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IoT Data Analytics Case Study - Packaging Films Manufacturer

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.

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  • SUPPLIER
  • Altizon Systems
    Altizon empowers Industrial Digital Revolutions globally by helping enterprises use machine data to drive business decisions. With a global footprint of over 100 enterprise users, Altizon is a leading Industrial IoT platform provider as recognized by Gartner, Forrester, BCG, Frost & Sullivan, and others.
  • INDUSTRIES
  • Chemicals
  • FUNCTIONS
  • Process Manufacturing
  • 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 company’s packaging films division analyze critical machines parameters settings and establish a correlation with film production quality.

  • CONNECTIVITY PROTOCOLS
  • 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
  • SOLUTION TYPE
  • SOLUTION MATURITY
  • Mature (technology has been on the market for > 5 years)
  • OPERATIONAL IMPACT
  • Impact #1
    [Efficiency Improvement - Operation]
    The solution helped achieve correlation10% reduction in quality degradation. It improved on-time delivery by 8% and reduced material wastage.
    Impact #2
    Impact #3
  • QUANTITATIVE BENEFIT
  • Benefit #1

     

    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.

    Benefit #2

    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.

    Benefit #3

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

  • USE CASES
  • Fog Computing
    Fog computing refers to a decentralized computing structure, where resources, including the data and applications, get placed in logical locations between the data source and the cloud; it also is known by the terms fogging and fog networking. The goal of this is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data needs to be transported on the network, improving overall network efficiency and performance. Fog computing can also be deployed for security reasons, as it has the ability to segment bandwidth traffic and introduce additional firewalls to a network for higher security.
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