Improvement is a process and sometimes it can feel like it is a one-step-forward-two-steps-back sort of shuffle. And just like any dance, knowing the steps to avoid can be critical. Here are some important ones to consider. In many ways they can be considered an onion, we systematically can address a problem layer and then work our way to the next.
The vague, ambiguous and poorly defined bucket concept called human error is just a mess. Human error is never the root cause; it is a category, an output that needs to be understood. Why did the human error occur? Was it because the technology was difficult to use or that the procedure was confusing? Those answers are things that are “actionable”—you can address them with a corrective action.
The only action you can take when you say “human error” is to get rid of the people. As an explanation the concept it widely misused and abused.
Human error has been a focus for a long time, and many companies have been building programmatic approaches to avoiding this pitfall. But we still have others to grapple with.
We like to build our domino cascades that imply a linear ordering of cause-and-effect – look no further than the ubiquitous presence of the 5-Whys. Causal chains force people to think of complex systems by reducing them when we often need to grapple with systems for their tendency towards non-linearity, temporariness of influence, and emergence.
This is where taking risk into consideration and having robust problem-solving with adaptive techniques is critical. Approach everything like a simple problem and nothing will ever get fixed. Similarly, if every problem is considered to need a full-on approach you are paralyzed. As we mature we need to have the mindset of types of problems and the ability to easily differentiate and move between them.
We remove human error, stop overly relying on causal chains – the next layer of the onion is to take a hard look at the concept of a root cause. The idea of a root cause “that, if removed, prevents recurrence” is pretty nonsensical. Novice practitioners of root cause analysis usually go right to the problem when they ask “How do I know I reached the root cause.” To which the oft-used stopping point “that management can control” is quite frankly fairly absurd. The concept encourages the idea of a single root cause, ignoring multiple, jointly necessary, contributory causes let alone causal loops, emergent, synergistic or holistic effects. The idea of a root cause is just an efficiency-thoroughness trade-off, and we are better off understanding that and applying risk thinking to deciding between efficiency and resource constraints.
Our problem solving needs to strive to drive out monolithic explanations, which act as proxies for real understanding, in the form of big ideas wrapped in simple labels. The labels are ill-defined and come in and out of fashion – poor/lack of quality culture, lack of process, human error – that tend to give some reassurance and allow the problem to be passed on and ‘managed’, for instance via training or “transformations”. And yes, maybe there is some irony in that I tend to think of the problems of problem solving in light of these ways of problem solving.
As it turns out, the reality-based, science-friendly communities and information sources many of us depend on also largely failed. We had time to prepare for this pandemic at the state, local, and household level, even if the government was terribly lagging, but we squandered it because of widespread asystemic thinking: the inability to think about complex systems and their dynamics. We faltered because of our failure to consider risk in its full context, especially when dealing with coupled risk—when multiple things can go wrong together. We were hampered by our inability to think about second- and third-order effects and by our susceptibility to scientism—the false comfort of assuming that numbers and percentages give us a solid empirical basis. We failed to understand that complex systems defy simplistic reductionism.
On point analysis. Hits many of the themes of this blog, including system thinking, complexity and risk and makes some excellent points that all of us in quality should be thinking deeply upon.
COVID-19 is not a black swan. Pandemics like this have been well predicted. This event is a different set of failures, that on a hopefully smaller scale most of us are unfortunately familiar with in our organizations.
I certainly didn’t break out of the mainstream narrative. I traveled in February, went to a conference and then held a small event on the 29th.
The article stresses the importance of considering the trade-offs between resilience, efficiency, and redundancy within the system, and how the second- and third-order impacts can reverberate. It’s well worth reading for the analysis of the growth of COVID-19, and more importantly our reaction to it, from a systems perspective.
Our goal should always be to reduce ignorance. Many unknown unknowns are just things no one has bothered to find out. What we need to do is ensure our processes and systems are constructed so that they recognize unknowns.
There are six factors that need to be explored to find the unknown unknowns.
Complexity: A complex process/system/project contains many interacting elements that increase the variety of its possible behaviors and results. Complexity increases with the number, variety, and lack of robustness of the elements of the process, system or project.
Complicatedness: A complicated process/system/project involves many points of failure, the ease of finding necessary elements and identifying cause-and-effect relationships; and the experts/participants aptitudes and experiences.
