1. Facility Location
Class 2 and/or 3

2. Objectives
Identify some of the main reasons organizations need to make location decisions
Explain why location decisions are important
Discuss the options that are available for location decisions
Give examples of the major factors that affect location decisions
Outline the decision process for making these kinds of decisions
Use the techniques presented to solve typical problems

3. Facility Location Problem
It is difficult to find a single location with all required characteristics at the desired level
For example:
A location in Besiktas may offer a highly skilled labor pool and proximity to customers but land costs may be too high.
Similarly, another location may offer low tax rates and minimal government regulations but may be too far from raw materials source or customer base.
Thus, facility location problem becomes one of selecting site (among several available alternatives) that optimizes a weighted set of objectives.

4. Logistics Management
Logistics management is the management of a series of macro-level transportation and distribution activities with the main objective of delivering the right amount of material (goods) at the right place at the right time at the right cost using the right methods.
Goods:
Raw materials
Subassemblies obtained from suppliers
Products shipped from plants to warehouses or customers
Logistics management problems can be classified into three categories:

5. What are these Categories:
Location Problems:
Location Problems involve determining the location of one or more new facilities in one or more of several potential sites.
The cost of locating each new facility at each of the potential sites is assumed to be unknown + operating and transportation cost of serving customers from this facility-site combination.
Allocation Problems:
Allocation Problems assume that the number and location of facilities are known and attempt to determine how each customer is to be served. That is, given
demand for goods at each customer center,
the production or supply
capacities at each facility, and
the cost of serving each customer from each facility,
the allocation problem determined how much each facility is to supply to each customer center.
Location – Allocation Problems:
Location – Allocation Problems involve determining not only how much each customer is to receive from each facility but also the number of facilities along with their locations and capacities.

6. Response Time 1 week-> 1 Distribution Center
Clientes
Centro distribución

7. Response Time 5 days-> 2 Distribution Center
Clientes
Centro distribución

8. Response Time 3 days-> 5 Distribution Center
Clientes
Centro distribución

9. Response Time 1 day-> 13 Distribution Center
Clientes
Centro distribución

10. Same Day Response --> 26 Distribution Centers
Customer DC

11. Response time vs. Number of facilities
Notes:
Number of Facilities

12. 1st Classification of Facility Location Problems
Single-Facility Location Problems
Single-Facility location problems deal with the optimal determination of the location of a single facility.
Multi-facility Location Problems
Multi-facility location problems deal with the simultaneous location determination for more than one facility.
Generally, single-facility location problems are location problems, but Multi-facility location problems can be location as well as location-allocation problems.
2nd Classification of Facility Location Problems
This classification of location problems is based on whether the set of possible locations for a facility is finite or infinite:
Continuous Space Location Problem
If a facility can be located anywhere within the confines of a geographic area, then the number of possible locations is infinite.
Discrete Space Location Problem
Discrete Space Location Problems have a finite feasible set of sites in which to locate a facility.

13. 1st Classification of Facility Location Problems
Single-Facility Location Problems
Single-Facility location problems deal with the optimal determination of the location of a single facility.
Multi-facility Location Problems
Multi-facility location problems deal with the simultaneous location determination for more than one facility.
Generally, single-facility location problems are location problems, but Multi-facility location problems can be location as well as location-allocation problems.
2nd Classification of Facility Location Problems
This classification of location problems is based on whether the set of possible locations for a facility is finite or infinite:
Continuous Space Location Problem
If a facility can be located anywhere within the confines of a geographic area, then the number of possible locations is infinite.
Discrete Space Location Problem
Discrete Space Location Problems have a finite feasible set of sites in which to locate a facility.
Because facilities can be located anywhere in a two-dimensional space, sometimes the optimal location provided by the continuous space model may be infeasible. For example, a continuous space model may locate a manufacturing facility on a lake!

14. 3rd Classification of Facility Location Problems
Solution Technique:
Minimization
Total cost of setting up and operating the new facilities (and serving the users)
The sum of distances to be traveled by the items
The number of facilities
Maximization:
Maximize the number of customers to be served
Maximize the revenue of a facility
Minimax:
Minimize the maximum distance travelled (eg. emergency facilities

15. Histogram Method

16. Weighted Factor Rating Method
Step 1: List all the factors that are important, i.e. have an impact on the location decision. Step 2: Assign appropriate weights (typically between 0 and 1) to each factor based on the relative importance of each. Step 3: Assign a score (typically between 0 and 100) for each location with respect to each factor identified in Step 1. Step 4: Compute the weighted score for each factor for each location by multiplying its weight with the corresponding score (which were assigned Steps 2 and 3, respectively). Step 5: Compute the sum of the weighted scores for each location and choose a location based on these scores.

