1. Review for Exam II This exam will be administered Wednesday, June 29, 2016, usual time and place

2. Mc Question 8  To add a module, like supply chain management, to the existing foray of modules that make up the ERP system, one must also add what:  a. another database engine  b. another module management engine  c. another client computer  d. nothing

3. Mc Question 15  The integrated group of business processes and activities that form the supply chain include all of the following except ________. A. procurement of services, materials, and components from suppliers B. production of the products and services C. distribution of products to the customers D. information technology  Hint: take a retailer perspective

4. MC Question 35  From the table in Question 32, which of the following represents the objective function for this problem? A. Min 4X 11 + 4X 12 + 4X 13 + 2X 21 + 5X 22 + 5X 23 + 3X 31 + 4X 23 + 3X 33 B. Max 4X 11 + 4X 12 + 4X 13 + 2X 21 + 5X 22 + 5X 23 + 3X 31 + 4X 23 + 3X 33 C. 4X 11 + 4X 12 + 4X 13 + 2X 21 + 5X 22 + 5X 23 + 3X 31 + 4X 23 + 3X 33 <= 17,000 D. 4X 11 + 4X 12 + 4X 13 + 2X 21 + 5X 22 + 5X 23 + 3X 31 + 4X 23 + 3X 33 >= 17,000

5. MC QUESTION 70: The number of daily calls received by a help desk between the hours of 9:00 a.m. to 10:00 a.m. can be described by the following probability distribution: CallsProbability 500.10 550.10 600.20 650.30 700.20 750.10 Use the following random numbers to simulate the number of calls to the help desk between 9:00 and 10:00 a.m. for the next five days: 39, 55, 75, 16, 70. If the first random number interval begins with 1, then the total number of calls received over the simulated five day period is ________ a. 375.b. 315. c. 325.d. 300. e. None of the above

6. In discrete/stochastic simulation, we are interested in  Entity idleness  Entity travel time  Entity time in the system  Resource utilization  All of the above

7. In discrete/stochastic simulation, which of the following components has time duration?  Events  Activities  Entities  Resources  All of the above

8. Discrete/stochastic simulation is appropriate for which of the following three decision environments  Decision Making (DM) under Certainty  DM under risk and uncertainty  DM under change and complexity

9. Math programming models, like the transportation and transshipment models we looked at, are appropriate for which decision making environment  Decision Making (DM) under Certainty  DM under risk and uncertainty  DM under change and complexity

10. What is reduced cost?  Reduced cost is only zero when the associated decision variable is nonzero. When the associated decision variable IS zero. It tells us by how much the associated cost//profit coefficient must be changed in order to get that decision variable to take a value above zero.

11. What is shadow price?  Shadow price measures the amount of change we can expect in the objective function value accruing from a unit increase in a constraint right-hand side.

12. In the example involving Southwest Airlines turns at a gate…..  What are the entities? Philip Peavy  Name some events… Jennifer Hendrix  Name some activities… Douglas Hallberg  What is the difference between an activity and an event? Jaclyn Davidson  What is the relationship between an event and an activity? Dylan West

13. Collin Christian Buster Brown Shoes # of work shoes # of dress shoes X1X2 X0X0 MAX12*600+16*20010400 S.T.1*600+2*2001000<=1,000Labor 1*600+1*200800<=1,000Leather 1*600+0*200600<=600Work Heels 0*600+1*200 <=500Dress Heels All X's >= 0 Which constraints have slack? (slack is the amount of resource that is unused) Which constraints have shadow prices of zero?

14. Lindsi Confer  If you increased the amount of labor available by 1 unit, how much increase will you get in the objective function? Constraints – Buster Brown shoes FinalShadowConstraintAllowable CellNameValuePriceR.H. SideIncreaseDecrease $J$5Labor10008 400 $J$6Leather800010001E+30200 $J$7Work Heels6004 400600 $J$8Dress Heels20005001E+30300

15. Jennifer Thompson  Suppose, in the Buster Brown problem you were to increase the number of dress heels by 100…..what would be the effect on the objective fcn? Constraints FinalShadowConstraintAllowable CellNameValuePriceR.H. SideIncreaseDecrease $J$5Labor10008 400 $J$6Leather800010001E+30200 $J$7Work Heels6004 400600 $J$8Dress Heels20005001E+30300

