Download presentation

Presentation is loading. Please wait.

Published byJaylen Garrish Modified over 2 years ago

1
1 1 Slide © 2001 South-Western College Publishing/Thomson Learning Anderson Sweeney Williams Anderson Sweeney Williams Slides Prepared by JOHN LOUCKS QUANTITATIVE METHODS FOR BUSINESS 8e QUANTITATIVE METHODS FOR BUSINESS 8e

2
2 2 Slide Chapter 13 Inventory Management: Independent-Demand n Economic Order Quantity (EOQ) Model n Economic Production Lot Size Model n An Inventory Model with Planned Shortages n Quantity Discounts for the EOQ Model n A Single-Period Inventory Model with Probabilistic Demand n An Order-Quantity, Reorder-Point Model with Probabilistic Demand n A Periodic-Review Model with Probabilistic Demand

3
3 3 Slide Inventory Models n The study of inventory models is concerned with two basic questions: How much should be ordered each timeHow much should be ordered each time When should the reordering occurWhen should the reordering occur n The objective is to minimize total variable cost over a specified time period (assumed to be annual in the following review).

4
4 4 Slide Inventory Costs n Ordering cost -- salaries and expenses of processing an order, regardless of the order quantity n Holding cost -- usually a percentage of the value of the item assessed for keeping an item in inventory (including finance costs, insurance, security costs, taxes, warehouse overhead, and other related variable expenses) n Backorder cost -- costs associated with being out of stock when an item is demanded (including lost goodwill) n Purchase cost -- the actual price of the items n Other costs

5
5 5 Slide Deterministic Models n The simplest inventory models assume demand and the other parameters of the problem to be deterministic and constant. n The deterministic models covered in this chapter are: Economic order quantity (EOQ)Economic order quantity (EOQ) Economic production lot sizeEconomic production lot size EOQ with planned shortagesEOQ with planned shortages EOQ with quantity discountsEOQ with quantity discounts

6
6 6 Slide Economic Order Quantity (EOQ) n The most basic of the deterministic inventory models is the economic order quantity (EOQ). n The variable costs in this model are annual holding cost and annual ordering cost. n For the EOQ, annual holding and ordering costs are equal.

7
7 7 Slide Economic Order Quantity n Assumptions Demand is constant throughout the year at D items per year.Demand is constant throughout the year at D items per year. Ordering cost is $ C o per order.Ordering cost is $ C o per order. Holding cost is $ C h per item in inventory per year.Holding cost is $ C h per item in inventory per year. Purchase cost per unit is constant (no quantity discount).Purchase cost per unit is constant (no quantity discount). Delivery time (lead time) is constant.Delivery time (lead time) is constant. Planned shortages are not permitted.Planned shortages are not permitted.

8
8 8 Slide Economic Order Quantity n Formulas Optimal order quantity: Q * = 2 DC o / C hOptimal order quantity: Q * = 2 DC o / C h Number of orders per year: D / Q *Number of orders per year: D / Q * Time between orders (cycle time): Q */ D yearsTime between orders (cycle time): Q */ D years Total annual cost: [(1/2) Q * C h ] + [ DC o / Q *]Total annual cost: [(1/2) Q * C h ] + [ DC o / Q *] (holding + ordering) (holding + ordering)

9
9 9 Slide Example: Barts Barometer Business n Economic Order Quantity Model Bart's Barometer Business (BBB) is a retail outlet which deals exclusively with weather equipment. Currently BBB is trying to decide on an inventory and reorder policy for home barometers. Barometers cost BBB $50 each and demand is about 500 per year distributed fairly evenly throughout the year. Reordering costs are $80 per order and holding costs are figured at 20% of the cost of the item. Barometers cost BBB $50 each and demand is about 500 per year distributed fairly evenly throughout the year. Reordering costs are $80 per order and holding costs are figured at 20% of the cost of the item. BBB is open 300 days a year (6 days a week and closed two weeks in August). Lead time is 60 working days.

