Application Support - Modelling Techniques
By Arun Vasudeva Rao, PMP®, Regional Manager - Technical Services at Torry Harris Business Solutions
Often, the CxO community of large multi-national firms approach THBS seeking help not just in planning for maintenance and life-cycle management of their IT estate, in other words - managed-services, but also in implementing such managed services using scientific techniques & frameworks that guarantee a phased RoI. A major part of such a managed-services initiative is constituted by ‘application support’, which forms the subject of discussion in this post.
Typically, we start with a ‘knowledge sharing’ initiative, to help our clients understand the relationship between support models and service levels. The idea is to clearly model the relationship between required service levels and cost, so the application owners take an intelligent decision on the service levels vs. cost. You will agree that the main cost for IT applications support today is directly proportional to the number of support staff deployed. This is very unlike a cloud hardware model, where resources can be ramped up or down based on real-time demand. In this article, we explore the characteristics of a support model and its similarities with that of a waiting line situation we face in everyday life. We apply queuing theory to help with optimal decision making.
We can visualize a set of issues or incident tickets being raised by application owners or end users to the application support team. The support team consists of a set of “servers” who resolve the issue and change the incident status to “fixed”. However, not every incident can be attended to immediately since all the support staff may be busy with an existing incident. Hence, the new incident gets into a queue. Queues or waiting lines form because people or things arrive at the servicing function, or server, faster than they can be served. However, this does not mean that the service operation is understaffed or does not have the overall capacity to handle the influx of customers. In fact, most businesses and organizations have sufficient serving capacity available to handle their customers in the long run. Waiting lines result because customers do not arrive at a constant, evenly paced rate, nor are they all served in an equal amount of time. Customers arrive at random times, and the time required to serve them individually is not the same. Thus, a waiting line is continually increasing and decreasing in length (and is sometimes empty), and it approaches an average rate of customer arrivals and an average time to serve the customer in the long run. For example, the checkout counters at a grocery store may have enough clerks to serve an average of 100 customers in an hour, and in any particular hour only 60 customers might arrive. However, at specific points in time during the hour, waiting lines may form because more than an average number of customers arrive, and they make more than an average number of purchases.
In this blog, we discuss the metrics that are critical to determine the relationship between service levels and cost to serve (or staffing levels) and describe the “Discrete event simulation” model to model incident management so that appropriate staffing levels can be arrived at depending on the required service levels.
An example:
When a banking customer visits a branch, a dispatcher helps categorize the request and direct him or her to the right counter where the request is serviced.
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