FACILITY LOCATION

0 Introduction

The previous chapter discussed the issues that pertain to the capacity expansion decision making process within a firm, with the size of capacity, timing of capacity addition, nature of capacity and its location being the principal decision parameters. This chapter aims to further that discussion by looking at the facility location decision in detail.

1 Definition

Stonebraker and Leong (1994: 182) define facility location as the determination of the best possible placement of a facility with respect to customers, suppliers and other facilities with which it interacts. Yang and Lee (1997) expand on this by stating that facility location decisions involve firms that are seeking to locate, relocate or expand their businesses. From these definitions it is possible to infer that location decisions are faced by both new firms determining their locations as well as existing firms determining how to react to changes in their operating environments.

It is argued that existing firms often have a bigger stake in location decisions than new organisations (Stevenson 2002: 356). The motivation for new firms is often different from that of existing firms reacting to changes in the market outlook (Blair and Premus 1987). New firms tend to be mostly influenced by access to present customers and access to growing markets while cost-minimisation is more important to existing firms choosing to expand.

Location decisions represent a key part of the strategic planning process of virtually every organisation (Stevenson 2002: 356). Location decisions, as part of a firms capacity planning strategy, are important owing to the impact that they have on a firms ability to meet its goals of meeting customer needs profitably. The location of a facility may have a significant impact on the firms performance objectives such as operating costs, delivery speed, dependability and flexibility that enable it to compete in the market place. They also normally involve long-term commitment of resources which are not easily reversible. They are thus not made lightly, and they usually involve long and costly studies of alternative locations before the eventual site is selected (Gaither and Frazier 1999: 241).

2 The need for location decisions

Vonderembse and White (1991: 207) aver that many of the principles involved in location decisions are the same irrespective of the nature of business since managers ought to consider the following factors;

• Costs of operating at a location, including costs to acquire the land and build the facility as well as costs for labour, taxes and utilities.
• The convenience of a particular location for customers, as well as the cost to transport materials to the facility and move finished product from the facility.
• Access to banking, educational and other activities that are important to the success of their organisation.

Thus changes to any of these factors may cause a firm to consider re-location in order to be able to meet its objectives more profitably. As Slack et al (2001: 159) state, location decisions are often motivated by either of two general reasons;

Changes in demand for goods and/or services

Changes in the supply of inputs to the operation

Heizer and Render (2003: 302) agree by stating that though firms make location decisions relatively infrequently, they do so usually either because demand has out grown the current plants capacity or because of changes in labour productivity, exchange rates, costs or local attitudes. However, it should also be noted that the demand could also reduce to such a level that it is no longer viable to have a firm in a particular location.

Changes in demand may be caused by demographic shifts of people as well as changes in their purchasing power. Changes in the supply of inputs is an all-encompassing phrase to take into consideration the cost and availability of raw materials as well as operating costs such as labour, utilities, transportation and taxes.

A special variation to the need for location decisions is when firms are considering an international facility location. Drawing from Canel and Khumawala (1996) and Ferdows (1997), the key motives may be categorised as follows;

• Access to low manufacturing-related costs such as labour, materials, transportation and financing in order to minimise costs of production thereby gaining a cost advantage. This can be triggered by the changes in the costs of supply inputs which may necessitate the relocation of an entire facility abroad.
• Access to markets results in easier access to customers as well as the ability to offer better customer service thereby helping to build customer loyalty. This can be triggered by changes in demand profiles both at home and abroad which may necessitate an overseas location to either service the overseas demand alone or even the home demand as well depending on the costs of production.
• Access to natural resources, technologies, skilled personnel and materials available in a certain part of the world may enable a firm to benefit from knowledge and exposure to world-class technology.
• Access to investment incentives, as well as the avoidance of tariff barriers, enables a firm to increase its access to markets while at the same time reducing its cost of production.

In summary, the motivation for international facility locations is essentially to gain competitive advantage in the market place.

3 Location options

The literature suggests that there are normally three or four options in location planning (Stevenson (2002: 357), Slack et al (2001: 160), and Heizer and Render (2003: 302));

• Expand an existing facility
• Shut down at one location and move to a larger site
• Add new locations while retaining existing ones
• Maintain the status quo (do nothing)

1 Expand an existing facility

As Vonderembse and White (1991: 225) aver, on-site expansion is very popular because it usually involves less capital investment. However, while acknowledging that expansion costs are often less than those of other alternatives, Stevenson (2002: 357) asserts that this option can only be attractive if there is adequate room for expansion coupled with the fact that the location has desirable features that are not readily available elsewhere.

Vonderembse and White (1991: 225) go on to sound a warning that on-site expansion may cause many problems, especially if it is an oft-repeated practice. This is because as more production space is added, material handling and storage become more difficult because inventory space is often converted to production.

2 Shut down at one location and move to a larger site

As Stevenson (2002: 357) argues, a firm must weigh the costs of a move with its resulting benefits against the costs and benefits of remaining in an existing location. Such drastic actions are normally triggered by significant changes in a firms operating environment occasioned by major changes in demand and/or cost and availability of inputs. Some examples of these are a shift in markets, an exhaustion of raw materials or a substantial increase in the cost of operations.

3 Add new locations while retaining existing ones

In the case when a firm experiences an increase in the demand placed on its current facility without a significant change in the cost and availability of inputs, the firm may consider putting up an additional facility to cater for this increased demand. However, as Stevenson (2002: 357) asserts, it is essential to take into account what the impact of adding a new facility will have on the whole firm. Thus the firm should consider whether it will be able to serve more customers profitably with the extra location. The costs of operating both plants need to be evaluated against the improved customer service that may be attained by the facility addition.

4 Maintain the status quo

Sometimes a firm may not be able to identify any benefits to be gained by using any of the above three alternatives, so in such circumstances the firm may then choose to maintain the status quo, at least for the time being (Stevenson (2002: 357).

4 Strategic importance of location decisions

Location decisions are strategically important owing to the impact that they have on a firms ability to meet its customer demand profitably. The literature is replete with references to location affecting the operating costs as well as the fact that once a decision is taken, it is often difficult to reverse thus the management has to live with the consequences of its location decision.

