frequent delivery of relevant manufacturing process insights wrapped around methodology and tools.Think of the core concepts and processes of lean production as an existence proof—creating and training associates on work approaches and problem-solving methodologies to both empower the front line and to quickly solve issues. By digitally augmenting workers with analytics and AI-powered video, we can get the next significant boost in ability. Training times can be significantly reduced and more value can be captured from the workforce with minimal additional demands being placed on it. Simultaneously, and importantly, one can develop an engaged workforce that is helping you focus your attention on the most impactful issues with its deep insights on problems to be solved. Ultimately, this leads you to reducing churn, since job satisfaction is at a different level.
People have an infinite capacity to learn. For example, by viewing the most relevant videos served up by the digital assistant, line associates and team leaders get to view what exactly needs to be fixed. Similarly, aggregate findings across shifts and workers can surface issues that associates can debug and address. The more this iterative process is used, the more is learned, and the smoother the process becomes.
In this way, the organization turns into a continuously learning organization—employees across the board are able to identify, prioritize, act, and establish the efficacy of the assembly procedures themselves, a task that has never been done continuously, in real time, in an unbiased manner. Manufacturers can use these digital assistants to drive personalized on-the-job training by identifying mistakes as they occur and retraining associates in real time—a major boon to risk mitigation. The beginnings of a new culture on the plant floor emerges.
Video Is a Universal Language
Let’s focus on training for a bit. Manufacturers have traditionally used two primary approaches to delivering quality: spend almost 30% of a worker’s time to check the work of upstream operators, and create/drive adherence to standardized work. Training to standardized work ensures new employees are ramped quickly without sacrificing quality and production is running smoothly faster. That said, training remains one of the biggest challenges to manufacturers despite its constant need and its positive impact to quality downstream.The fact is, whether from employee turnover or simply adjusting the workforce to changing demands, at least 20% of a whole team needs to be trained or retrained every year to become proficient on previously untrained tasks or as new products are added to the plant’s output. Cross-training improves the value of the associate to the manufacturer while cross-trained staff increases the manufacturer’s ability to be robust to the significant market changes we are witnessing today—a win-win scenario.
The current state of training and onboarding is as follows: an introduction to manufacturing processes, followed by a trainer-led employee shadowing period and more line specific a priori training, followed by intermittent verification and spot training. Typical training methods take 60 to 90 days to fully ramp up a line associate. With AI digital assistants, that time can be cut in half. Additionally, personalized verification, support, and retraining can occur as needed beyond that window—something never before possible without heavy human resources being deployed to manually monitor, track, and observe thousands of cycles.
Further, video allows for global operations to be unified in their standardization as it requires no translation, is agnostic to cultural differences, and communicates the nuances of the task almost impossible to photograph (much less describe in written instructions).
Also, people like learning through video. A 2021 survey by Wyzowl showed 69% of people say they’d most prefer to learn about a new product or service by watching a short video.2 (YouTube has transformed learning preferences forever.) Video is also much more stimulating than written training manuals, more memorable than spoken instructions, and can be performed in real time with the help of AI. If a photo is worth a thousand words, a video must be worth countless.
Designing for the Human
The design of production lines has historically been focused on the process, not the person. For example, it is self-evident that it makes zero sense to try to ask Tom Brady or Peyton Manning to be a receiver instead of the quarterback. Routinely, the equivalent is what we do on the plant floor; get people to fit into a predetermined role, then try to get the best out of them.With the unique insights video and AI-based digital assistants generate, what if the right worker is used for the right task, where his or her contribution is maximized and is accomplished effortlessly? Put another way, the process starts with putting the right workers in the right stations, then helping them improve continuously while also improving the manufacturing process. It allows engineers to use their creativity and subject matter expertise along with workers to solve problems, without having to stand for hours on the line with a clipboard and stopwatch. The digital assistant points out where the engineer and the line associate might want to pay a bit more attention (like a spellchecker), then lets the humans do what they do best—problem solve.
For example, if two workers line up their station tools differently when performing their assembly tasks, within the limitations of the prescribed standardized work, and one worker consistently has shorter cycle times, the digital assistant will flag this difference for his or her supervisor to dig deeper into it. Perhaps there is a quality concern to address, or maybe it is a better way to perform the given task, and the supervisor will update the standard work accordingly. Positive outliers like these often get overlooked because of the day-to-day firefighting of manufacturing. And the opportunity to celebrate, tap into, and promote a creative employee is lost.
This ability is particularly valuable in medical device manufacturing. Oftentimes, due to the stringent nature of product design and process verification within the medical device industry, initial production processes are locked in whether or not they are the best and most efficient process for the job. Digital tools fundamentally transform this stagnant process, and permit the alleviation of risk to the patient.
Opportunity
The ability to simultaneously capture more value, increase productivity, and increase employee satisfaction and retention all while reducing overall risk is rare. Video and AI-based digital assistants present the ability to do just that while being unobtrusive to the process. The chance to aid, train, and support the worker on the manufacturing line while gaining insight and actionable information across the organization is a massive opportunity to supercharge the workforce across medical device assembly operations.References
Dr. Prasad Akella is founder and chairman of Drishti, an AI-powered video analytics provider that augments workers in assembly operations to improve quality and efficiency.