16. Joshua Orfi  If you increased the amount of leather available by 1 unit, how much increase would you get in the objective function? Constraints-Buster Brown FinalShadowConstraintAllowable CellNameValuePriceR.H. SideIncreaseDecrease $J$5Labor10008 400 $J$6Leather800010001E+30200 $J$7 Work Heels6004 400600 $J$8 Dress Heels20005001E+30300

17. Kirushel Ejisu  The optimal solution calls for home many lbs of mix 1 to produce? Peanuts, Cashews, Almonds # of lbs of MIX 1 # of lbs of MIX 2 X1X2X0 MAX1.79*0+1.99*5250068460 Minus0.62875*0+0.686*52500 S.T.0.625*0+0.4*5250021000<=42,000Peanuts 0.375*0+0.4*5250021000<=21,000Cashews 0*0+0.2*5250010500<=14,000Almonds All X's >= 0

18. Mackenzie Wells Peanuts, Cashews Sensitivity  Which type of nut would you add more of to increase profitability? Peanuts, Cashews or Almonds?? Constraints FinalShadowConstraintAllowable CellNameValuePriceR.H. SideIncreaseDecrease $K$6Peanuts210000420001E+3021000 $K$7Cashews210003.2621000700021000 $K$8Almonds105000140001E+303500

19. Mitchell Williams  By how much must you adjust the objective coefficient of X2 in order to get it to take on a value greater than 0? Adjustable Cells FinalReducedObjectiveAllowable CellNameValueCostCoefficientIncreaseDecrease $E$3* X15600001.231251E+300.00875 $I$3* X20-0.0093333331.3040.0093333331E+30

20. Homework Problem #10-2 Average Total Raw MaterialsInventoryUnit Cost ($)Value oak8000648000 pine4500418000 brass fixtures120089600 stains300026000 joiners9001 Total82500 Work in Process frames200306000 drawers400104000 panels6005030000 chests12011013200 tables90 8100 Total61300 Finished Goods chests300500150000 coffee tables20035070000 Total220000 Cost of goods sold =3700000 Average aggregate value of inventory =363800 Inventory turns =10.17 Days of supply =35.89 HW 10-2—Not Assigned

21. HW 10-4 Homework Problem #10-4 Average Total Raw MaterialsInventoryUnit Cost ($)Value 172008.561200 245007.232400 3320015.449280 4480013.765760 5690010.572450 Total281090 Work in Process A100162001620000 B7013500945000 C606100366000 D3514400504000 Total3435000 Finished Goods X20787001574000 Y1065300653000 Z1086000860000 Total3087000 Cost of goods sold =18500000 Average aggregate value of inventory =6803090 Inventory turns =2.72 Days of supply =134.22

22. HW 10-6—not assigned Homework Problem #10-6 Supplier 1Supplier 2 Cost of goods sold836000014800000 Raw materials275000870000 Work-in-process62000550000 Finished goods33000180000 Inventory turns =22.69.3 Weeks of supply =2.35.6 Best

23. HW S11-12—not assigned Homework #S11-12 ClosingOpen PlantsAvailableEmployees PlantABCEmployeesTransferred 10600 2551040105 301007010 Demand558040 Transferred558040 Output =1660 Product Output (units/day): ClosingOpen Plant PlantABC 1586 210912 3768

24. Old HW S11-29 GrowerU.S. Ports (million lbs) Countries7. Mobile8. New Orleans9. Savannah10. JacksonvilleSupplyShipped 1. Brazil0.305.605.9 2. Columbia04.300 3. Indonesia01.202.63.8 4. Kenya2.8003.96.7 5. Cote d Ivoire002.50 6. Guatemala04.800 Shipped3.110.38.16.528 Capacity7.610.38.16.5 Plant Port11. New York 7. Mobile3.1 8. New Orleans10.3 9. Savannah8.1 10. Jacksonville6.5 Demand28 Shipped28 Transshipments: 7. Mobile0 8. New Orleans0 9. Savannah0 10. Jacksonville0 Cost = 2,206,000.00 Shipping Costs GrowerU.S. Ports ($ per milion lbs) Countries7. Mobile8. New Orleans9. Savannah10. Jacksonville 1. Brazil30000360002900041000 2. Columbia19000230002800035000 3. Indonesia53000470004500039000 4. Kenya45000540004800041000 5. Cote d Ivoire35000330002700029000 6. Guatemala14000170002400028000 Plant Port11. New York 7. Galveston61000 8. New Orleans55000 9. Savannah38000 10. Jacksonville43000