Heizer and Render (2003: 302) and Dilworth (2000: 176) point out that the location of a business greatly affects its costs such as transportation, wages, raw material costs and so on, thus it has a major impact on the overall risk and profit that the firm expects. Given that location can be such a major cost driver, the consulting firm McKinsey believes that a firms location ultimately has the power to make (or break) its business strategy (Bartness 1994).

Location decisions will normally involve long-term commitment of large capital investment that may not be easily reversible or even easily moved from one location to another (Stevenson 2002: 356, Vonderembse and White 1991: 207 and Yang and Lee 1997). Once a firm is committed to a specific location, many costs specific to operating at the site are then firmly in place and difficult to reduce. (Heizer and Render 2003: 302). Therefore the location decision affects a firms ability to compete since it affects the cost (Dilworth 2000: 179) as well as access to potential customers (Stevenson 2002: 356).

Given that the location decision implies a long-term commitment of capital resources, Stonebraker and Leong (1994: 183) emphasize that the location decision should be consistent with the long-term strategic goals of the firm instead of being focused strictly on market and operation resource issues. Thus, hard work to determine an optimal facility location is often a good investment (Heizer and Render 2003: 302).

4.1. Strategic role of facilities

Often when setting up facilities, firms need to consider their strategic roles taking into consideration the main motive for establishing the facility and the extent of the technical activities at the facility. Ferdows (1997) has identified six strategic roles of factories;

• Off-shore factory whose role is limited to producing specific items at low cost. The local management have little choice as to how production is organised with most if not all product technology dictated from the head office.
• Source factory that is established primarily for low cost production but where the local management are permitted to have more choice in determining how to exploit the opportunities the have to reduce costs.
• Server factory set up to serve specific regional markets in order to reduce distribution costs and also overcome tariff barriers in economic trading blocs. Its production technology is also likely to be dictated by its headquarters.
• Contributor factory set up to serve specific regional markets albeit with more authority to determine its process and product technology as well as choice of suppliers.
• Outpost factory set up in areas where advanced suppliers, research, competitors or consumers are located with a principal role of collecting information.
• Lead factories are centres of innovation, creating new processes, products and technologies for the entire firm. Its local management have more discretion on how the facilities are run and supplier development.

5 Objectives of location decisions

Firms are typically faced with the decision of where to place their facilities with respect to customers, suppliers and other facilities with which they interact. As Slack et al (2001: 161) note, the aim of the location decision is to achieve an appropriate balance between the operational costs, the level of customer service offered as well as the revenue potential of the operation. The priority that firms place on these objectives varies according to their long-term business strategies.

In general, profit-oriented organisations base their decisions on the profit potential whereas non-profit organisations strive to achieve a balance between cost and the level of customer service they provide (Slack et al 2001: 161 and Stevenson 2002: 357).

Another distinction has been made with regard to the nature of the business that a firm is involved in. Stevenson (2002: 369) notes that the manufacturing industry tends to be cost-focused thereby choosing locations that minimise the total costs of operation while service industries tend to be revenue focused using the location as a means to be closer to its customers thus boosting potential sales opportunities. Heizer and Render (2003: 311) concur by stating that whereas the focus in industrial-sector location analysis is on minimising costs, the focus in the service sector is on maximising revenue. They attribute this to the fact that manufacturing firms have found that costs tend to vary substantially between locations, while on the contrary service firms have found that location often has more impact on revenue than cost.

Stevenson (2002: 357) also notes that the location criteria can also depend on where a firm is in the supply chain. Therefore at the retail end of a chain, site selection tends to focus more on proximity to the market while at the beginning of a supply chain, site selection tends to focus on minimising the cost of operations by locating near the source of raw materials. He avers that businesses that are in the middle of the chain may choose to locate either near their suppliers or near their markets, depending on a variety of circumstances.

In general, the objective of location strategy is to maximise the benefit of the location to the firm depending on its circumstances (Heizer and Render 2003: 302).

6 General procedure for making location decisions

Stevenson (2002: 357) states that the way a firm approaches location decisions often depends on its size as well as its nature or scope. He further observes that new or small organisations tend to adopt a rather informal approach to location decisions. For instance, they typically locate in a certain area simply because the owner or founder lives there. On the contrary, large established firms, especially those that already operate in more than one location, tend to have a more formal approach considering a wider range of geographical locations. Thus a team may be responsible for the selection decision in the large firm while an individual may make the decision (Krajewski and Ritzman 2001: 413).

There seems to be a general consensus that formal location decisions are carried out in a set of three or four geographical-based stages. Schmenner et al (1987) confirmed in an empirical study of firms in the USA that the plant location process can be usefully approached on a geographical-staged basis, thus most location decisions today are often made in three stages (Martinich 1997: 266). This sequence can include a national/regional decision, a community decision and a site selection (Gaither and Frazier 1999: 241).

At each stage a set of location alternatives is identified, and then an agreed evaluation procedure is used to reduce the set of options at each stage (Martinich 1997: 266). The process begins with general geographic regions being evaluated using general criteria such as market proximity, then location alternatives within the chosen regions are evaluated using more specific criteria such as community attitude towards investment, and then finally sites within the chosen communities are then evaluated using site specific issues such as proximity to transportation networks or utilities.

In summary therefore, the general procedure for making location decisions usually consists of the following steps (Stevenson (2002: 358) and Krajewski (2001: 413));

1. Identify the criteria to be used for evaluating location alternatives and categorise according to importance
2. Identify location alternatives by narrowing down the choices from alternative regions to alternative communities and finally to specific sites as shown below;

a. Identify the general region for a location

b. Identify a small number of community alternatives

c. Identify site alternatives among the community alternatives

3. Evaluate the alternatives based on the criteria identified and make a selection

7 Factors affecting facility location and the location decision stages

As earlier noted, the location decision is based on achieving an appropriate balance between the site-specific operating costs (spatially variable costs), the level of customer service to be offered as well as the revenue potential of a site (Slack et al 2001: 161). An understanding of the factors that affect these three objectives is necessary in order to get a better appreciation of location decisions (Gaither and Frazier 1999: 241). Many of the factors are similar and what varies from industry to industry is the degree of importance placed upon them.

