It’s not hard to look around and see the influence that artificial intelligence (AI) is having on industries worldwide, and manufacturing is no different. For small and midsized businesses (SMBs), AI can seem inaccessible. But you don’t have to be a company the size of Boeing or Tesla to take advantage of AI in manufacturing.
For small manufacturers, the best AI use cases aren’t about transforming the production floor into a fully autonomous factory. It’s using AI to generate meaningful productivity improvements through AI-assisted decision making.
Smaller organizations have a very real opportunity to use focused implementation of AI to solve expensive, repetitive, and data-heavy problems without radically transforming their production.
Over the next 24 months, manufacturers don’t need to replace human workers with AI. They need to use AI and a Manufacturing 4.0 framework that augments human skill and expertise, connecting people, machines, processes, and data together to become more predictive, adaptive, and efficient.
What is Manufacturing 4.0?
Manufacturing 3.0 asked “can we automate this task?” Think of a robot loading a machine, or a conveyor moving parts without a human operator.
Manufacturing 4.0 takes the next step, asking “can we connect the process, collect useful data, understand what is happening, and use that information to make better decisions?”
AI is becoming increasingly important in the new connected manufacturing environment. Think of machines with sensors that collect data from the production system. That data is fed into an AI, which can make recommendations to humans.
Rather than simply automating processes, Manufacturing 4.0 is really about integrating information technology and operational technology to help human beings make better decisions.
You don’t need to completely automate your factory to begin using Manufacturing 4.0. A small 40-person factory using connected machines, digital work instructions, production dashboards, machine monitoring, and predictive maintenance can reap the benefits of smart manufacturing better than a heavily automated factory with systems that cannot communicate with each other.
Where AI Can Help Small Manufacturers See Rapid ROI
Small and midsized manufacturers share some characteristics that create opportunities to take advantage of AI and Manufacturing 4.0.
Many SMBs collect production data but fail to do anything with it. Engineers and management make key decisions without ever having access to that data. Maintenance, scheduling, quality and price quoting may rely heavily on individual experience, even when useful production data is available. On top of that, many organizations have extensive amounts of undocumented operational knowledge locked inside the minds of their most senior employees.
Each of these creates an opportunity for AI to make a meaningful impact in a small business, particularly because smaller companies can implement targeted improvements much faster than a larger enterprise.
To get the most out of AI, SMBs should focus on business problems where AI adoption can provide clear, actionable benefits that produce a rapid ROI.
Predictive Maintenance
With AI and Manufacturing 4.0, a predictive approach becomes possible. Equipment generates data from sources like vibration, temperature, and pressure. AI can detect patterns in the data that may indicate an increased risk of failure. Maintenance can then be scheduled before there is any unintended disruption.
This can reduce unplanned downtime and avoid the expense of emergency repairs. Maintenance can be scheduled during downtime, and can also potentially extend equipment life.
AI plays an important role, but manufacturers do need equipment capable of providing usable data. That’s where Manufacturing 4.0 readiness comes into play. A facility that is Manufacturing 4.0–ready has connected equipment and systems capable of collecting and sharing performance data about its machines. That data can be shared with and analyzed by AI systems to identify patterns that support predictive maintenance for small manufacturers.
Predictive Quality and Scrap Reduction
A similar approach can be applied to quality control. Traditionally, defects are identified only after they occur.
AI can be used to automate defect detection through visual and dimensional analysis, faster first article inspection, and process monitoring that can analyze patterns and identify when a process is drifting out-of-spec.
Instead of producing an entire run of parts before realizing a defect has occurred, AI helps quality become predictive. For example, AI can analyze data from machine sensors, inspection equipment, and production processes to identify patterns associated with defects or process drift. When those patterns appear, manufacturers can investigate and adjust the process before more defective parts are produced, reducing scrap and rework.
AI-Assisted Quoting and Capacity Planning
For SMBs, the impact of AI can reach beyond the shop floor. Manufacturers accumulate years of data on previous price quotes, job costs, material costs, and labor hours, machine hours, and production history.
AI-assisted quoting can take advantage of that data, analyzing historical information to help estimates of the labor, machine time, material requirements, and production costs needed for a new project.
The AI analysis helps support the estimator, engineer, or planner in building an accurate price quote for a contract, rather than eliminating their judgment. AI-assisted quoting can become more than a time saver. It can become a competitive advantage, helping SMBs produce faster, more consistent, and data-driven quotes at the right margin for the shop.
Engineering and Design Assistance
Software engineers are using AI to code faster, and the same advantages can be applied to manufacturing.
