Demand-side influences
Labour skills. The abilities of a local labour force can have an effect on customer reaction to the products or services which the operation produces. For example, ‘science parks’ are usually located close to universities because they hope to attract companies that are interested in using the skills available at the university.
The suitability of the site itself. Different sites are likely to have different intrinsic character- istics which can affect an operation’s ability to serve customers and generate revenue. For example, the location of a luxury resort hotel which offers up-market holiday accommoda- tion is very largely dependent on the intrinsic characteristics of the site. Located next to the beach, surrounded by waving palm trees and overlooking a picturesque bay, the hotel is very attractive to its customers. Move it a few kilometres away into the centre of an industrial estate and it rapidly loses its attraction.
Image of the location. Some locations are firmly associated in customers’ minds with a particular image. Suits from Savile Row (the centre of the up-market bespoke tailoring dis- trict in London) may be no better than high-quality suits made elsewhere but, by locating its operation there, a tailor has probably enhanced its reputation and therefore its revenue. The
Similar companies with similar needs often cluster together in the same location. For example, knitted garment manufacturers dominate parts of Northern Italy.
Perhaps the most famous location cluster is in the area south of San Francisco known as Silicon Valley, acknowledged as the most important intellectual and commercial hub of high-tech business. Yet Silicon Valley is being challenged by up-and-coming locations, especially in developing countries. Here are two examples.
Bangalore in India has for many years been attractive in the computer industry. Back in the 1980s the area attracted software code-writing business from Western multinationals attracted by the ready availability of well-educated, low-cost English-speaking software technicians. Now the area has attracted even more, and even more sophisticated, business. Companies such as Intel, Sun Microsystems, Texas Instruments and Cisco have a presence in the area and are using their Bangalore development centres to tackle cutting-edge projects. The biggest draw is still India’s pool of high-quality, low-cost software engineers. Each year Bangalore alone graduates 25,000 computer science engineers, almost the number who graduate in the entire USA. More significantly, the average wage of a top-class graduate software engineer is around one fifth of that in the USA. Nor is there any lack of multinational experience. For years Western (especially US) high-tech companies have employed senior Indian-born engineers. Equipped with Silicon Valley experience, some of these engineers are happy to return home to manage development teams.
Short case
Developing nations challenge
product and fashion design houses of Milan and the financial services in the City of London also enjoy a reputation shaped partly by that of their location.
Convenience for customers. Of all the demand-side factors, this is, for many operations, the most important. Locating a general hospital, for instance, in the middle of the countryside may have many advantages for its staff, and even perhaps for its costs, but it clearly would be very inconvenient to its customers. Those visiting the hospital would need to travel long distances. Because of this, general hospitals are located close to centres of demand. Similarly with other public services and restaurants, stores, banks, petrol filling stations etc., location determines the effort to which customers have to go in order to use the operation.
Locations which offer convenience for the customer are not always obvious. In the 1950s Jay Pritzker called into a hotel at Los Angeles airport for a coffee. He found that, although the hotel was full, it was also for sale. Clearly there was customer demand but presumably the hotel could not make a profit. That is when he got the idea of locating luxury hotels which could command high revenues at airports where there was always demand. He called his hotel chain Hyatt; it is now one of the best-known hotel chains in the world.
Location techniques
Although operations managers must exercise considerable judgement in the choice of altern- ative locations, there are some systematic and quantitative techniques which can help the decision process. We describe two here – the weighted-scoremethod and the centre-of-gravity method.
Weighted-score method
The procedure involves, first of all, identifying the criteria which will be used to evaluate the various locations. Second, it involves establishing the relative importance of each criterion and giving weighting factors to them. Third, it means rating each location according to each criterion. The scale of the score is arbitrary. In our example we shall use 0 to 100, where 0 represents the worst possible score and 100 the best.
An Irish company which prints and makes specialist packaging materials for the pharma- ceutical industry has decided to build a new factory somewhere in the Benelux countries so as to provide a speedy service for its customers in continental Europe. In order to choose a site it has decided to evaluate all options against a number of criteria, as follows:
● the cost of the site;
● the rate of local property taxation;
● the availability of suitable skills in the local labour force;
● the site’s access to the motorway network;
● the site’s access to the airport;
● the potential of the site for future expansion.
