“Together with his identical twin brother, Scott, he has laid the groundwork for the future of space exploration as the subjects of an unprecedented NASA study on how space affects the human body, which is featured in Scott’s New York Times best-selling memoir, Endurance: A Year in Space, A Lifetime of Discovery.”
“Currently, Mark is on the Commercial Crew Safety Board at Space X […] and is the co-founder of World View, a full-service commercial space launch provider.”
Endeavour to Succeed. College of DuPage, Department of Physics. February 14 2019.
I managed to attend Captain Mark Kelly’s talk in Chicago just the day before I was leaving for Barcelona’s Mobile World Congress. M. Kelly’s presence and insightful remarks commanded both admiration and utmost respect.
Among many other fascinating topics, he discussed NASA’s “None of US is as Dumb as All of Us“ as a reminder of the negative impact of ‘groupthink‘ in the context of faulty decision making. Most specifically, he referred to dramatic mistakes leading to the Space Shuttle Columbia disaster, which disintegrated upon re-entry in 2003.
“Large-scale engineered systems are more than just a collection of technological artifacts. They are a reflection of the structure, management, procedures, and culture of the engineering organization that created them.”
“They are also, usually, a reflection of the society in which they were created. The causes of accidents are frequently, if not always, rooted in the organization—its culture, management, and structure.”
“Blame for accidents is often placed on equipment failure or operator error without recognizing the social, organizational, and managerial factors that made such errors and defects inevitable.”
Nancy G. Leveson, MIT. Technical and Managerial Factors in the NASA Challenger and Columbia Losses: Looking Forward to the Future. Controversies in Science and Technology Volume 2, Mary Ann Liebert Press, 2008.
Groupthink is part of the taxonomy of well-known cognitive biases and takes hold when divergent thinking and disagreement are discouraged (and even repressed) as part of group dynamics.
Hindsight is 20/20 and, statistically speaking, ‘black swan’ events are characterized by seemingly random surprise factors. Groupthink can obfuscate the early detection of predictors such as leading outliers and anomalies, which left unattended can overwhelm a given system over time… and be the source of cascading effects and critical failure.
Groupthink’s negative impact compromises any best intentions such as organizational cohesiveness in the spirit of consensus, agility, productivity, timely project progress and de-escalation management.
Often times, there might be neither adequate situational and risk awareness nor a basis for sense making drawing from the comparative analysis that comes with diligent scenario planning.
Individuals and organizational cultures with a succesful track record can also experience complacency. Over-confidence fosters the sort of behaviors and decisioning that served the group well in the past.
Though, when in the mix of a changing environment defined by new parameters under the radar, only operating within the perimeter of a given set of core competences and comfort zones, makes those specific behaviors blindsight and betray the team’s mission and purpose.
Many plans do not survive first contact (or a subsequent phase for that matter) as their implementation creates ‘ripple effects’ of various shapes and propagating speeds. Some of that can be experienced as ‘sudden risk exposure.’ Once passed the ‘point-of-no-return,’ if that challenge is met with neither contingency planning nor the ability to timely course correct, pivot or even deploy a basic safety-net offsetting the impact, the project fails to ‘cross the chasm’ and is headed for what’s technically known as the ‘valley of death.’
This was one of the key issues discussed by Clyton M. Christiansen when I took his Harvard class on the ‘Innovator’s Dilemma,’ and is also a key point behind Risto Siilasmaa’s ‘Paranoid Optimism’ as well Paul Romer’s ‘Conditional Optimism,’ all of which advocate for scenario planning and sensing optimization to be able to calibrate or re-assess the path forward.
“Michael Shermer stated in the September 2002 issue of Scientific American, ‘smart people believe weird things because they are skilled at defending beliefs they arrived at for nonsmart reasons.”
Groupthink can also manifest itself by means of ‘eco chamber’ effects’ as misguided consensus amplifies what becomes a “self-serving” bias. That is, in effect, a closed feedback loop process that magnifies logical fallacies. These can come across as reasonable enough postulates, though if based on rushed judgement and selective focus they can also suffer from ‘confirmation bias.’ This is the case when new evidence is only used to back-up the existing belief system rather than share new light.
