1 / 65

Preparing for the Game

Preparing for the Game. The first 30 days of the game will be initialized this evening. Preparing for the Game. Download Data We will do one together. F ORECASTING. I NVENTORY. W AITING L INES. D ECISION T REES. G OING OVER THE M ODELS IN C LASS.

allan
Télécharger la présentation

Preparing for the Game

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Preparing for the Game The first 30 days of the game will be initialized this evening

  2. Preparing for the Game Download Data We will do one together

  3. FORECASTING INVENTORY WAITING LINES DECISION TREES GOINGOVER THEMODELS IN CLASS OBJECTIVE #1: SUPPORT THE LITTLEFIELD GAME STRATEGIC OPERATING PLAN LITTLEFIELD SIMULATION EXERCISE LITTLEFIELD

  4. GOINGOVER THEMODELS IN CLASS USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS SPECIAL TOPICS: POISSON PROCESS OBJECTIVE #2: POVIDE TRADITIONAL OVERVIEW FORECASTING SIMULATION EXERCISE LITTLEFIELD

  5. GOINGOVER THEMODELS IN CLASS USES: MINRELEVANTCOSTS EXERCISE & DERIVATION SPECIAL TOPIC: DEMAND NOT CONSTANT OBJECTIVE #2: POVIDE TRADITIONAL OVERVIEW INVENTORY SIMULATION EXERCISE: LITTLEFIELD

  6. GOINGOVER THEMODELS IN CLASS USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME OBJECTIVE #2: POVIDE TRADITIONAL OVERVIEW MINI CASE: PANTRY SHOPPERS WAITING LINES SPECIAL TOPIC: SERVER UTILIZATION SIMULATION EXERCISE LITTLEFIELD

  7. GOINGOVER THEMODELS IN CLASS USES: SEQUENTIAL DECISION MAKING UNDER UNCERTAINTY OBJECTIVE #2: POVIDE TRADITIONAL OVERVIEW SPECIAL TOPICS: RISK PERIOD LENGTHS DECISION TREES MINI CASE: ARCTIC INC SIMULATION EXERCISE LITTLEFIELD

  8. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS SPECIAL TOPICS: POISSON PROCESS FORECASTING SIMULATION EXERCISE LITTLEFIELD

  9. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS DEFINITIONS OF FORECASTING THREE LAWS OF FORECASTING ELEMENTS OF A GOOD FORECAST FORECASTING

  10. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS DEFINITION OF FORECASTING A FORECASTISA STATEMENTABOUTTHE FUTURE VALUEOF A VARIABLEOF INTEREST, e.g.: DEMAND LEARNING-CURVE EFFECTS (TASK COMPLETION TIMES) FORECASTING

  11. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS DEFINITION OF FORECASTING FORECASTING IS DRIVING ALONG CA99 AT 70 MPH YOUR WINDSHIELD AND SIDE WINDOWS PAINTED BLACK ONLY INFORMATION FOR STEERING  IN REARVIEW MIRROR FORECASTING

  12. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS THREE LAWS OF FORECASTING 1. FORECASTSARE ALWAYS WRONG 2. AGGREGATE FORECASTS ARE MORE ACCURATETHAN FORECASTSFOR SINGLE PRODUCTS 3. THE LARGER THE FORECAST HORIZONTHE LESS ACCURATETHE FORECAST WILL BE FORECASTING

  13. GIVENTHESELAWS OF FORECASTING, WHY DO WE FORECAST?

  14. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS WHY WE FORECAST FORECASTSARE ALWAYS WRONG – B-U-U-U-T … FORECAST ERROR MEASURES ARE VERY USEFUL THEY PROVIDE AN IDEA OF JUST HOW WRONG WE CAN BE ON AVERAGE FORECASTING

  15. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS WHY WE FORECAST USE THE FORECAST and the ERROR MEASURES together to CREATE a one-TAILED CONFIDENCE INTERVAL USED TO SET SAFETY STOCK OR ADDITIONAL SERVICE RESOURCES FORECASTING

  16. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS HOWDOWESET SAFETY STOCK? TOPIC OF ROP FORECASTING

  17. Preparing for the Game Plotting data Plot with charting tool Select columns A and B Click on the "Chart Wizard" Icon Under "Chart Type" on the left select "XY (Scatter)" On the right select "Scatter with data points connected by lines (third icon down on the left) When you are done customizing your chart click finish

  18. Preparing for the Game Adding trend lines Add a trend line right click your mouse anywhere on the curve select “Add a trend line” select the option that places a formula on the graph Forecast out 30 units

  19. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS SPECIAL TOPICS: POISSON PROCESS FORECASTING SIMULATION EXERCISE LITTLEFIELD

  20. PREPARINGFORTHE GAME IMPORTANT NOTEON DEMAND VARIANCE THE JOB ARRIVALS FOLLOW A POISSON DISTRIBUTION SPECIAL FEATURE OF THE POISSON DISTRIBUTION MEAN = VARIANCE RECALL: VARIANCE = (STANDARD DEVIATION)2 AS DEMAND INCREASES WITH TIME (SEE TREND LINE) SO DOES THE STANDARD DEVIATION OF DEMAND! INCREASING DEMAND WILL AFFECT EOQ INCREASING VARIANCE WILL AFFECT ROP

  21. USES: FORECAST + ERROR TO DETERMINE OPERATING REROURCE NEEDS SPECIAL TOPICS: POISSON PROCESS FORECASTING SIMULATION EXERCISE LITTLEFIELD

  22. PREPARINGFORTHE GAME FORECAST OPERATIONAL PLANNING PLANWHEN YOU WILL NEEDTO FORECASTAGAIN SEE “LITTLEFIELD_GAME.PDF” CONSIDERTHE QUANTITYOF DATA NEEDED IDEAL: 30 DAYS FUNCTIONAL?

