P3 Adaptive的动态

Transcript

Through this so here's an example of something I see very often so here at Axiom Dynamics, we make 3 products. We have our advanced machine. It's one of the most advanced machines you've ever seen that does amazing things. And sometimes people need our basic widgets to support our advanced machines and if you've got more than one advanced machine, you're going to need our smart sensors so the advanced machines can talk to one another. And so here we see we can see our historical sales. We can see our open orders by our. Our sales regions, we can see what's being forecasted, we can see our production schedule, we can see what's on hand and on order. And yes, it's all here and it's at my fingertips, but it's not, this isn't integrated in terms of how I'm presenting the analytics to the end user. I can click on regions and slice this data down, but it's not giving me any clarity as to the questions I laid out earlier. So let's walk through three simplified concepts that hopefully inspire some thinking amongst the group. Understanding if a demand forecast makes sense is a problem I've been fascinated with for a long time, and what I always try and ask customers is do we have a way to try and calibrate this forecast? If all were relying on is the forecast itself as it's come in from from the team, we're probably missing an opportunity to prove the analytics. Alright, so in this example. What we're looking at is we understand in our data. The accumulation of orders over time. So what we're seeing on the left here is the the orders that we're going to deliver in January is represented by the this top dark blue line. And we can see kind of the pace at which those orders come in and the pace at which the orders for February come in. So on and so forth and so on the right we see the forecast that's been provided by the sales team. But then what we can do in Power BI is we can say, OK, let's augment that forecast. With what we're actually seeing in our actual data, how fast our orders coming in, what's the volume? And so we can see things like in February, the pace is, you know, maybe slower than usual. And that's why we might project it's going to be actually down from the forecast or we can see April might be pacing much ahead of schedule and we're going to see we might project to finish ahead of schedule. And so of course we're able to run these calculations in the background to create the visualization on the right. Right. And so this just provides us a tool to understand, hey, is what we're being told by our forecast, does that jive with the data, something we talked about at P3A lot is does the story that we're hearing doesn't match up with the data that we're seeing? And so this is 1 technique that we can use to try and affect that. And of course, we can look at this by our, our three different finished goods. We can look at it by different regions, everything we'd expect from Power BI. So that's the first step I always want to try and consider is. Is there a way for me to calibrate this forecast and see if there's a way that I can rationalize if it does or doesn't make sense? So this is 11 technique we can apply to that idea. The second thing I want to understand is given my demand or given my projected demand. Can I meet that with my current production plan?