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1 Questions are taken from all PowerPoint slides.
Fourth test next week on last week and this week’s lectures, 40 MC questions. Study the PowerPoint and use the Study Guide that will be on the website. Questions are taken from all PowerPoint slides. Data questions: Know general trends, not numbers.

2 The Secondary Economic Sector
Manufacturing

3 Manufacturing Location To Do List
What it is and what it includes. Commodities, goods & bads, capital & consumer goods, durables-non-durables, finished-semi finished, obligatory-discretionary & elasticity. Manufacturing Location Theory: from weight watching to getting satisfaction: Basics: cost curves and surfaces. Weber and the weight loss hypothesis: maximization approaches. Losch and the pursuit of profit as a locational determinant: optimization approaches. Smith and the margins of profitability: behavioral approaches. Simon and satisficing: decision making approaches. Structural approaches and the international division of labour.

4 Definitions of “Things” and Basic Economic Concepts

5 Definitions Manufacturing is the term widely used to describe the process of taking raw materials and semi-finished components and creating semi-finished or finished products from them. Some industries, such as steel and computing, used the term fabrication instead of manufacturing. All economic activities are divided into an economic classification system using the North American Industrial Classification System (NAICS). DEFINITIONS

6 ECONOMIC ACTIVITY CLASSIFICATION SYSTEMS
Sectors Primary Primary Tertiary Tertiary Secondary Tertiary Tertiary Tertiary Quaternary Quaternary

7 Industry Group ECONOMIC ACTIVITY CLASSIFICATION SYSTEMS
NAICS 2012 CLASSIFICATION STRUCTURE – SECTORS Agriculture, forestry, fishing and hunting Mining, quarrying, and oil and gas extraction Utilities Construction Manufacturing Wholesale trade Retail trade Transportation and warehousing Information and cultural industries Finance and insurance Real estate and rental and leasing Professional, scientific and technical services Management of companies and enterprises Administrative and support, waste management and remediation services Educational services Health care and social assistance Arts, entertainment and recreation Accommodation and food services Other services (except public administration) Public administration ECONOMIC ACTIVITY CLASSIFICATION SYSTEMS Industry Group

8 ECONOMIC ACTIVITY CLASSIFICATION SYSTEMS
31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing 321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support Activities 324 Petroleum and Coal Products Manufacturing 325 Chemical Manufacturing 326 Plastics and Rubber Products Manufacturing 327 Non-Metallic Mineral Product Manufacturing 331 Primary Metal Manufacturing 332 Fabricated Metal Product Manufacturing 333 Machinery Manufacturing 334 Computer and Electronic Product Manufacturing 335 Electrical Equipment, Appliance and Component Manufacturing 336 Transportation Equipment Manufacturing 337 Furniture and Related Product Manufacturing 339 Miscellaneous Manufacturing NAICS: The Manufacturing Sector‘s 21 Classes 31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing 31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing 321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support Activities 324 Petroleum and Coal Products Manufacturing 325 Chemical Manufacturing 326 Plastics and Rubber Products Manufacturing 327 Non-Metallic Mineral Product Manufacturing 331 Primary Metal Manufacturing 332 Fabricated Metal Product Manufacturing 333 Machinery Manufacturing 334 Computer and Electronic Product Manufacturing 335 Electrical Equipment, Appliance and Component Manufacturing 336 Transportation Equipment Manufacturing 337 Furniture and Related Product Manufacturing 339 Miscellaneous Manufacturing

9 ECONOMIC ACTIVITY CLASSIFICATION SYSTEMS
31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing 321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support Activities 324 Petroleum and Coal Products Manufacturing 325 Chemical Manufacturing 326 Plastics and Rubber Products Manufacturing 327 Non-Metallic Mineral Product Manufacturing 331 Primary Metal Manufacturing 332 Fabricated Metal Product Manufacturing 333 Machinery Manufacturing 334 Computer and Electronic Product Manufacturing 335 Electrical Equipment, Appliance and Component Manufacturing 336 Transportation Equipment Manufacturing 337 Furniture and Related Product Manufacturing 339 Miscellaneous Manufacturing 31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing NAICS Code Hierarchy for Textile Mills to Six Digits 313 Textile Mills 3131 Fibre, Yarn and Thread Mills 31311 Fibre, Yarn and Thread Mills Fibre, Yarn and Thread Mills 3132 Fabric Mills Broad-Woven Fabric Mills Broad-Woven Fabric Mills Narrow Fabric Mills and Schiffli Machine Embroidery Narrow Fabric Mills and Schiffli Machine Embroidery Nonwoven Fabric Mills Nonwoven Fabric Mills Knit Fabric Mills Knit Fabric Mills 3133 Textile and Fabric Finishing and Fabric Coating 31-33 Manufacturing 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Clothing Manufacturing 316 Leather and Allied Product Manufacturing 321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support Activities 324 Petroleum and Coal Products Manufacturing 325 Chemical Manufacturing 326 Plastics and Rubber Products Manufacturing 327 Non-Metallic Mineral Product Manufacturing 331 Primary Metal Manufacturing 332 Fabricated Metal Product Manufacturing 333 Machinery Manufacturing 334 Computer and Electronic Product Manufacturing 335 Electrical Equipment, Appliance and Component Manufacturing 336 Transportation Equipment Manufacturing 337 Furniture and Related Product Manufacturing 339 Miscellaneous Manufacturing

10 Other terms and types of goods are also commonly used:
Other Definitions Other terms and types of goods are also commonly used: Commodities and commodification. Goods and ‘Bads’. Tangible, intangible, common, public goods. Finished and semi-finished products. Capital and consumer goods. Durable and non-durable goods. Discretionary and non-discretionary goods. DEFINITIONS

11 Commodities and Commodification
Commodities have two meanings in economics: It is a general terms that includes all goods and services produced to satisfy wants and needs. It is a specific term that refers to goods that have no qualitative differentiation across a market, such as wheat, pork bellies, oil. Most use of the term these days refers to qualitatively similar raw material goods. In fact the term commodification refers to the process by which goods are made similar. For example, the wine industry has been ‘commodified’ by mass consumption – that is, there is little difference between products at similar price points – terroir is no longer. DEFINITIONS

12 Goods and ‘Bads’ Can Get Ugly
The term ‘good’ is widely used in economics and is complicated in its definition. ‘Goods’ are things that satisfy a want or need and thus have a positive value. ‘Bads’ are things that have a negative value such as garbage. Goods and Bads derive their utility from people and as such: What’s a ‘good’ for one person may not be a good for another – e.g. cigarettes. DEFINITIONS

13 Goods and ‘Bads’ Can Get Even Uglier
Tangible goods are things you can touch and can be traded such as food, autos. Intangible goods are things you cannot hold but can traded such as information, expertise, video games. Services are not things and cannot be traded but do satisfy a want or need. Common goods are resources ‘owned’ by the people such as fish, timber, on crown land. Public goods are either free such as air or paid for by taxes such as schools. DEFINITIONS

14 Finished and Semi-finished products
Manufacturing at its base level refers to the production of goods from raw materials for sale either as: Finished products that are made for end consumer sale as consumer goods or corporate consumer goods. Semi finished products that are produced as further inputs to other goods or to other semi-finished products. These mostly work but some types of finished products can also be used as inputs – lumber for example is bought to build with and is also made into other stuff. Sometimes it is difficult to separate raw materials from manufactured goods. DEFINITIONS

