Facility Location Class 2 and/or 3 1
Facility Location Class 2 and/or 3 1. Objectives Identify some of the main reasons organizations need to make location decisions Explain why location.
Jan 11, 2016
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Facility LocationClass 2 and/or 3
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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
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Facility Location ProblemIt is difficult to find a single location with all
required characteristics at the desired levelFor 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.
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Logistics ManagementLogistics 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 materialsSubassemblies obtained from suppliersProducts shipped from plants to warehouses or
customersLogistics management problems can be classified
into three categories:
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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.
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Clientes
Centro distribución
Response Time 1 week-> 1 Distribution Center
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Clientes
Centro distribución
Response Time 5 days-> 2 Distribution Center
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Clientes
Centro distribución
Response Time 3 days-> 5 Distribution Center
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Clientes
Centro distribución
Response Time 1 day-> 13 Distribution Center
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Customer
DC
Same Day Response --> 26 Distribution Centers
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Response time vs. Number of facilities
Number of Facilities
Resp
on
seTi
me
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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.
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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.
1st Classification of Facility Location Problems
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!
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3rd Classification of Facility Location ProblemsSolution 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
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Histogram Method
A B C0
Su Enerji
Vergi Ulaştırma
İşçilik
Alternatives
CostWaterEnergyTaxTransporta-tionLabor
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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.
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Example 1: Weighted Factor MethodA 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.
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Weight
0.25
0.15
0.15
0.10
0.10
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
70
80
80
70
90
Winnipeg
90
60
45
90
90
65
95
90
Springfield
65
90
60
70
85
75
60
80
Score
Factors and weights for three locations
Steps 1, 2 and 3.
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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
8.00
5.60
6.30
78.15
Winnipeg
22.50
9.00
6.75
9.00
9.00
6.50
7.60
6.30
?
Springfield
16.25
13.50
9.00
7.00
8.50
7.50
4.80
5.60
?
Weighted Score
Weighted scores for three locations
Example 2: Weighted Factor Method
Supplement 7-20
Labor pool and climateProximity to suppliersWage ratesCommunity environmentProximity to customersShipping modesAir service
LOCATION FACTOR
.30
.20
.15
.15
.10
.05
.05
WEIGHT
80100
6075658550
Site 1
65919580909265
Site 2
90757280956590
Site 3
SCORES (0 TO 100)
Location Factor Rating
Supplement 7-21
24.0020.00
9.0011.256.504.252.50
77.50
Site 1
19.5018.2014.2512.00
9.004.603.25
80.80
Site 2
27.0015.0010.8012.00
9.503.254.50
82.05
Site 3
WEIGHTED SCORES
Site 3 has the highest factor rating
Break-Even Analysis
Total cost = fixed costs + variable costs (quantity):
Revenue = selling price (quantity)
Break-even point is where total costs = revenue:
QVCFTC
QSPR
VCSP
FQor
QSPQVCForRTC
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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?
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Example 1: Break-Even Analysis cont`d…
Break-even point:
Profit = total revenue – total costs
pairsVCSP
FQ 3250
9$25$
000,52$
000,12$
40009$000,52$400025$
QVCFQSPP
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Break-Even Analysis – Outsourcing
QVCFCQVCFC
QVCFCTC
QVCFCTC
MakeMakeBuyBuy
MakeMakeMake
BuyBuyBuy
:PointceIndifferen
:InsourcingofCostTotal
:gOutsourcinofCostTotal
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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?
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Interpretation: ◦ They anticipate selling 60,000 bagels (greater
than the indifference point of 56,000).◦ Therefore, make the bagels in-house.
000,56:
15.0$000,15$40.0$000,1$
QVCFCQVCFC MakeMakeBuyBuy
forSolve
:nCalculatio PointceIndifferen
Example: Break-Even Analysis – Outsourcing
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Cost-Profit-Volume AnalysisSteps:
◦ 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
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Cost-Profit-Volume Analysis, cont`d…
For a cost analysis, compute the total cost for each alternative location:
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Example: Cost-Profit-Volume AnalysisFixed and variable costs for four
potential plant locations are shown below:
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Example: Cost-Profit-Volume Analysis, cont`d…
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Example: Cost-Profit-Volume Analysis, cont`d…
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 12 200
ADANA 10 7 9 400
KONYA 8 4 7 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 12 200
ADANA 10 7 9 400
KONYA 8 4 300 7 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 200
ADANA 10 7 x 9 400
KONYA 8 4 300 7 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 200
ADANA 10 7 x 9 400
KONYA 8 4 300 7 100 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 200
ADANA 10 7 x 9 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 200
ADANA 10 7 x 9 200 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 x 200
ADANA 10 7 x 9 200 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 8 x 12 x 200
ADANA 10 200 7 x 9 200 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA BURSA DEMAND
TRABZON 11 200 8 x 12 x 200
ADANA 10 200 7 x 9 200 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
• TCBURSA =11*200+10*200+4*300+9*200+7*100=7900
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Minimum Cost Method
İSTANBUL ANKARA MERSİN DEMAND
TRABZON 11 8 10 200
ADANA 10 7 1 400
KONYA 8 4 6 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA MERSİN DEMAND
TRABZON 11 8 10 x 200
ADANA 10 7 1 300 400
KONYA 8 4 6 x 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA MERSİN DEMAND
TRABZON 11 8 x 10 x 200
ADANA 10 7 x 1 300 400
KONYA 8 4 300 6 x 400
CAPACITY 400 300 300 1000
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Minimum Cost Method
İSTANBUL ANKARA MERSİN DEMAND
TRABZON 11 200 8 x 10 x 200
ADANA 10 100 7 x 1 300 400
KONYA 8 100 4 300 6 x 400
CAPACITY 400 300 300 1000
• TCMERSİN =11*200+10*100+8*100+4*300+1*300=5500
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Minimum Cost MethodİSTANBUL ANKARA BURSA DEMAND
TRABZON 11 200 8 x 12 x 200
ADANA 10 200 7 x 9 200 400
KONYA 8 x 4 300 7 100 400
CAPACITY 400 300 300 1000
İSTANBUL ANKARA MERSİN DEMAND
TRABZON 11 200 8 x 10 x 200
ADANA 10 100 7 x 1 300 400
KONYA 8 100 4 300 6 x 400
CAPACITY 400 300 300 1000
• TCBURSA =11*200+10*200+4*300+9*200+7*300=7900
• TCMERSİN =11*200+10*100+8*200+4*300+1*300=5500
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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.
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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.
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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.
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Hybrid AnalysisAfter the factors are classified,
they are assigned numeric values:
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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.
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Hybrid Analysis
After LMi is determined for each candidate location, the next step is to select the one with the greatest LMi value
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Example: Hybrid AnalysisMole-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.
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Questions?
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