Brief Discussion 2

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Tomato’s vice president of manufacturing is against such a move because it increases manufactur- ing costs. She favors a promotion during the low-demand season because it levels demand and lowers production costs. S&OP allows the two to collaborate and make the optimal trade-offs.

The Base Case

We start by considering the base case that has already been discussed in Chapter 8. Each tool has a retail price of $40. Red Tomato ships assembled tools to Green Thumb, where all inventory is held. Green Thumb has a starting inventory in January of 1,000 tools. At the beginning of January, Red Tomato has a workforce of 80 employees at its manufacturing facility in Mexico. There are a total of 20 working days in each month, and Red Tomato workers earn the equivalent of $4 per hour. Each employee works eight hours on normal time and the rest on overtime. Because the Red Tomato operation consists mostly of hand assembly, the capacity of the produc- tion operation is determined primarily by the total labor hours worked (i.e., it is not limited by machine capacity). No employee works more than 10 hours of overtime per month. The various costs are shown in Table 9-1.

There are no limits on subcontracting, inventories, and stockouts. All stockouts are back- logged and supplied from the following month’s production. Inventory costs are incurred on the ending inventory in each month. The companies’ goal is to obtain the optimal aggregate plan that leaves at least 500 units of inventory at the end of June (i.e., no stockouts at the end of June and at least 500 units in inventory). The base demand forecast is shown in cells J5:J10 of Figure 9-1.

Table 9-1 Costs for Red Tomato and Green Thumb

Item Cost

Material cost $10/unit

Inventory holding cost $2/unit/month

Marginal cost of a stockout $5/unit/month

Hiring and training costs $300/worker

Layoff cost $500/worker

Labor hours required 4/unit

Regular-time cost $4/hour

Overtime cost $6/hour

Cost of subcontracting $30/unit

FIGURE 9-1 Base Case Aggregate Plan for Red Tomato and Green Thumb

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= (I0 + I6) / 2+ Itt=1

5 ∑( )

T = 5,367 6

= 895

= average inventory

average sales =

895 2,667

= 0.34 months

The first two factors increase the overall demand for Toyota, whereas forward buying simply shifts future demand to the present. It is important to understand the relative impact from the three factors as a result of a promotion before making a decision regarding the optimal timing of the promotion. In general, as the fraction of increased demand coming from forward buying grows, offering the promotion during the peak demand period becomes less attractive. Offering a promotion during a peak period that has significant forward buying creates even more variable demand than before the promotion. Product that was once demanded in the slow period is now demanded in the peak period, making this demand pattern even more costly to serve.

When to Promote: Peak or Off-Peak?

Green Thumb estimates that discounting a Red Tomato tool from $40 to $39 (a $1 discount) in any period results in the period demand increasing by 10 percent because of increased consump- tion or substitution. Further, 20 percent of each of the two following months’ demand is moved forward. Management would like to determine whether it is more effective to offer the discount in January or April. We analyze the two options by considering the impact of a promotion on de- mand and the resulting optimal aggregate plan.

IMPACT OF OFFERING A PROMOTION IN JANUARY The team first considers the impact of offering the discount in January. To simulate this option in the spreadsheet Chapter8,9-examples. xlsm, enter 1 in cell E24 (this sets promotion to be on) and 1 in cell E25 (this sets the promotion in Period 1, i.e., January). The new forecast accounts for the fact that consumption will increase by 10 percent in January and 20 percent of the demand from February and March is moved forward to January. Thus, with a January promotion, the new demand forecast for January is obtained by adjusting the base case demand from Figure 9-1 and is given by 1,600 ! 1.1 " 0.2 ! (3,000 " 3,200) # 3,000. The new demand forecast for February is 3,000 ! 0.8 # 2,400, and the new demand forecast for March is 3,200 ! 0.8 # 2,560. For a January discount, the demand forecast is as shown in cells J5:J10 of Figure 9-2. The optimal aggregate plan is obtained by running Solver in the spreadsheet and is shown in Figure 9-2. All data shown in Figure 9-2 are rounded to the nearest integer. For instance, the number of people laid off in Period 1 is 14.75 but is rounded to 15 in Figure 9-2. With a discount in January, the supply chain obtains the following:

Compared to the base case, offering a discount in January results in lower seasonal inventory, a somewhat lower total cost, and a higher total profit.

Profit over planning horizon = $221,485 Revenue over palnning horizon = $643,400

Total cost over planning horizon = $421,915

FIGURE 9-2 Optimal Aggregate Plan When Discounting Price in January to $39

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FIGURE 9-3 Optimal Aggregate Plan When Discounting Price in April to $39

IMPACT OF OFFERING A PROMOTION IN APRIL Now management considers the impact of offering the discount in April. To simulate this option in the spreadsheet Chapter8,9-examples.xlsm, enter 1 in cell E24 (this sets promotion to be on) and 4 in cell E25 (this sets the promotion in period 4, i.e., April). If Green Thumb offers the discount in April, the demand forecast is as shown in cells J5:J10 of Figure 9-3. The optimal aggregate plan is obtained by running Solver and is shown in Figure 9-3. All data shown in Figure 9-3 are rounded to the closest integer. For instance, the number of people laid off in Period 1 is 13.83 rounded up to 14 in Figure 9-3. Compared to discounting in January (Figure 9-2), discounting in April requires more capacity (in terms of workforce) and leads to a greater buildup of seasonal inventory because of the big jump in demand in April. With a discount in April we have the following:

Observe that a price promotion in January results in a higher supply chain profit, whereas a promotion in April results in a lower supply chain profit, compared to the base case of not running a promotion. As a result of the S&OP process, Red Tomato and Green Thumb decide to offer the discount in the off-peak month of January. Even though revenues are higher when the discount is offered in April, the increase in operating costs makes it a less profitable option. A promotion in January allows Red Tomato and Green Thumb to increase the profit they can share.