15 Capital and Consumer Goods
Finished and semi finished products are further classified into: Capital goods are goods that are used in the production of other goods (e.g. conveyor belts or welding robots, even factories themselves). Consumer goods are goods that are produced for end consumption through retailers to individuals, such as food, clothing, and autos. A special class of consumer goods known as corporate consumer goods also exists, such as aircraft, ships, railway engines. DEFINITIONS

16 Durable and Non-durable Goods
Consumer goods are again categorised into durable, semi-durable and non-durable goods. Durable goods are those products that are made to last and use repeatedly beyond a year, such as autos, fridges, bicycles. Semi-durable goods are those products designed to last a about a year or so and can be used repeatedly such as clothing, shoes, furniture. Non-durable goods are those designed to be consumed once and within a year, such as food, paper products, gasoline. DEFINITIONS

17 Proportional Distribution Within the Canadian Economy
Chained dollars are constant dollars calculated on the basis of a two year price index rather than a one year index. 100% 71% 33% 10% 6% 17% 100% 46% 14% 8% 24% 100% 30% 18% 52% Household Final Consumption Expenditure: Everything a household buys. Final consumption expenditure: HFCE plus taxes, debt servicing, interest paid and received, etc. Household Final Consumption Expenditure: Everything a household buys. Final consumption expenditure: HFCE plus taxes, debt servicing, interest paid and received, etc. Source: Statistics Canada, CANSIM, table DEFINITIONS

18 tangible intangible services common public
Goods tangible intangible services common public Finished Semi-Finished Capital Consumer Corporate Durable Semi-durable Non-durable Discretionary Non-discretionary DEFINITIONS

19 Factors of Production

20 Change in space and time.
Factors of Production Change in space and time. Space extensive/intensive Site Situation Semi skilled Flexible (e.g. money) Land Process Manufacturing Labour Capital Admin Fixed (e.g. buildings) Raw Materials Skilled Natural Resources Semi-finished components Process (e.g. water, power) FACTORS OF PRODUCTION

21 Proportions will vary depending on type of manufacturing:
Factors of Production Proportions will vary depending on type of manufacturing: Capital intensive – needs constant and large investments of flexible capital for raw materials and equipment. Examples are petro-chemical industries. Labour intensive – lower value added, less complex goods requiring high labour inputs due to difficulties of using machinery/robotics. Examples are the clothing and shoe industry. Raw material intensive – needs access to large, secure and constant inputs of base and process materials. Examples are iron and steel, pulp and paper. FACTORS OF PRODUCTION

22 Elasticity in Supply and Demand
Economic Concepts Elasticity in Supply and Demand

23 Elasticity From an economic point of view, durable goods are also (mostly) those types of goods that are discretionary purchases. Non-durable goods are those that are usually non-discretionary purchases. All goods are linked to price elasticity. Price elasticity is an economic concept that refers to the degree to which demand will fluctuate with changing price, and derives from the supply demand curve relationship. BASIC ECONOMIC CONCEPTS USED

24 Supply and Demand Curves
A basic concept in economics is supply and demand and how price effects the quantity that will be supplied and demanded. Thus… From Ecological Footprint website: What is World Overshoot Day? Beginning on October 9th and continuing through the end of the year, the world will be living beyond its ecological means. Ecological Footprint accounting shows that, as of September 23rd, humanity will have already consumed the total amount of new resources nature will produce this year. Each year Global Footprint Network calculates humanity’s Ecological Footprint (its demand on cropland, pasture, forests and fisheries) and compares it with global biocapacity (the ability of these ecosystems to generate resources and absorb wastes). Ecological Footprint accounting can be used to determine the exact date we, as a global community, begin running our annual ecological deficit. Designated “World Overshoot Day,” this year demand begins outstripping supply on October 9.     Overshoot has been called ‘the biggest issue you’ve never heard of.’ Yet despite its lack of publicity, its causes and effects are as simple as they are significant.In any given year, if trees are cut down faster than they grow back, then forests become smaller than the year before. If more fish are caught each year than spawn, there will be fewer fish in the sea. The consequences of our accumulating ecological debt also include global climate change, species extinction, insecure energy supplies, water shortages, and crop failure.  As humanity’s consumption of resources increases, World Overshoot Day creeps earlier on the calendar. Humanity’s first Overshoot Day was December 19, By 1995 it had jumped back a month to 21 November. Today, with Overshoot Day on October 9, humanity's Ecological Footprint is almost thirty per cent larger than the planet’s biocapacity this year. In other words, it now takes more than one year and three months for the Earth to regenerate what we use in a single year. What is Overshoot? Today, humanity uses about 30% more in one year than nature can regenerate in that same year. This is called “overshoot”. An ecological overshoot of 30% means that it takes one year and about three months for the Earth to regenerate what is being used by people in one year, creating an ecological deficit. We currently maintain this overshoot by liquidating the planet’s natural resources. For example we can cut trees faster than they re-grow, and catch fish at a rate faster than they repopulate. While this can be done for a short while, overshoot ultimately leads to the depletion of resources on which our economy depends. Overshoot is like ecological overspending. Just as any business that does not keep financial books will go bankrupt over time, we must document whether we’re living within our ecological budget or running an ecological deficit that will eventually deplete our renewable assets. How is Overshoot Day Calculated? [ world biocapacity / world Ecological Footprint ] x 365 = Overshoot Day Put simply, Overshoot day shows the day on which our total Ecological Footprint (measured in global hectares) is equal to the biocapacity (also measured in global hectares) that nature can regenerate in that year. For the rest of the year, we are accumulating debt by depleting our natural capital and letting waste accumulate. The day of the year on which humanity enters into overshoot is calculated by calculating the ratio of global available biocapacity to global Ecological Footprint and multiplying by 365. From this, we find the number of days of demand that the biosphere could supply, and the number of days we operate in overshoot. This ratio shows that in just 282 days, we demand the biosphere’s entire capacity for the year The 282nd day of the year is October 9th. … the higher the price of a product the less that gets demanded. … the higher the price of a product the more that gets produced. Demand Supply Spoiler Alert!! You’ve seen this all before. Price of a product per unit Quantity of units BASIC ECONOMIC CONCEPTS USED

25 Supply and Demand Equilibrium
When the two curves get put together we end up with an equilibrium price (quantity q will be made and bought at price p) being established by the market: the price is high enough to make it worth while for producers to produce the product and cheap enough for consumers to buy the product. Exceptions: Giffen goods – Goods where increases in price can lead to increase in demand. The classic example is staple products and the purchasing behaviour of poor people. For example, if the price of bread and meat both increase, poor people will buy less meat and more bread even though the latter product has increased in price as well because it is still cheaper and they have to eat. Veblen goods: Similar, but relates to very expensive goods bought because someone can afford them and to show status. Comes from Thorsten Veblen’s ideas on conspicuous consumption. Examples would be expensive cars – a Rolls Royce is a high quality vehicle, but how much higher quality is it than a high end Mercedes or Bentley? Even in the world of the rich, some can afford a Rolls and some ‘only’ a Bentley. Another example would be cosmetics – lowering the price of cosmetics does not increase demand for them, but the opposite – demand decreases. Quality is measured in terms of price. Demand p Price of a product per unit Supply q Quantity of units BASIC ECONOMIC CONCEPTS USED

26 When the price of a good or service changes by 1% and ...
Price Elasticity But supply and demand equilibrium isn’t a single relationship – it varies depending on type of good or service When the price of a good or service changes by 1% and ... …demand …it is called …and is typical of Doesn’t change Perfect inelasticity Nothing really, it’s theory Non -discretionary goods Changes by < 1% Inelastic Non -durable goods Changes by 1% Unitary elasticity Nothing really, it’s theory Discretionary goods Changes by >1% Elastic Durable goods Becomes infinite Perfect elasticity Nothing really, it’s theory The revenue earned from selling a product is affected by changes in price due to due to supply and demand relationships. But because of price elasticity demand changes differentially. BASIC ECONOMIC CONCEPTS USED