In the context of Decision Support Systems and Cognitive Analytics, the above reasoning deficits become root causes of errors impacting operations. That can involve both (a) Human-Human and (b) Human-Machine interactions, as well as impacting programming work resulting in (c) biased algorithms and automation pitfalls when left unsupervised.
Carisa Callini. Human Systems Engineering. NASA, August 7 2017. https://www.nasa.gov/content/human-systems-engineering
Carisa Callini. Spaceflight Human Factors. NASA, December 19 2018. https://www.nasa.gov/content/spaceflight-human-factors
Clayton M. Christensen. The Innovator’s Dilemma. Harvard Business Review Press, 1997.
COD Welecomes Astronaut Mark Kelly. Daily Herald, February 13 2019. https://www.dailyherald.com/submitted/20190201/cod-welcomes-astronaut-mark-kelly-feb-17
Geoffrey Moore. Crossing the Chasm. Haper Collins, 1991.
MIT Experts Reflect on Shuttle Tragedy. MIT News, February 3 2003. http://news.mit.edu/2003/shuttle2
Tim Peake. The Astronaut Selection Test Book. Century. London, 2018.
Scott Kelly. Endurance: A Year in Space, a Lifetime of Discovery. Knopf. New York, 2017.
Scott Kelly. Infinite Wonder. Knopf. New York, 2018.
Steve Young. Astronaut: ‘None of Us is as Dumb as All of Us.’ USA Today – Argus Leader, May 13, 2014. https://www.argusleader.com/story/news/2014/05/13/astronaut-none-us-dumb-us/9068537/
Will Knight. Biased Algorithms are Everywhere, and No One Seems to Care. MIT Technology Review, July 12 2017. https://www.technologyreview.com/s/608248/biased-algorithms-are-everywhere-and-no-one-seems-to-care/
Every once in a while we get to experience Murphy’s (dreaded) Law. This time around that had to do with stability issues with a media webcasting platform. We are now working on rescheduling NOKIA HFE18 under a different format. In parallel, we are also kicking off planning for HFE19… and we will take full advantage of lessons learned.
We regret any inconvenience that this eleventh hour change in plans might cause, and remain extremely grateful to both speakers and volunteers who have already invested time and efforts, which should not go to waste.
In the meantime, I’d like to volunteer just a handful of insights on the session that I was scheduled for and, therefore, keep the discussion going. The objective is to further improve what’s already available and allow for an even better session when we get to reconvene. Here is my session’s abstract to begin with.
THE SOFT & HARD NATURE OF ANYTHING DIGITAL
“Our quest to deliver productivity tools yielding operational excellence for DSPs, Digital Service Providers leads to the design of signature experiences by innovating in the process.”
“The Studio at Nokia Software’s Solutions Engineering is set to work with deceptively simple techniques and elegant sophistication… because neither oversimplification nor self-defeating complexity allow end-to-end systems to efficiently operate at digital speed and global scale.”
“This discussion intersects the soft and hard natures of dynamic systems by modeling Human Machine Systems (HMS) and the design of cybernetics. This practice focuses on critical success factors for the early acceptance and broader adoption of emerging technologies.”
“The work at the Studio embraces a renewed approach to QbD, Quality by Design, which is set to left-shift and unveil instrumental considerations at early design stages. The result is Nokia Studio’s QXbD, Quality Experiences by Design, optimizing for customer delight rather than table-stakes customer satisfaction.”
NI – WHAT IS NATURAL INTELLIGENCE? At the time of writing this, we humans possess NI, Natural Intelligence. NI involves naturally developed cognitive functions and models leveraged by the sort of biological beings, which humans happen to be. Intelligence (a) captures, (b) generates, (c) applies and (d) evolves knowledge. Our individual and collective brainpower can be gauged in terms of (e) skills and (f) talent levels, jointly with an understanding of (g) the underlying decisioning process and (h) our perceived experiences in context.
