As a leader and innovator in the IT industry, Masood has leveraged his mastery of business execution to oversee multi-million dollar initiatives to success.
One of the most frustrating moments of the human experience is waiting for the cable guy to show up. In most cases, rather than providing a specific appointment time, customers are given a range of 4-8 hours for the technician to arrive. Once on site, there is no guarantee all the equipment or parts necessary to complete the task will be available. So, what's missing link? In effect, the cable guy is not adequately managing his schedule's uncertain activity times!
Schedule is one of the three pillars of project management, along with its cohorts of cost and scope. Typically, in a linear project, activities happen in sequence. Task A is prerequisite for task B, task B is prerequisite for task C. . . and so on, and so forth. In this scenario, project tasks are treated much like an assembly line where the deliverable, or the result from a task, contributes to methodically building a final product or arriving at a desired outcome. When the schedule of a project is well-understood with fixed activity time estimates, or durations, it said to be deterministic,
This approach works well in projects that have both well-defined and well-understood activities. Life, however, is not so straightforward. Often, when there is technology or some sort of innovation involved, the work packages (or groups of similar tasks) are not so clear cut. For instance, what does the project schedule look like for the creation of a new social media product or smart phone application?
Two key strategies can be employed to produce higher quality project schedules when there is uncertainty around activity times:
- Identify work packages with unknown or uncertain activity times and break them down into a series of smaller work packages.
- Apply probabilistic scheduling methods to highly variable work packages.
Identifying and Breaking Down Work
The goal of work package decomposition is to maximize the amount of known, or well-understood, sets of work.
Consider the development of a new social media product for pets called BARK. The new social media empire would consist of a website, smartphone application, and devices attached to pets that live stream video. Investors like the concept, but they want to know when the team would be ready to go-live with a beta version. They have heard rumors of a competing product, scheduled for release within the next 12 months. The investors ask, 'Can BARK go-live in half that time?'
Breaking down the components of BARK is fairly straight-forward. A few things the product will need include a user management system to sign-up new accounts, a database to track user actions, and a simple user interface with a catchy logo. All of the common tasks can be easily identified and estimated based on experience or other industry references.
The major unknown, challenging the success of the BARK, is how the live-streaming of animals being cute will actually be executed. There is no existing wearable device that is specifically designed for dogs or cats to record video - let alone live-stream content.
Constructing the pet wearable device should be treated as a work package. Drilling down into it further showed the team that two-thirds of the development would involve bringing together known technologies familiar to BARK engineers, such as incorporating a mobile phone camera and WiFi communications. The key element that would differentiate the device would be centered around advanced stabilization.
Applying Probabilistic Scheduling
Given that the BARK team does not have the knowledge or background to develop an advanced video stabilization chip within the investor's timeline, they are left with only a couple of options: hire new staff with the relevant capabilities or bring in consultants. Consultants are typically hired to direct, or support the direction of new capabilities within a company. For the sake of the example, let's imagine that BARK was able to hire a NASA rocket scientist who developed video stabilization technology for space shuttle flights.
For the rocket scientist, creating the new video stabilization technology is doable and would be an adaptation of existing technologies. The initial estimate provided by the expert is 2 weeks for the new component. Given that there wasn't a lot of confidence expressed in the estimate, the BARK project manager starts asking challenge questions:
- How confident are you in the estimate?
- What are the risks that can impact the estimate?
- Will the component be production-ready within the estimated duration?
- Is testing included in the estimate?
A good project manager will offer the challenge questions in a manner that is not critical, but more conversational and show a genuine interest and commitment to success.
In the BARK example, it becomes evident that the 2-week estimate was very optimistic. Everything had to go perfectly to meet this timeline. A worst case scenario, where all risks materialize, would push component delivery to eight weeks. Most likely, the timeline looks more like four weeks. Now that the range of time required for building the component is well understood, it needs to be reflected in the project schedule.
Generally accepted project management practice uses the expected time formula as the basis for the schedule estimate. In this example the result of the formula is the sum of the optimistic time, 4 times the most likely time, and the pessimistic time all divided by six.
For the BARK team, this equated to (2 + 4 (4) + 8) / 6 = 4.33. Accordingly, the BARK schedule should reflect 4.33 weeks for completion of the advanced video stabilization technology.
Linear project schedules can be cookie cutter, leveraging experience from past project schedules. Estimating projects with uncertain activity times involves a two-step process to be applied to all work packages. First, breakdown large work packages into smaller and more well-understood work packages. This helps maximize the quantity of knowns. Second, apply probabilistic scheduling methods to help quantify the level of uncertainty and improve decision-making.
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