The Holy Grail?

I've spent the past few weeks tweaking a new tool to rate our suppliers on social performance. It's never going to be "perfect," but it's at a point where I think it's workable.

Ever since before I started working in CSR, I'd heard social performance metrics described as "the holy grail." Professors grandly alluded to the possibilities of tying "hard social metrics" to financial performance and building a stronger business case for CSR. Of course, no one suggested that social metrics would demonstrate that CSR does not have business value. That outcome would simply mean the metrics were "wrong."

In any case, I don't know whether or not we'll ever get to the social metrics that hard-core CSR professionals and academics seek. What I do know, however, is that people in other parts of my company need an easy way to understand CSR performance and I need to develop something that gives us directional insight into social performance. As it stands, my metrics system doesn't provide an absolute grade. The important part is that it serves as a springboard for discussion.

And my point in introducing these metrics into our company vocabulary isn't to help our contract factories strive for perfection. When it comes to social performance, it's about continuous improvement and metrics can help describe relative performance between entities or over time. I just hope my business partners understand that I want to provide these metrics in a certain spirit - to cultivate ongoing feedback, dialogue and improvement.

Building the Fundamentals

When I was interviewing for my new position, I was informed that my role would be to build a program from scratch. So, I expected to think through some of the company's risks, review processes and work toward making recommendations.

Little did I realize that I'd have to take a few steps back!

In my first week-and-a-half of work, I've been working with our Sourcing and Supply Chain teams to build very basic processes such as onboarding a new supplier and determining a scorecard of metrics to rate supplier performance. It's been a very steep learning curve and I've had to not only learn the company, but I've had to familiarize myself with the industry and the intricacies of a global supply chain.

Even though I'm completely out of my element and trying desperately to understand our quality standards, purchase order processes, product capabilities testing, costing and production processes, I've realized this gives me a very unique opportunity. It's been frustrating at times, but I actually have a seat at the table during these discussions and I'm responsible for ensuring that social and environmental factors are included in processes and measurements.

From my past experience and from speaking with other CSR practitioners, I think it's typical for CSR to be "layered" onto existing processes and systems. We focus on finding ways to integrate CSR into business processes and finding opportunities to influence process change. Here, we're building processes and systems together, incorporating CSR considerations from the outset!

While it's forcing me way out of my comfort zone and while we're moving at lightning speed (much faster than I've had to work in a long time), I'm very excited at the possible outcomes and having a voice at the very beginning.

Data Dependency

Most CSR practitioners, I believe, continue on a quest for data. We all want data to support our theses that good corporate citizenship adds value to the company. Value can come in the form of cost savings, enhanced reputation, employee engagement or any number of other shapes and sizes.

The tricky part about gathering data to support CSR is that there are very few defined ways to measure impact and value. And as such, there are very few methods or systems to gather meaningful CSR data within companies. Much of my job is spent trying to take the company's existing data and either performing some calculations to arrive at CSR impact metrics or finding proxies for the value I'm trying to describe.

Since returning to work from the holiday break, I've been working a lot with data for two separate projects. One has to do with calculating environmental impacts of a particular process and the other relates to measuring consumption of a particular resource across the enterprise.

For the first project, I've been trying to get at some raw data from other Business Units. In theory, this should be rather simple. I pick up the phone, call someone in another department, explain the project and describe my need. They, in turn, either take some time to "run some numbers" and send me back the relevant information or they refer me to someone else. Usually, it's the latter, so it typically takes a few days (yes, days!) to track down the right person. More often than not, the data that returns isn't quite what I'm looking for, so we work together until we arrive at something meaningful.

Well, this process has been playing out for nearly two months with a particular business partner and his unresponsiveness has resulted in me not being able to deliver on some promised metrics. I hated to throw him under the bus, but I had to bring in a VP to finally get him to send me the data he'd promised back in November. Approximately an hour later, I delivered the metrics that were expected of me.

In the second case, I recently received a spreadsheet with spend data from every cost center in the company (thousands) and I've been diligently pouring through it, figuring out which line item is relevant and which is outside the scope of this project. Essentially, I'm taking the spend data, translating it into units purchased and figuring out the resources necessary to create those units, resulting in the environmental impact of our consumption. The problem here is that I have to make sense of a spreadsheet that isn't necessarily meant for this purpose and is riddled with codes, acronyms and sequences of numbers I don't understand. It's time-consuming work and rather tedious, but hopefully will tell a good CSR story in due time.

With both examples, you see how CSR practitioners must rely on others to supply information before we can produce results. For my business partners, they can simply run a report through an existing information management system, but I have to spend time doing manual calculations because no system exists (or we can't afford such a system) to extract data in the format we need.