1. 14 – Advanced MRP Topics Dr. Ron Tibben-Lembke pp. 478-

2. MRP Priorities  First: Get installed, part of ongoing managerial process, get users trained Get installed, part of ongoing managerial process, get users trained Understand critical linkages with other areas Understand critical linkages with other areas Achieve high levels of data integrity Achieve high levels of data integrity Link MRP with front end, engine, back end Link MRP with front end, engine, back end  Then: Determine order quantities more exactly Determine order quantities more exactly Buffering concepts Buffering concepts Nervousness Nervousness

3. Ordering Policies  Dependent Demand Not independent demand Not independent demand Discrete – not continuous Discrete – not continuous Lumpy – may have surges Lumpy – may have surges  Complexity Reduces costs – ordering & holding Reduces costs – ordering & holding Anything other than lot-for-lot Increases lumpiness downstream Anything other than lot-for-lot Increases lumpiness downstream

4. Assumptions  All requirements must be available at start of period  All future requirements must be met, and can’t be backordered  System operated on periodic basis (e.g. weekly)  Requirements properly offset for LTs  Parts used uniformly through a period Use average inventory levels for holding cost Use average inventory levels for holding cost

5. Example Demands  Try several lot-sizing methods Economic Order Quantity Economic Order Quantity Periodic Order Quantity Periodic Order Quantity Part Period Balancing Part Period Balancing Wagner Within Wagner Within  Order cost = $300 per order = C P  Inventory Carrying cost = $2 / unit/ week = C H  Avg Demand = 92.1 / wk = D

6. EOQ  Minimizes total ordering & holding costs  Assumes demand same every period Definitely not always true for this use Definitely not always true for this use  Avg. demand and holding cost need same time units (e.g. per week)  Economic Lot Size:  Where: D = avg demand C P = ordering cost C H = holding cost

7. EOQ  Sqrt( 2 * 300 * 92.1 / 2) = 166

8. EOQ  Ordering cost = 6 * 300 = $1,800  Inv carry cost = 1,532.5 * 2 = $3,065  Total$4,865

9. Periodic Order Quantities  EOQ Gave good tradeoff between ordering & holding Gave good tradeoff between ordering & holding resulted in a lot of leftovers. resulted in a lot of leftovers.  Only order enough to get through a certain number of periods – no leftovers  How many? EOQ / avg. demand 166 / 92.1 = 1.805 ~ 2 weeks’ worth 166 / 92.1 = 1.805 ~ 2 weeks’ worth

10. Periodic Order Quantities  Ordering cost = 6 * 300 = $1,800  Inv carry cost =1,082.5 * 2 = $2,145  Total$3,945

11. Part Period Balancing  Increase the quantity until holding costs equal the ordering cost  Order 10 – holding = 10/2*2 = 10  Order 20 – holding = 10 + 10*1.5*2 = $40  Order 35 = 40 + 15*2.5*2 = $115  Order 55 = 115 + 20*3.5*2 = $255  Order 125 = 255 + 70*4.5*2 = $85

12. Part Period Balancing  Week 5:  Order 70: Holding = 10*0.5*2 = $10  Order 250: 10 + 180*1.5*2 = $550  So I could: Order 250 units, pay $300 in ordering and $540 holding, for a total of $840, Order 250 units, pay $300 in ordering and $540 holding, for a total of $840, Order 70 now, 180 next week, and pay $600 in ordering and $10 + 180*0.5*2=180 in holding = $790 Order 70 now, 180 next week, and pay $600 in ordering and $10 + 180*0.5*2=180 in holding = $790 Seems like the second option is best. Seems like the second option is best.

13. Part Period Balancing  When should we place a separate order? If 1.5*$2*D > 300. D>300/3 = 100  Whenever demand is >= 100, we might as well place a separate order.  What about week 9?  Order 230: holding = 230*0.5*2 = $230  Order 270: = 230 + 40*1.5*2 = $350  Order 280: = 350 + 10*3.5*2 = $420

14. Part Period Balancing

15. Wagner-Within  Mathematically optimal  Work back from planning period farthest in the future  Consider all possibilities: Order for 5, 4 and 5, 3 and 4, then 5, etc. Order for 5, 4 and 5, 3 and 4, then 5, etc. Uses “dynamic programming” – similar to linear programming Uses “dynamic programming” – similar to linear programming

16. Simulation Experiments  What is best under real-world conditions? Multiple levels to be concerned about Multiple levels to be concerned about Real-time changes Real-time changes

17. Buffering concepts

18. Nervousness