Managerial Levers to Improve Supply Chain Profitability

Having identified the factors that influence the optimal level of product availability, we now focus on actions a manager can take to improve supply chain profitability. We have shown in Section 13.2 that the costs of overstocking and understocking have a direct impact on both the optimal cycle service level and profitability. Two obvious managerial levers to increase profit­ability are thus

1. Increasing the salvage value of each unit increases profitability (as well as the optimal cycle service level).
2. Decreasing the margin lost from a stockout increases profitability (by allowing a lower optimal cycle service level).

Strategies to increase the salvage value include selling to outlet stores so leftover units are not merely discarded. Some companies, such as Sport Obermeyer, which sells winter wear in the United States, sell the surplus in South America, where the winter corresponds to the North American summer. The increased salvage value of the surplus allows Sport Obermeyer to pro­vide a higher level of product availability in the United States and increase its profits. The growth of online liquidators such as Overstock.com helps retailers by increasing their salvage value for overstocked products. Increasing the salvage value of leftover units allows a firm to increase profits by providing a higher level of product availability because the cost of excess inventory has been reduced.

Strategies to decrease the margin lost in a stockout include arranging for backup sourcing (which may be more expensive) so customers are not lost forever. The practice of purchasing product from a competitor on the open market to satisfy customer demand is observed and justi­fied by the earlier reasoning. In the MRO supply industry, McMaster-Carr and W.W. Grainger, two major competitors, are also large customers for each other.

The cost of understocking can also be decreased by providing the customer with a substi­tute product. Decreasing the cost of understocking allows a firm to increase profits by providing a lower level of product availability (because there are alternatives available to serve the cus­tomer), thus decreasing the amount of excess inventory at the end of the season.

The optimal cycle service level as a function of the ratio of the cost of overstocking and the cost of understocking is shown in Figure 13-2. Observe that as this ratio gets smaller, the optimal level of product availability increases. This fact explains the difference in the level of product availability between a high-end store such as Nordstrom and a discount store. Nordstrom has higher margins and thus a higher cost of understocking. It should thus provide a higher level of product availability than a discount store with lower margins and, as a result, a lower cost of stocking out.

Another significant managerial lever to improve supply chain profitability is the reduction of demand uncertainty. With reduced demand uncertainty, a supply chain manager can better match supply and demand by reducing both overstocking and understocking. A manager can reduce demand uncertainty via the following means:

1. Improved forecasting: Use better market intelligence and collaboration to reduce demand uncertainty.
2. Quick response: Reduce replenishment lead time so multiple orders may be placed dur­ing the selling season.
3. Postponement: In a multiproduct setting, postpone product differentiation until closer to the point of sale.
4. Tailored sourcing: Use a supplier with a short lead time, but perhaps higher cost, as a backup for a supplier that has a low cost but perhaps a long lead time.

Next, we study the impact of each of these actions on supply chain performance.

1. Improving Forecasts: Impact on Profits and Inventories

Companies have tried to better understand their customers and coordinate actions within the sup­ply chain to improve forecast accuracy. The use of demand planning information systems has also helped in this regard. We show that improved forecast accuracy can help a firm significantly increase its profitability while decreasing the excess inventory overstocked as well as the sales lost because of understocking. We illustrate the impact of improving forecast accuracy in Exam­ple 13-6 (see worksheet Example 13-6).

EXAMPLE 13-6 Impact of Improved Forecasts

Consider a buyer at Bloomingdale’s who is responsible for purchasing dinnerware with Christ­mas patterns. The dinnerware sells only during the Christmas season, and the buyer places an order for delivery in early November. Each dinnerware set costs c = $100 and sells for a retail price of p = $250. Any sets unsold by Christmas are heavily discounted in the post-Christmas sales and are sold for a salvage value of 5 = $80. The buyer has estimated that demand is nor­mally distributed, with a mean of m = 350. Historically, forecast errors have had a standard deviation of s = 150. The buyer has decided to conduct additional market research to get a bet­ter forecast. Evaluate the impact of improved forecast accuracy on profitability and inventories as the buyer reduces s from 150 to 0 in increments of 30.

