Following up on my last post about IEEE ERT 2018, here are a couple of charts for my “discussion brief,” which include a Human-Machine-System Capaility Mapping chart (above) and concept illustrations of the Experiential Decision Support System (below.) The charts’ text conveys context setting remarks, which I am also providing here.
The goal of furthering machine intelligence is to make humans more able and smarter: the opposite engineering approach typically becomes a source of self-defeating technical myopia waiting to happen and missed opportunities. This simple mapping exercise can be customized to assess and roadmap capability levels.
The more sophisticated automation becomes, the more obvious the criticality of the human factor in both consumer and enterprise environments… rather than less. And, in any case, customer acceptance and adoption criteria remain Quality’s litmus test for emerging technologies.
Digitalization is fostering (a) XaaS, (b) Self-Service, (c) the Shared Economy and the (d) Maker Movement. All elevate human involvement and drive the push for opening and democratizing technologies. These make (e) citizen science and citizen developers shape the next generation prosumers at mass market scale.
Digital Transformation initiatives embracing the above allow (f) nimbler enterprise teams to operate at far greater scale, scope and speed, and shift focus from routine operations to dynamic value creation coupled with extreme efficiencies.
This entails (g) interdisciplinary workstyles and collaborative organizational behaviors that include (h) customer co-creation models. In this new context, humans remain (i) the ultimate critical element in system reliability and safety. Left shifting Quality by Design (QbD) prioritizes Human-Centered-Design tools and processes to deliver high performance workforce automation systems.
Cost-effective Lean Ops systems intertwine analytics, automation, programmability and flexible systems integration. All optimized for dynamic behaviors given Soft System’s perpetual motion. This means designing “for-ever” rapid and seamless reconfigurability instead of just engineering “day 1” implementations.
Operational Excellence dictates system-wide as well as subsystem level visualization, and a combination of centralized & distributed closed loop controls under user friendly operational modes. Cognitive models involve Situational Awareness (SA,) Sense Making (SM,) Root Cause Analysis (RCA,) Scenario Planning (SP,) and ROA (Real Options Analysis.)
The Experiential element is not just about programming known rules and policies but, most importantly, it grows by assimiliating iterative learning in the context of cyclical automation: routine decisions and manual operations can be streamlined and colapsed, then switching to “exception” based management for that particular event.
Productivity calls for streamlining operations so that (a) waste can be eliminated & prevented, and (b) value based tasks can be performed effortlessly, in less steps, at speed & without error. High performance behaviors and sustainable competitiveness also call for the ability to (c) experiment and create new capabilities, as well as leveraging (d) process mining for customer journeys & value stream mapping (CJM & VSM) to continuously optimize them and guarantee service levels.
Service Operations Centers (SOC) should be equipped with Experiential Decision Support Systems (DSS) featuring (d) collaborative filtering, (e) actionable data stories conveying hindsight, insight & foresight and (f) adaptive cybernetics. Advanced visualization for both (f) intuitive & highly abstracted infographics and (g) scientific views is of the essence.
Quality is best addressed as a human experience, which determines (d) meaning and, therefore, the degree to which a system is lean vs. over-engineered or subpar (both being defective and carrying obvious and hidden costs.) A new take on QbD for Soft Systems, which are inherently fluid by definition, emphasizes acceptance testing probing for: usefulness & utility, usability & affectivity, consumability & serviceability and safety thru use cases and lifecycle events.
I am reviewing the Manifesto on Human Factors Engineering and making updates. In the meantime, what follows below was a draft introduction letter, which was left unpublished when releasing the Manifesto last year. Blue text shows new updates. As far as this post’s title is concerned, DX refers to Digital Experiences. The same acronym is also used for Digital Transformation initiatives.
Claude E. Shannon, the father of information theory, is credited with being the first to use the word “bit” in a ground-breaking paper published in the Bell Labs’ Research Journal in 1948. He defined a mathematical framework that defines information and how to encode and transmit it over communication networks.
John E. Karlin, the father of Human Factors Engineering and a peer of Shannon’s at Bell Labs, is credited with assembling the first business organization addressing the human side of the equation just a year earlier. His interdisciplinary team focused on how to interface and, therefore, best design communication systems that account for cognitive and behavioral matters, as well as form factor considerations for devices to be user friendly.
In the Age of Digital Transformation, the notion of “being digital” has transcended the sophisticated handling of binary digits and what we can do with tangible hardware. Data driven automation and the notion of zero-touch lead to the development of end-to-end digital systems that are largely software defined and autonomic. These are engineered to be highly efficient and to operate without human intervention… or so we thought.
