Neural networks in business: techniques and applications for the

Computers & Operations Research 27 (2000) 1023}1044

Neural networks in business: techniques and applications forthe operations researcher

Kate A. Smith!,*, Jatinder N.D. Gupta"

!School of Business Systems, Monash University, Clayton, VIC 3168, Australia"Department of Management, Ball State University, Muncie, IN 47306, USA

Abstract

This paper presents an overview of the di!erent types of neural network models which are applicable whensolving business problems. The history of neural networks in business is outlined, leading to a discussion ofthe current applications in business including data mining, as well as the current research directions. The roleof neural networks as a modern operations research tool is discussed.

Scope and purpose

Neural networks are becoming increasingly popular in business. Many organisations are investing inneural network and data mining solutions to problems which have traditionally fallen under the responsibil-ity of operations research. This article provides an overview for the operations research reader of the basicneural network techniques, as well as their historical and current use in business. The paper is intended as anintroductory article for the remainder of this special issue on neural networks in business. ( 2000 ElsevierScience Ltd. All rights reserved.

Keywords: Neural networks; Operations research; Business; Data mining

1. Introduction

Over the last decade, we have seen a rapid acceptance of new technologies like neural networksand data mining methodologies for solving a wide range of business problems. Many of theseproblems involve tasks that have typically been the domain of the operations researcher, like

*Corresponding author. Tel.: #61-3-9905-5800; fax: #61-3-9905-5159.E-mail address: [email protected] (K.A. Smith)

0305-0548/00/$ - see front matter ( 2000 Elsevier Science Ltd. All rights reserved.PII: S 0 3 0 5 - 0 5 4 8 ( 9 9 ) 0 0 1 4 1 - 0

forecasting, modelling, clustering, and classi"cation. As the business world becomes more excitedabout neural networks and data mining, however, it is important for the operations researcher torealise that these technologies are really their own.

While neural networks have developed from the "eld of arti"cial intelligence and brain model-ling, the operations research reader will recognise them for what they really are. Neural networksare nothing more than function approximation tools which learn the relationship between indepen-dent variables and dependent variables, much like regression or other more traditional approaches.The principal di!erence between neural networks and statistical approaches is that neural net-works make no assumptions about the statistical distribution or properties of the data, andtherefore tend to be more useful in practical situations. Neural networks are also an inherentlynonlinear approach giving them much accuracy when modelling complex data patterns. There areseveral types of neural networks, each with a di!erent purpose, architecture and learning algo-rithm, and these will be outlined in Section 2.

In Section 3, we brie#y review the history of neural networks from the perspective of businessapplications. Five stages of neural network development are identi"ed, together with the impacteach stage had on the business community. This leads into a discussion in Section 4 of the currentbusiness application areas where neural networks are "nding relevance. One of the main areaswhere neural networks are proving to be useful is data mining. Data mining is becoming extremelypopular in the business world, as a solution methodology to a wide variety of problems where thesolution is believed to be hidden in the data warehouse. Neural networks form the backbone ofmost of the data mining products available, and are an integral part of the knowledge discoveryprocess which is central to the methodology. This data mining methodology, as well as some of theother knowledge discovery techniques, will be discussed in Section 5.

Certainly, this is not the "rst paper to review neural networks. The developments in the "eld ofneural networks have been reviewed by several authors from various points of view. Wong et al.[1}3] categorise the available literature using the number of publications in each area to identifyprevious research and application trends, and identify future directions. Sharda [4] and Ignizio andBurke [5] review the applications of neural networks in the forecasting, prediction and operationsresearch "elds. Smith [6] surveys the application of neural networks to problems of combinatorialoptimization. Zhang and Huang [7] review the applications of neural networks in the area ofmanufacturing. A previous special issue of Computers and Operations Research by Ignizio andBurke [5] also presented some interesting developments in the use of arti"cial intelligence andevolutionary programming for solving operations research problems. This paper (and special issue)has a di!erent focus however. Our review emphasizes the historical progressions in the "eld ofneural networks and discusses the impact these had on the business community. The role of theoperations researcher in this current environment is then identi"ed by reviewing neural networkdevelopments in a series of application areas.

This paper thus aims to introduce the operations research reader to neural techniques whichappear to have been received rather sceptically to date. Neural networks and data mining are notmagic solutions to problems, despite the message purported by vendors of software products.Operations researchers are likely to "nd success when using these techniques however because theywill understand the process and are likely to adhere to the methodology. Due to the strong demandfrom business and industry, these approaches will become a valuable and highly marketable toolfor operations research groups in the near future.

1024 K.A. Smith, J.N.D. Gupta / Computers & Operations Research 27 (2000) 1023}1044

Fig. 1. Architecture of MFNN. (note: not all weights are shown)

2. Neural network models

In this section we provide details of three of the better known neural network models. Eachmodel is presented in terms of its purpose, architecture, and algorithm. Each of these models hassome similarity to more traditional statistical and operations research techniques, and the relation-ships to the analogous traditional techniques are discussed.

2.1. Multilayered feedforward neural networks

According to a recent study [2], approximately 95% of reported neural network businessapplication studies utilise multilayered feedforward neural networks (MFNNs) with the back-propagation learning rule. This type of neural network is popular because of its broad applicabilityto many problem domains of relevance to business: principally prediction, classi"cation, andmodelling. MFNNs are appropriate for solving problems that involve learning the relationshipsbetween a set of inputs and known outputs. They are a supervised learning technique in the sensethat they require a set of training data in order to learn the relationships.

