1 INTRODUCTION

Since the first edition of this volume was publishedin 1991, a significant amount of change hasoccurred in the area of ‘business geographics’. It canbe argued (with some important exceptions) thatGIS in business has moved from a peripheral,technical role to a mainstream management role.With GIS products such as MapInfo beingincorporated into Microsoft Office, the ubiquity ofGIS in business is impressive (see Elshaw Thrall andThrall, Chapter 23). Most, if not all, largecommercial and government organisations haveaccess to GIS technology in one form or another.This maturity, following a common life-cycle patternin IT applications, has naturally led to observersasking ‘where do we go from here?’ Has GIS becomeanother widely accepted technology that can existalongside spreadsheets, data warehouses, and otherbusiness IT applications – or is there somethingdistinctive about GIS and its relationship withbusiness application that remains untapped andoffers significant potential for exploitation? In thischapter we offer, in a pragmatic manner, anapproach and an argument that supports the latterrather than the former view.

Viewed from within the GIS industry, businessgeographics is seen as a tremendous success and weapplaud the efforts of the ‘GIS in business’community to promote the application of GIS inthe commercial area. The number of conferencesand the range of participants demonstrates aneffective collaboration of developers and appliers.However, viewed from a different perspective(that of contributing to the strategic planning ofbusiness organisations), the position is less clearcut. It often appears to be the case that GIS inbusiness is used simply to map out or representspatially referenced data. At one level this isreasonable and should be supported. At anotherlevel, it falls somewhat short of what we havetermed ‘intelligent GIS’ (Birkin et al 1996). We haveconsistently argued that the integration ofmodelling tools within a GIS environment creates awhole that is substantially more than the sum ofthe parts. In fact, it leads to the creation of decisionsupport systems that can address a much widerrange of operational and strategic issues thandesktop mapping. We would thus like to see GISmove from the mainstream to the strategic (seealso Gatrell and Senior, Chapter 66; Sugarbaker,Chapter 43).

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51GIS for business and service planning

M BIRKIN, G P CLARKE, AND M CLARKE

Within the realm of business and service planning, traditional GIS are weak so far aspredictive modelling is concerned, notably because they have no means of coping withcompetition. This chapter outlines a framework within which GIS can be linked with otheranalytical tools in such a way as to offer commercial organisations support in constructingstrategic development plans. We have consistently argued that the integration of modellingtools within a GIS environment creates a whole that is substantially more than the sum ofthe parts. In fact, it leads to the creation of ‘decision support systems’ for managers thatcan address a much wider range of operational and strategic issues than ‘desktopmapping’. The approach is illustrated by examples drawn from the authors’ work for majorretailing corporations in both Europe and the USA in terms of optimal siting of outlets andimproved distribution mechanisms.

Given this background, the aim of this chapter isto outline a framework within which GIS can be linked with other analytical tools in such a way as tooffer commercial organisations support inconstructing strategic development plans. Such aframework is likely to be built upon the followingsequence: data ⇒ information ⇒ intelligence ⇒action. The chapter will review the role of GIS inbusiness and marketing, and outline itscontribution in each of the above categories.Examples will be drawn from the fields of highstreet retailing, financial services, and automotiveretailing. An argument will be made that GIS isexcellent for examining ‘what is’ issues, but poorerat making ‘what if ’ forecasts and predictions. Theadvantages of GIS for data storage and handling,mapping, and the provision of basic levels ofinformation will be discussed first. Then we shallreview the kinds of spatial analysis tools that arecurrently available or recommended to retailanalysts. These include buffer and overlaytechniques and network analysis for store locationresearch and customer targeting. A key argumenthere will be that many of these techniques are nowsomewhat dated in terms of methods availableoutside GIS (both in theoretical terms as purveyedthrough the literature and in applied terms throughspecialist software and consultancy operations).