25. New HW S11-29 U.S. Port MobileNew OrleansSavannahJacksonvilleSupplyShipped Grower Country Brazil0.00 5.900.005.95.90 Colombia0.00 4.300.004.34.30 Indonesia0.00 3.803.83.80 Kenya0.00 6.706.76.70 Cote d'Ivoire0.00 2.500.002.52.50 Guatemala0.00 4.800.004.84.80 Shipped0.00 17.5010.502828.00 New York Mobile0.00 New Orleans0.00 Savannah17.50 Jacksonville10.50 Demand28.00 Shipped28.00 Cost $ 2,013,600 U.S. Port MobileNew OrleansSavannahJacksonville Grower Country 1Brazil $ 30,000 $ 36,000 $ 29,000 $ 41,000 2Colombia $ 19,000 $ 23,000 $ 28,000 $ 35,000 3Indonesia $ 53,000 $ 47,000 $ 45,000 $ 39,000 4Kenya $ 45,000 $ 54,000 $ 48,000 $ 41,000 5Cote d'Ivoire $ 35,000 $ 33,000 $ 27,000 $ 29,000 6Guatemala $ 14,000 $ 17,000 $ 24,000 $ 28,000 Shipping Costs U.S. PortNew York Mobile $ 61,000 New Orleans $ 55,000 Savannah $ 38,000 Jacksonville $ 43,000

26. HWS11-30 Shipping Costs ($): U.S. Warehouses PortNorfolkNew YorkSavannahSupplyShippedPortNorfolkNew YorkSavannah Hamburg4213055 Hamburg420390610 Marseilles00637863Marseilles510590470 Liverpool0370 Liverpool450360480 Shipped425063 Shipping Costs ($): Distribution Centers U.S. WarehousesDallasSt. LouisChicagoShippedWarehousesDallasSt. LouisChicago Norfolk0420 Norfolk756381 New York0050 New York12511095 Savannah603063Savannah688295 Demand604550 Shipped604550 Transshipment flows: Norfolk0 New York0 Savannah0 Cost =77362

27. HW S11-34—Not Assigned Homework #S11-34 U.S. Distributors Plants7. Texas8. Virginia9. OhioSupplyShipped 1. Germany0005.20 2. Belgium0006.30 3. Italy2.1004.52.1 Shipped2.100 Factories U.S. Distributors Plant4. PR5. Mexico6. PanamaShippedPlant7. Texas8. VA9. OhioShipped 1. Germany05.20 4. PR0000 2. Belgium006.3 5. Mexico005.2 3. Italy0002.16. Panama03.72.66.3 Demand2.13.77.8 Demand2.13.77.8 Shipped05.26.3 Shipped2.13.77.8 Transshipments: 4.PR0 5. Mexico0 6. Panama0 Cost =27120

28. HW S11-36—not assigned Shipping cost ($/container) U.S. PortAvailableContainers U.S. Port European Port4. Boston5. Savannah6. Mobile7. HoustonContainersShippedEuropean Port4. Boston5. Savannah6. Mobile7. Houston 1. Antwerp854000125 1. Antwerp1,7251,8002,3452,700 2. Cherbourg07015125210 2. Cherbourg1,8251,7501,9452,320 3. Barcelona00850160853. Barcelona2,0602,1752,0502,475 Containers Shipped85110100125 Shipping cost($/container) Inland PortIntermodalContainers Inland Port U.S. Port8. Ohio9. Texas 10. North CarolinaCapacityShippedU.S. Port8. Ohio9. Texas 10. North Carolina 4. Boston0085 4. Boston825545320 5. Savannah055 110 5. Savannah750675450 6. Mobile10000 6. Mobile325605690 7. Houston705501301257. Houston2705101,050 Intermodal Capacity170240140 550 Containers shipped170110140 Shipping cost ($/container) Distribution CentersContainers Distribution Centers Inland Port 11. Phoenix12. Columbus13. Kansas City14. Louisville 15. MemphisShippedInland Port11. Phoenix12. Columbus13. Kansas City 14. Louisville15. Memphis 8. Ohio850355001708. Ohio450830565420960 9. Texas04070001109. Texas880520450380660 10. North Carolina02000120140 10. North Carolina1,3503901,200450310 Demand856010550120 Containers Shipped856010550120 TransshipmentTotal cost = 1,179,400.00 Flows 0