The MFNN architecture is shown in Fig. 1 and consists of two or more layers of neuronsconnected by weights. The #ow of information is from left to right, with inputs x being passedthrough the network via the hidden layer of neurons to the output layer. The weights connectinginput element i to hidden neuron j are denoted by=

ji, while the weights connecting hidden neuron

j to output neuron k are denoted by <kj

.Each neuron calculates its output based on the amount of stimulation it receives from the given

input vector x. More speci"cally, a neuron's net input is calculated as the weighted sum of itsinputs, and the output of the neuron is based on a sigmoidal function indicating the magnitude ofthis net input. That is, for the jth hidden neuron

nethj"

N+i/1

=jixi

and yj"f (neth

j), (1)

K.A. Smith, J.N.D. Gupta / Computers & Operations Research 27 (2000) 1023}1044 1025

while for the kth output neuron

netok"

J`1+j/1

<kj

yj

and ok"f (neto

k). (2)

Typically, the sigmoidal function f (net) is the well-known logistic function

f (net)"1

1#e~jnet, (3)

where j is a parameter used to control the gradient of the function, although the only requirementis that it be bounded between 0 and 1, monotonically increasing, and di!erentiable.

For a given input pattern, the network produces an output (or set of outputs) ok, and this

response is compared to the known desired response of each neuron dk. The weights of the network

are then modi"ed to correct or reduce the error, and the next pattern is presented. The weights arecontinually modi"ed in this manner until the total error across all training patterns is reducedbelow some pre-de"ned tolerance level (or the network has started to òvertraina as measured bydeteriorating performance on the test set [8]).

The weight update rule for the output layer weights V is given by

<kj

(t#1)"vkj

(t)#cj(dk!o

k)o

k(1!o

k)y

j(t) (4)

and for the hidden layer weights W by

=ji(t#1)"w

ji(t)#cj2y

j(1!y

j)x

i(t)A

K+k/1

(dk!o

k)o

k(1!o

k)v

kjB. (5)

Proof that the e!ect of these weight updates minimizes the total average-squared error

E"

12P

P+p/1

K+k/1

(dpk!o

pk)2, (6)

where dpk

is the desired output of neuron k for input pattern p, and opk

is the actual network outputof neuron k for input pattern p), relies on the fact that the algorithm (known as the backpropaga-tion learning algorithm) performs steepest descent on this error function [8].

There are many training issues involved in applying MFNNs successfully, including ensuringthat the learnt relationships generalise well to new data. To ensure this, data are typically dividedinto a training and a test set, where the performance on the test set is used to indicate thegeneralisation of the neural network results. Other issues involve optimal selection of the manytraining parameters including the number of hidden neurons, the learning rate c, the initial weights,and the slope of the sigmoidal function j. Convergence to local minima of the error function (6) isalso a concern, since this means that the "nal combination of weights will always produce an error.Researchers have recently started using heuristics approaches like genetic algorithms instead of thebackpropagation learning rule to determine the optimal weights for the MFNN to minimise thetotal average-squared error [9}11].

The MFNN, with an algorithm for determining the optimal weights for a given training set ofdata (backpropagation or heuristic algorithm), can be seen as similar to any function approxima-tion technique like regression, where the weights are analogous to regression coe$cients estimated


Fig. 2. Architecture of Hop"eld neural network.

by least squares. The di!erence of course is the improved potential of the function approximationwhen learning highly complex and nonlinear data due to the increased number of free parameters.

2.2. Hopxeld neural networks

While MFNNs learn the relationships between inputs and outputs in a supervised manner,Hop"eld neural networks are completely di!erent, in function, architecture and approach. WithMFNNs, the neurons are connected in layers, and the weights are modi"ed throughout thealgorithm to re#ect the learning process. With Hop"eld networks however, there is no layerstructure to the architecture, and the weights do not change. Hop"eld networks [12] are a fullyinterconnected system of N neurons as shown in Fig. 2 for N"4. The weights of the network=

ijare "xed and symmetric (=

ij"=

ji), and store information about the memories or stable

states of the network. Each neuron has a state xiwhich is bounded between 0 and 1. Neurons are

updated according to a di!erential equation, and over time an energy function is minimised. Thelocal minima of this energy function correspond to the stable states of the network.

Hop"eld networks are principally used to solve optimisation problems of the kind familiar to theoperations researcher. Hop"eld and Tank [13] showed that the weights of a Hop"eld network canbe chosen so that the process of neurons updating simultaneously minimises the Hop"eld energyfunction and the optimisation problem.

Each neuron i updates itself according to the di!erential equation

dneti

dt"!

neti

q#

N+j/1

=ijxj#I

i, (7)

xi"f (net

i),

where f (.) is a sigmoidal output function bounded by 0 and 1 like (3), and q is a constant. Theseequations are similar to the calculation of a neuron output in the MFNN except that a constantterm I

ihas been added to the net input of each neuron, and the time dynamics are now continuous

(although the process is usually simulated with a discrete Euler approximation). Each time


a neuron is updated in this manner, the energy function

E"!

