Welcome everyone and thank you so much for being here. Appreciate introduction. My name is John Miller and I'm representing along with the other presenters are you and me and so our project is about and I'd just like to give a shout out to you hearing and getting started with the project goal is to reduce cost in there in the Transportation Department they're facing is that less than two percent of their shipments are currently a part of trying to identify every shipment we could possibly combine and therefore save them money so that the way to approach this problem is through energy programs. We have hundred different groups going into that later but groups. We've been able to identify almost fifteen thousand will generate over nine billion dollars So this is a graphic that the United States and there are in transportation. It's very expensive to distribute Now hundred vendors actually from the vendors and so. The problems they're facing in the current state. We have two different vendors shipping shipments to the same two different routes. So we're trying to combine the shipment to the origin of shipment and hearing that second shipment to the same route. Now this is a great way to start saving the money but it doesn't quite fully maximize the potential. So we've applied this same logic to it so very similarly coming from vendors going to the same and we're trying to figure out each shipment. So how are we getting this uprising started with individual shipments. We've created and. Shipments are created and the data we have over two hundred twenty thousand shipments from August twenty eighth. Well number of different. The first and most important is the shipment example here. Boston Massachusetts. When a truck driver gets over there in Los Angeles California because they meet with their vendors and so for this shipment here on average about three hundred dollars to make it back to the bridge this is very different from from where is it actually. Thank you. Three very. By ship. I'm James which was tough to go in excise and the value one and zero is now and some are logical as for exhale. It should be scale and can you can buy it gives becomes one of the why's and so that's what it did yes and S.K.Y. and you can check the function of the savings that are generated through these two stocks restocking So we check to see if the savings is positive from from combining shipments and if the goal is to maximize the total savings overall process just so there's some additional consideration you take into account here don't work the process so constrained one into their preferred to time constraints for a truck driver. So it can only be on a road for the next eleven hours on a certain day so that includes the time it takes for them to each of us need to go pick them up to have them back to the R.T.C. as well. Brakes they may take throughout the day and so that's constrained eleven hours are most important is greatest is number three there. So what that fixed to be only used one time. So every shipment can be hard to stop thinking part of the restock stuff they go and we also take the sequencing into production. It can be the first up and we're going to the second stuff in the freezer and so on and so this is strange sure is that. We don't use a shipment for a model to stop and free stuff it goes to make another example for you here unless you can see three shipments from the Pennsylvania area that were originally think of individually So three separate trucks one fifth of the shipments and lower than that the R.T.C. of Dallas Texas has a total distance of total cost associated with model found into a restavec So now it's sufficient for one for. To go out and you can see the distance. What used to be. And there's a much lower total So there's a saying that we generated from this. So from there we took you stock in free stuff browse and found vendor groups that we created which is the desire that is there in each group these vendors shit together frequently. So we look at the stuff if we can see which vendors are associated with them and so if throughout the course of the year vendor ship together by actions from these vendors together it makes sense to put them in a store and that's an example of start five vendors and they were there. And so confusing. This is if they see shipments from vendors in specific group shipping in certain opportunities to provide shipments into the stuff it goes and save significant money and overseas calls for a graph of the minimum and maximum reduction in overall shipments as a result of our optimization model excluding those shipments are already maxed out by weight and body constraints. We were able to take approximately seven thousand three hundred shipments teach R.D.C. and reduce that sent this reduction which can be as can be attributed to the formation of two and three stop because so on average we created six hundred twenty two stop the cops and two hundred nine three stop pick ups for each of our eighteen parties in the same graph. We see the minimum and maximum savings as a result of our stop formations on average are three hundred fifty thousand dollars to stop shipping it does one hundred sixty thousand dollars. For each of the eighteen R. he sees this five hundred ten thousand dollar savings compounds to about nine billion dollars across the entire dataset upon consolidating shipments. We took the vendors that were matched created two times per year to create groups of three four and five vendors. I would be created. Fifty two groups of three vendors thirteen groups of four vendors and three groups of five vendors for each of our eighteen R D C's amounting to one thousand two hundred vendors or so in order to navigate this large number of interviews that we created we developed this time so that broke for as uncomfortable to part time apart so adding a specific state vendor ID and R.T.C. the vendor chairs and sure bullets are selected vendors associated with as well as the vendor groups are selected vendors are part of our display. So for example in the first room you see there are some of the vendor has been matched with two other vendors twelve times over the course of the calendar year for our history five zero two three generating a total savings of roughly ten thousand dollars on the right side we see that are so if you vendor is found. In one of five vendors three groups of four vendors and two groups of three that was just to reiterate in any specific manner. Each vendor has been matched with any other vendor at least twice over the course of our hundred or so in conclusion. And yes it presented to us contained less than two percent of shipments as most opportunities through the formation of our editor program optimization model we were not only able to generate one thousand two hundred thirty three vendor groups but also bump up the number of Muslims stop shipments to fourteen percent through this process we are saving the Home Depot or possibly nine billion dollars in transportation and with that I'd like to open up for any questions. I've. Dangers of the use of the are you going to say You're saying there's a ghost or you're saving one if you get everything you want or are you going to let me stop while you're here that's your question. We're seeing chose not to say that and I just time constraints but the reason why just you know what people actually really want to see but realistically it becomes very feasible for me to do some of the constraints like so many hours of driving rain that's what we consider so we should really think there will be something that's a take place bettors or a switch or if that.