In its early days Facebook had a famous motto "Move fast and break things" once coined by Mark Zuckerberg. In fact, this principle is typical for every startup. In a wider sense, it is also well known fact of economics that developing economies grow much faster than developed ones.
When a company grows big it takes hundredths times more time, cost and effort to make the same change as in a small company. It creates a niche of survival for a small business. But it also creates a trap of immobility endangering the existence of large corporations. It can be viewed as a phenomenon of maturity or aging. Alternatively, one can see it as an objective and inevitable side effect of the size and complexity. Knowledge and experience too often degrade speed and appear a killing manifestation of age.
BPM is a crucial technology to leverage this fundamental contradiction between accumulated business experience and the ability to quickly respond to newly emerging challenges. BPM turns static array of accumulated business knowledge into dynamic models empowering quick and efficient decision making for larger organizations. It makes BPM true anti-aging lift saving aging organizations from degradation.
Move fast and build things consistently and systematically through model driven accumulation of business knowledge. This is a recipe of long and healthy life for any organization just overgrowing its startup age.
There always exists a discrepancy between a model of business process, however well designed and accurate, and real execution of this process in a business environment. The reason for this gap is an unforeseen depth and hidden details inherent to any real process. Real business model of organization is ultimately unlimited in its depth. Going from highest management levels, it descends to individual departments, client relations, production units, technical code of equipment and controllers etc. In vast majority of cases, it is impossible and senseless to build a complete model covering all and every fine detail of the business. Omitted lower layers of the model create (pseudo) random fluctuations during execution of the model. Real execution paths of a process never follow its model exactly. However, in case of the correct model, we can expect to see that an ensemble of execution paths statistically converges to the model as to its average path over a significant set of observation...
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