27 Elasticity and Demand Curves
Perfectly inelastic demand No change in demand no matter how price changes. 4 So: As price increases or decreases by 1 unit, demand decreases or increases by less than, equal to, or more than one unit. These are inelastic, unitary elastic, elastic. Inelastic Unit elasticity 3 Unit Change In Price Perfectly elastic demand Demand becomes infinite at the slightest price change. 2 Elastic 1 1 2 3 4 Unit Change in Quantity Demanded BASIC ECONOMIC CONCEPTS USED

28 BASIC ECONOMIC CONCEPTS USED
Types of Elasticity Unit change in price Empirical Theoretical Perfect inelasticity Perfect elasticity Inelastic Elastic Unitary 1 Unit change in demand 1=0 1<1 1 1=1 1>1 2 . n 1=∞ BASIC ECONOMIC CONCEPTS USED

29 BASIC ECONOMIC CONCEPTS USED
Inelastic Demand Elastic Demand BASIC ECONOMIC CONCEPTS USED

30 Spatial Cost Curves

31 Spatial Demand and Supply – Cost Curves
Fundamental concept in economic geography. Effects of overcoming distance on costs… for producers; for consumers. Effects of population density… on demand; on revenue. Effects of changing costs and revenues on profit; on spatial margins of profitability; on trade area boundaries. SPATIAL COST CURVES AND SURFACES

32 $ X Space The Basic Single Factory Spatial Cost Curve Costs
Costs vary over space due to transportation costs of getting product to markets or consumers to the product. Total cost = production costs + transportation costs $ Variable (transportation) cost component Cost curves are a simple and effective technique for illustrating the spatial implications of different cost, revenue and hence profit situations. The basic graph is a function of costs and revenues in dollars on the vertical axis, and space on the horizontal axis. The cost of locating anywhere on the space axis is a function of production costs at a given site plus, the transportation costs of shipping raw materials to the site,.... ... and the loss of revenue away from the site incurred when customers themselves have to pay for shipping the finished product to their stores (which leaves less money to buy product). The basic model looks like this:  Fixed (production) cost component. X Space SPATIAL COST CURVES AND SURFACES

33 The Basic Population Spatial Revenue Curve
Revenues vary over space due to variable population density and thus demand, and the cost to consumer of obtaining product. Total revenue = quantity demanded X price Quantity demanded = money available – travel costs $ Revenues Population Density Demand X Space SPATIAL COST CURVES AND SURFACES

34 $ X Spatial Implications of Variable Costs and Revenues Space Profit
Minimum costs and maximum revenues location the same. Therefore maximum profit point is the same. Costs Revenues $ Profit X Minimum cost location Maximum revenue location Spatial margins of profitability I.E Trade Area SPATIAL COST CURVES AND SURFACES Space

35 $ X Y Spatial Implications of Variable Costs and Revenues Profit
Minimum costs and maximum revenues locations differ. Therefore, in this case, maximum profit location is at maximum revenue location. Costs Revenues $ Profit X Y Minimum cost location Maximum revenue location Spatial margins of profitability I.E Trade Area SPATIAL COST CURVES AND SURFACES

36 $ X Z Y Spatial Implications of Variable Costs and Revenues Profit
Now maximum profit point is not at the minimum cost or the maximum revenue locations. Costs Revenues $ Profit X Z Y X=Minimum cost location Y=Maximum revenue location Z=Maximum profit location SMP SMP SPATIAL COST CURVES AND SURFACES

37 X Y The Basic Two Factory Spatial Cost Curve
Trade area boundary between X and Y Variable cost of transportation Costs Fixed unit cost of production X Y Space SPATIAL COST CURVES AND SURFACES

38 The Basic Two Factory Spatial Cost Curve
Plan View X Y SPATIAL COST CURVES AND SURFACES

39 X Y The Basic Two Factory Spatial Cost Curve With Revenue Curve
Trade area boundary between X and Y Unserved area between X and Y Costs v v Profit Profit X Y Space SPATIAL COST CURVES AND SURFACES

40 The Basic Two Factory Spatial Cost Curve Unserved area between X and Y
Plan View X Y Unserved area between X and Y SPATIAL COST CURVES AND SURFACES

41 What Happens to Market Boundary When ‘Y’ Reduces Unit Production Costs
Trade area boundary between X and Y Scale economies reduce unit costs X Y Market area of Y increases SPATIAL COST CURVES AND SURFACES Space

42 X Y The Basic Two Factory Spatial Cost Curve Plan View
New market boundary services un-served area. SPATIAL COST CURVES AND SURFACES

43 What Happens to Market Boundary When ‘Y’ Reduces Transportation Costs
Scale economies reduce transport costs Costs Trade area boundary between X and Y X Y Market area of Y increases SPATIAL COST CURVES AND SURFACES Space

44 X Y Adding a Revenue Curve Costs Profit Profit Space
Trade area boundary between X and Y Costs v v v Profit Profit Unserved area now gets served by Y Scale economies reduce unit costs X Y Market area of Y increases SPATIAL COST CURVES AND SURFACES Space

45 What Happens to Market Boundary When ‘Y’ Reduces Transportation Costs
Scale economies reduce transport costs Costs v v Profit Trade area boundary between X and Y X Y Market area of Y increases SPATIAL COST CURVES AND SURFACES Space

46 SPATIAL COST CURVES AND SURFACES
Cost Surfaces Cost isolines X3 X2 Costs X1 X Y SPATIAL COST CURVES AND SURFACES Space

47 SPATIAL COST CURVES AND SURFACES
Cost Surfaces Cost isolines X3 X2 X1 X Y SPATIAL COST CURVES AND SURFACES

48 Industrial Location Models

49 History of Industrial Location Models
The history of industrial location theory based on developing progressively more realistic models. Each subsequent theory, more or less, builds on the preceding models; that is, it builds on the perceived weaknesses of those previous models. Traditionally, the most influential variable has been transportation and the most influential process has been distance decay. The history of industrial location theory is the story of constructing progressively more realistic location models which concentrated on formulating a series of propositions, assumptions, conditions, and variables with which to analyze the manufacturing location decision. Each subsequent theory, more or less, builds on the preceding models; that is, it builds on the perceived weaknesses of those previous models. The essence of these so-called `normative' or `should-be' models is to identify the optimal location for a manufacturing plant after having identified the major processes and variables used in the decision, and controlled for all the rest. HISTORY AND FRAMEWORKS

50 How They Work Markets and raw material suppliers cannot be moved.
Labour and capital can. So the models depend on: Locations of markets and raw materials suppliers and the cost of transportation incurred in moving raw materials and finished products between markets, suppliers, and manufacturing plant. This has been the basis of the normative (theoretical) models of industrial location. The history of industrial location theory is the story of constructing progressively more realistic location models which concentrated on formulating a series of propositions, assumptions, conditions, and variables with which to analyze the manufacturing location decision. Each subsequent theory, more or less, builds on the preceding models; that is, it builds on the perceived weaknesses of those previous models. The essence of these so-called `normative' or `should-be' models is to identify the optimal location for a manufacturing plant after having identified the major processes and variables used in the decision, and controlled for all the rest. HISTORY AND FRAMEWORKS

