Operations Management Session 3. Too much info
Foundations of Facility Location Decisions
Definition of Facilities:
Facilities are physical locations where products are processed, stored, assembled, or fabricated, or where services are rendered.
Production Sites: Manufacturing plants, assembly factories, component fabrication shops.
Storage Sites: Distribution centers (DCs), warehouses, fulfillment centers, logistical hubs.
Retail & Service Locations: Retail stores, hospitals, post offices, educational institutions, hotel properties.
Strategic Character of Location Decisions:
Location decisions are strategic, long-term commitments made infrequently due to high capital investment and operational inertia.
Strategic location options available to management:
Expand: Increase production or storage capacity at an existing physical facility.
Add: Maintain existing facilities while constructing or leasing additional facilities elsewhere.
Close / Relocate: Shutdown an existing non-performing or sub-optimal facility and relocate operations to a new site.
Hierarchical Levels in Location Decisions
Facility location selection follows a three-step hierarchical framework:
Country Selection: Selecting the nation for investment based on macro-environment conditions.
Regional / State / City Selection: Selecting the specific sub-national region, state, or metropolitan area.
Site Selection: Choosing the specific physical plot, real estate parcel, or street location.
Key Success Factors (KSFs) Across Decision Levels
Country-Level Key Success Factors:
Political stability, political risk, regulatory framework, economic incentives, protection of intellectual property (IP), tax structures.
Macro-economic indicators (Gross Domestic Product, purchasing power, economic stability).
Proximity to core target markets.
Cultural norms and economic structures.
Infrastructure maturity (transportation networks, deep-water ports, rail connections, energy grids).
Labor force availability, technical skills, labor productivity, and wage rates.
Foreign exchange rates and currency fluctuation risks.
Regional / City-Level Key Success Factors:
Regional attractiveness (local taxation, climate, quality of life, cultural fit).
Labor availability, regional wage scales, and local worker attitudes toward industrial labor.
Availability and cost of utility services (water, power, gas, waste treatment).
Environmental laws, zoning codes, and regulatory restrictions.
State or municipal government tax incentives and fiscal subsidies.
Proximity to raw material sources and primary suppliers.
Proximity to end customers and regional distribution markets.
Real estate market prices, land acquisition costs, and construction costs.
Site-Level Key Success Factors:
Parcel size, physical topography, expansion capability, and purchase/lease cost.
Immediate access to transport networks (interstate highways, freight rail sidings, commercial airports, marine terminals).
Proximity to essential industrial service providers and supply bases.
Environmental impact compliance, noise ordinances, and community zoning regulations.
Local demographic profile, population density, traffic patterns, and customer accessibility.
Cost Evaluation, Labor Productivity, and Strategic Proximity
Tangible vs. Intangible Costs:
Tangible Costs: Direct financial outlays that can be precisely measured and quantified (e.g., labor costs, raw material costs, local taxes, freight and transportation costs, utility bills).
Intangible Costs: Qualitative criteria that are difficult to convert directly into monetrary values (e.g., quality of local education systems, employee work ethic, quality of transport infrastructure, strength and attitude of labor unions).
Labor Productivity Model:
Evaluating location alternatives based solely on nominal labor costs () is mathematically misleading; productivity must be incorporated into total unit cost evaluations.
Formula:
Comparative Benchmark (Germany vs. China):
Germany:
Labor Cost =
Daily Output =
Labor Productivity =
China:
Labor Cost =
Daily Output =
Labor Productivity =
Conclusion: Despite higher nominal wage rates in Germany, Germany yields higher overall labor productivity per euro () compared to China ().
Strategic Proximity Considerations:
Proximity to Markets: Vital for service facilities, retail stores, perishable items, or heavy goods where outbound freight cost is dominant.
Proximity to Suppliers: Critical for operations dependent on perishable raw materials, high-bulk raw inputs (weight-losing manufacturing processes), or tight JIT inbound delivery schedules.
Proximity to Competitors (Clustering):
Grouping competing firms in the same geographic zone provides access to specialized labor pools, advanced infrastructure, established supplier ecosystems, and consolidated customer traffic.
Examples: High-tech firms in Silicon Valley, wine production in the Bordeaux region, luxury retail on high-density city avenues.
Strategic Trade-offs: Single vs. Multiple Facilities
Efficiency vs. Responsiveness Trade-off:
Single Centralized Facility:
Responsiveness & Service Level: Low (longer delivery lead times to geographically dispersed customers).
Inventory Costs: Low (inventory aggregation reduces required total safety stock).
Fixed Costs: Low (single overhead cost, minimal facility setup and administrative costs).
