How BPM turns agility into sustainable business advantage?

Agile project management is becoming a popular practice, especially in IT development. It offers significant merit in splitting large missions into small sprints dynamically configurable across work teams. When applied creatively, agile approach may significantly improve business responsiveness to emerging challenges in dynamic economic environment.

Despite tempting benefits, agile management bears also significant business risks. Due to simplified decision making and direct informal communication agile approach may potentially dissolve centralized coordination and endanger strategic planning in higher level scope of the whole organization. For this reason, large corporations are typically very cautious on implementing agile practices and sustain from scaling them too widely.

How a company can combine all benefits of agile management and avoid associated risks? Key factor in achieving a balance is accurate business modeling. BPM technology brings necessary structuring and transparency lacked by agile techniques when used alone. Combination of BPM and agile ensures an ideal compound by optimally complementing rigid planning with competitive flexibility in task execution.

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CaseAgile Will Show BPMN Data Exchange between Visio and Leading BPMN Tools

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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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