29. Case Problem S11.1 Sites KingsportDanvilleMaconSelmaColumbusAllentownWhitewaterLos CanosDurasSupplyShipped Kingsport2501619000000285 Danville019600000800276 Macon002140000078292 Selma0017250360000303 Columbus000021406500279 Allentown038000250000288 Whitewater00000025000 Los Canos00000002500 Duras00000000250 Demand250 315330355 Shipped250 315330328 Total Cost =2630.00 Shipping Costs ($): Sites KingsportDanvilleMaconSelmaColumbusAllentownWhitewaterLos CanosDuras Kingsport064978121517 Danville60111012714910 Macon51103715132011 Selma91030316171619 Columbus712730147 12 Allentown871516140221618 Whitewater1214131772201210 Los Canos1592016141612015 Duras17101119121810150

30. HWS14-6 MachineOperators OperatorDrill PressLatheGrinderavailableassigned 101011 210011 300111 Operators demanded1113 Operators assigned111 Total time (mins)65 Time per operator per machine: Machine OperatorDrill PressLatheGrinder 1221835 2293028 3253618

31. HW S14-8—not assigned Homework #S14-8 Process: MoldingSmoothingPainting Constraints: AvailableUsageLeft over Budget ($)8.056.5 3,000 2462754 Available time (hrs)111 120 3684 Fiberglass (lbs)630 10,000 0 Process flow7-12 - - Process flow 12-10 - - Process flow7 -10 - - Process: Molding=15.87hours Smoothing=9.26hours Painting=11.11hours Profit= 19,444.44

32. HW S14-22—Not Assigned Homework Problem #S14-22 Nutrient Contributions CaloriesFatCholesterolIronCalciumProteinFiber Breakfast FoodServingUnitCost ($) gmg gg Bran cereal =0cup0.18900062035 Dry cereal =0cup0.221102044842 Oatmeal =1.025cup0.101002021253 Oat bran =0cup0.1290203864 Egg =0egg0.1075527013070 Bacon =0slice0.0935380020 Orange =0orange0.40650015211 Milk - 2% =1.241cup0.16100412025090 Orange juice =0cup0.50120000310 Wheat toast =2.975slice0.07651012633 Nutritional Requirements420203054002012 Nutritional Levels4201014.905.0240025.2212 Cost Per Meal = $0.509

33. HW S14-24 Homework #S14-24 PlantMine 1234CapacityProduction Cincinnati13801872220128 23016000190 Pittsburgh342072108280222 Demand (tons)11016090180 Production11016090180 Ash content0000 Sulfur content-2-3-3.24 Cost= 41,594 Shipping and processing cost ($/ton): Plant Mine1234 169717274 276747579 386898082

34. HW 13-12—not assigned Homework Problem #13-12 Demand =21600 Carrying cost =2.40 Order cost =80 Lead time =5 Q =1200.0 Total cost =2880.00 Reorder point =300.00

35. HW 13-20—not assigned Homework Problem #13-20 Carrying cost = $ 1.90 Ordering cost = $ 800 Demand =40000 QuantityPriceQDiscount QTotal Cost 13.405803.81 $ 147,027.24 100003.205803.8110000.00 $ 140,700.00 200003.005803.8120000.00 $ 140,600.00optimal 300002.85803.8130000.00 $ 141,566.67 400002.65803.8140000.00 $ 142,800.00 500002.45803.8150000.00 $ 144,140.00

36. HW S13-10—not assigned Homework Problem #S13-10 Probability of Sales Volume: Volume Price Variable Cost "Z" P(x)CumulativeVolumeMonthsRN1VolumeRN2PriceRN3CostProfit 0.12030010.89817000.589391250.880911800 0.180.1240020.89807000.211427230.43709800 0.200.3050030.72516000.043591220.593810-1800 0.230.5060040.56636000.939051270.6266101200 0.170.7370050.44945000.031211220.913511-3500 0.100.9080060.56766000.73029260.062081800 70.98818000.357548240.066783800 80.63086000.210563230.18069-600 Probability of Price:90.79257000.095235230.766310100 P(x)CumulativePrice100.13754000.129264230.24099-3400 0.07022110.50436000.863569260.8842110 0.160.0723120.41455000.615934250.47159-1000 0.240.2324130.12534000.597582250.694810-3000 0.250.4725140.91068000.867387260.5985103800 0.180.7226150.01773000.788519260.884711-4500 0.100.9027160.85177000.779089260.5655102200 170.92968000.029418220.778510600 180.12084000.765968260.969412-3400 190.67116000.287881240.956312-1800 Probability of Variable Cost:200.07183000.772055260.47079-3900 P(x)CumulativeCost 0.1708Average Z =-590 0.320.179 0.290.4910P(BE) =0.50 0.140.7811 0.080.9212 1.00