12

N+i/1

N+j/1

=ijxixj!

N+i/1

Iixi

(8)

is reduced. In fact, this energy function is a Liapunov function for the system and is guaranteed notto increase [12]. This proof relies on the fact that the neuron update rules (7) result in steepestdescent of the energy function (8), just like the weight update rules (4) and (5) of the MFNN withbackpropagation result in steepest descent of the error function (6).

The approach to solving optimisation problems using Hop"eld networks is to choose theweights =

ijand constant terms I

ito force the energy function and the optimisation objective

function to be equivalent. The optimisation problem is expressed as a single function to beminimised, which incorporates all costs and constraints of the problem using a penalty functionapproach. Notice that the weights=

ijare simply the coe$cients of the quadratic terms x

ixjin the

energy function, while the constant terms Iiare the coe$cients of the linear terms x

i. Once the

network weights and constants have been chosen, the neuron states xiare randomly initialised, and

the neurons begin updating in a random sequence according to di!erential Eq. (7). Over time, theenergy function minimises until the neuron states have stabilised, and the "nal neuron statescorrespond to a local minimum solution of the optimisation problem. This solution may notnecessarily be a feasible one or a good one since the penalty function treatment of the cost andconstraints means that a balance needs to be found between which components of the energyfunction are minimised. Penalty function parameters need to be selected to re#ect the relativedegree of di$culty in minimising each component of the energy function. Numerous researchershave tried to alleviate this problem by modifying the energy function form [14], or by analyticallychoosing values for the penalty parameters [15,16].

Clearly, Hop"eld networks are a steepest descent technique for solving an optimisation problemusing a penalty function approach. The performance of Hop"eld networks has been improved byincorporating hill-climbing strategies into the neuron update equations (7), like simulated anneal-ing [17]. Variations of the Hop"eld network include Boltzmann machines [18] and mean-"eldannealing [19]. Enhancements to these approaches such as neuron normalisation [19] haveenabled certain hard constraints to be enforced by the neuron updating, rather than relying ona penalty function approach. We refer the interested reader to Smith et al. [6,20] for a comprehens-ive discussion of the issue involved with using Hop"eld neural networks and their variations forsolving optimisation problems.

2.3. Self-organising neural networks

For many decades, statisticians have used discriminant analysis and regression to model thepatterns within data when there are labelled training data (with inputs and known outputs)available, and clustering techniques when no such data are available. These techniques "ndanalogies in neural networks, where MFNNs are used with backpropagation when training dataare available, and self-organising neural networks are used as a clustering technique when notraining data are available. Clustering has always been used to group the data based upon thenatural structure of the data. The objective of an appropriate clustering algorithm is that the degree


Fig. 3. Architecture of a SOFM with nine neurons.

of similarity of patterns within a cluster is maximised, while the similarity these patterns have withpatterns belonging to di!erent clusters is minimised.

Often patterns in a high-dimensional input space have a very complicated structure, but thisstructure is made more transparent and simple when they are clustered in a one, two or threedimensional feature space. Kohonen [21,22] developed self-organising feature maps (SOFMs) asa way of automatically detecting strong features in large data sets. SOFMs "nd a mapping from thehigh-dimensional input space to low-dimensional feature space, so the clusters that form becomevisible in this reduced dimensionality.

In comparison with the two previous neural network models discussed, the SOFM involvesadapting the weights to re#ect learning (like the MFNN with backpropagation) but the learning isunsupervised since the desired network outputs are unknown. Another signi"cant di!erencebetween the SOFM and the previous models is the architecture and the role of neuron locations inthe learning process. In the SOFM, input vectors are connected to an array of neurons, usuallyone-dimensional (a row) or two-dimensional (a lattice). Fig. 3 shows this architecture for n inputsand a square array of nine neurons.

When an input pattern is presented to the SOFM, certain regions of the array will become active,and the weights connecting the inputs to those regions will be strengthened. Once learning iscomplete, similar inputs will result in the same region of the array becoming active or `"ringa.Central to this idea is the notion of the ordering and physical arrangement of the neurons. WithSOFMs the ordering of the neurons is important since we are refering to regions of neurons "ring.If a neuron "res, it is likely that its neighbours will also "re, and thus for the "rst time we areconcerned with the physical location of the neurons. This idea has more biological justi"cationthan the other neural models, since the human brain involves large regions of neurons operating ina centralised and localised manner to achieve tasks. In the human brain, as in the SOFM, there isusually a clear `winning neurona which "res the most upon receiving an input signal, but thesurrounding neurons also get a!ected by this, "ring a little, and the entire region becomes active.

In order to replicate the response of the human brain in the SOFM, the learning process ismodi"ed so that the winning neuron (de"ned as the neuron whose weights are most similar to theinput pattern) receives the most learning, but the weights of neurons in the neighbourhood of the


Fig. 4. Concept of neighbourhood size for a rectangular array of neurons.

winning neuron are also strengthened, although not as much. It is appropriate at this point tode"ne the concept of a neighbourhood in relation to the architecture of the SOFM. For a lineararray of neurons, the neighbours are simply the neurons to the left and right of the winner. This iscalled a neighbourhood size of one. To achieve the e!ect of an active region of neurons, we need toconsider larger neighbourhood sizes, as shown in Fig. 4 for rectangular array of neurons, witha hexagonal neighbourhood structure.

