Cross-disciplinary architecture is among crucial and often neglected factors of BPM initiatives. Compatible multi-domain modeling is rarely available or seen as priority in BPM projects in favor of local automation tasks. However, exactly transparent and scale-able process collaboration across an organization determines long term success of the digital transformation.
The ability to capture a consistent view of an organization through precise mapping of relevant notations is a cornerstone of all subsequent BPM implementation. The goal of BPM is not a replacement of existing processes and technologies but their skilled alignment, which should ensure most effective and transparent interaction of the existing corporate infrastructure at minimum cost and modification.
Unfortunately, too many BPM initiatives go as vendor driven disruptions, revolutions, breaks through and other scenarios similarly dubious in terms of attractiveness and suitability for a continuous corporate growth. BPM is merely about bringing a harmony into established business structures, which is a skill too rarely found or distinguished as a demand.
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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