 Hello everyone, welcome to this course on supply chain digitization which is offered by IIM Mumbai. This is jointly offered by three of the faculty members, myself, Professor Priyanka Verma along with my colleagues, Professor Sushmita and Professor Deepakrita. If you remember in our last session on supply chain segmentation, we started our discussion about understanding the different supply chain challenges and while searching for the solution to manage these challenges, we found that segmentation is one of the very interesting way of looking into the possible solutions. Going forward, we have explored what are the different ways in which the supply chain segmentation strategies can be implemented and we got enough ways of looking into our supply chain with different views so that we can have proper strategies for managing those particular supply chains. We are going to explore few of those segmentation strategies in detail from this session onwards and some of the analytical way of following these strategies in order to see that what better can be done for taking up the right decisions. The first example which I am going to cover today is about product segmentation and this is a very interesting way of looking into the supply chain in a different method because we will be using some analytical approaches for taking some valid decisions. So as we have already discussed about what is product segmentation where we can classify our products in terms of in different ways. It can be depending on certain product characteristics or it can be depending upon their requirements and so on. So let us see with this example that how we have captured some of the very interesting ways of segmenting our products and this can be employed by any of the industries in deciding their appropriate strategies. So we are going to start with a case on this. Suppose there is a company who is in the business of electronic gadgets and generally it offers a huge variety of electronic gadgets and for all of these SKUs what is being offered by the company they have recorded their weekly sales for a long duration and also they are trying to capture the order variability. So here we are going to use two characteristics to create our product segmentation that is the weekly sales and the order variability. So let us see that how we can use this information to create the segmentation. So something more about the company, the company is already providing different type of SKUs which can include like your smartphones, tablets, smart watches and many other similar products. So we can see that the company is into a business where it is providing large number of varieties of SKUs and again as we can understand from this example this is a very critical challenge because in order to understand that what should be the right supply chain or what should be the right way for managing a particular product is a very critical task. So if you can have certain segmentation strategies for looking into these products the decisions related to it can be much better. So this example continues and it can be seen that the given case is shown for a company which is actually operating in two different cities that is city A and city B and interestingly it is trying to provide the product through both online and retail channels. So we can cover as many number of channels as we have already seen in the previous sessions but in this example we are going to focus only for two cities and for two different channels. Now talking about the weekly sales the company has reported an annual sales revenue of 1 billion dollar which is a overall annual sales but we also have the overall sales information on a weekly basis. So this is annual sales information going forward how to capture the demand variability. We understand that the demand never remains constant there is always a possibility that demand goes up or down or there is a small variability across the demand. So what is the better way of capturing this demand variability? So demand variability as already discussed is actually talking about the fluctuations that you observe in the orders or it can also be the changes in the number and size of the orders that the company receives. So both the fluctuation as well as the change in the number and size of orders together is getting considered as demand variability. But interestingly the demand variability if you are able to measure it will help us in understanding that how your demand for a particular SQ is it unpredictable or is it stable. Such type of decisions can be taken very easily with the help of this particular method. So going forward let us see what further details are given to us about this case. This case is dealing about 20 SQ that is there are 20 SQ which the given company is providing and we have already been given with the data for these 20 SQs. This demand data is on a weekly basis and it is given to us for 2 cities under our planning this is for city A and city B both. So if you look into the data further the given information is available to us for 8 weeks for last 8 weeks we have got the weekly data about the demand and now using this data we have to calculate the demand variability. In order to calculate demand variability very interestingly we use this concept called as coefficient of variation which is a very widely known statistical step for calculating this variability. So let us see what is the formula of coefficient of variation it is a very simple formula which talks about coefficient of standard deviation is given as standard deviation divided by mean that is sigma by mu and talking about the mean the mean is nothing but the sum of whatever quantities that you have received divided by the number of orders and the standard deviation as we all know is given as the square root of x i minus x bar whole square by n whereas we know that x i are the individual orders and x bar is the average of the orders and n is your number of orders. So we can use this information about mean and standard deviation to calculate our coefficient of variation. So many a times there is a confusion that when to use standard deviation or when to use coefficient of variation remember that