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1 utdallas.edu/~metin SC Design Facility Location Sections 4.1, 4.2 Chapter 5 and 6.

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Presentation on theme: "1 utdallas.edu/~metin SC Design Facility Location Sections 4.1, 4.2 Chapter 5 and 6."— Presentation transcript:

1 1 utdallas.edu/~metin SC Design Facility Location Sections 4.1, 4.2 Chapter 5 and 6

2 2 utdallas.edu/~metin Outline u Frequency decomposition of activities u A strategic framework for facility location u Multi-echelon networks u Analytical methods for location

3 3 utdallas.edu/~metin Frequency Decomposition u SCs are enormous u It is hard to make all decisions at once u Integration by smart decomposition u Frequency decomposition yields several sets of decisions such that each set is integrated within itself

4 4 utdallas.edu/~metin Frequency Decomposition u Low frequency activity, ~ once a year, high fixed cost –R&D budget –Capacity expansion budget u Moderate frequency activity, ~ once a month –Cancellation of specific R&D projects depending on experimental outcomes –Specific machines to purchase u High frequency activity, ~ once a day, low fixed cost –What experiments to start / continue today –What to produce

5 5 utdallas.edu/~metin Facility Location: The Cost-Response Time Frontier An inventory location based point of view Local Finished Goods (FG) Inventory Regional FG Inventory Local WIP (work-in-process) Central FG Inventory Central WIP Central Raw Material and Custom production Custom production with raw material at suppliers Cost Response Time Hi Low Hi 7-Eleven Sam’s Club Regional Central Pull the inventory upstream

6 6 utdallas.edu/~metin Service and Number of Facilities Number of Facilities Response Time

7 7 utdallas.edu/~metin Customer DC Where inventory needs to be for a one week order response time - typical results --> 1 DC

8 8 utdallas.edu/~metin Customer DC 5 day order response time - typical results -- > 2 DCs

9 9 utdallas.edu/~metin Customer DC 3 day order response time - typical results -- > 5 DCs

10 10 utdallas.edu/~metin Customer DC Next day order response time - typical results --> 13 DCs

11 11 utdallas.edu/~metin Customer DC Same day / next day order response time - typical results --> 26 DCs

12 12 utdallas.edu/~metin Inbound and outbound shipping with more facilities More inbound shipping and less outbound shipping with more facilities. Less (inbound + outbound) shipping costs with more facilities, if economies of scale in transportation. SupplierManufacturerCustomer Add more facilities.SupplierManufacturerDistributorRetailerCustomer Inbound shipmentOutbound shipment Inbound shipmentOutbound shipment

13 13 utdallas.edu/~metin Costs and Number of Facilities Costs Number of facilities Total SC Inventory Transportation Facility costs No economies of scale in shipment size, SC covers a larger portion with each facility. With economies of scale in inbound shipping to retailers.

14 14 utdallas.edu/~metin Percent Service Level Within Promised Time Transportation Cost Build-up as a function of facilities Cost of Operations Number of Facilities Inventory Facilities Total Costs Labor

15 15 utdallas.edu/~metin Network Design Decisions u Facility function: Plant, DC, Warehouse: What facility performs what function –Packaging at the manufacturer or warehouse –Should a rental computer return location run diagnostic tests on the returned computers or should the testing be done at major warehouses? u Question arising from CRU Computer Rental Case done in OPRE6302 u Facility location –Starbucks opened up at UTD student apartments in 2005 but closed in 2006! –Recall Japanese 7-eleven and their blanketing strategy –SMU’s experimentation with Plano campus: u Capacity allocation –SOM car park took 80 cars in 2005 and expanded in 2006 to take about 110 cars. u Supply and market allocation: Who serves whom –By location: UT Austin serves central Texas students –By grade: UT Arlington serves undergraduate students

16 16 utdallas.edu/~metin Strategic Factors Influencing Location Decisions u Strategic Facilities Global Customers Offshore VW plants in Mexico Serving Latin America Source Nike plants in Korea Regional Customers Server Suziki’s Indian venture Maruti Udyog Contributor Maruti Udyog Lead facility Lockheed Martin’s JSF in Dallas Outpost facility Facilities in Japan; Toyota Prius

