Imagine a situation of you being in complete control of all business processes in your organization. You would know exactly how long each activity takes to complete, how much time is spent waiting, and where all bottlenecks are located.
With this near data-omniscience you could easily identify problematic processes, calculate waste hours, and effortlessly follow the audit trails of a full workflow management system.
Now, you can stop imagining; process mining makes this scenario a reality.
It is said that “knowledge is power”. While this might be true, then following the same analogy, unused “power” is just hot air - wasted energy, and for our purposes, wastedinsights.
Therefore, process mining begs for action based on the insights gained in the analysis. These insights allow management to focus their optimization on exactly where the investment would have the most profound organizational impact, if acted upon.
Effective process mining provides complete knowledge about the past and present events, though to be truly omniscient one would have to be able to tell of future events. The mythical oracles of ancient Greece have long passed and we need to look for modern-day fortune-tellers: This is where the science of Monte Carlo (MC) simulations enters the scenario.
In a business setting where variables are often unknown, always stochastic, and usually of high magnitude, making statistically accurate and classical predictions are often quite difficult in the best of cases. To handle this unpredictable nature of future events, nuclear physicists working on the Manhattan Project coined the term MC-simulations, inspired by the roulette tables of the Monte Carlo casinos. These simulations were used to predict expectation values for effects following quantum-mechanical reactions based entirely on past events and statistical probability.
Connecting the dots
By replacing the random quantum-physics reactions with well-established and well-understood business processes, we can suddenly apply MC-simulations in a much more accessible environment. This is done by leveraging process mining to shed light on every possible variation of the business workflows, thereby creating a solid foundation for the MC-simulations. Suddenly, fortune is not so much about luck but more about clever application of data and previous practices.
After having selected an activity for optimization, the management team will then be able to simulate how the process landscape of tomorrow will be affected by the changes today. The simulations are now able to give a statistically sound expectation value for the probability of each workflow.
The physicists at the Manhattan Project applied MC-simulations to build the first nuclear weapon. CERN scientists used this method in the search for the Higgs boson. Now, we can harness that same science to enhance businesses everywhere.
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NNIT Digital Transformation Insights is a regular column where prominent NNIT consultants share their thoughts on current and future digital transformation.
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