Making the Most of the Quality 4.0 Journey

In my previous blog, I explored how to optimise data and actionable information by increasing network connectivity. In this post, I will continue the theme of connectivity to examine how companies can more closely integrate quality into the production process.

Greater connectivity is one of the central principles defining the fourth industrial revolution (Industry 4.0), driven by what are becoming increasingly familiar technologies and concepts like big data, Internet of Things (IoT), cyber-physical systems, and cloud computing. These innovations provide unprecedented opportunities to bring quality closer to production and drive a significant increase in productivity, but to achieve this there are some common challenges that quality needs to overcome.

While looking to the opportunities of tomorrow, quality teams must recognise where they are still trying to solve the problems of yesterday. Among these challenges, some of the most prevalent are inefficiencies caused by fragmented systems, manual metrics calculations, and ineffective supplier communication. These kinds of issues need to be resolved before organisations can fully leverage the huge amounts of data created by IoT and machine-to-machine (M2M) technology. So how do teams ensure they are managing data efficiently to get the most out of it and taking the steps that will integrate quality into production to achieve more connected Quality 4.0?

Connected Key Performance Indicators (KPIs) Fed by Collected Data
To operate connectedly and globally, you need to measure likewise. This will give you a global view of processes, enabling you to analyse how systems are working together and where potential problems lie. Some important KPIs to consider include:

  • Efficiency metrics, such as overall equipment effectiveness (OEE), which essentially measures the percentage of time the organisation spends creating good parts in the ideal cycle time with no stop time. Other efficiency metrics include throughput, schedule attainment, and capacity utilisation.
  • Process capability metrics, such as coefficient of process capability (Cpk), which compares how closely processes are running according to their specification relative to their natural variabilities.

HxGN SMART Quality Connectivity

To maximise the visibility of this information and your ability to leverage it, it can be beneficial to make use of mobile-based real-time analytics and dashboards like those in HxGN SMART Quality, that collect all the relevant information and data and provide a user-friendly platform to perform analyses.

Collaborative Work Across Different Systems
Deeper integration and comprehensive data integrity relies on reducing levels of media and technology discontinuity. Ultimately, this will involve adopting inline measurement techniques that are not only aligned with the production process but also integrate automated feedback cycles to ensure that quality is a proactive part of the process, producing insightful, real-time actionable information that enables you to make changes as they are needed rather than after the fact. This level of connectivity should certainly include supplier and customer integration to create a truly end-to-end process and allow users to fully understand how quality is impacting the value chain.

Real-Time Communication
Connecting data points allows you to ensure you are getting the right information to the right people or machine at the right time. Tools like HxGNSMART Quality aggregate data from these multiple sources, allowing users to analyse results in context and real-time, implementing resourcing and workflow changes along the way to drive efficiency.

Quality 4.0 is a journey, but each step to merge quality throughout production is crucial to make that significant productivity leap for your organisation. In my next blog I’ll be taking a close look at the Smart Factory.


  • Benjamin Bickel

    Benjamin Bickel is the Product Manager for HxGN SMART Quality at Hexagon Manufacturing Intelligence. He studied mechatronics, industrial engineering and business administration and holds a diploma in industrial engineering as well as a master’s degree in business administration. Before he joined Hexagon, he worked for several years as a business consultant for company specialising in technology consulting. He himself specialises in project and product management for the software industry, and innovation management.

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