We present the application of an
unsupervised snap drift neural network (USDNN) in
the context of market prediction for small and
medium enterprises (SMEs). The method is applied to
small firm data collected from twenty seven
participating SMEs across greater London. The
motivation of this research is aimed at the
significance of artificial neural networks in
addressing the perceived failure associated to smaller
firms irrespective of area of operation and business
type. Hence, the work presented here provides crucial
information for creating requirements for a small
firm specific model that can be used to support
growth or survival in predefined markets

Lee, S.

Palmer-Brown, Dominic

Walcott, Terry H.

UEL Research Repository at University of East London

  University of East London Institutional Repository: http://roar.uel.ac.uk   This paper is made available online in accordance with publisher policies. Please scroll down to view the document itself. Please refer to the repository record for this item and our policy information available from the repository home page for further information.  Author(s): Walcott, Terry H; Palmer-Brown, Dominic; Lee, Sin Wee. Article title: Early SME Market Prediction using USDNN Year of publication: 2008 Citation: Walcott, T.H; Palmer-Brown, D and Lee, S.W. (2008). "Early SME Market Prediction using USDNN",In Proceedings of the International Conference of Computational Intelligence and Intelligent Systems (ICCIIS'2008), London,United Kingdom, July 2-4,2008. Early SME Market Prediction using USDNN  Terry H. Walcott, Member, IAENG, Dominic Palmer-Brown and Sin Wee Lee       Abstract—We present the application of an unsupervised snap drift neural network (USDNN) in the context of market prediction for small and medium enterprises (SMEs). The method is applied to small firm data collected from twenty seven participating SMEs across greater London. The motivation of this research is aimed at the significance of artificial neural networks in addressing the perceived failure associated to smaller firms irrespective of area of operation and business type. Hence, the work presented here provides crucial information for creating requirements for a small firm specific model that can be used to support growth or survival in predefined markets.      Index Terms— Data Analysis, Market Prediction, SME, Unsupervised Learning, USDNN  I. INTRODUCTION The small firm sector often the major contributor towards the gross domestic product of large economies nonetheless suffers from an inability to predict its future businesses. As a result there is a divide between struggling small and medium enterprises (SMEs) and those considered to be growing enterprises. Therefore, for the truly small firms there must be an element of continuity that can initiate success. Consider the barter system of trading, it has allowed goods and services to be traded long before the existence of money. This means that for survival to be initiated then early man had to decipher what was to be considered most important and as such determine the bargaining tool to meet his needs. For smaller firms to establish this need they must be given an opportunity to deduce factors that are beneficial for sustaining growth. Therefore, efficiency in marketing is a core requirement for SME survival. For small firm marketing to be considered efficient, such firms must be able to determine the needs of the customer in advance. As such, practice based marketing (distribution of goods based on market needs) will be instrumental. It is with this approach that small firms will be more suited in ensuring that the products and services created will be accepted by its customers. Essentially, the small firm must be able to acquire new customers (determining their specific needs) and retain existing customers (satisfy) if they are to remain a force in their respective market of operation. II. RELATED WORK The emphasis that has been duly placed on business survival has led to a continuous adaptation of neural network technology in the general business domain. This adaptation can be attributed to ANNs ability to model non- linear systems [1]. According to [2], such systems have benefited the business sector because of the ease in which they can be used to analyse data a company has at its disposal. Interestingly, a large proportion of prior works in business has focused on operations management, insurance, banking, finance, and marketing and of course the general retail sectors [3]. Key literature suggests that neural networks are considered the likeliest data analysis tool because of its feature discovery capabilities. Hence a large proportion of the work published in this business domain (under prediction) has largely focused on the areas of  bank failure prediction [4],[5]; exchange rate prediction[6]; Firm failure Prediction[7],[8]; stock price prediction [9],[10]. All of which suggests that conflicts in markets have led to large firms considering implementing neural networks to sustain growth and initiate competitive advantage. Marketing