In case of booking a service appointment, the system has to allocate certain resources with proper skills to execute required service tasks.

Once you have to overbook resources you need to understand how to distribute working hours among personnel. The best practice is based on how skilled resources are aiming at the most skilled person being overbooked to the smallest extent.

How would one calculate that? This is the topic I want to describe in my article.

Various overbooking algorithms are available for this subject. For instance, the methods include but are not limited to ‟proportional”, ‟sequential”, ‟progressive” or ‟conditional” overbooking methods. The abundance of options can guarantee that one can easily write a book on this matter.

Drawing upon personal experience there is a trend to use the ‟spread” algorithm which best fits dealers demand. When it comes to the ‟spread” method it does not really matter if a certain person can complete the task on his own. An intention is to distribute working hours amongst all resources considering skills a given resource has. This algorithm ensures that less skilled resources are booked more while more skilled people are booked less. Therefore, more skilled people are going to be more available. This point is crucial!

Let’s get down to details!

Assuming the following scenario: One workshop has 4 technicians with 4 different types of skills. Mechanic (MECH), Diagnostic (DIAG), Painting (PAINT), and Body shop (BODY). See example below:

John is the most experienced person in the workshop (4 skills). Now, let’s get to how to distribute working hours if for example 7 MECH Hours are needed for doing a service task but the workshop is short of MECH skill. Using the spread overbooking algorithm the following math formula is considered:

Conclusion: John, who is the most experienced person is less overbooked then others.

As a result, one can see some decimals, but it does not really matter. These numbers do not bother end-users but are crucial for Dealer Management Systems to handle the calculation of the overbooking.

I hope that my article was insightful.

I’m looking forward to receiving your feedback!

Dr. Juraj Hanus, hanus@taxapa.com

Data Analytics & Automotive Applications Expert for Dealerships