“[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.
IEEE CQR-ETR 2018: “Discuss and identify the RAS (Reliability, Availability and Serviceability) challenges, requirements and methodologies in the emerging technology areas like the Cloud Computing, Wireless/Mobility (with focus on 5G technologies), NFV (Network Functions Virtualization), SDN (Software Defined Networking), or similar large-scale distributed and virtualization systems.”
“Discuss the RAS requirements and technologies for mission-critical industries (e.g., airborne systems, railway communication systems, the banking and financial communication systems, etc.), with the goal to promote the interindustry
sharing of related ideas and experiences. Identify potential directions for resolving identified issues and propose possible solution.”
Session Title: A Programmatic Approach for an Artificial Intelligence Code of Conduct.
Today’s DX, Digital Transformation, programs are all the rage, but it takes a fair amount of double clicking and inquisitive questioning to separate facts from vaporware. DX typically involves a wide variety of game changing initiatives intersecting analytics, automation, programmability, software-defined systems, end-to-end integration, service-level composition and controls… all coming together to optimize for Quality as a differentiated and value-based Human Experience. Therefore, Customer Delight metrics (rather than outmoded customer satisfaction ones) are set to redefine the “Q” in CQR, Communications Quality & Reliability in 5G.
While the Telecoms industry rallies toward a zero-touch automation paradigms, which some happen to position as a Human-“OFF”-the-Loop panacea, this session will expose the need for considering, and possibly pivoting, to the kind of Operational Excellence that can only be delivered by adaptive HMS, Human-Machine-Systems instead.
Note the rise of Dataviz (Data and Science Visualization,) ML’s (Machine Learning’s) Collaborative Filtering, AI’s (Artificial Intelligence’s) RecSys (Recommender Systems) and a renewed take on Cybernetics are driving innovation in HILT and HOTL (Human-“IN”-The-Loop and Human-“ON”-the-Loop, Computing,) as well as delivering effective mass-personalization with Affective Computing powered by Human Dynamics’ analytics.
Telecoms’ pioneered HFE, Human Factors Engineering: a holistic systems engineering discipline addressing people (culture, workstyle, skills,) processes (procedures, methods, practices,) and technologies (crafts, tools, systems) so that we can best humanize technology and make a compelling difference across the value chain at all levels. We are now embarked on a new journey.
The sought after outcome of any Digital Service Provider, DSP, is to be instrumental to our Citizens’ Quality Experiences with new service experimentation, transactions and growth models. This takes agility and dynamic system-wide (horizontal and vertical) behaviors, which prompt effortless operability at unprecedented speed, scale and scope. Our work permeates design, development, delivery and serviceability, and continuous intertwined lifecycles instead of lock-step waterfalls.
In this context, AI, Artificial Intelligence, enables us, humans, to envision and implement capabilities beyond the reach of legacy systems’ last gasps. By the same token, practices that might have appeared to serve us well in the past, are exposing their limitations when becoming latency-prone barriers. A successful path forward takes augmented Human-Machine Intelligence. A programmatic approach for an AI’s Code of Conduct would enable us to best model AI’s behavior, design better human-network interactions and collaborate on standardization.
I am on my way to Mobile World Congress and last night I had the opportunity to speak at DevMynd’s “Agile Software in a Hardware World.” That panel discussion featured BMW Technology Corporation (BMW, Mini, Rolls-Royce,) Monsanto’s “The Climate Corporation,” and Nokia Software Group, which I was proud to represent. The venue, 1KFulton, is a century-old and former cold storage building in the Fulton Market neighborhood, home to Google’s Chicago campus.
Reflecting on that panel discussion, small group conversations and one-on-one chats before and after the event, I think that it is fair to state the following:
(A) software is undergoing a defining moment while re-shaping industries. “Software defined instruments and systems” have superseded capabilities of hardware-centric deployments.
In other words, economic value and profitability are migrating from conventional products to software dominated environments that control tools, systems, and processes.
In this new context, (B) collaborative undertakings (co-creation, open source,) platforms, modularization and mashups are paving the way for rapid experimentation and for a wide-range of services to surface.
Back to economics… a venture capital firm operating in the Silicon Valley shared with me that when comparing current investments with equivalent old-school ones, they experienced x3 times time-to-market speed at 1/3 of the investment, which allows them to better diversify risk and fund more start-ups in the process.
Moreover, we are now operating at (C) unprecedented speed, scale and scope. For that reason alone, software should improve our ability to “pivot” and dynamically adapt to changing circumstances.
Most plans don’t survive first contact and many start-ups and emerging technologies don’t survive the so-called “crossing-the-chasm” or “Valley of Death.” So, remaining lean and embracing continuous/iterative improvement are of the essence. That’s a quality mantra rather than an excuse for forgoing best quality practices.
Back to economics again: quality management’s definition of “customer satisfaction” is now table-stakes and compliance in that area drives low-cost commoditization. “Customer delight” is the higher benchmark that commands a premium and the kind of margins enabling us to re-invest to further innovate.
Let’s now state the obvious, “customers” are human beings, aren’t they? Interestingly enough, the more sophistication and diversification, the higher the need for (D) humanizing technology so that we can better create, consume, use and democratize any digital services. In turn, this has fostered (E) Design Thinking as a leading innovation practice that intersects art and science. Design Thinking addresses HMS, Human-Machine-Systems, by prioritizing HCD, Human-Centered-Design.
In terms of economic effectiveness and efficiency, that means outcome-oriented system-sizing, rather than over-engineering waste. It also means the definition of meaningful and purposeful requirements: some are designed to meet customer satisfaction metrics, while others are explicitly thought out to exceed that baseline and, hence, to actually deliver the X-Factor prompting customer delight. All key to customer acceptance and adoption growth.
Better yet, one of the event’s participants volunteered the fact that “good design” factoring intuitive interaction, advanced dataviz (data visualization) and effortless controls was proven to shrink the sales cycle by literally half: not only customers perceived and experienced the service’s tangible value early, the sales team was also able to approach more customers in that timeframe. Innovative Human-Computer-Interaction based on information design, value based tasks, streamlined processes, intuitive data visualization, effortless controls and overall UX, User Experience, double as compelling demonstration tools.
This is a side note: that has already become a critical success factor in Artificial Intelligence’s new developments, AI being software’s top transformational exponent as DSS, Decision Support Systems for humans and/or machines become quintessential. I will detail that in another post.
One last thought… (F) software’s pervasiveness has also brought along Agile development practices. These include “user stories” borrowing a Design Thinking technique by which application features are defined by synthesizing human optics (persona/outcome/rationale) to put technical myopia at bay.
After all, we should all be in the business of making tech human. Otherwise, what would negating or ignoring that say about each of us and our collective culture?