Initially the neighbourhood size around a winning neuron is allowed to be quite large toencourage the regional response to inputs, but as the learning proceeds, the neighbourhood size isslowly decreased so that the response of the network becomes more localised. The localisedresponse, which is needed to help clearly di!erentiate distinct input patterns, is also encouraged byvarying the amount of learning received by each neuron within the winning neighbourhood. Thewinning neuron receives the most learning at any stage, with neighbours receiving less the furtheraway they are from the winning neuron.

Let us denote the size of the neighbourhood around winning neuron m at time t by Nm(t). Theamount of learning that every neuron i within the neighbourhood of m receives is determined by

c"a(t) exp(!DDri!r

mDD/p2(t)), (9)

where ri!r

mis the physical distance (number of neurons) between neuron i and the winning

neuron m. The two functions a(t) and p2(t) are used to control the amount of learning each neuronreceives in relation to the winning neuron. These functions can be slowly decreased over time. Theamount of learning is greatest at the winning neuron (where i"m and r

i"r

m) and decreases the

further away a neuron is from the winning neuron, as a result of the exponential function. Neuronsoutside the neighbourhood of the winning neuron receive no learning.

Like the other neural network models considered thus far, the learning algorithm for the SOFMfollows the basic steps of presenting input patterns, calculating neuron outputs, and updatingweights. The di!erences lie in the method used to calculate the neuron output (this time based onthe similarity between the weights and the input), and the concept of a neighbourhood of weightupdates. The steps of the algorithm are as follows:


Step 1: Initialise} weights to small random values} neighbourhood size N

m(0) to be large (but less than the number of neurons in one

dimension of the array)} parameter functions a(t) and p2(t) to be between 0 and 1

Step 2: Present an input pattern x through the input layer and calculate the closeness (distance) ofthis input to the weights of each neuron j:

dj"DDx!w

jDD"S

n+i/1

(xi!w

ij)2.

Step 3: Select the neuron with minimum distance as the winner mStep 4: Update the weights connecting the input layer to the winning neuron and its neighbouring

neurons according to the learning rule

wji(t#1)"w

ji(t)#c[x

i!w

ji(t)],

where c"a(t) exp(!DDri!r

mDD/p2(t)) for all neurons j in N

m(t)

Step 5: Continue from STEP 2 for ) epochs; then decrease neighbourhood size, a(t) and p2(t):Repeat until weights have stabilised.

SOFMs have been predominantly used for clustering and feature extraction, "nding application asa data mining technique. As such, they are comparable to traditional clustering techniques like thek-means algorithm [23]. There has also been quite a signi"cant amount of research undertaken inusing SOFMs for solving optimisation problems as an alternative to the Hop"eld neural networksdiscussed in the previous section. This involves combining the ideas of the SOFM with the elasticnet algorithm [24] to solve Euclidean problems like the travelling salesman problem [25,26]. Inrecent work, a modi"ed SOFM has been used to solve broad classes of optimisation problems byfreeing the technique from the Euclidean plane. We refer the reader to Smith et al. [6,20] for moredetails of this and other self-organising approaches to optimisation.

2.4. Other neural network models

There are many other di!erent types of neural network models, each with their own purpose andapplication areas. Most of these are extensions of the three main models we have discussed here.Their potential application to problems of concern to the business world and the operationsresearcher is unclear, but they are referenced here for completeness. These other neural networkmodels include adaptive resonance networks [27], radial basis networks [28], modular networks[29], neocognitron [30], brain-state-in-a-box [31], to name just a few.

3. History of neural networks in business

The history of neural network development can be divided into "ve main stages, spanning over150 years. These stages are shown in Fig. 5, where key research developments in computing and


Fig. 5. The "ve stages of neural network research development, and its business impact.


neural networks are listed along with evidence of the impact these developments had on thebusiness community. The subdivision of this history into "ve stages is not the only viewpoint, andmany other excellent reviews of historical developments have been written [8,32,33]. The "vestages proposed here, however, each re#ect a change in the research environment and theresourcing and interests of business.

Much of the preliminary research and development was achieved during Stage 1 which here isconsidered to be pre-World War II (i.e. prior to 1945). During this time most of the foundations forfuture neural network research had been formed. The basic design principles of analytic engineshad been invented by Charles Babbage in 1834, which became the forerunner to the modernelectronic computer. The ability of these analytic engines and adding machines to automate tediouscalculations led to their widespread use by 1900 (the US government used such machines for the1890 national census), and International Business Machines (IBM) was founded in 1914 to capturethis market. Meanwhile researchers in psychology had been exploring the human brain andlearning. William James' 1890 book Psychology (see James [34]) discussed some of the earlyinsights researchers had into the nature of brain activity. In 1904, Ivan Pavlov received a NobelPrize for his work on conditional learning (see Schultz [35]), which became extremely importantfor subsequent researchers in neural networks. Between the two World Wars, Alan Turinginvestigated computing devices which used the human brain as a paradigm, and the "eld ofartixcial intelligence was born. This "rst stage of preliminary research concludes with the "rst basicattempts to mathematically describe the workings of the human brain. McCulloch and Pitts' (1943)paper entitled À logical calculus of the ideas immanent in nervous activitya proposed a simpleneuron structure with weighted inputs and neurons which are either òna or ò!a [36]. At thisstage, however, these neural networks could not learn, and the lack of suitable computing resourcessti#ed experimentation.