17 17 utdallas.edu/~metin Factors Influencing Location Decisions u Customer response time and local presence u Operating costs u Technological, –Availability and economies of scale (fixed operational costs) »Semiconductor manufacturing takes place only in 5-6 countries worldwide u Infrastructure, electricity, phone lines, suppliers u Macroeconomic, –Tariffs, exchange rate volatility, economic volatility –Economic communities: Nafta, EU, Pacific Rim, Efta u Politic, stability u Logistics and facility costs u Competitive –Positive externalities »Nissan in India develops car suppliers which can also supply Suziki in India. »Toyota City »Shopping Malls »DFW Telecom corridor hosting Alcatel, Ericsson, Nortel, … –Negative externalities, see the next slide

18 18 utdallas.edu/~metin Negative externality: Market Splitting by Hotelling’s Model 0 a b 1 ab 1-a-b Suppose customers (preferences, e.g. sugar content in coke) are uniformly distributed over [0,1] How much does firm at a get, how about firm at b ? If a locates first, where should b locate? If a estimates how b will locate in response to a ’s location, where should a locate?

19 19 utdallas.edu/~metin A Framework for Global Site Location PHASE I Supply Chain Strategy PHASE II Regional Facility Configuration PHASE III Desirable Sites PHASE IV Location Choices Competitive STRATEGY INTERNAL CONSTRAINTS Capital, growth strategy, existing network PRODUCTION TECHNOLOGIES Cost, Scale/Scope impact, support required, flexibility COMPETITIVE ENVIRONMENT PRODUCTION METHODS Skill needs, response time FACTOR COSTS Labor, materials, site specific GLOBAL COMPETITION TARIFFS AND TAX INCENTIVES REGIONAL DEMAND Size, growth, homogeneity, local specifications POLITICAL, EXCHANGE RATE AND DEMAND RISK AVAILABLE INFRASTRUCTURE LOGISTICS COSTS Transport, inventory, coordination

20 20 utdallas.edu/~metin Comparing Locations Objectively According to McKinsey Global Institute on HBR Jun p.91 u Draw up a list of possible locations u Define the decision criteria –Six common criteria used by companies »1. Cost of operating »2. Availability of the skills »3. Sales potential in the adjacent markets »4. Risk of doing the business »5. Attractiveness of living environments »6. Quality of infrastructure u Collect data for each location u Weight the criteria »Fortisbank of Belgium, wants to enter new large markets, gives highest weight to 3. »Citibank, wants a location for a captive IT center, gives the highest weight to 4. u Find risk data at –Economist intelligence unit: –UN Development Program: u Rank locations according to weighted sum of their scores u Assess the dynamics of the labor pool »Availability of skilled labor –Top tier universities in the cities (How many top Business schools in Dallas?).

21 21 utdallas.edu/~metin Analytical Models for SC Design u Objective functions »Private sector deals with total costs u minimizes the sum of the distances to the customers »Public sector deals with fairness and equity u minimizes the distance to the furthest customer –Location of emergency response units u Demand allocation »Distance vs. Price vs. Quality: Recall Hotelling model u Demand pattern over a geography: Discrete vs. Continuous u Feasibility check »Ante vs. Post u Distances »Euclidean vs. Rectilinear »Triangular inequality

22 22 utdallas.edu/~metin Network Optimization Models u Allocating demand to production facilities u Locating facilities u Determining capacity Which plants to establish? How to configure the network? Key Costs: Fixed facility cost Transportation cost Production cost Inventory cost Coordination cost

23 23 utdallas.edu/~metin A transportation network Defined by data K, D and c D2D2 m demand points D4D4 D3D3 D1D1 n supply points K1K1 K2K2 K3K3 c 11 c 12 c 14 c 22 c 23 c 31 c 32 c 34

24 24 utdallas.edu/~metin Demand Allocation Model: Transportation Problem Which market is served by which plant? Which supply sources are used by a plant? Given m demand points, j=1..m with demands D j Given n supply points, i=1..n with capacity K i Send supplies from supply points to demand points x ij = Quantity shipped from plant site i to customer j Each unit of shipment from supply point i to demand point j costs c ij

25 25 utdallas.edu/~metin A transportation network Defined by data K, D, c and f m demand points D4D4 D3D3 D2D2 D1D1 n supply points f 1,K 1 f 2,K 2 f 3,K 3 c 11 c 12 c 14 c 22 c 23 c 31 c 32 c 34 Which supply point operates? y 1 =yes or no y 2 =yes or no y 3 =yes or no

26 26 utdallas.edu/~metin Plant Location with Multiple Sourcing Which market is served by which plant? Which supply sources are used by a plant? None of the plants are open, a cost of f i is paid to open plant i At most k plants will be opened y i = 1 if plant is located at site i, 0 otherwise x ij = Quantity shipped from plant site i to customer j How does cost change as k increases?

