Groups of models

1. Groups of models • Intra-Enterprise Planning • Enterprise Planning itself • Single Facility Location Models • Multiple Facility Location Models

2. Groups of models • Intra-Enterprise Planning • Planning of facility • Machinery, offices, distances • Ways of transportation • Safety

3. Groups of models • Enterprise Planning itself • Finding the SPOT • What needs to be located • Locations to chose from

4. Factors influencing the choice of location 1.Proximity to market. 2.Integration with organization. 3.Availability of labour and skills. 4.Availability of amenities. 5.Availability of transport. 6.Availability of inputs. 7.Availability of services. 8.Suitability of land and climate.

5. Factors influencing the choice of location 9.Regional regulations. 10.Room for expansion. 11.Safety requirements. 12. Site cost. • Political, cultural and economic situation. • Special grants, regional taxes and import/export barriers. • Analysis of the factory site • Issue of a development plan. • Choice of the shape of the building.

6. Graphic Approach • Single Facility Location • 2-dimensional Graph • Iso-Cost-Lines • Geopgraphical Map of Costlines

7. Graphic Approach Isocost lines Transportation cost lines radiating from point of facility

8. Graphic Approach

9. Grid Method • Center of Gravity Approach • Depended on the Demand Rate • Facility positioned at the center of demands (volume rates) • Only costs related to distance are important

10. Grid Method • Distances from Facility or warehouse to customers • Decicion between diffrent possible locations • Lowest cost model

11. Grid Method Σ V R X i i i i X= Σ V R i i i Σ V R Y i i i i Y= Σ V R i i i V=volume R=transportation rate X Y=coordinates for points X Y=coordinates for facility Vertical index number Y Horizontal index number X

12. Service Elasticity of Demand • Adds to Grid Method • Delivery time needed • Volume of goods • Order cycle time • Depending on price of goods and distance

13. Service Elasticity of Demand • Customers care about order cycle time Volume demanded Ratio of delivery time

14. Algorithmic and Cluster Method • Clusters of Facility locations • Begin with a facility at each demand or market site • Reduce number by grouping/clustering • Determine the centroid (center of gravity) and place a new facility • Total costs of this reduced number of locations

15. Algorithmic and Cluster Method Example: - one or more warehouses are to be located to serve 5 primary markets (Frankfurt, Köln, Berlin, Hamburg, München) - needed are the costs for transportation per unit per km (0.10 €)

16. Algorithmic and Cluster Method - fixed costs per single warehouse - 1,000,000 € construction - 500,000 € carrying costs per period - transportation costs depend on volume and distance to customer - here 50,000 goods per warehouse per time period - begin with 5 warehouses, so distance is 0

17. Algorithmic and Cluster Method • distances of locations are given by maps Ffm Köl Ber HH Muc Ffm - 200 560 500 400 Köl 200 - 550 350 600 Ber 560 550 - 300 600 HH 500 350 300 - 800 Muc 400 600 600 800 -

18. Algorithmic and Cluster Method - so each warehouse would cost 1,500,000 € per period (7,500,000 € total) - Frankfurt and Köln become one cluster and are served by one warehouse - new warehouse situated in the middle of both cities 100 km distance

19. Algorithmic and Cluster Method - so only 1,500,000 € need to be spend for both cities, but - transportation: 100,000 goods * 100km * 0.10 € =1,000,000 Totals: 4 warehouses 6,000,000 € + transportation costs 1,000,000 € = 7,000,000 €

20. Simulation and Sampling Methods • Mathematic Method • Relaying on the figures and numbers given by „Real World“ • Simulation of development made by computer • Decision for location made by results

21. Simulation and Sampling Methods • Customer: customer location, annual volume of demand, types of products, size of orders • Warehouses: company owned warehouses or rented?, fixed costs for administration and operation,storing costs

22. Simulation and Sampling Methods • Availability of products at factories and distribution costs • Freight costs depending on location of warehouse and factory • Delivery costs depending on location of warehouse to customer and size of shipments

23. Simulation and Sampling Methods Start Read in all customer order data and locations Volume shipment orders Preprocessing programm Orders filled through warehouse system Read in freight rates, warehousing costs, taxes, etc. Read warehouse location configuration to be evaluated Test programm Cost of warehouse location configuration Yes Is another run desired Stop No

24. Heuristic Methods • „any principle or device that contributes to the reduction in the average search to a solution“ • Rule of thumb • Not the optimum solution may be found • Depending on the quality of heuristics used

25. Heuristic Methods • Kuehn-Hamburger model (classic heuristic model) • Locations with the greates promise are those at or near concentration of demand • Near-optimum warehousing systems can be developed if ar each stage the warehouse offering the greatest cost savings is added • Only a small subset of all posible warehouse location needs to be evaluated to determine which warehouse should be added

26. Heuristic Methods • The Kuehn-Hamburger warehouse location model is still one of the most comprehensive models available • Multiple products • Fixed and variable warehousing costs • Warehouse capacity • Factory capacity • Effect of delivery time on customer service • Actual transportation rates

27. Discrete Optimizing Model • This model assumes that fixed and volume related costs are the same at each center • 3 procedures have been used to generate the matrices • One based on strictly on random generation of binary matrices • One which is biased to take advantage of possible economies of scale • And one which picks the least-transportation-cost solution as a starting point

28. Discrete Optimizing Model • Procedure1: • Random generation. This random localized search procedure takes a random binary matrix with columns representing the markets and rows representing the centers for the assignment of a warehouse to each market. It evaluates the cost of that assignment of a warehouse to each market. It evaluates the cost of that assignment.Then changes are made in this matrices, this randomizing leads to all possible alternatives and to a near optimal solution.

29. Discrete Optimizing Model • Procedure 2: • Economies of scale. In this procedure the search is conducted with the starting positions utilizing the economies of scale. Specifically, the initial matrix assigns all markets to one warehouse and in turn evaluates each column to see wether it is best to assign all markets to one warehouse.

30. Discrete Optimizing Model • Procedure 3: • Least transport cost. This procedure searches the columns of the cost matrix for the least-cost route and assigns each market to that center that has the least transportation cost to serve that market. Then each distribution center in turn has ist service area extended to additional markets, and other centers are closed if it is shown to be less costly.