One of the values that we bring is that we can help our clients to decide what scope to cut by providing them with a framework that links quantifiable business objectives to specific features. We create an objective chain to do this and it helps to spotlight features that don’t feed into the core business purpose. Typically our stakeholders are able to cut a minimum of 10% of features and as much as 90% while achieving their objectives.

What we are finding though is that it is sometimes a challenge because the features that are good for the company are not necessarily good at making the lives of the people using the software easier or better.

We have recently run into a case (I’m changing the dollar values and the features for confidentiality reasons) where the business side of our client had identified $50 million in potential savings each year in an area of the business related to giving discounts. The issue was that the discounts were being calculated manually and there was the serious possibility that customers were claiming millions in discounts twice. The discounting system and rules are very complex with overlapping effectivity dates, products, regions and discount rules.

The business was confident in the $50 million number based on industry studies that showed that typical companies were giving away 5% more than they needed to in improperly calculated discounts. However, no one could identify specific types of problems that might be leading to double payment. We did a little research and analysis and decided that the biggest risk area was multiple overlapping discounts and so made that the focus.

There were several issues that came up. The most critical was that the users of the system and the business team while acknowledging the problem with overlapping discount agreements, were basing their decisions on the efficiency of the team. The calculating team is an offshore team with 8 people focused on this portion of the process. The company had done a study to show that full automation could reduce headcount from 8 to 2 people.

However our view was that the savings associatied with reducing headcount from 8 to 2 people was so minimal that it wasn’t worth the effort in the beginning when we were faced with such a large amount in overpayments. Instead we felt that focusing on the features that would automate detection of overlapping agreements were absolutely critical. Deploying those features as quickly as possible was paramount because of the massive revenue leak associated with the problem. Leaving a majority of the process manual would actually be ok if the system had the ability to determine when multiple discounts were being applied to the same purchase and thus eliminate the lost dollars.

It turns out that the business simply didn’t want to fund the project unless their work was decreased, even though in the overall scheme the cost savings was minimal. In the end they approved funding for a first phase that has full automation but does not actually focus on detection of the business case driving error conditions. The detection of the error conditions will come in later phases, so ultimately they will get the business value.

We see this often where at the level of individual features, the subject matter experts have mandatory features that will make their life easier but don’t necessarily contribute to the business case for the project. These features create a death by a thousand cuts situation. Our methodology can identify the “unnecessary” features, but ultimately it is up to the client to decide if the business case is really the highest priority.