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
S V R X
i i i i
X
S V R
i i i
S V R Y
i i i i
Y
S V R
i i i
Vvolume Rtransportation rate X Ycoordinates
for points X Ycoordinates 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
Preprocessing programm
Volume shipment orders
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.