37. Exam Format  100 multiple choice  no problems  Closed-book  Closed-notes  Closed-neighbor  BRING---pencil, calculator, orange scantron sheet

38. Exam Coverage  Chapter 10, Chapter 11, Supplement to Ch 11, Supplement to Ch 13, Supplement to Ch 14 and Chapter 15- second half  LP problems in the supplement to Chapter 14, but not the content of Chapter 14—will cover that later.

39. Typical problems—  Inventory with Independent Demand  Problems involving calculation of inventory turns and days of supply  Production Scheduling Problem  Transportation problem  LP formulation problem  Interpretation of LP SENSITIVITY output

40. Typical Discussion Problems  Transshipment problem  Linear programming formulation  Be able to draw schematics of mainframe/glass architecture, client/server architecture and N-tier architecture

41. Chapter 15 – ERP  Inventory for Dependent Demand will NOT be covered….  Exam coverage of this chapter starts on page 700

42. What were five motivations for transitioning from mainframes  Absence of data integration  36 month backlogs at centralized MIS shops  Idle CPU cycles on desktops  Mainframes were expensive bottlenecks  Support for Internet and thin clients  Quicker, cheaper development times through REUSE

43. What is the information architecture modern ERP systems are currently based on?  Mainframe/glass house  Client/server  N-tier distributed  None of these

44. Every application software package consists of 1. Presentation management component 2. Business logic management component 3. Data management component 4. All of the above 5. 1 and 2 only

45. ERP  Is software that organizes and manages a company’s business processes by sharing info across functional areas  Large caps have been there and done that—transitioned to ERP  Mid and small caps are getting there  The road to implementation has been rough

46. More ERP  Based on an N-tier distributed architecture  Not on mainframe glasshouse

47. Advantages of N-tier architecture  Provides for data integration  Better usage of MIPS on both PCs and servers  Solves the 36-month backlog of the centralized MIS shop  Enables a better career path for the MIS professional

48. N-Tier distributed architecture  Is decentralized or centralized, or some combination of these (which?)  Utilizes thick clients or thin clients (which?)

49. ERP Modules  Sales & distribution  Production & Materials Management  Quality management  Human resource management  Project management  Accounting and controlling/finance  Supply chain management  Customer relationship management

50. ERP Terms  Best-of-breed  Collaborative product commerce  Customer relationship management  Supply chain management  XML

51. Re-engineered Computer Architectures  Started with mainframe/glasshouse  Migrated to client/server  Evolved to N-tier distributed

52. Why did such re-engineering occur?  There was no data integration  MIPs on mainframes were hugely expensive and very much in demand  MIPs on PCs were idle 95% of the time and extremely cheap  Backlogs for MIS shops were at 36 months  Developing new applications were slow and expensive

53. Distributed architectures solved these problems  Data resides behind a single database engine

54. Components of any Software Application

55. Components in brief

56. Mainframe Architecture (circa 1993) Mainframe Computer

57. Problems with Mainframe Architecture  Absence of data integration, resulting in little enterprise visibility  The applications are maintainable only by the centralized MIS shop, which is overloaded, resulting in 36 month lead times to get revisions effected  Every application had to be built from scratch, line-by-line, resulting in large cost and long lead times to create new applications

58. More problems with Mainframe Architecture  No reuse was possible  These mainframe apps were accessed on networked PC’s via IBM 3278 terminal emulation software that was completely incompatible with the windows GUI applications—meaning no cut and paste  Mainframes were computational bottlenecks  Desktop PCs sat idle 99% of the time