17. Example 1: Weighted Factor Method
A payroll processing company has recently won several major contracts in the Midwest region of the United States and Central Canada and wants to open a new, large facility to serve these areas.
Because customer service is so important, the company wants to be as near its “customers” as possible. A preliminary investigation has shown that Minneapolis, Winnipeg, and Springfield, Illinois are the three most desirable locations, and the payroll company has to select one of these.
A subsequent thorough investigation of each location with respect to eight important factors generated the raw scores and weights. Using the location scoring method, determine the best location for the new payroll processing facility.

18. Factors and weights for three locations
Steps 1, 2 and 3.
Factors and weights for three locations
Score
Weight
0.25
0.15
0.10
0.08
0.07
Factor
Proximity to customer
Land and construction prices
Wage rates
Property taxes
Business taxes
Commercial travel
Insurance costs
Office services
Minneapolis 95 60 70 80 90
Winnipeg 90 60 45 65 95
Springfield 65 90 60 70 85 75 80

19. Weighted scores for three locations
Steps 4 and 5.
Factor
Proximity to customer
Land and construction prices
Wage rates
Property taxes
Business taxes
Commercial travel
Insurance costs
Office services
Sum of weighted scores
Minneapolis
23.75
9.00
10.50
7.00
8.00
5.60
6.30
78.15
Winnipeg
22.50
6.75
6.50
7.60 ?
Springfield
16.25
13.50
8.50
7.50
4.80
Weighted Score
Weighted scores for three locations

20. Example 2: Weighted Factor Method
Labor pool and climate
Proximity to suppliers
Wage rates
Community environment
Proximity to customers
Shipping modes
Air service
LOCATION FACTOR
.30
.20
.15
.10
.05
WEIGHT 80
100 60 75 65 85 50
Site 1 91 95 90 92
Site 2 72
Site 3
SCORES (0 TO 100)

21. Location Factor Rating
24.00
20.00
9.00
11.25
6.50
4.25
2.50
77.50
Site 1
19.50
18.20
14.25
12.00
4.60
3.25
80.80
Site 2
27.00
15.00
10.80
9.50
4.50
82.05
Site 3
WEIGHTED SCORES
Site 3 has the highest factor rating

22. Break-Even Analysis
Total cost = fixed costs + variable costs (quantity):
Revenue = selling price (quantity)
Break-even point is where total costs = revenue:

23. Example 1: Break-Even Analysis
A firm estimates that the fixed cost of producing a line of footwear is $52,000 with a $9 variable cost for each pair produced. They want to know:
If each pair sells for $25, how many pairs must they sell to break-even?
If they sell 4000 pairs at $25 each, how much money will they make?

24. Example 1: Break-Even Analysis cont`d…
Break-even point:
Profit = total revenue – total costs

25. Break-Even Analysis – Outsourcing

26. Example: Break-Even Analysis – Outsourcing
Bill & Nancy plan to open a small bagel shop.
The local baker has offered to sell them bagels at 40 cents each. However, they will need to invest $1,000 in bread racks to transport the bagels back & forth from the bakery to their store.
Alternatively, they can bake the bagels at their store for 15 cents each if they invest $15,000 in kitchen equipment.
They expect to sell 60,000 bagels each year.
What should they do?

27. Example: Break-Even Analysis – Outsourcing
Interpretation:
They anticipate selling 60,000 bagels (greater than the indifference point of 56,000).
Therefore, make the bagels in-house.

28. Cost-Profit-Volume Analysis
Steps:
1.Determine the fixed and variable costs for each alternative
2.Plot the total-cost lines for all alternatives on the same graph
3.Determine the location that will have the lowest total cost (or highest profit) for the expected level of output
Assumptions
1.Fixed costs are constant for the range of probable output
2.Variable costs are linear for the range of probably output
3.The required level of output can be closely estimated
4.Only one product is involved

29. Cost-Profit-Volume Analysis, cont`d…
For a cost analysis, compute the total cost for each alternative location:

30. Example: Cost-Profit-Volume Analysis
Fixed and variable costs for four potential plant locations are shown below:

31. Example: Cost-Profit-Volume Analysis, cont`d…

32. Example: Cost-Profit-Volume Analysis, cont`d…

33. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 12 200
ADANA 10 7 9
400
KONYA 4
CAPACITY
300
1000

34. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 12 200
ADANA 10 7 9
400
KONYA 4
300
CAPACITY
1000

35. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
CAPACITY
1000

36. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000

37. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000

38. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000

39. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000

40. Minimum Cost Method İSTANBUL ANKARA BURSA DEMAND TRABZON 11 8 x 12 200
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000

41. Minimum Cost Method TCBURSA =11*200+10*200+4*300+9*200+7*100=7900
İSTANBUL
ANKARA
BURSA
DEMAND
TRABZON 11
200 8 x 12
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000
TCBURSA =11*200+10*200+4*300+9*200+7*100=7900

42. Minimum Cost Method İSTANBUL ANKARA MERSİN DEMAND TRABZON 11 8 10 200
ADANA 7 1
400
KONYA 4 6
CAPACITY
300
1000

43. Minimum Cost Method İSTANBUL ANKARA MERSİN DEMAND TRABZON 11 8 10 x
200
ADANA 7 1
300
400
KONYA 4 6
CAPACITY
1000

44. Minimum Cost Method İSTANBUL ANKARA MERSİN DEMAND TRABZON 11 8 x 10
200
ADANA 7 1
300
400
KONYA 4 6
CAPACITY
1000

45. Minimum Cost Method TCMERSİN =11*200+10*100+8*100+4*300+1*300=5500
İSTANBUL
ANKARA
MERSİN
DEMAND
TRABZON 11
200 8 x 10
ADANA
100 7 1
300
400
KONYA 4 6
CAPACITY
1000
TCMERSİN =11*200+10*100+8*100+4*300+1*300=5500

46. Minimum Cost Method TCBURSA =11*200+10*200+4*300+9*200+7*300=7900
İSTANBUL
ANKARA
BURSA
DEMAND
TRABZON 11
200 8 x 12
ADANA 10 7 9
400
KONYA 4
300
100
CAPACITY
1000
TCBURSA =11*200+10*200+4*300+9*200+7*300=7900
İSTANBUL
ANKARA
MERSİN
DEMAND
TRABZON 11
200 8 x 10
ADANA
100 7 1
300
400
KONYA 4 6
CAPACITY
1000
TCMERSİN =11*200+10*100+8*200+4*300+1*300=5500

47. Hybrid Analysis
A disadvantage of the Qualitative method discussed earlier is that location decision is made based entirely on a subjective evaluation. Although Quantitative method overcomes this disadvantage, it does not allow us to incorporate unquantifiable factors that have a major impact on the location decision.
Example:
The Quantitative techniques can easily consider:
transportation cost, and
operational costs,
but intangible factors such as;
the attitude of a community toward businesses,
potential labor unrest,
reliability of auxiliary service providers are difficult to capture though these are important in choosing a location decision.
Therefore, we need a method that incorporates subjective as well as quantifiable cost and other factors.

48. Hybrid Analysis
A multi-attribute, single-facility location model based on the ones presented by Brown and Gibson (1972) and Buffa and Sarin (1987).
This model classifies the objective and subjective factors important to the specific location problem being addressed as:
• critical,
• objective, and
• subjective.
The meaning of objective and subjective factors is obvious. The meaning of critical factors needs some discussion.

49. Hybrid Analysis
In every location decision, usually at least one factor determines whether or not a location will be considered for further evaluation.
For instance, if water is used extensively in a manufacturing process (e.g. a brewery), then a site that does not have an adequate water supply now or in the future is automatically removed from consideration.
This is an example of a critical factor.

50. Hybrid Analysis
After the factors are classified, they are assigned numeric values:

51. Hybrid Analysis
Assume that we have m candidate locations and p critical, q objective and r subjective factors. We can determine overall critical factor measure (CFMi), objective factor measure (OFMi), and Subjective Factor Measure (SFMi) for each location i with these equations.

52. Hybrid Analysis
After LMi is determined for each candidate location, the next step is to select the one with the greatest LMi value

53. Example: Hybrid Analysis
Mole-Sun Brewing Company is evaluating six candidate location; Montreal, Plattsburgh, Ottawa, Albany, Rochester, and Kingston for a new brewery.
The two critical, three objective and four subjective factors that management wishes to incorporate in its decision making are summarized in the table (next slide).
The weights of the subjective factors are also provided in the table. Determine the best location if the subjective factors are to be weighted 50% more than the objective factors.

54. Questions?