51 Types of Models Theoretical models of industrial location can be divided into two main types: Economic optimisation models that seek the single best location for a plant, where best usually meant most profit. Economic sub-optimisation models that seek those areas of profitability where the plant can make money, but not necessarily the maximum profit. Traditional models most appropriate to the location of: Basic heavy industry of the type formed early in the industrialisation process. The individual factory as the object of study. The individual entrepreneur as the decision maker. And, traditionally, the most influential variable has been transportation and the most influential process has been distance decay. That is, since markets and raw material suppliers cannot be moved but labour and capital can, then it is the locations of those markets and raw materials suppliers, and the cost of transportation incurred in overcoming the distance between markets, suppliers, and manufacturing plant, that has been the basis of the normative models of industrial location. The traditional models are most appropriate to the location of: basic heavy industry of the type formed early in the industrialisation process the individual factory as the object of study the individual entrepreneur as the decision maker. Theoretical models of industrial location can be divided into two main classes: economic optimisation models that seek the single best location for a plant, where best usually meant most profit; economic sub-optimisation models that seek those areas of profitability wherein the plant can make money, but not necessarily the maximum profit. HISTORY AND FRAMEWORKS

52 Major Locational Attribute of Model SUB-OPTIMISING MODELS
Who’s Who Modeling of Industrial landscape starts in 1909 and all models are explicitly locational using variation in costs/revenues over space. Person Major Locational Attribute of Model  OPTIMISING MODELS Weber Least cost location. Palander Least cost location, variable prices, leading to market areas. Hoover Least cost location with variable transportation costs. Losch Maximum profit point (variable revenues). Hotelling Locational interdependence & market areas. SUB-OPTIMISING MODELS Isard Locational triangle & economic substitution. Smith Margins of profitability. Simon Satisficing. Pred Behavioral matrix. HISTORY AND FRAMEWORKS

53 The Beckmann Framework
SELLING PRICE OF FINISHED PRODUCTS Uniform In Space Not Uniform In Space Uniform Plants are Best locations are In Space indifferent to those that maximise location sales and hence profits COST OF FACTORS OF Best locations are PRODUCTION Best locations Not those that maximise difference Uniform minimise costs and between costs and In Space hence maximise revenues and hence profits maximise profits HISTORY AND FRAMEWORKS

54 The Beckmann Framework and Spatial Cost Curves
SELLING PRICE OF FINISHED PRODUCTS Uniform In Space Not Uniform In Space COST OF FACTORS OF PRODUCTION Plants are indifferent to location Best locations are those that maximise sales and hence profits Best locations are those that minimise costs and hence maximise profits Best locations maximise difference between costs and revenues and hence maximise profits Revenues $ $ Costs Space Space $ $ Space Space SPATIAL COST CURVES AND SURFACES

55 Weber’s Model

56 Weber's Industrial Location Theory
Alfred Weber developed this model in 1909 to explain the location of manufacturing. It has been modified greatly over the years to better suit more modern views on manufacturing location. But it still has some utility in examining the location of what are called high weight loss industries (such as the steel industry). It also serves as another good example of the normative model and the assumptions on which such models must be based.  WEBER

57 Assumptions of the Model
There are two types of raw materials: localised or point sources (which may nonetheless cover considerable areas such as timber stands) and ubiquitous sources, which are readily available throughout the system (such as water). Markets are fixed point locations (but can be metropolitan areas). Transportation costs are proportional to distance. Transportation costs are the same on finished products as for raw materials. Processing costs are constant over space (no scale economies). Labour costs are constant over space. Demand is constant; i.e. the producer can sell all they can produce. Producers are profit maximisers. WEBER

58 Operational Principles of the Model
The assumptions isolate the effect of transportation costs on location while holding all other variables constant. In order to put the model into operation Weber developed several fundamental principles: The ton-miles concept. The weight loss hypothesis involving … pure, gross, and ubiquitous raw materials that uses… the material index. Isodapanes or cost surfaces. WEBER

59 The Ton-Miles Concept This concept is based on the transportation assumptions and includes two elements: weight to be moved (tons) distance to be covered (miles) Hence, combined, these yield the respective ton-miles value for a given commodity/distance combination: To ship 200 tons of a raw material 10 miles to the plant gives a ton-miles value of 200*10=2000 ton-miles. Likewise, to ship 1 ton over 2000 miles would yield the same ton-miles value. Quantitatively they are same, qualitatively they are different - it is easier to ship the 200 tons 10 miles by canal than to ship the 1 ton 2000 miles by different modes of transportation. WEBER

60 Ton-Miles and Best Economic Location
Given the assumptions of fixed revenue, fixed processing costs, identical transport costs, etc., then the best economic location for the plant is going to be at the least cost location. Since the only cost affecting the model is transportation cost, then the least cost location is going to be that location where transportation costs are minimised. Therefore… The best location is the one where the total ton-miles value for a plant is minimised. WEBER

61 The Weight Loss Hypothesis
This concept is based on the different types of raw materials that exist: Localised raw materials: Found in one place so exert a locational pull and have two types: Pure localised raw materials: All raw material used in the product – e.g. cotton. Gross localised raw materials: Only part of the raw material used so it involves weight loss during the production process – e.g. iron ore. Ubiquitous raw materials: Found virtually everywhere so don’t exert a locational pull - e.g. water. This concept is based on the different types of raw materials that exist: localised raw materials, which exert a locational pull because they are only found in one place. ubiquitous raw materials, which do not exert any locational pull because they are found virtually everywhere, such as water. There are also two types of localised raw materials: pure localised raw materials, in which virtually all the raw material ends up in the product; for example, cotton in yarn manufacture. gross localised raw materials, in which there is some weight loss of the raw material during the production process, either through (a) direct waste (as with the rock ore during the manufacture of pig iron, or with steel scrap in the manufacture of machinery), or through (b) consumption during the production process (as with coal in the manufacture of steel). Thus, with pure localised raw materials there is little or no weight loss in processing, and with gross localised raw materials there is weight loss in processing. From this weight loss concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the GROSS LOCALISED RAW MATERIALS are procured.“ WEBER

62 The Weight Loss Hypothesis
To summarise: With pure localised raw materials there is little or no weight loss in processing; With gross localised raw materials there is weight loss in processing. With ubiquitous materials, there is a weight gain in processing. From this weight differential concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the gross localised raw materials are procured.“ This concept is based on the different types of raw materials that exist: localised raw materials, which exert a locational pull because they are only found in one place. ubiquitous raw materials, which do not exert any locational pull because they are found virtually everywhere, such as water. There are also two types of localised raw materials: pure localised raw materials, in which virtually all the raw material ends up in the product; for example, cotton in yarn manufacture. gross localised raw materials, in which there is some weight loss of the raw material during the production process, either through (a) direct waste (as with the rock ore during the manufacture of pig iron, or with steel scrap in the manufacture of machinery), or through (b) consumption during the production process (as with coal in the manufacture of steel). Thus, with pure localised raw materials there is little or no weight loss in processing, and with gross localised raw materials there is weight loss in processing. From this weight loss concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the GROSS LOCALISED RAW MATERIALS are procured.“ WEBER