59. First solution: Client/server architecture Server (DM) Clients (PM, BL) Database

60. These were known as thick clients  Because they contained both the presentation management (PM) and the business logic (BL) components of the application  Notice how the application is distributed across the network, residing in two computing boxes—the client or desktop and the server

61. First solution: Client/server architecture Server (DM) Thick Clients (PM, BL) Database

62. Advantages of Client/server architecture  All Data are all accessible behind the Server which runs the data management portion of the application—usually an Oracle Database engine  Now the marketing guy can see where his customer’s job is, and whether the customer is current with his payments, among other ‘things’

63. Advantages of client/sever architecture  The IT professional could sit shoulder-to- shoulder with the end-user and develop applications as well as make changes to existing software rapidly, without a 36 month backlog  For new applications, there were huge reuse opportunities—in particular, the IT professional does not have to create a DM component—the Oracle engine can be reused

64. Problems with Client/server  It wasn’t Internet compatible  It required an IT professional to install software on the end-user’s personal computer (the client)  It required an IT professional to work closely with the non-IT professional  There were no career paths for IT professional hired in marketing, finance, accounting, manufacturing, etc.

65. Modern solution of today: N- TIER DISTRIBUTED ARCHITECTURE  This is a distributed architecture like client/server, but now the application is distributed across three or more computing boxes on the network

66. N-Tier distributed Architecture Data Server (DM) Thin Clients (1/2PM) Database ApplicationServer 1 ApplicationServer 2

67. Take a closer look at the Application Servers Application Server runs the business logic component and half ot thepresentation management component—theportion the serves out the web pages

68. Comments on N-Tier Distributed Architecture  Clients are called ‘thin’ because the only thing running on them is the Internet Browser  The IT professional doesn’t have to install anything on the client  More re-use is possible—specifically that browser

69. Advantages of N-Tier Distributed Architecture  Like Client/server, it accommodates enterprise visibility because the data are integrated  Applications can be built rapidly because there is abundant reuse  The DM module is reused  Half of the PM component is reused  There are reuse opportunities within the rest of the PM component and the BL component as well

70. More advantages of N-Tier  IT professionals don’t have to be remotely loaned out to marketing, management, accounting and finance  They can now be centrally located and managed where career paths will exist for them

71. Application Servers do Two things  They serve out web pages upon request  They do all of the business logic processing.

72. ERP Modules  Finance/Accounting  Sales Marketing  Production/Materials Management  Human Resources  Supply Chain Management  Customer Relation Management

73. These modules would be placed in a  Thin client  Data server  Application server  Mainframe WHICH??

74. ERP Implementation  Analyze business processes  Choose modules to implement  Align level of sophistication  Finalize delivery and access  Link with External Partners

75. Customer Relationship Management  CRM software plans and executes business processes that involve customer interaction, such as marketing, sales, fulfillment, and service (not manufacturing)  CRM is focused on customers, not products

76. Collaborative Product Commerce  Software concerned with new product design and development, as well as product lifecycle management

77. Connectivity  A common data management component  API’s (Application Programming Interfaces)  EAI (Enterprise Application Integration)  XML (Extensible Markup Language)  Dr. Viator (accounting) teaches a course in this language

78. Chapter 10--Supply Chain Management  Plants/warehouses/distribution/ information infrastructure  Most of America’s product gets moved by _____ (air, water, rail, truck, pipeline).  What is COVISINT??  What benefits accrue from SCM?

79. What’s new and exciting in SCM??  Information Technology (specifically enterprise visibility)  Has changed everything  SCM Software modules within ERP systems  I2 Technologies  Has reduced uncertainty  Which has reduced _____________  Which is a form of _______________

80. Inventory turns  Calculated on an annual basis  The more, the better  Inputs:  Cost of goods sold  Average aggregate value of inventory

81. Average aggregate value of inventory  Calculated by taking the product of the unit cost with the number of units and then summing these products for all inventory categories

82. Days of supply  Avg agg value of Inv*365/Ann cost of goods sold  Or simply… 365/inventory turns

83. Manufacturing Inventory Types  Raw materials inventory  Work-in-process inventory  Finished goods inventory

84. Supply Chain Management Terms  Bullwhip effect  Collaborative planning, forecasting and replenishment  Continuous replenishment  Core competencies  Cross-docking  E-business  E-marketplaces  E-procurement  EDI  Inventory turns  Landed cost  Logistics  Order fulfillment  RFID  Sourcing  Vendor-management inventory  Warehouse management system