Dilworth (2000: 182) reiterates that location decisions often involve a broad array of factors that can influence revenue, cost or both and consequently may affect profits. Location factors have been considered and classified in a variety of ways within the literature (Alberto, 2000, Badri et al., 1995, Chase et al 2001: 374-376; Dilworth 2000: 182; Evans et al 1993: 225; Meredith 1999: 176-179; Noori and Radford 1995: 219; Russell and Taylor, 1998: 387-390; Sule, 1994: 550).

Dilworth (2000: 182) has summarised these factors into three general categories;

1. Market-related factors strategic marketing issues involving the locations of present and future demand as well as that of competition.
2. Tangible cost factors factors that are easily quantifiable such as transportation, labour, utilities, site costs, construction costs and taxes.
3. Intangible factors factors that are not easily quantifiable such as international considerations, zoning and legal regulations, environmental factors, local attitude toward industry, room for growth, climate, quality of life factors such as schools, churches, hospitals, recreational opportunities and so on.

Historically, access to markets, labour, raw materials and transportation were considered to be the most important variables when firms were deciding where to locate, expand or move. However, more recent studies have shown that although these traditional factors are still the most important, their dominance has been reduced as other factors have been recognised as influential (Blair and Premus 1987). These non-traditional factors include quality of life factors for the employees and community attitudes towards the investment.

In concurrence with Blair and Premus findings, Jungthirapanich and Benjamin (1995) carried out a chronological survey of research studies undertaken between 1875 to 1990 on general industrial location which revealed that, often in the past, a small number of quantitative factors such as transportation and labour costs were considered when firms made a location decision, but that more recently an increasingly wide range of both qualitative and quantitative factors have become evident. However it must be noted that the nature and the importance of the various factors may change significantly over time (Epping, 1982) as the conditions of production change (Blair and Premus, 1987).

One of the results of Jungthirapanich and Benjamins literature review was a hierarchy of location factors relevant to locating manufacturing facilities in the United States of America. Many of the factors are applicable in other parts of the world and are listed below;

1. Market proximity to markets and local consumers purchasing power
2. Transportation cost and availability of land, water and air transportation systems
3. Labour cost, quality and availability of skilled and semi-skilled workers
4. Site considerations size & cost of land and cost of plant construction/leasing
5. Raw materials cost and availability of raw materials
6. Utilities cost and availability of energy, fuel and water
7. Government concerns taxes, incentives
8. Community environment housing, education, health/medical facilities, security, environmental issues, cost of living, business climate, physical climate

7.1 Regional Stage

The definition of a region varies from firm to firm and may be a continent, a country, a part of a country, a state or province within a country, or a metropolitan area. In the first stage of the location decision making stage, the macroeconomic, market and legal factors tend to be dominant with the following being of importance (Martinich 1997: 267);

• Market proximity Firms have to take into consideration market characteristics such as customer population concentrations and trends as well as their purchasing power so as to decide on where to locate facilities. Depending on the nature of the business, facilities may be located away from ultimate consumers if the transportation costs are not prohibitive. Economic factors such as tariff barriers may force a firm to locate within a trading bloc so as to benefit from access to all countries within the bloc at no extra tax. Service operations usually consider close proximity as essential unless the services are provided by telecommunication.

• Proximity to Raw Materials and Suppliers Firms tend to locate near their suppliers or at the source of their raw materials for a number of reasons. Some firms have to locate close to their raw materials out of necessity (Stevenson 2002: 360) owing to the nature of the operation for instance the mining industry has to be located close to its raw material. Other firms have to take into consideration the perishability of the raw material (Stevenson 2002: 360 and Heizer and Render 2003: 360) a case in point being firms dealing with dairy products. Transportation costs also play an important role in the location decision when the raw materials are heavy or bulky thus liable to high transportation costs as well as when initial processing involves the raw material losing a significant amount of bulk thus making it cheaper to transport after processing (Stevenson 2002: 360 and Heizer and Render 2003: 360). Therefore manufacturing facilities find proximity to raw materials and other material inputs is extremely important in order to reduce transportation cost and delivery time (Martinich 1997: 268) as well as maintaining lower inventories (Krajewski and Ritzman 2001: 410).

• Proximity to parent firms facilities In a number of firms, some plants supply parts to other facilities or rely on other facilities for management and staff. Such ties require frequent coordination and communication necessitating a close proximity to other firm facilities (Krajewski and Ritzman 2001: 410).

• Cost and availability of utilities Manufacturing firms need to be certain of the availability and quality of utilities due to the potentially high costs of unutilised capacity. The cost is important as it then affects the final price that they can expect to charge customers.

• Labour supply and unionisation The primary labour considerations are the cost and the availability of labour (skilled and unskilled), labour productivity, worker attitudes towards work, and the nature of labour unionisation (Stevenson 2002: 361). Heizer and Render (2003: 303) observe that management should be interested in the net effect of the combination of productivity and wage rate which would give rise to a lower labour cost per unit produced (labour content of the product)

• Exchange rates and currency risk Whereas wage rates and productivity may make a country location appear economical, unfavourable exchange rates might negate any savings. One reason for the increase in foreign manufacturing investments by Japanese manufacturers was the appreciation of the Japanese yen which substantially reduced the profit margins (Chopra and Meindl 2001: 308).

• National taxes This is an important consideration in international location decisions as the tax structures vary from country to country not only in the level of taxation but also in the form.

• Legal restrictions Another consideration in international local decisions are the legal restrictions prevailing in different countries. These affect the viability of an operation and they may be in the form of import restrictions on the inputs required for production or home content laws that dictate that a certain minimum amount of value must be added from locally available inputs.

7.2 Local/Community Stage

Having chosen a region, the next stage involves choosing among the communities or areas within the region and at this stage, additional location factors become relevant (Martinich 1997: 269);

• Taxes Local communities depend on taxation to provide services and these taxes may be in form of property taxes, local development levies and so on. These local taxes may vary considerably within the same region and firms consider these because of their impact on costs and the wage bill.