10
10 Slide Example: Barts Barometer Business n Total Variable Cost Model Total Costs = (Holding Cost) + (Ordering Cost) Total Costs = (Holding Cost) + (Ordering Cost) TC = [ C h ( Q /2)] + [ C o ( D / Q )] TC = [ C h ( Q /2)] + [ C o ( D / Q )] = [.2(50)( Q /2)] + [80(500/ Q )] = 5 Q + (40,000/ Q )

11
11 Slide Example: Barts Barometer Business n Optimal Reorder Quantity Q * = 2 DC o / C h = 2(500)(80)/10 = Q * = 2 DC o / C h = 2(500)(80)/10 = n Optimal Reorder Point Lead time is m = 60 days and daily demand is d = 500/300 or Lead time is m = 60 days and daily demand is d = 500/300 or Thus the reorder point r = (1.667)(60) = 100. Bart should reorder 90 barometers when his inventory position reaches 100 (that is 10 on hand and one outstanding order). Thus the reorder point r = (1.667)(60) = 100. Bart should reorder 90 barometers when his inventory position reaches 100 (that is 10 on hand and one outstanding order).

12
12 Slide Example: Barts Barometer Business n Number of Orders Per Year Number of reorder times per year = (500/90) = 5.56 or once every (300/5.56) = 54 working days (about every 9 weeks). n Total Annual Variable Cost TC = 5(90) + (40,000/90) = = $894.

13
13 Slide Example: Barts Barometer Business Well now use a spreadsheet to implement the Economic Order Quantity model. Well confirm our earlier calculations for Barts problem and perform some sensitivity analysis. This spreadsheet can be modified to accommodate other inventory models presented in this chapter.

14
14 Slide Example: Barts Barometer Business n Partial Spreadsheet with Input Data

15
15 Slide Example: Barts Barometer Business n Partial Spreadsheet Showing Formulas for Output

16
16 Slide Example: Barts Barometer Business n Partial Spreadsheet Showing Output

17
17 Slide Example: Barts Barometer Business n Summary of Spreadsheet Results A 16.15% negative deviation from the EOQ resulted in only a 1.55% increase in the Total Annual Cost.A 16.15% negative deviation from the EOQ resulted in only a 1.55% increase in the Total Annual Cost. Annual Holding Cost and Annual Ordering Cost are no longer equal.Annual Holding Cost and Annual Ordering Cost are no longer equal. The Reorder Point is not affected, in this model, by a change in the Order Quantity.The Reorder Point is not affected, in this model, by a change in the Order Quantity.

18
18 Slide Economic Production Lot Size n The economic production lot size model is a variation of the basic EOQ model. n A replenishment order is not received in one lump sum as it is in the basic EOQ model. n Inventory is replenished gradually as the order is produced (which requires the production rate to be greater than the demand rate). n This model's variable costs are annual holding cost and annual set-up cost (equivalent to ordering cost). n For the optimal lot size, annual holding and set-up costs are equal.

19
19 Slide Economic Production Lot Size n Assumptions Demand occurs at a constant rate of D items per year.Demand occurs at a constant rate of D items per year. Production rate is P items per year (and P > D ).Production rate is P items per year (and P > D ). Set-up cost: $ C o per run.Set-up cost: $ C o per run. Holding cost: $ C h per item in inventory per year.Holding cost: $ C h per item in inventory per year. Purchase cost per unit is constant (no quantity discount).Purchase cost per unit is constant (no quantity discount). Set-up time (lead time) is constant.Set-up time (lead time) is constant. Planned shortages are not permitted.Planned shortages are not permitted.

20
20 Slide Economic Production Lot Size n Formulas Optimal production lot-size:Optimal production lot-size: Q * = 2 DC o /[(1- D / P ) C h ] Q * = 2 DC o /[(1- D / P ) C h ] Number of production runs per year: D / Q *Number of production runs per year: D / Q * Time between set-ups (cycle time): Q */ D yearsTime between set-ups (cycle time): Q */ D years Total annual cost: [(1/2)(1- D / P ) Q * C h ] + [ DC o / Q *]Total annual cost: [(1/2)(1- D / P ) Q * C h ] + [ DC o / Q *] (holding + ordering) (holding + ordering)

21
21 Slide Example: Non-Slip Tile Co. n Economic Production Lot Size Model Non-Slip Tile Company (NST) has been using production runs of 100,000 tiles, 10 times per year to meet the demand of 1,000,000 tiles annually. The set-up cost is $5,000 per run and holding cost is estimated at 10% of the manufacturing cost of $1 per tile. The production capacity of the machine is 500,000 tiles per month. The factory is open 365 days per year.