Stage 2 is characterised by the age of computer simulation. In 1946, Wilkes designed the "rstoperational stored-program computer. Over the ensuing years, the development of electroniccomputers progressed rapidly, and in 1954 General Electric Company became the "rst corporationto use a computer when they installed a UNIVAC I to automate the payroll system (see Turbanet al. [37]). The advances in computing enabled neural network researchers to experiment withtheir ideas, and in 1949 Donald Hebb wrote The Organization of Behaviour, where he proposeda rule to allow neural network weights to be adapted to re#ect the learning process explored byPavlov [38]. In 1954, Marvin Minsky built the "rst NeuroComputer based on these principles. Inthe summer of 1956, the Dartmouth Summer Research Project was held and attracted the leadingresearchers at the time. The "eld of neural networks was o$cially launched at this meeting.Rosenblatt's Perceptron model soon followed in 1957, and many simple examples were used toshow the learning ability of neural networks. By this stage the "elds of arti"cial intelligence andneural networks were causing much excitement amongst researchers, and the general public wassoon to become captivated by the idea of `thinking machinesa. In 1962, Bernard Widrow appearedon the US documentary program Science in Action and showed how his neural network could learnto predict the weather, blackjack, and the stock market. For the remainder of the 1960s thisexcitement continued to grow.

Then in 1969 a book was published which severely dampened this enthusiasm. The bookwas Minsky and Papert's Perceptrons (1969), that proved mathematically that Perceptrons areincapable of learning any problem containing data that are linearly inseparable [39]. The


consequence of their book was that much neural network research ceased. This is the third stage,commonly called the `quiet yearsa from 1969 until 1982. During this time, however, there weresigni"cant developments in the computer industry. In 1971 the "rst microprocessor was developedby the Intel Corporation. Computers were starting to become more common in businessesworldwide, and several computer companies and software companies were formed during themid-seventies. SPSS Inc. and Nestor Inc. in 1975 and Apple Computer Corporation in 1977 area few examples of companies which formed then, and later became heavily involved in neuralnetworks. In 1981, IBM introduced the IBM PCt which brought computing power to businessesand households across the world. While these rapid developments in the computing industry wereoccurring, some researchers started looking at alternative neural network models which mightovercome the limitations observed by Minsky and Papert. The concept of self-organisation in thehuman brain and neural network models was explored by Willshaw and von der Malsburg [40],and consolidated by Kohonen in 1982 [21]. This work helped to revive interest in neural networks,as did the e!orts of Hop"eld [12] who was looking at the concepts of storing and retrievingmemories. Thus, by the end of this third stage, research into neural networks had diversi"ed, andwas starting to look promising again.

From 1983 until 1990 marks the 4th Stage where neural network research blossomed. In 1983the US government funded neural network research for the "rst time through the DefenceAdvanced Research Projects Agency (DARPA), providing testament to the growing feeling ofoptimism surrounding the "eld. An important breakthrough was then made in 1985 whichimpacted on the future of neural networks considerably. Backpropagation was discovered indepen-dently by two researchers [41,42] which provided a learning rule for neural networks whichovercame the limitations described by Minsky and Papert. In actual fact, backpropagation hadbeen proposed by Werbos [43] while he was a graduate student some 10 years earlier, butremained undiscovered until after LeCun and Parker had published their work. The backpropaga-tion algorithm enabled any complex problem to be learnt without the limitations of Perceptrons.Within years of its discovery the neural network "eld grew dramatically in size and momentum.Rumelhart and McClelland's (1986) book [44], Parallel Distributed Processing, became the neuralnetwork `biblea. In 1987, the Institute of Electrical and Electronic Engineers (IEEE) held the 1stInternational Conference on Neural Networks, and these conferences have been held annually eversince. Many neural network journals emerged over the next few years, with notable ones beingNeural Networks in 1988, Neural Computation in 1989, and IEEE Transactions on Neural Networks in1990. During this stage of rapid growth, the business world remained fairly untouched by neuralnetworks. A few companies specialising in neural networks formed such as NeuralWare Inc. in1987, and the reputation of neural networks in the business community was beginning to grow, butit was not until the next stage that neural network made their real and lasting impact in business.

In 1991, the banks started to use neural networks to make decisions about loan applicants andspeculate about "nancial prediction (see Ref. [45]). This marks the start of the 5th Stage. Withina couple of years many neural network companies had been formed including Neuraltech Inc. in1993 and Trajecta Inc. in 1995. Many of these companies produced easy-to-use neural networksoftware containing a variety of architectures and learning rules. A survey of neural networksoftware products available in 1993 listed over 50 products, the majority of which were designed tobe run on a PC under Microsoft Windows (see DTI [46]). The impact on business was almostinstantaneous. By 1996, 95% of the top 100 banks in the US were utilising intelligent techniques


including neural networks [47]. Within competitive industries like banking, "nance, retail, andmarketing, companies realised that they could use these techniques to help give them a `competi-tive edgea. In 1998, IBM announced a company-wide initiative for the estimated $70 billionbusiness intelligence market. Research during this 5th stage still continues, but it is now moreindustry driven. Now that the business world is becoming increasingly dependent upon intelligenttechniques like neural networks to solve a variety of problems, new research problems areemerging. Researchers are now devising techniques for extracting rules from neural networks, andcombining neural networks with other intelligent techniques like genetic algorithms, fuzzy logicand expert systems. As more complex business problems are tackled, more research challenges arecreated.

