Closing the loop to make better decisions

It’s not hard to make decisions when you know what your values are.”
Roy Disney

In this earlier post about decision engineering, I spoke of “closing the loop” to help operators to make better decisions using their OSS / DSS (Decision Support System) tools.

Today, I thought I’d use a diagram to better show what I had in mind.

OSS / DSS feedback loop

The following helps to describe this diagram:

  1. Action – The operator is assigned a task to perform (eg raising an order, fixing a fault, designing a new service, capacity planning, network augmentation design, etc). The operator comes with their own knowledge that will help them to make decisions
  2. DSS – The operator is assisted by the DSS (Decision Support System) to make decisions relating to each step in the workflow of the task assigned to them. The DSS provides assistance based on a knowledge base of optimal decisions / workflow-actions
  3. Workflow – Whether through gamification techniques being adopted, inquisitiveness of operators, exceptions in this individual activity / data-set or just natural variation, there will be occasional differences in the ways that operators process their workflow / activities
  4. Delta – Each operator’s key metrics (ie Y-X) are then compared with the optimal decisions / workflow-actions stored in the Benchmark Database for that action
  5. Benchmark – If this operator’s activity / workflow is more optimal than the previous best held in the knowledge base then the decision engineering rules held in the knowledge base will be updated
  6. Closing the Loop – The new optimal decisions / workflow-actions are then fed back into the DSS for the next operator’s activities, hopefully helping the operator to make better decisions within the complex OSS decision framework

This becomes a stepping-stone to a loop that is more automated by machine-learning / AI techniques. The DSS could consist of:

  • RPA (Robotic Process Automation)
  • AI (Artificial Intelligence)
  • ML (Machine Learning); or
  • Just plain old user training (BTW. The benchmark data can be really helpful for planning training scenarios too)

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