63 Locational Types – At The Gross Material Source
Based on the weight loss hypothesis and the ton-miles concept three basic location types can be postulated: Location at the gross raw material source This would occur when there was a large weight loss because you would minimise the ton-miles by shipping as little waste as possible. That is, it involves gross localised raw materials. 2. The Weight Loss Hypothesis: This concept is based on the different types of raw materials that exist: i. localised raw materials, which exert a locational pull because they are site constrained; that is, only found in one place. ii. ubiquitous raw materials, which do not exert any locational pull because they are found virtually everywhere; for example water. There are also two types of localised raw materials: i. pure localised raw materials, in which virtually all the raw material ends up in the product; for example, cotton in yarn manufacture. ii. gross localised raw materials, in which there is some weight loss of the raw material during the production process, either through (a) direct waste (as with the rock ore during the manufacture of pig iron, or with steel scrap in the manufacture of machinery), or through (b) consumption during the production process (as with coal in the manufacture of steel). Thus, with pure localised raw materials there is little or no weight loss in processing, and with gross localised raw materials there is weight loss in processing. From this weight loss concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the GROSS LOCALISED RAW MATERIALS are procured." The reason for this is fairly simple: - when assembling the raw materials for production, transportation costs have to be paid on both the waste part and the useable part of the raw material. - therefore, there is a rational attempt to minimise unnecessary transportation costs on the waste part. The conceptual simplicity of Weberian theory, which is its strength, also hides its basic weakness: how do you discern what are ubiquitous and localised raw materials, and further, what are pure and what are localised? Apart from the obvious examples of water and iron ore, where do all the other myriad raw materials fit in, and especially, how do you treat semi-finished products? Notwithstanding the problems, based on the weight loss hypothesis and the ton-miles concept (where the best location is found where ton-miles are minimised) three basic and broad location types can be postulated: 1. location at the raw material source would occur when there was a large weight loss (you would minimise the ton-miles by shipping as little waste as possible; 2. location at the market would occur when there was a large weight gain over the weight of the localised materials, and this would occur when large amounts of a ubiquitous product were involved (you would minimise the ton-miles by not shipping the ubiquitous raw material; 3. Location at an intermediate site (including the market or the material source) could occur if there was no weight gain or weight loss; that is, if the raw material was pure localised. In practice, intermediate locations are unlikely to occur, as we shall see when Hoover's work is examined. WEBER

64 Locational Types – At An Intermediate Site
Location at an intermediate site This includes a market or a material source or anywhere in between and would occur if there was no weight gain or weight loss. That is, if the raw material was pure localised. In practice, intermediate locations are unlikely to occur due to curvilinear transport costs and break of bulk costs. 2. The Weight Loss Hypothesis: This concept is based on the different types of raw materials that exist: i. localised raw materials, which exert a locational pull because they are site constrained; that is, only found in one place. ii. ubiquitous raw materials, which do not exert any locational pull because they are found virtually everywhere; for example water. There are also two types of localised raw materials: i. pure localised raw materials, in which virtually all the raw material ends up in the product; for example, cotton in yarn manufacture. ii. gross localised raw materials, in which there is some weight loss of the raw material during the production process, either through (a) direct waste (as with the rock ore during the manufacture of pig iron, or with steel scrap in the manufacture of machinery), or through (b) consumption during the production process (as with coal in the manufacture of steel). Thus, with pure localised raw materials there is little or no weight loss in processing, and with gross localised raw materials there is weight loss in processing. From this weight loss concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the GROSS LOCALISED RAW MATERIALS are procured." The reason for this is fairly simple: - when assembling the raw materials for production, transportation costs have to be paid on both the waste part and the useable part of the raw material. - therefore, there is a rational attempt to minimise unnecessary transportation costs on the waste part. The conceptual simplicity of Weberian theory, which is its strength, also hides its basic weakness: how do you discern what are ubiquitous and localised raw materials, and further, what are pure and what are localised? Apart from the obvious examples of water and iron ore, where do all the other myriad raw materials fit in, and especially, how do you treat semi-finished products? Notwithstanding the problems, based on the weight loss hypothesis and the ton-miles concept (where the best location is found where ton-miles are minimised) three basic and broad location types can be postulated: 1. location at the raw material source would occur when there was a large weight loss (you would minimise the ton-miles by shipping as little waste as possible; 2. location at the market would occur when there was a large weight gain over the weight of the localised materials, and this would occur when large amounts of a ubiquitous product were involved (you would minimise the ton-miles by not shipping the ubiquitous raw material; 3. Location at an intermediate site (including the market or the material source) could occur if there was no weight gain or weight loss; that is, if the raw material was pure localised. In practice, intermediate locations are unlikely to occur, as we shall see when Hoover's work is examined. WEBER

65 That is, it involves large quantities of a ubiquitous product.
Locational Types – At the Market Location at the market This would occur when there was a large weight gain because you would minimise the ton-miles by not shipping the ubiquitous raw material. That is, it involves large quantities of a ubiquitous product. 2. The Weight Loss Hypothesis: This concept is based on the different types of raw materials that exist: i. localised raw materials, which exert a locational pull because they are site constrained; that is, only found in one place. ii. ubiquitous raw materials, which do not exert any locational pull because they are found virtually everywhere; for example water. There are also two types of localised raw materials: i. pure localised raw materials, in which virtually all the raw material ends up in the product; for example, cotton in yarn manufacture. ii. gross localised raw materials, in which there is some weight loss of the raw material during the production process, either through (a) direct waste (as with the rock ore during the manufacture of pig iron, or with steel scrap in the manufacture of machinery), or through (b) consumption during the production process (as with coal in the manufacture of steel). Thus, with pure localised raw materials there is little or no weight loss in processing, and with gross localised raw materials there is weight loss in processing. From this weight loss concept, and based on the transportation assumptions of the model, Weber developed the weight loss hypothesis: "The production plant is attracted towards those locations where the GROSS LOCALISED RAW MATERIALS are procured." The reason for this is fairly simple: - when assembling the raw materials for production, transportation costs have to be paid on both the waste part and the useable part of the raw material. - therefore, there is a rational attempt to minimise unnecessary transportation costs on the waste part. The conceptual simplicity of Weberian theory, which is its strength, also hides its basic weakness: how do you discern what are ubiquitous and localised raw materials, and further, what are pure and what are localised? Apart from the obvious examples of water and iron ore, where do all the other myriad raw materials fit in, and especially, how do you treat semi-finished products? Notwithstanding the problems, based on the weight loss hypothesis and the ton-miles concept (where the best location is found where ton-miles are minimised) three basic and broad location types can be postulated: 1. location at the raw material source would occur when there was a large weight loss (you would minimise the ton-miles by shipping as little waste as possible; 2. location at the market would occur when there was a large weight gain over the weight of the localised materials, and this would occur when large amounts of a ubiquitous product were involved (you would minimise the ton-miles by not shipping the ubiquitous raw material; 3. Location at an intermediate site (including the market or the material source) could occur if there was no weight gain or weight loss; that is, if the raw material was pure localised. In practice, intermediate locations are unlikely to occur, as we shall see when Hoover's work is examined. WEBER

66 The Material Index The material index is a measure of how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of finished product 1 ton of cotton/1 ton of cloth = MI of 1 4 tons of iron ore/1 ton of iron = MI of 4 0.1 ton of flavouring/1 ton of beer = MI of 0.1 And interpreted these are: 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: WEBER

67 The Material Index Interpretation
Pure localised raw = material index of 1.0 Location is intermediate. Gross localised raw material = material index of >1.0 Location is at the material source. Ubiquitous material = material index of <1.0 Location is at the market. Note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water. The ton-miles concept and weight loss hypothesis can be demonstrated with an example. 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: WEBER