4. Overview of business applications

Over the last decade, neural networks have found application across a wide range of areas frombusiness, commerce and industry. In this section, an overview is provided of the kinds of businessproblems to which neural networks are suited, with a brief discussion of some of the reportedstudies relevant to each area. This overview is based upon some excellent review articles [3,48,49],as well as many published studies.

4.1. Marketing

The goal of modern marketing exercises is to identify customers who are likely to respondpositively to a product, and to target any advertising or solicitation towards these customers.Target marketing involves market segmentation, whereby the market is divided into distinct groupsof customers with very di!erent consumer behaviour. Market segmentation can be achieved usingneural networks by segmenting customers according to basic characteristics including demog-raphics, socio-economic status, geographic location, purchase patterns, and attitude towardsa product [50]. Unsupervised neural networks can be used as a clustering technique to automati-cally group the customers into segments based on the similarity of their characteristics [51].Alternatively, supervised neural networks can be trained to learn the boundaries between customersegments based on a group of customers with known segment labels, i.e. frequent buyer, occasionalbuyer, rare buyer [52].

Once market segmentation has been performed, direct marketing can be used to sell a product tocustomers without the need for intermediate action such as advertising or sales promotion.Customers who are contacted are already likely to respond to the product since they exhibit similarconsumer behaviour as others who have responded in the past. In this way, marketers can saveboth time and money by avoiding contacting customers who are unlikely to respond. Bounds andRoss [53] showed that neural networks can be used to improve response rates from the typical oneto two percent, up to 95%, simply by choosing which customers to send direct marketing mailadvertisements to.

Neural networks can also be used to monitor customer behaviour patterns over time, and tolearn to detect when a customer is about to switch to a competitor. The electronic storage of dailytransaction details enables us to anticipate consumer behaviours based upon learnt models, and


strategies can be devised for retaining customers who are identi"ed as likely to switch toa competitor (also known as `churna). Analysis of market research is also an area whereneural networks can be of bene"t. Moutinho et al. [54] applied neural networks to analyseresponses to advertising and to determine the factors in#uencing the usage of ATMs by a bank'scustomers.

4.2. Retail

Businesses often need to forecast sales to make decisions about inventory, sta$ng levels, andpricing. Neural networks have had great success at sales forecasting, due to their ability tosimultaneously consider multiple variables such as market demand for a product, consumers'disposable income, the size of the population, the price of the product, and the price of com-plementary products [52]. Forecasting of sales in supermarkets and wholesale suppliers has beenstudied [55,56] and the results have been shown to perform well when compared to traditionalstatistical techniques like regression, and human experts.

The second major area where retail businesses can bene"t from neural networks is in the area ofmarket basket analysis (see Bigus [57]). Hidden amongst the daily transaction details of customersis information relating to which products are often purchased together, or the expected time delaybetween sales of two products. Retailers can use this information to make decisions, for example,about the layout of the store: if market basket analysis reveals a strong association betweenproducts A and B then they can entice consumers to buy product B by placing it near product A onthe shelves. If there is a relationship between two products over time, say within 6 months of buyinga printer the customer returns to buy a new cartridge, then retailers can use this information tocontact the customer, decreasing the chance that the customer will purchase the product froma competitor. Understanding competitive market structures between di!erent brands has also beenattempted with neural network techniques [51].

4.3. Banking and xnance

One of the main areas of banking and "nance that has been a!ected by neural networks is tradingand xnancial forecasting. Neural networks have been applied successfully to problems like deriva-tive securities pricing and hedging [58], futures price forecasting [59], exchange rate forecasting[60] and stock performance and selection prediction [61}64]. The success stories are numerousand have received much attention.

There are many other areas of banking and "nance that have been improved through the use ofneural networks though. For many years, banks have used credit scoring techniques to determinewhich loan applicants they should lend money to. Traditionally, statistical techniques have driventhe software. These days, however, neural networks are the underlying technique driving thedecision making [65,66]. Hecht-Nielson Co. have developed a credit scoring systems whichincreased pro"tability by 27% by learning to correctly identify good credit risks and poor creditrisks [48]. Neural networks have also been successful in learning to predict corporate bankruptcy[67}69].

A recent addition to the literature on neural networks in "nance is the topic of wealth creation.Neural networks have been used to model the relationships between corporate strategy, short-run


"nancial health, and the performance of a company [70]. This appears to be a promising new areaof application.

Financial fraud detection is another important area of neural networks in business. VisaInternational have an operational fraud detection systems which is based upon a neural network,and operates in "ve Canadian and 10 US banks [71]. The neural network has been trained todetect fraudulent activity by comparing legitimate card use with known cases of fraud. The systemsaved Visa International an estimated US$40 million within its "rst six months of operation alone[72]. Neural networks have also been used in the validation of bank signatures [73], identifyingforgeries signi"cantly better than human experts.

