In this second module, we're going to discuss supply chain analytics. As we discussed before, this is probably the most important capability that retailers need to develop to navigate successfully the digital transformation in the supply chains and their business as a whole. I would like to start by asking the question on where to start with analytics. This can sound trivial, but it's a very important step to recognize what is the best way to start with analytics can lead to success in the process. If you just dive into analytic without stepping back and thinking about this for a moment, you can set yourself to failure. Let me start by discussing three and promising approaches. Things that based on my experience is not going to work well. The first one is when someone shows up and says, hey, we're here to help, do you have a problem to solve? That can be very nice and full of good intentions. However, it lacks the motivation. This is fundamental to succeed when you embark yourself in an analytic process. You need to have a clear motivation, both from the execution side, but also to detect the value that the analysis that you're going to do is going to bring to the company. The second and promising approach, I call it boil the ocean. This means that you basically has no focus. You are willing to take any analytic project and start working on it without stepping back and asking the question on, is this really needed? Is the investment in effort, resources, and time going to be justified when and if we find an answer? The third one and last and promising approach based on my experience is people that shows up and says, well, three years, $10 million from now, it's going to be great. So the idea that by throwing resources both in time and money is going to lead to a successful outcome. This is not the case. You need to make sure that the project and the analytics endeavor that you are embarking yourself has a clear value to the business. We are not doing analytics in these contexts just for the sake of it, we want to connect the analytic effort to the value to the business. What we need, in other words, is good motivation, focus, and a clear business value. What is the promising approach? Well, to have success in the analytic effort, I want to start at a high level business problem that is tightly defined. I want to tackle a question that is something I will be able to address once I know the answer of my analytic project and then have evident business value and show a business result. In other words, I want to tackle a question that is something I can handle in terms of scope, but it's at the same time allowing me to change something in the business that everybody in the organization agree that is relevant. I don't want to start with a problem that is interesting for the sake of interests because that is something that is not going to drive success in the business. I don't want to tackle a problem that is so broad and so big that even if I learn the answer, I won't be able to implement something. What I need to do is to find the right level of ambition in my analytic effort that will allow me to start, give a first step, and create the momentum in the organization to make everybody agree that this is the right path forward. I want to focus on a project that is tightly defined, have a clear outcome, and it's of clear business impact to everybody around me. When I think about the analytics effort, there are two main characteristics that are relevant to consider. One is the business impact that I'm going to have, and the other one is the complexity. I can think of the different layers of these two axis as ways to be more and more sophisticated in the analytics approach. In terms of the business impact, it is a combination of the individual impact of that analytic effort and the scalability. How many times I'm going to be making these particular decision? How many times I'm going to be able to replicate the analytics effort? On the other axis, I want to make sure that I understand the complexity, what we need in order to handle this project. In terms of information, in terms of data handling, in terms of knowledge. Because there are something that are easier to do than others. As I move along in these two axis, I will be moving from more basic stuff that I will be able to handle without a lot of complexity. But at the same time, the business impact is relatively small. Two more sophisticated stuff that will require a lot of complexity, but the potential upside, an impact to the company is really, really large. This business impact and complexity map is extremely important. Once we have completed the first step of identifying the relevant business problem that is readily addressable. I want to make sure that I placed those potential projects in this map. Once I do that, I will see that there are some of these projects that are extremely complex and the business impact is low. Those are clearly unattractive projects. I probably won't bother to tackle those because the payoff is not enough. At the same time, I can very quickly identify those projects that the complexity is relatively low, but the business impact is high. Those are no-brainers, things I want to start right away. There is no discussion that, that is a good place to start. Once we tack on those, then we need to be able to navigate where most project we learnt. Is that tension between the complexity I want to handle, the complexity I'm able to handle, and the potential business impact? As we are going to see next, that different layer of complexity and business impact can be characterized and evaluated at the different steps. Next, I'm going to introduce a framework that can help you think about analytics and how to evaluate this tension between complexity and business impact.
