Monday, 2 April 2018

Data Driven Sustainability Improvement


Last week I met Bianca van Walbeek of Klimaatgesprekken together with several of my colleagues to kick off our sustainability initiative. In this initiative, we will explore with Bianca’s help, how we as a person and as company can become more sustainable. Also, we will develop innovative ways to support our customers in achieving a more sustainable way of doing business. Our sustainability initiative is part of our firm wide CSR programme which has as main topics People, Society and Planet. Last year our focus was on Society, which resulted in our participation in theWeekend Academie to help educate children from less prosperous neighbourhoods (see this link for more). This year we’ll focus on Planet. Stating that our ambition is to become more sustainable is easy, turning it into an actionable plan, following it through and achieving measurable impact will not be that easy. It’s however not different from the types of challenges we help our customers solve.  In fact, the approach to becoming more sustainable is similar to the approach to become more cost efficient, more customer centric, more data driven, more digital or more innovative. Let me illustrate.

If you want to improve, you require a baseline and a way to monitor your progress. As Peter Drucker stated:” What gets measured gets improved”. However, as so many things can and get measured these days, the real challenge is to identify what metrics best express the goals you want to achieve. With the right set of metrics and the data to calculate them you can track your progress and get guidance in deciding on your actions. This is also what we did. We started by using descriptive analytics to calculate our current carbon footprint and used it to get insights on what the key drivers of our footprint are. Analysing my own carbon footprint, I found that a major part of it is flying abroad, using the car to attend customer meetings and driving to our office in Amsterdam. Flying is the big contributor, so need to think on how to reduce my impact there. Second is using my car to attend customers meetings and to drive to the office. I found out that driving with a low average speed, for example during rush hours, causes more carbon emissions per km than at medium average speeds. However, high average speeds (>100 km/h) cause the emissions per km to rise again.  This suggests that avoiding rush hours, avoid driving at high speed or taking the train will reduce my footprint. That’s exactly what I’m going to do.


Just having the numbers however is not sufficient to achieve our goals. To engage our colleagues and get their support we need to inform them on what we want to achieve and why. Also we want to keep them updated on how we are doing and what we did with their suggestions. We will accomplish this by using digital visual management support, like iObeya, showing our current performance, actions undertaken, results and expected impact of the actions we plan to take to become more sustainable.  Next to enabling the engagement of our colleagues, iObeya will also allow us to work as a virtual team which will reduce our need to travel and allow us to make efficient use of our time. As you can tell from the above example, one relative simple action, adopting iObeya, can have multiple positive impacts (footprint reduction, travel cost reduction, efficiency increase) on our objectives and will use up some of our available resources (It budget, it-support hours, electricity). Usually you’re not considering just one action but multiple, each with specific impact on your objectives and resources requirements, which will complicate your decision making. Which subset of actions will give the best outcome and utilise our available resources in the best way?

To answer the last question, we need predictive and prescriptive analytics. First, we need to gather data, analyse it, and use predictive analysis to model resource usage and impact of each initiative on our sustainability metrics and how they interact. Next, we will need a prescriptive analytics model to help us choose the best combination of initiatives. The prescriptive analytics model off course uses the predictive models to model the impact and resource usage of each of the individual initiatives. Next, we need to create a learning loop in which we will measure the impact of the initiatives we have chosen to pursue. We will use newly gathered data to calibrate the predictive models and get better estimates for the predicted impact and resource usage, update the prescriptive model wen new initiatives should be considered and use the prescriptive model to re-optimise our set of initiatives to assure that we have chosen the best possible set of initiatives to achieve our goals.

Data driven improvement cycle

The above approach will work for any improvement initiative, yours as well, whether it is improving the sustainability of your organisation, or for example making it more innovative. Key elements of the approach are to make explicit and measurable what counts, share objectives and progress to engage and mobilise your people. Use predictive and prescriptive analytics to find the best set of initiatives given limited resources and make sure to create a learning loop by continuously gathering relevant data on the actual outcomes of your decisions and using it to calibrate the decision support models and your decision process.

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