Sales Forecast for Pickup Truck Parts:

Sales Forecast for Pickup Truck Parts: A Case Study on Brake Rubber Mojtaba Kamranfard University of Semnan Semnan, Iran mojtabakamranfard@gmail.com Kourosh Kiani Amirkabir University of Technology Tehran, Iran Kourosh.kiani@aut.ac.ir Abstract In this paper we address sales forecasting of brake rubber for 1600 pickup truck manufactured by Iran Khodro Co. To this end, we use two different methods named Neural Network (NN) and regression model. Further, we develop two types of neural networks, one general network and a set of monthly networks. Results reveal that when data are nonlinear and chaotic, traditional models like regression are less likely to be useful. In these cases we can use nonlinear models like neural networks. It is shown that general network is not a useful tool for forecasting sales of brake rubber, whereas monthly networks are accurate and useful for this purpose. Keywords- neural network, regression, sales forecasting, automotive industry, chaotic data. I. INTRODUCTION In the retail area, specifically within the order management in the supply chain, providing a precise mechanism for forecasting demand is a key factor in the success of large corporations [6]. Therefore, a key issue that defines the success of a manufacturing organization is its ability to adapt easily to the changes of its business environment. To this end, it is very useful for a modern company to have a good estimate of how key indicators are going to behave in the future, a task that is fulfilled by forecasting [16]. An efficient forecasting system can improve machine utilization, reduce inventories, achieve greater flexibility to changes and increase profits. In particular, sales forecasting is very important, as its outcome is used by many functions in the organization [12]. In fact, nowadays due to the competitive environment, the globalization, the irreducible manufacturing lead times and the uncertainty of the customer s demand, the sales forecast is a fundamental success factor of most companies [8]. Several forecasting models, such as regression models [1], exponential smoothing and Box & Jenkins models [10], neural networks [9] or fuzzy systems [8], have been developed and provide satisfactory results in different domains [20]. However, the performance of these models strongly depends on the field of application, the forecasting goal, the user experience, and the forecast horizon [11]. Automotive industry is of a great importance in Iranian economy. Several companies are active in this field and some of them have begun to export their products to Middle East countries. Iran Khodro Co. is one of these major manufacturers. This company manufactures and assembles several types of automobiles widely used by Iranian people, each of which are made up of different parts that can be unusable due to several reasons like car accidents, normal wear and tear and so on. Therefore, it is important for these companies and their suppliers to make appropriate decisions about their volume of production, their methods of production, their methods of material storage, and some other issues. In order to cope with the sever competition environment, manufacturers need to rest on some reliable information for decision making and therefore sales forecasting is viewed as a key factor [14]. Due to the abovementioned arguments, this paper deals with monthly sales forecasting of brake rubber for 1600 pickup truck manufactured by Iran Khodro Co. We use two different models in this paper and compare their results. These models are called neural networks and time series. To the best of our knowledge, it is the first Iranian paper in this field and further researches may be needed to improve the results. This paper is organized as follows. In the next section we review existing literature on forecasting using different methods. Section 3 provides data selection and research method of this research. In section 4 we provide the results and discuss their implications. Finally, section 5 presents conclusion and suggestions for future researches. II. LITERATURE REVIEW A. Forecasting Time Series Traditionally, the most commonly used forecasting techniques are statistical methods, such as Multiple Regression model in [22] and time series models such as moving average, exponential smoothing and the Box Jenkins autoregressive integrates moving average (ARIMA) methods in [17]. We can classify these models as linear and nonlinear, depending on the nature of the model they are based on [16]. In the real world the relationship between the factors or the past time series data (independent variables) and the sales (dependent variables) are always nonlinear and quite chaotic [3]. Another alternative approach is an Econometric Model which its goal is to investigate the