22
22 Slide Example: Non-Slip Tile Co. n Total Annual Variable Cost Model This is an economic production lot size problem with D = 1,000,000, P = 6,000,000, C h =.10, C o = 5,000 D = 1,000,000, P = 6,000,000, C h =.10, C o = 5,000 TC = (Holding Costs) + (Set-Up Costs) TC = (Holding Costs) + (Set-Up Costs) = [ C h ( Q /2)(1 - D / P )] + [ DC o / Q ] = [ C h ( Q /2)(1 - D / P )] + [ DC o / Q ] = Q + 5,000,000,000/ Q = Q + 5,000,000,000/ Q

23
23 Slide Example: Non-Slip Tile Co. n Optimal Production Lot Size Q * = 2 DC o /[(1 - D / P ) C h ] Q * = 2 DC o /[(1 - D / P ) C h ] = 2(1,000,000)(5,000) /[(.1)(1 - 1/6)] = 2(1,000,000)(5,000) /[(.1)(1 - 1/6)] = 346,410 = 346,410 n Number of Production Runs Per Year The number of runs per year = D / Q * = 2.89 times per year. The number of runs per year = D / Q * = 2.89 times per year.

24
24 Slide Example: Non-Slip Tile Co. n Total Annual Variable Cost How much is NST losing annually by using their present production schedule? How much is NST losing annually by using their present production schedule? Optimal TC =.04167(346,410) + 5,000,000,000/346,410 = $28,868 Current TC =.04167(100,000) + 5,000,000,000/100,000 Current TC =.04167(100,000) + 5,000,000,000/100,000 = $54,167 Difference = 54, ,868 = $25,299 Difference = 54, ,868 = $25,299

25
25 Slide Example: Non-Slip Tile Co. n Idle Time Between Production Runs There are 2.89 cycles per year. Thus, each cycle lasts (365/2.89) = days. The time to produce 346,410 per run = (346,410/6,000,000)365 = 21.1 days. Thus, the machine is idle for = days between runs.

26
26 Slide Example: Non-Slip Tile Co. n Maximum Inventory Current Policy: maximum inventory = (1- D / P ) Q * = (1- 1/6 )100,000 83,333 = (1- 1/6 )100,000 83,333 Optimal Policy: Optimal Policy: maximum inventory = (1- 1/6 )346,410 = 288,675. n Machine Utilization The machine is producing tiles D / P = 1/6 of the time.

27
27 Slide EOQ with Planned Shortages n With the EOQ with planned shortages model, a replenishment order does not arrive at or before the inventory position drops to zero. n Shortages occur until a predetermined backorder quantity is reached, at which time the replenishment order arrives. n The variable costs in this model are annual holding, backorder, and ordering. n For the optimal order and backorder quantity combination, the sum of the annual holding and backordering costs equals the annual ordering cost.

28
28 Slide EOQ with Planned Shortages n Assumptions Demand occurs at a constant rate of D items per year.Demand occurs at a constant rate of D items per year. Ordering cost: $ C o per order.Ordering cost: $ C o per order. Holding cost: $ C h per item in inventory per year.Holding cost: $ C h per item in inventory per year. Backorder cost: $ C b per item backordered per year.Backorder cost: $ C b per item backordered per year. Purchase cost per unit is constant (no quantity discount).Purchase cost per unit is constant (no quantity discount). Set-up time (lead time) is constant.Set-up time (lead time) is constant. Planned shortages are permitted (backordered demand units are withdrawn from a replenishment order when it is delivered).Planned shortages are permitted (backordered demand units are withdrawn from a replenishment order when it is delivered).