Dynamism: The volatility or the propensity of elements and relationships to change.
Equivocality: Knowledge management is a critical enabler of product and project life cycle management. If the information is not crisp and specific, then the people who receive it will be equivocal and won’t be able to make firm decisions. Although imprecise information itself can be a known unknown, equivocality increases both complexity and complicatedness.
Perceptive barriers: Mindlessness. This factor includes a lot of our biases, including an over-reliance on past experiences and traditions, the inability to detect weak signals and ignoring input that is inconvenient or unappealing.
Organizational pathologies: Organizations have problems, culture can have weaknesses. These structural weaknesses allow unknown unknowns to remain hidden.
The way to address these six factors is to evaluate and challenge by using the following approaches:
Interviews with stakeholders, subject matter experts and other participants can be effective tools for uncovering lurking problems and issues. Interviewers need to be careful not to be too enthusiastic about the projects they’re examining and not asking “yes or no” questions. The best interviews probe deep and wide.
Build Knowledge by Decomposing the System/Process/Project
Standard root cause analysis tools apply here, break it down and interrogate all the subs.
Identifying the goals, context, activities and cause-effect relationships
Examining the complexity and uncertainty of each element to identify the major risks (known unknowns) that needed managing and the knowledge gaps that pointed to areas of potential unknown unknowns.
Construct several different future outlooks and test them out (mock exercises are great). This approach accepts uncertainty, tries to understand it and builds it into the your knowledge base and reasoning. Rather than being predictions, scenarios are coherent and credible alternative futures built on dynamic events and conditions that are subject to change.
Communicate Frequently and Effectively
Regularly and systematically reviewing decision-making and communication processes, including the assumptions that are factored into the processes, and seeking to remove information asymmetries, can help to anticipate and uncover known unknowns. Management Review is part of this, but not the only component. Effective and frequent communication is essential for adaptability and agility. However, this doesn’t necessarily mean communicating large volumes of information, which can cause information overload. Rather, the key is knowing how to reach the right people at the right times. Some important aspects include:
Candor: Timely and honest communication of missteps, anomalies and missing competencies. Offer incentives for candor to show people that there are advantages to owning up to errors or mistakes in time for management to take action. It is imperative to eliminate any perverse incentives that induce people to ignore emerging risks.
Cultivate an Alert Culture: A core part of a quality culture should be an alert culture made up of people who strive to illuminate rather than hide potential problems. Alertness is built by: 1) emphasizing systems thinking; 2) seek to include and build a wide range of experiential expertise — intuitions, subtle understandings and finely honed reflexes gained through years of intimate interaction with a particular natural, social or technological system; and 3) learn from surprising outcomes.
By working to evaluate and challenge, to truly understand our systems and processes, our risk management activities will be more effective and truly serve to make our systems resilient.
We often think that complicated and complex are on a continuum, that complex is just a magnitude above complicated; or that they are synonyms. These are actually different, and one cannot address complex systems in the same way as complicated. Many improvement efforts fail by not seeing the difference and they throw resources at projects that are bound for failure because they are looking at the system the wrong way.
Complicated problems originate from causes that can be individually distinguished; they can be addressed piece by piece; for each input to the system there is a proportionate output; the relevant systems can be controlled and the problems they present admit permanent solutions.
Complex problems result from networks of multiple interacting causes that cannot be individually distinguished and must be addressed as entire systems. In complex systems the same starting conditions can produce different outcomes, depending on interactions of the elements in the system. They cannot be addressed in a piecemeal way; they are such that small inputs may result in disproportionate effects; the problems they present cannot be solved once and for ever, but require to be systematically managed and typically any intervention merges into new problems as a result of the interventions dealing with them; and the relevant systems cannot be controlled – the best one can do is to influence them, or learn to “dance with them” as Donella Meadows said.
Lets break down some ways these look and act different by looking at some of the key terminology.
Causality, the relationship between the thing that happens and the thing that causes it
Linear cause-and-effect pathways allow us to identify individual causes for observed effects.
Because we are dealing with patterns arising from networks of multiple interacting (and interconnected) causes, there are no clearly distinguishable cause-and-effect pathways.
This challenges the usefulness of root cause analysis. Most common root cause analysis methodologies are based on cause-and-effect.