research to date has predominantly focused on forecasting (price and sales); segmentation analysis and consumer behaviour. The neural network marketing literature has provided works that have focused on telephone analysis using quantitative data [11],[12]; qualitative response models for decision making[13]; predicting market responses for fast moving goods[14];determination of market response functions [15]; purchase product frequency prediction[16], airline passenger forecasting [17]; industrial market segmentation classification [18]; future order processing [19]; target marketing [20]; using neural networks to determine early warning of loan risk assessment [21] and small business loan default [22]; Proceedings of the World Congress on Engineering 2008 Vol IWCE 2008, July 2 - 4, 2008, London, U.K.ISBN:978-988-98671-9-5 WCE 2008predicting corporate bankruptcy using neural networks [23]; Analysis of customer satisfaction data [24]; Marketing decision support systems [25],  and staff selection [26] and the evaluation of quality in small firms [27]. III. THE SNAP-DRIFT ALGORITHM  The snap drift algorithm is based on both adaptive resonance theory and learning vector quantization. Snap indicates that learning in such a network will take place when network performance is poor while on the other hand drift indicates detailed learning when the performance of the network is good. As snap-drift is based on ART and LVQ then it has to be appreciated that it can be used as a strategic tool for monitoring performance within a self-organising active network. In order to achieve the networks primary objectives both systems must compare inputted patterns in order to determine a level of change in the systems performance feedback propagation unit [28].                   Fig. 1. shows the SDNN architecture[28]  The figure above represents the Snap-drift neural network.  Input patterns (I) are presented to the drift half of this network then each pattern will be grouped based on the general features exhibited in the data (or input patterns). Successive epochs will ensure that this type of neural network toggles between snap and drift learning. For each even epoch (inputs such as 2, 4, 6) the algorithm executes the snap half of the algorithm and for each odd number epoch (such as 1, 3, 5) the algorithm executes the drift half of the algorithm. This type of an algorithm is useful for exploratory data analysis as it uses continuous self-organisation in order to discover features as they arise in the data. Hence, for each new execution of the algorithm a new set of classes are created. Each class contains a collection of inputs that are to be considered similar to those of its members. IV. METHODOLOGY A six part questionnaire was used as the main research instrument in this study in order to determine the feasibility of neural networks when applied to small and medium enterprises.  Themes covered areas such as the company foundations; firm’s management structure; employee profile; firm threats; firm competitiveness; innovative and technological capabilities. Collected questionnaires (27 collected) were then tabulated using the subheadings of each section of the questionnaire.   A. Data Pre‐processing and Variable Selection The responses made to each question were transposed using data scaling, data normalisation and coarse coding respectively. Closed question responses were mostly scaled or normalised. Open ended responses were ideally pre-processed using coarse coding as this was considered satisfactory for these kinds of responses in order to preserve key similarities and variations between patterns. Our analysis consisted of sixty two variable types created directly from all questions on the questionnaire. At this stage in our research, data analysis was conducted using all sixty responses.   V. RESULTS Our responses were collected from twenty seven participating companies across five distinctive operational areas.  Company Types Number of Companies Public Carriage Office Transportation 6 Restaurants 18 Education Recruitment 1 Financial Services 1 Cosmetics/ Hair Design 1 Table 1 shows breakdown of areas of operation of research participant companies. From the responses collected (see Table 1) it is evident that firms operating in the restaurant sector were the dominant participants in our research so far.  Proceedings of the World Congress on Engineering 2008 Vol IWCE 2008, July 2 - 4, 2008, London, U.K.ISBN:978-988-98671-9-5 WCE 2008Classes Inputs One  3,13,15,20 Three 5,8,10,18,19,21,22,24,26,27 Four 6,9,12,17 Five 1,2,5,7,11,14,16,23,25 Table 2 shows the classification of each input using snap-drift neural network. Table 2 shows the classification of each input by the snap-drift neural network. Each input consists of sixty variables representing a particular company. The assigned weights were used to interpret these distinctive classifications.  