Big Data

Adaptive Program Management in Challenging Budgetary Times

data analysisBudget cuts are ever-looming; an innovative approach to out-year planning will help limit the impact to your program. Traditional program management approaches yield traditional program management results. This is not meant to be an encouraging statement. No program wants to end up like the Sydney Opera House – 10 years late and 14 times over budget. Most programs experience cost and schedule overruns.

So, why use the same old tired management techniques on your program?

The Traditional Program Management Paradigm

Most program managers recognize the need to create a program schedule, cost estimate and risk register early in the program lifecycle. However, these artifacts end up taking a backseat once execution issues emerge. Fighting “daily fires” to get the program back on track (reactive) is not effective. Adopting a proactive approach will allow the program team to predict what’s coming and avoid the next major obstacle.

A Better Approach: Be Certain That Uncertainty Exists

Events are subject to uncertainty. If a program does not have uncertainty built into cost and schedule estimates, the program will likely experience schedule and cost overruns. Recent advances in data science and predictive analytics now allow program managers and analysts to model their program’s outcome before work even begins.

This approach is as simple as integrating traditional program management artifacts and overlaying them with uncertainty distributions for simulation. In the real world, a five-day task often won’t take exactly five days to perform. It may take three days or five days, sometimes even ten days or more.

A better approach begins by capturing a range of potential outcomes for each schedule anPolaris Blog Imaged cost variable.

These parameters are then run through Monte Carlo simulations, generating thousands of iterations to create a model of the program. Once complete, the program manager can view a realistic assessment of cost/schedule feasibility, isolate the key drivers of cost and schedule growth, and perform rapid what-if and trade-off analysis.

The House Always Wins

Only an estimated 5% of sports bettors profit in the long run. Why? The rules, fees and conditions in place favor the betting institution or “the house.” Vegas odds-makers and sportsbooks forecast outcomes using the Monte Carlo method. In contrast, sports bettors often rely on traditional approaches (e.g., picking favorites and gut instinct) to make their bets. Program managers who rely on subject matter expert opinion and hunches to guide their decisions are no different.

Tying It All Together – From the Casino to Your Program

The future is impossible to predict, but now it’s possible to model outcomes with high degrees of accuracy. What will it take? A willingness to deviate from the traditional, often ineffective program management approaches and embrace techniques that are more effective. If you were to emulate an outcome, which would you pick? The Sydney Opera House, which took 14 years to complete instead of its 4-year plan and cost 14 times more than estimated? Or, “the house,” which is always positioned to win in the long run?

To learn more about how to adopt a bolder program management approach, see Booz Allen’s Polaris solution.

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