29
29 Slide EOQ with Planned Shortages n Formulas Optimal order quantity:Optimal order quantity: Q * = 2 DC o / C h ( C h + C b )/ C b Q * = 2 DC o / C h ( C h + C b )/ C b Maximum number of backorders:Maximum number of backorders: S * = Q *( C h /( C h + C b )) S * = Q *( C h /( C h + C b )) Number of orders per year: D / Q *Number of orders per year: D / Q * Time between orders (cycle time): Q */ D yearsTime between orders (cycle time): Q */ D years Total annual cost:Total annual cost: [ C h ( Q *- S *) 2 /2 Q *] + [ DC o / Q *] + [ S *2 C b /2 Q *] [ C h ( Q *- S *) 2 /2 Q *] + [ DC o / Q *] + [ S *2 C b /2 Q *] (holding + ordering + backordering) (holding + ordering + backordering)

30
30 Slide Example: Hervis Rent-a-Car n EOQ with Planned Shortages Model Hervis Rent-a-Car has a fleet of 2,500 Rockets serving the Los Angeles area. All Rockets are maintained at a central garage. On the average, eight Rockets per month require a new engine. Engines cost $850 each. There is also a $120 order cost (independent of the number of engines ordered). Hervis has an annual holding cost rate of 30% on engines. It takes two weeks to obtain the engines after they are ordered. For each week a car is out of service, Hervis loses $40 profit. Hervis has an annual holding cost rate of 30% on engines. It takes two weeks to obtain the engines after they are ordered. For each week a car is out of service, Hervis loses $40 profit.

31
31 Slide Example: Hervis Rent-a-Car n Optimal Order Policy D = 8 x 12 = 96; C o = $120; C h =.30(850) = $255; C b = 40 x 52 = $2080 Q * = 2 DC o / C h ( C h + C b )/ C b Q * = 2 DC o / C h ( C h + C b )/ C b = 2(96)(120)/255 x ( )/2080 = 2(96)(120)/255 x ( )/2080 = = S * = Q *( C h /( C h + C b )) S * = Q *( C h /( C h + C b )) = 10(255/( )) = = 10(255/( )) =

32
32 Slide Example: Hervis Rent-a-Car n Optimal Order Policy (continued) Demand is 8 per month or 2 per week. Since lead time is 2 weeks, lead time demand is 4. Thus, since the optimal policy is to order 10 to arrive when there is one backorder, the order should be placed when there are 3 engines remaining in inventory.

33
33 Slide Example: Hervis Rent-a-Car n Stockout: When and How Long How many days after receiving an order does Hervis run out of engines? How long is Hervis without any engines per cycle? Inventory exists for C b /( C b + C h ) = 2080/( ) =.8908 of the order cycle. (Note, ( Q *- S *)/ Q * =.8908 also, before Q * and S * are rounded.) An order cycle is Q */ D =.1049 years = 38.3 days. Thus, Hervis runs out of engines.8908(38.3) = 34 days after receiving an order. Hervis is out of stock for approximately = 4 days.

34
34 Slide EOQ with Quantity Discounts n The EOQ with quantity discounts model is applicable where a supplier offers a lower purchase cost when an item is ordered in larger quantities. n This model's variable costs are annual holding, ordering and purchase costs. n For the optimal order quantity, the annual holding and ordering costs are not necessarily equal.

35
35 Slide EOQ with Quantity Discounts n Assumptions Demand occurs at a constant rate of D items per year.Demand occurs at a constant rate of D items per year. Ordering Cost is $ C o per order.Ordering Cost is $ C o per order. Holding Cost is $ C h = $ C i I per item in inventory per year (note holding cost is based on the cost of the item, C i ).Holding Cost is $ C h = $ C i I per item in inventory per year (note holding cost is based on the cost of the item, C i ). Purchase Cost is $ C 1 per item if the quantity ordered is between 0 and x 1, $ C 2 if the order quantity is between x 1 and x 2, etc.Purchase Cost is $ C 1 per item if the quantity ordered is between 0 and x 1, $ C 2 if the order quantity is between x 1 and x 2, etc. Delivery time (lead time) is constant.Delivery time (lead time) is constant. Planned shortages are not permitted.Planned shortages are not permitted.

