Mini-Masterclass Episode 11: No More Novels on the Tablet!

No More Novels on the Tablet: How to Create Good Maintenance Documentation

You are introducing a new maintenance system and are looking forward to complete reports, clean feedback, and a reliable data basis.

But after a few weeks, the reality looks sobering.

In the ticket it says:

"Pump broken."

After the repair follows:

"Works again."

The system is running again. But the company has learned little from the incident.

Which component was affected? What did the technician observe? What measure helped? Is the fault completely resolved, or does something need to be done at the next planned shutdown?

This information is missing not because your employees know nothing. It's missing because no one likes typing a novel on a small tablet out in the field.

The Input Barrier Determines Data Quality

Many companies treat poor maintenance data as a discipline problem.

Employees should document more thoroughly, fill out the specified fields better, and adhere more consistently to the new processes. So, training, mandatory fields, and additional work instructions follow.

But this turns an operational problem into an employee problem.

A technician often works under time pressure. Production is waiting, the environment is noisy, and his hands may be dirty or restricted by gloves. In this situation, he is supposed to write a detailed error description on a small screen.

The higher the input effort, the shorter the documentation.

The result is predictable: The technician only enters what is necessary to formally complete the process.

"Pump broken" is not usable information

A short message may be enough to trigger an order. But it is not enough for the long-term improvement of maintenance.

Between "Pump broken" and a truly useful fault report, important information is missing:

• Where exactly did the problem occur?
• What noise or behavior was observed?
• How long has the anomaly existed?
• Under what operating conditions does it occur?
• What tests have already been carried out?
• What measure helped?
• Is further repair necessary?

Without this information, the company can hardly recognize recurring errors. Subsequent shifts do not understand what actually happened. New employees cannot learn from the event.

With the next similar problem, the troubleshooting starts again.

Thus, valuable contextual knowledge is lost, even though it was immediately available to the technician.

The Most Important Trick is Rarely in the Ticket

Often, the most valuable insights are not the official standard measures, but the experiences from the specific repair.

Perhaps the technician had to assemble a component in a specific order. Perhaps an unusual noise indicated the cause. Perhaps the connection could only be loosened with a specific hand movement.

Such hints can save crucial minutes or hours during the next deployment.

But exactly this knowledge often disappears at the keyboard.

The employee could explain the entire context. He just doesn't want to type it awkwardly on a "tiny screen."

Therefore, the problem is not a lack of knowledge.

The problem is the wrong interface.

The Interface Must Adapt to the Human

Many industrial applications require humans to adapt to the logic of the software.

He must know which field expects what information. He must navigate through menus, select technical terms, and translate his observations into the system's structure.

A modern system should reverse the work.

The employee describes the process as he would explain it to a colleague. The software then takes over the structuring.

Because speech is our most natural form of communication.

We speak faster, more thoroughly, and more spontaneously than we write on a small screen keyboard. At the same time, we can express connections that do not fit into a single selection field.

100 Percent Information with Zero Percent Typing Effort

Imagine your technician presses a button after the repair and speaks:

"The flange on pump 3 is leaking. I tightened it, but the gasket must definitely be replaced at the next shutdown."

This short statement already contains several relevant pieces of information:

• affected asset: pump 3,
• affected component: flange or gasket,
• observed problem: leakage,
• immediate action taken: flange tightened,
• current status: temporarily stabilized,
• necessary follow-up action: replace gasket at the next shutdown.

The technician did not have to search for individual fields or type a long report. Yet, a much better feedback was created than with a brief "done."

This is the idea behind 100 Percent Information Content with Zero Percent Typing Effort.

Artificial Intelligence Takes Over the Structure

A voice message alone is not yet a structured data set. It would have to be listened to again later and manually transferred.

Here, artificial intelligence supports.

A modern assistant can transcribe the spoken feedback, recognize the relevant content, and convert it into a structured message.

From the free description, for example, arise:

• system assignment,
• error description,
• affected component,
• action taken,
• current condition,
• open follow-up task,
• priority and timing.

If important information is missing, the system can ask targeted questions:

"Which gasket is affected?"

"Can the pump continue to operate until the next shutdown?"

"When is the next planned shutdown?"

The employee remains the technical expert. The artificial intelligence takes on the role of an attentive documentation assistant.

The Information Belongs in the Existing System

A speech-based solution must not create a new data island.

The goal is not to collect voice messages in a separate application. The processed information must reach where the company needs it.