4.4. Insurance

There are many areas of the insurance industry which can bene"t from neural networks. Policyholders can be segmented into groups based upon their behaviours, which can help to determinee!ective premium pricing. Prediction of claim frequency and claim cost can also help to setpremiums, as well as "nd an acceptable mix or portfolio of policy holders characteristics [74].The insurance industry, like the banking and "nance sectors, is constantly aware of the need todetect fraud, and neural networks can be trained to learn to detect fraudulent claims or unusualcircumstances. The "nal area where neural networks can be of bene"t is in customer retention [74].Insurance is a competitive industry, and when a policy holder leaves, useful information can bedetermined from their history which might indicate why they have left. O!ering certaincustomers incentives to stay, like reducing their premiums, or providing no-claims bonuses,can help to retain good customers. Unfortunately, the competitive nature of the insuranceindustry means that few details of successful applications of neural networks have been published.The data mining company Trajecta (http://www.trajecta.com) advertises success within theinsurance industry, as does Risk Data Corporation (a subsidiary of Hecht}Nielson Company).Risk Data Corporation used neural networks to detect fraudulent insurance claims for theWorkers' Compensation Fund of Utah, as well as estimating the "nancial impact of predictedclaims [75].

4.5. Telecommunications

Like other competitive retail industries, the telecommunications industry is concerned with theconcepts of churn (when a customer joins a competitor) and winback (when an ex-customer returns).Neural Technologies Inc., is a UK-based company which has marketed a product called DA ChurnManager. Speci"cally tailored to the telecommunications industry, this product uses a series ofneural networks to: analyse customer and call data; predict if, when and why a customer is likely tochurn; predict the e!ects of forthcoming promotional strategies; and interrogate the data to "nd themost pro"table customers. Telecommunications companies are also concerned with product sales,since the more reliant a customer becomes on certain products, the less likely they are to churn.Market basket analysis is signi"cant here, since if a customer has bought one product froma common market basket (such as call waiting), then enticement to purchase the others (such ascaller identi"cation) can help to reduce the likelihood that they will churn, and increases pro"tabil-ity through sales.


There are also many other applications of neural networks in the telecommunications industry,and while these are more engineering applications than business applications, they are of interest tothe operations researcher because they involve optimisation. These include the use of neuralnetworks to assign channels to telephone calls [76], for optimal network design [77] and for thee$cient routing and control of tra$c [78].

4.6. Operations management

There are many areas of operations management, particularly scheduling and planning, whereneural networks have been used successfully. The scheduling of machinery [79], assembly lines[17], and cellular manufacturing [80] using neural networks have been popular research topicsover the last decade. Other scheduling problems like timetabling [81], project scheduling [82] andmultiprocessor task scheduling [83] have also been successfully attempted. All of these approachesare based upon the Hop"eld neural network [12] and the realisation of Hop"eld and Tank [13]that these networks could solve complex optimisation problems. Recently, alternative neuralnetwork approaches like neuro-dynamic programming [84] have also been used to solve relatedproblems.

The use of neural networks in various operations planning and control activities are reviewed byGaretti and Taisch [85] and cover a broad spectrum of application from demand forecasting toshop #oor scheduling and control. Balakrishnan et al. [86] use neural networks to integratemarketing and manufacturing functions in an organization. A unique feature of this paper is the useof both supervised and unsupervised learning modes in the neural network design. In addition,using scheduling of jobs as an example, Gupta et al. [87,88] describe the use of neural networks forselecting the most appropriate heuristic algorithm to use to solve a practical problem in operationsmanagement. Neural networks have also been used in conjunction with simulation modeling tolearn better manufacturing system design [89].

The other area of operations management which bene"ts from neural networks is quality control.Neural networks can be integrated with traditional statistical control techniques to enhance theirperformance. Examples of their success include a neural network used to monitor soda bottles tomake sure each bottle is "lled and capped properly [90]. Neural networks can also be used asa diagnostic tool, and have been used to detect faults in electrical equipment [91] and satellitecommunication networks [92].

Project management tasks have also been tackled using neural networks. Lind and Sulek [93]report the use of MFNNs to forecast project completion times for knowledge work projects, whileSmith et al. [94] use neural networks for estimating several software metrics in software develop-ment projects.

4.7. Other industries

In this section we have examined some applications of neural networks to various sectors ofbusiness: marketing, retail, banking and "nance, insurance, telecommunications, and operationsmanagement. There are of course many other industries which have bene"tted from neuralnetworks over the last decade. Many commercially available products incorporate neural networktechnology. IBM's computer virus recognition software IBM AntiVirus uses a neural network to


detect boot sector viruses. In addition to the viruses it was trained to detect, the software has alsocaught approximately 75% of new boot viruses since the product was released. Sensory Inc. haveused neural networks to create a speech recognition chip, which is currently being used inFisher}Price electronic learning aids, and car security systems. Companies like Siemens use neuralnetworks to provide automation for manufacturing processes, saving operating costs and improv-ing productivity. Handwritten character recognition software like that used in Apple Computer'sNewton MessagePad uses neural network technology as well. Details about many of these applica-tions can be found in Knoblock [49].

