Tag Archives: Manufacturing Technology

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 ↑

It’s no secret that manufacturing and supply chain organizations are constantly in pursuit of a greater degree of efficiency. This is the key to remaining competitive in both increasingly contentious markets.

It’s also no secret that attaining a higher degree of efficiency is harder than it looks. Supply chain organizations have faced disruption from multiple angles, with decentralized distribution, competitors with a higher level of digitalization, and the deglobalization of trade causing them to fall behind. Similarly, manufacturers are attempting to ride out the silver tsunami and the resulting gap in team member experience while doing so.

Automation is already impacting both industries for the better, providing accurate analytics, monitoring and limiting resource expenditure, and removing manual tasks from employee dockets. But newer technological innovations promise to be a massive boon for both industries, optimizing operations, further streamlining decision-making, and enhancing productivity. Digital twins technology offers insights that revolutionize traditional manufacturing and supply chain management – and we’re about to break down exactly how.

What is Digital Twins Technology?

A common misconception that surrounds the topic of digital twins technology is that it’s just another form of 3D modeling – a sensor, a software platform, or a particularly creative application of artificial intelligence (AI).  Digital twins are, in fact, none of these things.

Digital twins are an amalgamation of technologies that work in tandem to record, model, and simulate projects in real time. The technologies involved in this process will range according to organizations’ capabilities and needs but often include sensors, augmented reality tools, modeling software, and AI. Far from a simple model, digital twins technology tests, records, and reports key data points to leadership, unlocking agile decision-making on an unprecedented level.

Let’s quickly break down some of the use cases for digital twins in supply chain and manufacturing organizations:

• Predicting future bottlenecks with algorithmic insights: Instead of reacting to bottlenecks as they crop up, digital twins can model manufacturing and supply chain processes proactively. This empowers leaders to make decisions to avoid or break bottlenecks before they occur, instead of manually going back over data after an incident occurs to discover what went wrong.
• Assessing alternate plans of action: Unforeseen variables can always impact your ability to deliver – unless, that is, you have an algorithm on your side that can predict likely pain points and chart alternative plans of action. Digital twins can be used to test the viability of plan Bs and Cs, allowing you to react quickly when disaster strikes.
• Organization-wide transparency: When you create a digital twin, it stores its information in a widely accessible single source of truth, allowing stakeholders across the organization to see and understand what’s going on at every level.

Manufacturers in particular will see a massive value-add from digital twins technology, as it can be used to:

• Develop and virtually test stronger concrete;
• Improve yield sizes and limit defects in steel production;
• Accurately estimate costs and inventory;
• Design and create environmentally resistant structures.

While it’s not the most buzzed about technological innovation on the market, digital twins are certainly one of the more useful types of technology for manufacturers and supply chain organizations.

Digital Twins, Your Network, and Expanding Your Infrastructure

Digital twinning also has implications for your network, especially if you’ve already made the switch from copper to fiber. Employing digital twins technology necessitates a high capacity for data transference, as a large quantity of data will be consistently transferred to your single source of truth. While switching from copper to fiber can somewhat fill that need, depending on your network’s capacity and the quality of the components within, you may find that your current network doesn’t adequately support your data-transmitting needs.

Taking the step to convert to a dark fiber network is one possible solution, as dark fiber networks grant a robust, scalable network infrastructure that is entirely customizable according to need. Organizations that need to expand their bandwidth while also maintaining network security and consistent uptime may consider switching to dark fiber, as it is a high-capacity, consumer-controlled network that can effectively replace inferior infrastructure overnight.

Another option is actually using digital twins technology to replicate and reinforce your network. Creating a network digital twin allows you to connect tasks with network performance, granting you control over all facets of your network’s lifecycle. Similarly to how digital twinning allows you to identify bottlenecks and potential impediments to swift service throughout your operations, network digital twinning replicates those benefits for your network.

Either option will allow you to boost your network’s performance while also granting you a greater degree of visibility into and control over said network. This is key when using a technology like digital twins, which can consume quite a bit of bandwidth, as it allows you to reap the benefits of this technology without any unintended consequences.

Digital twins technology can empower manufacturers and supply chain organizations to drive efficiency, regaining a competitive edge in markets overrun with disruptions. With the right solution and the infrastructure to support it, you’ll find efficiency, customer satisfaction, and profits spike.

*This article is written by Ainsley Lawrence. View more of Ainsley’s articles here.

Back to top ↑

If there’s one certain thing about the food and beverage industry, it’s the fact that nothing is certain – not when it comes to trends, anyway. Take nutrition as a classic example. At one time, low fat was all the rage; now it’s high fat, combined with low carbs. In the late 1990s and early 2000s, the all-protein diet from lean meat sources was popular. Today, plant-based protein is on more and more dinner menus.

But there is one constant in the food and beverage business: consumers’ demand for top quality, originality and convenience. Thus, the heat is always on for manufacturers to deliver on Americans’ high expectations.

The best way to come through is to understand what’s on the menu, in terms of food manufacturing challenges facing producers and developing strategies for how to overcome them. Our ebook “Understanding the Challenges Facing Modern Food and Beverage Producers” details some of the potential obstacles producers face and offers suggestions that can help turn sour situations into sweet solutions.

Download our ebook today to learn more about current food manufacturing challenges and discover solutions to key areas including:

Contact us today for help fine-tuning your workflows or improving yield and productivity.

Back to top ↑