Question

Senior Management of a large organization is deciding on a corporate estimation and commitment strategy for engineering. Select the most fitting option:

1. Establish unrealistic and broad expectations so to motivate engineers going an extra mile in an attempt to reach these apparently distant goals. A “reach for the stars” approach.

2. Under Promise and Over Deliver. Strategy assuming that customers appreciate pleasant surprises.

3. Have Product Management provide market requirements and then pad them with a consistent buffer of 27.5 percent.

4. Request from SMEs (Subject Matter Experts) who are responsible for building the product, to decompose each sub-system into affected architectural components and then match them with requirements with the goal of deriving an estimate for each change based on previous experience.

Answer 1

a)NO. Setting up unrealistic expectations will not work as it doesn't guarantee that deadlines will be met. It will also create artificial pressure on employees and may lead to their burnouts, the employees might even leave jobs due to that.

b)NO. It might be not an option as under promising is nothing but giving false information to the clients which can have repercussions in the future. The competition can also take advantage of this by promising better timelines for the clients. Over time, the clients will also come to know about this strategy.

c) NO. The consistent buffer of 27.5 percent will not work always. The projects with large timelines like a year will lead to unnecessary long timelines which can, in turn, affect the productivity and the budget.

d)YES. This approach will work because this approach looks into the modular approach instead of the monolithic estimation. The decomposition and looking into the subcomponents help in coming up with better estimation for the overall task. Without using the sum of subcomponents if we estimate directly the whole task, it won't give good results and might have large error value. Also using the past experience and leveraging the experience of SMEs will be good at doing precise estimation.