In terms of forecasting and predicting theimpacts of changes in distribution patterns we thenargue that current proprietary GIS are less useful.This has now been recognised by many GISvendors and ‘solved’ by introducing suites oflocation models, particularly location–allocationmodels and spatial interaction models(see Church, Chapter 20; Getis, Chapter 16).This recognises the usefulness of these tools forforecasting and ‘what if ’ style predictions.However it is our belief that making simple(often one parameter) models available for appliedproblems can be as dangerous as not having suchmodelling power in the first place, mainly becauseit is unlikely that such simple models will be able toreproduce accurately the complex consumerbehaviour patterns seen in modern retail markets.The alternative is to customise GIS so that themodelling tools become the main analysis tools,supported by good graphics and data processing.We will demonstrate how such (disaggregated)models can help add intelligence in the schemadescribed above through examples of a variety ofbusiness situations that require action plans.

2 THE CORPORATE GROWTH MODEL

The geographical development of most retail andbusiness organisations has taken place in ahaphazard way. Few organisations can point to asystematic plan for network development which hasinvolved prioritising potential target locations. Thatis not to say that there has not been an overall‘grand plan’. Indeed, many companies will havedecided historically on either contagious diffusionstrategies (expansion from a headquarters location:see the example of the UK Kwiksave grocery chainin Sparks 1990) or hierarchical diffusion strategies(involving location in the largest cities first). Somemay also have gone as far as publishing lists oftarget sites (Kwiksave again is a good example – seeLangston et al 1997). However, it would seem fair tosay that such lists are often merely rankings ofshopping centre by size, rather than the result ofany proactive spatial analysis of competitive retailmarkets. This lack of investment in locationalanalysis does not surprise those who have dealingswith senior management in retail organisations. Themajority of these managers (if not all) will beaccountants, lawyers, or business managers notaccustomed or trained to acknowledge theimportance of the branch location network in otherthan a superficial fashion (hence the importance of,and interest in, the arguments in section 3 below).In a fascinating recent appraisal of the importanceof dealer networks in the Caterpillar organisation(Fites 1996) there is discussion on the value ofdealers in terms of customer care and help inshaping new technical developments. Discussion ofthe relationship between sales and dealer location isless prominent although Fites does recognise thevalue of local marketing and knowledge of localcustomer needs. The only reference to the locationof dealers in respect to sales in his publicationcomes with reference to Mexico where salesdropped during a recession and many competitordealers were closed:

‘When the good times returned, we were the onlyones with a viable dealer organisation in Mexico,and we got the vast majority of the business.’ (Fites1996: 92)

Such recognition of national dealer locationsneeds to be mirrored at the regional and local levels.Part of the problem lies with the fact that manyorganisations believe few new opportunities exist.

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Their networks probably developed in piecemealfashion over the years, especially where mergers andacquisitions have also added to the uneven nature ofspatial corporate growth. In addition, manyorganisations believe their network is complete in thesense that they no longer concentrate seriousresources on location research, often believing homemarkets to be saturated. When we began to workwith W H Smith (the largest bookshop andstationery chain store in the UK; see Birkin andFoulger 1992) they expressed surprise at theopportunities which were revealed through theappraisal of local markets simply because they hadgot used to the belief that their network of brancheswas complete and UK shopping opportunities weresaturated. This attitude is common amongst retailersdespite the fact that, in most retail sectors, companymarket shares vary significantly from region toregion and substantially within regions themselvesand that there is widespread regional variation inprovision indicators such as floorspace per head ofpopulation (Langston et al 1997: see Figure 1). Thelack of investment in location research is even moresurprising in relation to opportunities provided byoverseas markets (Wrigley 1993).

It is good news for those involved with spatialanalysis that the situation is slowly changing. Thismust partly be attributed to the success of GIS vendorsfaithfully spreading the message at an increasingnumber of business conferences, through site visits,and through magazine articles. It is also a reflection ofan increasing number of academics getting involvedwith consultancy (see Clarke et al 1995). Before weassess the contribution of GIS to business applications,it is useful briefly to rehearse the arguments concerningthe importance of geography in the distribution andtargeting of goods and services.