68 Material-----------------distance 10km--------------Market
The Material Index – Single Material and Market Only Given the following single material source, single market example, where would the plant be located if the materials were pure, gross, and ubiquitous? Material distance 10km Market Material Index Ton - miles if plant Ton - miles if plant located at materials located at market COMPARE source Pure (1:1) 1 ton FP*10km=10 1 ton RM*10 = 10 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: Gross (2:1) 1 ton FP*10km=10 2 tons RM*10 = 20 Ubiquitous (0.5:1) 1 ton FP*10km=10 0.5 tons RM*10 = 5 Pure locates anywhere (MI=10 vs. 10) Gross locates at the materials source (MI=10 vs. 20) Ubiquitous locates at the market (MI=10 vs. 5) WEBER

69 Material-----------------------5km---------------------Market
The Material Index – Single Material and Market Only If the plant were located halfway between: Material km Market Pure: (1 ton RM*5)+(1 ton FP*5) = 10 ton-miles Gross: (2 ton RM*5)+(1 ton FP*5) = 15 ton-miles Ubiquitous: (0.5 ton RM*5)+(1 ton FP*5) = 7.5 ton-miles Location decisions are the same because ton-miles either increases or stays the same. Pure locates anywhere (MI=10 & 10) Gross locates at the materials source (MI=10 & 20) Ubiquitous locates at the market (MI=7.5 & 5) 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: WEBER

70 ● ● ● The Material Index – Multiple Materials and Markets RM#1 3:1
10 km 6 km 8 km MARKET Plant at RM#1 Ton-miles = (2 rm2*10)+(1 fp*6) = 26 Plant at RM#2 Ton-miles = (3 rm1*10)+(1 fp*8) = 38 Plant at Market Ton-miles = (3 rm1*6)+(2 rm2*8) = 34 Weber’s general rule about location: If the weight of a single gross localised material exceeds the sum of all other raw materials, then that’s the least cost location. 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: WEBER

71 The Locational Polygon for Iron Ore Smelting
Reduction coal 8:1 Iron Ore 2:1 Charcoal 2:1 Scrap iron 1:1 Heating coal 2:1 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: So, in this case we use Weber’s Rule: =7, which is less than 8 so plant goes to reduction coal material source. WEBER

72 The problem looks like this:
The Locational Polygon When no single raw material dominates, as is usually the case for products requiring many inputs, we can use the idea of the location polygon to locate the best plant site because now it will not be at either the market or materials source. It is quite possible that an intermediate site would be the lowest ton-miles point, and the problem becomes one of how to find this intermediate location. The problem looks like this: WEBER

73 The Locational Polygon for Zinc Smelting
Reduction coal 8:1 Zinc concentrate 2:1 Slab zinc 4:1 ? 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: Fireclay 2:1 Heating coal 3:1 In this case: =11, which is more than 8 so plant goes somewhere in the middle. WEBER

74 The Locational Polygon
Now it is no longer possibly to ascertain the location using the simple arithmetic of ton-miles, even though the least ton-miles point is still the optimum location. There are three ways to do find this location: Mechanically (Varignon Frame) Progressive approximation (brute strength) Mathematically with vectors WEBER

75 Weights (material indices) pull pointer to optimum location.
Varignon Frame Weights (material indices) pull pointer to optimum location. Pulleys Weights WEBER

76 Joint them up with a triangle.
Progressive Approximation G1 * G3 * Joint them up with a triangle. G2 * * Guess 3 least cost locations within the locational polygon and calculate their material indices. 3. The Material Index: The material index is a measure of just how much weight loss is involved for a particular raw material, and is expressed as a simple ratio of the weight of localised raw material to the weight of finished product: material = weight of localised material index weight of final product Such that, 1.use of a pure localised raw material leads to a material index of 1.0; thus location can be intermediate. 2.use of gross localised raw material leads to a material index of >1.0; thus location is at the material source. 3.use of ubiquitous material leads to a material index of <1.0; thus location is at the market [note that in the case of a product that uses a great deal of ubiquitous material, the localised material in the formula may be very small, as in the case of beer making where the product is mostly water.] The ton-miles concept and weight loss hypothesis can be demonstrated with an example. Given the following single material source, single market example, where would the plant be located if the materials were either pure, gross, or ubiquitous? material miles market source X area Material Ton-miles if Ton-miles if Index plant located at Plant located (rm:fp) materials source at market Pure (1:1) 1ton fp*10miles=10 1ton rm*10miles=10 Gross (2:1) 1ton fp*10miles=10 2tons rm*10miles=20 Ubiq. (.5:1) 1ton fp*10miles=10 .5tons rm*10miles=5 Thus a product requiring gross localised raw materials will be located at the materials source (10 ton-miles), a product requiring ubiquitous r.m. will be located at the market (5 ton-miles), and a product requiring pure r.m. can locate anywhere along the line between the materials and the market because its ton-miles will be equal at 10. If the location of the plant were at X (5miles), then the ton-miles calculations would be: Pure 1 ton rm * 5 miles + 1 ton fp * 5 miles = 10t-m Gross 2 tons rm * 5 miles + 1 ton fp * 5 miles = 15t-m Ubiq. .5 tons rm * 5 mile + 1 ton fp * 5 miles = 7.5tm Thus far we have only considered a single material source, but the model works just as well with more than one. Consider: 3:1 rm# rm#2 2:1 * * 10 miles 6 miles miles * market Calculation of the ton-miles for each location, either rm #1, rm #2 or market is: Plant at rm#1 ton-miles = (2*10)+(1*6) = 26 Plant at rm#2 ton-miles = (3*10)+(1*8) = 38 Plant at market ton-miles = (3*6)+(2*8) = 34 In this case, the best place to locate the plant is at raw material #1 site. Weber derived a general rule about location which does not require the researcher to calculate ton-miles if the weight of a single gross localised material exceeds the sum of all other raw material. If this is the case, then the plant location is at the site of this heaviest raw material. Otherwise, there is need to calculate the location of the plant within a location polygon: Guess 3 more points within the second (red) polygon. If their values are less than the original 3 then you are approximating the least cost point. Repeat until you hit the least cost point (material index values will start to increase. # # # WEBER

77 Using Vector Mathematics
The use of vector mathematics is complex and this section is added only for completeness; we shall not be discussing the actual mathematics of vector analysis. This method uses the mathematics of vectors to estimate numerically the actions of the Varignon Frame. The Varignon frame is based on the physical concept of the reaction of masses (the weights) acting in different directions. Vectors are used in physics to represent the movement of objects, and hence the physical movement of the Varignon frame can be estimated mathematically with vectors.  WEBER

78 These were called isodapanes, or lines of equal transportation cost.
 Weber also developed a tool that estimated the total cost surface, and showed how these can be used to identify the least cost transportation point. These were called isodapanes, or lines of equal transportation cost. Since we are not interested solely in either the cost of shipping raw materials to the plant, or finished product to the market, but in the total cost of shipping, what we want is a total transportation cost surface. Weber began by developing two sets of isovectures (or lines of equal transportation cost), one for shipping raw materials from the source to the plant and the other for shipping finished products from the plant to the market.  The final contribution of Weber's we shall look at is his concept of the total cost surface, and how these can be used to identify the least cost transportation point. Weber developed what he called isodapanes, or lines of equal transportation cost. Since we are not interested solely in either the cost of shipping raw materials to the plant, or finished product to the market, but in the total cost of shipping, what we want is a total transportation cost surface. Weber began by developing two sets of isovectures (or lines of equal transportation cost), one for shipping raw materials from the source to the plant and the other for shipping finished products from the plant to the market.  WEBER