27 27 utdallas.edu/~metin Plant Location with Single Sourcing Each customer has exactly one supplier Which market is served by which plant? Which supply sources are used by a plant? None of the plants are open, a cost of f i is paid to open plant i y i = 1 if plant is located at site i, 0 otherwise x ij = 1 if market j is supplied by factory i, 0 otherwise Can a plant satisfy the demand of two or more customers with this formulation?

28 28 utdallas.edu/~metin Case Study: Applichem Demand Allocation

29 29 utdallas.edu/~metin Applichem Demand Allocation (1982) Demand Mexico Canada Frankfurt Gary Sunchem Mexico30 Canada26 Latin America 160 Europe 200 U.S.A 264 Japan Venezuela 220 Capacity

30 30 utdallas.edu/~metin Applichem Production Network 1982 (with duties) Venezuela Annual Cost = $72,916,400 Mexico Canada Frankfurt Sunchem Mexico Canada Latin America Europe U.S.A Japan Gary, Indiana

31 31 utdallas.edu/~metin Applichem Production Network 1982 (without duties) Mexico Canada Latin America Mexico Canada Venezuela Frankfurt Gary Sunchem Europe U.S.A Japan Annual Cost = 66,328,100 Without duties, Venezuela and Canada plants are closed and Frankfurt satisfies the excess Canada, Latin America and USA demand. There is consolidation without duties.

32 32 utdallas.edu/~metin 1981 Network. Mexico Canada Venezuela Frankfurt Gary Sunchem Mexico Canada Latin America Europe U.S.A Japan Annual Cost = $79,598,500

33 33 utdallas.edu/~metin 1981 Network (Sunchem Closed) Mexico Canada Venezuela Frankfurt Gary Sunchem Mexico Canada Latin America Europe U.S.A Japan Annual Cost = $82,246,800

34 34 utdallas.edu/~metin Cash Flows From Sunchem Plant

35 35 utdallas.edu/~metin Value of Adding 0.1 M Pounds Capacity (1982) Capacity should be evaluated as an option and priced accordingly. Shadow (dual) prices from LP tells you where to invest.

36 36 utdallas.edu/~metin Gravity Methods for Location Ton Mile-Center Solution Given n delivery locations, i=1..n, a i, b i : Coordinates of delivery location i d i : Distance to delivery location i F i : Annual tonnage to delivery location i Locate a warehouse at (x,y)

37 37 utdallas.edu/~metin Gravity Methods for Location Change the distance Given n delivery locations, i=1..n, a i, b i : Coordinates of delivery location i d i : Distance to delivery location i F i : Annual tonnage to delivery location i Locate a warehouse at (x,y)

38 38 utdallas.edu/~metin Chapter 6 Network Design in an Uncertain Environment

39 39 utdallas.edu/~metin A tree representation of uncertainty u One way to represent Uncertainty is a binomial tree u Up by 1 down by -1 move with equal probability T steps

40 40 utdallas.edu/~metin Decision tree –One column of nodes for each time period –Each node corresponds to a future state »What is in a state? u Price, demand, inflation, exchange rate, your OPRE 6366 grade –Each path corresponds to an evolution of the states into the future –Transition from one node to another determined by probabilities –Evaluate the cost of a path starting from period T and work backwards in time to period 0.

41 41 utdallas.edu/~metin Evaluating Facility Investments: AM Tires. Section 6.5 of Chopra. Now U.S. Demand = 100,000; Mexico demand = 50,000. Demand is not to be met always. But selling more increases profit. 1US$ = 9 pesos. Sale price $30 in US and 240 pesos in Mexico. Future Demand goes up or down by 20 percent with probability 0.5 and Exchange rate goes up or down by 25 per cent with probability 0.5.