3 THE GEOGRAPHY OF SERVICE PROVISION

Ask any UK retail company about their currentmarket share for different products in the UK andthe vast majority will provide the correct answer.There is good market information at this coarse levelof spatial resolution. However, ask the samecompanies for a regional breakdown and they maybegin to struggle. This is often more interesting sincenational figures usually mask very large regionalvariations. Figures 2 and 3 show the variations inregional market shares for LADA and BMW cars

respectively in the early 1990s. The variations inregional performance are clear to see. Suchvariations even occur in the business-to-businesssectors where historically many companiesmonopolise markets in traditional strongholds butare very weak in other geographical areas. Forexample, Shell Chemicals has very strong marketshares in northwest England and around GreaterLondon. Referring back to the Fites (1996) articlecited above, Caterpillar are equally likely to haveuneven international and national market sharesgiven the location of its global network of dealers.

Although these regional variations are oftenstriking, the actual variations within regions arenormally even greater. The vast majority of serviceorganisations will struggle to provide market shareestimates for major cities within regions, let alone for

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Fig 1. Spatial variations in retail provision: multiple grocerfloorspace per household in Britain.Source: Langston et al (1997)

4.74.13.53.02.4 = Mean1.91.3

Standard deviationsfrom mean

0 100 200 miles

0 100 200 300 km

smaller geographical areas such as postal districts. Aswe mentioned above, this is partly because seniormanagers are not geographically aware and do nottypically think in this way. Figure 4 shows the actualmarket penetrations for a major car retailer in the twoUK counties of Hampshire and Dorset by postaldistrict. This particular manufacturer has a nationalshare of roughly 2.5 per cent. As Figure 4 shows, thepostcode district averages within Hampshire andDorset range between less than half and more thantwice this average. The distance decay effect aroundeach dealer is also evident. If geography (or dealerlocation) were unimportant, then local marketpenetrations for individual manufacturers would bemuch flatter and nearer to national figures (evenallowing for random variations). This phenomenon isnot peculiar to the UK. Figure 5 shows the samerelationship between market share and dealer locationfor a leading motor company in Puerto Rico.

A second obvious factor which influences localgeographical performance is the location of

customer ‘types’. Kwiksave have a target market ofUK social groups D and E, traditionally low- or un-skilled workers more interested in cheaper pricesthan store layout or store choice (‘no frills retailing’).Similarly, Toyota are more likely to sell ‘top of therange’ cars in areas of high affluence and their two-seater MR2 sports car in areas containing bothwealthy and typically young professional workers.Hence customer targeting is very important instrategic planning, and the geodemographicsindustry aims to supply information to companieson geographical variations in such customer types.Clearly the geographical balancing act is to combinedistribution and sales points with the greatestconcentration of potential customers.

We have long argued (Birkin et al 1987) that, tounderstand the geography of service provision, weneed to understand the relationships betweencustomers, sales, and markets. For some servicesectors these data are readily available but currentlyremain greatly underused. Financial institutions, for

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Fig 2. Regional market shares for Lada cars in the UK.

Percentage of totalmarket

< 0.650

0.650 – 0.960

0.960 – 1.280

1.280 – 1.600

1.600 – 1.910

> 1.910

0 100 200 miles

0 100 200 300 km

Fig 3. Regional market shares for BMW cars in the UK.

Percentage of totalmarket

< 1.720

1.720 – 2.210

2.210 – 2.710

2.710 – 3.200

3.200 – 3.690

> 3.690

0 100 200 miles

0 100 200 300 km

example, have superb data on their customers andtheir place of residence. In Britain, the motorindustry can purchase sales information for theircompany and all the competition from the Societyof Motor Manufacturers and Traders (SMMT),including customer postcode addresses bymanufacturer and model type. In these twoinstances, companies are likely to be in thecommonly stated position of being ‘data rich butinformation poor’ in that there is so much raw datathat processing and analysis without computer-based technology is virtually impossible (or at best

extremely time-consuming). Even if data areprovided on computer (or by computer tape), it isour experience that it is often simply printed out onreams of computer paper and analysed bytraditional eyeballing methods.