Transportation Costs: High (long-distance inbound transport from suppliers and long-distance outbound shipping to final customers).
Multiple Decentralized Facilities:
Responsiveness & Service Level: High (shortened delivery lead times due to customer proximity).
Inventory Costs: High (safety stock requirements scale up across every operating site).
Fixed Costs: High (multiplied capital investments, duplicated facilities, higher administrative overhead).
Transportation Costs: Low outbound shipping costs (facilities are situated near demand centers).
Sectoral Strategic Drivers:
Retail & Service Sector: Primary objective is revenue maximization. Location strategy prioritizes proximity to high-density customer segments, high traffic volume, and market accessibility.
Industrial & Manufacturing Sector: Primary objective is cost minimization. Location strategy prioritizes proximity to cheap labor, low raw material costs, favorable utility tariffs, and efficient freight transportation.
Global Corporate Case Studies
Lotus Bakeries:
Constructed a new manufacturing plant in Thailand (operational by 2026) to expand Biscoff cookie supply across the Asia-Pacific region.
Strategic drivers: Central location within Asia, robust local food processing industry, access to raw material inputs, large labor pool, tax advantages of Thailand's Eastern Economic Corridor, shorter transit times, reduced transport costs, and lower supply chain carbon footprint.
Adidas Speedfactory:
2016–2017: Constructed highly automated, robotic "Speedfactory" plants in Ansbach (Bavaria, Germany) and the United States to manufacture ~1 million pairs of running shoes close to major consumer markets, offsetting rising Asian labor wages and high international freight rates.
2019–2020: Terminated the automated Speedfactory operations in Germany and the U.S., choosing instead to redeploy the automation technologies into existing high-volume production facilities across Asia.
McDonald's:
Opened its flagship Russian restaurant at Pushkin Square in Moscow on January 31, 1990 (during the Soviet era), attracting tens of thousands of customers.
Fully exited the Russian market in May 2022 after more than 30 years of continuous operation due to geopolitical instability.
Tesla:
Selected Monterrey, Mexico, for its new Gigafactory Mexico facility to produce next-generation electric vehicles, supporting an operational target to manufacture by the end of the decade.
Amazon:
Operates over 1,300 fulfillment centers, sorting hubs, and delivery stations globally as of 2026 to maximize customer order delivery speed.
Decathlon:
Expanded globally with 1,751 total retail stores as of May 2024 (e.g., 325 stores in France, 175 in Spain, 163 in Italy, 125 in India). Announced a 100 million euro expansion investment in India over 5 years.
Hilton Hotels:
Expanded hotel footprints across developing and emerging markets, including new hotel construction in Accra, Ghana, and plans to more than quadruple its hospitality presence in Saudi Arabia.
Ahlstrom:
Announced plans on April 19, 2024, to evaluate the closure or sale of its paper processing plant in Bousbecque, France.
Analytical Decision Models for Facility Location
Factor Rating Method
Overview: Quantitative model that evaluates location options by combining qualitative and quantitative factors into a weighted total score.
Procedural Steps:
Identify Key Success Factors (KSFs) relevant to the location decision.
Assign a numerical weight () to each factor reflecting its relative strategic importance (sum of weights = or ).
Establish a standard scoring scale (e.g., to , or to ).
Score each candidate location () for each factor.
Multiply the score by the weight for each factor and sum the products to find the total weighted score for each candidate:
Recommend the location alternative with the highest total weighted score.
Limitations: Method is subjective in factor weighting and scoring; fails to incorporate direct financial cost metrics.
Locational Break-Even Analysis
Overview: Cost-volume-profit technique that calculates total operating costs for candidate locations as a function of annual production volume ().
Mathematical Formulation:
Procedural Steps:
Determine fixed costs () for each candidate location (land, facility lease, taxes, plant overhead).
Determine variable cost per unit () for each location (direct labor, raw materials, shipping, utilities).
Plot total cost curves for each location on a coordinate graph (-axis = production volume , -axis = total annual cost).
Calculate crossover / break-even volume points between competing cost lines by setting :
Select the location that achieves the lowest total cost at the target production volume.
Limitations: Model ignores spatial market locations, distance to suppliers, revenue differences, and product quality factors.
Center of Gravity Method
Overview: Spatial mathematical technique used to determine the optimal location for a central warehouse, distribution center, or plant that minimizes total weighted transportation distance or freight cost.
Mathematical Formulas: Where:
= Spatial grid coordinates of market or supply location .