AI-assisted CAD and generative design can result in faster product designs and iterations, and better manufacturability of products. Simulations and digital twins, which create digital representations of physical machines, can incorporate real-word data to simulate process changes, model equipment behavior, and identify potential failures.
Productivity and AI-Generated Work Instructions
One of the biggest hidden challenges of manufacturers is unstructured and tribal knowledge. Decades of knowledge is often scattered across spreadsheets, PDFs, quality records, and maintenance logs, as well as in the minds of individual employees who’ve held their positions for years.
When an experienced machinist, engineer, supervisor, or other key role leaves, if their institutional knowledge isn’t preserved, they take that knowledge with them.
AI systems can help organize this scattered knowledge and turn it into searchable resources, draft work instructions, training materials, troubleshooting guidance, and equipment knowledge that employees can access when they need it.
For example, an AI system can analyze existing documents such as standard operating procedures, equipment manuals, maintenance records, and quality documentation, then organize it into a searchable format. Employees can use natural-language questions to find specific information when they need it. AI can also use that information to create drafts of new work instructions or training materials, which employees can review and validate.
How Do Small Manufacturers Get Started With AI?
Small and midsized businesses don’t always have access to the same level of resources that large organizations do. That means their implementation of AI must be strategic.
Step 1: Identify a Business Problem
Instead of asking “where can we use AI?”, ask “where are we losing money?”
Many small manufacturers have issues with scrap and rework, unplanned downtime, scheduling problems, and engineering bottlenecks. Identify which issue can be addressed with AI that will have the biggest impact on your business, and put your focus into that.
Step 2: Gather Data
AI and Industry 4.0 thrive on data, so the next step is finding it. Ask yourself, what data and information do we already have about this business problem? Do we collect it manually or digitally? Is that information reliable, where is it stored, and can our systems communicate?
Step 3: Choose a High-Value Use Case
SMBs can’t tackle all their challenges at once. Instead, pick one single, high-value AI initiative. It should ideally have a high business cost, measurable baseline, accessible data, and a relatively low implementation complexity. The one that meets these criteria the best gets your full attention.
Step 4: Establish a Baseline
Before you can solve a problem, you must first quantify it. If you’re looking at a predictive maintenance problem, that could be the number of downtime hours a machine had in the past year. Solving a scheduling problem could look at how many orders were late due to scheduling constraints. Whatever problem you choose to address, the baseline gives you a starting point you can use to measure the success of your initiative.
Step 5: Pilot
This is where you develop a plan for implementing your AI solution. But you shouldn’t make sweeping changes yet. You want to test the new technology on a limited process, machine, or product.
Step 6: Measure Against Your Baseline
Now you can compare the results of your pilot against the baseline. Was there a change in downtime, scrap, labor hours, or any other data point you chose to measure?
If it worked, great. If it doesn’t, you go back to the drawing board.
Step 7: Scale What Works
When you land on something that works, you can scale it up to the rest of your process.
You don’t need to become an AI company overnight. You just need to take one opportunity at a time to identify, implement, measure, and scale AI and smart manufacturing technologies that can improve your business.
Is Your Manufacturing Company Ready for AI?
AI and Manufacturing 4.0 readiness shouldn’t be viewed simply as purchasing new software or equipment. Your organization needs to determine whether you have the data, process, systems, and people needed to successfully adopt the new technology.
AI is only as good as the data you feed it. So ask yourself: do you have access to production and equipment data? Is that data reliable? Can your systems communicate with each other? Are key processes documented, or does knowledge live only in the heads of your employees?
Data is an important factor, but your workforce readiness matters too. Manufacturers can benefit by introducing basic AI literacy training across the organization. Engineers, planners, and supervisors can benefit from deeper training. Data and process owners should receive the most in-depth AI training. These leaders can help provide the foundation for future AI initiatives.
Remember, for SMBs the goal shouldn’t be to turn into an autonomous factory that replaces human manufacturing expertise and leadership. The best AI use cases for small manufacturers lie in augmenting your existing workforce’s skill and capabilities, so that they can spend less time searching for information and analyzing data, while AI helps them solve problems and make decisions more effectively.
The key is to make the most of limited resources by choosing one high-value problem to solve that can produce a measurable business result.
For California manufacturers looking for AI manufacturing, Manex can help you assess your Manufacturing 4.0 readiness, and identify practical AI or automation opportunities. Together, we can develop an AI implementation roadmap that is focused on producing measurable ROI as quickly as possible.
If you’re ready to see where AI and Manufacturing 4.0 can take your manufacturing operation, contact Manex to speak with our manufacturing automation consultants.