Analysis:

In this case, we have

Cost of understocking = C_u = p – c = $250 – $100 = $150
Cost of overstocking = C_o = c – s = $100 – $80 = $20

Using Equation 13.1, we have

The optimal order size is obtained using Equation 13.2 and the expected profit using Equa­tion 13.3. The order size and expected profit as forecast accuracy (measured by standard devia­tion of forecast error) varies are shown in Table 13-3.

Example 13-6 illustrates that as a firm improves its forecast accuracy, expected quantity overstocked and understocked declines and expected profit increases. This relationship is shown in Figure 13-3.

2. Quick Response: Impact on Profits and Inventories

Quick response is the set of actions a supply chain takes to reduce the replenishment lead time. Supply chain managers are able to improve their forecast accuracy as lead times decrease, which allows them to better match supply with demand and increase supply chain profitability. We have discussed the benefits of lead time reduction for regularly stocked items such as detergent in Chap­ter 12 (see Example 12-6). We now focus on the benefits of lead time reduction for seasonal items.

To illustrate the issues, consider the example of Saks Fifth Avenue, a high-end department store, which purchases cashmere shawls from India and Nepal. The selling season for cashmere shawls is about 14 weeks. Historically, replenishment lead times have been on the order of 25 to 30 weeks. With a 30-week lead time, the buyer at Saks must place the entire order well before the start of the sales season. It is difficult for a buyer to make an accurate forecast of demand this far in advance. This results in high-demand uncertainty, leading the buyer to order either too many or too few shawls each year.

Typically, buyers are able to make accurate forecasts once they have observed sales for the first week or two in the season. If lead times can be shortened to facilitate the use of actual sales when placing part of the seasonal order, there can be significant benefits for the supply chain. Consider the situation in which manufacturers are able to reduce replenishment lead time to six weeks. This reduction allows the buyer at Saks to break up the entire season’s purchase into two orders, each covering seven weeks of demand. The first order is placed six weeks before the start of the sales season. The buyer orders what the store expects to sell over the first seven weeks of the season. The first order must be placed without observing any sales. Once the season starts, the buyer observes sales for the first week and places a second order after the first week for the final seven weeks of the season. When placing the second order, the buyer can use sales informa­tion from the first week of the season. The improved accuracy of the buyer’s predictions allows Saks to use the second order to better match supply and demand, resulting in higher profits.

When multiple orders are placed in the season, it is not possible to provide formulas like Equations 13.1 to 13.5 that specify the optimal order quantity, the expected profit, expected over­stock, and expected understock. Rather, we must use simulation (see Appendix 13F) or approxi­mations to identify the impact of different ordering policies. We illustrate the impact of being able to place multiple orders in a season using approximations on the Saks example discussed earlier.

The buyer at Saks must decide on the quantity of cashmere shawls to order from India and Nepal for the upcoming winter season. The unit cost of each shawl is c = $40, and the shawl retails for p = $150. A discount store purchases any leftover shawls at the end of the season for 5 = $30 each. After the sales season of 14 weeks, any leftover shawls are sold to the discount store.

Before the start of the sales season, the buyer forecasts weekly demand to be normally distributed, with a mean of D = 20 and a standard deviation of s_D = 15. We compare the impact of the following two ordering policies:

1. Supply lead time is more than 15 weeks. As a result, a single order must be placed at the beginning of the season to cover the entire season’s demand.
2. Supply lead time is reduced to six weeks. As a result, two orders are placed for the season, one to be delivered at the beginning of the season and the other to be placed at the end of week 1 and delivered at the beginning of week 8.

For policy 2, we assume that once the buyer sees sales for the first week, she is able to forecast demand for the first seven-week period accurately (this approximation allows us to quantify the benefits of the second order). She is still not able to predict sales for the second seven-week period. In terms of her forecasting ability for the second seven-week period, we con­sider two scenarios—one in which the buyer’s forecast accuracy does not improve for the second order (i.e., the standard deviation of forecast demand stays at 15), and another in which it improves and the buyer is able to reduce the standard deviation of the forecast to 3 instead of 15. We also assume that demand is independent across weeks.