That feat can only be accomplished by undertaking a holistic design approach which, paradoxically, highlights the larger context and the new nature of Human-Machine-Systems. Otherwise, we would incur a technical myopia where presumably good technology ends up addressing the wrong problems or causing new ones that offset the intended benefits. In the digital age, technical prowess alone does no longer guarantee success: impressive inventions can fail to “cross the chasm,” fall in the “valley of death,” and never become true innovations to their creators and investors’ dismay. Passing the Turing Test just to plunge into the uncanny valley paradox also reinforces that point.
Note: the above draft chart is not self-explanatory, requires some updating and I will better address it on another post… but I’d like to leave this version here for ongoing discussions and feedback.
Being digital entails a new breed of jobs enabled by workforce automation. Any of us may become a citizen developer who can leverage self-service and intuitive decision support systems to create, blend and program services, because automation does the heavy lifting under the hood. Interdisciplinary collaboration is now within reach as teams involving individuals from different domains can effectively share these tools and the underlying resources to overcome the pitfalls and diminishing returns of organizational fragmentation. Enterprises can better innovate and further business opportunities by engaging in rapid experimentation with nimbler teams working at greater scale and scope, and by doing so at unprecedented speed.
At the time of writing this, and in the foreseeable future, no enterprise system is left alone without a human being accountable for its performance (or lack of thereof) since our skills and judgement remain key to critical and ultimate decision making. The more sophisticated the environment, the more obvious, as smaller agile teams become responsible for systems operating at greater scale, scope and speed. Dr. Alonso Vera, Chief at NASA’s HSI (Human Systems Integration) Division, states that “humans are the most critical element in system safety, reliability and performance […] across the gamut of applications even as increasingly intelligent software systems come on-line,” Human-Centered Design and Operations of Complex Aerospace Systems 2017.
It should also be noted that best practices in A.I. are promoting the kind of augmented and collaborative intelligence that only Human-On-The-Loop and Human-In-The-Loop Computing can deliver. A.I. is also powering up Affective Computing to make day-to-day digital services be contextual, empathic and adaptive, and allowing for mass-personalization at scale. We are also leveraging Natural Language Processing coupled with Dataviz helping better search, discover and visualize insight and foresight with interactive infographic quality, instead of just rendering data overloading screens and overwhelming navigation.
These are all good reasons to further our understanding of how to best leverage analytics, automation and programmability to design enterprise and consumer systems driven by a human-centered approach. The desired outcome is greater utility, frictionless consumability, dynamic adaptation and, last but not least, extreme ease of use at any level throughout a service’s lifecycle. That’s the fertile ground that enables new cross-pollination opportunities to enable a better future, which continuous improvement sets in constant motion and, hence, always is in the making.
Being digital is a human experience and, as such, it involves human affects. That relates to how we perceive our predominantly analog world and the diversity of our social and cultural fabrics. We interact with a great deal of objects and services of all sizes which can, and will be, digitized and automated in some fashion. We will continue to lead our lives in a variety of changing contexts and perform numerous tasks throughout the day, some routinely and some exercising more demanding skills with both low and high tech in that mix. So, it pays to think of Human Factors Engineering as not only having pioneered human-centered-design, but as an endless source of serial innovation for Creative Technologists to address our evolving lifestyles and quality of life in the DX Age.
“Service Design is big. Being holistic, it includes the researching, envisioning and orchestrating of service experiences that happen over time and across multiple touch points with many stakeholders involved, both frontstage and backstage.”
“At Service Design Week, we seek to strip away any fluff, examining service design methods and processes at their core, and unpack the practical tools and skill-sets, hard and soft, needed for this way of working. Service Design Week will gather service design leaders from various functions and disciplines across all flavors of Service Design. With content for all levels of Service Design maturity, we look forward to drawing both fledging and experienced service designers.”
I am looking forward to joining Service Design Week and I would like to thank Michel DeJager and the team at the International Quality & Productivity Center for their kind invitation. My talk will discuss C3LM, Customer Co-Creation Lifecycle Methodology, in the context of Blended Service Design, which I will take care of defining and demystifying in my talk.
I am proud to share that C3LM is the recipient of a Nokia Innovation Award. My work seeks to interweave a set of known and brand new interdisciplinary practices to best address end-to-end solutions for complex and dynamic environments, also known as soft systems given their organic and morphing nature. And, most importantly, achieving that by optimizing for the delivery of quality experiences while humanizing low and high tech in the process.