= Quantity, freight volume, or shipment frequency demanded/supplied by location Detailed Solved Numerical Exercises
Exercise 1: Factor Rating Method (Theme Park Selection)
In evaluating an overseas expansion to Europe, 5 Flags over Florida compared France (Dijon) and Denmark (Copenhagen). Factors and weights included labor availability and attitude (25%25% weight, France score 7070, Denmark score 6060), people to car ratio (5%5% weight, France score 5050, Denmark score 6060), per capita income (10%10% weight, France score 8585, Denmark score 8080), tax structure (39%39% weight, France score 7575, Denmark score 7070), and education and health (21%21% weight, France score 6060, Denmark score 7070). Calculating total weighted scores yields ScoreFrance=(0.25×70)+(0.05×50)+(0.10×85)+(0.39×75)+(0.21×60)=17.50+2.50+8.50+29.25+12.60=70.35ScoreFrance=(0.25×70)+(0.05×50)+(0.10×85)+(0.39×75)+(0.21×60)=17.50+2.50+8.50+29.25+12.60=70.35. For Denmark, the calculation yields ScoreDenmark=(0.25×60)+(0.05×60)+(0.10×80)+(0.39×70)+(0.21×70)=15.00+3.00+8.00+27.30+14.70=68.00ScoreDenmark=(0.25×60)+(0.05×60)+(0.10×80)+(0.39×70)+(0.21×70)=15.00+3.00+8.00+27.30+14.70=68.00. Because France achieves a higher weighted score (70.35>68.0070.35>68.00), France is the preferred location.
Exercise 2: Locational Break-Even Analysis (Three US Cities)
A company considers building a plant with a selling price of 120 /unit and an expected sales volume of x=2000 unitsx=2000units. Location cost structures include Akron (FC=$30,000FC=$30,000, VC=$75/unitVC=$75/unit), Bowling Green (FC=$60,000FC=$60,000, VC=$45/unitVC=$45/unit), and Chicago (FC=$110,000FC=$110,000, VC=$25/unitVC=$25/unit). Cost functions are defined as TCAkron=30,000+(75×x)TCAkron=30,000+(75×x), TCBowling Green=60,000+(45×x)TCBowling Green=60,000+(45×x), and TCChicago=110,000+(25×x)TCChicago=110,000+(25×x). The crossover volume between Akron and Bowling Green is found by solving 30,000+75x=60,000+45x ⟹ 30x=30,000 ⟹ x=1,000 units30,000+75x=60,000+45x⟹30x=30,000⟹x=1,000units. The crossover point between Bowling Green and Chicago is solved via 60,000+45x=110,000+25x ⟹ 20x=50,000 ⟹ x=2,500 units60,000+45x=110,000+25x⟹20x=50,000⟹x=2,500units. Consequently, Akron is lowest cost for volumes 0≤x<1,000 units0≤x<1,000units, Bowling Green is optimal for volumes 1,000<x<2,500 units1,000<x<2,500units, and Chicago is optimal for volumes x>2,500 unitsx>2,500units. At the expected volume of x=2,000 unitsx=2,000units, total revenue equals 120×2,000=$240,000120×2,000=$240,000. Profit calculations yield ProfitAkron=240,000−[30,000+(75×2,000)]=$60,000ProfitAkron=240,000−[30,000+(75×2,000)]=$60,000, ProfitBowling Green=240,000−[60,000+(45×2,000)]=$90,000ProfitBowling Green=240,000−[60,000+(45×2,000)]=$90,000, and ProfitChicago=240,000−[110,000+(25×2,000)]=$80,000ProfitChicago=240,000−[110,000+(25×2,000)]=$80,000. Bowling Green is selected because it yields the maximum profit of $90,000$90,000.
