Just as with preceding industrial revolutions, Industry 4.0 signaled a major shift in how products are manufactured and delivered. Today, smart factories are taking a digital approach to manufacturing products, drastically improving time-to-market and efficiency, reducing costs and boosting productivity. Yet, in the race to digitize the factory, one area that has often been overlooked is quality management. Today, Quality 4.0, a concept coined by LNS Research, is taking its rightful place in the most recent industrial revolution.
As the price of poor quality becomes more visible – from product recalls to lawsuits leading to poor results and weakened brand reputations – Quality 4.0 is gaining traction. It’s no wonder, considering that according to a recent State of Quality Management Survey, 96% of manufacturers have had a product recall in the past few years.
Quality 4.0 provides a data-driven approach to managing quality, so that production is not just measured according to how quickly products are produced, but on their level of quality, along with the quality of every related item and transaction across the supply chain.
A Quality 4.0 approach integrates new technologies, such as digital twins, simulation testing, AI, mobile solutions, SaaS and the Industrial Internet of Things (IIoT) into traditional quality management strategies. It leverages key technologies to collect data enterprise-wide, from various sources to provide visibility and manage workflows, processes and protocols. It also leverages analytics to find meaning behind the data and use it to anticipate or solve business challenges.
However, according to Boston Consulting Group’s Aug. 2019 report, “Quality 4.0 Takes More Than Technology,” technology is only one aspect to the new revolution. The others are people and skills. Participants in the Boston Consulting Group (BCG) study noted that a shortage of skills is the main impediment to Quality 4.0 adoption. Based on the report, soft skills, such as change management, communication and teaming, are the most critical skills for success, while also acknowledging the role of analytics and big data skills.
Surprisingly, the BCG study found that relatively few participants believe that their company is prepared to implement Quality 4.0. “Only about one in four say that their company possesses a detailed digital transformation strategy and roadmap or has state-of-the-art knowledge about Industry 4.0.”
So what does it take to become prepared for Quality 4.0 success? Consider the following.
Quality 4.0 requires strategic business alignment. A commitment to Quality 4.0 requires involvement from the C-level suite to be successful. It ensures that quality is seen as a strategic business imperative, rather than an operational process, and enables stronger adoption and retention. The idea that quality contributes to revenue by improving customer retention and bolstering perception of the brand must be clearly reinforced from the top down.
Quality is a journey that needs a purpose. Before setting out on the Quality 4.0 journey, manufacturers must understand why they’re going there and what the destination will look like. They need to uncover their pain points and unique obstacles. These challenges might include siloed processes, insufficient documentation, inconsistent and limited data, improperly trained employees, a minimal amount of corrective action and disconnected suppliers. When quality becomes embedded in the organization, it stops being a separate thing and just becomes part of the day to day mantra of how an organization operates. Manufacturers don’t have to remind employees to consider quality – it just is.
Investment in the quality tech stack requires an integrated approach. Digital technologies that enable an effective Quality 4.0 program, such as cloud computing, machine learning, the Industrial Internet of Things (IIoT) or mobile, must be part of a strategic roadmap for improving operational performance, identifying and addressing quality problems preemptively, ensuring regulatory compliance and, ultimately, enhancing brand reputation.
Data is king when it comes to Quality 4.0. While sound data is crucial to all areas of business, storing, managing, and retrieving, it enterprise-wide continues to be a challenge for many manufacturers. Advanced analytics and AI are playing a role, helping them collect and analyze key quality metrics such as manufacturing efficiency, customer satisfaction or supplier performance. The driver to superior quality is the intelligence gathered from patterns found in the data that can lead to sound business decisions and process improvements.
An obstacle to Quality 4.0 is a lack of qualified talent. According to the BCG study mentioned above, “only one-third of participants understand how digitization will change quality management roles and skills.” And, the study points out that even fewer believe that their company has the right people in place to run a Quality 4.0 initiative or attract related talent. A shortage of software talent, as well as quality expertise is an industry challenge, yet it can be overcome. Companies that are unable to find the skills they need in the workforce can invest in outside training of their people; support internal champions to train existing staff and build a culture of quality enthusiasts; and work alongside strategic partners to build appropriate tech stacks.
The path to Quality 4.0 is paved with disruptive technology, processes and people committed to traveling down the long and winding road to its successful arrival. Those manufacturers that embrace it are rewarded with an elevated place in the market, higher valuation, positive brand recognition, higher customer satisfaction, all due to improved efficiency, less product defects, and reduced costs. It’s well worth the journey.
David Isaacson, senior director, product marketing , ETQ, has over 25 years of experience in software product marketing and product management. David has successfully brought SaaS products to market for a variety of industries and high-growth companies. He has worked for software companies such as Anaqua, VFA/Accruent, and Oracle, where he led the product management team responsible for integrating analytics into the Oracle database.