Widespread digitalization in our everyday activities is not just far reaching, but is also leading to a renaissance in Human Factors disciplines. The delivery of “effective quality services” with “highly efficient end-to-end solutions” is the reason for being and rationale behind creating C3LM. This new brave world entails Blended Services that intersect Data Science, Automation and Programmability, all orchestrated with Human Centered Design in mind.
My talk will also cover how we can best experience Artificial Intelligence and how to make it transparent to Blended Services. That will be a sneak preview in advance to another talk that I’m giving early next year. In case you have already heard what Elon Musk has to say about AI, let me share that Human Factors Engineering has been revisited and redefined to come to the rescue. More on that when we get to meet at Service Design Week : )
Here is the event’s registration page. See you in Boston : )
Pictures courtesy of Service Design Week.
“Innovation is a risky business and the failure rate is high. Traditional approaches to consumer research may exacerbate the problem. There are many shortcomings with traditional research approaches, and one of the main ones is that data collection focuses on what people say they do, rather than on what is actually driving behavior.” – Behavioral Science – Do people do what they say will do? by Innovia.
Tim works for Innovia Technology and will be visiting Nokia’s Chicago Technology Center, Naperville Campus, on Monday, May 8. He is a physicist from University of Cambridge, UK, with a research background on ballistics who has spent the past 15 years addressing human factors led innovation.
Tim will share insights from recent projects as well as highlights of work done for Nokia back in 2003. About 15 years have gone by and he will conduct a retrospective to unveil who ended up implementing those concepts in today’s market.
Post May 8 Session Notes – Tim’s talk covered the need for gaining a deeper understanding of people as both individuals and collectives to best inform the design of new products, services and business models. Tim emphasized the value of a holistic approach to problem solving and a focus on behavioral drives. He stated that conventional research solely looking at attitudes and beliefs can miss critical insights.
Nokia’s community can access Tim’s presentation and recording on my work blog.
I am now taking the chance to share my thoughts on this topic and, whether we call it “stated vs. observed behavior” or “reported vs. actual paradoxes,” the point is that those of use working on Human Factors Engineering and/or leveraging Design Thinking cannot just rely on product or service requirements as described by customers and end users themselves.
Therefore, on location ethnographic research coupled with instrumentalizing objects, tools and environments to gather telemetry as they are being used over their useful lives are also of the essence, given user permission as this entails privacy concerns.
“According to Alan Mulally, former Ford Motor Company CEO, Henry Ford said that if, when he founded his company, he had asked potential customers what they wanted, they would have said faster horses.” – Quote Investigator.
Hawthorne Works was a Western Electric factory in the Chicago area, which is part of Bell Labs’ outstanding legacy.
I’m now inserting a side personal note: I now live Chicagoland and have worked with Bell Labs, now part of Nokia.
More than a century ago, going all the way back to the 1920s and 30s, Hawthorne Works undertook a study to assess what lighting levels correlated to higher productivity levels.
However, research findings revealed that (a) worker’s awareness of being observed in the context of (b) paying attention to their needs in the workplace elevated their motivation and productivity, which trumped other factors such as lighting levels whether they would be set low or high.
I would also like to share another interesting observation. This one involving Bell Lab’s own John Karlin:
“The Times, who refer to Karlin as widely considered the father of human-factors engineering in American industry, relates an amusing story of an earlier project–one that demonstrates his keen understanding of human behavior: an early experiment involved the telephone cord.”
“In the postwar years, the copper used inside the cords remained scarce. Telephone company executives wondered whether the standard cord, then about three feet long, might be shortened.”
“Mr. Karlin’s staff stole into colleagues’ offices every three days and covertly shortened their phone cords, an inch at time. No one noticed, they found, until the cords had lost an entire foot. From then on, phones came with shorter cords.”
Once again, I’d like to thank Tim for his talk and for the also interesting discussions that preceded and followed that session. We both agree on the positive impact of holistic and interdisciplinary practices, which lead to a disciplined and robust approach to defining value based outcomes.
This is about innovative solutions humanizing technology in everyone’s best interest. So, it definitely pays to leverage Behavioral Sciences and Behavioral Economics when addressing serial innovation programs.
“Rapidly advancing technologies require humans to make critical decisions in increasingly dynamic and complex environment. Human factors studies human interaction with increasingly intelligent and automated engineering systems to address safe, efficient and cost-effective operations, maintenance and training” – Areas of Ingenuity – Human Systems Integration at NASA Ames Research Center.
“SVS works closely with scientists in the creation of visualizations, animations, and images in order to promote a greater understanding of Earth and Space Science research activities at NASA and within the academic research community” – Scientific Visualization Studio.