Exercise 3: Locational Break-Even Analysis (International Comparison)
In an international location comparison, cost structures vary across candidate nations. Under Scenario Variant A, Cuba (Havana) features FC=125,000 €FC=125,000€ and VC=15 €/unitVC=15€/unit, the US (Dallas) features FC=25,000 €FC=25,000€ and VC=70 €/unitVC=70€/unit, and Italy (Milan) features FC=40,000 €FC=40,000€ and VC=55 €/unitVC=55€/unit. Calculating crossover points shows Italy vs. US at 40,000+55x=25,000+70x ⟹ 15x=15,000 ⟹ x=1,000 units40,000+55x=25,000+70x⟹15x=15,000⟹x=1,000units, and Italy vs. Cuba at 40,000+55x=125,000+15x ⟹ 40x=85,000 ⟹ x=2,125 units40,000+55x=125,000+15x⟹40x=85,000⟹x=2,125units. Thus, Italy is preferred for production volumes between 1,0001,000 and 2,125 units2,125units. Under Scenario Variant B, Cuba features FC=110,000 €FC=110,000€ and VC=25 €/unitVC=25€/unit, the US features FC=30,000 €FC=30,000€ and VC=70 €/unitVC=70€/unit, and Italy features FC=60,000 €FC=60,000€ and VC=45 €/unitVC=45€/unit. Crossover calculations show Italy vs. US at 60,000+45x=30,000+70x ⟹ 25x=30,000 ⟹ x=1,200 units60,000+45x=30,000+70x⟹25x=30,000⟹x=1,200units (or x=1,000 unitsx=1,000units if US VC is 75 €/unit75€/unit), and Italy vs. Cuba at 60,000+45x=110,000+25x ⟹ 20x=50,000 ⟹ x=2,500 units60,000+45x=110,000+25x⟹20x=50,000⟹x=2,500units. Thus, the US is optimal for volumes 0<x<1,000 units0<x<1,000units, Italy for volumes 1,000<x<2,500 units1,000<x<2,500units, and Cuba for volumes x>2,500 unitsx>2,500units.
Exercise 4: Center of Gravity Method (Cities A, B, and C)
A firm serves three demand cities: City A at (x1,y1)=(0,0)(x1,y1)=(0,0) with demand QA=10QA=10, City B at (x2,y2)=(100,300)(x2,y2)=(100,300) with demand QB=30QB=30, and City C at (x3,y3)=(200,50)(x3,y3)=(200,50) with demand QC=20QC=20, giving total demand ∑Qi=60∑Qi=60. Applying the Center of Gravity Method yields xf=(0×10)+(100×30)+(200×20)60=7,00060≈116.67xf=60(0×10)+(100×30)+(200×20)=607,000≈116.67 and yf=(0×10)+(300×30)+(50×20)60=10,00060≈166.67yf=60(0×10)+(300×30)+(50×20)=6010,000≈166.67. The optimal coordinates for the distribution center are (116.67,166.67)(116.67,166.67).
Exercise 5: Center of Gravity Method (Four US Cities)
A company evaluates locating a central warehouse to serve Chicago at (30,120)(30,120) with demand 20002000, New York at (130,130)(130,130) with demand 10001000, Pittsburgh at (90,110)(90,110) with demand 10001000, and Atlanta at (60,40)(60,40) with demand 20002000, totaling ∑Qi=6000∑Qi=6000. Calculating coordinates yields xf=(30×2000)+(130×1000)+(90×1000)+(60×2000)6000=400,0006000≈66.67xf=6000(30×2000)+(130×1000)+(90×1000)+(60×2000)=6000400,000≈66.67 and yf=(120×2000)+(130×1000)+(110×1000)+(40×2000)6000=560,0006000≈93.33yf=6000(120×2000)+(130×1000)+(110×1000)+(40×2000)=6000560,000≈93.33. The optimal warehouse coordinates are (66.67,93.33)(66.67,93.33).
Exercise 6: Stella Artois Brewery Network Location
To determine the location for a new Stella Artois brewery across a supply and demand network, data includes Hops Supplier 1 at (296,40)(296,40) with volume 1,0001,000 (5%5%), Hops Supplier 2 at (341,61)(341,61) with volume 3,0003,000 (15%15%), Beer Market 1 at (304,60)(304,60) with volume 1,0001,000 (5%5%), Beer Market 2 at (289,119)(289,119) with volume 10,00010,000 (50%50%), and Beer Market 3 at (369,292)(369,292) with volume 5,0005,000 (25%25%), for a total network volume of 20,000 units20,000units (100%100%). Calculating coordinates using percentage weights yields xf=(296×0.05)+(341×0.15)+(304×0.05)+(289×0.50)+(369×0.25)=14.80+51.15+15.20+144.50+92.25=318.00xf=(296×0.05)+(341×0.15)+(304×0.05)+(289×0.50)+(369×0.25)=14.80+51.15+15.20+144.50+92.25=318.00 and yf=(40×0.05)+(61×0.15)+(60×0.05)+(119×0.50)+(292×0.25)=2.00+9.15+3.00+59.50+73.00=147.00yf=(40×0.05)+(61×0.15)+(60×0.05)+(119×0.50)+(292×0.25)=2.00+9.15+3.00+59.50+73.00=147.00. The optimal brewery coordinates are (318,147)(318,147).