• Economic incentives Incentives are offered to reduce the cost of doing business in a particular area in exchange for economic opportunities created for the area. Thus local authorities commonly entice firms to locate within their areas by providing incentives which can take the form of direct financial assistance such as free land, low-cost loans and tax reductions, as well as indirect incentives such as constructing or improving roads, providing for employee training, or modifying zoning restrictions.

• Attractiveness of the community Firms today have to increasingly evaluate the quality of life issues that would affect the willingness of their employees to relocate as well as the ability to attract and retain experienced skilled labour. Important considerations are the quality of housing, rate of crime, quality of schools, religious institutions, and facilities for recreation.

• Compatible industry The availability of industry supplying the inputs required to support a facility is another important consideration. Locating a production facility close to those of competitors is often advantageous for it implies that a supply of skilled labour probably already exists, suppliers of needed materials and services are available, and that the community accepts the industry.

• Transportation network This is dependent on the nature of industry as well as the specific mode of transportation that is deemed to be cost-effective. The availability of a desirable mode of transportation in an area would sway the location decision.
• Community government policy and attitude This is important because most firms do not want to locate in areas that are where they are not wanted.

• Environmental regulations These place limitations on the nature of businesses that may invest in an area. Costs of operations may be affected if the environmental regulations impose expensive means of waste disposal on a firm.

7.3 The Site Decision

With community decision, the next stage is to identify and select specific facility sites. As Martinich (1997: 270) notes, some of the important factors at this stage are refinements of factors considered at earlier stages with the following factors being important;

• Access to utilities (water, electricity, fuel and gas) networks.
• Access to transport networks.
• Zoning restrictions These limit the possible locations of sites to those designated for industrial purposes.
• Land cost (acquisition or rental) and construction costs.
• Size and space for expansion This is an important aspect as Schmenner (1982: 94) found that firms most common reason for moving their facilities was lack of room for expansion. Tong (1979) in a survey of 242 foreign-owned manufacturing firms also found that availability of a site and space for expansion was one of the most important factors affecting their location decisions.
• Proximity to related industry such as competitors, suppliers, training establishments, research facilities, financial services, legal services and so on.

Table Importance of Location Factors at Different Levels of Decision Making

Source: Stonebraker, P.W. and Leong, G.K. (1994). Operations strategy: focusing competitive excellence. Allyn and Bacon, p. 184.

8 Summary of Location Factors

The discussion so far has been focused on the hierarchical location decision process which starts with the country/global region decision followed by the sub-region or intra-country decision, then a community decision and finally a site decision. Drawing from the preceding discussion and MacCarthy and Atthirawong (2003), the major factors involved in facility location decisions involving manufacturing plants may be summarised as;

1. Costs It has been identified that facility location decisions involving manufacturing plants tend to focus on cost minimisation. These costs include transportation costs, labour costs, costs of utilities (water and electricity), telecommunication costs, land costs, site construction costs and site leasing costs.
2. Labour Apart from cost, the major considerations are the availability and quality of labour (skilled and unskilled), productivity, existence and nature of labour unionisation, nature of industrial relations and worker attitudes to work.
3. Infrastructure Given the importance of transportation in both inbound and outbound logistics, firms need to consider the availability, quality and reliability of transportation modes in their location decisions. In addition they consider the quality and reliability of utilities (water and electricity) and telecommunication systems that are essential for the running of the facility.
4. Quality of Life These factors affect the willingness of employees to relocate as well as the ability to attract and retain experienced skilled labour. Important considerations are the quality of housing, security, quality of schools, universities, hospitals, physical climate, religious institutions, facilities for recreation, cost of living and community attitude towards the new industry.
5. Market proximity Important considerations here are the size of the market, its purchasing power, population growth trends and proximity to customer population concentrations in order to give a better customer service while minimising transport costs.
6. Proximity to raw materials and suppliers As already noted, firms tend to locate near their suppliers or at the source of their raw materials for three main reasons; necessity, perishability and transportation costs. Issues to consider are the quality and reliability of suppliers as well as the presence of alternative suppliers.
7. Proximity to competition Avoiding areas where competitors are already well established can be beneficial. However, the presence of a competitors production facility in an area would also imply that a supply of skilled labour probably already exists, suppliers of needed materials and services are available, and that the community accepts the industry.
8. Proximity to parent firms facilities Closeness to a firms other facilities will help in co-ordination and control of the manufacturing activities.
9. Economic factors These factors affect the attractiveness and cost of operating in a particular location and they include the tax structure and tax incentives, financial incentives, the stability of the exchange rate and the economic growth rate.
10. Government and regulatory factors These factors affect the ease of doing business in a country and they include the attitude of the government towards inward investment, the political and economic stability of the government, regulations on the setting up of local corporations, environmental regulations, and regulations on transferring of money outside the country.
11. Site considerations The main considerations here are the physical conditions of the site which might affect its suitability as well as availability of space for expansion and the attitude of the immediate local community to a location.

Table Summary of major factors affecting location decisions

Adapted from MacCarthy, B.L. and Atthirawong, W. (2003). Factors affecting location decisions in international operations a Delphi study. International Journal of Operations and Production Management, Vol. 23 (7), pp. 797.

9 Evaluating location decisions

Given the variety of facilities with their unique characteristics and location factors, a variety of analytical evaluation methods exist to aid on decision making (Martinich 1997: 266). There are four major methods that are used for solving location problems: locational break-even analysis, the centre-of-gravity method, the transportation model and the factor-rating method (Heizer and Render 2003: 306). However, Gaither and Frazier (1999) note that given the immense amount of data needed to compare facility location alternatives, location decisions can end up being very complex. They argue that this is because so many variables are related in complicated ways while at the same time so much uncertainty is present in the process that it is mentally difficult to juggle all the information simultaneously. Therefore the analysis techniques tend to analyse only part of the relevant information in order to simplify the process, leaving the decision maker with the burden of intelligently integrating the results of the analysis with the remainder of the information in order to make the final location decision.

9.1 Locational break-even analysis

The location of a facility greatly affects both the fixed and the variable costs that the firm incurs thereby having a major impact on the profit that a firm may expect (Heizer and Render 2003: 302). Some of the costs that may be influenced by location are transportation, taxes, wages, raw material costs and rents. Just as in the case of capacity decision making, break-even analysis can also be used to compare location alternatives on the basis of quantitative factors that can be expressed in terms of total cost.