We first start with buyers placing a single order for the season. Given that the season lasts 14 weeks and demand is independent across weeks, we obtain the following (using Equation 12.1):

Using Equation 13.1, the optimal cycle service level is given by

The optimal order quantity for a single order is obtained using Equation 13.2:

 For an order of 358 shawls, we obtain

Expected profit with a single order (using Equation 13.3) = $29,767

Expected overstock (using Equation 13.4) = 79.8

Expected understock (using Equation 13.5) = 2.14

Given that the cost of overstocking is $10 per shawl and the cost of understocking is $110 per shawl, we obtain

Expected cost of overstocking = 79.8 X $10 = $798
Expected cost of understocking = 2.14 X $110 = $235

If there were no demand uncertainty, demand over the season would be 280 shawls with a profit of 280 X $110 = $30,800. Observe that expected profit is reduced by $30,800 – $29,767 = $1,033 = $798 + $235 because of uncertainty. Thus, uncertainty reduces the expected profit by the expected cost of overstocking and understocking.

From the preceding analysis, it also follows that the reduction of uncertainty as a result of shortening lead times will increase profits in the season by $1,033 at most.

We now describe a procedure that can be used to estimate the benefit of placing two orders in a season. We assume that the first order aims to cover demand for the first seven weeks and the second order for the last seven weeks. Given that the buyer will see the first week of demand before placing the second order, we have assumed that she will accurately be able to predict sales in the first seven-week period. Thus, her second order can take into account any leftover inven­tory from the first order.

First, consider that no improvement in forecast accuracy occurs after observing the first period demand (standard deviation of weekly demand remains 15). For each seven-week period, we obtain the following:

The optimal cycle service level is maintained at 0.92. Using Equation 13.2, we obtain the size of the first order to be

For an order of 195 shawls, we obtain

Expected profit from seven weeks (using Equation 13.3) = $14,670

Expected overstock (using Equation 13.4) = 56.4

Expected understock (using Equation 13.5) = 1.51

Recall that the buyer can accurately predict sales over the first seven-week period when she places the second order at the end of week 1. Thus, any overstock resulting from the first order will be used to adjust the size of the second order. We also assume that any understock in the first seven weeks is served from shawls that arrive in the second order. Given that the desired starting inventory for the second seven-week period is 195 shawls, the expected overstock at the end of the first seven-week period is 56.4 shawls, and the expected understock is 1.51 shawls, the second order will be only 195 – 56.4 + 1.51 = 140.11 shawls, on average. Given that all overstock from the first seven-week period is used to lower the order size for the second seven- week period and all understock is served from the second order, there is no overstock or under­stock cost from the first order. There is an expected overstock of 56.4 shawls (this is the expected overstock when starting the second seven-week period with 195 shawls) and an expected under­stock of 1.51 shawls at the end of the season.

The expected profit at the end of the season is thus given by the sum of the expected profit from each seven-week period and the overstock and understock cost recovered from the first seven-week period as follows:

Expected profit from season = $14,670 + (56.4 X $10) + (1.51 X $110) + $14,670 = $30,070

We add (56.4 X $10) + (1.51 X $110) to the expected profit from the first seven-week half because there is effectively no overstock that must be sold to the discount store at the end of the first seven weeks or understock where margin is lost. Our analysis indicates that allowing for a second order in the season increases profits by $30,070 – $29,767 = $303 even if there is no improvement in forecast accuracy for the second seven-week period. The profit increase will be smaller, however, if we assume that customers who do not find the product at the end of the first seven weeks are unwilling to wait for the second order to arrive. Observe that as a result of allow­ing a second order, the total order quantity has decreased from 358 shawls to 195 + 140.1 = 335.1 shawls. The expected overstock at the end of the season has decreased from 79.8 to 56.4 shawls and the expected understock has decreased to 1.51.

