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Cloud-based Data Analytics Platform for Injection Molding Machines

An intelligent system that helps companies manage and predict maintenance and improve the performance of molding machines. It collects data from battery-powered sensing devices and uses multiple connectivity technologies to transfer data to the cloud.


Industrial IoT platform for injection molding machines: from sensor data to intelligent insights


Industrial Manufacturing


T&M (time and materials)



  • Firmware Developers
  • Back-end Developer
  • Business Analyst
  • Project Manager



Our customer wanted to build an intelligent Industrial IoT solution that would allow them to:

  • Easy to install systems for injection molds
  • Avoid intervention into the mold design and technical process
  • Carry out the system maintenance during injection mold services

The company addressed Softeq to test the feasibility of their idea, choose the technology stack, and outline the scope of work.

We began the project with a Business Analysis phase.

Solution Requirements

A market-ready solution would:

  • Track KPIs of molding operations in real time
  • Collect mold condition and process data from sensors installed on injection molds
  • Detect abnormal behavior and notify operators before the defects result in a failure

For the first phase, the system had to consist of three parts:

  • Temperature and mold movement sensors for analysis of the mold behavior and process
  • Edge device that collects and analyzes data sending the crucial information to the cloud
  • AWS cloud for data analysis and mold behavior prediction


Following the Business Analysis phase, we have made the following assumptions:

  • The system collects equipment performance data via battery-operated IIoT sensor devices (ISD). The devices use custom bare-metal firmware
  • The data is transferred to a cloud server via a custom Linux-driven edge device
  • IEEE 802.15.4 is the primary connectivity standard. This provides an opportunity to implement popular connectivity technologies like Bluetooth, Zigbee, and Z-Wave
  • The platform uses AWS data storage, processing, and visualization tools

As a first step, the Softeq hardware team will create a custom ISD sensor device. Then we'll conduct field tests to make sure the data is produced in a format suitable for further Machine Learning-assisted analysis. The results will help developers refine the requirements for the sensing devices. Next, we will integrate ISDs with the AWS cloud services.


The solution we proposed goes beyond a traditional monitoring system. It helps companies
improve their manufacturing process through:

  • Lowering their equipment maintenance costs
  • Reducing downtime
  • Increasing asset life span
  • Reducing plastic waste

Currently, there are no alternatives to the Krammer Technology system on the market. The
competing solutions with similar functionality are either wired or much more expensive.