  23. USES: MINRELEVANTCOSTS INVENTORYMANAGEMENT EXERCISE & DERIVATION SPECIAL TOPIC: DEMAND NOT CONSTANT SIMULATION EXERCISE: LITTLEFIELD

  24. ASSUMPTIONS of theEOQMODEL EOQ Model • Only one product is involved • Annual demand requirements known • Demand is even throughout the year • Lead time does not vary • Each order is received in a single delivery • There are no quantity discounts

  25. Annual carrying cost Annual ordering cost Total cost = + Q D S H TC = + 2 Q OBJECTIVE: MINRELEVANTCOSTS EOQ Model

  26. USES: MINRELEVANTCOSTS INVENTORYMANAGEMENT EXERCISE & DERIVATION SPECIAL TOPIC: DEMAND NOT CONSTANT SIMULATION EXERCISE: LITTLEFIELD

  27. INVENTORYMANAGEMENT EXERCISE & DERIVATION D = Annual demand H = Annual holding cost - $/unit/year S = Fixed order cost Example D = 50,000 units H = $1 /unit/year S = $1,000 Operate 50 weeks/year

  28. Cost Minimization Goal The Total-Cost Curve is U-Shaped Annual Cost Ordering Costs Order Quantity (Q) QO (optimal order quantity)

  29. Deriving the EOQ Using calculus • take the derivative of the TC function • set the derivative (slope) equal to zero • solve for Q.

  30. Deriving the EOQ The total cost curve reaches its minimum where the carrying = ordering costs

  31. USES: MINRELEVANTCOSTS INVENTORYMANAGEMENT EXERCISE & DERIVATION SPECIAL TOPIC: DEMAND NOT CONSTANT SIMULATION EXERCISE: LITTLEFIELD

  32. Q Usage rate Quantity on hand Reorder point Time Place order Receive order Receive order Receive order Place order Leadtime TheINVENTORY CYCLE EOQ Model Profile of Inventory Level Over Time

  33. When to Reorder with EOQ Ordering • Reorder Point - When the quantity on hand of an item drops to this amount, the item is reordered • Safety Stock - Stock that is held in excess of expected demand due to variable demand rate *. • Service Level - Probability that demand will not exceed supply during lead time. • *Applies to lead-time uncertainty as well – but not in the Littlefield exercise

  34. Determinants of the Reorder Point • The rate of demand • The lead time Demand* • Stockout risk (safety stock) * and/or lead time variability – but not in the Littlefield Exercise

  35. Maximum probable demand during lead time Quantity Expected demand during lead time ROP Safety stock Time LT Safety Stock Safety stock reduces risk of stockout during lead time

  36. Service level Risk of a stockout Probability of no stockout Quantity ROP Expected demand Safety stock 0 z z-scale Reorder Point The ROP based on a normal Distribution of lead time demand

  37. So What’s the D _ _ _ _ _ d ROP Equation? LetL = lead timed = demand rate (demand expressed in days)s = standard deviation of demandSL = required service levelz = standard normal variateROP = d x L + z xs xSqrt(L)

  38. So What’s the D _ _ _ _ _ d ROP Equation? ExampleL = 19 daysd = 24 units/days = 4 units/daySL = 98%z = 2.05ROP = d x L + z xs xSqrt(L)ROP = (24)(19) + (2.05)(4)sqrt(19)ROP = 492 units (round up)

  39. DEMAND NOT CONSTANT Inventory Level of raw kits at Littlefield (not an average)

  40. USES: MINRELEVANTCOSTS INVENTORYMANAGEMENT EXERCISE & DERIVATION SPECIAL TOPIC: DEMAND NOT CONSTANT SIMULATION EXERCISE: LITTLEFIELD

  41. SIMULATION EXERCISE: LITTLEFIELD INVENTORYMANAGEMENT

  42. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES SPECIAL TOPIC: SERVER UTILIZATION MINI CASE: PANTRY SHOPPERS SIMULATION EXERCISE LITTLEFIELD

  43. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES SPECIAL TOPIC: SERVER UTILIZATION MINI CASE: PANTRY SHOPPERS SIMULATION EXERCISE LITTLEFIELD

  44. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES SPECIAL TOPIC: SERVER UTILIZATION MINI CASE: PANTRY SHOPPERS SIMULATION EXERCISE LITTLEFIELD

  45. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES Example of an M/M/1 queue[1] [1] Picture source: Heizer and Render. Production and Operations Management.

  46. WAITING LINES The following information is typically given:

  47. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES Assumptions of the M/M/1 queue Population Source  assumed infinitely large Queue capacity  infinite Queue discipline  FCFS

  48. USES: CONTROL OPERATING CHARACTERISTICS AVERAGE LINE LENGTH AVERAGE WAITING TIME WAITING LINES Assumptions of the M/M/1 queue More Customers are Patient That is, they do not Balk Renege Jockey

More Related