From our analysis, we observe three important consequences of being able to place a sec­ond replenishment order in the season after observing some sales:

1. The expected total quantity ordered during the season with two orders is less than that with a single order for the same cycle service level. In other words, it is possible to provide the same level of product availability to the customer with less inventory if a second, follow-up order is allowed after observing some sales.
2. The average overstock to be disposed of at the end of the sales season and the expected understock is less if a follow-up order is allowed after observing some sales.
3. The profits are higher when a follow-up order is allowed during the sales season.

In other words, as the total quantity for the season is broken up into multiple smaller orders, with the size of each order based on some observed sales, the buyer is better able to match supply and demand and increase profitability for Saks. These relationships are shown in Figures 13-4 and 13-5.

We now consider the case in which the buyer improves her forecast accuracy for the sec­ond order after observing some of the season’s demand. As a result, the standard deviation of weekly demand forecast drops from 15 to 3 for the second seven-week period. In this setting, the first order stays at 195 shawls, as discussed earlier. For the second order, however, we must account for the fact that the standard deviation of weekly demand has dropped to 3. As a result, we obtain

The optimal cycle service level is maintained at 0.92. Using Equation 13.2, we obtain the desired number of shawls at the beginning of the first seven weeks to be O₁ = 195 as before and that at the beginning of the second seven weeks to be O₂ where:

As in the previous analysis, we assume that the buyer is accurately able to predict sales for the first seven-week period after observing sales for the first week. She thus accounts for the over­stock and understock at the end of the first seven week period when placing her second order. Given an expected overstock of 56.4 shawls and an expected understock of 1.51 from the first order, the net second order is thus 151 – 56.4 + 1.51 = 96.11 shawls. With 151 shawls at the start of the second seven weeks, we obtain

Expected profit from second order(using Equation 13.3) = $15,254

Expected overstock (using Equation 13.4) = 11.3

Expected understock (using Equation 13.5) = 0.30

Again observe that there is no overstock cost at the end of the first seven weeks and we have assumed that customer orders that are understocked in the first order are served from the second order. Thus, the net profits for the season are $14,670 (first seven weeks) + 56.4 X $10 (no over­stock at the end of first seven weeks) + 1.51 X $110 (understock is served from second order) + $15,254 (second seven weeks) = $30,654. If forecast accuracy improves as a result of observing early seasonal demand, the season’s profit increases by $30,654 – $29,767 = $887. The expected overstock at the end of the season has now declined to 11.3 units and the expected understock to 0.3 units. A second order and improved forecast accuracy as a result of seeing early season sales thus increase profits and decrease overstocks and understock.

Zara, the Spanish apparel retailer, has built its entire strategy around quick response. At a time when most of its competitors were cutting costs by outsourcing production to low-cost countries, Zara focused on reducing response time by setting up production facilities in Spain. While competitors had lead times that ranged from three to nine months, Zara was able to reduce its design-to-shelf lead times to three to four weeks. Given a three-month sales season (for each of fall, winter, spring, and summer), competitors were forced to make sourcing decisions well before the start of a season. In contrast, Zara divided the three-month sales season into three one- month periods. For the first month, Zara decided on quantities without knowing what sales would be. These quantities, however, were much lower than what the competition was required to order for the entire three-month season. For the second month, Zara made its production decisions after observing the first week of demand (Zara also observed demand at its competition). For the third month, Zara made its production decisions after observing the entire first month of sales. In each instance, observing sales allowed Zara to significantly improve its forecast accuracy. The result was that Zara was able to bring in more of what was selling without wasting precious pro­duction capacity on what was not likely to sell. Quick response allowed Zara to respond to trends rather than have to predict them. This resulted in higher profits for Zara because it produced what was selling and had less overstock and understock. The New York Times reported in 2006 that “Zara books 85 percent of the full ticket price for its merchandise while the industry average is 60 percent.”