Other service organisations (e.g. high streetretailers) may not be as fortunate as the onesmentioned above: for them, data may be poorer andless easily accessible. However, it is likely thatthrough a mixture of internal data (invoices, creditcard information, transaction data), public domaininformation (in the UK the Census of Population,

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Fig 4. Market penetration for a leading motor distributor by postcode district, with dealer locations shown as squares.

Dorchetser

Bournemouth

AndoverBasingstoke

Winchester

Bognor Regis

Percentage of total marketby postcode district

< 1.100

1.100 – 1.830

1.830 – 2.560

2.560 – 3.290

3.290 – 4.030

> 4.030

0 5 10 miles

0 5 10 15 km

Fig 5. Market share of a major motor manufacturer in Puerto Rico. Dealer locations are shown by dots.

Market share of a majormotor vehicle manufacturer

in Puerto Rico

> 13.2

9.1 – 13.2

6.9 – 9.1

4.6 – 6.9

≤ 4.6Dealer location

0 10 miles

0 5 10 km

Census of Employment, Family Expenditure Survey,etc.) and agency information (market reports fromEuromonitor, the Economic Intelligence Unit, Auditsof Great Britain, and more specialist sources such asSMMT for the motor industry and AMI for thechemical industry) it is possible to piece together alarge volume of market information; similar marketinformation can be assembled in other countries.Indeed, the first major benefit from developing acomputer-based information strategy is often the‘data audit’ associated with piecing together relevantmaterial on markets and customers.

The most immediate benefit of GIS itself lies in theability to store large volumes of such data within aframework which allows quick and efficient retrieval(see Goodchild and Longley, Chapter 40). Ingeographical terms, this obviously relates toinformation pertaining to different spatial areas(and possibly spatial scales) as well as the medium ofdisplay through modern computer graphics. We haveseen in the examples shown above that spatial dataand information can be ‘brought to life’ and hencemore easily understood if displayed in map as well astabular form. It is at this stage of the informationsystem that simple arithmetic operations can greatlyadd to the information base. Data retrieved can befiltered (or sorted by some key category), ranked, andadded together. For example, if the datasets includecensus variables on characteristics of either thepopulation or the household, then these are likely tobe available for small geographical regions. They canthen be combined with household information setsavailable only at the national level to produce newestimates for small geographical regions. Marrying theCensus variables on household social class withexpenditure on products (in the UK from the annualFamily Expenditure Survey) can produce demandestimates by product groups not normally published(see an example of this approach for the estimation ofdemand for books in Germany shown in Figure 6).

GIS can also provide useful methods of datalinkage through polygon overlay where differentlayers of data can be superimposed (see Martin,Chapter 6). The ability to link data together turnsdata into information. In the financial servicesindustry, data on different types of account holderscould be linked to provide customer profiles of thesorts of persons most likely to have a basic currentaccount, a mortgage, or to be financially activeholding many accounts. Targeting new depositaccounts could thus be made more sophisticated

through this data overlay procedure – but see Curry,Chapter 55, for an appraisal of the privacy issuesthat this sort of operation raises. Whilst manyorganisations are getting better at producinginformation, there is still a shortfall in good spatialanalysis: we argue that it is the ability to analyse thistype of information which provides marketintelligence and the ingredients for action. Thus, tocontinue the financial services example, we could goon to assess the role that different types ofcustomers play in adding to profit levels and tryingto work out a strategy for optimising profits. Oncewe understand the profit implications of new clients,then the search is on to find other cases of these andsimilar customer types for added value products.

4 GIS AND MODELS IN RETAIL ANALYSIS

We will now illustrate the advantages of combiningGIS and models (‘intelligent GIS’) using fourexamples of issues or problems which can arise inthe retail industry. The first explores the day-to-dayactivities of marketing and branch networkplanning. The last two are examples of activitieswhich take place more infrequently but whichpresent obvious geographical problems to solve.We will demonstrate that intelligent GIS can offersolutions which lead to effective action plans.

