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Table 1 MDC Atlanta Chicago Dallas Hagerstown Kansas City Los Angeles Ravenna Seattle Totals Demanda Di Ii Embarques 26,070,000 23,321,000 13,244,000...

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Table 1 Demanda Di Ii MDC Embarques Estoque Atlanta 26,070,000 3,784,333 Chicago 23,321,000 2,188,417 Dallas 13,244,000 2,159,250 Hagerstown 38,193,000 5,824,583 Kansas City 15,950,000 1,592,333 Los Angeles 21,470,000 3,666,500 Ravenna 25,853,000 2,918,250 SeaTle 4,922,000 959,833 Totals 169,023,000 23,093,500 Table 2 Pedido por Canal Por Canal Jan Fev Mar Abr Mai Jun Jul Ago Set Out Nov Dez ±otal ano C&I Itens pedidos 46307 55013 44683 54528 48492 42230 46709 50983 46792 65775 57932 47152 606596 B/O 10795 13084 11083 11974 10173 7759 7979 11382 8719 10850 9571 6910 120279 % de serviços 76.70% 76.20% 75.20% 78.00% 79% 81.60% 82.90% 77.70% 81.40% 83.50% 83.50% 85.30% 80.08% Consumidor Itens requisitados 24709 28023 21511 23487 29644 21204 24089 25958 26182 37272 33650 25482 321211 B/O 4214 5081 3331 3651 4373 2801 2925 3480 3196 4797 3652 2074 43575 % de serviços 82.90% 81.90% 84.50% 84.50% 85.20% 86.80% 87.90% 86.60% 87.80% 87.10% 89.10% 91.90% 86.35% OEM Itens requisitados 1038 1396 1028 1260 1058 1019 1208 1215 1147 1526 1279 1122 14296 B/O 301 387 289 325 252 225 256 278 228 315 224 193 3273 % de serviços 71.00% 72.30% 71.90% 74.20% 76.20% 77.90% 78.80% 77.10% 80.10% 79.40% 82.50% 82.80% 77.02% ±O±AL ±otal Itens requisitados 72054 84432 67222 79275 79194 64453 72006 78156 74121 104573 92861 73756 942103 34.10% ±otal B/O 15310 18552 14703 15950 14798 10785 11160 15140 12143 15962 13447 9177 167127 Media % de serviços 76.87% 76.80% 77.20% 78.90% 80.13% 82.10% 83.20% 80.47% 83.10% 83.33% 85.03% 86.67% 1 Demanda ±otal: 169,023,000 Inventario ±otal 23,093,500 Demanda Consu 57,628,568 lampadas Inventario Consumidor 7,873,753 Table 3 Custo do transporte e tempos de reabastecimento por MDC CD Principal e transporte s/ carga com ±empo de reabaste±empo de reabastecimento de saida Atlanta 600 2 2 Chicago 350 1 2 Dallas 1200 3 2 Hagerstown 475 1 2 Kansas City 700 2 3 Los Angeles 1800 2 2 Ravenna 250 1 2 Sea±le 1800 6 2 LOC 600 1 2 0 10,000,000 20,000,000 30,000,000 40,000,000 50,000,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 f(x) = 0.1398426776x - 67891.2379372417 R² = 0.8255736569 Linear Regression Embarques anuais Estoque médio
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AMERICAN LIGHTING PRODUCTS Teaching Note Strategy American Lighting Products is a manufacturer of fluorescent lamps in various sizes for industrial and consumer use. As frequently happens in business, top management has requested that inventories be reduced across the board, but it does not want to sacrifice customer service. Sue Smith and Bryan White have been asked to eliminate 20 percent of the finished goods inventory. Their plan is to reduce the number of stocking locations and, thereby, eliminate the amount of inventory needed. Of course, they must recognize that with fewer stocking points, transportation costs are likely to increase and customer delivery times may increase as well. On the other hand, facility fixed cost may be reduced. The purpose of this case is to allow students to examine inventory policy and planning through aggregate inventory management procedures. They also can see the connection between location and inventory levels. Answers to Questions (1) Evaluate the company's current inventory management procedures. The company's procedures for controlling inventory levels are at the heart of whether inventory reductions are likely to be achieved through inventory consolidation. The company appears to be using some form of reorder point control for the entire system inventory, but it is modified by the need to produce in production lot sizes. It is not clear how the reorder point is established. If it is based on economic order quantity principles, then the effect of the principles becomes distorted by the need to produce to a lot size that is different from the economic order quantity. Therefore, average inventory levels in a warehouse will not be related to the square root of the warehouse's throughput (demand), i.e., throughput raised to the 0.5 power.1 Rather, the throughput will be raised to a higher exponent between 0.5 and 1.0. The above ideas can be verified by plotting the data given in Table 1 of the case and then fitting a curve of the form I = TP . Note: The curve can be found from standard linear regression techniques when the equation is converted to a linear form through a logarithmic transformation, i.e., lnI = ln + lnTP. The results are shown in Figure 1. The inventory curve is I = 2.99TP 0.816 with r = 0.86, where I and TP are in lamps. The projected inventory reduction can be calculated by using this formula. From the plot of the inventory data, we can see that there is substantial variation about the fitted inventory curve. There is not a consistent turnover ratio between the warehouses. This probably results from the centralized control policy. On the other hand, improved control may be achieved by using a pull procedure at each MDC. The data available in the case do not let us explore this issue. Based on the economic order quantity formula, the average inventory level (AIL) for an item held in inventory can be estimated as AIL = Q / 2 = 2 DS / IC / 2 . Collecting all constants into K, we have AIL=K(D)0.5, where D is demand, or throughput. 