Linearity, the relationships between elements of a process and the output
Every input has a proportionate output
Outputs are not proportional or linearly related to inputs; small changes in one part of the system can cause sudden and unexpected outputs in other parts of the system or even system-wide reorganization.
Think on how many major changes, breakthroughs and transformations, fail.
Reducibility, breaking down the problem
We can decompose the system into its structural parts and fully understand the functional relationships between these parts in a piecemeal way.
The structural parts of the system are multi-functional — the same function can be performed by different structural parts. These parts are also richly inter-related i.e. they change one another in unexpected ways as they interact. We can therefore never fully understand these inter-relationships
This is the challenge for our problem solving methodologies, which mostly assume that a problem can be broken down into its constituent parts. Complex problems present as emergent patterns resulting from dynamic interactions between multiple non-linearly connected parts. In these systems, we’re rarely able to distinguish the real problem, and even small and well-intentioned interventions may result in disproportionate and unintended consequences.
One structure-one function due to their environments being delimited i.e. governing constraints are in place that allows the system to interact only with selected or approved types of systems. Functions can be delimited either by closing the system (no interaction) or closing its environment (limited or constrained interactions).
Complicated systems can be fully known as a result and are mappable.
Complex systems are open systems, to the extent that it is often difficult to determine where the system ends and another start. Complex systems are also nested they are part of larger scale complex systems, e.g. an organisation within an industry within an economy. It is therefore impossible to separate the system from its context.
This makes modeling an issue of replicating the system, it cannot be reduced. We cannot transform complex systems into complicated ones by spending more time and resources on collecting more data or developing better maps.
Some ideas for moving forward
Once you understand that you are in a complex system instead of a complicated process you can start looking for ways to deal with it. These are areas we need to increase capabilities with as quality professionals.
Methodologies and best practices to decouple parts of a larger system so they are not so interdependent and build in redundancy to reduce the chance of large-scale failures.
Use storytelling and counterfactuals. Stories can give great insight because the storyteller’s reflections are not limited by available data.
Grace Duffy is the keynote speaker. I’ve known Grace for years and consider her a mentor and I’m always happy to hear her speak. Grace has been building on a theme around her Modular Kaizen approach and the use of the OODA Loop, and this presentation built nicely on what she presented at the Lean Six Sigma Conference in Phoenix, at WCQI and in other places.
Audits as a form of sustainability is an important point to
stress, and hopefully this will be a central theme throughout the conference.
The intended purpose is to build on a systems view for preparation for an effective audit and using the OODA loop to approach evolutionary and revolutionary change approaches.
Grace starts with a brief overview of system and process and then from vision to strategy to daily, and how that forms a mobius strip of macro, meso, micro and individual. She talks a little about the difference between Deming and Juran’s approaches and does a little what-if thinking about how Lean would have devoted if Juran had gone to Japan instead of Deming.
Breaking down OODA (Observe, Orient, Decide Act) as “Where
am I and where is the organization” and then feed into decision making.
Stresses how Orient discusses culture and discusses understanding the culture.
Her link to Lean is a little tenuous in my mind.
She then discusses Tom Pearson’s knowledge management model
with: Local Action; Management Action; Exploratory Analysis; Knowledge
Building; Complex Systems; Knowledge Management; Scientific Creativity. Units
all this with system thinking and psychology. “We’re going to share shamelessly because
that’s how we learn.” “If we can’t have fun with this stuff it’s no good.”
Uniting the two, she describes the knowledge management
model as part of Orient.
Puts revolutionary and evolutionary change in light of
Juran’s Breakthrough versus Continuous Improvement. From here she covers
modular kaizen, starting with incremental change versus process redesign. From
there she breaks it down into a DMAIC model and goes into how much she loves
the measure. She discusses how the human brain is better at connections, which
is a good reinforce of the OODA model.
Breaks down a culture model of Culture/Beliefs,
Visions/Goals and Activities/Plans-and-actions influenced by external events
and how evolutionary improvements stem out of compatibility with those. OODA is
the tool to help determine that compatibility.
Discusses briefly on how standardization fits into systems
and pushes a look from a stability.
Goes back to the culture model but now adds idea generation
and quality test with decisions off of it that lead to revolutionary
improvements. Links back to OODA.
Then quickly covers DMAIC versus DMADV and how that is
another way of thinking about these concepts.
