Quality Engineering in the Era of Industry 4.0

With the proliferation of digital technologies on the shop floor, quality engineering is adapting to a new manufacturing reality.

The good news is that Industry 4.0 technologies–IoT, big data, AI, and computer vision, among others–all promise to enhance quality efforts. Given that quality professionals are increasingly tasked with coordinating the human and technological components of quality programs, finding success will mean maximizing resources in both of these areas.

This post will survey trends in quality engineering, and suggest some ways quality engineers can refine their skillset to deliver the most value to their organizations.

The Big Trends: Big Data and Soft Skills

A panel of quality experts agreed on two skill sets that will be most important for quality engineers in Industry 4.0: big-data analytics and soft skills, such as interpersonal relationship management, critical thinking, and creativity.

Tulip analytics dashboard
New analytics dashboards can simplify big data problems

Big data analytics–The modern factory produces an overwhelming amount of data. New machine monitoring technologies constantly record machine data, and IoT increasingly captures the human dimension of quality as well. As server capacity and monitoring technology improve, engineers will have access to data on a previously unimaginable scale. Increasingly, quality engineers will be expected to use this data to inform their suggestions. As factories come to rely on this data for continuous improvement and quality assurance, quality engineers will need to understand, if not master, the use of large data sets and basic data science for quality related insights.

This data can help quality engineers predict changes in quality before they happen. One quality engineer noted how big data analytics helped their predictive efforts: “We used big data and analytics to predict early indications of deviations from the standard process and potential excursions. This would also provide us with information on possible next steps if an alert was received.”

Soft Skills–Just as important as big data analytics, if not more, are soft skills. Increasingly, quality engineering means attending to the human dimension of production. Creative problem solving and people skills will help keep quality engineers central to any manufacturing operation.

As data scientists know, big data sets don’t solve problems by themselves. Messy data need to be gathered and organized. They require human intervention and interpretation. Quality engineers will increasingly do the hard, creative work that makes data analysis possible.

In order to succeed in Industry 4.0, quality engineers will need to be able to frame quantitative manufacturing problems. They will need to understand how to marshal a surplus of data to provide the insights they desire.

Communication will also be critical for the Industry 4.0 quality engineering.

With new technology will come new questions, new areas for testing, and unknown unknowns. Quality engineers will find themselves in a position where they need to explain new concepts and initiatives to technical and lay audiences. They’ll:

  • Explain new tests and systems to associates
  • Communicate results to management
  • Justify the use or acquisition of new systems to procurement
  • Articulate increasingly complex work to a wide variety of stakeholders
  • Advocate on behalf of customers
  • Mediate between stakeholders

Quality engineers who can navigate these personal relationships will help their operations get the most out of a digital transformation.

Specific Industry 4.0 impacts

Beyond these general trends, there are several areas where industry 4.0 will impact quality engineers.

Improving the feedback loop–With better data will come better visibility into the full range of factory processes. This means quality engineers will be able to suggest areas for corrective and preventive action earlier and more accurately. Engineers will be able to see new relationships between each of the nodes in the feedback loop, and new mechanisms can be put in place to assure quality at every stage.

Improved prevention/Proactive maintenance–Data collected from IoT connected machines will help quality engineers prevent non-conformances and allow operators to repair machines and smooth processes before interruptions. Accelerated stress testing, Real Time Process monitoring, and sophisticated modeling tools will make quality engineering more proactive than reactive.

Tulip Fishbone Ishikawa Diagram
Root Cause Analysis App

Root cause analysis–With more information and a connected factory, it will be easier for quality engineers to separate signal from noise, to identify the root cause of problems. This can take the form of advanced, AI driven Statistical Process Control from machine monitoring data, or from enhanced attention to the human dimensions of quality collected from instrumented applications.

Integrated quality controls–Industry 4.0 will let the quality engineer integrate their quality controls into machines and processes in ways that exceed the capabilities of many Quality Management Systems (QMS). Artificial Intelligence, in particular, will help QEs design validation and QE systems that learn as they work, become more efficient and accurate with every iteration.

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