Good Decisions. Bad Outcomes
Tuesday, 23 November, 2010 Leave a comment
The article below is of particular interest to me and my colleagues at Ontonix because it reinforces the approach that we advocate and that is supported by our unique technology.
Business is no longer about linear relationships or processes with the post-industrial resilience suggested by terms like “supply chain”. As was highlighted in this recent blog (video) Eric Berlow: How complexity leads to simplicity, when dealing with the modern [non-linear] business, apparently, “good” decisions can often have the opposite of the desired effect without a holistic view of the system…more lessons from nature.
You may not, immediately, get the connection but this is a small extract from a report by US National Academies/National Research Council and the Federal Reserve Bank of New York collaborated on an initiative to “stimulate fresh thinking on systemic risk”.
…back to business
Technological advancements, financial and product inter-connectedness have accelerated in recent years as our demand for newer, faster, better products and services has grown. But with our ability to develop more and better solutions comes greater complexity of operations, technology and relationships required to meet these demands.
Complexity is the enabler but is also a source of considerable risk. That is why it needs to be measured and managed – Ontonix have developed the means to do so.
We have established that every system, from the human body to IT systems, business and manufacturing processes have a finite limit [critical complexity] in proximity to which the system becomes unstable, unmanageable and unpredictable.
Systems cannot survive beyond the point of critical complexity without losing functionality or adding new “structure”. Follow the link for further Complexity Facts.
A scientific, NOT consultancy-based, methodology – applied in healthcare – aerospace – automotive – CAE…
Measuring the current structural robustness of the business [system] – “fitness” or resilience
Determining the current and maximum sustainable levels of complexity within the system
Mapping and measuring the effectiveness of internal and external [network] interdependencies
Identifying sources of risk – incl. complexity – that lie beyond the “risk horizon” for conventional RM
Assessing the individual and cumulative impact of business “risk” decisions accelerated “feedback loop”
Monitoring and Managing the operations – processes – ecosystem – strategy away from fragility
100% Quantitative analysis of strengths, weaknesses, opportunities and threats
Provide “crisis anticipation” – additional insulation against contagion [systemic risk]
Treating the business as a dynamic [non-linear] system, with a focus upon building redundancy and proactive loss prevention to survive the impact of randomness [risk and uncertainty] upon the
This extract is taken from an article, Recognizing & Managing Uncertainty, which is one of the “Concepts” series from Daedalus Oversight [leave a comment or drop me an email if you would like a copy].
…Even with a command of the risks, managers need a sound understanding of control and process dynamics to consistently perform well. The understanding is counter-intuitive to many engrained practices of managing and controlling business environments.
I propose that: rather than being able to control any situation, resources, process or organization, you only have a strong influence over a limited, finite environment. I will outline some fundamentals
from science and current business literature that back this proposal and then demonstrate it through real examples in the business and natural world. The detailed understanding of these scientific concepts is not needed, but a realization of their conclusions and applicability to management challenges is essential.
Managers have to cope with issues of control in dynamic, complex and interdependent environments. Dynamic situations can be described as Linear or Non-Linear. Linear relationships mathematically can be described as factors of the first power of a variable e.g. production output’s relationship to productivity and time. However, most dynamic systems have non-linear, higher power or function relationships (e.g. x2, x3, sin x). It may be surprising to hear that although there are equations to describe a single planet’s movement around the sun, as soon as you introduce a second planet, the equations for three bodies become unsolvable…
More interesting information from Farnam Street.
We can’t entirely avoid outcome-based decisions. Still, we can reduce our reliance on stochastic outcomes. Here are four ways companies can create more-sound reward systems.
1. Change the mind-set. Publicly recognize that rewarding outcomes is a bad idea, particularly for companies that deal in complex and unpredictable environments.
2. Document crucial assumptions. Analyse a manager’s assumptions at the time when the decision takes place. If they are valid but circumstances change, don’t punish her, but don’t reward her, either.
3. Create a standard for good decision making. Making sound assumptions and being explicit about them should be the basic condition for getting a reward. Good decisions are forward-looking, take available information into account, consider all available options, and do not create conflicts of interests.
4. Reward good decisions at the time they’re made.Reinforce smart habits by breaking the link between rewards and outcomes.
Our focus on outcomes is understandable. When a company loses money, people demand that heads roll, even if the changes are more about assuaging shareholders than sound management. Moreover, measuring outcomes is relatively easy to do; decision-making–based reward systems will be more complex. But as I’ve I said before, “It’s hard” is a terrible reason not to do something. Especially when that something can help reward and retain the people best able to help you grow your business.
Read the entire article.