Using Scientific Systems Thinking to resolve chronic issues
There are some issues that societies across the world have been forced to struggle with for signficant periods of time without any clear solution. Some examples of such problems are increasing pollution levels, the ubiquitous corruption, amongst many others. Just like societies at large, many organizations, too, grapple with chronic issues such as stagnant market share, disappointing on-time delivery record, continuous delays in new product development projects, pressure to offer sales discounts, etc. Thus, be it in an organizational or in a social system, some problems are “sticky”, and persist despite multiple attempts to solve them.
At Vector, we hold that, these ‘sticky’ problems remain unresolved because of the following obstacles
Inability to establish the definitive “Why” of a problem
Different stakeholders tend to have contradictory points of view on the real reason behind a problem. Each would also, often, offer a different but apparently “obvious” solution to the problem from their point of view. This leads to a stalemate on what has to be done to solve the issue.
The ‘Obvious’ solution does not work
Even when an attempt is made to implement one of these “obvious solutions”, more often than not, for the actions taken on one part of the system, there would be severe negative ramifications on some other part of the system. When faced with these negative ramifications, there would either be a strong resistance to the solution’s implementation, or pressure to roll-back the solution later.
Conventional problem analysis and diagnostics, as widely practised, especially in solving business problems, either ignore or exacerbate the above obstacles because of the following entrenched belief systems
1. Acceptance of Subjectivity
When it comes to business management, it is widely accepted that problems cannot be posed well like in Mathematics or the hard sciences. Business Management is partly science and partly art, is the widely held belief amongst practicing managers. Heuristic rules based on experience is considered good enough by many. “I have more experience on these matters”, is always presented as a clinching argument.
Because of the above acceptance of subjectivity around the nature of management studies, enough attention is usually not paid to the thinking rigour required to establish causality behind business situations, as in the hard sciences. Some assume that hard data can be used to settle disputes of causal knowledge. But it does not work either, because direct data is usually never available for all business variables under discussion. At the same time, basic statistics also do not help directly establish causality. They just reveal association or correlations between variables. Correlations, as they say, is not causation!
The Principle of Objectivity assumes that reality has a clear answer about a problem, and that conflicting viewpoints are nothing but incorrect interpretation of this reality. Hence, in the hard sciences and in Mathematics, where belief in objectivity is fundamental, it is assumed that problems can be “well posed”, which means that the problem is so well defined that one can conclude if an answer is right or wrong by logical argumentation using established laws, mathematical calculations and/or by controlled experimentations.
2. ‘Reductionist Analysis’ approach for resolving chronic issues
Much of formal business analysis depends on the approach of breaking a problem down to its constituent parts, and drilling each one of them further – like the fish bone analysis. This reductionist analysis is good enough for understanding problems of high cost structures or identifying non-value adding tasks in a process.
But it is ineffective to solve chronic issues of an organization such as stagnant sales, low production output in the first week of the month, significant forced discounting in retail stores, and so on.
The reductionist analysis approach assumes that the drill down variables are usually independent of each other, and, hence, separate initiatives can be launched to impact each one of them independently. Cumulatively they will deliver the desired impact.
But business variables are inherently dependent on each other; many times, forming non-linear cause of effect loop as shown below. Ignoring the interdependencies is another reason why solutions do not deliver.
Acceptance of differing or conflicting causal narratives, combined with incorrect diagnosis methods lead to stickiness of chronic problems. As a way out, many organizations just rely on industry level heuristics, euphemistically called ‘bench-marking’ to solve these problems. If others are doing something, then maybe it is the right thing to do. This approach leads to bad implementation of borrowed ideas, as the unidentified conditions of an organization throw seemingly great ideas out of gear. Many organization have a long unrecorded history of ideas copied for implementation, but later reversed to prevent deterioration.
Vector’s Approach: Scientific Systems Thinking
To overcome the above weaknesses of conventional business diagnostics, Vector uses a thinking process approach that combines the philosophy of deductive reasoning methods of the Greek philosopher, Aristotle; the Systems
Phases of Business diagnostics and Principles of Thinking
01
Problem Definition
Why is there a need to change?
Is it the core problem or just a symptomatic issue?
How big is the problem and what are the direct and indirect impact of it?
02
Solution Detailing
What is the change that needs to be implemented?
Is the change a win-win for all stakeholders?
Is the change risk free without any negative effect either in short or long run?
03
Implementation Design
Will the change affect current performance levels.
What should be done to prevent the pain of transition? Where will the capacity come for transition phase?
Can the sequencing design generate the desired capacity for transition ?
04
Implementation
Is there a need for pilot? How do we design the pilot?
Did the implementation give the desired results or was there new causal factors in play?