relationship between the external economic variables and the final sales. Srinivasan [4] proposed a forecasting model based on Econometric Model integrated with Neural Network and proved that it could have better performance measure when compared to traditional approaches. Recently, with the development of the Artificial Intelligence techniques, several methods are found to have better performance than traditional models when applied to forecasting problems and Artificial Neural Networks (ANNs) are the most commonly used tools. After being trained by historical data, ANNs can be used to predict the sales in the future [13]. Many researchers have successfully applied ANNs to solve forecasting problems as in [18]-[19]. The field of Artificial Intelligence (AI), such as Neural Network (NN), Fuzzy Theory, Expert System (ES), Genetic Algorithms (GA), and Rule Induction and so on has been rapidly developed in recent years [14]. B. Traditional Methods Sales forecasting is only an effective management method if and when it helps managers make company s decisions in an uncertain environment. Sales forecasts can be produced by many linear and nonlinear models [5]. Traditional methodologies of sales forecasting can be divided as follows: 1. Qualitative method Qualitative method belongs to more subjective approaches, which transform qualitative data into qualitative estimates based on estimations and opinions. This method is best adopted when the available data are not sufficient, e.g. when a new product is introduced to the market. Generally it includes Delphi Method, Market Research, Panel Consensus, Visionary Forecast and Historical Analogy, etc. 2. Time series analysis It mainly forecasts the future demands by using the data of demands in the past. In traditional forecasting models, no matter how long the forecasting time takes, it can be done by the available information analyzed through any kinds of statistical methods. The most popular models are Moving Average, Exponential Smoothing, Box Jenkins, and Trend Projections, etc. 3. Cause and effect analysis This method adopts the technique of linear regression and attempts to investigate the cause-and-effect relationship between the predicted items and other related factors. This method can be used when the historical data can provide enough information to analyze and explain the relative factors of the forecasting items. The most commonly used models include Regression analysis and Economic Model, etc [15]. However, the methodologies that have been used in sales forecasting are typically time series that, as mentioned above, can be classified as linear or nonlinear, depending on the nature of the model they are based on [16]. Linear models, like autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) are the most popular methodologies, but their forecasting ability is limited by their assumption of a linear behavior and thus, it is not always satisfactory [4]. In order to address possible nonlinearities in time series modeling, researchers introduced a number of nonlinear methodologies, including nonlinear ARMA time series models. Their main drawback is that the type of nonlinearity is not known in advance and the modeler needs to select the structure of the model by trial and error [16]. In this study we use regression model to forecast the sales and compare its results with results of neural network. C. Neural Networks (NNs) As discussed by Kuo and Xue (1998), the new developed Artificial Intelligence (AI) models have more flexibility and can be used to estimate the non-linear relationship, without the limits of traditional Time Series models [15]. Advanced artificial intelligence technologies, like artificial neural networks (ANNs) and fuzzy logic systems use more sophisticated generic model structures, and can obtain better results [16]. Obviously, the key question concerns the accuracy of each modeling method. To this end, a number of studies have been conducted to compare the aforementioned methods. Adamo L. Santana et al. (2012) discussed different methods of artificial intelligence in solving load forecasting problems and concluded that artificial neural networks have been one of the most prominently accepted models in this field [2]. Zhang, Patuwo, and Hu (1998) did a comprehensive review of the literature concerning the utilization of ANNs in forecasting problems in various areas. ANNs performed equally well with linear methods in 30% and better in 56% of the cases reviewed. In a subsequent study by Stock and Watson (1999) linear and nonlinear methods were compared and it was found that in terms of forecasting performance, combinations of nonlinear methods are better than combinations of linear methods. Additionally, feedforward neural networks (FNNs) that constitute a special ANN architecture performed equally well or better than traditional methods in more than half of the cases [16]. In another study, Makridakis & Hibon (2000) examined the FNN topology and the most popular forecasting methodologies and commercial software in several test cases. The results showed that FNNs did not exhibit good performance, which is due to the nature of the available data. As pointed out by Balkin (2001) only 25% of the data sets exhibited strong nonlinearity, while the lengths of the series were insufficient for the model building in most cases. Zhang (2003) pointed out that no single method is best in every situation and that combining different models in an effective and efficient way to improve forecasting accuracy [21]. III. METHODOLOGY A. Data Selection Iran Khodro Co. is one of biggest manufacturers in Iranian automotive industry. This company manufactures and assembles several types of automobiles widely used by Iranian people, each of which are made up of different parts