when we are talking about standard deviation this it measures like how much demand tends to vary around the average. So standard deviation always measures the fluctuation which the demand is having and which is around the average it is in a way if we can say that it is trying to measure the absolute variability of the demand whereas when we talk about COV the COV or the coefficient of variation is very interesting it tries to measure the variability relative to the average demand. So it is in a way it tries to normalize and if we can look into this further it allows you to compare the products with different levels of demand. If you have got 3 product or a 5 product as the number of SKUs that you are being that you are offering so you can see their COVs and even if their demands are different you can compare the variations of the demand using this COV very easily using this coefficient of variation very easily. So that is the major difference between standard deviation and coefficient of variation we will be using these 2 concepts to create or to calculate our demand variability. Now let us look into the data about both the cities. So we have been given with 8 weeks of data and for all these weeks of data which is available to us we have calculated the average of the demand for all the 20 SKUs similarly we have calculated the standard deviations for the same and for every SKU as you can see from here we have divided the standard deviation with the average demands which help us in getting our coefficient of variation. So we can see that for all these 20 SKUs we have calculated their respective average and we have calculated their standard deviation and using the formula of coefficient of variation which is sigma by mu we have calculated the coefficient of variation. So we can see that for some of the SKUs the coefficient of variation is 0.3 some is 0.5 and some is 0.1 and so on. So there is an indication that there is a variability of demand for all of these SKUs. The similarly the same exercise is done for city B as well the question is why we have done it for both the cities separately. This is to understand the pattern of consumption of these 20 SKUs for both the cities and then accordingly if you have planning to have to individually look for these cities the correspondingly you can use this information to drive the common strategy which is applicable to both the cities as per their classification of the products based on the variability. So how this is done let me try to show you with one excel sheet which includes all the data about the 8 week data and in parallel to that the calculations that we have done for calculating the average demand and the standard deviation is also shown to you. So as you can see from this excel sheet this is your raw data and as you can see from here this is the data for the first 8 weeks and it indicates that which SKU is used in which city and what is the volume of that particular SKU. The same exercise is done for last 8 weeks and you can see there is a huge data corresponding to both the cities for last 8 weeks. So we have been given with this information about the available data. Now let us see that how we can use it. So we have to find out the SKU wise what is the average demand and similarly what is the standard deviation for that. So we can simply use some of the formulas which are already available in excel where we are trying to find out the unit sold only for SKU 1 but only for we will come to this table first. So we will try to first show the evaluation for city A first and we have to calculate the SKU wise the average demand for particularly for city A. For this part we can use a simple average, simple excel function where it will find out that which SKU you are trying to calculate its corresponding demand and for which city and using this as the condition it will average down the unit sold for that particular SKU for that particular city and correspondingly the standard deviation can also be calculated. If we have these values for SKU 1 for city A we can take we can calculate the coefficient of variation and the same exercise can be repeated for all the 20 SKUs for city A very easily. So this excel sheet is also provided for your reference you can easily look into the formulas that we have used for calculating these averages value and also the standard deviation by applying the conditional requirements for SKU wise and also for city wise. The same exercise is repeated for city B as well here also you can see that for all the SKU all the 20 SKUs the average demands are calculated the standard deviations have been calculated by providing the conditional requirement for SKU and for the respective SKU and for the respective city using this to information the coefficient of variation is calculated and now you can see from these two tables that is the orange color table and the green color tables that we have the information about the SKUs being sold in city A and their coefficient of variation. Similarly we now know what are the coefficient of variation of these 20 SKUs in city B as well. So we already have discussed about coefficient of variation and its role that how it can be used for understanding the variability of the demand. So here we have captured these details and now let us try to make a scatter plot out of it it is very easy you can simply use the chart available options over here and try to create the scatter plot. So for your reference we have already provided you the solution you can see into this solution as well and let us see that what is kept on the x axis and what is kept on the y axis. So here if we observe that on the x axis we have the coefficient of variation which is capturing the demand variability and on the y axis we have the average weekly sales which we have already calculated for all the SKUs. Now here the blue dots are indicating the COVs for city number A and similarly the orange dots are indicating the coefficient of variation for city B. So we can see that some of the SKUs have got very low demand variability but their average weekly sales are very high and similarly there are some SKUs where demand variability or the coefficient is very