36
36 Slide EOQ with Quantity Discounts n Formulas Optimal order quantity: the procedure for determining Q * will be demonstratedOptimal order quantity: the procedure for determining Q * will be demonstrated Number of orders per year: D / Q *Number of orders per year: D / Q * Time between orders (cycle time): Q */ D yearsTime between orders (cycle time): Q */ D years Total annual cost: [(1/2) Q * C h ] + [ DC o / Q *] + DCTotal annual cost: [(1/2) Q * C h ] + [ DC o / Q *] + DC (holding + ordering + purchase) (holding + ordering + purchase)

37
37 Slide Example: Nick's Camera Shop n EOQ with Quantity Discounts Model Nick's Camera Shop carries Zodiac instant print film. The film normally costs Nick $3.20 per roll, and he sells it for $5.25. Zodiac film has a shelf life of 18 months. Nick's average sales are 21 rolls per week. His annual inventory holding cost rate is 25% and it costs Nick $20 to place an order with Zodiac. If Zodiac offers a 7% discount on orders of 400 rolls or more, a 10% discount for 900 rolls or more, and a 15% discount for 2000 rolls or more, determine Nick's optimal order quantity. If Zodiac offers a 7% discount on orders of 400 rolls or more, a 10% discount for 900 rolls or more, and a 15% discount for 2000 rolls or more, determine Nick's optimal order quantity D = 21(52) = 1092; C h =.25( C i ); C o = 20 D = 21(52) = 1092; C h =.25( C i ); C o = 20

38
38 Slide Example: Nick's Camera Shop n Unit-Prices Economical, Feasible Order Quantities For C 4 =.85(3.20) = $2.72For C 4 =.85(3.20) = $2.72 To receive a 15% discount Nick must order at least 2,000 rolls. Unfortunately, the film's shelf life is 18 months. The demand in 18 months (78 weeks) is 78 X 21 = 1638 rolls of film. If he ordered 2,000 rolls he would have to scrap 372 of them. This would cost more than the 15% discount would save.

39
39 Slide Example: Nick's Camera Shop n Unit-Prices Economical, Feasible Order Quantities For C 3 =.90(3.20) = $2.88For C 3 =.90(3.20) = $2.88 Q 3 * = 2 DC o / C h = 2(1092)(20)/[.25(2.88)] = (not feasible) Q 3 * = 2 DC o / C h = 2(1092)(20)/[.25(2.88)] = (not feasible) The most economical, feasible quantity for C 3 is 900. The most economical, feasible quantity for C 3 is 900. For C 2 =.93(3.20) = $2.976For C 2 =.93(3.20) = $2.976 Q 2 * = 2 DC o / C h = 2(1092)(20)/[.25(2.976)] = Q 2 * = 2 DC o / C h = 2(1092)(20)/[.25(2.976)] = (not feasible) (not feasible) The most economical, feasible quantity for C 2 is 400. The most economical, feasible quantity for C 2 is 400.

40
40 Slide Example: Nick's Camera Shop n Unit-Prices Economical, Feasible Order Quantities For C 1 = 1.00(3.20) = $3.20For C 1 = 1.00(3.20) = $3.20 Q 1 * = 2 DC o / C h = 2(1092)(20)/.25(3.20) = (feasible) Q 1 * = 2 DC o / C h = 2(1092)(20)/.25(3.20) = (feasible) When we reach a computed Q that is feasible we stop computing Q's. (In this problem we have no more to compute anyway.)

