Quality 4.0 Transition: What You Need & How

Now more than ever, digital transformation is profoundly changing manufacturing, processing, and production industries all around the world. Recent research conducted by Raoul Sisodia and Daniel Villegas Forero indicates how to transition into Quality 4.0. In our blog, based on their research findings, we zoom in on Quality 4.0 initiatives.

First things first: How can we define Quality 4.0? 

The concept is derived from the fourth industrial revolution, called Industry 4.0. It refers to the digitalisation of total quality management (TQM) and how digital tools can impact technology, process, and people. 

The ISO 9001:2015 standard provides us with guidelines for the adoption of a quality management system related to TQM. As it proposes seven Quality Management principles that should work under a process approach:

• Customer focus.
• Leadership.
• Engagement of people.
• Process approach.
• Improvement.
• Evidence-based decision making.
• Relationship management.

However, how to transition into Quality 4.0 remains abstract and intangible for many companies.

It is important to note that in the context of Industry 4.0, quality should be considered as the discovery of data sources, root causes, and insights about products and organisations by augmenting and improving upon human intelligence. The changes that come with digitalisation, automation, big data, and cybersecurity must therefore be considered as organisational issues.

Sisodia et al. proposed a general roadmap for transitioning to Quality 4.0, which consists of six sequential phases that can be applied to various organizations:

1. Assessing Readiness Level: This phase involves evaluating the maturity level of Industry 4.0, the stability of processes and data flows, as well as monitoring relevant regulations and standards.
2. Setting Up: This includes aligning the strategy with Industry 4.0, developing business cases, securing management support, anticipating changes, and managing knowledge effectively.
3. Involving Stakeholders and Systems: This phase focuses on addressing changes in roles and competencies, involving suppliers and customers, and enhancing the interoperability of systems.
4. Finding New Ways to Deliver Insights: This phase emphasizes the importance of discovering innovative methods to provide insights that support decision-making and operational improvements.
5. Creating Value: Value creation overlaps with all the other phases, as it occurs at each stage of the transition.
6. Managing Data: This requires establishing adequate infrastructure for data administration and data flows, ensuring that relevant insights are delivered to the right people in a timely manner to enhance their work.

Seven tools and technologies that can be utilized to enhance quality and facilitate its digitalization.

Statistics and data science

Drive value through predictions, finding patterns, and generating viable models and solutions. Identify causal and noncausal relationships through data aggregation, data classification, real-time pipelines, and dynamic modeling that generates knowledge related to problem-solving

Enabling technologies

Always related to the latest developments in connectivity like sensors, mobile devices, networks, Internet of Things (IoT), Industrial Internet of Things (IIoT), integrated systems, Virtual Reality, and cloud computing. Also related to how to manage documentation.

Big data

Related to the infrastructure for managing and analysing large data sets that arrive very fast, in different formats, with high variation in data quality, from different stakeholders, it could be easily modified, and there may be restrictions on its use.

Blockchain

Permanent monitoring for allowing transactions to happen only if quality objectives are met. Contributes to ensuring data quality, trust, and to developing a quality culture.

Artificial intelligence (AI)

For making complex decisions like computer vision, chatbots, and robotics.

Machine learning (ML)

It helps when heuristics are used for decision making and also for forecasting, filtering of information, and recommendation systems. Helps a company to do jobs better by finding levers within the processes that can ensure consistency and alignment across the whole organisation. The uncovering of relationships helps build a safety and quality culture.

Neural networks and deep learning

Used for forecasting and complex pattern recognition. It incorporates layers with special functions.

Subsequently, the role of quality professionals continues to change, as they need to help their organisations make the vital connection between quality excellence and their ability to thrive in disruption, using quality principles to enable transformation and growth. With Quality 4.0, quality professionals all be more capable of answering questions about:

• Product robustness.
• Process excellence.
• Customer satisfaction.
• Risks in new product development.
• Traceability.
• Transparency.

Quality professionals possess the necessary skills to lead digital transformation in organisations, such as:

• Systems thinking.
• Decision-making based on data.
• Leadership for organisational learning.
• Continuous improvement processes.
• The ability to understand the consequences of decisions taken regarding society, environment, and ethics.

Furthermore, the role of existing inspections will evolve as well. Since the measuring of performed operations will be done by automated equipment built into the machines and production lines, inspectors will have to shift their role. Inspectors will play crucial roles in making decisions related to measuring processes, analyzing collected data, and taking appropriate preventive measures to improve existing processes.

Quality 4.0 blends new technologies with traditional quality methods to arrive at new optimum performance, operational excellence, and innovation.