high but their weekly sales is quite low. This is the very interesting concept and we can also see that it is not necessary that the product which is sold in city A will have the same combination of COV versus weekly sales just like in city B also. So it is not necessary that the product is getting consumed at both the places in similar pattern. So this is quite clearly visible but yes they are quite nearby as well. So they have almost similar pattern in both the cities which helps us in understanding their demand variability. Now going forward the supply chain manager now want to decide about the distribution policy for these SKUs and he has decided to make a distribution center which is going to serve the requirement of city A and city B respectively. Now what is the role of this distribution center and how the inventory can be managed or the products which we have segmented based on the demand variability, how that can be managed and what type of supply chain can be suggested. So let us see that how we can use this data to decide on this part. So in this table if you refer from here what we have done is that we have combined the demand of both city A and city B or the weekly demand and the joint average demand and the joint average joints and a deviation is calculated for all the 20 SKUs and correspondingly the coefficient of variation is also calculated. So the purpose of this exercise is to understand what is the variability that the distribution center is facing which is responsible for serving the requirement of both the city A and city B. Now once we have this information we have plotted them over here just like previously as you can see from this graph that the demand variability is again kept captured on the x-axis and the average weekly sales is captured on the y-axis and the joint demand variability for both city A and city B and for the 20 SKUs are shown over here. So we can see from here that the demand variability on the x-axis is trying to show us those SKUs which has got very low demand variability or very low COVs but they have got high weekly sales and correspondingly there is a case which is talking about particularly this blue dot if you can see it can be inferred very easily that the coefficient of variation is very high for this unit for this SKU but the average weekly sales is the one of the lowest for particularly for this SKU. So now once we have done this type of analysis let us go back to our analysis further and see that what can be done next to it. So going forward we have already seen the summary of the weekly sales data for both city A and city B and how this coefficient of variation can be calculated. Similarly we combined the weekly sales data for both the cities and then we have tried to calculate the combined average demand and also the standard deviations for the same and from here you can see that this coefficient of variation is actually trying to show you the total weekly sales at both city A and city B. So this we can see that it is related with the distribution center which is responsible for serving the requirement of both city A and city B. So we are trying to find out the variability at the distribution center. So we have already calculated the COVs or the coefficient of variation and along with that we also now know their average weekly sales this we already discussed that the blue dots are indicating for city A and the orange dots are indicating for city B. So we can see that how these SKUs are being sold in both the cities and what is their corresponding demand variability and their weekly sales. We continued the exercise where we combined both the demand from city A and city B and we have a combined plot for every SKU concerning considering their sales as well as the demand variability. Now till this part we have already discussed in our previous Excel example. The next decision is how do I use this information which can be used for deciding the right supply chain strategy. So here let us try to build a quadrant system which is trying to divide the whole area into four parts and which depends upon your demand variability and your average weekly sales. So here we can see that we have tried to formulate a quadrant and let us try to understand the each and every quadrant what is the role that they are playing and what is the right supply chain strategy for the same. So if we look into this first quadrant you can see from here that particularly for this quadrant the coefficient of variation is one of the is on the lower side and parallely if we look into the weekly sales it is one of the highest side. So we can say that this is a case where high volume these are the essential products we can classify them like this which are having the characteristics of high volume whereas the demand variability is quite low. So these are some products which are generally always have are in demand they are constantly required and that is why the volume for them is quite high whereas the demand variability is quite low. Now in this type of situation if you remember from our previous session this is more about having the feature of stable products and where it is driven by the forecast and most it is the supply chain associated with it is more stable. So strategy that can be followed for this case is a push strategy ensuring that the supply chains that are being designed are efficient in nature. Similarly we have another case where the volume if you see is considerably low but the coefficient of variation is on the higher side. So we can see that it is a case where the volume is low but the demand variability is quite high. Again this type of feature or this type of characteristics can be seen for such products which are more customized and they are like having characteristics of special edition collectibles or so on. So which are which requires more and more customization and thereby the best strategy for managing these type of product is a pull strategy. But we also have two more quadrants and we can see from here that what is the right way for managing these two quadrants further. So if you look into the third quadrant we can see from this side this quadrant have a very high demand variability