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Tag Archives: AI
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.”
Warehouse operations are critical to any manufacturing business. From holding inventory to delivering items, the process must be as swift and efficient as possible. Earlier practices such as document management and communication have been a significant step, but growth and progression in the supply chain call for more.
The rise of the Internet has been a key event in improving warehouse operations. As technology progresses, there are even more ways to optimize the supply chain, and ensure every item or employee is included.
The Need to Streamline Warehouse Operations
Warehouse operations offer many opportunities for error while meeting tight deadlines. Brand owners must recognize these areas for improvement and see what can be done to reduce mistakes. Streamlining translates to more accurate and faster processing, which equates to higher customer satisfaction.
Warehouse operational efficiency also translates to long-term time and cost savings. Next-gen technology can streamline warehouse operations using fewer minutes and dollars resulting in increased productivity.
Remember to include workers when integrating these new electronics. Forty-two percent of workers fear job loss from automation and new technologies. However, the reality is humans are responsible for tool management and strategy execution. Train them to work with these items rather than against them.
Vital Next-Gen Technologies in the Warehouse
Some facilities may incorporate multiple next-gen technologies, while others only incorporate one. The most important factor is to assess what works best for a specific set of operations and makes sense investment-wise.
Automation and Robotics
Certain warehouse operations are rather repetitive. It can be the same cycle of picking out a product, packing it, adding a shipping label and sending it off. Automating these processes with robots can take care of these mundane tasks, shifting focus to more pressing concerns in the facility.
Smaller establishments can still find ways to introduce automation. For example, installations like conveyor belts move items along the facility. Automated labeling machines can transfer the necessary information.
Certain equipment can also improve staff safety. For example, about 70 worker fatalities occurred in forklift-related accidents across different sectors. Self-operating forklifts simplify warehouse transportation and prevent hazardous contact.
Blockchain Technologies
Blockchain technology is a key database streamlining data storage and information sharing. Warehouse management entails plenty of information about product quantity and delivery. Many parties — like suppliers, manufacturers and distributors — are involved.
The blockchain ensures information is accessible and interconnected. What’s ideal about this next-gen ledger tech is it keeps data under wraps. Each block is secure in nature because it requires verification and permission.
Thus, blockchain technology is ideal for various financial transactions. If a distributor pays a manufacturer for production, they should process the transaction through this network. It has a suitable layer of encryption while executing those actions.
Internet of Things
The Internet of Things (IoT) is a flexible alternative to blockchain technology. By employing this network, a warehouse can generate connections between products and machines through sensors and software. If one product is removed, the system will detect it and send an update.
The IoT enables warehouses to receive real-time data about the movement of their shipments. This cuts down the slower steps in inventory management and prompts communication between devices so all parties in the supply chain can stay up to date.
It is possible to fuse both next-gen technologies in warehouse operations. The blockchain establishes trust, while the IoT improves connectivity, refining the process of sharing information among multiple parties.
Artificial Intelligence
Multiple industries are utilizing artificial intelligence (AI) in business processes. While most people find its use helpful in customer service, 40% of business owners use AI for inventory management and 30% for supply chain operations. Warehouses can use their programs to collect and organize data in the long run.
AI can also generate different presentations and reports based on the data it receives. Manufacturers with multiple facilities can upload their information and send a prompt to receive specific information about their inner workings.
AI can also provide business recommendations on streamlining operations with predictive analytics. However, these programs’ output depends on the data set given, and there are limits to the predictions they can make depending on the amount of variation.
The next best thing to do with this output is to conduct a comprehensive data analysis. Use the information to set metrics for evaluation in the future. If one area is faltering, make actionable decisions to influence processing in the facility.
Cybersecurity
As effective as next-gen technologies in warehousing are, new problems arise. The Identity Theft Resource Center found supply chain attacks impacted more than 10 million people in 2022. Each facility and its streamlined performance are vulnerable to these cyber threats.
Focus on preventive measures to maintain the order of operations. Investing in a firewall adds a layer of protection to warehouse information. Add intrusion detection systems to alert business owners of any breaches.