1 122 FIGURE 1 Plot of MDC average inventory vs. annual throughput. (2) Should establishing the LOC be pursued? One of the ideas proposed in the case is to consolidate all Consumer product line items into one large order center (LOC). Evaluating the impact of the LOC on inventory reduction requires that an assumption be made as to how much demand and associated inventory of the total belongs to Consumer products. Table 2 of the case gives the order and back order breakdown by sales channel. Using this data, total consumer demand is 312,211 line items, or 33.4 percent of the total line items. The assumption is that the same percentage applies to total demand. Hence, Consumer demand is 33.4%169,023,000 = 56,453,682 lamps. From the inventory-throughput curve, we can estimate the amount of inventory needed at the single LOC. That is, I = 2.997(56,453,682)0.816 = 6,339,684 lamps. If Consumer products account for 33.4% of total inventory, then there are 33.4%23,093,500 = 7,713,229 lamps in Consumer inventory. The reduction that can be projected is 7,713,229 - 6,339,684 = 1,373,545 lamps for a reduction of Reduction = 1,373,545 100 = 17.8% 7,713,229 in
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Consumer inventory levels, but only a 6 percent reduction in overall inventory levels. The 20 percent reduction goal is not achieved. Other alternatives need to be explored. (3) Does reducing the number of stocking locations have the potential for reducing system inventories by 20 percent? Is there enough information available to make a good inventory reduction decision? The second alternative proposed in the case is to reduce the number of MDCs from eight to a smaller number. In order to evaluate this proposal, it needs to be determined which MDCs will be consolidated and the associated total demand flowing through the consolidated facilities. The inventory-throughput relationship can then be used to estimate the resulting inventory levels. For example, if the Seattle and Los Angeles MDCs are combined, the consolidated demand would be 4,922,000 + 21,470,000 = 26,392,000 lamps. The combined inventory is projected to be I = 2.997(26,392,000)0.816 = 123 3,408,852 lamps, compared with the inventory for the two locations of 4,626,333, as shown in Table 1. This yields a 26.3 percent reduction from current levels. Table 1 shows other possible MDC consolidations and the resulting inventory reductions that can be projected. TABLE 1 Inventory Reduction for Selected MDC Combinations, in Lamps Combined Combined Inventory MDC combination demand inventory reduction Seattle/Los Angeles 26,392,000 3,408,852 1,217,481 Kansas City/Dallas 29,194,000 3,701,403 50,181 Chicago/Ravenna 49,174,000 5,664,257 -557,590 Atlanta/Dallas 39,314,000 4,718,862 1,224,721 Kansas City/Chicago 39,271,000 4,714,650 -933,900 Ravenna/Hagerstown 64,046,000 7,027,231 1,715,607 K City/Dallas/Chicago 52,515,000 5,976,377 -36,377 Ravenna/H'town/Chicago 87,367,000 7,508,054 3,423,196 Atlanta/Dallas/K City 55,264,000 5,242,351 2,293,566 From the MDC combinations in Table 1, proximity to each other is a primary consideration in order to not increase transportation costs or jeopardize delivery service any more than necessary. Several options can be identified that yield a 20 percent inventory reduction. These are: Inventory reduction, lamps 1,217,481 3,423,196 4,640,677 1,217,481 1,224,721 1,715,602 4,157,804 1,217,481 1,715,602 2,293,566 5,226,649 Total inventory reduction Option 1 MDC combinations LA/Seattle Ravenna/H'town/Chicago Total reduction LA/Seattle Kansas City/Hagerstown Ravenna/Hagerstown Total reduction LA/Seattle Ravenna/Hagerstown Atlanta/Dallas/K City Total reduction 20.1% 2 18.0% 3 22.6% Options 1 and 3 achieve the 20 percent reduction goal, although other MDC combinations not evaluated may also do so. The maximum reduction would be achieved with one MDC. The total inventory would be I = 2.997(169,023,000)0.816 = 15,512,812 lamps, for a system reduction of 32.8 percent. However, we must recognize that as the number of warehouses is decreased, outbound transportation costs will increase. Inbound transportation costs to the combined MDC will remain about the same, since 124 replenishment shipments are already in truckload quantities. Some difference in cost will result from differences in the length of the hauls to the warehouses. On the other hand, outbound costs may substantially increase, since the combined MDC locations are likely to be more removed from customers then they are at present. Outbound transportation rates will be higher, as they are likely to be for shipments of less-than-truckload quantities. If the sum of the inbound and outbound transportation cost increases is greater than the inventory carrying cost reduction, then the decision to reduce inventories must be questioned. Calculating all transportation cost changes is not possible, since the case study does not provide sufficient data on outbound transportation rates. However, they should be determined before and after consolidation to assess the tradeoff between inventory reduction and transportation costs increases. On the other hand, inbound transportation costs can be found, as shown below for option 1, where the consolidation points are Los Angeles and Hagerstown. Annual demand, lamps Location Seattle 4,922,000 Los Angeles 21,470,000 Ravenna 25,853,000 Hagerstown 38,193,000 Chicago 23,321,000 Total 113,759,000 a (4,922,000/35,000)1800 = 253,131 TL rate, $/TL 1800 1800 250 475 350 Transport cost, $ 253,131a 1,104,171 184,664 518,334 233,210 2,293,510 Combined
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