that can be unusable due to several reasons like car accidents, normal wear and tear and so on. Mazda Yadak Co. provides these parts and after sales services for customers. We have used Mazda Yadak s database for the purpose of forecasting in this paper. After classifying and organizing data, we decided to forecast sales amount of brake rubber for 1600 pickup truck on a monthly basis. It was because this part is extremely prone to wear and tear and has been bought a lot by automobile owners. In addition, its available data were partly sufficient for the purpose of this paper. Available data for this part includes amounts of sales in 9 successive years, which is between 1999 and 2007. It should be noted that data are not available for December of these years. Whether this is due to inappropriate documentation, unavailability of data in reality or any other reason, we cannot forecast sales in this specific month. Therefore, we perform forecasting for remaining 11 months. So we have 11 monthly networks. In the following we discuss how we developed our forecasting networks. B. Research Method We developed two different types of neural networks for the purpose of forecasting in this paper: a general network and a set of monthly networks. The general network is a multipurpose one which can be used to forecast sales of every month, while each of monthly networks is developed to forecast sales of a specific month. Entries of general network are month number, price of the part in related month, importance factor of the month and accumulative sales of the part. Monthly networks have four entries, which are average of sales in the specific month up to the previous year, sales of the month in previous year, standard deviation and sales in previous month. It should be noted that, as mentioned above, we do not have data for December and so there will be no network for this month. Therefore, we have one general, and 11 monthly networks. Finally we also perform sales forecasting using regression model and provide the results. In next section we present the results and discuss them. IV. RESULTS AND DISCUSSION In the following we show a comparison between neural network and regression results. Table I shows the results of general network and compares them with results of regression. As can be seen in the table, results of neural network is better than regression, but still the error percentage is high and unacceptable. Error percentage for NN is 465.1, whereas for regression is 1062.8, which both are very high and there is a need to make them smaller. It is worth mentioning that in this paper we have used Mean Absolute Percentage Error (MAPE) which better shows forecasting smoothness of each method. Table II shows the results of monthly networks and compares them with results of regression. As we can see in the table, results of neural network is better than results of regression in 9 months and regression model provides better results only in 2 months. In other words, monthly networks perform better except in June and November. Using monthly networks, error percentages are much more acceptable than the general network and lower than regression error percentages. It seems that forecasting performance of monthly networks is better that general network and regression model. Therefore, using monthly networks reduces the error rate and they seem to be a better tool than just one general network. In addition, using monthly networks is more practical, because they use more specific data as inputs. TABLE I. COMPARISON BETWEEN GENERAL NN AND REGRESSION FORECAST Observed data NN forecast Regression forecast 6 332 782 4767 8112 6576 2081 3774 4453 1059 2058 3138 240 774 3182 1840 366-1400 6324 4886 954 4028 2776 1304 1482 2886 1384 1106 2470 1756 378 2782 2226 4462 8416 5451 4767 1938 2571 2081 3774 4453 1059 2058 3138 240 774 3182 Error percentage 465.1 1062.8 We observe that error percentage of neural network in June and November is higher than regression s and in other months, forecasting performance of neural network is better than regression. Therefore, it can be concluded that neural networks are better tools for the purpose of this paper. However, to improve generalizability of the results, it is needed to conduct further researches and studies. There are some points we should explain. First, as we see in the tables, some of observed data are the same, like observed data for August, September, October and November in table II. This can be due to inappropriate data