An assistant can create a complete ticket from the spoken description and then transfer the data to SAP or another enterprise resource planning system, ERP for short.

SAP thus remains the leading background system. However, the employee receives a much easier way to bring in high-quality information.

The speech input does not replace existing processes. It forms a comprehensible bridge between the workshop and the enterprise system.

Good Documentation Begins with Good User Guidance

Companies often invest a lot of money in data platforms, evaluations, and artificial intelligence. At the same time, the capture of initial data remains complicated.

But every subsequent analysis depends on what was documented during the disruption.

A dashboard cannot reconstruct a missing error description. An algorithm can only recognize recurring causes if they have been comprehensibly recorded. A new employee can only learn from previous repairs if their context has been preserved.

Therefore, data quality does not begin in reporting.

It begins at the moment when an employee enters information.

The simpler this moment is designed, the more complete and reliable the data will be.

Speech Makes Different Employees Connectable

Not every technician formulates equally confidently in writing. Language skills, writing habits, and experience with digital systems differ.

A speech-based operation can reduce these barriers.

The employee describes the technical matter in his own words. The system assists with structuring and can prepare the content uniformly.

This creates more comparable reports without every technician having to use the same formulations or understand a complicated data model.

This is particularly relevant for international teams. Employees can explain a matter in their preferred language, while the system provides the information in the required form.

The key remains: The employee's technical observation is preserved, rather than being lost to linguistic or formal barriers.

Fast Input Does Not Mean Less Control

Speech input must not mean that unchecked statements are automatically adopted as binding.

The employee should be able to review the prepared feedback before sending it. Critical information can additionally be secured through approvals or queries.

The difference lies in the effort.

The technician does not have to write the report from scratch. He checks whether the system has correctly understood and structured his statement.

This combines speed with traceability in the process.

The Benefit Extends Far Beyond Documentation

More detailed reports are not only relevant for management. They improve the daily work of the entire maintenance team.

• Better Shift Handoffs The next shift immediately recognizes what happened, what measure was taken, and what task is still open.
• Faster Troubleshooting Previous disruptions contain more context. Technicians can compare similar cases and reuse known solutions.
• Preservation of Experiential Knowledge Practical tricks and observations remain in the company, instead of existing only in the memory of individual employees.
• More Reliable Analyses Recurring errors, problematic components, and effective measures can be recognized more reliably.
• Shorter Repair Times When relevant experiences are quickly accessible, the team does not have to start from scratch with every disruption.

How to Pragmatically Introduce Speech Input

You don't have to immediately overhaul the entire maintenance process. Start with a clearly defined use case.

1. Select a Problematic Process Start, for example, with fault reports or technical feedback where particularly little context is captured today.
2. Define the Necessary Information Determine which information is truly necessary for a usable data set. Avoid unnecessary mandatory fields.
3. Enable Free Speech The employee should be able to describe the process naturally at first, without having a fixed form in mind.
4. Ask Targeted Questions The assistant supplements missing information with a few understandable queries.
5. Connect to Existing Systems The structured results flow into the existing SAP, ERP, or maintenance system.
6. Measure Data Quality Compare the new reports with the previous state. Check, for example, completeness, processing time, and quality of shift handoffs.

This turns a technical function into a measurable improvement process.

The Most Important Insights of the Episode

The central message is: **Don't demand novels on the tablet. Let your technicians speak.**

The most important key takeaways:

• Short tickets are usually not a knowledge problem. The input is too cumbersome in everyday work.
• High input barriers destroy contextual knowledge. Observations, noises, and practical tricks are not documented.
• The interface must adapt to the human. The technician should not have to learn the language of the system.
• Speech is the most natural form of input. Employees can explain matters faster and more completely.
• Artificial intelligence structures the information. It recognizes systems, errors, actions, and open tasks.
• The results belong in the existing SAP or ERP system. Speech input complements existing processes instead of creating new silos.
• Better inputs create better analyses. Data quality begins directly at the machine.
• Valuable experiential knowledge is preserved. Subsequent shifts and new employees can learn from previous cases.

Therefore, don't just ask if your team documents sufficiently.

Ask yourself how easy you make it for your employees to pass on their knowledge.

Because no one likes writing a novel out at the machine.

But almost everyone can explain to a colleague in a few sentences what happened.

• Reduce typing effort in reports and feedback
• Guide technicians through the process by voice
• Automatically transfer structured information to SAP or ERP