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Cloud-Based Data Analytics Platform for Injection Molding Machines

An intelligent system for the management and predictive maintenance of molding machines.

Krammer Technology is a startup that provides consulting services to industrial businesses. It was working on an industrial IoT solution that transforms molds into intelligent devices.

Project Information
Engagement model

T&M (time and materials)




Firmware Developers


Back-end Developer


Business Analyst


Project Manager

More Details


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.



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.


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

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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.