4.1 Marketing and store revenue predictions

Most organisations are interested in analysingregional or local markets to understand bothexisting performance (‘what is?’ analysis) and topredict the impacts of changing the distributionnetwork in some way (‘what if ?’ analysis). Havingused simple overlay procedures to identify targetsites, the GIS literature often suggests a combinationof buffer and overlay analysis to calculate storerevenues for existing and new (potential) stores (seeElshaw Thrall and Thrall, Chapter 23; Waters,Chapter 59; Beaumont 1991a, 1991b; Elliott 1991;Howe 1991; Ireland 1994; Reid 1993; Reynolds1991). This works by first estimating how farconsumers are willing to travel to a store (existing orpotential). The result of this exercise will be either atravel time (say 20 minutes) or a distance (say nomore than three miles). The second stage is then todelimit an area around that store (a buffer) thatmarks the limit of that time or distance factor in

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each direction outwards from the store. The revenueaccruing to that store can then be estimated byoverlaying the consumer spending power that resideswithin the postal sectors (or parts of sectors) orzipcodes that lie within that buffer. Unfortunately,the assumption is often made that customers close tothe store are as likely to travel to the store as arethose at the edge of the buffer. When there arecompetitor stores within the catchment area buffer,then revenue prediction becomes even moreproblematic. Normally, the so-called ‘fair share’method is used (see Beaumont 1991a, 1991b) wherethe total amount of revenue generated by all storesin the buffer is divided simply by that number ofstores. This could be made more sophisticated by

basing ‘fair share’ on store size and perhaps storeownership. However, even with these refinements,the revenue predictions made are likely to be fraughtwith danger. When the supermarket catchmentsoverlap one another, the GIS software merges theminto one polygon along with all the spatial attributesassociated with that buffer. Clearly, the problem isgreater when there are two or more stores which arelocated close together and thus effectively have verysimilar catchment areas. Although some of theseissues have been addressed in relation to catchmentarea analysis (see Davies and Rogers 1984), thesolutions do not yet appear in many GIS packages.

On the other hand, spatial interaction modelling isideally suited to the problems of defining realistic

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Fig 6. Estimated demand for books in former West Germany.

Value of market inthousands of DM

30800 – 64000

16000 – 30800

10000 – 16000

5500 – 10000

0.0 – 5500

0 30 60 miles

0 30 60 90 km

catchment areas and estimating store revenues. A study area is divided into a set of residential zones,say postal sectors or zipcodes, and all centres andoutlets are identified. The models thus attempt toquantify exactly how many customers in eachdemand zone will patronise each and every store inthe region. Since customers to different stores willcome from a variety of demand zones, overlappingcatchment areas are expected and explicitly handled.The model is calibrated on existing flow data, afterwhich a new outlet can be introduced and its impactassessed. Although these models have been aroundfor 25 years, only recently has their full potentialbeen realised as better spatial data and computerhardware/software became available (Birkin 1996;Clarke and Wilson 1987). Examples of these modelsand their use is provided by Birkin et al (1996).

It should be acknowledged that these types ofmodel are increasingly to be found in proprietary GIS(see Maguire 1995 for developments in ARC/INFO).Although this should provide a more sophisticatedlevel of analysis, there remains the general problem ofthe lack of flexibility in model design and outputs aswell as a number of practical problems. The argumentconcerning the lack of flexibility which is oftenneeded to handle a variety of often unique businessquestions has been provided by Birkin (1996). Thepractical problems have been explored by Benoit andClarke (1997). They found model selection a majorproblem for the uninitiated. An analysis for a localhealth care facility will typically require a differentmodel to that for an automotive dealer. A reasonablequestion from the new investor who has recentlypurchased the GIS package might be: ‘Which modelshould I use?’ Second, there is typically not a useful orrelated calibration method imparted to the potential

user, so it becomes necessary to devise one’s ownmethod. This particular problem makes it eminentlydifficult for a retail manager initially to comprehendwhat to do for his or her own store location analysisunless he or she has access to extensive knowledge onretail modelling.