42 42 utdallas.edu/~metin AM Tires How many states in period 2? Consider US demand 4 or 3 states Consider the rest also 4x4x4 or 3x3x3

43 43 utdallas.edu/~metin AM Tires Four possible capacity configurations: Both dedicated Both flexible U.S. flexible, Mexico dedicated U.S. dedicated, Mexico flexible Consider the both flexible configuration For each node solve the demand allocation model. PlantsMarkets U.S. Mexico U.S. Mexico

44 44 utdallas.edu/~metin AM Tires in period 2: Demand Allocation for DUS = 144; DMex = 72, E = Compare this formulation to the Transportation problem. We maximize the profit now. 1.1=240/ =30-110/ =( )/14.06

45 45 utdallas.edu/~metin AM Tires: Demand Allocation for DU = 144; DM = 72, E = 14.06; Cheap Peso Plants Markets U.S. Mexico U.S. Mexico 100K; $15 44K; $21.2 6K; $9.2 Profit =Revenue-Cost US Production’s contribution=100,000*15-1,100,000=$400,000 Mex Production’s contribution=44,000* *9.2-4,400,000/14.06=$675,055 Profit(DU = 144; DM = 72, E = 14.06; Period 2; Both flexible)=$1,075,055

46 46 utdallas.edu/~metin AM Tires: Demand Allocation for DU = 144; DM = 72, E = 8.44; Expensive Peso Plants Markets U.S. Mexico U.S. Mexico 100K; $15 44K; $16 6K; $15.4 US Production’s contribution=100,000*15-1,100,000=$400,000 Mex Production’s contribution=44,000* *15.4-4,400,000/8.44 = =$275,073 Profit(DU = 144; DM = 72, E = 8.44; Period 2; Both flexible)=$675,073

47 47 utdallas.edu/~metin AM Tires: Demand Allocation for DU = 144; DM = 72, E = 5.06; Very Expensive Peso Plants Markets U.S. Mexico U.S. Mexico 78K; $15 22K; $ K; $25.7 US Production’s contribution=78000* *31.4-1,100,000=$760,800 Mex Production’s contribution=50000*25.7-4,400,000/5.06=$415,435 Profit(DU = 144; DM = 72, E = 8.44; Period 2; Both flexible)=$1,176,235 Cheap Peso profit=$1,075K; Expensive Peso profit=$675K; Very Expensive Peso profit=$1,176K

48 48 utdallas.edu/~metin Facility Decision at AM Tires Make profit computations for the first year nodes one by one: Compute the profit for a node and add to that (0.9)(1/8)(Sum of the profits of all 8 nodes connected to the current one)

49 49 utdallas.edu/~metin Capacity Investment Strategies u Single sourcing is risky u Hedging Strategy –Risk management? »Too much capacity or too little capacity »E.g. 200 leading financial services companies are examined from Every other company struck at least once by a risky event. u Source: Running with Risk. The McKinsey Quarterly. No »Managers unfamiliar with risk often focus on relatively simple accounting metrics as net income, earnings per share, return on investment, etc. –Match revenue and cost exposure u Flexible Strategy –Excess total capacity in multiple plants –Flexible technologies u More will be said in aggregate planning chapter

50 50 utdallas.edu/~metin Summary u Frequency decomposition u Factors influencing facility decisions u A strategic framework for facility location u Gravity methods for location u Network-LP-IP optimization models u Value capacity as a real option

51 51 utdallas.edu/~metin Location Allocation Decisions PlantsWarehouses 1 2 Which plants to establish? Which warehouses to establish? How to configure the network? Markets

52 52 utdallas.edu/~metin p-Median Model Inputs: A set of feasible plant locations, indexed by j A set of markets, indexed by i D i demand of market i No capacity limitations for plants At most p plants are to be opened d ij distance between market i and plant j y j = 1 if plant is located at site j, 0 otherwise x ij = 1 if market i is supplied from plant site j, 0 otherwise

53 53 utdallas.edu/~metin p-Center Model Replace the objective function in p-Median problem with Min Max {d ij x ij : i is a market assigned to plant j} We are minimizing maximum distance between a market and a plant Or say minimizing maximum distance between fire stations and all the houses served by those fire stations. An example with p=3 stations and 9 houses:

54 54 utdallas.edu/~metin p-Covering Model x i = 1 if demand point i is covered, 0 otherwise y j = 1 if facility j is opened, 0 otherwise N i facilities associated with demand point i If j is in N i, j can serve i Can you read constraint (*) in English?

55 55 utdallas.edu/~metin Other Models u p-Choice Models –Criteria to choose the server: distance, price? u Models with multiple decision makers –Franchise model


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