What’s Quality 4.0, why is it important, and how can you use it to gain competitive advantage? Did you know you can benefit from Quality 4.0 even if you’re not a manufacturing organization? That’s right. I’ll tell you more next week.
Since then, hundreds more have followed to help people understand more about quality and process improvement in theory and in practice. I started writing because I was in the middle of my PhD dissertation in the Quality Systems program at Indiana State, and I was discovering so many interesting nuggets of information that I wanted to share those with the world – particularly practitioners, who might not have lots of time (or even interest) in sifting through the research. In addition, I was using data science (and some machine learning, although at the time, it was much more difficult to implement) to explore quality-related problems, and could see the earliest signs that this new paradigm for problem solving might help fuel data-driven decision making in the workplace… if only we could make the advanced techniques easy for people in busy jobs to use and apply.
We’re not there yet, but as ASQ and other organizations recognize Quality 4.0 as a focus area, we’re much closer. As a result, I’ve made it my mission to help bring insights from research to practitioners, to make these new innovations real. If you are developing or demonstrating any new innovative techniques that relate to making people, processes, or products better, easier, faster, or less expensive — or reducing risks and building individual and organizational capabilities — let me know!
I’ve also learned a lot in the past decade, most of which I’ve spent helping undergraduate students develop and refine their data-driven decision making skills, and more recently at Intelex (provider of integrated environment, health & safety, and quality management EHSQ software to enterprises and smaller organizations). Here are some of the big lessons:
People are complex. They have multidimensional lives, and work should support and enrich those lives. Any organization that cares about performance — internally and in the market — should examine how it can create complete and meaningful experiences. This applies not only to customers, but to employees and partners and suppliers. It also applies to anyone an organization has the power and potential to impact, no matter how small.
Your data are your most valuable assets. It sounds trite, but data is becoming as valuable as warehouses, inventory, and equipment. I was involved in a project a few years ago where we digitized data that had been collected for three years — and by analyzing it, we uncovered improvement opportunities that when implemented, saved thousands of dollars a week. We would not have been able to do that if the data had remained scratched in pencil on thousands of sheets of well-worn legal paper.
Self-awareness must be cultivated. The older you get, and the more experience you gain, the more you know what you don’t know. Many of my junior colleagues (and yours) haven’t reached this point yet, and will need some help from senior colleagues to gain this awareness. At the same time, those of you who are senior have valuable lessons to learn from your junior colleagues, too! Quality improvement is grounded in personal and organizational learning, and processes should help people help each other uncover blind spots and work through them — without fear.
Most of all, I discovered that what really matters is learning. We can spend time supporting human and organizational performance, developing and refining processes that have quality baked in, and making sure that products meet all their specifications. But what’s going on under the surface is more profound: people are learning about themselves, they are learning about how to transform inputs into outputs in a way that adds value, and they are learning about each other and their environment. Our processes just encapsulate that organizational knowledge that we develop as we learn.
Want to find out what Quality 4.0 really is — and start realizing the benefits for your organization? If so, check out the October 2018 issue of ASQ’s Quality Progress, where my new article (“Let’s Get Digital“) does just that.
Quality 4.0 asks how we can leverage connected, intelligent, automated (C-I-A) technologies to increase efficiency, effectiveness, and satisfaction: “As connected, intelligent and automated systems are more widely adopted, we can once again expect a renaissance in quality tools and methods.” In addition, we’re working to bring this to the forefront of quality management and quality engineering practice at Intelex.
Quality 4.0 Evolution
The progression can be summarized through four themes. We’re in the “quality as discovery” stage today:
Quality as inspection: In the early days, quality assurance relied on inspecting bad quality out of items produced. Walter A. Shewhart’s methods for statistical process control (SPC) helped operators determine whether variation was due to random or special causes.
Quality as design: Next, more holistic methods emerged for designing quality in to processes. The goal is to prevent quality problems before they occur. These movements were inspired by W. Edwards Deming’s push to cease dependence on inspection, and Juran’s Quality by Design.
Quality as empowerment: By the 1990’s, organizations adopting TQM and Six Sigma advocated a holistic approach to quality. Quality is everyone’s responsibility and empowered individuals contribute to continuous improvement.
Quality as discovery: Because of emerging technologies, we’re at a new frontier. In an adaptive, intelligent environment, quality depends on how:
quickly we can discover and aggregate new data sources,
effectively we can discover root causes and
how well we can discover new insights about ourselves, our products and our organizations.”
I don’t use cash often, so I haven’t been to an ATM machine in several months. Regardless, I’m fully accustomed to the pattern: put card in, enter secret code, tell the machine what I want, get my money, take my card.
This time, my money was taking a looonnnnnnnngggg time to pop out.
Maybe there’s a problem with the connection? Maybe I should check back later? I sat in my car thinking about what the best plan of action would be… and then I decided to read the screen. (Who needs to read the screen? We all know what’s supposed to happen… right? Once, I was even able to use an ATM machine entirely in the Icelandic language just because I knew the pattern.)
PLEASE TAKE YOUR CARD TO DISPENSE FUNDS, it said.
This is one of the simplest and greatest examples of poka-yoke (or “mistake-proofing”) I’ve ever seen. I had to take my card out and put it away before I could get my money! I was highly motivated to get my money (I mean, that’s the specific thing I came to the ATM to get). Of course I’m going to do what you want, ATM! The machine forced me to take my card — and prevented me from accidentally leaving my card in the machine. This could be problematic for both me and the bank.
