Sample APO Dashboards

Overview

This tab represents current performance of 6 selected "key" KPIs, as well as average performance over time for the past 3 months. This is a division-level look at data from many projects, and can be drilled into by levels of the organization. Results are a combination of operational data from external systems and risk associated with contextual data from surveys.
*Some information in this slideshow has been blurred out for privacy reasons.

Score Card

This scorecard represents a portfolio view of several projects, with focus on several selected KPIs. The portfolio can be reorganized to sort by risk associated with any of the available KPIs. This solution is delivered at a 100-150 person IT shop with a nearly even split between on-shore and off-shore resources.

Project

This screen represents a slightly more detailed view of project performance, including performance trends over time by reporting period (in this case, the customer assesses teams twice a month).

People

At a more detailed level, staff count "swells" are shown over time to identify changes in projects with high resource consumption. Specific "key" metrics are also teased out as significant criteria to watch over time - any downward trending results in warnings.

Satisfaction

Satisfaction is reported as a purely survey-based representation of data. Summary results of satisfaction on 6 criteria are reported, with the ability to drill down by role, portfolio, organization, or project. Results are reported over time as well as current status.

Support

This implementation is tracking ticket closure by month - reporting on overall resolution rates, and total open/closed within the month. In this example, the customer wants to show projects in the list even if they do not have any tickets to report for the month (normal behavior would be to show only the projects with tickets in the latest month displays). Totals are then rolled up to report over time, showing incidents by project, as well as overall for the entire organization. In this example, data is completely fed by an external system.

Predictors

Based on research by industry experts like Vic Basili, Leon Kappelman and Mary Sumner, four key measures were identified as important influencers into a projects likelihood to complete successfully. The predictive measures are:

RPL

The Rework Prevention Level (RPL) is an indicator of a project (or collection of projects) ability to avoid rework. Research shows that 40% of project cost is due to rework and the RPL measure is help identify projects that might be in risk of these rising project costs.

SMART

The SMART measure assess project tasks against the following five criteria:

1. Is it specific?
2. Is it measurable?
3. Is it achievable?
4. Is it relevant?
5. Is it timely?

If project tasks don't meet the above criteria the project may see problems around quality, schedule and cost - all areas that lead to project failure.

PAL

The Process Adherence Level (PAL) is an overall score a project (or collection of projects) health score with regard to process adherence. Research shows that projects that do not follow standardized processes are likely to see problem down the road.

SMPL

The level of participation by senior management in a project is a strong indicator of the project's likelihood to succeed. This measure seeks to gauge that level of participation from both a management and project team perspective.