Major companies are not easy to transform digitally due to inertia of a large structure and deeply rooted traditions. Diverse IT landscape and distributed faculties make it especially difficult and expensive to migrate established business routine.
On the contrary, smaller companies can quickly transform their operations into digital field due to limited footprint and simpler workflows. For the same reason, an effect of innovation appears much quicker, is easier to measure and correct depending on obtained results.
Smaller companies simply cannot afford being inefficient, unlike their bigger counterparts, which have at least a temporal security belt of size and established market share. Quick technical and operational optimization offered by digital tools is the primary factor of growth for an emerging business.
Digital transformation offers to smaller companies unique competitive advantages by effectively replacing costly manual operations by affordable and adaptive automation of business processes. Digital operations in smaller companies are also much easier to control due to limited scale and complexity.
All these factors explain explosive growth of digital automation in smaller organizations far superseding respective evolution of large corporations and creating key competitive advantage of modern small business.
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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