What emerges from this discussion is the complete diversity of the application areas which arereaping the advantages and bene"ts of neural networks. The important point about these applica-tions is that they have e!ectively driven research over the last decade. Banks cannot reject a loanapplicant because their neural network advised them that the applicant would be a bad risk. Theymust provide reasons why the application was not successful, and give suggestions as to how theapplicant could improve their chances next time. Because of these legal requirements, researchersare now working on extracting rules from neural networks [95,96]. High demands for speech andcharacter recognition software means that researchers are constantly striving for faster and moree$cient algorithms to achieve the task. These demands from business and industry will continue todrive research well into the next century.

5. Data mining

And what is the role of data mining in this discussion? Data mining is an area which is captivatingthe business world at the moment, and the operations researcher can "nd many opportunities forengaging in consulting work or collaborative research with companies interested in data mining.Data mining has emerged over recent years as an extremely popular approach to extractingmeaningful information from large databases and data warehouses [97]. The increased computerisa-tion of business transactions, improvements in storage and processing capacities of computers, aswell as signi"cant advances in knowledge discovery algorithms have all contributed to the evolutionof the "eld [57]. Neural networks (MFNNs and SOFMs) form the core of most commercial datamining packages such as the SAS Enterprise Miner and the IBM Intelligent Miner. Other tools likeregression, classi"cation (decision) trees, and advanced statistics modules are also often included.

To the operations researcher, data mining is an opportunity to use traditional techniques, neuralnetworks, and other ìntelligent techniquesa to help an organisation achieve their potential. Whiledata mining may therefore appear to be about using old techniques under a new name, it is themethodology of data mining and the new range of applications that are generating the excitement.There have been many studies published recently that demonstrate the bene"ts that can be broughtto an organisation through data mining [74,98}101].

Data mining has not been without criticism, however, and it appears that some data miningprojects have been unsuccessful for a variety of reasons [102]. Perhaps the most perceptive quoteon this topic comes from Small [102], who observes:

The new technology cycle typically goes like this: Enthusiasm for an innovation leads tospectacular assertions. Ignorant of the technology's true capabilities, users jump in without


adequate preparation and training. Then, sobering reality sets in. Finally, frustrated andunhappy users complain about the new technology and urge a return to `business as usuala.Certainly an understanding of the individual techniques that fall under the umbrella of data

mining, as well as adherence to a methodology, can prevent this scenario from occuring. It is forthis reason that the operations researcher is likely to "nd success when applying data mining: theapproach is a natural extension of an existing problem solving methodology. We refer theinterested reader to Berry and Lino! [103] for an excellent introduction to data mining method-ologies and techniques.

6. Conclusion

This paper has reviewed neural network techniques in business from the perspective of theoperations researcher. The three main neural network approaches to solving business problemshave been introduced: multilayered feedforward neural networks, Hop"eld neural networks, andself-organising neural networks. Each of these techniques "nds natural analogy with more tradi-tional statistical and operations research techniques, and these analogies have been discussed.

There has been a certain amount of hype associated with neural and ìntelligenta techniqueswhich appears to have made the academic community sceptical about their merits. This is partlydue to the turbulent history of neural network development, which has been discussed in Section 3.This paper has aimed to clarify the potential of these techniques in comparison with moretraditional approaches. The operations research reader will recognise neural network approachesto solving business problems as very similar to statistical methods, with some relaxation ofassumptions and more #exibility.

We have also provided an overview of some of the many business applications that have beensuccessfully tackled using neural networks. Data mining is one of the booming application areas atthe moment, and is an area where the operations researcher can "nd projects with industry. Neuralnetwork research is now being driven by industry, as more business problems are attempted andnew research challenges emerge. Given the need for any successful research area to be responsive tothe interests of industry, the role of emerging technologies like neural networks and data mining inoperations research is clear.

Acknowledgements

The authors express deep appreciation to the "ve reviewers for their constructive comments andsuggestions that improved the presentation of this paper.

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Kate A. Smith is a Senior Lecturer in the School of Business Systems at Monash University, Australia. She holdsa B.Sc(Hons) in Mathematics and a Ph.D. in Electrical Engineering, both from the University of Melbourne, Australia.She is Director of the Data Mining Research Group in the Faculty of Information Technology at Monash University. Dr.Smith has published a book on neural networks in business, and over 40 journal and international conference papers inthe areas of neural networks, combinatorial optimization, and data mining. Journals she has published in includeComputers and Operations Research, European Journal of Operational Research, IEEE Transactions on Neural Networks,INFORMS Journal of Computing, Location Science, Journal of the Operational Research Society, IEEE Journal on SelectedAreas in Communications, etc. Dr. Smith serves as a referee for many journals in the "eld, and is a member of theorganizing committee for several international data mining and neural network conferences.

Jatinder N.D. Gupta is Professor of Management, Information and Communication Sciences, and Industry andTechnology at the Ball State University, Muncie, Indiana, USA. He holds a Ph.D. in Industrial Engineering (withspecialization in Production Management and Information Systems) from Texas Tech University. Coauthor of a text-book in Operations Research, Dr. Gupta serves on the editorial boards of several national and international journals.Recipient of an Outstanding researcher award from Ball State University, he has published numerous research andtechnical papers in such journals as Computers and Operations Research, International Journal of Information Management,Journal of Management Information Systems, Operations Research, IIE Transactions, Naval Research Logistics, EuropeanJournal of Operational Research, etc. His current research interests include information technology, scheduling, planningand control, organizational learning and e!ectiveness, systems education, and knowledge management.


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