The arguments for integrating specialistmodelling software (where highly disaggregatedmodels can often be constructed) and GIS have beenmade elsewhere and the term ‘intelligent GIS’(Birkin 1996; Birkin et al 1996) is broadlysynonymous with ‘spatial decision support systems’(Shiffer, Chapter 52; Densham 1991; Densham andRushton 1988). However, it is important here toemphasise the benefits. Since the models deal withspatial interactions, we can not only predict accuraterevenues but also analyse resultant flow patterns.This, in turn, facilitates the calculation of a widerange of residence-based and facility-basedperformance indicators as a matter of routine (seeBertuglia et al 1994; Birkin 1994; Clarke and Wilson1994) and also allows the analyst to construct areaor regional ‘typologies’. Examples of thesetypologies are given in Table 1. In this case, basicdata on market performance have been turned intouseful information. Having identified appropriateregional strategies, the resulting action plans canthen be tested using the models in a ‘what if ?’fashion. The most common of these is the testing ofnew store openings. Again, the power of the modelsover traditional GIS comes with the accurateprediction of new catchment areas which are alwayslikely to be skewed in some way (again because ofthe location of the competition) rather than thesimple, common assumption of circular travel timesor distance buffers.

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Table 1 Four typologies of regional performance and management actions.

Low Market share High

Type 2 Type 1

Low market share High market shareHigh sales per branch High sales per branch

Extend branch network Maintain status quo

Type 3 Type 4

Low market share High market shareLow sales per branch Low sales per branch

Reconfigure branch network Rationalise branch network

High

Sales perbranch

Low

4.2 Launch of a new product

A special type of ‘what if ?’ analysis concerns thedistribution of a new market product. If this is anew type of chocolate bar, then an organisationwould probably be safe retailing this from allnewsagents and confectionery stores. However, whatabout a new £30 000+ ($48 000+) motor vehicle?GIS could highlight areas containing the mostaffluent consumer groups as a useful starting point.Existing dealer locations could then be overlaid tosee which dealers might be in the most appropriatelocations to sell cars valued at over £30 000. Howmany cars each dealer would actually sell is a muchharder question to address with proprietary GIS. Ifthe company has over 1000 dealers and hopes to sell10 000 vehicles then it works out at ten per dealer.Realistically, however, some dealers are likely to sell50, others fewer than five. This suggests we need aselective distribution policy which involvesaccurately estimating how many each dealer is likelyto sell and hence which dealers should take priority.Figure 7 shows the results of a model-basedappraisal of the likely sales of a new luxury car forcompany X from its dealers in the southeast ofEngland and hence the dealers most likely to be ableto sell 50 or more.

4.3 Mergers and acquisitions

Historically, mergers and acquisitions have been akey method of generating corporate growth. Mostretail organisations have developed their networksand their market shares through acquiring regionalplayers in this way (see Guy 1994; Kay 1987). Thereare several explicitly geographical issues involvedwith such action. The first is relevant when decidingon which organisations may be ripe for merger ortake over. Different organisations have differentregional and local market shares. By consideringthese variations prior to action, it may be possible togrow rapidly by achieving high market shares inregions where previously market share was very low.It was largely for these reasons that Kwiksavebought out Shoprite and Tesco bought out WilliamLow in Scotland (see Sparks 1996a, 1996b). Oncesuch action is complete, a second set of issuesemerges. Does the combined network of stores nowgive that organisation the best or at least a gooddistribution network? In the case of the twoexamples mentioned above, the answer is likely to be‘yes’. However, with many mergers or buy-outs thecombined organisation is left with many competingoutlets in the same locations (a good recent UKexample is the Leeds and Halifax building society

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Fig 7. ‘Optimal’ dealers to sell new luxury cars in southeast England.

Reading

Bracknell

Woking Sevenoaks Canterbury

Haywards Heath

Portsmouth

(Savings and Loan) merger where both had a highpresence in the Yorkshire region as well as similarnetworks nationally).

