8 best practices for IoT asset management
IoT is fundamentally changing asset management: fewer failures, better performance, transparent processes. Here are the 8 most important steps to using IoT successfully:
Set clear goals: Focus on efficiency, cost reduction and quality. Example: Foxconn increased plant efficiency by 17%.
Select suitable IoT hardware: Sensors, connectivity (e.g. LPWAN, GPS) and test pilot projects.
Using data for maintenance: Predictive maintenance by monitoring parameters such as vibration and temperature.
Integration into existing systems: Analysis of existing technology, modernization and use of protocols such as MQTT.
Ensure security: GDPR compliance, encryption and regular security checks
Step-by-step introduction: pilot phases, risk management and clear measurement of success.
Monitor performance data: Real-time monitoring to optimize costs, efficiency and reliability.
Long-term monitoring: visualization of trends, integration into ERP systems and data-driven decisions.
Comparison of the most important IoT concepts
Range | Goal | Example technology |
|---|---|---|
Connectivity | Data transmission | LPWAN, GPS, MQTT |
Maintenance | Predictive maintenance | Sensor data analysis |
Security | Protection against attacks | Encryption, GDPR |
Integration | Modernize old systems | MQTT, XMPP |
By taking these steps, companies can operate their systems more efficiently, reduce costs and remain competitive.
The integration of AI and IoT
The video “The integration of AI and IoT” by TURILYTIX shows how artificial intelligence and the Internet of Things are transforming industrial production. It describes scenarios in which machines and sensors work together autonomously to optimize processes, predict maintenance requirements and increase safety. This combination is intended to make factories more efficient, intelligent and future-proof.
1. set measurable goals
Clear and measurable goals are crucial for successful IoT asset management. According to studies, 86% of manufacturers focus on improving overall equipment effectiveness (OEE) as a key performance indicator [2]. The focus here is on areas such as efficiency, performance and costs.
Key performance indicators and targets
Performance indicator | Significance for companies | Typical objective |
|---|---|---|
Work efficiency | 79 % of manufacturers | Optimize production processes |
Production output | 78 % of manufacturers | Increase output |
Cost reduction | 77 % of manufacturers | Reduce operating costs |
Quality improvement | 76 % of manufacturers | Improve product quality |
Supply chain resilience | 73 % of manufacturers | Increase security of supply |
Practical examples for KPIs
Bonfiglioli provides a practical example: the Italian manufacturer of drive components was able to quadruple its productivity with the help of IoT-based smart factory solutions. This was made possible by the use of collaborative robots, automated transport systems and digital twins [2].
Another example is Foxconn: the electronics manufacturer increased plant efficiency in one factory by 17% by using real-time data and AI for optimization [2].
Implementation and standards
To achieve the targets effectively, you should assign a responsible person to each performance indicator. Work with precise data, check it regularly and respond to deviations with clear measures.
Also follow ISO 55001, which requires continuous assessment and reporting on asset performance, asset management and the effectiveness of the management system [1].
2. select suitable IoT hardware
Choosing the right IoT hardware is crucial for effective asset management. Sensors and devices must provide precise data and at the same time be easily compatible with existing systems.
Overview of connectivity options
Technology | Field of application | Advantages | Special features |
|---|---|---|---|
Wired | Production halls | Interference-free and reliable data transmission | Particularly suitable for environments with a lot of metal |
RFID | High-quality goods | Precise tracking, cost-efficient | Limited range |
LPWAN | Large-scale systems | Long range, energy efficient | Perfect for many distributed objects |
GPS/Satellite | Outdoor areas | Global coverage | Ideal for remote locations |
Practical implementation
Start with a test installation on a tried and tested machine to collect initial data and check the functionality. The following points should be taken into account:
Data acquisition: IoT sensors should be directly connected to the factory network.
Signal processing: Sensor data must be converted into digital form by a PLC or data logger.
Connectivity: In environments with a lot of metal, wired connections are often the better choice.
Important criteria for sensor selection
The choice of sensors depends on the specific requirements of the system. The relevant measured variables include
Key production figures
Consumption data
Wear characteristics
Environmental factors
A well thought-out sensor selection lays the foundation for all other digital processes in asset management.
Step-by-step introduction
A pilot project on a single machine offers the opportunity to gain initial experience and gradually expand the system. This basis makes it easier to improve data-based processes in other areas.
3. use data for maintenance planning
Once the right hardware has been selected (see section 2), the next step is to use the collected data effectively. IoT data can be used to make maintenance plans more precise. By continuously monitoring important parameters, potential problems can be identified and rectified at an early stage.
Important parameters for maintenance monitoring
Measured variable | Purpose | Typical limit values |
|---|---|---|
Vibration | Indications of wear and imbalance | 2.8-11.2 mm/s |
Temperature | Checking thermal load | 60-85°C |
Oil pressure | Ensuring lubrication | 2.5-4.0 bar |
Power consumption | Detection of overloads | ±15 % of the rated power |
Note: These values are guidelines and can be adjusted depending on the system.
