Service management - Applying simple queue model to address service management challenge

A simple Server queue model can be applied to address some of the challenges identified in the blog http://insightful-journey.blogspot.com/2008/07/service-management-issues-with-using.html:

The following step-wise approach can be adopted on the same:

1. Demand characterization is nothing but the l for a unique combination of the service factors (Service type, service scope, service domain and service category). The best way to represent such a set of demand characterization is defined using the mathematic notation below:

l = l_ijklm

i Î {Incident management, Problem Management, Change requests/enhancement..}

j Î {Level 1, Level 2, Level 3, Level 4}

k Î {Business, Infrastructure, Application…}

l Î {sev 1, sev 2, sev 3, sev 4…)

m Î {Java, .Net, Tibco, Oracle, …)

2. Capacity for providing service can be determined based on the service rate for each combination of service factor.

m = m_ijklm

i Î {Incident management, Problem Management, Change requests/enhancement..}

j Î {Level 1, Level 2, Level 3, Level 4}

k Î {Business, Infrastructure, Application…}

l Î {sev 1, sev 2, sev 3, sev 4…)

m Î {Java, .Net, Tibco, Oracle, …)

3. The optimal service level objective can be set by setting factors:
1. Utilization percentage (as indicated above) 70-90% but not 100%. (Note that a model which is 100% utilized is unstable)
2. Healthy backlog for tickets to smoothen the demand-capacity gaps. This can be determined as the number of request in queue for each combination of service factors
3. Balanced service time for service tickets and a cap on target improvement. Uncapped service improvement target leads to instability in system and disproportionate cost to maintain such a model.
4. Assuming a typical utilization of 90% we can determine the number of resources for each demand:

r _ijklm (number of resources) = l _ijklm /m _ijklm/90%

Total number resources R = å r _ijklm

This is a classical optimization problem where one can apply constraint theory for solving it. (We will cover this in detail later).

4. Designing the optimal demand-supply model can be determined by applying constraint theory for an optimal engagement.
1. Objective function is the minimum resources to manage the engagement (note we are not using a function to minimize the cost due to complexity introduced in the system due to workforce, the levels, delivery center utilized and so on)
2. Constraints are defined by cap on service requests, backlogs and service time for each combination of service factors,
5. Execution and sustenance of the service model requires one to have the right value stream map (a.k.a. service process), waste elimination by continuously eliminating non value added activities (for example reducing the infused management team for transition engagement), improving turn around time for service tickets by measuring and optimizing time for value added activities (one can also apply other engineering techniques such as DSM, concurrent engineering, etc.), using multi-skilling of resources through ongoing training, and so on.
6. Continuous improvement involves driving some transformation initiatives similar to ones identified in point 5 above to reach the ideal state as determined by the server queue modeling in Step 2.

Your comments & thoughts welcome!