If spatial duplication is the case, then theorganisation is likely to want to rationalise itsnetwork through branch closure. The key questionsare then which branches to close and where shouldtransferred accounts be located, whilst at the sametime considering the impacts of such closures on thedesire to maintain market share. Although theintelligent GIS could be used to evaluate such storeclosures, a more effective action plan would alsoinclude refurbishments, change of fascias, and newbranch openings – in other words, a systematicappraisal of all opportunities for the combinedorganisation. Tables 2 and 3 show an example ofrecent work we have undertaken for a new financialorganisation in the UK following merger. Its mainproblem is to combine the former branch networksof the two organisations into one network for thenew organisation. Table 2 shows a nationwide actionplan which has resulted from a set of detailedmodelling scenarios based on closures,reconfigurations (changing fascia name from one ofthe poorer performing branches of one of the oldorganisations to the name of the new organisation),refurbishments, and new branch openings. Theaction plan is clearly very different for each majorUK geographical region. If we consider thesoutheast of the UK, we can see the more detailed

action plan in Table 3. Plate 41 shows the combinedimpacts of these changes on market share. Thepressure on the new organisation is to rationalise byclosing branches (in order to reduce costs). However,it is also important to minimise the impacts ofclosure programmes. From our action plan, theclosures inevitably result in a loss of market share inthe south and east of the region but we believe theimpact is minimised with closures in these areas andmore than offset by new openings in far morelucrative markets to the north and west.Refurbishment of those branches which are left inthe south and the east of the region may well bringback market shares to their previous levels. Swann(Chapter 63) describes analogous problems in therealm of rationalising military operations.

4.4 Optimising retail networks

We saw in section 3 that one of the uses commonlymade of a GIS is to overlay different data layers toproduce new information. Often this procedure takesplace in conjunction with a set of rules governing sitelocations, such as finding sites where several criteria aremet – for example, flat land, proximity to a motorwayaccess point, and areas with a catchment populationgreater than 100 000. Whilst this is useful in dealingwith many environmental problems, it is less useful inbusiness. Again this is mainly because of the problemsof dealing adequately with the competition (i.e.

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Table 2 National action plans for the amalgamation of two building societies: number of stores affected.

Action East Anglia Inner London Midlands Yorks and Northnorthwest

New entry 18 29 25 1 3Removal 1 4 3 10 5Rationalise 1 8 0 2 1Reconfigure 4 22 1 3 0

Scotland southeast southwest National totalsNew entry 8 16 10 110Removal 6 6 3 38Rationalise 1 8 0 21Reconfigure 5 10 1 56

whereNew entry = High potential, no presence: add new storesRemoval = Poor performance, low potential: close branches of one former companyRationalise = Poor performance, low potential: close both former company branchesReconfigure = High potential, low market share: invest to upgrade branches

allocating population between stores). Thus, forbusiness applications, other types of optimisationprocedure may be more useful. Optimisation methodssuch as linear programming attempt to find the bestsolution to a stated problem subject to a number ofconstraints being satisfied (see Church, Chapter 20).They first appeared in the geographical literature tohandle difficult operational research issues such as the‘travelling salesman problem’. Nowadays a muchlarger class of methods exists, particularly in the non-linear programming area. The great advantage of thesemethods over traditional GIS approaches is the way inwhich they can handle spatial interaction. For example,in site location problems they can consider a particularsolution (say, 50 particular sites chosen from 500possible ones) and calculate catchment populationsand potential revenues very easily. Where existing GIScan help is possibly in identifying local sites. Certainly,a marriage of methods could well prove invaluable.

