Data science and machine learning have surged in prominence over the past few years, and digital transformation seems to be on everyone’s agenda. Have you ever wondered why? Even though quality engineering has long been a data-driven pursuit, we now have the potential to get even deeper insights from our data because of several recent innovations:
- Computing power per dollar has increased steadily (e.g. through adoption of GPUs).
- Open-source software packages with powerful machine learning algorithms are freely available, reliable, robust, and well-maintained.
- Infrastructure for data storage and management is readily available and cost-effective.
- Cloud-based software, platforms, and infrastructure helps companies focus on their core competencies and scale rapidly when needed.
- Algorithms are often more revealing when Big Data is available.
It’s easy and cheap to collect data but using it to generate actionable insights can be more challenging. Think, for example, about the digital displays available to a production manager supervising a factory. The factory has multiple product lines, with hundreds of variables and attributes to monitor, as well as reaction plans to oversee and track. Although software systems are available to make these tasks easier, getting it right can still be daunting. Synthesizing data across multiple subsystems in your operations or multiple divisions in your organization to uncover opportunities for improvement can be messy, uncertain, or even impossible.
Quality 4.0 aims to break down these barriers by providing “organizational omniscience” and real-time performance innovation. (Radziwill, 2018a; 2018b) Our new technology-infused quality management strategies will answer questions like:
How can we leverage connected, automated, intelligent systems — made up of people and machines — to make processes more efficient and effective, save time and money, and produce better products more consistently?
How can we use emerging technologies to support engagement and collaboration that help connected and empowered individuals achieve the organization’s goals?
Quality 4.0 will help us more quickly assess compliance and customer satisfaction, adapt to changes in the environment and the market, and optimize business processes through systems integration – whether the object we’re working with is a process, a product, a person, or an intelligent software system. The accompanying digital transformation will help us enrich human intelligence while leveraging machine intelligence, automate processes that were previously labor intensive, and connect more richly to our processes and to each other.
One day, smart, hyperconnected, and intelligent agents will be deployed in environments where humans and machines cooperate effortlessly to achieve their shared goal of continuous improvement. With real-time visibility and transparency, you’ll be able to take operations one step further by anticipating process and product issues before they happen. You can trigger contingency plans, adapt to environmental changes, and discover and prioritize opportunities for continuous improvement based on anticipated impact and the potential for reduced risk.
As a result, Quality 4.0 strategies emphasize real-time visibility, intelligent decision support, and improved communication between people, systems, and machines. Nikon, for example, recently announced a Quality 4.0 strategy emphasizing real-time measurement: automating measurement systems and inspections, and centralizing the results in an accessible data warehouse. By digitizing as much as possible, Nikon aims to shorten the time required to make decisions about production processes. This will improve availability and productivity.
Some organizations that are struggling with compliance, continuous improvement, or building a quality culture may worry that they will miss out on the business value promised by Quality 4.0 initiatives. Fortunately, it’s not too late at all. The foundation for Quality 4.0 is digital data, supported by an integrated management system that can serve as the nexus for your quality data and records. Having all of your environment, health and safety, and quality data in one place gets people working together and reduces the need for data integration later. (BLR Media, 2018) Cloud-based systems further reduce risk, allowing your organization to scale without the burden of keeping key systems functioning.
If your data is on paper, or if you’re drowning in it, your data can’t work for you. Where should you begin?
In this webinar “The Quality 4.0 Revolution: Reveal Hidden Insights Now with Data Science and Machine Learning”, we’ll show you what data scientists do, what kinds of problems in quality and process improvement can be solved with machine learning, and how to get started — even if your processes are still manual and your data analysis is still primarily in Excel. No technical expertise is necessary! Watch your free webinar now!
BLR Media. (2018). Implementing New EHS Software: Revealing Value, Gaining Buy in and Engaging Employees. Intelex Insight Report, available from https://www.intelex.com/resources/insight-report/implementing-new-ehs-software-value-buy-and-engagement
Radziwill, N. (2018a, August 7). What is Quality 4.0? https://community.intelex.com/explore/posts/what-quality-40%C2%A0
Radziwill, N. (2018b, October). Let’s Get Digital: The many ways the fourth industrial revolution is reshaping the way we think about quality. Quality Progress, American Society for Quality, p. 24-29. Available from http://asq.org/quality-progress/2018/10/basic-quality/lets-get-digital.html