1. Resource Optimization for Information systems support using Heat Maps
Overcoming Challenges and Complexities in Project Management
AJAY KUMAR / SUNEEL WATTAL

2. Introduction.
This paper studies the scope and impact of resource optimization for supporting Information Systems in an organization.
The study includes the design and development of an automation tool to create heat maps for work load and resourcing for various tasks, thereby comparing the heat maps to arrive at an optimal resource mix to carry out the Information Systems support.
The authors designed and implemented a spreadsheet based tool for supporting Information Systems of a financial services organization. We have discussed the various attributes and parameters to be considered for creation of heat maps and emphasized on the modalities for developing such heat maps.

3. Material and Methods
The Information systems support team provides IT infrastructure support to a multitude of users globally and needs to carry out resource optimization and resource balancing activities at periodic intervals of time.
The support team operates in a 24*7 model across shifts as
Three rotating shifts of 9 hours each (with a one hour overlap).
Two additional shifts - one for India Business hours (General Shift) and one for US Business Hours (Prime Shift).
Any recoded incident has an associated unique Incident number and a severity.
Incidents are designated with a severity ranging from 1 to 3 (Sev 1 being a critical issue).
The severity of an incident is decided on the basis of the number of affected users and the business impact.
The support team is expected to address or resolve an incident according to its severity.

4. Methods..
For creating the workload heatmap, the base data was pulled in from the Incident Management system as : (i) Incident Number (ii) Severity (iii) Status (iv) Incident Summary (v) Reported by (vi) Creation Group (vii) Reported Date (viii) Owner Group (ix) Global issue (x) Owner (xi) CI number The incident report is pasted onto an excel sheet. We have added two columns for "Day of the week" and the "Time". Necessary formulae were plugged in to calculate these fields automatically from the “Reported Date” field, which denotes the timestamp of an incident.

5. Challenge..
Skilled and qualified people are a vital component of any information system.
Organizations invest heavily in Human capital, still there is always a demand for skilled resources.
It is a challenging task to ensure that delivery teams are adequately and efficiently resourced.
While it is not cost-effective to remain over-resourced, under-resourcing would lead to poor service delivery.
We must be able to allocate the resources available in the most efficient way possible, always bearing in mind that we have to achieve the delivery goals.
Resource optimization targets to match the available human resources with the needs of the support team in order to meet the delivery requirements.
Optimization consists in achieving desired results with minimum usage of the resources.

6. Design…
While the delivery team has resources allocated through all the shifts, they do face situations of resource shortage when they cannot resolve the incidents within time.
On the other hand, there could be a scenario with too many resources sitting idle with a sparse inflow of incidents.
The delivery team requires an automation to ensure resource deployment to be commensurate with the work inflow.
During the workshops with the Delivery Managers, subject matter experts and system analysts, it became evident that the incident inflow follows a trend over the course of the day.
It was also apparent that several important activities like database maintenance, server patching, reporting and financial processing are carried out on specific days of the week.
Thus, the analysis needs to be carried out over the 'time of the day' and the 'day of the week'.

7. Design…
Having defined the two parameters over which we would analyze the behavior, the next step was to identify the attributes to be analyzed.
It was realized that the solution comprises of two components - workload analysis and resource analysis.
It was planned to develop two heat maps - one for the workflow and one for the resource.
These two heat maps can then be compared. For an optimal resource allocation, the heat maps for workload and resourcing should be identical.
The resourcing would then be tweaked around till the resource heat map resembles the workload heat map. The framework for the tool was developed after multiple iterations.

8. Design…
The incident report is pasted onto an excel sheet. We have added two columns for "Day of the week" and the "Time". Necessary formulae were plugged in to calculate these fields automatically from the “Reported Date” field, which denotes the timestamp of an incident. The incident sheet looks as under:

9. Design…
The "Time of the day" (on a 24 hour scale) and "Day of the Week" form the two axis of this table, with the number of incidents being totaled up for a particular Time-Day combination. The Heatmap uses Red color to depict high values and Green to depict Low ones, with Yellow being the medium ones. The Heatmap looks like follows:

10. Calculations…
The tool converts the shift timings from IST to EST and counts the number of weekdays in the. The number of resources available each hour are computed and plotted on a pivot table. The Pivot Table depicts the average number of resources available on each Time-Day combination. The Resource heat map looks like this:

11. Interpretation…
When we compare the workload heatmap and resource heatmap, we observe as under:
High workload on Sunday 08 to 10 hrs.
Peak load on Thu - 09 and Sat 12 hrs.
Generally, there is High load 08 to 10 hrs. and low load from 22 to 03 hrs.
High resourcing from 8 to 11 hrs. with low resourcing from 20 to 02 hrs.
In order to optimize the resource allocation, we proposed the following actions (based on comparing the two heat maps):
Increase Resource on Sun 08 and 09 hrs., Sat 12 hrs.
Increase Resource from 22 to 01 hrs.
Reduce resources from 04 to 07 hrs., 11 to 16 hrs.

12. Conclusion…
The automation tool was deployed successfully and has demonstrated results in resource optimization. We analyzed data over a three month period. Heatmaps were created for workload and resourcing. These heatmaps were compared and the rota was tweaked around to have optimal resource allocation. A heatmap for the revised rota was then generated and compared with the workload heatmap. Post revision, the workload and resource heatmaps showed increased similarity, indicating an optimal resource utilization. The tool has been in operations since six months and is being used effectively.

13. Reusable Concept…
This is a best example of TIME MANAGEMENT process as per the PMI Methodology.
This help us to create reusable tool across industry and support skills.
This can be further altered based on industry need outside IT industry.
We can consider this as one of the mitigation on complexity and overcoming challenge in resource management.