This is the 3rd post in our 5 part series on Resource Management. Post #1 covered Allocation. Post #2 was about using Effort vs Duration. Now in this post we'll discuss Collecting Data to make informed Resource Management decisions. When I reflect on our client engagements, there are a few common issues which come to mind. One is the lack of data collection which leads to inconsistent decision making.
There is a movement right now with Machine Learning and AI. Why are they becoming popular and trending to be the way we make decisions across industries? Let's take a look at the definition of Machine Learning. Paraphrasing from the many definitions which can be found out there, Machine Learning is computers "learning" from data analysis.
The premise is that through data, machines will learn and therefore make decisions consistently and at a higher success rate than a human who might be using intuition or emotion to factor into the decision-making process.
Resource Management is an extremely challenging task for many organizations. There is a constant struggle with annual planning, unexpected requests, emergency requests, and the over-promising Sales team! We've talked about these challenges with our clients and have captured some valuable insights.
A large percentage of clients follow a "just in time" mindset, shifting resources to the project/effort with the closest deadline. Others place the best resources on the highest profile customers and use the less experienced resources on the remaining work. Others don't even have a method to the madness and just assign resources in a seemingly random selection process.
We always ask, "Can you show us the data which supports your decision making process?" The typical response is, "We don't have data, that's just the way we've always done it." So, how is that working for you? Are you finding your customers are satisfied? Are your employees frustrated? Are you regularly missing deadlines and missing out on hitting profit forecasts?
We get to work with our clients building a process to collect data. It doesn't have to be sophisticated or an expensive software purchase. Sometimes we even find they are already collecting the data but didn't even know it. They may have the data in an ERP, Payroll, PM or other existing system.
Once a data collection process is in place we then analyze the data looking for learnings. In one engagement we found projects with the "best resources" were more often delivered late compared to projects staffed with less experienced resources. What did the data tell us? The "best resources" were always over allocated. The thinking was they are the best and can handle the extra work. But, the data told a different story. The projects with less experienced resources were more frequently delivered on time and when we looked at the resource allocations we discovered they never over allocated those resources.
Taking our cue from the data, we scaled back the allocations on the "best resources" and as expected the project success rates started to improve. There were also some additional unexpected benefits. Team morale improved and customer satisfaction increased.
Gut instinct was telling the client, the best resources can handle more work. Makes sense, right? They are the best resources they can handle the extra workload and still deliver. When we examined the data and removed intuition from the equation we found a different answer.
So how are you making your Resource Management decisions? Are you using data to make informed decisions or a you using "years of experience", "gut instinct", "it's the way we've always done it" and all the other data-less processes???