41
41 Slide Example: Nick's Camera Shop n Total Cost Comparison Compute the total cost for the most economical, feasible order quantity in each price category for which a Q * was computed. TC i = (1/2)( Q i * C h ) + ( DC o / Q i *) + DC i TC i = (1/2)( Q i * C h ) + ( DC o / Q i *) + DC i TC 3 = (1/2)(900)(.72) +((1092)(20)/900)+(1092)(2.88) = 3493 TC 2 = (1/2)(400)(.744)+((1092)(20)/400)+(1092)(2.976) = 3453 TC 1 = (1/2)(234)(.80) +((1092)(20)/234)+(1092)(3.20) = 3681 Comparing the total costs for 234, 400 and 900, the lowest total annual cost is $3453. Nick should order 400 rolls at a time. Comparing the total costs for 234, 400 and 900, the lowest total annual cost is $3453. Nick should order 400 rolls at a time.

42
42 Slide Probabilistic Models n In many cases demand (or some other factor) is not known with a high degree of certainty and a probabilistic inventory model should actually be used. n These models tend to be more complex than deterministic models. n The probabilistic models covered in this chapter are: single-period order quantitysingle-period order quantity reorder-point quantityreorder-point quantity periodic-review order quantityperiodic-review order quantity

43
43 Slide Single-Period Order Quantity n A single-period order quantity model (sometimes called the newsboy problem) deals with a situation in which only one order is placed for the item and the demand is probabilistic. n If the period's demand exceeds the order quantity, the demand is not backordered and revenue (profit) will be lost. n If demand is less than the order quantity, the surplus stock is sold at the end of the period (usually for less than the original purchase price).

44
44 Slide Single-Period Order Quantity n Assumptions Period demand follows a known probability distribution:Period demand follows a known probability distribution: normal: mean is µ, standard deviation is normal: mean is µ, standard deviation is uniform: minimum is a, maximum is b uniform: minimum is a, maximum is b Cost of overestimating demand: $ c oCost of overestimating demand: $ c o Cost of underestimating demand: $ c uCost of underestimating demand: $ c u Shortages are not backordered.Shortages are not backordered. Period-end stock is sold for salvage (not held in inventory).Period-end stock is sold for salvage (not held in inventory).

45
45 Slide Single-Period Order Quantity n Formulas Optimal probability of no shortage:Optimal probability of no shortage: P(demand < Q *) = c u /( c u + c o ) P(demand < Q *) = c u /( c u + c o ) Optimal probability of shortage:Optimal probability of shortage: P(demand > Q *) = 1 - c u /( c u + c o ) P(demand > Q *) = 1 - c u /( c u + c o ) Optimal order quantity, based on demand distribution:Optimal order quantity, based on demand distribution: normal: Q * = µ + z normal: Q * = µ + z uniform: Q * = a + P(demand < Q *)( b - a ) uniform: Q * = a + P(demand < Q *)( b - a )

46
46 Slide Example: McHardee Press n Single-Period Order Quantity Model McHardee Press publishes the Fast Food Restaurant Menu Book and wishes to determine how many copies to print. There is a fixed cost of $5,000 to produce the book and the incremental profit per copy is $.45. Any unsold copies of the book can be sold at salvage at a $.55 loss. Sales for this edition are estimated to be normally distributed. The most likely sales volume is 12,000 copies and they believe there is a 5% chance that sales will exceed 20,000. How many copies should be printed?

47
47 Slide Example: McHardee Press n Single-Period Order Quantity m = 12,000. To find note that z = 1.65 corresponds to a 5% tail probability. Therefore, (20, ,000) = 1.65 or = 4848 (20, ,000) = 1.65 or = 4848 Using incremental analysis with C o =.55 and C u =.45, ( C u /( C u + C o )) =.45/( ) =.45 Using incremental analysis with C o =.55 and C u =.45, ( C u /( C u + C o )) =.45/( ) =.45 Find Q * such that P( D < Q *) =.45. The probability of 0.45 corresponds to z = Thus, Find Q * such that P( D < Q *) =.45. The probability of 0.45 corresponds to z = Thus, Q * = 12, (4848) = 11,418 books Q * = 12, (4848) = 11,418 books

48
48 Slide Example: McHardee Press n Single-Period Order Quantity (revised) If any unsold copies of the book can be sold at salvage at a $.65 loss, how many copies should be printed? C o =.65, ( C u /( C u + C o )) =.45/( ) =.4091 C o =.65, ( C u /( C u + C o )) =.45/( ) =.4091 Find Q * such that P( D < Q *) = z = -.23 gives this probability. Thus, Q * = 12, (4848) = 10,885 books Q * = 12, (4848) = 10,885 books However, since this is less than the breakeven volume of 11,111 books (= 5000/.45), no copies should be printed because if the company produced only 10,885 copies it will not recoup its $5,000 fixed cost of producing the book.