AI – WHAT IS ARTIFICIAL INTELLIGENCE? Intelligence that is not naturally occurring, simulated knowledge in other words. This is generated by programmable artifacts consuming, processing and producing data under closed loop models. Whether working with individual or networked machine intelligence, there is neither information derived from mindfulness nor the type of general purpose sense making that match those of the human experience. The year is 2018… and that’s where state of the art is today.
GI – WHAT IS GENUINE INTELLIGENCE? Earlier in the year I introduced this topic at Design Thinking 2018 (plenary session) and at IEEE Emerging Technologies Roundtable (invitation only workshop.) Coincidentally, both were held in Austin, TX, back in May. I proposed thinking about GI as the outcome of NI powered by AI.
By the way, “genuine” means acting in bonafide. To be clearer: with honesty and without the intention to deceive. Given the trade-offs (pros and cons) that NI and AI bring to the table, GI gets us a step closer to productive bonafide systems.
GI is, therefore, the outcome of purposely crafting optimal technology solutions that augment human possibilities. This is addressed by Human Factors Engineering interdisciplinary science given HFE’s holistic approach and focus on value driven Human-Machine-Systems, HMS.
Quick side note: those of you into Lean and Lean Six Sigma can approach this topic with Jikoda (autonomation.) Ditto for anyone working on Human-in-the-Loop Computing, Affective Computing, RecSys (Recommender Systems,) Human Dynamics and Process Mining with Machine Learning or, better yet, xAI, Explainable Artificial Intelligence.
DDESS – The most tangible design work entails the delivery of DDESS, Digital Decision & Execution Support Systems. This is where GI gets interesting because we need to apply new optics to take a fresh look at what Operational Excellence is (and is not) moving forward.
In a nutshell DDESS’ purpose is to reveal and inform decisions and to make decisions, all in context. But, I will pause here as this topic will be better covered in subsequent posts… just one more thought: DDESS addresses decision support for (NI) humans, (AI) machines, and (GI) human-machine systems. Coming to terms with that one insight alone becomes a critical success factor.
Some other thought… it turns out that, in today’s day and age, projects that are techno-centric heavy only succeed a fraction of the time, 10% or so by some estimates. Selective memories tend to focus and celebrate the 10% that make it… but that is a terrible ROI, Return on Investment, which inflicts (1) severe technical debt, (2) latency costs in systems engineering and (3) a huge opportunity cost as funding and good efforts could have been put to work for more productive endeavours.
By many other well documented and more recent accounts, HCD, Human Centered Design, happens to flip that ratio as designers are obsessed with optimizing for user acceptance and frictionless adoption from day one. HFE takes painstaking work on purposeful and value driven technological solutions where a smart combination of Outside-IN-innovation and Inside-OUT-ingenuity happens to make all the difference.
“[They] lost their quality leadership to new, aggressive competition. The most obvious consequence was lost of market share (…) [due to] quality features that were perceived as better meeting customer needs [and] they did not fail in service as often.”
“Loss of market share is not the only reason behind [it] (…) a second major force has been the phenomenon of life behind the quality dikes. We have learned that living in a technological society puts us at the mercy of the continuing operation of the goods and services that make a society possible (…) without such quality we have failure of all sorts (…) at the least these failures involve annoyances and minor costs. At their worst they are terrifying.”
“A third major force has been the gathering awareness by companies that they have been enduring excessive costs due to chronic quality-related wastes (…) about a third of what we do consists of redoing work previously done (…) lacking expertise in the quality disciplines, they are amateurs in the best sense of that word.”
J.M. Juran’s assessment on Quality issues in the 1960s-70s.
What follows are some of the insights driving the work that I’m doing on reviewing, leveraging and updating QbD (Quality by Design) in the context of today’s fast growing and all-encompassing digitalization.