Why have I never seen this before? Why don’t other ATMs do this? I went on an intellectual fishing expedition and found out that no, the idea is not new… Lockton et al. (2010) said:
A major opportunity for error with historic ATMs came from a user leaving his or her ATM card in the machine’s slot after the procedure of dispensing cash or other account activity was complete (Rogers et al., 1996, Rogers and Fisk, 1997). This was primarily because the [ATM dispensed the cash] before the card was returned (i.e. a different sequence for Plan 3 in the HTA of Fig. 3), leading to a postcompletion error—“errors such as leaving the original document behind in a photocopier… [or] forgetting to replace the gas cap after filling the tank” (Byrne and Bovair, 1997). Postcompletion error is an error of omission (Matthews et al., 2000); the user’s main goal (Plan 0 in Fig. 3) of getting cash was completed so the further “hanging postcompletion action” (Chung and Byrne, 2008) of retrieving the card was easily forgotten.
The obvious design solution was, as Chung and Byrne (2008) put it, “to place the hanging postcompletion action ‘on the critical path’ to reduce or eliminate [its] omission” and this is what the majority of current ATMs feature (Freed and Remington, 2000): an interlock forcing function (Norman, 1988) or control poka-yoke (Shingo, 1986), requiring the user to remove the card before the cash is dispensed. Zimmerman and Bridger (2000) found that a ‘card-returned-then-cash-dispensed’ ATM dialogue design was at least 22% more efficient (in withdrawal time) and resulted in 100% fewer lost cards (i.e. none) compared with a ‘cash-dispensed-then-card-returned’ dialogue design.
I don’t think the most compelling message here has anything to do with design or ATMs, but with the value of hidden gems tucked into research papers. There can be a long lag time between generating genius ideas and making them useful to real people.
One of my goals over the next few years is to help as many of these nuggets get into the mainstream as possible. If you’ve learned something from research that would benefit quality or business, get in touch. I want to hear from you!
Lockton, D., Harrison, D., & Stanton, N. A. (2010). The Design with Intent Method: A design tool for influencing user behaviour. Applied ergonomics, 41(3), 382-392.
In previous articles, we introduced Quality 4.0, the pursuit of performance excellence as an integral part of an organization’s digital transformation. It’s one aspect of Industry 4.0 transformation towards intelligent automation: smart, hyperconnected(*) agents deployed in environments where humans and machines cooperate and leverage data to achieve shared goals.
Automation is a spectrum: an operator can specify a process that a computer or intelligent agent executes, the computer can make decisions for an operator to approve or adjust, or the computer can make and execute all decisions. Similarly, machine intelligence is a spectrum: an algorithm can provide advice, take action with approvals or adjustments, or take action on its own. We have to decide what value is generated when we introduce various degrees of intelligence and automation in our organizations.
How can Quality 4.0 help your organization? How can you improve the performance of your people, projects, products, and entire organizations by implementing technologies like artificial intelligence, machine learning, robotic process automation, and blockchain?
A value proposition is a statement that explains what benefits a product or activity will deliver. Quality 4.0 initiatives have these kinds of value propositions:
Augment (or improve upon) human intelligence
Increase the speed and quality of decision-making
Improve transparency, traceability, and auditability
Anticipate changes, reveal biases, and adapt to new circumstances and knowledge
Evolve relationships and organizational boundaries to reveal opportunities for continuous improvement and new business models
Learn how to learn; cultivate self-awareness and other-awareness as a skill
Quality 4.0 initiatives add intelligence to monitoring and managing operations – for example, predictive maintenance can help you anticipate equipment failures and proactively reduce downtime. They can help you assess supply chain risk on an ongoing basis, or help you decide whether to take corrective action. They can also improve help you improve cybersecurity: documenting and benchmarking processes can provide a basis for detecting anomalies, and understanding expected performance can help you detect potential attacks.
(*) Hyperconnected = (nearly) always on, (nearly) always accessible.
Quality is the “totality of characteristics of an entity that bear upon its ability to meet stated and implied needs.” (ISO 9001:2015, p.3.1.5) Quality assurance is the practice of assessing whether a particular product or service has the characteristics to meet needs, and through continuous improvement efforts, we use data to tell us whether or not we are adjusting those characteristics to more effectively meet the needs of our stakeholders.
But what if the entity is a chatbot?
In June 2017, we published a paper that explored that question. We mined the academic and industry literature to determine 1) what quality attributes have been used by others to determine chatbot quality, we 2) organized them according to the efficiency, effectiveness, and satisfaction (using guidance from the ISO 9241 definition of usability), and 3) we explored the utility of Saaty’s Analytic Hierarchy Process (AHP) to help organizations select between one or more versions of chatbots based on quality considerations. (It’s sort of like A/B testing for chatbots.)
“There are many ways for practitioners to apply the materialin this article:
The quality attributes in Table 1 can be used as a checklist for a chatbot implementation team to make sure they have addressed key issues.
Two or more conversational systems can be compared by selecting the most significant quality attributes.
Systems can be compared at two points in time to see if quality has improved, which may be particularly useful for adaptive systems that learn as they as exposed to additional participants and topics.”