49
49 Slide Reorder Point Quantity n A firm's inventory position consists of the on-hand inventory plus on-order inventory (all amounts previously ordered but not yet received). n An inventory item is reordered when the item's inventory position reaches a predetermined value, referred to as the reorder point. n The reorder point represents the quantity available to meet demand during lead time. n Lead time is the time span starting when the replenishment order is placed and ending when the order arrives.

50
50 Slide Reorder Point Quantity n Under deterministic conditions, when both demand and lead time are constant, the reorder point associated with EOQ-based models is set equal to lead time demand. n Under probabilistic conditions, when demand and/or lead time varies, the reorder point often includes safety stock. n Safety stock is the amount by which the reorder point exceeds the expected (average) lead time demand.

51
51 Slide Safety Stock and Service Level n The amount of safety stock in a reorder point determines the chance of a stockout during lead time. n The complement of this chance is called the service level. n Service level, in this context, is defined as the probability of not incurring a stockout during any one lead time. n Service level, in this context, also is the long-run proportion of lead times in which no stockouts occur.

52
52 Slide Reorder Point n Assumptions Lead-time demand is normally distributedLead-time demand is normally distributed with mean µ and standard deviation. with mean µ and standard deviation. Approximate optimal order quantity: EOQApproximate optimal order quantity: EOQ Service level is defined in terms of the probability ofService level is defined in terms of the probability of no stockouts during lead time and is reflected in z. no stockouts during lead time and is reflected in z. Shortages are not backordered.Shortages are not backordered. Inventory position is reviewed continuously.Inventory position is reviewed continuously.

53
53 Slide Reorder Point n Formulas Reorder point: r = µ + zReorder point: r = µ + z Safety stock: zSafety stock: z Average inventory: 1/2( Q ) + zAverage inventory: 1/2( Q ) + z Total annual cost: [(1/2) Q * C h ] + [ z C h ] + [ DC o / Q *]Total annual cost: [(1/2) Q * C h ] + [ z C h ] + [ DC o / Q *] (holding(normal) + holding(safety) + ordering) (holding(normal) + holding(safety) + ordering)

54
54 Slide Example: Roberts Drug n Reorder Point Model Robert's Drugs is a drug wholesaler supplying 55 independent drug stores. Roberts wishes to determine an optimal inventory policy for Comfort brand headache remedy. Sales of Comfort are relatively constant as the past 10 weeks of data indicate: Week Sales (cases) Week Sales (cases) Week Sales (cases) Week Sales (cases)

55
55 Slide Example: Roberts Drug Each case of Comfort costs Roberts $10 and Roberts uses a 14% annual holding cost rate for its inventory. The cost to prepare a purchase order for Comfort is $12. What is Roberts optimal order quantity?

56
56 Slide Example: Roberts Drug n Optimal Order Quantity The average weekly sales over the 10 week period is 120 cases. Hence D = 120 X 52 = 6,240 cases per year; C h = (.14)(10) = 1.40; C o = 12. C h = (.14)(10) = 1.40; C o = 12. Q * = 2 DC o / C h = (2)(6240)(12)/1.40 = 327 Q * = 2 DC o / C h = (2)(6240)(12)/1.40 = 327

57
57 Slide Example: Roberts Drug The lead time for a delivery of Comfort has averaged four working days. Lead time has therefore been estimated as having a normal distribution with a mean of 80 cases and a standard deviation of 10 cases. Roberts wants at most a 2% probability of selling out of Comfort during this lead time. What reorder point should Roberts use?