Locational break-even analysis involves the use of the cost-volume analysis to make an economic comparison of location alternatives (Stevenson 2002: 373 and Heizer and Render 2003: 308). The three steps of break-even analysis are (Stevenson 2002: 373):

1. Determine the fixed and variable cost associated with each location alternative
2. Plot the total cost-lines costs for all location alternatives on the same graph
3. Select the location with the lowest total cost for the expected level of output or alternatively select the location with the highest profit.

This method assumes the following;

1. Fixed costs are constant for the range of probable output.
2. Variable costs are linear for the range of probable output.
3. The required level of output can be closely estimated.
4. Only one product is involved.

In order to carry out a cost analysis, the total cost for each location is computed as follows;

Total cost = FC + (vQ)

where;

FC = Fixed cost, v = Variable cost per unit and Q = Quantity or volume of output

Conversely, in order to carry out a profit analysis, the total profit is computed for each location as follows;

Total profit = Revenue Total cost

= Q R [FC + (v Q)] where R = Revenue per unit

= Q (R-v) FC

Both the cost analysis and the profit analysis will yield a graph showing different ranges of output at which either of the location options renders the best result of either the lowest cost or the highest profit. The following example can help to illustrate how this method can assist decision makers as to which location to consider given the expected outputs.

Given the following fixed and variable costs for four potential plant locations;

Plotting the total cost lines for the different locations gives the following graph;

Figure Break-even analysis graph

From the graph, it is possible to conclude that initially location B is optimal for low output levels but that with increasing output levels location C and later location B become superior. In other words, the optimality crosses over from location B to C and eventually location C to A. The crossover points can either be read from the graphs or computed mathematically as the point of crossover will occur when the total costs of either location are equal.

Therefore in order to find out the crossover point from location B to C;

Total cost at location B = Total cost at location C

x x

x

x

The crossover point from C to A occurs when;

Total cost at location C = Total cost at location A

x x

x

x

From the graph and the calculation above, it is possible to conclude that location A is optimal for all expected outputs below 5,000 while location C is optimal for all expected values between 5,000 and 11,111, and location B is optimal for all expected values above 11,111. However, it is also evident that location D is never an optimal location.

9.2 The centre of gravity method

The previous technique enables the evaluation of several existing locations so as to identify the optimal location. However, it is sometimes necessary to carry out an initial analysis to identify an optimal location without taking into consideration any fixed or existing location alternatives thus the optimal location can literally be anywhere. The centre of gravity method can be used in such a case as it is a method used to determine the location of a distribution centre that will minimise distribution costs (Heizer and Render 2003: 309 and Stevenson 2002: 376).

The centre of gravity method considers the location of markets (or suppliers), the volume of goods to be shipped to those markets (or to be received from suppliers) and the shipping costs in receiving inputs or delivering outputs, in order to find the best location for a distribution centre or plant. The centre of gravity method assumes that distribution costs are directly proportional to the distance that a product is shipped as well as the quantity of product shipped.

The method entails the use of an accurate map that shows the destination (source) locations. Then a co-ordinate system is overlaid on the map so as to determine the relative locations as illustrated below.

Figure Co-ordinate system showing relative location of three facilities

The centre of gravity is determined by the following equations below;

x-coordinate of the centre of gravity =

y-coordinate of the centre of gravity =

where d_ix= x-coordinate of location i

d_iy= y-coordinate of location i

Q_i= Quantity of goods moved to or from location i

Given that the centre of gravity assumes that only the load shipped and the distance shipped affect cost, there is a risk that the optimal location found may be in an area with inherently high operational costs such as labour costs, land costs, utility costs and so on. It is also likely that a nearby location may offer a lower cost site albeit with slightly increased distribution costs. In such a scenario, Vonderembse and White (1991: 220) advise that management will have to make a judgement about the costs saved by moving away from the centre of gravity as compared to the customer/supplier inconvenience caused by choosing a site away from the centre of gravity as well as the increased costs of operation.

Example

A firm would like to find out the optimal location for its new distribution centre relative to four suppliers. The locations of the four suppliers A, B, C and D and the annual number if trailer loads that will be transported to the distribution centre are as shown in the table below.

The solution using the centre of gravity method is illustrated in the table below;

= = 238 = = 444

The centre of gravity is (238, 444)

Load Distance Technique

There is a variation of the centre-of-gravity method, called the load-distance technique, where a set of existing location alternatives are evaluated unlike in the centre of gravity method where one optimal location is sought from an infinite range of possibilities (Russell and Taylor 1998: 396).

Just as in the centre of gravity, a co-ordinate system is developed and the principal parameter is based on load shipped and distance shipped. This parameter is called a load-distance value and it is a measure of weight and distance. The use of distance stems from the fact that several location factors relate directly to distance, for instance proximity to markets, suppliers and resources. The load-distance method is therefore a mathematical model that is used to evaluate locations based on proximity factors with the objective of minimising the total weighted loads shipped into and out of the facilities (Krajewski and Ritzman 2001: 414). For a single potential location, a load-distance value is calculated as follows;

LD =

where;

LD = the load-distance value

l_i = the load expressed as a weight, number of trips or units

d_i = the distance between the proposed site and location

The distance d_i in this formula is the travel distance which can also be determined from a map. It can also be computed using the following formula for the straight-line (Euclidean) distance between two points;

d_i =

or the Rectilinear distance which measures the distance between two points with a series of 90⁰ turns as along city blocks.

d_i =

where (x,y) = co-ordinates of the proposed site and (x_i,y_i) = co-ordinates of existing facility

The load-distance technique is applied by computing a load-distance value for each potential facility location. The implication is that the location with the lowest value would result in the minimum transportation cost and thus would be preferable.

Example

In a variation of the example above, the firm would now like to evaluate three different sites it has identified for its new distribution centre relative to four suppliers. The locations of the four suppliers A, B, C and D and the annual number of trailer loads that will be transported to the distribution centre are as shown in the table below.