After consultation with its property agents the company identifies three sites which seem to be broadly acceptable. These are known as sites A, B and C. The company also investigates each site and draws up the weighted-score table shown in Table 6.2. It is important to remember that the scores shown in Table 6.2 are those which the manager has given as an indication of how each site meets the company’s needs specifically.
Nothing is necessarily being implied regarding any intrinsic worth of the locations.
Likewise, the weightings are an indication of how important the company finds each criterion in the circumstances it finds itself. The ‘value’ of a site for each criterion is then calculated by multiplying its score by the weightings for each criterion.
Worked example
Weighted-score method Centre-of-gravity method
The centre-of-gravity method
The centre-of-gravity method is used to find a location which minimizes transportation costs.
It is based on the idea that all possible locations have a ‘value’ which is the sum of all trans- portation costs to and from that location. The best location, the one which minimizes costs, is represented by what in a physical analogy would be the weighted centre of gravity of all points to and from which goods are transported. So, for example, two suppliers, each send- ing 20 tonnes of parts per month to a factory, are located at points A and B. The factory must then assemble these parts and send them to one customer located at point C. Since point C receives twice as many tonnes as points A and B (transportation cost is assumed to be directly related to the tonnes of goods shipped) then it has twice the weighting of point A or B. The lowest transportation cost location for the factory is at the centre of gravity of a (weightless) board where the two suppliers’ and one customer’s locations are represented to scale and have weights equivalent to the weightings of the number of tonnes they send or receive.
For location A, its score for the ‘cost-of-site’ criterion is 80 and the weighting of this criterion is 4, so its value is 80 ×4 =320. All these values are then summed for each site to obtain its total weighted score.
Table 6.2 indicates that location C has the highest total weighted score and therefore would be the preferred choice. It is interesting to note, however, that location C has the lowest score on what is, by the company’s own choice, the most important criterion – cost of the site. The high total weighted score which location C achieves in other criteria, however, outweighs this deficiency. If, on examination of this table, a company cannot accept what appears to be an inconsistency, then either the weights which have been given to each criterion, or the scores that have been allocated, do not truly reflect the company’s preference.
Table 6.2 Weighted-score method for the three sites
Criteria Importance Scores
weighting
Sites
A B C
Cost of the site 4 80 65 60
Local taxes 2 20 50 80
Skills availability 1 80 60 40
Access to motorways 1 50 60 40
Access to airport 1 20 60 70
Potential for expansion 1 75 40 55
Total weighted scores 585 580 605*
*Preferred option.
A company which operates four out-of-town garden centres has decided to keep all its stocks of products in a single warehouse. Each garden centre, instead of keeping large stocks of products, will fax its orders to the warehouse staff who will then deliver replenishment stocks to each garden centre as necessary.
The location of each garden centre is shown on the map in Figure 6.7. A reference grid is superimposed over the map. The centre-of-gravity coordinates of the lowest-cost location for the warehouse, Gand H, are given by the formulae:
G=∑xiVi
∑Vi
Worked example
➔
and
H= where
xi=the xcoordinate of source or destination i yi=the ycoordinate of source or destination i
Vi=the amount to be shipped to or from source or destination i.
Each of the garden centres is of a different size and has different sales volumes. In terms of the number of truck loads of products sold each week, Table 6.3 shows the sales of the four centres.
∑yiVi
∑Vi
In this case
G= =5.34
and
H= =2.4
So the minimum cost location for the warehouse is at point (5.34, 2.4) as shown in Figure 6.7. That is, at least, theoretically. In practice, the optimum location might also be influenced by other factors such as the transportation network. So if the optimum loca- tion was at a point with poor access to a suitable road or at some other unsuitable location (in a residential area or the middle of a lake, for example) then the chosen location will need to be adjusted. The technique does go some way, however, towards providing an indication of the area in which the company should be looking for sites for its warehouse.
(2 ×5) +(3 ×10) +(1 ×12) +(4 ×8) 35
(1 ×5) +(5 ×10) +(5 ×12) +(9 ×8) 35
Table 6.3 The weekly demand levels (in truck loads) at each of the four garden centres
Sales per week (truck loads)
Garden centre A 5
Garden centre B 10
Garden centre C 12
Garden centre D 8
Total 35
Figure 6.7 Centre-of-gravity location for the garden centre warehouse