79 ● ● Constructing Isodapanes – The Isotims Material Market
$70 $60 $50 $50 $60 $70 $40 $40 $30 $30 $20 $20 $10 $10 Material Market To get our total cost surface, the isovectures are drawn until they overlap, then each set of intersections of the same total value are joined with a line. That is, the $1 market isovecture crosses the $14 materials isovecture to give us the $15 isodapane. Likewise, the $2 market isovecture crosses the $13 materials isovecture to give us another $15 isodapane. By joining all the various combinations of market and materials isovectures of the same value where they cross, we get the isodapane of the same value. [OH:DRAWN ISODAPANE SURFACE] The isodapane surface shown in the figure is a simple one, involving only one pure localised raw material source and one market. This is evident because the least cost isodapane, the $15 isodapane, is a straight line between the material source and the market. This tells us that the plant can locate anywhere on that least cost line - thus the material must be pure localised otherwise the location would have to be either at the material source (gross localised) or at the market (ubiquitous). This is fairly unrealistic, and, as you may have guessed, there are ways in which the isodapane surface can be made to simulate more realistic conditions. Cost of shipping finished products to the marketplace from a plant located anywhere on the landscape. Cost of shipping raw materials from material source to anywhere on the landscape. WEBER

80 ● ● Constructing Isodapanes – The Isotims $60 isodapane ● ● ● ● ● ● ●
$70 $60 $50 $50 $60 $70 $40 $40 $30 $30 $20 $20 $10 $10 $60 isodapane Material Market $70 isodapane $80 isodapane WEBER

81 ● ● Constructing Isodapanes – The Isotims $60 isodapane ● ● ● ● ● ● ●
$70 $60 $50 $50 $60 $70 $40 $40 $30 $30 $20 $20 $10 $10 $60 isodapane Material Market $70 isodapane $80 isodapane WEBER

82 ● ● Constructing Isodapanes $60 isodapane ● ● ● ● ● ● ● $70 isodapane
Material Market $70 isodapane $80 isodapane WEBER

83 Isodapanes – Relaxing Assumptions
The isodapane surface shown in the figure is a simple one, involving only one pure localised raw material source and one market. This is evident because the least cost isodapane, the $60 isodapane, is a straight line between the material source and the market - the plant can locate anywhere on that least cost line so the material must be pure localised. This is fairly unrealistic, but there are ways in which the isodapane surface can be made to simulate more realistic conditions. WEBER

84 Gross localised raw materials. Change values on material isotims.
Relaxing Transportation Assumptions on Isodapane Surfaces: [YOU SHOULD REVIEW THE TRANSPORTATION ECONOMICS NOTES IN THIS VOLUME BEFORE TACKLING THIS SECTION] We can relax some of the transportation cost assumptions of the isodapane surfaces quite easily, by changing the widths, values and functions of the isovectures. i. Using Gross Localised Materials: [OH:DRAWN, GROSS MATERIALS] This modification involves changing the value of the material source isovectures. When a gross material is used, with, for example, a 2:1 material index, then the isodapane surface can be simulated by doubling the raw material isovecture values. The isodapane surface alters such that the material source becomes the lowest transportation cost location. WEBER

85 Ubiquitous raw materials. Change values on market isotims.
ii. Using Ubiquitous Materials: This modification involves changing the value of the market isovecture. [OH:DRAWN, UBIQUITOUS MATERIALS] When ubiquitous materials are used with, for example, a 0.5:1 material index, then the isodapane surface can be simulated by doubling the market isovecture values. The isodapane surface alters such that the market becomes the lowest transportation cost location. WEBER

86 Higher transport costs on finished products than raw materials.
Change values on market isotims. iii. Higher Shipping Costs on F.P. than R.M.: This modification involves changing the value of the market isovecture. [OH:DRAWN, HIGHER TRANSPORT COSTS ON FP] When transport costs are higher on the finished product as opposed to the raw materials (as is usually the case), then the isodapane surface can be simulated by increasing the value of the market isovecture by the difference in costs. The isodapane alters such that the market becomes the lowest transportation cost location. WEBER

87 Tapered transport costs on all products and raw materials.
Consecutive widening of isotims. . Tapering Freight Rates: This modification involves changing the width of the isovectures. [OH:DRAWN, TAPERING] Tapering freight rates are used to reflect how the fixed transportation cost component diminishes with distance and hence gradually lowers the total shipping costs. It is achieved by increasing the widths of the isovectures WEBER

88 Adding terminal handling costs on all products and raw materials.
Add a handling cost to isotims at market and material source. iv. Adding Terminal Handling Costs: This modification involves changing the values of all isovectures. [OH:DRAWN, TERMINAL COSTS, STEPPED RATES] When terminal handing charges are added, we have to include two sets for all intermediate isovectures. This is because at any other points on the landscape except the materials source or the market, there will be terminal handling costs both to offload the raw material and onload the finished product. At the materials source and the market, only one set of costs are required, either to onload finished product at the materials site, or to offload raw materials at the market. WEBER

89 Stepped transport costs on all products and raw materials.
Change function of isotims from continuous to discrete values. That is, each band becomes a zone. X Y v. Stepped Rates: This modification involves changing the function of the values on the isovectures, such that changing values become discrete units and not continuous. [OH:DRAWN, SAME AS ABOVE] When transportation rates are stepped (as they usually are), then the space between each set of isovectures becomes a zone with the isovecture value attached to it. In this way, each isovecture represents a boundary between two discrete zones. Thus the isodapane surface is comprised of cost zones rather than the continuous cost surface so far discussed. In the Figure `x' and `y' pay the same, as do `a' and `b'. Location `z' on the other hand, while closer to `y' and `b' than either `x' or `a', must pay more ($7 rather than $6). WEBER

90 Critical Isodapane $25 $20 X $15 $10 Z Y
Incentive offered at X and Y is $10, therefore: $20 X $15 Cost at Without Incentive With Incentive Z $10 $10-$0=$10 Y $20 $20-$10=$10 X $25 $25-$10=$15 $10 Z Y Without incentive Z is the least cost location. With incentive both Z and Y are least cost locations but X is still not. Critical Isodapane WEBER

91 Critical Isodapanes and External Scale
Least cost locations. Critical isodapanes due to urbanisation economies within overlaps. But there is a C,D,E overlap where all three will save more because they form a larger agglomeration with attendant extra savings. A A&C save C A&B save C&E save C&D save Relaxing Other Assumptions: [OH:FIG 4.3, SMITH, FIVE FIRMS] The case is thus: - Five firms operate on a landscape, each within its own locational triangle (polygon); i.e. these points within the triangles represent the least cost transportation points. - The circles represent the critical isodapanes for each firm. - They find that they can cut costs by $20/unit if three or more can agglomerate through the savings accruing from external scale economies. - Therefore firms C, D & E will save money by grouping together in the dark shaded area. - Firms A & B will not because that area is beyond their critical isodapanes; it would end up costing them money. C,D&E save B B&D save E D&E save D WEBER

92 Summary of Classic Models Maximum revenue/profit location
Palander, Losch, Hoover all followed Weber and played with the effects of variable costs, revenues and profit as we saw earlier in cost curves and surfaces. All tried to find the ‘best’ location. Costs Revenues $ Profit X Y Minimum cost location Maximum revenue/profit location OTHER CLASSICAL MODELS