85. Chapter 13 – Inventory Management  Inventory for Independent demand  { Not manufacturing inventory, usually— more like retail inventory}

86. Carrying costs  Rent  Lighting/heating  Security  Interest (on borrowed capital tied up in inventory)  Taxes  Shrink/obsolescence/theft Can also be expressed as a % of product cost A rule of thumb is 30%

87. Ordering costs—costs related to Transportation Shipping Receiving Inspection

88. Shortage costs  This is an opportunity cost  Is ignored in the simple models you will be using, by assuming that there are no shortages

89. Back-order costs  Will assume impatient customers who must have the product they wish to buy NOW.  So back-ordering is not considered in the simple models we looked at

90. Continuous Inventory Systems  Constant order amount, called the EOQ  EOQ = Economic Order Quantity  Fixed annual deterministic demand  Minimizes  Holding (carrying) costs  Ordering costs  Uses re-order point to determine when to order  Time between orders is not fixed

91. EOQ models also have  No shortages/back-ordering  Constant lead time  Instantaneous or finite replenishment  Can take into consideration price discounting  When doing so, three costs are minimized jointly: Ordering costs, holding costs and purchase costs taken over a year’s time

92. If the quantity ordered is less than the EOQ, then  Ordering costs will be greater than holding (carrying) costs

93. ABC Classification—what is the point??  To concentrate, focus on the those items in inventory that constitute the highest dollar value to the firm  Class A items constitute 5-15% of the items and 70 to 80% of the total dollar value to the firm  Class B items constitute 30% of the inventory items but only 15% of the dollar value  Class C items constitute 50 to 60% of the items but only 5 to 10% of the dollar value

94. ABC Classification..  Class A items are tightly controlled  Class B items less so  Class C items even less  Dollar values are computed by multiplying the unit cost by the annual demand for the item  This technique is used in all auto parts inventory control systems and has been for 15 years

95. Periodic inventory systems are….  Fixed Time period systems  NOT  EOQ Models  The time between orders is fixed, the re-order point is fixed, but the order amount is not

96. Which gives you lowest holding cost?  Instantaneous replenishment  Finite (non-instantaneous) replenishment  Quantity discounts  WHICH OF THE ABOVE GIVES YOU LOWEST TOTAL ORDERING COST?

97. How do we calculate a re- order point?  Lead time in days times the daily demand plus the safety stock  Safety stock equals the service level (usually 3 for z) times the standard deviation of daily demand times the sq. rt. of lead time.  (You will be given the formulas)

98. How do we calculate…  Time between orders?  Production days in a year / # of orders  Run length  EOQ or order quantity / daily Production rate

99. Safety Stocks and Service Levels  Safety stock = Z value * std. dev. of daily demand * sqrt(lead time)  For 95% service level, use Z value of 1.65  For 99% service level, use Z value of 3

100. Inventory Terms  ABC system  Carrying costs  Continuous inventory system  Dependent demand  EOQ  Fixed-order quantity system  Fixed time period system  Capacity  Independent demand  Inventory  In-process inventory  Non-instantaneous receipt  Order cycle  Quantity discount  Stockout  Service level  Efficiency

101. Simulation  Two types—  Continuous deterministic  VENSIM is an example  Discrete stochastic  PROMODEL is an example  Each of these two types differ by method of time advance

102. Time advance in continuous deterministic simulation  Time is advanced in small, equidistant increments  The simulation engine is really integrating differential equations

103. Time advance in discrete stochastic simulation  Time is advanced from event to event  The simulation engine maintains a stack of discrete events chronologically ordered in time, called an events calendar  The next event to occur is popped off the stack and processed.  The result of processing the event is that more events are generated and subsequently get saved on the events calendar

104. MONTE CARLO—  The computer-generation of random numbers using an

105. Which simulation gestalt uses activities, events, entities and their attributes?  Continuous deterministic?  Discrete stochastic?

106. The Excel function RAND() generates…  Normally-distributed random variates  Gamma-distributed random variates  Uniformly-distributed random numbers  Exponentially-distributed random variates

107. To get a non-uniform random variate, we often start with  A normal random variate  A lognormal random variate  A uniform random number  A triangular random variate

108. To get a non-uniform random variate, we often use…  The central limit theorem  The law of large numbers  The inverse function theorem  All of the above