Steps towards data-based maintenance
The introduction of a data-based maintenance strategy takes place in three steps:
Record and analyze data
Sensors continuously record operating data. With the help of modern software, this data is automatically analyzed to detect deviations from normal operation.Predictive maintenance
Machine learning evaluates the data and predicts possible failures. This allows maintenance work to be planned in good time [3].Adapt maintenance cycles
Sensor data enables maintenance intervals to be adapted to actual wear. This prevents breakdowns and reduces costs – maintenance costs account for up to 60 % of production costs in some industries [4].
Practical implementation
The introduction of such a strategy includes:
Determining the relevant measuring points and installing the appropriate sensors
Defining alarm limit values
Training of maintenance personnel for handling the systems
Quality assurance
To ensure success, the following points should be checked 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?
The targeted use of IoT data can reduce unplanned downtime and increase the efficiency of systems.
4. connect to existing systems
The integration of IoT solutions into existing production and company systems requires a clear strategy. Find out here how you can efficiently integrate your existing systems into your IoT infrastructure.
Inventory and analysis
A thorough analysis of the existing systems is the first step. The following table provides an overview of important test areas:
Analysis area | Aspects to be examined | Why it is important |
|---|---|---|
Sensor technology | Existing sensors, retrofit requirements | Basis for data acquisition |
Interfaces | Available communication protocols | Enables data exchange |
Data formats | Supported standards, conversion | Ensures system compatibility |
Modernization of existing systems
The modernization of older systems takes place in three steps:
Technical evaluation
Checking the existing systems
Identification of technical weaknesses
Assessment of integration possibilities
Feasibility study
Selection of suitable sensors and control technologies
Development of customized solutions
Implementation
Installation during scheduled maintenance
Staff training for smooth operation
After the technical evaluation, suitable communication protocols are selected.
Protocols and data transmission
Various protocols are available for communication between machines (M2M):
MQTT: Saves resources and is suitable for simple data transfers.
CoAPPerfect for embedded systems with limited resources.
XMPP: Offers additional functions for more complex communication requirements.
Monitoring and alerting
IoT systems enable real-time monitoring of important operating parameters such as:
Voltage and current consumption
Power factor
Temperature
Operating hours
If limit values are exceeded, notifications are automatically sent to the responsible employees by e-mail or SMS.
With a well thought-out strategy, even older systems can be digitized without any problems. In the next section, you will learn how to ensure the security of your systems.
IoT asset management in Salesforce from logicline
Today, your customers expect self-service portals that offer them quick access to documentation or spare parts. At the same time, your internal processes, from resource planning to IoT-supported maintenance, need to mesh perfectly. With platform solutions such as Salesforce and our complementary modules, we create a seamless connection between your customers, your installed base and your teams.
5. protect 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
The following table summarizes key security areas and their implementation:
Security area | Measures | Relevance for IoT assets |
|---|---|---|
Authentication | Multi-stage verification | Protects against unauthorized access |
Encryption | End-to-end encryption | Secures sensitive data transfers |
Access control | Role-based authorizations | Controls who accesses which data |
Integrity check | Checksums for quality assurance | Ensures the integrity of the data |
Data protection and GDPR compliance
In addition to technical security, data protection is an important aspect. Data should only be collected and stored to the extent necessary for analysis and maintenance work. An automated deletion concept can help to comply with these requirements and avoid unnecessary mountains of data.
A multi-layered security architecture is crucial here. Niheer Patel, Product Manager at Real-Time Innovations, puts it in a nutshell:
“Security is the cornerstone of data protection. Securing an IIoT infrastructure requires a rigorous in-depth security strategy that protects data in the cloud, over the internet, and on devices.” [5]
Security measures for edge computing
Edge platforms place special demands on security. Important measures are
Regular firmware updates for Edge devices
Isolated network segments for critical systems
Permanent monitoring of system activities
Encrypted communication between edge and cloud systems
Regular safety checks
Bassam Zarkout, Executive Vice President at IGnPower, emphasizes the importance of continuous monitoring:
“Protecting IIoT data during the lifecycle of systems is one of the critical foundations of trustworthy systems.” [5]
Security audits and penetration tests are essential for identifying and eliminating vulnerabilities at an early stage. Both technical and organizational aspects are examined.
The next section looks at how continuous monitoring can contribute to safety in the long term.
6. create step-by-step implementation plans
The introduction of IoT asset management requires a well-thought-out strategy. According to current figures, around 80% of IoT projects fail due to complex integration processes and a lack of scalability [6]. A clear roadmap is therefore essential. The following phases form the basis for technical implementation.
Phase-based implementation
The central phases of implementation and their main aspects are summarized here:
Phase | Focal points | Success factors |
|---|---|---|
Preparation | Clarify requirements, plan resources | Define goals, involve stakeholders |
Pilot phase | Carry out tests, validate processes | Small test group, quick feedback |
Roll-out | Implement integration and training | Gradual scaling, offer support |
Optimization | Monitor performance, adapt processes | Continuous improvements, use data |
Prepare technical infrastructure
The technical infrastructure is a decisive factor. 40% of managers see IT weaknesses as the main problem in digital projects [6]. The following points are particularly important:
Development of flexible storage solutions for data
Integration of existing systems and sensors
Establishing secure communication channels
Implementation of backup mechanisms
Consider risks
A good implementation plan must take possible risks into account. With measures such as authentication, encryption and regular audits, companies can protect their IIoT systems and comply with data protection regulations [6]. Once risks have been minimized, the measurement of success can begin.