Covers Gina Wickman’s concept of visionary and integrator from Traction.
Ties back OODA to effective auditing: focus on patterns and
not just numbers, Grasp the bigger picture, be adaptive.
This is a big sprawling topic for a key note and at times it
felt like a firehose.. Keynotes often benefit from a lot more laser focus. OODA
alone would have been enough. My head is reeling, and I feel comfortable with
this material. Grace is an amazing, passionate educator and she finds this
material exciting. I hope most of the audience picked that up in this big gulp
approach. This system approach, building on culture and strategy is critical.
OODA as an audit tool is relevant, and it is a tool I think we should be teaching better. Might be a good tool to do for TWEF as it ties into the team/workplace excellence approach. OODA and situational awareness are really united in my mind and that deserves a separate post.
After the keynote there are the breakout sessions. As always, I end up having too many options and must make some decisions. Can never complain about having too many options during a conference.
First Impressions: The Myth of the Objective & Impartial Audit
First session is “First Impressions: The Myth of the
Objective & Impartial Audit” by William Taraszewski. I met Bill back at the
2018 World Conference of Quality Improvement.
Bill starts by discussing how subjectivity and first
impressions and how that involves audits from the very start.
Covers the science of first impressions, point to research of bias and how negative behavior weighs more than positive and how this can be contextual. Draws from Amy Cuddy’s work and lays a good foundation of Trust and Competence and the importance in work and life in general.
Brings this back to ISO 19011:2018 “Guidelines for auditing
management systems” and clause 7.2 determining auditor competence placing
personal behavior over knowledge and skills.
Brings up video auditing and the impressions generated from
video vs in-person are pretty similar but the magnitude of the bad impressions
are greater and the magnitude of positive is lower. That was an interesting
point and I will need to follow-up with that research.
Moves to discussing impartiality in context of ISO
19011:2018, pointing out the halo and horn effects.
Discusses prejudice vs experience as an auditor and covers
confirmation bias and how selective exposure and selective perception fits into
our psychology with the need to be careful since negative outweighs.
Moves into objective evidence and how it fits into an audit.
Provides top tips for good auditor first impressions with
body language and eye contact. Most important, how to check your attitude.
This was a good fundamental on the topics that reinforces some basics and goes back to the research. Quality as a profession really needs to understand how objectivity and impartiality are virtually impossible and how we can overcome bias.
Auditing Risk Management
Barry Craner presented on :Are you ready for an audit of your risk management system?”
Starts with how risk management is here to stay and how it is in most industries. The presenter is focused on medical devices but the concepts are very general.
“As far possible” as a concept is discussed and residual risk. Covers this at a high level.
Covers at a high level the standard risk management process (risk identification, risk analysis, risk control, risk monitoring, risk reporting) asking the question is “RM system acceptable? Can you describe and defend it?”
Provides an example of a risk management file sequence that matches the concept of living risk assessments. This is a flow that goes from Preliminary Hazard analysis to Fault Tree Analysis (FTA) to FMEA. With the focus on medical devices talks about design and process for both the FTA and the FMEA. This is all from the question “Can you describe and defend your risk management program?”
In laying out the risk management program focused in on personnel qualification being pivotal. Discusses answering the question “Are these ready for audit?” When discussing the plan asks the questions “Is your risk management plan: documented and reasonable; ready to audit; and, SOP followed by your company?”
When discussing risk impact breaks it down to “Is the risk acceptable or not.” Goes on to discuss how important it is to defend the scoring rubric, asking the question”Well defined, can we defend?”
Goes back and discusses some basic concepts of hazard and harm. Asks the questions “Did you do this hazard assessment with enough thoroughness? Were the right hazards identified?” Recommends building a example of hazards table. This is good advice. From there answer the question “Do your hazard analses yield reasonable, useful information? Do you use it?”
Provides a nice example of how to build a mitigation plan out of a fault tree analysis.
Discussion on FMEAs faultered on detection, probably could have gone into controls a lot deeper here.
With both the PTA and FMEA discussed how the results needs to be defendable.
Risk management review, with the right metrics are discussed at a high level. This easily can be a session on its own.
Asks the question “Were there actionable tasks? Progress on these tasks?”
It is time to stop having such general overviews at conferences, especially at a conference which are not targeted to junior personnel.