Supply chain profit

Once the manufacturer and retailer decisions are made, the combined profit across the two firms is termed the supply chain profit. Notice that when the profits of the manufacturer and retailer are added together, the wholesale price level does not affect this total as it is merely a transfer payment from the retailer to the manufacturer. The supply chain as a whole thus attempts to choose a capacity level Kthat will maximize supply chain profit. 

Intuitively, the supply chain manager chooses a capacity level that sets the expected revenue associated with increasing capacity equal to the expected cost associated with increasing capacity. Thus, following the newsvendor model, the optimal capacity has to satisfy 

This capacity level and the associated supply chain profit maximize the profits of the supply chain. 

Consider an example with a retailer whose demand follows a uniform distribution with values between 8 and 22, see Table 5.1. Thus the probability of demand taking each value between 8 and 22 is equal to T. Suppose r = A, w = 2, c = 0.6, = 0.5. Following the steps defined ear- 

Thus the optimal capacity level is obtained as K = 20, using the values in Table 5.2. 

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The corresponding supply chain profit can be calculated as 40.32. Table 5.2 shows the steps in this calculation for the supply chain profit. 

Again the retailer s profit is the sum of the entries in the last column and is equal to 28. Notice that the supply chain profit is lower than the maximum supply chain profit possible of 40.32. Note that for any wholesale price such that c + q < w < r, then Kq > K, if the service levels are greater than 50%. Thus the supply chain profits are not maximized, and the supply chain is termed uncoordinated. Notice that this remains the case even when the wholesale price varies from the current level. In general,... 

Figure 5.2 shows the manufacturer, retailer and supply chain profits for different possible values of the manufacturer capacity. Notice from this picture that it is optimal for the manufacturer to choose a capacity of 17 units because that capacity maximizes the manufacturers profits. Notice, however, that the capacity level does not maximize the supply chain profits. This example illustrates that wholesale price agreements may not be able to coordinate a supply chain. 

Figure 5.2 Manufacturer, retailer and supply chain profits for different K values...

Notice that this agreement also generates a Pareto-improving contract. Also, because the supply chain profit attains the maximum possible level, the agreement coordinates the supply chain. As before, any of the different w and p combinations correspond to the different possible splits of the total supply chain pie of profits. Details regarding the negotiations to split increased profits will be left out of this discussion. 

It is possible to choose such that the retailer purchases an amount equal to the capacity in the supply chain profit maximizing system. This is... 

Table 5.9 shows the corresponding calculations for the maximum supply chain profit, which is equal to 37.53. 

The retailers and manufacturers profits and supply chain profit for different lvalues are shown in Figure 5.4. In Figure 5.4, notice that the retailers expected profits are maximized 2xK= 15, as we calculated earlier. However, at that inventory level, the supply chain profit, which is the sum of manufacturers and retailers profits is 36.03, which is lower than the maximum supply chain profit obtained earlier. This is observed in Figure 5.4, which shows that the maximum supply chain profit is attained 21 K= 18, rather than at the inventory decision of 15 obtained in this case. 

This difference in supply chain profit arises because of double marginalization, i.e., the retailer does not see the supply chain margin associated with each sale realized or lost and thus makes inventory decisions that are lower than the supply chain optimal decisions (for service levels > 0.5). 

The key message is that in many supply chain contexts coordinating agreements can deliver significant improvements that all involve expanding the supply chain profit pie, thus enabling Pareto-improving profit situations. 

Often the difficulty of forecasting demand during a retail promotion means that the retailer may order too much or too little. In addition, if the retail pricing information is not communicated to the manufacturer, then a forecasting rule that adjusts orders based on shipments will result in the manufacturer carrying high inventories between retail promotions. Sharing planned retail promotion information decreases these costs and thus improves supply chain profits. 

In many industries, the provision of spare parts and associated services represents a significant component of supply chain profits. Some studies [23] estimate US sales of spare parts and after-sales services to be 8% of the annual gross domestic product (GDP) or 1 trillion. Others [28] surest, for example, that in 2001, General Motors earned relatively more profits from its 9 billion in after-sales revenues than it did from 150 billion in car sales. Another estimate [124] suggests that the total cost of ownership of a product may far exceed the amount spent on the initial product purchase and may vary between five and twenty times the original product cost. The main conclusion from these studies is that managing spare parts supply chains and related services after a product is sold may have a significant impact on both primary demand as well as on profits. 

The optimization models discussed in this chapter had a single objective— either to minimize supply chain costs or to maximize supply chain profitability, in case the product prices vary by location or customer. However, customer demand fulfillment and service are also important in designing a supply chain network. More recently, supply chain risk is emerging to be another important criterion (Supply chain risk is discussed in detail in Chapter 7). Hence, recent applications of optimization models have used multiple criteria optimization models for decision making. 

The network should be evaluated using multiple supply chain performance metrics that include supply chain profit, risk, and responsiveness. 

In terms of quantitative approach, some of the supply chain design sffidies have included the topic of supply chain disruption into consideration in order to enhance both supply chain profit and supply chain resilience. Falasca et al. (2008) have extended the supply chain design characteristics concept in Craighead et al. (2007) and proposed a quantitative measure for those characteristics. For instance, supply chain density can be measured as the number of nodes divided by the average... 

The main issues to be addressed in the bi-criteria model are to (i) determine the numbers and locations of suppliers, (ii) select the appropriate set of warehouse locations and capacity levels in the network, and (iii) determine the optimal manufacturing and distribution of products through the selected set of facilities. The conflicting objectives of the model are (1) maximizing supply chain profit and (2) maximizing supply density. 

As mentioned in Sect. 1, two important and conflicting objective functions are considered in the formulation of the supply chain network design problem (1) maximizing supply chain profit and (2) maximizing supply density. 

First Objective Maximizing Supply Chain Profit (Z/)... 

The supply chain profit comprises of the following cost components ... 

Therefore, the supply chain profit objective can be formulated as follows. 

The profit maximization objective has a tendency toward a centralized supply chain network by selecting 13 out of 20 suppliers in order to minimize the total cost. The supply chain profit is 13,248,680, representing the ideal profit value, and the supply density is 1.34. On the other hand, the density maximization objective has a tendency toward a decentralized supply chain network by selecting all 20 suppliers. The supply chain profit is 12,055,457, and the supply density value is 96.63, representing the ideal density value. More suppliers are selected when decision maker gives higher priority to the density objective, as shown in Table 3. 

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