Industrial Engineering

Quality 4.0: Taking Quality to its Next Level

Quality 4.0: Taking quality to its next level

ISE Magazine June 2020 Volume: 52 Number: 6

By Jiju Antony


The term Quality 4.0 was coined by Dan Jacob, research director and principal analyst with LNS research, a leading manufacturing research and advisory firm. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and improved business models.

Quality 4.0 is using myriad technologies such as cyber-physical systems (CPS), internet of things (IoT) and cloud computing to meet the quality of design, quality of conformance and quality of performance requirements of an industry. The ap-plication of digital technologies can change the quality in various ways; for instance, an organization can monitor processes and extract data from real-time sensors. The big data generated from these sensors can be further analyzed to predict quality is-sues and maintenance needs of the organization so that defects and breakdowns can be significantly reduced.

Quality 4.0 will drive improvements across the value chain. This article collates and analyzes the essential ingredients from the existing literature which are essential for the effective implementation of Quality 4.0.

The key ingredients for the effective implementation of Quality 4.0 are as follows:

Leadership. Quality 4.0 requires a leadership style that con-siders innovation and learning. One of the widely used styles in innovation and learning is transformation leadership (Muthuraj Birasnav, “Knowledge Management and Organizational Performance in the Service Industry: The Role of Transformational Leadership Beyond the Effects of Transactional Leader-ship,” Journal of Business Research, 2014). The transformational leadership style at present is limited to idealized influence, inspirational motivation, intellectual stimulation and providing vision. Therefore what we need is knowledge-oriented leader-ship, which is more specific to learning and innovation. This style of leadership combines both transactional and transformation styles.

Organizational culture. This influences members of the organization, such as influencing their behavior, performance outcomes and an organization’s external environment. The four types of organizational culture such as clan, adhocracy, hierarchy (Kim S. Cameron and Robert E. Quinn, Diagnosing and Changing Organizational Culture: Based on the Competing Values Framework, 2011) and market will play an important role in Quality 4.0. However, more research with a variety of organizations with different sizes and nature has to be carried out to understand the impact of various cultures and leadership styles for the successful implementation of Quality 4.0.

Top management support. The backing of management is critical in enhancing the incorporation of Quality 4.0 technology into the business strategy. A transparent and visible top management support encourages positive user attitudes toward a quality 4.0 system. Top management support within an organization can encourage the practices and behaviors that lead to quality performance throughout the organization.

Training. For Quality 4.0, a range of new skills are required for quality professionals and training will play a major role as different skills might be required at different levels (such as quality engineers, managers, directors, etc.). The Quality 4.0 skills needed would be technical, such as installing and operating IT, radio frequency identification tags and big data analysis. There would also be a requirement of transformational skills such as adaptability, critical thinking, creativity and social skills such as teamwork and knowledge transfer (Burkhard Schallock, Christoffer Rybski, Roland Jochem and Holger Kohl, “Learning Factory for Industry 4.0 to Provide Future Skills Beyond Technical Training,” Procedia Manufacturing, 2018).

Use for strategic advantage. By using modern technologies of Quality 4.0, organizations can create better quality products and services and thereby create a price-value advantage over the competitors. The data on how customers use the products enhances a company’s ability to segment the customers, customize the products, set prices to better capture value and extend value-added services (Michael E. Porter and James E. Heppelmann, “How Smart, Connected Products are Trans-forming Competition,” Harvard Business Review, 2014).

Organizations that compete on quality using digital technologies therefore should use an operational strategy based on continuous improvement using both digital technologies and big data.

Using prescriptive analytics algorithms for quality metrics. Poor metrics is one of the primary barriers for accomplishing quality objectives. The current quality metrics such as defect rate, failure rate, etc., primarily describe what happened, why it happened and what might happen next. It seldom describes what actions are to be taken in a prescriptive manner (B. Pedersen, “The Quality Leader’s Guide to Quality 4.0,” 2017).

Prescriptive analytics algorithms in quality management can provide two levels of human intervention for decision-making (John Hagerty, Planning Guide for Data and Analytics, Gartner, 2017). The first level is a decision support system. The second level of prescriptive analytics will be based on intelligent algorithms that will result in decision automation through ma-chine learning. This type of prescription algorithm will help in implementing the prescribed action in an automated and self-regulating manner.

Handling big data. The development of affordable sensors, improved data acquisition systems and fast communication systems in the cyber-physical systems of Industry 4.0, a large amount of data is generated that can be used by quality management systems. The big data will enable the understanding of customers’ needs in a holistic or all-encompassing manner, as almost all customers’ needs will be mapped and analyzed.

In terms of a Kano model, the threshold/basic attributes, performance attributes and excite/delight attributes can be accurately analyzed using big data. The end-to-end integration across the product life cycle is one of the striking features of Industry 4.0. This will result in a large amount of product us-age data (Tim Stock and Günther Seliger, “Opportunities of Sustainable Manufacturing in Industry 4.0.,” Procedia Corp., 2016) that can be used by manufacturers to monitor the quality and reliability of the product. Consequently, the quality of performance can also be effectively monitored by collecting and analyzing the product usage data in customers’ hands through an automated manner using artificial intelligence.

Quality 4.0 is an emerging research area and this article proposes the key ingredients for its effective implementation. Included are essential ingredients from the existing literature. The next stage of the research is to evaluate these ingredients and explore further to see if any other essential ingredients are missing.

The agenda for future research includes a global study into Quality 4.0 to understand the key ingredients for its implementation from practitioners’ perspectives and the challenges or barriers in the implementation, to be followed by the development of a self-assessment organizational cultural readiness framework for Quality 4.0.

References: IISE Magazine June 2020 (