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IEEE ETR 2018, Emerging Technologies Reliability Roundtable – Human Factors Session (2)

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.


IEEE ETR 2018, Emerging Technologies Reliability – Human Factors Session

IEEE ETR 2018 on Twitter

ETR turned out to be a very productive undertaking and I would like to thank IEEE’s Spilios Markis, Chi-Ming Chen and Chris Mayer for all the help provided prior and during workshop.

My contribution focusing on addressing the unprecedented flexibility of advanced software defined systems and artificial intelligence. That intersection defines game changing technologies leading to zero-touch automation and, therefore, fostering self-service opportunities at both operational and service consumption levels.

“Zero touch” implies extreme automation to its fullest while self-service reveals that this new order elevates the criticality of HMS (Human Machine Systems.) More touch points surface compared to what legacy technologies allowed given their constraint and restricted nature. That prompts a new take on HCI (Human Computer Interaction) and QbD (Quality by Design) to best deliver service quality throughout: concept exploration and service definition, fulfilment and adaptation, assurance and security… across multi-domain, highly decomposed, re-configurable and exceptionally dynamic end-to-end systems involving integration and service delivery in continuous motion.

These are thought out to (a) dramatically optimize support personnel ratios and (b) shift staff’s attention and efforts to value based activities and innovation. These are small agile teams and new talent tasked with jobs involving (c) far greater scale with (d) a wider interdisciplinary scope, and all to be performed at (e) digital speed. In this next-level productivity and more demanding and challenging context, success relies on new tools embracing Design Thinking’s HCD (Human-Centered-Design.)

That is applied to capability models and subsequent modes of operation for (f) HITL (Human “IN” The Loop) Computing largely devoted to  deep domain expertise supported by Science Visualization, as well as (g) HOTL (Human “ON” the Loop) for system-wide supervisory responsibilities and ease of service creation and onboarding. HOTL draws from highly abstracted Visualization techniques and Low Code Development revealing the behavior of end-to-end systems and subsystems and adequate flow control.

These are coupled with effective Cybernetics gearing up for context aware 360-closed-loop-control, zooming in and out between distributed and central levels. Last but not least, effective and efficient tools that are characterized by ease of use and consumability do attract many more new users from many more different domains to interact with these systems in a self-service fashion and create new business opportunities as a result.


d.SCI: Intersecting Information Design, Dataviz and Cognitive Art

I am joining a discussion on Information Visualization and Interaction Design… and the integral role of Cognitive Art to deliver innovative HCI (Human-Computer-Interfaces.)

Heare are sample projects that I have been involved in. This set showcases: multi-modal user interfaces, metaphorical abstractions, and cognitive models, as well as ergonomic form factors that optimize for extreme ease of use.

d.SCI refers to a methodology that I am working on which purposely intersects design and science. In this particular discussion, human congition and affect are the topics of interest.

Project Portfolio - 1

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Project Portfolio - 7

Project Portfolio - 6b

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Project Portfolio - 10


Debunking Design Thinking Myths

“Reflecting the diversity of the agenda, we are thankful for the support of our advisory board. The board is integral to the development and execution of Design Thinking, supporting the strategic positioning of the brand and advising to the content and participants that matter most. Hear from some of the greatest minds in Design Thinking as they shed a light on its mysteries and separate fact from fiction.”

Debunking Design Myths CoverDesign Thinking Advisory Board.jpgDesign Thinking - Jose de Francisco 1

Design Thinking - Jose de Francisco 2

Design Thinking Agenda



Agile Software in a Hardware World


“The world of IoT and connected devices is expanding rapidly. We all carry super computers in our pockets and interact with everything from home automation, cars, consumer electronics, and healthcare devices.”

“In this complex hardware + software environment the product development cycle can be tricky. For example, you can’t just follow agile software practices by the book when you’re building a connected pace maker. So how do we approach product development when the stakes are high and the moving parts are many? During this discussion we’ll be tackling topics such as:”

“How do you roadmap a product which includes both hardware and software components? How does agile development fit in? How does the regulatory landscape affect how we approach development and iteration? How do you build teams around these integrated products? And how do you keep them in sync and working together?”


I’d first like to thank the team at DevMynd for their kind invitation. I am looking forward to joining the panel discussion in Chicago this coming Thursday, February 22. In the meantime, I will welcome any comments and insights as I gear up for this discussion.

I’m working on outlining some of the myths, dilemmas and trade-offs that I have encounter as an Industrial Designer and in Product Management.

From a design perspective, there are two topics worth looking at: Design Thinking as a Human-Centered methodology and its outcomes in terms of: (a) utility, (b) usability, (c) consumability, (d) affectivity and (e) the composite and differential value of the resulting digital experiences that involve software and hardware.

This “new brave world” equips us with the freedom to explore new form factors, cognitive models and, most impoartantly, the development human x technology networks. Some of the specifics come down to design semantics re-defining HMS, Human-Machine-Systems, in the context of multi-modal user interfaces and innovative interactions where Machine Learning and new visualization paradigms happen to surface.

From a Product Management viewpoint, there is a need for also pondering about how to best leverage Design Thinking beyond Industrial Design and Software Development to talkle product and service strategy. Here my focus gravitates toward addressing: (a) success factors and (b) limiting factors under control, as well as (d) other determining factors beyond our area of influence that can impact the difussion of innovations either possitively or negatively. Moreover, I like to couple business model innovation with behavioral economics and information network effects.

This construct really boils down to capturing the essence behind (e) stakeholders’ acceptance criteria and (f) the users’ engagement, adoption and growth rates. This means defining capability and maturity levels and how to best factor for the fact that they adapt and evolve over time. Obviously, this leads to taking a close look at how to best intersect Lean and Agile practies, but not only, so that we can lead and navigate constantly changing environments in “digital time.”

Let’s get down to a more tactical level: end-to-end system design entails a mix of loosely and tightly coupled elements, and a platform approach to operate at speed, scale and wider scope that what black boxes can match. A reality check unveils a hybrid world where decisions on capacity and performance levels, as well as serviceability and dependency levels drive decisions toward optimizing for distributed systems and, therefore, the rising value of end-to-end solutions vs. point solutions only.

In that context, inter-disciplinary teams involving creative technologists and domain experts make our organizations effectively diverse, smarter and innovative. Otherwise, self-defeating arrogance, conflicting silos and technical myopia can make pre-production and production be costlier by promoting unncessary friction and getting everyone to work harder and harder rather than smarter. Typically, that negates productivity, forces a number corrective actions, and significantly shifts and/or downsizex sought after results.

The beauty of the Studio’s human-experience-centered practice is a healthy obssession for delivering “meaning.” The definition of “meaningful outcomes” (rather than churning outputs) makes these organizations behave based on value and impact. We strive to foster not just customer satisfaction and net promoter scores, but measurable customer delight and network effects (superior and service-level performance indicators) which, in turn, set and streamline technical requirements.

Long story short, the Studio’s mindset (critical thinking / wonder & discovery / problem solving) and workstyle (collaborative / experiential / iterative / adaptive) help explain why creative technologysts are instrumental and serial innovation engines for the digital age.


Footnote: the term “team of creative technologysts” was first coined by Nokia Bell Labs back in the 1940s to single out the differentiated value of inter-disciplinary undertakings. In the late forties, Bell Labs’ Clauded Shannon pioneered Information Theory and John Karlin set up the first Human Factors Engineering in industry. That HFE team was formed by a pyschologist, a statistician (the father of quality control visualization,) an engineer, and a physicist.