Set up performance measurement
Monitoring the implementation is essential. Important key figures:
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 and acceptance.
Enabling scalability
74% of companies are already working on IIoT strategies or have implemented them [6]. A flexible architecture and adaptable processes are crucial to efficiently manage growing data volumes without exploding costs. Such solutions ensure long-term success.
7. monitor performance data
The next step after the introduction of IoT systems is the continuous monitoring of performance data. This monitoring makes it possible to operate IoT systems efficiently, minimize downtimes and make informed decisions based on data.
Key performance indicators
Certain key figures play a central role in performance monitoring. These can be divided into four main areas:
Goodwill | Key figures |
|---|---|
Operational efficiency | Real-time monitoring, predictive maintenance, system utilization |
Cost savings | Maintenance costs, energy consumption, downtimes |
Reliability and safety | Error detection, safety standards |
Data-based decisions | Data analysis, reporting |
These key figures form the basis for the technical implementation of monitoring.
Technical implementation
Effective monitoring requires the use of certain technical components:
IoT sensors
These sensors continuously measure operating data such as temperature, vibrations and energy consumption. The strategic positioning of the sensors ensures that all relevant data is recorded.MQTT broker
The broker collects and standardizes data from various systems. This creates a uniform database that can be used for analyses.Analysis software
Special software evaluates the collected data and provides specific recommendations for action. These can include preventive or corrective measures.
In addition to the technical infrastructure, modern AI methods offer further opportunities to make monitoring more efficient.
Improvements through AI
The use of AI-supported systems opens up new possibilities. AI can detect anomalies at an early stage, optimize maintenance cycles and reduce unplanned downtimes.
“Companies continue to look for ways to leverage connectivity and IoT messaging for innovation and improvement. Maintaining optimal asset performance delivers critical business benefits such as operational efficiency, cost reduction and compliance.” [7]
8. long-term monitoring of system performance
Continuous monitoring of asset performance plays a key role in an effective IoT asset management strategy. With regular analysis and observation, companies can better decide when maintenance, modernization or replacement of assets is necessary. These processes build on the methods described in performance monitoring.
Visualize performance trends
In order to recognize long-term trends, a clear presentation of the data is crucial. Many modern platforms offer various visualization options for this purpose:
Visualization type | Field of application | Advantages |
|---|---|---|
Line diagrams | Development over time | Recognition of trends and patterns |
Bar charts | Comparison of data | Clear comparison |
Dashboards | Real-time data | Quick overview and status check |
Integration into existing systems
Following the successful integration of IoT technologies, data can be seamlessly integrated into existing systems such as ERP software, SAP environments, HANA databases or MES. This facilitates end-to-end monitoring and analysis of plant performance.
Implementation in practice
Practical examples show that a structured approach is crucial in order to use IoT data effectively. Current 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
For successful and data-driven monitoring of system performance, companies should consider the following steps:
Set up individual dashboards: Concentrate on the most important key figures.
Carry out regular analyses: Use the data to identify trends at an early stage.
Automate reports: Ensure that relevant information is continuously available.
These approaches enable companies to monitor their investments in a targeted manner and make well-founded asset management decisions.
Conclusion
Successful IoT asset management requires a clear and structured approach. The best practices presented make it clear that digital transformation involves far more than just installing sensors.
Important success factors
Three key aspects are crucial for effective implementation:
Success factor | Meaning | Implementation recommendation |
|---|---|---|
Guidance | Management commitment | Establishment of a cross-divisional management team |
Data quality | Focus on relevant information | Selection of suitable sensors and measuring points |
Integration | Connection to existing systems | Step-by-step migration and adaptation of processes |
These factors form the basis for the development of concrete action steps.
Next steps for companies
Implementation begins with a thorough analysis of the existing infrastructure. The focus here is on the scalability and adaptability of the solutions. Particularly in the context of Industry 4.0, end-to-end digital networking is essential to ensure the international competitiveness of German companies.
“The Industrial Internet of Things (IIoT) will determine whether manufacturing SMEs and corporations, as well as Germany as a production location, survive in international competition.” – Markus Dohm [8]
These measures lay the foundation for long-term success in asset management.
Long-term perspective
The digitalization of systems is an ongoing process. Companies should consider the following points in particular:
Governance structures: Clear responsibilities create long-term stability.
Security concepts: Preventive measures protect against risks.
Employee development: Regular training promotes long-term efficiency.
By consistently implementing these approaches, systems can be operated more efficiently, maintenance costs can be reduced and availability can be increased. By integrating modern IoT technologies, German manufacturers can consolidate their market position and prepare themselves optimally for future challenges. Only by closely linking all measures can the digital transformation in asset management stay on track.
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