8 best practices for IoT asset management

For manufacturers of machinery and equipment, the installed base is their most valuable service asset—and at the same time, their biggest source of data. Those who leverage the IoT effectively can turn sensor data into predictable maintenance, shorter downtime, and new service revenue. This guide shows the 8 most important steps for IoT asset management that really makes an impact in aftermarket service.

At a glance: Successful IoT asset management starts with clear goals, the right hardware and a clean database. Linking IoT data with the digital machine file, Salesforce service processes, self-service portals and AI-supported decision-making logic is crucial for machine manufacturers. If you take all eight steps consistently, you can turn the installed base into a real growth driver.

Overview article: For a comprehensive introduction—covering everything from sensors and edge computing to IoT platforms and the service context in Salesforce—see our Pillar on IoT Data in the Service Portal: How Machinery Manufacturers Put Sensor Data to Work.

The 8 steps at a glance

1. Set measurable goals – define and prioritize service KPIs such as first-time fix rate, MTTR, and equipment efficiency.
2. Select the appropriate IoT hardware —tailor sensors, connectivity (LPWAN, GPS, MQTT), and pilot setup to the machines.
3. Using data for maintenance planning – Predictive maintenance through real-time monitoring of vibration, temperature, and power consumption.
4. Connect to existing systems – integrate sensors, ERP, Salesforce, and service portals without any data gaps.
5. Protecting data and systems – Ensuring GDPR compliance, encryption, and role-based access control.
6. Roll out in phases —pilot, scaling, optimization—with clear success criteria for each phase.
7. Monitor performance data – real-time dashboards for plant utilization, maintenance costs, and downtime.
8. Long-term planning – visualizing trends, feeding data back into ERP and CRM systems, and embedding data-driven decision-making.

Comparison of the most important IoT concepts

With these modules, machine manufacturers can operate their systems more efficiently, reduce service costs and generate new sales in the aftermarket.

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Set measurable goals

Clear and measurable goals are crucial for successful IoT asset management. In the mechanical engineering sector, service KPIs such as first-time fix rate, MTTR, and NPS are particularly important—not just the traditional Overall Equipment Effectiveness (OEE). OEE is the established performance indicator in mechanical engineering, but in aftermarket service, availability, maintenance costs, and customer satisfaction are equally important.

Key performance indicators and targets

Real-world example

A mass-production manufacturer with several thousand machines in the field uses first-time fix rate and MTTR as key performance indicators. As soon as vibration and temperature readings for each machine are recorded in the service file, a service call can be prepared before the technician arrives: the correct spare part is on hand, the cause of the fault has been narrowed down, and a second visit is unnecessary. The manufacturer then makes this same database available to its customers as a self-service offering via a customer portal on Salesforce.

Implementation and standards

Assign a responsible person to each KPI. Work with accurate data, review it regularly, and respond to deviations with clear actions. Also, follow the ISO 55001 standard—it requires continuous assessment of plant performance, asset management, and the effectiveness of the management system.

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Selecting the Right IoT Hardware

Choosing the right IoT hardware determines whether you receive usable data. Sensors and devices must measure precisely and be easily compatible with existing systems.

Connectivity options in comparison

Practical implementation

Start with a pilot on a single machine to collect initial data and validate the setup. You should take the following points into account:

• Data acquisition: Connect IoT sensors directly to the machine or factory network.
• Signal processing: Convert sensor data into digital form using a PLC or data logger.
• Connectivity: In metal-containing environments, wired connections are often the better choice.

Important criteria for sensor selection

The selection depends on the requirements of the machine. The relevant measured variables include

• Key production figures (quantities, cycle times)
• Consumption data (energy, pressure, lubricant)
• Wear characteristics (vibration, temperature, power consumption)
• Environmental factors (humidity, dust, position)

A well-considered choice of sensors lays the foundation for all subsequent digital processes in asset management—and should be linked to the digital machine file so that the recorded data can be immediately assigned to a specific machine, a customer, and a maintenance history.

Using data for maintenance planning

Once the right hardware has been selected, the next step is to make effective use of the collected data. IoT data allows for more precise maintenance planning—potential issues are identified early on and resolved before downtime occurs.