From our previous discussion, quick response is clearly advantageous to a retailer in a sup­ply chain—with one caveat. As the manufacturer reduces replenishment lead times, allowing for a second order, we have seen that the retailer’s order size drops. In effect, the manufacturer sells less to the retailer. Thus, quick response results in the manufacturer making a lower profit in the short term if all else is unchanged. This is an important point to consider, because decreasing replenishment lead times requires tremendous effort from the manufacturer, yet seems to benefit the retailer at the expense of the manufacturer. The benefits resulting from quick response should be shared appropriately across the supply chain. This was easier for Zara, which was vertically integrated into responsive manufacturing and retailing. It can be a challenge, however, for retail­ers that outsource manufacturing.

3. Postponement: Impact on Profits and Inventories

As discussed in Chapter 12, postponement refers to the delay of product differentiation until closer to the sale of the product. With postponement, all activities prior to product differentiation require aggregate forecasts that are more accurate than individual product forecasts. Individual product forecasts are required close to the time of sale when demand is known with greater accu­racy. As a result, postponement allows a supply chain to better match supply with demand. Post­ponement can be a powerful managerial lever to increase profitability. It can be particularly valuable if customers are willing to wait for delivery. If the supply chain can postpone product differentiation until after receiving the customer order, a significant increase in profits and reduc­tion in inventories can be achieved.

The major benefit of postponement arises from the improved matching of supply and demand. There is a cost associated with postponement, however, because the production cost using postponement is typically higher than the production cost without it. For example, the production process at Benetton, where assembled knit garments are dyed, costs about 10 per­cent more than if dyed thread is knitted. Similarly, when retailers mix paint at stores rather than at the factory, manufacturing costs increase because there is a loss of economies of scale in mixing. Given the increased production cost from postponement, a company should ensure that the inventory benefits of postponement are larger than the additional costs.

Postponement is valuable for a firm that sells a large variety of products with demand that is unpredictable, independent, and comparable in size. We illustrate this using the example of Benetton selling knit garments in solid colors. Starting with thread, two steps are needed to com­plete the garment—dyeing and knitting. Traditionally, thread was dyed and then the garment was knitted (Option 1). Benetton developed a procedure whereby dyeing was postponed until after the garment was knitted (Option 2).

Benetton sells each knit garment at a retail price p = $50. Option 1 (no postponement) results in a manufacturing cost of $20, whereas Option 2 (postponement) results in a manufac­turing cost of $22 per garment. Benetton disposes of any unsold garments at the end of the season in a clearance for 5 = $10 each. The knitting or manufacturing process takes a total of 20 weeks. For the sake of discussion, we assume that Benetton sells garments in four colors. Twenty weeks in advance, Benetton forecasts demand for each color to be normally distributed, with a mean of m = 1,000 and a standard deviation of s = 500. Demand for each color is independent. With Option 1, Benetton makes the buying decision for each color 20 weeks before the sale period and holds separate inventories for each color. With Option 2, Benetton forecasts only the aggregate uncolored thread to purchase 20 weeks in advance. The inventory held is based on the aggregate demand across all four colors. Benetton decides the quantity for individual colors after demand is known. We now quantify the impact of postponement for Ben­etton. All analysis is detailed in the spreadsheet Chapter13-postponement-Benetton. See work­sheet Benetton for the following analysis.

With Option 1, Benetton must decide on the quantity of colored thread to purchase for each color. For each color we have

Retail price, p = $50

Manufacturing cost, c = $20

Salvage value, 5 = $10

Using Equation 13.1, we obtain the optimal cycle service level for each color as

Using Equation 13.2, the optimal purchase quantity of thread in each color is

Thus, it is optimal for Benetton to produce 1,331 units of each color. Using Equation 13.3, the expected profit from each color is

Expected profits = $23,644

Using Equations 13.4 and 13.5, the expected overstock and understock for each color is

Expected overstock = 412
Expected understock = 15

Using Option 1, across all four colors Benetton thus produces 4 X 1,331 = 5,348 sweaters. This results in an expected profit of 4 X 23,644 = $94,516, with an average of 4 X 412 = 1,648 sweaters sold on clearance at the end of the season and 4 X 15 = 300 customers turned away for lack of sweaters.

