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.
Artist’s rendering of Bitcoin. THERE ARE NO ACTUAL COINS THAT LOOK LIKE THIS. Don’t ever let anyone sell you one.
Today, many cryptocurrencies lost ~35-50% of their value. Reddit even posted contact information for the National Suicide Prevention Hotline in /r/cryptocurrency, knowing how emotional investors were bound to be today. Bitcoin, which was nearly $20K in mid-December and has been hovering near $14K this past week, dropped nearly $4K and almost sunk below the $10K milestone. I usually track the price of Bitcoin at http://bitcointicker.co, which can show the posted prices from several exchanges (web locations where people go to buy and sell, like Ebay). There are hundreds of cryptocurrencies and many of them dropped in value today.
Why did the prices drop so much on Tuesday? Here are some likely influences:
The government of South Korea announced its plans to prepare a bill banning cryptocurrency trading (specifically Bitcoin, Ethereum, Ripple); trading volume has been high in South Korea this past year, and the transactions have propped up global cryptocurrency prices.
Market prices are usually driven by supply and demand — for example, if there aren’t that many lobsters available in a particular area at a particular time, and you go to a restaurant hoping to order one — you’ll pay a premium. But that price is also influenced by the quality of the product, the image of the product, which influences your perception of its value. Quality reflects how well something satisfies stated and implied needs or expectations.
Value, however, is quality relative to price, and influenced by image. And people are not always rational: they’ll pay a premium for image, even if the value of a product isn’t particularly high. Just think of all the Macs on display at schools, coffee shops, and airports. Price is related to value… usually, price goes up as value goes up.
Where’s the value of cryptocurrency? A Bitcoin does not, on its own, have any inherent value — just like a dollar or a Euro (a “fiat currency”). But the prospect of an asset that will increase in perceived value — where you can buy low, hold (sometimes just for a few days), and sell high because there are lots of people willing to buy it from you — will have perceived value. Hundreds of early adopters — or “Bitcoin millionaires” — are getting people excited about the prospect of making small investments and reaping huge rewards. That this has happened so recently lends a mystique to ownership of cryptocurrencies and Altcoins (or “alternatives to Bitcoin,” like Ether) in addition to the novelty.
Value is attributed to things by people, and cryptocurrencies are no exception. The quality of the currency itself, and the technical solidity of the platform upon which one is based, isn’t really tied to the cryptocurrency price right now — although this will probably change as knowledge and awareness increases.
Is this the end of Bitcoin? That’s doubtful — there are too many innovators who insist on exploring the technological landscape of cryptocurrencies and blockchain technology, and lots of investors willing to fund them. In the meantime, there are unlikely benefits: because cryptocurrencies are not yet mainstream, a “crypto crash” is not as likely to ripple through the whole economy (no pun intended) like the subprime mortgage crisis of 2008. But if you do decide to buy cryptocurrency, don’t invest any more than you can afford to lose.
The fourth industrial revolution is characterized by intelligence: smart, hyperconnected agents deployed in environments where humans and machines cooperate to achieved shared goals — and using data to generate value. Quality 4.0 is the name we give to the pursuit of performance excellence in the midst of this theme of technological progress, which is sometimes referred to as digital transformation.
The characteristics of Quality 4.0 were first described in the 2015 American Society for Quality (ASQ) Future of Quality Report. This study aimed to uncover the key issues related to quality that could be expected to evolve over the next 5 to 10 years. In general, the analysts expected that the new reality would focus not so much on individual interests, but on the health and viability of the entire industrial ecosystem.
The World Economic Forum (WEF) has also been keenly interested in these changes for the past decade. In 2015, they launched a Digital Transformation Initiative (DTI) to coordinate research to help anticipate the impacts of these changes on business and society. They recognize that we’ve been actively experiencing digital transformation since the emergence of digital computing in the 1950’s:
Because the cost of enabling technologies has decreased so much over the past decade, it’s now possible for organizations to begin making them part of their digital strategy. In general, digital transformation reveals that the nature of “organization” is changing, and the nature of “customer” is changing as well. Organizations will no longer be defined solely by their employees and business partners, but also by the customers who participate – without even explicitly being aware of their integral involvement — in ongoing dialogues that shape the evolution of product lines and new services.
New business models will not necessarily rely on ownership, consumption, or centralized production of products or provision of services. The value-based approach will accentuate the importance of trust, transparency, and security, and new technologies (like blockchain) will help us implement and deploy systems to support those changes.
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.”
You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.
This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.
The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.