Important parameters for maintenance monitoring

Note: These values are guidelines and are adjusted depending on the machine type.

Steps towards data-based maintenance

1. Collecting and analyzing data – Sensors continuously collect operational data. Modern software automatically analyzes this data to detect deviations from normal operation.
2. Predictive Maintenance – Machine learning predicts potential failures. Maintenance work is scheduled in advance. Learn more in the article “How to Implement Predictive Maintenance with IoT.”
3. Adjust maintenance cycles – Sensor data allows maintenance intervals to be adjusted based on actual wear and tear. Unplanned downtime is avoided and maintenance costs are reduced because maintenance is no longer performed according to a fixed schedule, but rather based on the actual condition of the equipment.

Practical implementation

• Determine relevant measuring points and install suitable sensors
• Define alarm thresholds – tailored to the machine type and operating mode
• Train maintenance personnel to handle the systems
• Link results to the digital machine file so that all maintenance remains historicized

Quality assurance

Check regularly:

• Are the measured values plausible?
• Is all relevant data recorded?
• Are the maintenance measures having the desired effect?
• Do limit values need to be adjusted?

Through the targeted use of IoT data, you can reduce unplanned downtime and increase the efficiency of your systems.

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IoT Asset Management on Salesforce – by logicline

Sensor data alone is of little use. It is only when it is combined with machine records, service history, and customer context that true value is created. With IOTAM—IoT Asset Management on Salesforce —we integrate telemetry, installed base, and service processes into a single platform:

• A 360° view of every machine in the field—including configuration, maintenance history, real-time IoT data, and service tickets.
• Self-service portals with machine cockpit, spare parts store and ticketing on Salesforce Experience Cloud.
• Automated service processes from sensor alarms to ticket routing and field service deployment.
• AI-supported recommendations through Agentforce and our Service Decision Intelligence (SDI).

→ More about IOTAM and the digital machine file → Listing on Salesforce AppExchange

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Connect to existing systems

The integration of IoT solutions into existing production and company systems requires a clear strategy. This is how you can efficiently integrate your existing systems into the IoT infrastructure.

Inventory and analysis

A thorough analysis of the existing systems is the first step:

Modernization of existing systems

1. Technical Assessment – Review of existing systems, identification of technical vulnerabilities, evaluation of integration options.
2. Feasibility study – selection of appropriate sensors and control technologies, development of customized solutions.
3. Implementation – Installation during scheduled maintenance, training of staff to ensure smooth operation.

Protocols and data transmission

Various protocols are available for communication between machines (M2M):

• MQTT – Saves resources and is suitable for simple data transfers.
• CoAP – Perfect for embedded systems with limited resources.
• OPC UA – The industry standard for machine and plant communication.
• REST/HTTPS – For integration with cloud platforms such as Salesforce.

From the sensor to the service file

What matters isn’t that data flows—it’s that it ends up in the system where service decisions are made. For machinery manufacturers, that’s usually Salesforce. Learn more about how sensor data can be utilized in the service portal: Making IoT Data Usable in the Service Portal.

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Protecting Data and Systems

The security of IoT infrastructures requires a well thought-out combination of technical and organizational measures. Especially in Germany, where data protection laws such as the GDPR play a central role, structured protection is essential.

Important safety measures

Data protection and GDPR compliance

Data should only be collected and stored to the extent necessary for analysis and maintenance work. An automated deletion concept helps to comply with these requirements and avoid unnecessary mountains of data. You can find an in-depth look at security aspects in our article IoT data in service portals: minimizing security risks.

Security measures for edge computing

Edge platforms place special demands on security:

• Regular firmware updates for Edge devices
• Isolated network segments for critical systems
• Permanent monitoring of system activities
• Encrypted communication between edge and cloud

Regular safety checks

Security audits and penetration tests are essential for identifying and addressing vulnerabilities early on—both technical and organizational.

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Introduce gradually

Implementing IoT asset management requires a well-thought-out strategy. According to a Cisco study, only about a quarter of IoT projects are considered a complete success—most stall as early as the proof-of-concept phase, often due to complex integration processes and a lack of scalability. A clear roadmap is therefore essential.

