1. ShipSmart TM : The Model and the Computer Science Jonathan Sheffi University of Maryland College Park April 17, 2000

2. Outline l The EOQ Model l Reliability and Safety Stock l Internal Structure of ShipSmart l ShipSmart™ Example l Object-Oriented Programming

3. Tradeoff: Production Batch vs. Inventory similar to Tradeoff: Shipment Size vs. Inventory Production setup costs vs. Inventory carrying costsProduction setup costs vs. Inventory carrying costs Transportation costs vs. Inventory carrying costsTransportation costs vs. Inventory carrying costs Lot Size Inventory

4. A simple business: Order fixed quantity of goods at fixed intervals Order fixed quantity of goods at fixed intervals Sell at constant rate Sell at constant rate In Stock Time Inventory costs = quantity x time Economic Order Quantity Model

5. A Single Receiving Cycle

7. X

8. X

9. X Y Smaller (and more frequent) shipments imply lower inventory carrying costs A Single Receiving Cycle

10. Tradeoffs: Smaller shipments low inventory costs high transportation costs Large shipments high inventory costs low transportation costs In addition: Inventory cost-in transit

11. InventoryCarryingCosts/Unit In-TransitInventory Shipment Size Inventory Costs

12. Shipment Size LTL Rate Actual rate curve Formula rate curve Transportation Costs

13. Basic Tradeoffs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment)

14. Basic Tradeoffs Transportation costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment)

15. Basic Tradeoffs Transportation costs Cycle inventory costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment)

16. Basic Tradeoffs In-Transit Inventory Transportation costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment) Cycle inventory costs

17. Basic Tradeoffs In-Transit Inventory Transportation costs Total inventory costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment) Cycle inventory costs

18. Basic Tradeoffs In-Transit Inventory Total logistics costs Transportation costs Total inventory costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size ($/item) COSTS Shipment Size (items/shipment) Cycle inventory costs

19. Basic Tradeoffs In-Transit Inventory Total logistics costs Transportation costs Total inventory costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size Opt. size ($/item) COSTS Shipment Size (items/shipment) Cycle inventory costs

20. Basic Tradeoffs In-Transit Inventory Total logistics costs Transportation costs Total inventory costs Logistics Cost Components as a Function of Shipment Size Logistics Cost Components as a Function of Shipment Size Opt. size ($/item) COSTS Shipment Size (items/shipment) Least cost Cycle inventory costs

21. Total Logistics Cost Safety Stock Costs In Transit Inventory Costs Transportation Costs Cycle Inventory Costs Other Costs Elements of Total Logistics Costs

22. TotalLogisticsCosts = 2 * (Cycle Inventory Costs) (Transportation Costs) (Demand Rate) In-transitInventoryCostsSafetyStockCosts OtherCosts Analytical Expressions OptimalShipmentSize = 2 * (Transportation costs) (Demand rate) (Cycle Inventory Costs)

23. Transit Times Transit delivery times vary in random fashion. Transit delivery times vary in random fashion. Each mode and carrier is characterized by a distribution of delivery times. Each mode and carrier is characterized by a distribution of delivery times.

24. Inventory Time Effect of Shipment Delay Regular on-time delivery

25. Inventory Time Delay One delivery delayed Effect of Shipment Delay

26. Inventory Time Delay Delivery pattern with a single delay Effect of Shipment Delay

27. Inventory Time Delay Erratic delivery pattern Effect of Shipment Delay

28. Effect of Safety Stock Inventory Time Safety stock level

29. Inventory Time Avoiding stock-out Effect of Safety Stock

30. Transit Time Probability Transit Time Density Function

31. Transit Time Probability Average Time Transit Time Density Function

32. Transit Time Probability 95 Percentile Average Time Transit Time Density Function

33. Transit Time Probability 95 Percentile Average Time Safety Time Transit Time Density Function

34. Transit Time Probability 95 Percentile Average Time Safety Time Safety Stock for 95% Fill Rate = (Safety Time) x (Sales Rate) Transit Time Density Function

36. The Internal Structure l Calculations are performed for each option separately l In case there is one segment: –Optimal shipment size is calculated using the EOQ formula –Takes into account minimum frequency and storage capacity –Calculates all other output values

37. The Internal Structure l For multiple segments: –Rank vehicle capacities –Find how many vehicles are needed for each segment –Loop over the second largest, third largest, etc. –Use optimal solution to calculate all output values (including the segment solutions)

38. ShipSmart Example

39. Tactics for the Rail Operator l Reduce shipment price l Tighten delivery time l Tighten reliability l Compete for lower valued commodities l Compete for commodities which ship in high volume

40. Procedural vs. OOP l Procedural programming: –Organize system around procedures that operate on data (do-something...) (do-another-thing ) l Object-oriented programming: –Organize system around objects that receive messages ( 'do-something ) ( 'do-another-thing) –An object encapsulates data and operations

41. Advantages of OOP Simplicity: software objects model real world objects, so the complexity is reduced and the program structure is very clear Modularity: each object forms a separate entity whose internal workings are decoupled from other parts of the system Modifiability: it is easy to make minor changes in the data representation or the procedures in an OO program. Changes in the behavior of an object do not affect any other part of a program, since the only public interface that the external world has to an object is through messages Extensibility: adding new features or responding to changing operating environments can be solved by introducing a few new objects and modifying some existing ones Maintainability: objects can be maintained separately, making locating and fixing problems easier Reusability: objects can be reused in different programs

42. Elements of OOP l Class : –specifies the common behavior of entities l Instance : –A particular object or entity of a given class

43. Space War Class Diagram SHIP position: velocity: num-torps: POSITION VELOCITY MOVE SHIP? ATTACK DISPLAY CLOCK-TICK EXPLODE PLANET position: POSITION PLANET? DISPLAY CLOCK-TICK TORPEDO position: velocity: target: proximity-fuse: TORPEDO? POSITION VELOCITY MOVE DISPLAY CLOCK-TICK

44. Space War Class Diagram SHIP position: velocity: num-torps: POSITION VELOCITY MOVE SHIP? ATTACK DISPLAY CLOCK-TICK EXPLODE PLANET position: POSITION PLANET? DISPLAY CLOCK-TICK TORPEDO position: velocity: target: proximity-fuse: TORPEDO? POSITION VELOCITY MOVE DISPLAY CLOCK-TICK Ships and torpedoes have some behavior that is the same – is there are way to capture this commonality?

45. Space war game with Inheritance MOBILE-THING position: velocity: MOBILE-THING? POSITION VELOCITY MOVE SHIP num-torps: SHIP? ATTACK DISPLAY CLOCK-TICK EXPLODE TORPEDO target: proximity-fuse: TORPEDO? DISPLAY CLOCK-TICK is-a l SHIP class is a specialization or sub- class of the MOBILE-THING class – SHIP is-a MOBILE-THING – SHIP inherits the state and behavior of MOBILE-THING l MOBILE-THING class is a super-class of the SHIP and TORPEDO classes PLANET position: POSITION PLANET? DISPLAY CLOCK-TICK has-a target

46. Any Questions? ? ? ? ? ? ? Jon Sheffi jsheffi@mit.edu