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Optimised scheduling through Probabilistic Modeling

by crystalball_manager on 23rd April 2008

Scheduling customers for virtually any process or service (event) can be notoriously inaccurate and unreliable, especially when there are numerous factors affecting the duration of the event, or there are numerous event types.

The resultant effect of scheduling difficulties includes wait time for the service provider (staff), wait time for the customers, and later than expected end-of-workday. In addition to wait time, either early or later-than-scheduled start or finish times can result in other forms of waste and non-value added activity.

Optimised scheduling balances voice-of-the-customer (VOC) with the voice-of-the-process (VOP), and achieving this balance reduces inefficiencies associated with not meeting needs of the customer and/or the business.

This presentation uses curve fitting of historical data (of the duration of the events scheduled), and Monte Carlo analysis to determine the cumulative effect of those distributions on subsequent scheduled events.

Presented by Rob Allen, AIT Group.



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