Phase-based implementation

Prepare technical infrastructure

IT shortcomings are among the most common obstacles in digital projects. Key factors include:

• Flexible storage solutions for data
• Integration of existing systems and sensors
• Secure communication channels
• Backup mechanisms
• Salesforce connection as a central service system

Consider risks

A good implementation plan addresses potential risks at an early stage. With authentication, encryption and regular audits, companies protect their IIoT systems and comply with data protection regulations.

Set up performance measurement

• System availability: Regular performance tests ensure stable availability.
• Data quality: Automatic checks help to detect errors at an early stage.
• User acceptance: Training and support promote use.
• Business benefits: First-time fix rate, MTTR, maintenance cost ratio as hard key figures.

Enabling scalability

A flexible architecture and customizable processes are essential for efficiently managing growing volumes of data. Solutions such as the installed base integration on Salesforce are designed specifically for this purpose.

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Monitor performance data

Continuous monitoring follows the implementation. Monitoring makes it possible to operate IoT systems efficiently, minimize downtimes and make informed decisions based on data.

Key performance indicators

Technical implementation

• IoT sensors – continuously measure operational data such as temperature, vibration, and energy consumption.
• MQTT broker – collects and standardizes data from various systems to create a unified database.
• Analytics software – analyzes the collected data and provides specific recommendations for action.
• Salesforce Integration – brings data to where service decisions are made.

Improvements through AI

AI-powered systems detect anomalies early on, optimize maintenance cycles, and reduce unplanned downtime. But this depends on the AI having access to a clean, contextualized database—otherwise, it will hallucinate instead of helping. To learn how to avoid this, read the article “When Agentforce Hallucinates: Why AI in Customer Service Needs Its Own Knowledge Base.”

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Take a long-term view

Continuous monitoring of system performance plays a key role in an effective IoT asset management strategy. Regular analysis and observation make it possible to plan maintenance, modernization or the replacement of systems.

Visualize performance trends

Integration into existing systems

Once successfully integrated, IoT data can be seamlessly integrated into ERP software, SAP environments, HANA databases or MES. In mechanical engineering, however, the central hub is usually Salesforce: this is where machine data, customer information, service contracts and maintenance orders come together.

Implementation in practice

Practical examples show that a structured approach is crucial. Modern IoT platforms enable real-time insights into energy and performance data. This allows companies to react more quickly to changes and use large amounts of data efficiently for preventive maintenance and optimization.

Decisions based on data

• Custom dashboards – Focus on the most important metrics
• Regular analyses – identifying trends early on
• Automate reporting – ensure relevant information is always available
• Support service decisions with evidence —not gut feelings, but a transparent data foundation

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Bonus: From IoT data to service decisions

The eight best practices create the data basis. However, machine builders only gain the greatest leverage when IoT data interacts with service history, technical knowledge and diagnostic logic. This connectivity layer— Service Decision Intelligence (SDI) —uses sensor data to make informed decisions: Which machine should be serviced first? Which action is most worthwhile? Which technician with what expertise? SDI combines telemetry, CRM context, technical documentation, and diagnostics into an intelligence layer for AI agents—with source references and data ownership remaining with the machine manufacturer. → More about Service Decision Intelligence

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Conclusion

Successful IoT asset management isn’t just a sensor project—it’s a service transformation. Sensors provide the data, but you can only unlock the business value when machine data, customer context, and service processes come together. Three success factors are decisive:

If you follow all 8 steps consistently, you will operate systems more efficiently, reduce maintenance costs and develop service into a growth driver. The installed base does not become a burden, but your company’s greatest service opportunity. The first step, therefore, is not about choosing a tool, but rather conducting a structured assessment: Which machines currently generate which service value, and where are the greatest untapped opportunities? The Installed Base Assessment delivers the data foundation and a concrete leverage report within 4–6 weeks—the basis for implementing the eight best practices in the correct order. If you already have a structured foundation and want to specifically evaluate how IoT data translates into service value in Salesforce, schedule an initial consultation right away.