Note that this analysis is possible only because the retailer and manufacturer have an S&OP process that facilitates collaboration during the planning phase. This conclusion supports our earlier statement that it is not appropriate for a supply chain to leave pricing decisions solely in the domain of retailers and aggregate planning solely in the domain of manufacturers, with each having individual forecasts. It is crucial that forecasts, pricing, and aggregate planning be coordinated in a supply chain.

The importance of a collaborative S&OP process is further supported by the fact that the optimal action is different if most of the demand increase comes from market growth or stealing market share rather than forward buying. We now illustrate the scenario in which a discount leads to a large increase in consumption.

When to Offer a Promotion if Discount Leads to a Large Increase in Consumption

Reconsider the situation in which discounting a unit from $40 to $39 results in the period demand increasing by 100 percent (instead of the 10 percent considered in the previous analysis) because of increased consumption or substitution. Further, 20 percent of each of the two following months’ demand is moved forward. The supply chain team wants to determine

Profit over planning horizon = $211,283 Revenue over palnning horizon = $650,140

Total cost over planning horizon = $438,857

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whether it is preferable to offer the discount in January or April under these conditions. To simulate this scenario, change the entry in cell H24 (increase in consumption) of spreadsheet Chapter8,9-examples.xlsm from 0.10 (10 percent) to 1.00 (100 percent). Set the entry in cell E24 to 1 to set the promotion to be on. The base case when no promotion is offered remains unchanged as shown in Figure 9-1. We now repeat the analysis for the cases in which the promotion is offered in January (off-peak) and April (peak).

IMPACT OF OFFERING A PROMOTION IN JANUARY For a January promotion, set the entry in cell E25 to 1(Period 1, January). If the discount is offered in January, observe that the January demand forecast is obtained as 1,600 ! 2 " 0.2 ! (3,000 " 3,200) # 4,440. This is much higher than the same forecast in Figure 9-2 because we have assumed consumption in the promotion month increases by 100 percent rather than the 10 percent assumed earlier. The demand forecast for a January promotion with a large increase in consumption is shown in cells J5:J10 of Figure 9-4.

The optimal aggregate plan is obtained using Solver and is shown in Figure 9-4. All data shown in Figure 9-4 are rounded to the closest integer. For instance, the number of people laid off in Period 2 is 10.5 rounded to 10 in Figure 9-4.

With a discount in January the team obtains the following:

Observe that a January promotion when consumption increase is large results in a higher profit than the base case (Figure 9-1).

IMPACT OF OFFERING A PROMOTION IN APRIL For an April promotion, set the entry in cell E25 to 4 (Period 4, April). If the discount is offered in April, observe that the April demand fore- cast is obtained as 3,800 ! 2 " 0.2 ! (2,200 " 2,200) # 8,480. With a promotion in April and a large increase in consumption, the April peak is much higher in Figure 9-5 compared to peak demand in Figure 9-4 (with a January promotion). For an April promotion with a large increase in consumption, the resulting demand forecast is as shown cells J5:J10 of Figure 9-5. The opti- mal aggregate plan is obtained using Solver and is shown in Figure 9-5.

With a discount in April the team obtains the following:

Profit over planning horizon = $247,320 Revenue over palnning horizon = $783,520

Total cost over planning horizon = $536,200

Profit over planning horizon = $242,810 Revenue over palnning horizon = $699,560

Total cost over planning horizon = $456,750

FIGURE 9-4 Optimal Aggregate Plan When Discounting Price in January to $39 with Large Increase in Demand

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FIGURE 9-5 Optimal Aggregate Plan When Discounting Price in April to $39 with Large Increase in Demand

When comparing Figures 9-5 and 9-4, observe that with an April promotion (Figure 9-5), there are no layoffs and the full workforce is maintained. The April promotion requires a much higher level of seasonal inventory and also uses stockouts and subcontracting to a greater extent than a January promotion. It is clear that costs will go up significantly with an April promotion. The interesting observation is that revenues go up even more (because of a larger consumption increase) making overall profits higher with an April promotion compared to a January promo- tion. As a result, when the increase in consumption from discounting is large and forward buying is a small part of the increase in demand from discounting, the supply chain is better off offering the discount in the peak-demand month of April even though this action significantly increases supply chain costs.

Exactly as discussed earlier, the optimal aggregate plan and profitability can also be deter- mined for the case in which the unit price is $31 (enter 31 in cell H31) and the discounted price is $30. The results of the various instances are summarized in Table 9-2.

From the results in Table 9-2, we can draw the following conclusions regarding the impact of promotions:

1. As seen in Table 9-2, average inventory increases if a promotion is run during the peak period and decreases if the promotion is run during the off-peak period.

2. Promoting during a peak-demand month may decrease overall profitability if there is a small increase in consumption and a significant fraction of the demand increase results from a forward buy. In Table 9-2, observe that running a promotion in April decreases profitability when forward buying is 20 percent and the demand increase from increased consumption and substitution is 10 percent.

Table 9-2 Supply Chain Performance Under Different Scenarios

Regular Price

Promotion Price

Promotion Period

Percentage of Increase in Demand

Percentage of Forward

Buy Profit Average

Inventory

$40 $40 NA NA NA $217,725 895

$40 $39 January 10% 20% $221,485 523

$40 $39 April 10% 20% $211,283 938

$40 $39 January 100% 20% $242,810 208

$40 $39 April 100% 20% $247,320 1,492

$31 $31 NA NA NA $73,725 895

$31 $30 January 100% 20% $84,410 208

$31 $30 April 100% 20% $69,120 1,492

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