Where Small Manufacturers Should Start with Data and Automation

What if being late to adopt digital technology actually gives you a competitive advantage? During a recent Moore on Manufacturing episode, Ira Sharp Jr., Director of Product Management at Phoenix Contact USA, shared a counterintuitive insight that should encourage every small and mid-sized manufacturer still hesitant about Industry 4.0: companies just starting their digital journey today may be better positioned than those who began years ago. This discussion explored practical strategies for small manufacturers to implement data collection and automation without breaking the bank, while building a foundation that scales with business growth.

The Current Reality: Doing More with What You Have

Sharp, who analyzes quarterly reports from IoT Analytics tracking CEO priorities across industries, identifies one persistent theme dominating manufacturing conversations: “doing more with the same stuff you have, whether that’s the same people, the same machines or the same operations.”

For large manufacturers like Frito-Lay or Pfizer, this means deploying teams of specialists and investing millions in data infrastructure. But small and mid-sized manufacturers face different constraints. “Sometimes they can do it and a lot of times they can’t,” Sharp explains. “And it’s not because they don’t want to. It’s because they just don’t have the budget. They don’t have the funding. They don’t have the ability to do it.”

This resource gap creates a critical challenge. Small to mid-size manufacturers comprise the majority of American manufacturing. Without helping these companies adopt digital tools, the entire industry ecosystem suffers.

The Surprising Advantage of Starting Now

Sharp revealed an insight that challenges conventional wisdom about technology adoption: “Small to midsize manufacturers actually have a strategic advantage over some of the large guys.”

The reason? Many large manufacturers invested heavily in data collection systems 5-10 years ago. “They already have an installed base and they have a structure that they have to deal with,” Sharp notes. These legacy systems often can’t easily accommodate modern architectures or emerging best practices.

In contrast, small manufacturers starting today work with a clean slate. “Starting green field to start with a new architecture with what we have today is very different than starting 10 years ago,” Sharp explains. The tools available now, whether for data collection, storage, or analysis, have changed dramatically in just the past three years.

However, Sharp emphasizes this advantage won’t last forever. When asked about timing, he’s direct: “The best time was yesterday, the best and the next best time is now.”

Where to Begin: The Problem-First Approach

For small manufacturers wondering where to start, Sharp recommends a clear framework: identify a specific problem with measurable ROI.

“You need to have a problem you’re trying to solve,” he states. “Particularly if you’re small to midsize because you need to have—I hate to say it, but you need to have an ROI.”

He suggests power monitoring as an accessible entry point that many manufacturers can relate to. By installing current transformers and monitoring devices on machinery, companies can track energy consumption patterns, but the insights extend far beyond utility bills.

“You can correlate it to the operator that’s running the machine, how often it’s running, is the machine producing the same yield at different power levels. Maybe you can see wear and tear on the machine,” Sharp explains. This single data source can inform decisions about maintenance cycles, operator training, production scheduling, and equipment optimization.

Build for Tomorrow While Solving Today’s Problems

While focusing on immediate ROI is essential, Sharp cautions against short-term thinking. “You need to think a couple steps further so that you can scale it up.”

Many manufacturers implement point solutions. Adding power monitoring that feeds directly to a cloud dashboard, for example. These systems work, but they create problems down the road. When the company wants to add vibration monitoring or track PLC alarms, they must build entirely new data pipelines.

Sharp recommends using standardized architectures like ISA-95 from the start. “While it may be challenging on the onset and make the initial project harder, it will make the next step much easier.”

The Data Collection Philosophy: Move More, Store Selectively

One of Sharp’s most practical recommendations addresses a common concern: getting overwhelmed by data volume.

His solution? Use an architecture called the Unified Namespace (UNS). Essentially, a central hub where data flows through but isn’t necessarily stored permanently. “Not everything needs to be stored all the time,” he explains. “Not everything is right for every application.”

This approach allows companies to capture data from operators, temperature sensors, cycle counts, and other sources without the cost of storing everything. “Getting as much data as possible up so that you can make incremental improvements at the data level is so much easier in the long run,” Sharp advises.

The key advantage: “It’s a lot easier to go to your database and start recording an extra variable later on if it’s already been moved” versus shutting down production to reprogram at the machine level.

Beyond Efficiency: The Succession Planning Benefit

Sharp points out that data collection serves another critical function for small manufacturers: knowledge transfer.

“Eventually, you can’t do that, right?” he says, referring to owner-operators who personally run equipment. “You will get to a point where you have to give it to somebody else and training that new operator and training the new people to do it is critically important.”

By collecting data on optimal machine performance and operator behavior, companies create digital training models. “If you’re collecting the data and you can model what good behavior, good operator behavior looks like, whether it be in business or on a particular machine, you can guide so much better digitally than you could if you were just standing there.”

The Equipment Manufacturer Opportunity

Sharp also addresses a less obvious audience: equipment manufacturers (OEMs) who build machinery for other manufacturers.

“If you’re collecting the data and making that IT data available while you’re still running your OT, your PLC operations, you’ve now made your machine digitally ready,” he explains. This creates differentiation in competitive markets and opens new business models.

He envisions equipment manufacturers using aggregated data from hundreds of installations to continuously optimize machine performance. Then, delivering those improvements to customers through software updates, similar to how Tesla enhances vehicle performance remotely.

Practical Next Steps for Small Manufacturers

Sharp offers specific advice for manufacturers ready to begin:

1. Start with home automation as a low-cost learning tool. “Get involved in home automation and do something like Home Assistant with some Raspberry Pis,” he suggests. This demonstrates data collection principles at minimal investment. 
2. Engage with integration partners who specialize in manufacturing automation. Don’t try to build expertise entirely in-house. 
3. Connect with the community. Sharp mentions organizations like CESMII (Clean Energy Smart Manufacturing Innovation Institute), a Department of Energy-funded group that helps small to mid-size manufacturers adopt advanced manufacturing techniques. 
4. Think about tax strategies. As our hosts noted, research and development tax credits and depreciation strategies can offset the short-term cash flow impact of automation investments.

The Margin Pressure Reality

The conversation acknowledged current economic pressures facing manufacturers. Rising material costs, labor expenses, and potential tariff impacts are squeezing margins across the industry.

“Every pricing conversation that our manufacturing clients are having with their customers or their suppliers—in those conversations are promises to improve efficiency to try to cut costs,” explained host Mike Sibley. These efficiency commitments have become part of pricing negotiations in ways not seen before.

Without data to identify improvement opportunities, manufacturers struggle to deliver on these promises. The ability to shave even small percentages off production costs can mean the difference between absorbing cost increases or passing them entirely to customers, potentially losing business to more efficient competitors.

Take Action Today

Small manufacturers face a unique moment: modern tools are more accessible than ever, legacy system constraints don’t apply, and the competitive advantages of early adoption remain available. But this window won’t stay open indefinitely.

Whether you’re exploring your first automation project or looking to scale existing initiatives, the principles remain the same: start with a real problem, build for scalability, and collect more data than you currently need.

The manufacturers who act now, thoughtfully, strategically, but decisively, will position themselves to compete effectively regardless of what economic or technological changes come next.

Contact our manufacturing team today to discuss how data-driven operations can improve your efficiency and profitability.