Under Option 2, Benetton has to decide only the total number of sweaters across all four colors to be produced, because they can be dyed to the appropriate colors once demand is known. In this case, we have

Retail price, p = $50

Manufacturing cost, c = $22

Salvage value, 5 = $10

Using Equation 13.1, the optimal cycle service level for each color is

Given that demand for each color is independent, total demand across all four colors can be evaluated using Equation 12.15 to be normally distributed, with a mean of μ_A and a standard deviation of σ_A, where

Using Equation 13.2, the optimal aggregate production quantity for Benetton is given by OA, where

Under Option 2, it is optimal for Benetton to produce 4,524 undyed sweaters, to be dyed as demand by color is available. The expected profit with postponement is evaluated using Equation 13.3 as

Expected profits = $98,092

Using Equations 13.4 and 13.5, the expected overstock is 715 and the expected understock is 190. Thus, postponement increases expected profits for Benetton from $94,576 to $98,092. Expected overstock declines from 1,648 to 715, and the expected understock declines from 300 to 190. Clearly, the use of postponement and production using Option 2 is a good choice for Benetton in this case.

The benefits of postponement decrease significantly if demand across the different colors is positively correlated. In the Benetton example, we find that postponement is not valuable if the correlation coefficient across each color is p = 0.2 or higher.

The benefits of postponement also decrease significantly if demand is more predictable. If the standard deviation of demand for each color decreases to 300 or less, our analysis shows that Option 2 with postponement results in lower profits than Option 1 without postponement.

Postponement is not very effective if a large fraction of demand comes from a single prod­uct. The benefit from aggregation is small in this case, whereas the increased production cost applies to all items produced. We illustrate this idea once again using Benetton as an example (see worksheet Postponement with dominant prod).

Assume that demand for red sweaters at Benetton is forecast to be normally distributed, with a mean of μ_red = 3,100 and a standard deviation of σ_red = 800. Demand for the other three colors is forecast to be normally distributed, with a mean of μ= 300 and a standard deviation of σ = 200. Observe that red sweaters constitute about 80 percent of demand.

Under Option 1, the optimal cycle service level CSL* is 0.75, as evaluated earlier. Using Equation 13.2, the optimal production of red sweaters is given by

Using Equation 13.3, the expected profit from red sweaters is $82,831. Using Equa­tion 13.4, the expected overstock of red sweaters is 659; using Equation 13.5, the expected understock of red sweaters is 119. For each of the other three colors, we can similarly evalu­ate the optimal production to be O^* where

This results in an expected profit of $6,458, an expected overstock of 165, and an expected understock of 30 for each of the other three colors. Across all four colors, Option 1 thus results in the following:

Total production = 3,640 + 3 X 435 = 4,945

Expected profit = $82,831 + 3 X $6,458 = $102,205

Expected overstock = 659 + 3 X 165 = 1,154

Expected understock = 119 + 3 X 30 = 209

Under Option 2, Benetton has to decide only the total production across all four colors. Given that demand for each color is independent, total demand across all four colors can be evaluated using Equation 12.13 to be normally distributed, with a mean of p_A and a standard deviation of s_A, where

Under Option 2, we repeat all calculations to obtain the following:

Total production = 4,457

Expected profit = $99,876

Expected overstock = 623

Expected understock = 166

In this case, Benetton sees its profits decline even though both overstock and understock have decreased as a result of postponement. This is because a large fraction of demand is from red sweaters, which can already be forecast with reasonably good accuracy. Postponement and the resulting aggregation thus do little to improve the forecasting accuracy of red sweaters. They do, however, improve the forecasting accuracy for the other three colors, but they represent a small fraction of demand. Meanwhile, the production costs increase for all sweaters (including red sweaters). As a result, the increased production costs outweigh the benefits from postponement across all colors.

