Altizon Systems > Case Studies > IoT Data Analytics Case Study - Packaging Films Manufacturer

IoT Data Analytics Case Study - Packaging Films Manufacturer

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 IoT Data Analytics Case Study - Packaging Films Manufacturer - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Process Analytics
  • Sensors - Pressure Sensors
Applicable Industries
  • Packaging
Applicable Functions
  • Quality Assurance
Use Cases
  • Machine Condition Monitoring
  • Predictive Quality Analytics
  • Root Cause Analysis & Diagnosis
About The Customer
A leading manufacturing company specialized in packaging films production implements Altizon’s Datonis Mint, a smart IoT solution to reduce quality degradation.Learn how Altizon’s Datonis MInt –Manufacturing Intelligence solution helped
The Challenge

The company manufactures packaging films on made to order or configure to order basis. Every order has a different set of requirements from the product characteristics perspective and hence requires machine’s settings to be adjusted accordingly. If the film quality does not meet the required standards, the degraded quality impacts customer delivery causes customer dissatisfaction and results in lower margins. The biggest challenge was to identify the real root cause and devise a remedy for that.

The Solution

In the ‘packaging film’ manufacturing process, the films are finally wound downstream on a ‘winder’ machine and slit to order on a ‘slitting’ machine. The solution demanded an ability to process historical machine data and establish requiredcorrelation between machine settings and production output. The deployed solution helped collect historical performance data. It gave the quality team the ability to monitor critical machine settings parameters, ascertain that they are in statistical control, and eventually correlate the process and machine data with different types of quality failures. The solution (Datonis MInt) also helped to identify critical to quality parameters from thirty parameters to two (pressure and tension) using correlation analytics. A prescriptive quality model was built based on the collected data to recommend the machine settings for the product configuration for every new order. This helped reduce quality degradation and achieve predictable quality.

Data Collected
Process Parameters, Machine Performance, Machine Settings
Operational Impact
  • [Efficiency Improvement - Operation]
    The solution helped achieve correlation10% reduction in quality degradation. It improved on-time delivery by 8% and reduced material wastage.
Quantitative Benefit

    Real-time process visibility: The IoT Solution helped gain real-time visibility into critical machine parameters and send alerts in case of quality issues.

    Solution Scalability: With the success of the pilot project and payback in less than the months, the solution is scaled in multiple plants.

  • Prescribed Machine settings: Data analytics helped establish a correlation between machine setting parameters and film production quality. The the machine setup improved quality consistency and throughput predictability.

  • Quality improvement: The solution helped the achieve correlation10% reduction in quality degradation. It improved on-time delivery by 8% and reduced material wastage.

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