Exercise 7: Factor Rating Method (Madrid vs. Barcelona)
A distribution center selection between Madrid and Barcelona considers labor availability (40%40% weight, Madrid 8080, Barcelona 7070), transportation access (35%35% weight, Madrid 7070, Barcelona 8585), and proximity to customers (25%25% weight, Madrid 9090, Barcelona 8080). Calculating weighted scores gives ScoreMadrid=(0.40×80)+(0.35×70)+(0.25×90)=32.00+24.50+22.50=79.00ScoreMadrid=(0.40×80)+(0.35×70)+(0.25×90)=32.00+24.50+22.50=79.00 and ScoreBarcelona=(0.40×70)+(0.35×85)+(0.25×80)=28.00+29.75+20.00=77.75ScoreBarcelona=(0.40×70)+(0.35×85)+(0.25×80)=28.00+29.75+20.00=77.75. Madrid is selected due to its higher total score (79.00>77.7579.00>77.75).
Slide Practice Problems
Additional practice problems reinforce these decision models. In an in-class Factor Rating practice comparing Germany and China with factors labor productivity (40%40% weight, score 22 for both), image (30%30% weight, Germany 22, China 11), and proximity to supply (30%30% weight, Germany 00, China 22), total weighted scores are ScoreGermany=(0.4×2)+(0.3×2)+(0.3×0)=1.4ScoreGermany=(0.4×2)+(0.3×2)+(0.3×0)=1.4 and ScoreChina=(0.4×2)+(0.3×1)+(0.3×2)=1.7ScoreChina=(0.4×2)+(0.3×1)+(0.3×2)=1.7, making China preferred (1.7>1.41.7>1.4).
In a Locational Break-Even practice comparing Germany (FC=110,000 €FC=110,000€, VC=25 €/unitVC=25€/unit) and China (FC=60,000 €FC=60,000€, VC=45 €/unitVC=45€/unit), equating total costs via 110,000+25x=60,000+45x ⟹ 20x=50,000 ⟹ x=2,500 units110,000+25x=60,000+45x⟹20x=50,000⟹x=2,500units shows Germany is optimal for production volumes exceeding 2,500 units2,500units.
In a Global Center of Gravity practice with France (Paris) at (48,2)(48,2) (25%25% sales), China (Beijing) at (39,116)(39,116) (25%25% sales), and Germany (Berlin) at (52,13)(52,13) (50%50% sales), the optimal coordinates are xf=(0.25×48)+(0.25×39)+(0.50×52)=47.75xf=(0.25×48)+(0.25×39)+(0.50×52)=47.75 and yf=(0.25×2)+(0.25×116)+(0.50×13)=36.00yf=(0.25×2)+(0.25×116)+(0.50×13)=36.00, yielding coordinates (47.75,36.00)(47.75,36.00).
For Slide 47 Practice Exercise 1 on French retail sites
factors rent cost (40%40% weight), customer traffic (25%25% weight), and proximity to suppliers (35%35% weight) yield scores of ScoreLille=(0.40×8)+(0.25×6)+(0.35×7)=7.15ScoreLille=(0.40×8)+(0.25×6)+(0.35×7)=7.15, ScoreMarseille=(0.40×6)+(0.25×9)+(0.35×8)=7.45ScoreMarseille=(0.40×6)+(0.25×9)+(0.35×8)=7.45, and ScoreParis=(0.40×4)+(0.25×8)+(0.35×9)=6.75ScoreParis=(0.40×4)+(0.25×8)+(0.35×9)=6.75, selecting Marseille (7.457.45).
For Slide 47 Practice Exercise 2 on French plant break-even, options Paris (FC=140,000FC=140,000, VC=50VC=50), Bordeaux (FC=70,000FC=70,000, VC=70VC=70), and Lille (FC=50,000FC=50,000, VC=80VC=80) yield crossovers Lille vs. Bordeaux at 10x=20,000 ⟹ x=2,000 units10x=20,000⟹x=2,000units and Bordeaux vs. Paris at 20x=70,000 ⟹ x=3,500 units20x=70,000⟹x=3,500units, making Bordeaux optimal for volumes between 2,0002,000 and 3,500 units3,500units.
Finally, for Slide 47 Practice Exercise 3 on warehouse network coordinates, Warehouse A at (10,30)(10,30) (500 tons500tons), Warehouse B at (40,10)(40,10) (300 tons300tons), and Warehouse C at (30,40)(30,40) (200 tons200tons) yield xf=(10×500)+(40×300)+(30×200)1000=23.00xf=1000(10×500)+(40×300)+(30×200)=23.00 and yf=(30×500)+(10×300)+(40×200)1000=26.00yf=1000(30×500)+(10×300)+(40×200)=26.00, giving optimal coordinates (23,26)(23,26).