Next, we discuss how tailored postponement can be an effective strategy when complete postponement is not appropriate.

4. Tailored Postponement: Impact on Profits and Inventories

In tailored postponement, a firm uses production with postponement to satisfy a part of its demand, with the rest being satisfied without postponement. Tailored postponement produces higher profits than when no postponement is used or all products are manufactured using postponement. Under tailored postponement, a firm produces the amount that is predictable using the lower-cost production method without postponement. The firm produces the portion of demand that is uncertain using post­ponement. On the portion of the demand that is predictable, postponement provides little value in terms of increased forecast accuracy. The firm thus produces that portion using the lower-cost method to lower the manufacturing cost. On the portion of demand that is uncertain, postponement signifi­cantly improves forecast accuracy. The firm is thus willing to incur the increased production cost to achieve the benefit from the improved matching of supply and demand. We now illustrate the idea of tailored postponement, returning to the example of Benetton (see worksheet Tailored postponement).

One way to implement tailored postponement is to produce high-demand, predictable products without postponement and produce only the unpredictable products using postponement. Let us return to the Benetton data with red sweaters constituting about 80 percent of demand. Recall that demand for red sweaters at Benetton is forecast to be normally distributed, with a mean of μ_red = 3,100 and a standard deviation of σ_red = 800. Demand for the other three colors is forecast to be normally distributed, with a mean of μ = 300 and a standard deviation of σ = 200. We evalu­ated that postponing all colors decreases profits for Benetton by more than $2,000 (from $102,205 to $99,876). However, if we tailor postponement so red sweaters are made using the traditional method and only the other colors are postponed, profits actually increase by $1,009, to $103,213.

A more sophisticated approach to postponement separates all demand into base load and variation. The base load is manufactured using the low-cost method without postponement, and only the variation is made using postponement. This more sophisticated form of tailored sourc­ing is more complex to implement but can be valuable even when all products being postponed have similar demand, as we illustrate next (see spreadsheet Table13-4). Consider the scenario in which Benetton is selling four colors, and the forecast demand for each color is normally distrib­uted, with a mean of m = 1,000 and a standard deviation of s = 500. We have observed earlier that the use of complete postponement (every sweater is postponed) in this instance increases profits at Benetton from $94,576 to $98,092.

We now consider a situation in which Benetton applies tailored postponement and uses both Option 1 (dye thread and then knit garment) and Option 2 (dye the knit garment) for production. For each color, Benetton identifies a quantity Q₁ (the base load) to be manufactured using Option 1 and an aggregate quantity Q_A to be manufactured using Option 2, with colors for the aggregate quantity being assigned when demand is known. We now identify the appropriate tailored post­ponement policy and its impact on profits and inventories. There is no formula that can be used to evaluate the optimal policy and profits in this case. We thus resort to simulations to study the impact of different policies. The results of various simulations are shown in Table 13-4. From Table 13-4, we see that Benetton can increase its expected profit by using a tailored postponement policy under which Q₁ units of each color are produced using Option 1 and Q_A units are produced using Option 2. The resulting profit is higher than if all units are produced entirely using Option 1 (no postponement) or Option 2 (complete postponement). It is best to select Qi such that it is quite likely that demand for each color is Q1 or higher. The tailored postponement policy exploits this fact and produces Q₁ units using Option 1, which has a low cost. Q_A units are produced using Option 2 so demand uncertainty can be reduced by aggregation.

5. Tailored Sourcing: Impact on Profits and Inventories

In tailored sourcing, firms use a combination of two supply sources, one focusing on cost but unable to handle uncertainty well, and the other focusing on flexibility to handle uncertainty, but at a higher cost. For tailored sourcing to be effective, having supply sources such that one serves as the backup to the other is not sufficient. The two sources must also focus on different capa­bilities. The low-cost source must focus on being efficient and should be required to supply only the predictable portion of the demand. The flexible source should focus on being responsive and be required to supply the uncertain portion of the demand. As a result, tailored sourcing allows a firm to increase its profits and better match supply and demand. The value of tailored sourcing depends on the reduction in cost that can be achieved as a result of one source facing no vari­ability. If this benefit is small, tailored sourcing may not be ideal because of the added complex­ity of implementation. Tailored sourcing may be volume based or product based, depending on the source of uncertainty.