The co-ordinates of the three sites under consideration are as follows;

Site 1: x₁ = 360, y₁ = 180

Site 2: x₂ = 420, y₂ = 450

Site 3 : x₃ = 250, y₃ = 400

The distances between the proposed sites (1, 2 and 3) and each existing facility (A, B, C and D) are computed using either the straight-line formula or the rectilinear formula for d_i.

The rectilinear distance from A (200, 200) to 1 (360, 180) will be;

d_A =

=

= 160 + 20 = 180

Working out the rest of the distances and taking into consideration the loads to be shipped, the table below with the load-distance values is generated.

Since site 3 (250, 400) has the lowest load-distance value, it would be assumed that this location would also minimise transportation costs.

The centre of gravity method, as well as the related load-distance method, is commonly used by firms planning distribution centres as well as firms in the service sector who depend on customers visiting their facilities to consume their services. In fact retailing location studies, both in the past and at present, have mostly been based on the gravity method which was structured on two principles that stress proximity to markets (Gaither and Frazier 1999: 247);

1. Customers are attracted to a location in direct proportion to the population in the immediate region of the location
2. Customers are attracted to a location in inverse proportion to the square of the distance that they must travel to the location.

9.4 Transportation method

Whereas the previous techniques are particularly useful when deciding on where to have a single location serving a number of demand points, some firms are faced with a situation where they have to decide where to place a new plant in a network of plants at different locations that are serving several demand points. The transportation method is a specialised algorithm that may be used to determine the minimum transportation cost that would result if a potential new location were to be added to an existing system (Stevenson 2002: 375). In the transportation model, the transportation costs are treated as a direct linear function of the number of units shipped. Other factors that may differ among the locations, such as production costs, may then be included in the analysis provided that they can be determined on a per-unit basis.

Therefore the objective of the transportation model is to determine the best pattern of shipments from several points of supply (sources) to several points of demand (destinations) so as to minimise the total production and transportation cost (Heizer and Render (2003: 311). The transportation model is a linear programming technique that finds an initial feasible solution and then makes step-by-step improvement until an optimal solution is reached.

The transportation model can be used to compare location alternatives in terms of their impact on the total distribution costs for a system. Therefore if there were three alternative locations for a new facility, then the transportation methods would be used to determine the optimal solution which has the least cost for each of these alternatives when added to the network separately. This information can then be included in the evaluation of location alternatives.

Use of the transportation model implies that certain assumptions are satisfied. The major ones are (Stevenson 2002: 376);

The items to be shipped are homogeneous (i.e. they are the same regardless of their source or destination)

Shipping cost per unit is the same regardless of the number of units shipped.

There is only one route or mode of transportation being used between each origin and each destination.

The information needed to use the model consists of the following (Stevenson 2002: 376);

1. A list of the origins and each ones capacity or supply quantity per period.
2. A list of the destinations and each ones demand per period.
3. The unit cost of shipping items from each origin to each destination.

An example of a problem that requires solving by the transportation method is shown below in a transportation table.

Factory A can supply 100 units per period

Cost of shipping one unit from factory A to warehouse 1

Figure Transportation tableau

Step 1: Define the decisions variables

A₁ = number of units to be shipped from Factory A to Warehouse 1 per period

A₂ = number of units to be shipped from Factory A to Warehouse 2 per period

C₄ = number of units to be shipped from Factory C to Warehouse 4 per period

Step 2: Formulate the objective function

Min Z = 4A₁ + 7A₂ + 7A₃ + A₄ + 12B₁ + 3B₂ + 8B₃ + 8B₄ + 8C₁ + 10C₂ + 16C₃ + 5C₄

Step 3: Formulate the constraints

A₁ + A₂ + A₃ + A₄ 100 Factory A capacity

B₁ + B₂ + B₃ + B₄ 200 Factory B capacity

C₁ + C₂ + C₃ + C₄ 150 Factory C capacity

A₁ + B₁ + C₁ = 80   Warehouse 1 requirements

A₂ + B₂ + C₂ = 90   Warehouse 2 requirements

A₃ + B₃ + C₃ = 120   Warehouse 3 requirements

A₄ + B₄ + C₄ = 160 Warehouse 4 requirements

Using the transportation method, an initial solution is found by applying the northwest corner method wherein the upper-left-hand (northwest) corner of the tableau is assigned as large a shipment as possible. If the column constraint of column 1 is satisfied, then next cell is selected by moving right to the next column (column 2). If the row constraint of row A is satisfied then the next cell is selected by moving down to the next row (row B). This process goes on till all the possible cells have been filled thereby giving an initial solution. It must be noted that this first solution is not feasible as no consideration was taken of the costs of moving the products from the factories to the warehouses.

Initial solution

Cost = (80*4) + (20*7) + (70*3) + (120*8) + (10*8) + (150*5) = £2,460

The stepping stone method is then used to test for optimality of the initial solution. This is done by determining for each unfilled cell how the total cost of the objective function would change if the shipments corresponding to the unfilled cells were increased by one unit and the filled cells were adjusted in value to remain feasible. The table below shows a case of this being done in which the shipments from factory A to warehouse 3 (A₃) are increased by one unit. In order to balance this, the shipments from factory A to warehouse 2 (A₂) must be reduced by one unit while the shipments from factory B to warehouse 2 (B₂) must increase by one unit and finally the shipments from factory B to warehouse 3 (B₃) must decrease by one unit.

The total costs have changed as follows;

A₃ increases by 1 cost increases by (7*1)

A₂ decreases by 1 cost decreases by (7*1)

B₂ increases by 1 cost increases by (3*1)

B₃ decreases by 1 cost decreases by (8*1)

Thus the net effect of this change would be (+7-7+3-8 = -5) which would imply that increasing shipments from factory A to warehouse 3 would decrease the cost by £5 for each unit shipped. Thus the firm would consider this in finding an optimal solution.

This exercise is continued for the rest of the unfilled cells giving rise to the table below. The path (A₃-A₂-B₂-B₃-A₃) is unique in that it must begin and end at an unfilled cell, thus for the rest of the unfilled cells the following conditions must be fulfilled while doing this path analysis (Martinich 1997: 301);

1. The path can only move horizontally or vertically in the tableau; it can not move diagonally.
2. A path can only change direction at a filled cell, though it does not have to do so as it can actually pass through the filled cell.
3. A path can cross over itself at a 90⁰ angle, but it can not backtrack over itself.

