The implication of product variety goes beyond to product settings and beyond simple set up times. Product variety will also impact the distribution system of all operations. The more we will segment demand into smaller and smaller segments the harder will it be to accurately predict the amount. Consider the following example; how many shirts in blue of size L was a gap here in Philadelphia sell tomorrow morning. One. Two, three, four, or five. I'd be surprised if we would get this forecast right, even within 50 to 100%. Now ask yourself, how many shirts across all colors, sizes. Stores will pick up sales in all of the next quarter. Probably we can get that forecast within ten or twenty% of the real number. The reason for that is the more we added it to forecast, we added uncertainty, and the uncertainty starts behaving following the laws of statistics, then it becomes easier to plan. This will be the focus of this session. To make distribution decisions, we typically need a forecast of demand. The problem with demand forecast is, they're not always right. We face what is called demand uncertainty. When we describe demand uncertainty, we think of demand as a distribution, drawn from some underlying distribution. Let's think of a running shoe company. The running shoe company has two models. Model one and model two. It makes forecasts for model one and model two but thinking about the mean or the expected amount of shoes that they going to sell and it's just common in operations and statistics to use a Greek symbol Mu to capture this and the standard deviation of that demand which we're gonna call sigma. Now we might think about sigma as the amount of uncertainty that the firm faces. However, sigma or loan the standard deviation of loan the standard deviation of loan is not a good proxy for the amount of variability or uncertainty in demand. A thousand running shoes standard deviation, is it a big number or small number? That really depends on the mu on the mean. If I'm having a 1,000 standard deviation for an expected demand of 2,000, we would call this probably a lot of statistical variation. However, if it's a 1,000 standard deviation for a million shoes, that would be relatively little. With this in mind, we define the coefficient of variation, also CV, coefficient of variation as a ratio between the standard deviation. And, the mean. Now consider a competitor, of our running shoe business. It has some how managed to combine shoes one and two in to one model. Think of this a second, shoes for men and shoes for female runners. Where as this company has like just one common shoe for everybody. Let's assume for sake of argument that the demand for the female running shoes and the demand for the male version are independent of each other. Moreover let's assume that the market sizes are roughly similar and the market uncertainty is also roughly similar. So, in other words the Mu1 is equal to the Mu2 and the Mu, the Sigma one is equal to the Sigma two. Now, what's gonna be the demand for company two over on the right? The expectation is simply gonna be mu one + mu two which is = two, assuming that they are the same. Which is = to two mu. How about the standard deviation. To find the standard deviation of the combined demand, we have to look at the square root of sigma one squared plus sigma two squared plus two times the covariance between demand one. And demand two. We assumed independence, and so this fellow here is gonna be equals to zero. And we also assumed that sigma one is equals to sigma two, which leaves us here with the square root of two times, sigma one or sigma two squared. I can simplify this and write, this is the square root of two, times sigma. Now, how about a coefficient of variation? Well, again, I have already computed now the standard deviation. And so the standard deviation / the mean, which is two mu is gonna be = to one over the square root of two sigma / mu. So you notice that the firm here to the right is facing a lower variability of demand, then the firm here on the left. So we see that by combining demands I'm able to reduce the demand variability as measured by the co efficient of variation or put differently is in combine and demand the standard deviation by demand goes up slow than the underlying mean. This effect is called demand pooling. Pooling demand or aggregating demand is a way for others to reduce uncertainty. That would make it much easier for us to get the right amount of orders in the right place. Notice that pooling does not always require independence. It works on nicely mathematically if the two demands are independent. But even if there's a correlation between the demand of product one and product two pooling still offers tremendous benefits. In this session, we went from the left to the right, and asked ourselves what would happen if I could combine the two products. Now just put this argument on its head. Ask yourself, what would happen if I'm a company offering one running shoe and I'm thinking about customizing it now and offer more variety, offer a product for the male runners and to female runners. You'll notice that as we're fragmenting the demand, so if pooling goes this way. Here we are fragmenting demand. As we're fragmenting demand, and increasing the amount of demand on certainty. Now let's go back to our comparison of McDonalds and Subway that we started in the module on Process and Analysis. We said that McDonalds followed some make-to-stock strategy. Make to stop means that they make the burgers before having the orders. In contrast we said that Subway produces made to order, why then. Let's think about this. At Mcdonalds, the choice of burgers is limited You can have the cheeseburger. You can have the hamburger, the big mac, but you cannot have the sandwich customized your way. This limited set of offering keeps the demand variability relatively low and let's McDonald's come up with reasonably good forecast of demand. At Subway and subway customization strategy this does not work. There's so many versions in which you can have your Subway sandwich made that it would be impossible for Subway to hold one in inventory for every possible offering. Notice that we observe the same. Two strategies in the computer industry. We have the dell model that is basically playing the subway strategy. We are taking the orders of customers and through the web site and makes the computer to order. Apple on the other hand plays the McDonald's strategy. They offer a handful of variants. These are so popular that customization is typically not necessary, that allows them to reduce demand variability and get some edge to supply, between supply and demand reasonably correct. In this session, I introduce the concept of demand pooling. By pooling the demand across multiple items in a product line or multiple locations in a distribution network, I can decrease the demand uncertainty. In other words, I can tame the uncertain demand. This is a direct consequence of the laws of statistics. By reducing the demand to uncertainty, I also reduce the supply/demand mismatches. I have fewer customers that are disappointed because they couldn't get the shirt and size and model that they wanted. And vice versa, I have fewer shirts that nobody wanted to buy. Pulling is probably one of the most powerful inside in concept in operation management. We will see it again and again in the remainder of this course.