but we also know the volume is also high. So this is something that is applicable on consumer electronics and which we understand that because of the change in the technologies and all. So there is a continuous introduction of new and better type of products in the system but it is also having high volume. So because of this we can say that there is a high variability of the demand but volume is also high. So in this case a combination of push and pull strategy can be implemented and depending upon the requirement you can decide upon the push and pull boundary where it can be placed in your supply chain. So we already had discussed about push pull boundary in the previous sessions you can refer to that and you can make some examples. You can look into some examples where this customization is also offered but in what way and how this push pull boundary is being decided. The last quadrant is about some basic electronic components or you can look into the characteristics where the demand variability is quite low and also the average weekly sales you can see is also low. So both the combination of sales and demand variability is on the lower side and again the best strategy to manage for these type of businesses is to have a combination of push and pull strategy together. A very simple example is some non-seasonal apparel basics or some basic electronics components which are required for the same. So again there is no specific strategy over here a right combination of push and pull strategy needs to be decided by looking into the requirements of these particular type of products. So we can see from here very clearly that how we have interestingly used these two concepts of demand variability and weekly sales to decide about these four type of products and according to that you can select the right supply chain strategy for the same. This is a very interesting example about product segmentation which is based on demand variability and the weekly sales. Let us pick up one more example of product segmentation but this is a very famous concept which is used for the sourcing and procurement related decision. We will try to cover it quickly. This is given by Peter Kralczyk and the matrix which is shown over here is called as Kralczyk matrix. So here on the x-axis if you have your supply risk and if you try to cover information about the impact on profit on y-axis we can try to make this Kralczyk matrix. So when it comes about what is supply risk, supply risk is related to the availability of the product followed by how many number of suppliers you have or what is the competitive demand or also is it possible to make or buy that particular component or if there is any type of risk involved with that. So there are many other factors which helps you in deciding the supply risk and talking about the profit impact this is all related to the volume of the product that is required to be purchased or what is the percentage of the total purchase cost or if directly or indirectly what is the impact of that component on the business growth. So considering these two aspects if we want to classify our components that are required for manufacturing we can use this Kralczyk matrix very nicely. So broadly if you see there are four quadrants in this and these are referred as leverage, strategic bottleneck and non-critical. So we will look into this one by one in detail. So talking about non-critical quadrant these are the components you see that where the product is much standardized and the process is very efficient and these are having the characteristics of abundant supply. So we can see that in this case there are many suppliers available to you and you can decide about your strategies to manage those suppliers very easily because there is enough options available in this particular group of components. When we talk about the leverage items again we see from this case that these items have got a very high impact on profit but talking about the supply risk the supply risk is on the lower side. So this combination of items again is indicating that they are available in abundant but there is because there is a huge impact on profit. So there is the option of having right type of negotiations and that is why it requires good pricing strategy. So exploitation of full purchasing power can be done for this particular items. When we talk about the strategic items these are again having a very high supply risk and also the impact on profit is also very high. So these are some very critical items and which affect the overall business for a long term on a long term basis and that is why a long term relationship is expected from the supplier and totally depends on the collaborations with the suppliers for those who are responsible for providing your strategic items and that is why there is a different strategy for managing the suppliers of this particular group of components. The last group is about bottleneck where the products are having high supply risk but either impact on operations or on profit is very low. So it simply means that they have an unavailability of these component will have a very minimal effect on the profit but the supply risk of their availability is very high and because of that the process can get affected. So in this type of suppliers there is a very low control on the suppliers and it is quite challenging to manage this particular type of components. So we can see so far. So we have seen so far about the ways in which the components can be accordingly classified in the procurement decisions and this is a very interesting and basic matrix which is available in literature given by Peter Kraljik and can be accordingly used for deciding their relationships with the corresponding suppliers and also for managing these components so that the impact on profit is maximized. So in summary in today's sessions we have discussed about possible way of product segmentation in using some analytical techniques and also we have referred to Kraljik matrix to understand the classification of the components by following the supplier risk and profit impact characteristics. With this we will close today's session. Thank you. Have a nice day.