From the table above it is evident that the net effects of increasing the shipments in A₃ and A₄ are negative which implies that they can cause a further reduction in costs. The current solution is thus not optimal as it can be improved upon by making changes to A₃ and A_4.

In order to obtain an improved solution, the unfilled cell that has the highest negative net effect (A₄) is increased by the maximum possible number of units that can fit within the row and column constraints in order to have the maximum effect of reducing the total cost. In order to do this, the path (A₄-A₂-B₂-B₄-A₄) used to evaluate the net effect of a change in A₄ is used and the filled cells that would have had to be reduced in order for A₄ to be increased are appraised. In this case the filled cells that would have to be reduced are A₂ which has 20 units and B₄ which has 10 units. Therefore in order to fulfil the row and column constraints cell A₄ can only be increased by a maximum of 10 units. This then causes the table to change as below in order to maintain feasibility of the table.

Checking for optimality yields the following table which indicates that the solution is still not optimal.

Second solution

Cost = (80*4) + (10*7) + (80*3) + (120*8) + (10*1) + (150*5) = £2,350

The solution is further refined to give rise to the optimal solution as shown below;

Checking for optimality shows that the solution is feasible as indicated below;

Optimal solution

Cost = (80*£4) + (10*£7) + (90*£3) + (110*£8) + (10*£1) + (150*£5) = £2,300

Therefore a similar analysis can be done using new values for factory D instead of C so as to evaluate the difference that either alternative location would have on the overall costs.

9.5 Factor rating

The methods of location evaluation so far discussed have tended to focus on the quantitative-based approach charged with minimising travel time, distance or total cost of a facility. These traditional methods are particularly useful in selecting an optimal location when it is known that such quantitative factors are dominant (Martinich 1997: 272). However as Stevenson (2002: 376) notes, a typical location decision today involves both quantitative and qualitative inputs, which tend to vary from situation to situation depending on the needs of each organisation. Therefore these quantitative approaches are inadequate when faced with qualitative factors that are difficult to quantify or convert into numerical terms (Martinich 1997: 273).

The literature has indicated a growing influence of the qualitative factors in location decision making today (Blair and Premus 1987) and in some cases these qualitative factors have been found to be dominant when compared to quantitative factors (Gaither and Frazier 1999: 251). This then calls for methods of analysis that attempt to synthesise the qualitative and quantitative factors as an aid to decision making.

Some of the qualitative factors as seen in earlier sections include housing, cost of living, availability of labour, climate, community activities, education and health services, recreation, churches, union activities, local transportation systems, proximity of similar industrial facilities and community attitudes. These factors all work in tandem with quantitative factors such as annual operations costs to influence the suitability of a particular location.

Factor rating is a general approach that is useful for evaluating a given alternative as well as comparing among alternatives. Considering that some of these factors are more important than others, it is possible for decision makers to use weightings to make the decision process more objective. In other words, factor rating enables decision makers to incorporate their personal opinions, based on their professional and private experience of the industry, together with quantitative information in the decision process.

The factor-rating method is popular because a wide variety of factors, from education to recreation to labour skills, can be objectively included (Heizer and Render 2003: 306). Stevenson (2002: 376) concurs by stating that the value of factor rating is that it presents a rational basis for the evaluation and comparison among alternative facilities by establishing a composite value for each alternative that summarises all related factors.

The factor-rating method is comprised of a number of steps as indicated below (Heizer and Render (2003: 307), Martinich (1997: 273) and Stevenson (2002: 376));

1. Develop a list of relevant factors that are relevant for screening locations while taking into consideration the trade-off between completeness and simplicity. . It is important not to omit factors that may be important while at the same time it is not necessary to include factors that have a very small effect on the decision.
2. Assign a weight to each factor to reflect its relative importance in the firms objectives, thus a factor with a weight of 0.6 is deemed to be twice as important as a factor with a weight of 0. The weights assigned should add up to 1.
3. Develop a scale for each factor  using for example a 0 - 10 or a 0 - 100 point scale
4. Have management score each location for each factor, using the scale in step 3
5. Multiply the score by weights for each factor and total the score for each location
6. Make a recommendation based on the maximum point score, considering the results of quantitative approaches as well.

An example drawn from Stevenson (2002: 376) is shown below to illustrate how the factor-rating method can be used in comparing location alternatives.

In the example above, alternative 2 would be the preferred choice as it has the highest weighted score though the final choice would also depend on the results of the quantitative analysis. In other cases the differences between the weighted scores of several alternatives may be so small that it may not be proper to select the alternative with the highest weighted score. This is because in such cases, a small change in any of the factors or weights could give rise to a different optimal alternative.

As Martinich (1997: 274) asserts, the purpose of the factor-rating is not only to select or screen alternatives, but also to help the decision maker attain a better understanding of the options available and the factors that are important. He argues that after computing the total weighted score for each alternative, it would be imperative to understand why one alternative scored higher than the others and this would entail studying the strengths and weaknesses of each alternative. It may then be necessary to re-appraise the ratings each alternative received for each factor rated, re-appraise the weights assigned to each factor and finally re-compute the weighted score for each alternative especially in the cases when a decision is sensitive to minor changes (Heizer and Render 2003: 307).

As an example of the popularity of this method, Camp (1994) notes that when Mercedes Benz decided to locate its plant in Alabama, all of the finalists in the running for the plant were rated on a point system based on the following factors: suitability of the work force, educational environment, the site, quality of life, business climate, training programs, operating flexibility and transportation/logistics.

9.5.1 Analytical Hierarchy Process

One of the limitations of the factor rating approach is that it requires the specification of subjective weights in order to evaluate a composite rating of different factors (Tyagi and Das 1997). The analytical hierarchy process (AHP) is a multi-criteria decision process that has been widely applied to rate decision alternatives involving mainly qualitative factors as opposed to quantitative factors (Zahedi 1986) by reducing the subjectivity. The AHP can be regarded as an improvement of the factor rating method.