One methodology which has already beenimported into GIS is the location–allocation modelwhich finds the optimal locations for supply pointsgiven a spatially non-uniform pattern of demand(see Ghosh and Craig 1984; Ghosh and Harche 1993).The optimisation criterion is usually distanceminimisation. The model can be run for any numberof supply points. This kind of model is limited in itsapplied value since existing supply points are likelyto be sub-optimally located and extremely difficultor expensive to relocate. However it does give someindication of an idealised pattern of businessactivity and it is particularly valuable in looking atthe best locations for sales representatives or areamanagers. Given that the primary problem for such‘reps’ or area managers is accessibility, then thesemodels are very useful for assigning better newlocations from which to serve either an existingpattern of clients to salespersons (or area managersto stores) or a new set (which themselves can beworked out in an optimal sense). Figures 8 and 9 showtwo examples – the optimal locations for new cardealer locations in the Seattle/Tacoma region of theUSA and the Czech Republic, respectively. Theoptimisation objective in Seattle/Tacoma was to findsites which maximised sales for two new locationsfor Toyota whilst at the same time minimising thenumber of deflections from existing dealers. Clarkeand Clarke (1995) estimated the potential financialbenefits of such an optimisation procedure if thiscould be replicated throughout the USA: thepredicted profits alone ran into many hundreds ofmillions of dollars! Greater details on the mechanicsof optimisation appear in Birkin et al (1995) andWilson et al (1981).

5 CONCLUSIONS

We have argued that existing GIS for business andservice planning do not provide sufficient analyticalpower to solve many important geographicalproblems which arise in areas such as sales,marketing, and advertising. We believe highlyflexible information systems are required whichcombine database manipulation and high-qualitymap and graphical output with spatial modellingtechniques. Although some of this can be achievedthrough existing proprietary GIS packages, these aretoo often restrictive and unable to offer the rightsorts of analyses to business organisations. It is

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Table 3 Detailed strategy for development insoutheast region.

New entry Hempstead Valley Ashford (Middlesex)Woodley RomseyFrimley FleetRyde (Isle of Wight) WeybridgeRainham (Kent) RingwoodSilverhill OxtedAddlestone EghamHythe (Kent) Broadstairs

Removal Newhaven MidhurstNew Milton RyePolegate Hampden Park

Rationalise Farnham Haywards HeathSeaford BexhillDorking SouthseaLittlehampton Burgess Hill

Reconfigure Harley UckfieldSteyning EsherShoreham HaslemereRustington GodalmingLymington Tenterden

Expansion1 Crawley WinchesterCosham SevenoaksNewbury ReigateNorth End WindsorBanstead HaslemereRustington

1 where Expansion = High potential, low market share: open additional branch

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Fig 8. Two new optimal locations for Toyota dealers in Seattle/Tacoma which minimally impact on existing dealers.The land area is divided into census tract units.

Existing Toyota dealer

Optimal dealer

Fig 9. Optimal locations of existing dealers in the Czech Republic.

Autocentrum Pribram

Autoprodej Chum

Autochandel

FOPO

Autopravna KrumlHornat

K & N VolyneAuto Kriz

A. Charouz-Zapad

Autocentrum Plasy

Albert

Aredj

Mlcak

Roubicek

Autotop

AutobohemiaHomolka Autocentrum Dorda

Cermak-I.E.S

Bospor

Detax

ACP PardubiceBaca & Jelinek

Autoprodej Sedlak

Lundyje CaslavCentrum

Borovicka

Tipa

Atoma Autoservis

Automobil

Autospol

Cartip

K & L Autoservis

Dankar

Maly & Velky

Service dealers

Sub dealers

Main dealers

CZ Dist

0 15 30 miles

0 15 30 45 km

crucially important that GIS solutions can address thetypes of key strategic planning issue we have describedabove, rather than force organisations to ‘shoe-horn’their problems into the analytical toolboxes availablein current GIS packages. If this is not done, then webelieve GIS will remain a low-order planning tool,assisting in the process of data manipulation andmapping but offering few long-term solutions orflexible action plans for distribution planning.Fortunately, the opportunities are still growing. Weare convinced there are many new markets for GIS toexplore. The area of telecommunications is one (seeFry, Chapter 58), offering enormous potential forgood spatial analysis in distribution planning andtarget marketing as opposed to traditional GISinterests in physical network planning. If we canconvince strategic planners in these new areas of theflexibility which is possible with GIS and spatialanalysis then exciting times still lie ahead.

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