93 Critique of Classical Optimising Models
 The past thirty years has seen considerable criticism of the traditional approaches to industrial location theory. Traditional models: Had a preoccupation with finding optimal locations (least cost, maximum revenue or maximum profit) when location decision making is suboptimal. Revolved around the concept of economic humans. Ignored the organisation of firm and the way in which a firm's organisational structure evolves over time. Relied on neo-classical micro economics and ignore the political economy.  That traditional models have had a preoccupation with finding optimal locations (least cost or maximum profit), and have thus failed to recognise the suboptimal nature of much plant location practice. However, the problem with this criticism is that these earlier theories never intended to say anything about what is, but only about what should be - they were normative models. A theory provides us with a benchmark against which to test reality, as much as with an explanation of that reality. Related to (1), traditional theory has revolved around the concept of economic humans; human decision makers who are completely rational, know all, see all, and are completely efficient; in other words, not human beings at all. The point of criticism asked: "Where are the real people?“ Similar to (2), the organisation of firms in traditional location theory bear little resemblance to firms in the real world, or, more importantly, to the way in which a firm's organisational structure evolves over time. The reliance on neo-classical economics has prevented the development of a theory of industrial location based on the political economy. That is, a theory which adequately accounts for the role of the state in economic activity. The purpose of this section is to examine some ideas and models of suboptimal location and decision making behaviour. OTHER CLASSICAL MODELS

94 Sub-optimality and Satisficing
Suboptimality refers to making location decisions that may not seek the `best' decisions from an economic perspective. Suboptimal locations are locations that do not return as much profit as is possible, but do return enough to survive. Suboptimal decision making has its roots in the concept of satisficing that was developed in the late 1950s by Herbert Simon, an administrative sciences theorist, and it is based in psychology and sociology as much as in economics. SUB OPTIMAL MODELS

95 Sub-optimality and Satisficing
`Satisficing behaviour', as Simon called it, was based on his theory of bounded rationality. Bounded rationality meant that people made decisions based on the limited amounts of information, and the different abilities they had to process it. Therefore, people made decisions that were satisfactory under a given set of circumstances, as opposed to their decisions being the best, as the classical models demanded. Thus a satisficing human could replace the traditional economic human. SUB OPTIMAL MODELS

96 Sub-optimality and Satisficing
But how would this work since firms still have to make a profit? "As long as capricious choice costs no more than entrepreneurial profit, it is still consistent with theory“ August Losch "We may regard freedom to indulge in suboptimal behaviour as spatially constrained.“ David Smith That is, a plant can depart from the optimum location only as far as it can make a profit. But how would theory accommodate this character who explicitly did not and could not seek to maximise profits? Losch gives a clue, even though he used the economic human concept, in keeping with his logical positivist approach to analysis: "As long as capricious choice costs no more than entrepreneurial profit, it is still consistent with theory“ In other words, as Smith puts it: "We may regard freedom to indulge in suboptimal behaviour as spatially constrained.“ That is, a plant can depart from the optimum location only as far as it can make a profit. And this notion gives the idea of spatial margins of profitability, as developed by David Smith in the mid 1960s.  SUB OPTIMAL MODELS

97 $ X Spatial Margins of Profitability – David Smith Space Profit Costs
Revenues $ Profit X Minimum cost location Maximum revenue/profit location Spatial margins of profitability I.E Trade Area SUB OPTIMAL MODELS Space

98 X Y The Basic Two Factory Spatial Cost Curve With Revenue Curve
Trade area boundary between X and Y Unserved area between X and Y Costs v v Profit Profit These become the spatial margins of profitability. X Y Space SUB OPTIMAL MODELS

99 The Basic Two Factory Spatial Cost Curve
Plan View X Y Unserved area between X and Y based on spatial margins of profitability SUB OPTIMAL MODELS

100 How Incentives and Disincentives Work
Either subsidize non-profitable areas or tax profitable ones – or do both. Subsidies must offset revenues earned throughout the SMP. Tax Costs v v Profit Profit Subsidy X Z Y Space SUB OPTIMAL MODELS

101 Changing SMP and Changing Transportation Mode
When transportation mode changes SMP can change as well, opening up many more locations for plants. This happened with containerization in the 1970s. Revenues Costs of truck shipping Costs of rail shipping Costs A second example comes from Taylor (1970) and the British ironfounding industry, which was centred in the Midlands of Britain. [OH:FIG 12.8, SMITH P313] In this example note that the cost curve is uniform across space and it is the revenue curve which dictates where the SMP will lie. When railroad shipping dominated the ironfounding industry the SMP were much narrower than when truck transportation took over in the 1950s. Ostensibly, with the transfer to truck freighting and the expansion of the SMP virtually all of Britain became viable as sites for the ironfounding industry. Z SMP Truck SMP Rail SMP Rail SMP Truck Space SUB OPTIMAL MODELS

102 Changing SMP and Urbanisation
Profitable locations at T1 Non-profitable locations at T1 SMP Freeway T1 T3 T2 SUB OPTIMAL MODELS

103 Judgment, intuition, knowledge
Pred Matrix - Decision Making is Influenced by the Quality and Quantity of Information high Mostly good decisions BUT… Even the smartest people with the best information can make poor decisions AND not so smart people with little information can luck out! So this is a probability of making better decisions. Quality and Quantity of INFORMATION Increasingly better decisions Mostly poor decisions low high low INTELLECTUAL ABILITY Judgment, intuition, knowledge SUB OPTIMAL MODELS

104 SMP and Pred Matrix ● ● ● ● ● ● ● ● ● ● ● SMP Intellectual Ability
low high high Mostly good decisions SMP Quality and Quantity of Information ‘Max profit point X Mostly poor decisions low X X X SUB OPTIMAL MODELS

105 Structuralism Behavioral approaches moderate the assumptions of the classic models by demonstrating that optimal locations are rarely achieved. Structural theories, on the other hand, represent a clean break from those assumptions. The premise of structuralism is that the economy is driven by long term trends, ... ... that result from efforts to maximise or maintain profits (the capitalist motive), ... ... in a constantly evolving class struggle environment (Marxist perspective). Currently, these trends are manifested in the passage from what has been termed Fordism to Post Fordism. STRUCTURALISM

106 Fordism Fordism was characterised by:
Standardisation of consumer goods; Mass production techniques; Relatively high wages, that allowed ... …mass consumption of produced goods; Production therefore concentrated in large industrial cities of first world nations that had the labour, demand, skill, capital to do all the above. STRUCTURALISM

107 Post Fordism was characterised by:
Reduced standardisation of goods. Transfer of considerable productive capacity from developed to less developed countries. Tendency for large corporations to contract out. Loss of industrial employment. Deterioration of worker's wages and conditions. Deterioration of the economic base of, particularly, the inner city. Increased competition between labour blocs in and between cities.  Furthermore, corporations have segmented the production process to take maximum advantage of the labour skill-labour cost balance provided by different countries and regions. STRUCTURALISM

108 The ‘division’ of labour into: Expectations and Reactions:
Results of Post Fordism The ‘division’ of labour into: Brawn work: routinized production stages locate in areas offering unskilled, cheap labour, while … Brain work: less standardised products and stages will locate in areas where a skilled labour force that expects higher wages is available. Expectations and Reactions: Low skilled developed nation labour expects high skilled wages, the result being… manufacturers shift their brawn work to areas of cheaper labour, while maintaining the brain work functions in the home nation. STRUCTURALISM

109 Results of Post Fordism
Explicit in Post Fordism are these concepts: Third World industrialisation. First World deindustrialization or industrial restructuring. Internationalisation of the division of labour and the production process. Globalisation of the economy. all of which alter and are altered by: Labour/capital rates, flows and balances. Trade patterns. Political and corporate structures. The results are the post oil crisis decades of confusion and seeming chaos. STRUCTURALISM


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