We provide a lot of recommendations to the client in the course of an IOE analysis. Particularly in our Scenario Planning process, we propose improvements to the client’s work environment and compare the merits and trade-offs of our ideas. Hey, if you go with an all-office layout, your square footage will be 250,000 sf, and at the other extreme, an all-workstation layout will be 30% less than that. But we also weigh in on the qualitative differences between options, not just the numbers. Your employees will have better isolation from distraction in offices, but they’ll interact and collaborate more easily in workstations. But qualitative comparisons are more subjective than cold, hard numbers, and can’t be mixed together in a spreadsheet to get a single answer from a bunch of different factors.
When you start taking a lot of qualitative variables into consideration, potential pitfalls crop up:
Are we remembering to consider all the different factors that define a quality?
What are the important qualities that we want to consider?
Will I weigh a given factor the same today as I do tomorrow? I don’t want my mood to impact the value I assign to something . . .
How do twenty different subjective factors impact each other in determining a qualitative value? That’s complicated. What about thirty or forty? Too complicated for a person to blend together and estimate!
We wanted to solve these problems and add qualitative analysis to our Scenario Planning process. We even wanted to use computing power to help us find the best answers among the nearly infinite number of possible scenarios. So, we created an entire new approach in Excel (yep, using numbers) to apply consistent, repeatable qualitative scores to the same potential work environments that we have long compared in terms of square feet and lease costs.
We were telling our clients about qualities like focused work, collaboration, flexibility, and others. These Attributes and the comparisons between them were the outputs we wanted to produce. The many Features – spatial ones like having a lot of natural light, but also cultural and technical ones like flexible hours and good communication tools – are the inputs we use to help us make decisions about the attributes. Lastly, Workspace Percentages – the ratio of offices to workstations to remote workers – critically alter the qualitative aspects of a workplace as they shift from a majority of one to another.
We made a list of all the Features we wanted to consider, all the Attributes we wanted to score, and had several experienced OPXers come to a consensus on the relative merits of each Feature to for each Attribute. Then we took all that data and the various Workspace Percentages we were already including in each of our Scenario Planning exercises, and built a fairly sophisticated rules engine to compute and chart the qualitative Attribute values, scoring each from 0 to 10.
When we were finished, the resulting scenario analyzer immediately became a permanent part of our Scenario Planning process. We found we were able to provide valuable additional decision support to our clients in their quests. to find the right future environment for their organizations. In fact, we found that the complex synthesis of hundreds of data points and thousands of automatically generated scenarios was so easy to see the value of that we created a web-based version of the scenario analyzer and made all of this analytical expertise and power available to end users as ESP (kind of an acronym for Electronic Scenario Planning, but really about seeing the future). ESP is available free of charge at opxesp.com.