Effective asset management is key to gaining a strong competitive edge in manufacturing. It has a significant impact both on operational productivity and the company’s bottom line.Â
The advent of IoT has radically transformed asset management: remote asset tracking, automated asset workflows, and predictive asset maintenance are just some of the ways in which IoT is revolutionizing asset management.
Big Data and IoT in asset management help make things and spaces smart and connected. It equips management with important information related to the status, location, and conditions of the assets, enabling continuous monitoring in real-time. They also provide real-time alerts, predictive analytics, automatic reporting, and data insights to help manufacturers identify and address any opportunities for improvement.
IoT and Big Data systems for asset management engage a wide range of technologies like GPS, beacons, sensors, and meters as well as data analytics, visualization, and management tools.
For example, in a storage facility, a sensor-based IoT in asset management can continuously monitor the temperature and humidity and, using actuators, maintain balanced conditions for the safe storage of sensitive items like medical products.Â
Ultimately, IoT-powered solutions enhance throughput and quality, resulting in better business outcomes for manufacturers. In this two-part blog series, we’ll dive deep into the role of IoT in asset management, how it works, use cases, and the potential benefits, allowing you to transition successfully to IoT-based asset management.
How does IoT in asset management work?
The general structure of an IoT network consists of devices to interact with the environment, a gateway for collecting data and communicating with the cloud, and the cloud platform for storing, processing, and analyzing the data.
Step 1: Data acquisition and transmission to the cloud
Sensors collect data from the ambiance and then send it to the cloud with the help of an intermediary device. They measure environmental parameters like temperature, light, moisture, etc., and transmit this information to the device.
Devices act as controlling units of sensors. They collect the information from the sensors, pre-process it and then send it to the cloud. Running a self-cleaning cycle or reducing the temperature are excellent examples of pre-processing activities controlled by devices.Â
Step 2: Data Processing
Once the data is transmitted, the cloud receives messages from the devices which are securely connected to it. Then, the messages are published for the subscribers to read and data pipelines are generated from the device to their destination. Some of these platforms include BigQuery, BigTable, and Cloud Storage. The data processing stage involves the following tools.
Device Manager: A device manager is a tool that contains a device registry, where each authenticated device is registered and configurations are set.
Communication Protocols: These protocols help the devices to communicate with the cloud. Protocols like the MQTT bridge or HTTP bridge are used depending upon the requirement.
Serverless functions: These are single-purpose, programmable functions, which are hosted on infrastructure. These functions are event-driven and perform the intended actions only after the event gets triggered.
Beam pipeline:Â They assist in building data pipelines to process the entire data workflow. Apache Beam is a widely used programming model that optimizes data pipelines for simplifying large-scale data processing.
Case study: Asset Management for Construction
Step 3: Data Analytics and Machine Learning
After processing the data, it is analyzed to develop actionable insights. Data analysis and ML are performed on the cloud. The accumulated data in storage systems of the cloud becomes accessible to BigQuery and Cloud Bigtable. Following tools are used to extract valuable insights from data:
ML Models:Â They are used to detect and identify anomalies from the devices.
Data Warehousing Tool:Â It stores and runs a query on the data to obtain insights. A serverless, highly scalable, low-cost enterprise data warehouse makes the data analysis more effective. AWS Redshift, open-source Hadoop, or GCP Bigquery are some of the effective data warehousing services. While selecting an appropriate data warehousing tool, it is essential to consider:
- Data processing type (batch or real-time)
- On-demand scalability
- Efficiency in querying and storage of structured data
Step 4: Generation of dashboards and reportsÂ
Data reporting and dashboarding solutions are the final aspects of an IoT-based asset management system. The data gets turned into customized reports that are easy to read and share, enabling you to make better business decisions and improve operations. Google Datastudio, Kibana, and Tableau are some of the most widely used tools for this purpose.
Final Thoughts
By now, you must have grasped the value of streamlining IoT in asset management and how IoT-based asset management systems work. In the next blog, we’ll explore the use-cases and benefits of IoT-based asset management.Â
If you’re looking to leverage a robust asset management tool for your business, look no further. Hakuna Matata’s state-of-the-art solution empowers you with complete visibility of your assets right from procurement to disposal.
Our custom web and mobile applications allow you to monitor asset availability and performance on the fly. Using our solution, you can automate asset management, scale asset utilization and increase efficiency.Â
What are you waiting for? Hurry up and schedule a free demo with our experts today! Transform asset management and harness the power of smart operations.Â
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