58
58 Slide Example: Roberts Drug n Optimal Reorder Point Lead time demand is normally distributed with m = 80, = 10. Since Roberts wants at most a 2% probability of selling out of Comfort, the corresponding z value is That is, P ( z > 2.06) =.0197 (about.02). Hence Roberts should reorder Comfort when supply reaches m + z = (10) = 101 cases. The safety stock is 21 cases.

59
59 Slide Example: Roberts Drug n Total Annual Inventory Cost Ordering: ( DC o / Q *) = ((6240)(12)/327) = $229 Holding-Normal: (1/2) Q * C o = (1/2)(327)(1.40) = 229 Holding-Safety Stock: C h (21) = (1.40)(21) = 29 Holding-Safety Stock: C h (21) = (1.40)(21) = 29 TOTAL = $487 TOTAL = $487

60
60 Slide Periodic Review System n A periodic review system is one in which the inventory level is checked and reordering is done only at specified points in time (at fixed intervals usually). n Assuming the demand rate varies, the order quantity will vary from one review period to another. (This is in contrast to the continuous review system in which inventory is monitored continuously and a fixed- quantity order can be placed whenever the reorder point is reached.) n At the time a periodic-review order quantity is being decided, the concern is that the on-hand inventory and the quantity being ordered is enough to satisfy demand from the time this order is placed until the next order is received (not placed).

61
61 Slide Periodic Review Order Quantity n Assumptions Inventory position is reviewed at constant intervals (periods). Inventory position is reviewed at constant intervals (periods). Demand during review period plus lead time periodDemand during review period plus lead time period is normally distributed with mean µ and standard deviation. is normally distributed with mean µ and standard deviation. Service level is defined in terms of the probability ofService level is defined in terms of the probability of no stockouts during a review period plus lead time period and is reflected in z. no stockouts during a review period plus lead time period and is reflected in z. On-hand inventory at ordering time: IOn-hand inventory at ordering time: I Shortages are not backordered.Shortages are not backordered. Lead time is less than the length of the review period.Lead time is less than the length of the review period.

62
62 Slide Periodic Review Order Quantity n Formulas Replenishment level: M = µ + zReplenishment level: M = µ + z Order quantity: Q = M - IOrder quantity: Q = M - I

63
63 Slide Example: Ace Brush n Periodic Review Order Quantity Model Joe Walsh is a salesman for the Ace Brush Company. Every three weeks he contacts Dollar Department Store so that they may place an order to replenish their stock. Weekly demand for Ace brushes at Dollar approximately follows a normal distribution with a mean of 60 brushes and a standard deviation of 9 brushes. Once Joe submits an order, the lead time until Dollar receives the brushes is one week. Dollar would like at most a 2% chance of running out of stock during any replenishment period. If Dollar has 75 brushes in stock when Joe contacts them, how many should they order?

64
64 Slide Example: Ace Brush n Demand During Uncertainty Period The review period plus the following lead time totals 4 weeks. This is the amount of time that will elapse before the next shipment of brushes will arrive. Weekly demand is normally distributed with: Weekly demand is normally distributed with: Mean weekly demand, µ = 60 Mean weekly demand, µ = 60 Weekly standard deviation, = 9 Weekly standard deviation, = 9 Weekly variance, 2 = 81 Weekly variance, 2 = 81 Demand for 4 weeks is normally distributed with: Mean demand over 4 weeks, µ = 4x60 = 240 Mean demand over 4 weeks, µ = 4x60 = 240 Variance of demand over 4 weeks, 2 = 4x81 = 324 Variance of demand over 4 weeks, 2 = 4x81 = 324 Standard deviation over 4 weeks, = (324) 1/2 = 18 Standard deviation over 4 weeks, = (324) 1/2 = 18

65
65 Slide Example: Ace Brush n Replenishment Level M = µ + z where z is determined by the desired stockout probability. For a 2% stockout probability (2% tail area), z = Thus, M = (18) = 277 brushes As the store currently has 75 brushes in stock, Dollar should order: = 202 brushes The safety stock is: z = (2.05)(18) = 37 brushes

66
66 Slide The End of Chapter 13

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google