VI. CONCLUSIONS The snap-drift neural network has been applied to small firm data. Thus demonstrating that irrespective of type of company there is some correlation in regards to how these firms conduct themselves in business. Our research work presented here is limited in regards to what has already been achieved. However, the associated weights within the snap-drift algorithm have demonstrated that correlations exist between 33% of our input variables (i.e. a third of the sixty variables under examination). This suggests that our ideal network may perform better if we were to use fewer input variables for classification. REFERENCES  [1] T.Walcott, D. Palmer-Brown, G.Williams, H. Mouratidis and S.W. Lee, “An Assessment of Neural Network Algorithms that could aid   SME Survival”, Proceedings of ACT, School of Computing and Technology Conference, 2007. [2] K.A. Smith, and J.N.D. Gupta, “Neural Networks in business: techniques and applications for the operations researcher”, Computers and Operations Research, vol.27, pp.1023-1044, 2000. [3] B.K. Wong, V.S.  Lai, and J. Lam, “A bibliography of neural network business applications research: 1994-1998”, Computers and Operations Research, vol.27 pp.1045-7, 2000. [4] L.M. Salchenberger, E.M. Cinar, and N.A. Lash, “Neural Networks: A tool for predicting thrift failures”, Decision Sciences, .vol.23, no.4, pp. 899-916, 1992. [5] K.Y. Tam and M.Y. Kiang, “Managerial applications of neural networks: The case of bank failure predictions”, Management Science, vol.38, no.7, pp: 926-947, 1992. [6] C.M. Kuan and T. Liu, “Forecasting exchange rates using feedforward and recurrent neural networks”, Working Paper Series No. 92-0128, University of Illinois at Urbana-Champaign, 1992. [7] G. Udo, “Neural network performance on bankruptcy classification problem”, Computers and Industrial Engineering,25(1-4), pp. 377-380,1993. [8] J. Tsukada, and S. Baba, “Predicting Japanese Corporate Bankruptcy in terms of financial data using neural networks”, Computers and Industrial Engineering, vol.27, no.1-4, pp.445-448, 1994. [9] M.W. Kattan, D.A. Adams and M.S. Parks, “A comparison of machine learning with human judgement”, Journal of Management Information Systems, vol.9, no.4, pp.37-57, 1993. [10] L. Kryzanowski, M.Galler, and D.W. Wright, “Using artificial neural networks to pick stock”, Financial Analysts Journal, vol.49, no.4, pp.18-21, 1993. [11] K. Moore, R. Burbach, and R. Heeler, “Using neural networks to analyse qualitative data”, Marketing Research: A magazine of Management and Applications, vol.7, no.1, pp.34-39, 1995. [12] C.Hsu, “Generalizing Self-Organizing Map for Categorical Data”, IEEE transactions on Neural Networks’, vol.17, no.2, pp. 294-304, 2006. [13] J.V.Hansen, J.B. McDonald and J.D. 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Schaefer, ‘Forecasting international airline passenger traffic using neural networks’, Logistics and Transportation Review, vol.31, no.3,pp.239-251,1995. [18] K.E.Fish, J.H.Barnes and M.W.Aiken, “Artificial Neural Networks- A new methodology for industrial market segmentation”, Industrial Marketing Management, vol.24, pp.431-438, 1995. [19] Y.B Moon and R. Janowski, “A neural network approach for smoothing and categorizing noisy data”, Computers in industry, vol.26, no.1, pp.23-39, 1995. [20] J. Zahavi, and Levin, N, “Issues and Problems in applying neural computing to target marketing”, Journal of Direct Marketing, 9(3), 33-45, 1995. [21] B.Yang, L.X. Hi, H. Ji, and J. Xu, “Early Warning system for loan risk assessment using artificial neural networks”, Knowledge Based Systems, vol.14, pp.303-306, 2001. [22] J. I. Yegorova, B.H. Andrews, J.B. Jensen, B.J. Smoulk, and S. Walczak,  “A successful neural network-based methodology for predicting small business loan default”, Credit Research Foundation, vol.7, no.4, pp.31-42,2001. [23] D.Mitchell and R.Pavur, “Using Modular neural networks for business”, Management Decision, vol.40, no.1, pp.58-63, 2002. [24] L.Gronholdt and A.Martensen, “Analysing Customer Satisfaction Data: “A comparison of regression and artificial neural networks”, International Journal of Market Research, vol.47, no.2, 2005. [25] R.A.Proctor, “Marketing decision support systems: A role for neural networks”, Marketing Intelligence and Planning, vol.10, no.1, pp. 21-26, 1992. [26] R.A.Proctor, “An expert system to aid in staff selection: A neural network approach”, International Journal of Manpower, vol.12, no.8, pp.18-21,1991 [27] A.Turkyilmaz, G. Gencyilmaz, S. Zaim, and A.A. Nikov, “A neural networks based evaluation of quality in the SMEs: An application in Turkish Textile Sector”, Proceedings of the 5th International Symposium on Intelligent Manufacturing Systems, pp. 1230-1238, 2006. [28] S.W. Lee, D. Palmer-Brown, C.M. Roadknight, “Performance-guided neural network for rapidly self-organising active network management” (Invited paper), Neurocomputing, vol.61C, pp. 5 – 20, 2004.  Proceedings of the World Congress on Engineering 2008 Vol IWCE 2008, July 2 - 4, 2008, London, U.K.ISBN:978-988-98671-9-5 WCE 2008

Early SME Market Prediction using USDNN

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Early SME Market Prediction using USDNN

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