How manufacturers can close the factory machine visibility gap

Making factory data visible to the enterprise is becoming a strategic priority for manufacturers because it helps improve productivity, resilience, energy efficiency, and decision-making. Yet many companies still struggle to capture reliable machine data for visibility across existing plants, lines, and assets.

For many industrial companies, the barrier is not a lack of ambition. It is a function of lack of connectivity of disparate and aging assets. Therefore the ability to clip a sensor onto a machines power cable, measure electricity flow and use AI to determine if the machine is off, idle, running or behaving unusually – is an effective way to gather  real-time information from the machines that make products, consume energy and use materials. The factory floor is where value is created, but without the insights from machines, it can remain the least visible part of the business.

That is why the collaboration between SAP and Guidewheel is important for manufacturers looking to modernize operations in a practical, scalable way. The opportunity is not simply to collect more data. It is to help companies turn machine-level truth into business-level improvement.

Across manufacturing, leaders have invested heavily in digital transformation. Planning is more advanced. Enterprise processes are more connected. Analytics are more accessible. Yet many factories still depend on manual reporting, delayed production updates, disconnected spreadsheets, or specialized systems that only cover certain machines or lines.

This challenge is especially acute in brownfield environments. Most manufacturers run a mix of old and new assets from different vendors, with varying control systems, connectivity options, and levels of automation. Each machine matters, but not every machine was designed to speak the same digital language.

As a result, teams often lack a consistent real time view of what is running, idle, down, consuming energy, or causing the gap between plan and reality. Leaders may know output is below target, but not many see the patterns quickly enough to act. Maintenance and sustainability teams may have data, but not the continuous machine context needed to prioritize action.

Guidewheel helps manufacturers monitor machine performance and act quickly to drive ROI across the factory floor by using non invasive sensors that attach to the power cables of existing equipment-.. Its technology can capture machine activity by monitoring electrical signals, enabling companies to understand whether equipment is running, stopped, idle, or behaving differently than expected. Because the approach does not depend on machines being new, standardized, or already digitally enabled, it can be applied across a broad range of production assets of any make, age, or model.

This matters because manufacturers need solutions that meet the reality of the factory. Many cannot afford multi-year programs before they start improving performance. They need a faster way to generate useful signals from existing equipment, prove value, and scale across lines, plants, and regions.

Guidewheel’s approach is designed for the people closest to production as well as the leaders responsible for performance. Executives, operators, supervisors, maintenance teams, and plant managers, can use machine-level insight to see what is happening, identify constraints, compare performance, and focus improvement efforts where they matter most.

The conversation about factory data sits at the center of three strategic manufacturing priorities. First, productivity pressure is intensifying. Manufacturers are expected to increase output without proportionally increasing labor, capital, or energy use. Better machine visibility can help teams understand where capacity is being lost and take action based on facts rather than assumptions.

Second, resilience requires faster response. Supply disruptions, labor constraints, and demand swings make it harder to rely on static plans. When machine status and performance data are visible sooner, teams can spot issues earlier, rebalance work, and make better decisions before small disruptions become missed shipments.

Third, sustainability is becoming an operational discipline. Energy efficiency is increasingly tied to production behavior, asset utilization, scheduling, product mix, maintenance, and cost. Machine-level insight helps companies connect energy consumption to the way work is actually performed.

Guidewheel is relevant across manufacturing environments where machine utilization, uptime, energy use, and throughput are critical. This includes discrete industries such as industrial manufacturing, automotive, high tech, aerospace and defense, machinery, components, and equipment. In these industries, operations often involve mixed fleets of assets, variable production volumes, engineering changes, quality requirements, and pressure to deliver more customized products faster.

Discrete manufacturers face a particular challenge because production performance is often tied to many individual process steps, machines, tools, and work centers. A bottleneck in one operation can affect the entire order flow. A recurring short stop can erode productivity without appearing as a major downtime event. A maintenance issue can affect quality, scrap, and delivery reliability. When machine data is fragmented or delayed, it becomes difficult to see these patterns at scale.

The value for customers begins with speed to insight. Better factory and machine data can shorten the time between identifying a performance problem and acting on it. Rather than waiting for a large-scale modernization program to finish, teams can begin acting on uptime, downtime and energy patterns on the equipment they already have.

Manufacturers can improve overall equipment effectiveness by understanding not only whether machines are running, but also correlate performance  by line, shift, product, site, or operating condition. This supports more focused continuous improvement, more informed capital planning, and more consistent execution across plants. Connected manufacturers can drive compelling ROI, recovering hundreds of thousands or even millions of dollars with these insights.

Customers can also reduce unplanned downtime by using machine energy usage and stoppage patterns to guide maintenance priorities. They can identify when machines consume power while idle, compare energy intensity across production conditions, and connect efficiency initiatives to financial and operational outcomes.

Machine insight becomes more valuable when it is understood in business context. A stoppage is not just a technical event; it can affect labor efficiency, customer commitments, inventory, revenue, service levels, and margin. The SAP and Guidewheel collaboration points toward a future in which factory signals are easier to connect with enterprise decisions.

Artificial intelligence is reshaping how manufacturers approach maintenance, production planning, and performance improvement. But AI is only as effective as the data behind it. Fragmented machine signals, manual inputs, and coverage gaps limit what AI models can learn—and how quickly they can deliver value at scale.

This is where data completeness becomes a strategic differentiator. Manufacturers who can capture consistent, , real-time signals from across their entire operations create the foundation that AI needs to detect patterns, generate reliable predictions, and surface actionable insight across lines, plants, and regions.

Guidewheel is working with SAP to extend machine data into AI-ready data packages—normalizing machine signals such as runtime, downtime, utilization, energy consumption, cycle time, and anomaly patterns into structured formats that AI models can act on. Rather than training on inconsistent or partial data, manufacturers can build from a more complete picture of how their assets actually behave.

For manufacturers developing or scaling AI strategies, this has tangible implications. Predictive maintenance models improve when trained on continuous machine behavior data rather than periodic snapshots or manual records. Production optimization models become more reliable when they account for actual operating conditions across shifts, lines, and sites. Energy efficiency algorithms can identify patterns that only become visible when consumption data is captured consistently across the entire asset fleet.

The underlying principle is straightforward: better data produces better AI outcomes. The path to AI-enabled manufacturing does not begin with algorithms. It begins with the signals those algorithms depend on. Manufacturers that close the factory data gap today are also building the data foundation that makes AI investments more effective tomorrow.

Manufacturers do not need more isolated data. They need trustworthy signals that help people make better decisions in the flow of work. They need a clearer line of sight between the factory floor and the business outcomes that matter: productivity, resilience, sustainability, quality, customer delivery, and profitable growth.

For discrete manufacturers and other industrial companies, the path forward is not only about building smarter factories from scratch. It is about making existing factories smarter and faster. By helping connect machines of any age, make, and model, Guidewheel can give customers a practical starting point for measurable improvement. And by working with SAP, factory-level truth can become part of a broader business conversation—one that connects frontline performance to enterprise value.

In a manufacturing environment defined by uncertainty, speed, and accountability, the companies that win will be the ones that understand their operations clearly and act on that understanding quickly. Closing the factory data gap is no longer a niche technology project. It is becoming a strategic requirement for industrial performance.

See this in action at the S Factory.