Tag Archives: Manufacturing Processes

Remember when artificial intelligence (AI) was a glimmer on the horizon? And then ChatGPT stormed onto the scene and people were convinced every job out there was soon going to be replaced by a bot? Now it turns out, not so much.

As awesome (and we don’t use that word lightly) as AI is, it’s only as good as the data it has to work with. At USC Consulting Group, we’re finding this is especially true when we’re using AI for predictive analytics. AI doesn’t like variation, and there can be a lot of that in manufacturing processes.

Here’s a look into this issue and how to handle it.

A short primer into AI and predictive analytics

AI is a broad term describing computer systems that perform intelligent tasks, like reasoning, learning, problem solving, and more. Not so obvious is predictive analytics, which is the ability to forecast future outcomes using AI based on data. You’re already familiar with it, to a certain degree. If you’ve ever had a recommendation from Netflix based on what you’ve watched in the past, that’s it. In a nutshell.

Netflix’s use of predictive analytics created a seismic shift in consumer expectations. This technology also has the potential to transform operating procedures and processes for many industries.

It’s extremely powerful when dealing with processes in which multiple predictors are influencing outcomes. It has the ability to tell us which path to take in order to achieve a desired outcome, even when process patterns and trends are changing.

It means greater precision and accuracy, speed and increased efficiency, the holy grails for any manufacturer.

But there is a fly in this cyber ointment.

Variation.

AI doesn’t like it and – low and behold – that means humans are necessary in this process in order for predictive analytics to achieve its potential.

What is variation?

When we’re talking about manufacturing processes, what exactly does variation mean?

In manufacturing, variation is the difference between an actual measure of a product characteristic and its target value. Excessive variation often leads to product discard or rework.

When you’re dealing with high process variation and instability, it degrades efficiency, consistency and ultimately, profits. A key manufacturing performance objective is the establishment of stable and predictable processes that limits variation – minimum variation around target values.

A main focus for USC Consulting Group is to identify the root causes of variation and address them. Generally, it boils down to people, components and materials.

Some examples to causes of variation include:

• Poor product design
• Poorly designed processes
• Unfit operations
• Unsuitable machines/equipment
• Untrained operators
• Variability from incoming vendor material
• Lack of adequate supervision skills
• Changing or inadequate environmental conditions
• Inadequate maintenance of equipment
• Inadequate or changing environmental conditions

It can be one of these factors, several, or something else. But whatever it is, it’s impeding our ability – and the bot’s – to predict outcomes.

Minimizing variation with our Customized Quality System (CQS)

Every situation is different. The cause of variation on one manufacturing line isn’t going to be the same on another. USCCG assesses and evaluates client processes, then applies a customized approach using a series of tools, techniques and methods that is most applicable in addressing the causes of variability. This customized approach enables USCCG to address variability in an efficient manner. We call it our Customized Quality System (CQS).

We review processes from “the cradle to the grave” and identify the highest-impact operations, then drill down to the tasks and steps within those operations until we uncover the culprits.

Although every situation is different, the general roadmap includes:

• Carefully defining the problem
• Selecting the right team
• Objectively identifying high-impact operations
• Drilling down into the tasks within those operations
• Brainstorming possible causes on those high-impact tasks
• Recommending and implementing deeply focused corrective actions
• Controls so it doesn’t happen again

Removing variability through our CQS not only has an immediate impact on improved product conformance but also paves the way for AI to do its job in predictive analytics, i.e., we want predictions with minimum variability.

It’s just one way USC Consulting Group is using the human touch to make sure AI is up to the job.

Read more about this in our free eBook, “AI and Machine Learning: Predicting the Future Through Analytics.”

Back to top ↑

Studies have shown that over 40% of workers across various industries spend a significant portion of their workweek on repetitive manual tasks. In the manufacturing sector, these tasks often involve data collection and manual data entry, which many consider to be inefficient given the availability of advanced automation software in today’s market.

Innovative automation programs are designed to automatically collect, upload, or synchronize data into a system of record. This automation can help eliminate production bottlenecks and streamline manufacturing processes, ultimately improving output. Moreover, automation can significantly reduce the risk of human error, which can lead to injuries. In fact, a majority of workers (nearly 60%) believe that they could save six or more hours per week if the repetitive aspects of their jobs were automated.

Automation is not limited to the field personnel, as managers are also looking to streamline their own tasks. A renowned technological research and consulting firm predicts that by 2024, 69% of day-to-day managerial work will be fully automated. Examples of automatable managerial tasks include approvals, sign-offs, status updates, and confirmation requests. Increased efficiency in these operations can free up time for employees at all levels to contribute more strategically to the success of a business.

In addition to automation, cutting-edge robotic technology is also being utilized in many manufacturing organizations. Programmed robots or robot-controlled machines that use artificial intelligence (AI) can enhance a company’s assembly, material handling, and processing capabilities. Robots excel in predictable environments and can handle physically demanding or monotonous tasks that may negatively impact employee well-being or morale. This results in increased productivity and reduced labor costs.

Another type of robot gaining popularity is the collaborative robot, or cobot, which is specifically designed for direct human-robot interaction. Cobots are relatively new but are projected to have exponential growth in the market, with an estimated worth of nearly $2 billion by 2026, up from $590.5 million in 2020. Industry experts predict that by 2025, 34% of industrial robots sold will be cobots. Cobots are cost-effective, safe, and flexible, making them an ideal tool for small and mid-sized manufacturers to modernize their operations, reduce redundant tasks, improve productivity, and achieve peak performance.

To learn more about the impact of repetitive tasks in manufacturing and how technology can counter them, please refer to the infographic below:

Back to top ↑