documentation or any other reason. Regardless of the reason, we have relied on available data and presented our results based on data documented in Mazda Yadak s database. Data accuracy and appropriate data documentation are important issues which should be addressed in future researches. Second, as can be seen in the tables, we have 16 observed data for the general network, whereas the number of observed data for each monthly network is 2. This is due to the fact that we have data for 9 years, with the exception of data for December of each year. In sum, we have data for 99 months. We used 83 data as training data set, and 16 data as testing data set for the general network. In case of monthly networks, we have only 9 data for each of them, 7 of which are training data and 2 of which are testing data. Third, in order to obtain reliable results, each neural network has been run for 10 times, and the results presented in tables are averages of these 10 results. For example, fig. 1

shows the results of one random run of general neural network which, as explained in table I, is not accurate enough and contains a large level of error. The line in the figure below represents the trend of the network in forecasting sales, and each point corresponds with an observed amount of sales. TABLE II. COMPARISON BETWEEN MONTHLY NNS AND REGRESSION FORECAST Month Observed NN forecast Reg. forecast data January 755 857 634 1840 1739 1744 Jan. Error ----- 9.5 10.6 February 487 486 0 6324 6978 5688 Feb. Error ----- 5.2 54.9 March 12720 12467 12403 4028 4819 3051 Mar. Error ----- 10.8 13.4 April 817 512 1606 1482 1352 1158 Apr. Error ----- 23 59.2 May 11550 5809 11552 1106 1058 413 May Error ----- 27 31.3 June 2254 945 2041 378 186 279 Jun. Error ----- 54.4 17.8 July 6 512-941 4462 1352 3037 Jul. Error ----- 23 59.2 August 4767 4837 5318 4767 4372 3081 Aug. Error ----- 4 23.5 September 2081 2065 2031 2081 2066 2031 Sep. Error ----- 0.7 2.4 October 1060 1061 1067 1060 1063 1065 Oct. Error ----- 0.2 0.5 November 240 251 243 240 243 245 Nov. Error ----- 5.8 1.7 Fig. 1. Results of a random run of general neural network. V. CONCLUSION In this paper we addressed sales forecasting of brake rubber for 1600 pickup truck using two different methods, which are neural networks and regression model. Then we compared results of these two methods and analyzed the results. We developed two types of neural networks, one general network and 11 monthly networks. Comparing the results, we found that general network produces a large error percentage and thus is not appropriate for the purpose of this paper. But using monthly networks revealed that they are useful tools for sales forecasting. We saw that traditional approaches like regression model are not appropriate when data are nonlinear and chaotic. In these cases, we can use nonlinear models like neural networks. We also found that there may be some inappropriate documentation in data used. This is an important issue which is needed to be dealt with in future researches. REFERENCES [1] A. D. Papalexopoulos, T. C. Hesterberg, A regression-based approach to short term system load forecasting, IEEE Trans. Power Syst, volume 5, pp. 1535-1547, 1990. [2] A. L. Santana et al., predict-decision support system for load forecasting and inference: a new undertaking for Brazilian power suppliers, Electrical Power and Energy Systems, volume 38, pp. 33-45, 2012. [3] A. Fiordaliso, a nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems, International Journal of Forecasting, volume 14, pp.367-379, 1998. [4] D. Srinivasan, evolving artificial neural networks for short term load forecasting, Neural Computing, volume 23, pp. 265-276, 1998. [5] F. K. Wang, K. K. Chang and C. W. Tzeng, using adaptive network-based fuzzy inference system to forecast automobile sales, Expert Systems with Applications, volume 38, pp. 10587-10593, 2011. [6] F. T. Garcia et al., intelligent system for time series classification using support vector machines applied to supply chain, Expert Systems with Applications, article in press, 2012. [7] G. P. Zhang, time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, volume 50, pp. 159-175, 2003. [8] H. Matilla, R. King, and N. Ojala, retail performance measures for seasonal fashion, Journal of Fashion Marketing Management, volume 6, pp. 340-351, 2002. [9] H. Yoo, R. L. Pimmel, short term load forecasting using a self-supervised adaptive neural network, IEEE Transact, Power Syst, volume 14, pp. 779-784, 1999. [10] J. H. Park, Y. M. Park, K. Y. Lee, composite modeling for adaptive short term load forecasting, IEEE Transact. Power Syst, volume 6, pp. 450-457, 1991. [11] J. S. Armstrong, principles of forecasting-a handbook for researchers and practitioners, Kluwer Academic Publishers, Norwell, MA, 2001. [12] J. T. Mentzer, and C. C. Bienstock, sales forecasting management: understanding the techniques, systems, and management of the sales forecasting process, Thousand Oaks, CA: Sage publications, 1998.

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