I am dusting off my research from 2010 on the 3Q Model. Back then I was a senior manager at Alcatel-Lucent’s Solutions & Technology Introduction Department. My current role is Senior Studio Director at Nokia Software’s Solutions Engineering. Note that the scope is End-to-End Solutions. These are holistic system-wide (cross-sectional and longitudinal) undertakings intersecting different domains to deliver the higher value of the whole. I have discussed QbD for Digital Transformation projects at the Design Thinking 2018 event and at the IEEE (Institute of Electrical and Electronics Engineers) conference on CQR (Communications Quality and Reliability) back in April and May of this year. Interestingly enough, both events were held in Austin, Texas.
QbD was first coined by Juran, a renown pioneer of quality practices, whose work on that specific topic started in the mid 80s. He linked Quality to customer satisfaction and reliability as the two dimensions to focus on:
“Features” were defined as “quality characteristics,” which meant properties intended to satisfy specific customer needs. That would also include “promptness of delivery,” “ease of maintenance,” and “courtesy of service” to name some examples. “The better the features, the higher the quality in the eyes of customers.”
As far as reliability and, therefore, replicability and consistent performance, “freedom from deficiencies” conveyed the fact that “the fewer the deficiencies the better the quality in the eyes of customers.” A “deficiency” is a failure that triggers dissatisfaction, which calls for incurring higher costs to redo prior work.
“Fitness for use” was mentioned as an attempt to capture the above two together. The so-called Juran Trilogy entails Quality Planning, Quality Control, and Quality Improvement.
More than three decades have passed since Juran started to work on “New Steps for Planning Quality into Goods and Services.” Let’s decompose QbD’s acronym at face value and distill its essence.
As a designer, my belief & practice system focuses on “serial innovation” consistently delivering superior value. This is achieved by means of purposeful and elegant solutions equipped with capability models and optimal functionality leading to Quality Experiences.
Customer Delight, rather than just satisfaction, being the sought after outcome. This applies to both small and large undertakings, and as A. Kay, a pioneer in graphical user interfaces, best put it, “simple things should be simple, complex should be possible.”
Following that train of thought, “Designing Quality into Solutions” should become center stage to: (a) collaborative and iterative ideation, (b) agile development, (c) continuous delivery and (d) the dynamic diffusion of (e) new and mass-customizable digital services for consumer and enterprise markets, as well as no-for-profit. Overall, QoB is key to Operational Excellence.
In a world where “Continuous Improvement” leads to incremental and breakthrough innovations, Quality’s critical KPI, Key Performance Indicator, can be expressed in terms of measurable advances in QoUX, the Quality of the Users’ Experiences. These are lagging (outcome) metrics that are far from static because they evolve within and over lifecycles. Therefore, reliability is not just applied to production operations, but also to the solution’s consistent performance and serviceability over time and under changing scenarios and events.
Given Quality’s unequivocal narrative around the “experiential” paradigm and, therefore, human-centric-optics, QbD’s best work should optimize for user “delight,” which is defined as superior “satisfaction,” rather than just aiming for requirements compliance.
It is very tempting to rally around core competencies within comfort zones that exist, and then settling on just aiming for “customer satisfaction” around “must-meet” baseline requirements. Though, that might not suffice given the necessity to innovate and better compete by leveraging unique sources of sustainable differentiation.
Let’s now state the obvious: “designing” Quality Experiences into digital solutions is best addressed by means of Human-Centered methodologies that optimize for (f) users’ “acceptance criteria” and (g) the kind of “adoption levels” that foster user base growth.
The opposite approach would risk the adverse effects (and hidden costs) that can be incurred when technical myopia leads the way. A. Cooper’s “The Inmates are Running the Asylum” captures that very well. His book is referenced below.
Just for the record, the year is 2018 and we are gearing for a pervasive digital world dominated by software defined systems. The 4th Industrial Revolution’s floodgates are set wide-open.