Physical security installments can also protect warehouses. For example, surveillance cameras log who accesses company computers during and outside active hours. Biometric technology is also a good touch for tracking and access control.
Optimize Warehouse Operations with Digitalization
Speed and effectiveness are crucial in warehouses. Next-gen technologies have made great strides in equipping facilities with these attributes, so take advantage of them to strengthen operations.
*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.
Since the industrial revolution, every technological advancement has been viewed through the lens of its effect on jobs. Will I be obsolete? Can a machine do my job better than I can? Are the bots coming for me? If my skills are rendered obsolete, what will I do?
The plain truth is, sometimes machines can do the job better, faster or more efficiently than a human can. Think of the advent of the sewing machine. Even your grandmother’s old Singer model is a whole lot faster, more precise and efficient than she is working with a needle and thread. The art and craft of sewing isn’t lost or obsolete, but for sheer volume and exact replication, you can’t beat the machines.
What’s happening now with artificial intelligence (AI) in manufacturing is a little bit like that. People on all levels of the manufacturing chain want to know if AI is taking over.
The answer is no. Don’t think of it as a takeover. Think of it as more of a transformation. It’s already happening, and it’s not all bad.
AI’s current impact on manufacturing
Artificial intelligence is seeping into the manufacturing workplace in a couple of important ways.
Automation: Much like the sewing machine and indeed all of the industrial revolution, AI has the power to automate repetitive tasks previously done by humans. Operating machinery, tasks on the assembly line, even inspecting products for defects – all of these things are increasingly being automated.
Efficiency: AI can help us optimize processes and procedures, leading to greater efficiency on the line and as a whole.
New job creation. Yes, you read that right. Whereas AI may reduce the amount of jobs focused on repetitive tasks, it is also creating jobs that we haven’t seen before in the manufacturing realm, including specialized programmers, engineers, and technicians. It means companies will need people with different skill sets, and the savvy employers will dig in and train the people they already have to take on these new roles.
Predictive analytics
At USC Consulting Group, we’ve already been using AI with some of our manufacturing clients, specifically in the area of predictive analytics. We spell it all out in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics,” but here is the gist of it in a nutshell.
By now, we all know what AI is — computer systems that perform intelligent tasks, like reasoning, learning, problem solving, decision making, and natural language processing, among others.
Machine learning is a subset of AI. It is, technically, a set of algorithms that can learn from data. Instead of having to be programmed, the computer learns on its own based on data.
Predictive analytics is one output of machine learning. It is the ability to forecast future outcomes based on data. It’s like having a crystal ball that’s informed by vast amounts of complex algorithms and data.
You’re already familiar with predictive analytics but may not know it. You know how Amazon suggests an item for you to buy based on past purchases, or Netflix queues up new shows based on what you’ve already watched? That’s predictive analytics in action.
Much like 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.
The benefits of using AI in predictive analytics are many, including:
- Greater precision and accuracy. Yes, humans do the programming. But AI can analyze mountains of data and identify complex patterns people might miss.
- AI can analyze vast amounts of data in a snap. This helps companies make decisions faster.
- Increased efficiency. All of this accuracy and speed leads to greater efficiency, output and ultimately, profits.
Bottom line: AI needs us
AI is a powerful tool we’ve used at USCCG to help our clients achieve greater efficiency, productivity, and profits.
But here’s the thing about that. It’s a tool. And it’s only as good as the data we supply. Any variation, and there can be skewed results.
As we all know, life is not a data set. Variation is happening all around us, all the time, even in projects where we need great precision.
That’s why the bots are never going to replace humans. They need us as much as we need them. At USCCG, we have more than 50 years of experience making process improvements, finding hidden opportunities for efficiency, creating leaner systems and helping companies thrive. For the next 50, AI will be one tool we use to help achieve that.
Read more about this innovative technology, including a specific case study about how AI works in practice, in our eBook, “AI and Machine Learning: Predicting the Future Through Data Analytics.”