The AHP quantifies decision makers subjective judgements by assigning corresponding numerical values based on the relative importance of factors under consideration. A conclusion can be reached by synthesizing the judgements to determine the overall priorities of variables (Saaty, 1994a). The analytic hierarchy process (AHP) technique has been found to be useful in use in handling the facility location problem, since it is particularly effective for multi-attribute decisions that involve both tangible and intangible factors (Alberto 2000).

AHP builds in objectivity into the factor rating process in the comparative analysis where a method called pair-wise comparison is applied to determine priority weights of location qualitative factors. Pair-wise comparison is a key step in the AHP location model and it focuses on comparing any two factors at a time in order to determine how much one factor is more important than the other in the attainment of the objective being considered. This is useful as decision makers will probably find it easier to offer relative rather than absolute preference information. The relative importance of each factor is then rated by a measurement scale that converts the decision makers evaluations into quantitative measures. The instrument used in this research is a discrete scale, from 1 to 9, with 1 representing equal importance of two factors and 9 the highest possible importance of one factor over another (Saaty, 1994a) as indicated in the table below.

Table Analytic Hierarchy Process Comparison Scale

Adapted from Saaty, T.L. (1994), How to make a decision: the analytic hierarchy process. Interfaces, Vol. 24 (6), p. 26.

The three major steps involved in the AHP solution approach are (Saaty, 1994b):

1. Problem decomposition the problem is decomposed into elements grouped on different levels to form a chain of hierarchy. Each element is decomposed into sub-elements until the lowest level of the hierarchy.
2. Comparative analysis the relative importance of each element at a particular level in the hierarchy on the attainment of an element at the next higher level is measured by a procedure of pair-wise comparison drawn from decision makers prioritisation of the elements using a rating scale.
3. Synthesis of priorities the priority weights of elements at each level is computed using eigenvector or least square analysis. The process is repeated for each level of the hierarchy until a decision is finally reached by overall composite weights.

Figure A decision hierarchy using the AHP with k levels

Source: Zahedi, F. (1986). The analytic hierarchy process - a survey of the method and its applications. Interfaces, Vol. 16 (4), pp. 97.

For example if a firm was faced with a location choice between two places basing on three parameters costs, labour and infrastructure, the problem could be solved as below;

Pair-wise comparison of the three location parameters

The above pair-wise comparison indicates that costs are deemed to have moderate importance when compared with labour and strong importance when compared with infrastructure in the choice of a location. Normalising the values in the columns and then averaging the normalised values across the rows gives rise to the relative weights of the parameters in the location decision for the firm. It is important to ensure the consistency of the above preferences, thus for instance since costs are moderately favoured over labour and strongly favoured over infrastructure, it should then be expected that labour should be at least more favoured than infrastructure.

Pair-wise comparison of the locations based on costs, labour and infrastructure

Pair-wise comparison then follows to the next level of the decision by comparing the different locations based on the parameters identified for the location decision.

This process then yields ratings for the different location as based on the decision makers perception of the parameters vis-à-vis the locations. These ratings (scores) can then be utilised in a factor-rating table as indicated below which would imply that location B was a more optimal location than location A.

In summary, Heizer and Render (2003) offer a classification of the issues involved in the two major types of facilities in the table below.

Table Location Strategies (Service versus Goods-Producing Organisations)

Source: Heizer, J. and Render, B. (2003). Operations Management (7^th Edn). Pearson Education Ltd, p312.

10 Conclusion

With the increasing globalisation of the world economy, firms are increasingly forced to consider manufacturing locations on a global scale rather than on an in-country scale. Some of the major motivations are;

access to low manufacturing-related costs so as to minimise costs of production

access to existing and new markets in order to offer better customer service

access to natural resources, technologies, skilled personnel and materials

access to investment incentives, as well as the avoidance of tariff barriers

Therefore location decisions today often begin with an identification of the global region that the firm intends to manufacture in before evaluating countries in that region, then communities and finally sites within those communities.

Notwithstanding whether a location decision is local or international, extreme care has to be taken when making location decisions since they are normally long-term decisions requiring substantial investment. Further to this, location decisions directly affect the cost of operations thus impacting on a firms profit potential. Location decisions are complex because they require the consideration of a number of related variables which are often faced with uncertainty. Current analytical techniques are unable to consider all the variables, thus they simplify the problem by tending to focus on a few variables leaving the job of interpreting the results with respect to the rest of the variable with the management.

Over the years, advancements in production technology as well as other aspects of technology have changed the focus of location decisions from that of simply focusing on access to raw materials, labour, markets and transportation to include quality of life issues such as quality of housing, rate of crime, quality of schools, religious institutions, and facilities for recreation. Although these traditional factors are still the most important, their dominance has diminished.

The major factors identified in location decisions include, costs (labour, transport, utilities, telecommunication, land, construction/leasing), labour (availability and quality of labour, nature of labour unionisation, nature of industrial relations and worker attitudes to work), infrastructure (availability, quality and reliability of transportation modes; quality and reliability of utilities (water and electricity) and telecommunication systems), quality of life, market proximity (size of the market, purchasing power, proximity to customer population concentrations and population growth trends), proximity to raw materials and suppliers (the quality and reliability of suppliers, quality of raw materials and availability of alternative suppliers), proximity to competition, proximity to parent firms facilities, economic factors (tax structure and tax incentives, financial incentives, exchange rate stability and economic growth rate), government and regulatory factors (government attitude towards investment, political and economic stability of the government, regulations on the setting up and operating local corporations, environmental regulations, and capital transfer regulations) and site considerations (physical conditions, availability of space for expansion and attitude of immediate local community to a location).

A number of location evaluation methods exist but they are mainly cost-based quantitative approaches. However, the increasing importance of qualitative factors in location decisions has created a need for evaluation approaches that are able to incorporate both quantitative and qualitative factors that are encountered in the decision making process. Factor rating methods are by far the most popular and the application of the analytical hierarchy process to the factor rating methods helps to reduce the subjectivity in the rating of the factors.

Having identified these motivations and major factors, the next chapter will attempt to identify the key factors that are prevalent in location decisions involving the expansion of capacity by way of international manufacturing using a number of case studies of firms making such decisions.