Why do we claim the results were because of the intervention and nothing else ?
What needs to be done for scale up and sustenance of results?
The human mind is a “story-telling” machine. We have the ability to develop any causal narrative for a given problem, and fall in love with it after more confirming validations. Confirmation bias is a bug of the human brain, and without a methodological rigour, one might end up having a world view that is out of synch with reality. Managers with "departmental outlook” tend to have a biased view of reasoning behind issues. Vendors of IT tools view problems through the eyes of their tools, and strongly feel that the lack of IT tools is the reason behind problems. Bosses in a power structure, at times, impose their world view about problems on the organisation.
To overcome the above biased viewpoints in business diagnostics, Vector follows the methodology and thinking tools that focus on the following principles to ensure that the diagnosis of business situations is devoid of experiential biases, and has all the necessary characteristics of a “well posed” problem.
To make a causal reasoning well posed, the methodology followed by Vector has the following rules.
Language Clarity
For constructing a causal narrative, the language used should be clear, and key words should be well defined. Vague definitions or jargon should be avoided. Sophistication in language, commonly used in consulting circles to impress the client, is devalued in Vector. Good communication implies that the sender and the listener draw the same meaning for the words used in it.
Deduction from Supporting theory
All cause and effect narratives should be verbalised along with underlying theories or principles being used to logically derive the effect from the cause. Arguments have to be deduced from these principles. No argument can be argued for validity based on generalized from a few observations or examples. (Others are doing it, and hence it must be the right thing to do!)
Falsifiability
Any causal narrative that is developed should be critically falsifiable and or testable. Explanations should be non-variable in light of contradictory evidence or arguments.
This implies arguments should have clear boundary conditions on which it can fail.
Principle of Convergence
Even when reasoning is done as per the above method, asking more whys to the reasons offered can lead to a circular loop of reasons, as given below, more so , with wicked problems.
Complex systems, like an organization, have inter-departmental problems connected with each other to form runaway vicious loops or rigid stagnant ones that force an organization to move from one extreme to another. For example, actions taken to cut inventory to release working capital but a consequent drop in sales at a later period, forces one to increase inventory back to original levels. Such loops always have single leverage point for intervention to reverse the character of the loop into a positive reinforcing loop.
View the video on this to learn about leverage point interventions in complex systems.
Predictability
Proposed cause, particularly if it is non-measurable, should not only explain the observed effects but also predict additional unique effects that can be checked and measured for validations.
Listen to the podcast on how important it is to create sound logical explanations that are non-variable and testable.
Solution Detailing
Resolving Conflicts/Paradoxes/Contradictions using Bounded Creativity The circular loops of reasons is because of contradictory relationships inherent to many variables. Improvement in one can lead to deterioration of another, forcing managers to ping-pong based on improvement-deterioration cycles. Any solution that compromises one to benefit the other is always a temporary patch work. Innovative solutions are ones that find a way out of these conflicts or paradoxes by invalidating assumptions behind them, and which enable a positive correlation between variables.
Risk-free Transition Sequencing Many great ideas fail to fructify in implementation because the transition phase needs huge temporary management capacity that can impact short term results. This creates the classic conflict between the short term and the long term needs of the company. Short term needs always triumph while the long term needs get either lip service or go slow in deployment. This is one of the reasons many transformation projects fail or move at a sluggish pace.
However if implementation is well focussed on the leverage point, the short term needs are always met, while creating capacity for taking the next step. This approach eventually creates the desired long term effect.
Implementation sequencing requires one to always focus on the active leverage point of the organization. Any implementation that is in a different area does not provide any business benefit, and can potentially be damaging to the organization.
Focussing on one leverage point at a time brings a lot of organizational focus, as the entire bandwidth of the organization is made available for the important few. With each successful implementation, the leverage point is expected to shift, requiring another shift of focus. This way one can develop a rapid transformation plan that is sequential, rather than launching many projects together.
Listen to the podcast to understand why great solutions fail during execution because of bad sequencing design.
Implementation
Even when solutions and implementation sequencing are well thought out following the above principles, reality can throw up surprises both in terms of positive and negative results beyond the expectations of the theory. Hence, before implementation start, it is important for change agents to create a map of intermediate outcomes that they expect to see, and check if each outcome is range bound as expected. The control group for comparison is clearly defined before start of the implementation.
If outcomes are out of range, then investigations are required to be done with control groups established for comparison. The checks are made to identify any new interfering variables or validity of boundary conditions of the solution for validations. This helps in developing new or modifying solutions on an ongoing basis.
Listen to the podcast to understand how to deal with mystery analysis in implementations.
Using scientific systems thinking to resolve chronic issues