Low and high tech perform best when playing a supporting role. Technology enables “Services” which justify it, otherwise the so-called Chasm and Valley of Death wait around the corner. It pays to emphasize that “Services” are defined by “Use Cases.” So, it shouldn’t take much effort to see that “Use(case)ability” (“usability” being the proper term) is a CSF, Critical Success Factor. “Fitness for use” in other words.
Let’s take that further and couple “usability” with designing for usefulness,” “utility,” “consumability & serviceablity” as well as “affectivity” because perception and human affects orient satisfaction and dissatisfaction levels.
QbD cannot be put to work without adequately addressing Human Dynamics, which entails psychological (e.g. cognitive models, information architecture) physiological (e.g. device form factor, workstation ergonomics) and social dimensions (e.g. network effects increasing value for users.) That happens to be the SoW (Scope of Work) of HFE’s (Human Factors Engineering) interdisciplinary teams in Design Studios… and the topic of my next post on QbD’s Intellectual Capital.
A few more thoughts…
In spite of one’s day-to-day work and/or belief system being either closer to or removed from the kinds of jobs and tasks that make tech human, it makes sense to engage in meaningful outcome oriented and goal driven practices by applying HCD, Human-Centered-Design. The purpose is delivering quality and achieving customer acceptance and delight, given that customers are human beings. That is the reason why Design Thinking has outgrown the field of industry design and is applied to a wide variety of domains and disciplines nowadays.
Tech’s roller-coaster industry is packed with well intended technologies that fail. We all know that this is a fiercely competitive environment in constant change. Though, it is also true that, in many of those cases, UX, User Experience, professionals were not engaged at any part of the process, or were purposely involved at the back-end, or were called to come to the rescue in the eleventh hour. That leaves no room for Design to make a difference. Superficial changes just amount to bells-and-whistles and shiny-objects to disguise the underlying quality issues that are likely to re-surface at some point.
QbD’s top objective should be excelling at effectively & efficiently addressing our customers’ acceptance and adoption criteria. That remains true even in the context of full automation. Humans still get promoted and demoted (or fired) based on those system’s performance. D. Newman’s recent article on Forbes magazine rightly states that “you cannot run your business without people (…) you cannot operate technology without people (…) research have shown that people are a critical component for digital transformation.”
Today’s best practice calls for “reverse engineering” solutions by working from that human-centered understanding around Human Machine Systems (HMS.) That is substantially different from only relying on a far riskier “if you build it, they will come” model and its costlier brute-force mindset.
When dealing with challenging, intractable and complex projects, overlooking that fact typically results in exponential project risk and plenty of the, otherwise, avoidable zig-zagging course corrections ahead (e.g. opportunity costs in financial analysis and hidden and latency costs in systems engineering.)
Agile’s iterative development and ability to pivot shouldn’t be a refuge for either subpar or no design effort, but a vehicle to best implement QbD and augment development capacity while minimizing technical debt. This is why this revision of QbD for today’s tech industry calls for Design Sprints to lead the way.
Last but not least, before dismissing this QbD revision as a philanthropic and humanistic only endeavor, I suggest deep thinking around its (1) business criticality and (2) contribution to risk mitigation.
J. de Francisco
Bell Labs, Distinguished Member of Technical Staff
Nokia Software, Senior Studio Director @ Solutions Engineering
A. Cooper. The Inmates are Running the Asylum. Why High-Tech Products Drive Us Crazy and How to Restore the Sanity, Sams Publishing, 2004.
D. Newman. 3 Reasons People are Critical for Digital Transformation Success. Forbes, June 2018.
J. de Francisco. IEEE ETR 2018, Emerging Technologies Reliablity Roundtable – Human Factors Session (2). Innovarista: Innovation at Work, July 2018 innovarista.org
J. de Francisco. IEEE ETR 2018, Emerging Technologies – Human Factors Session. Innovarista: Innovation at Work. May 2018 innovarista.org
J.M. Juran. Juran on Quality by Design: the New Steps for Planning Quality into Goods and Services, The Free Press, 1992.