In volume-based tailored sourcing, the predictable part of a product’s demand is produced at an efficient facility, whereas the uncertain portion is produced at a flexible facility. Benetton provides an example of volume-based tailored sourcing. Benetton required retailers to commit to about 65 percent of their orders about seven months before the start of the sales season. Benetton subcontracted production of this portion without uncertainty to low-cost sources that had long lead times of several months. For the other 35 percent, Benetton allowed retailers to commit orders much closer to or even after the start of the selling season. All uncertainty was concen­trated in this portion of the order. Benetton produced this portion of the order in a plant it owned that was very flexible. Production at the Benetton plant was more expensive than production by the subcontractor. However, the plant could produce with a lead time of weeks, whereas subcon­tractors had a lead time of several months. A combination of the two sources allowed Benetton to reduce its inventories while incurring a high cost of production for only a fraction of its demand. This allowed it to increase profits.

Volume-based tailored sourcing should be considered by firms that have moved a lot of their production overseas to take advantage of lower costs. The lower costs have also been accompanied by longer lead times. In such a situation, having a flexible local source with short lead times can be an effective complement to the long-lead-time overseas supplier even if the local source is more expensive. Long lead times require large safety inventories, and the result­ing mismatch of supply and demand hurts profits. The presence of the local source allows the firm to carry lower safety inventories and supply any excess demand from the local source.

The most effective combination is for the overseas source to focus on replenishing cycle inven­tories, ignoring uncertainty. The local source is used as a backup any time demand exceeds the inventory available.

Allon and Van Mieghem (2010) describe a high-tech manufacturer of wireless transmis­sion components with facilities in China and Mexico. The Chinese facility was cheaper but had lead times that were five to ten times longer than those from Mexico. A simulation study indi­cated that the use of tailored sourcing was the most effective strategy in this case. Allon and Van Mieghem (2010) recommend a tailored base-surge (TBS) inventory policy, under which a con­stant base load is sourced from the cheaper source (China, in this case), with the responsive source (Mexico, in this case) being used any time inventory drops below a threshold. Their simulations indicate that sourcing roughly 75 percent of the demand from the cheaper source as base load, with the rest coming from the responsive source as needed, is a fairly effective tai­lored sourcing policy in practice. Their results show that the fraction of demand allocated as base load to the cheaper source increases as the demand and the cost difference with the respon­sive facility grow. The fraction of demand allocated as base load to the cheaper source decreases as the reliability of the cheaper source decreases or the volatility of demand and the holding cost of inventory grow.

In product-based tailored sourcing, low-volume products with uncertain demand are obtained from a flexible source, whereas high-volume products with less demand uncertainty are obtained from an efficient source. An example of product-based tailored sourcing is that used by Levi Strauss. Levi sells standard-sized jeans as well as jeans that can be customized. Standard jeans have relatively stable demand, whereas demand for custom jeans is unpredictable. Custom jeans are produced at a flexible facility, whereas standard jeans are produced at an efficient facil­ity. Zara also follows such a product-based tailored sourcing strategy, obtaining more than half its production from responsive plants in Europe, with the rest coming from lower-cost plants in Asia. Zara’s most fashionable items that have the least predictable demand are made in respon­sive European facilities. Clothes that are more predictable and can sell for longer periods, such as basic T-shirts, are sourced from the cheaper Asian facilities.

In some instances, new products have uncertain demand, whereas well-established prod­ucts have more stable demand. Product-based tailored sourcing may be implemented with a flex­ible facility focusing on new products, and efficient facilities focusing on the well-established products. This is often the case in the pharmaceutical industry, for instance.

Source: Chopra Sunil, Meindl Peter (2014), Supply Chain Management: Strategy, Planning, and Operation, Pearson; 6th edition.