www.ijaser.com 2012 by the authors Licensee IJASER- Under Creative Commons License 3.0 editorial@ijaser.com Research article ISSN 2277 9442 Forecasting Product Sales for Masters Energy Oil and Gas Using Different Forecasting Methods 1 Ezeliora Chukwuemeka Daniel, 2 Chinwuko Emmanuel Chuka 1 Department of Industrial and Production Engineering, Nnamdi Azikiwe University Awka, Anambra State, Nigeria. 2 Department of Industrial and Production Engineering, Nnamdi Azikiwe University Awka, Anambra State, Nigeria. Doi: 10.6088/ijaser.020300007 Abstract: This research work addresses the problem of forecasting for product sales using different forecasting techniques and models. It focuses on the application of different forecasting methods in the case study company. Product sales of their monthly sales data were gathered in the case study company over the months for three years. The data were collected, modeled and analyzed using different forecasting methods. The models and methods used were three period moving average, weighted moving average and simple exponential smoothing models. These were used to forecast for monthly and quarterly using the historical data collected. From the result analysis, forecasts of three years in the future were developed. This is to their future sales of the products within the next three years in the future. In conclusion, there is always a need to use more than one forecasting methods, to avoid errors during forecasting and to choose a better forecast. Keywords: Forecasting, Moving Average, Weighted Moving Average, Simple Exponential Smoothing Theoretical Background of Forecasting The forecasting techniques were developed in the 19th century. For instance, the regression techniques are used to forecast in business to predict sales and other variables. As well some techniques are developed recently and have been recognized by the business community at large. Recently with development of more sophisticated forecasting techniques along with the proliferation of personal computers and associated software forecasting received more and more attention by business managers in all types of businesses. In addition, managers are aware of the fact that they must use the correct technique so that the forecasts are accurate as possible and use wisely the forecasting techniques available in the modern times. If managers use in appropriate forecasting techniques and it may lead them to make poor decisions. A particular focus in forecasting is on the errors that are inherent in any forecasting procedure and an endeavor to make the inevitable errors as small as possible [8]. The use of forecasting has been made over the past and there is a need to see their analysis, results and effect of their forecast in other to have more concentration and a better idea in which particular method of forecast is to be adopted. Dwaikat Nidal, (2009) recognized that different forecasting methods were appropriate in different situations, to become familiar with the various methods of forecasting and to learn measures for analyzing the performance of forecast methods. Fischer Martin, (2010) briefly description of how energy demands can be modeled as a function of calendar data, meteorological data and economic variables was established. 241 *Corresponding author (e-mail: cezeliora@gmail.com) Received on May 2013; Accepted on May 2013; Published on June 2013
Latham Andrew, (2008) observed that product-based organizations need to forecast demand to minimize the cost of inventory while ensuring that their clients have access to their products. Ihueze and Okafor, (2009) focused on the establishment of an optimum forecast model that predicts future production trends of 7UP Bottling company, using Sixty (60) months time series data of 7UP bottling company to ascertain the presence of seasonal variation and trend components of the data. However, multidimensional forecast model where establish. Allen and Fildes, (2001) made some Observations which show that error-correction models (ECMs) are only sometimes an improvement even when variables are cointegrated. Evidence is even less clear on whether or not to different variables that are nonstationary on the basis of unit root tests. While some authors recommend applying a battery of misspecification tests, few econometricians use (or at least report using) more than the familiar Durbin-Watson test. Consequently, there is practically no evidence on whether model selection based on these tests will improve forecast performance. Limited evidence on the superiority of varying parameter models hints that tests for parameter constancy are likely to be the most important. Finally, econometric models do appear to be gaining over extrapolative or judgmental methods, even for short term forecasts, though much more slowly than their proponents had hoped. Ibrahim et al, (2011) emphasis on adaptability to changes in the business environment and on addressing market and customer needs proactively. Changes in the business environment due to varying needs of the customers lead to uncertainty in the decision requirements from the supplier. Flexibility is needed in the value stream map (VSM) to counter the uncertainty in the decision for requirements from supplier. A model was presented, which encapsulates the market sensitiveness, process integration, information driver and flexibility measures of VSM demands from supplier and grantee customer requirements. The model is consist s of two phases, the first phase is a mathematical model explores the relationship among customer demand, quality, and service level and the leanness and agility of VSM in fast moving consumer goods. The second phase is a quality assurance process of establishing evidence that provides a high degree of preventive that a product involves acceptance of fitness for purpose with customers. Muruvvet and Jayashankar, (1999) observed the coordination mechanisms through penalty schemes between manufacturing and marketing departments which enable the organizations to match demand forecasts with production quantities. Two possible organizational structures ± centralized and Decentralized were considered. In the decentralized case, a single period model problem were developed where demand is uncertain and the marketing department provides a forecast to manufacturing which in turn produces a quantity based on the forecast and the demand distribution. In the centralized case, marketing and manufacturing jointly decide on the production quantity. Among other results they show that by setting suitable penalties one can generate the same result in a decentralized system as that obtained from a centralized system. Suwan-Achariya and Roumtham, (2012) show there results no analysis of product classification that causes the cooperatives fail to recognize how to control the quantity of the incoming and outgoing stocks appropriately. The results also showed no demand forecasting, resulting that certain products were ordered in higher quantity than stock demand on respective period, thereby affecting the evaluation of the inventory management performance, leading to a loss to certain kind of products, increased logistics cost, and 242
deviation of data calculated for reserve management variables. Jun and Kim, (2012) investigate the effect of sticker shock on both incidence and quantity by using multivariate quantity outcomes in three categories. We found that the effect of sticker shock on quantity was significant in all categories. Also, we found that the sticker shock effect on incidence within multinomial choice outcomes was insignificant, whereas the effect on incidence within multivariate choice outcomes was significant in yogurt category. Ihueze and Onyechi, (2011) noted that Minitab software was used to analyze the past demand and production data in order to establish production and demand model and to provide for future forecast demand and production. Furthermore, from this research work, it was observed that time-series analysis and forecasting methods are best technique to be used for forecasting of the data and the data collected are periodic. However, the forecasting methods and its models were used to analyze and predict the product types investigated in the case study of company. Although, there products are plastic. Their methods of production used are injection and extrusion methods. Table 1: Product Sales for Masters Energy Oil and Gas Year Month M. Code KEROSINE DIESEL PETROLEUM 2010 Jan 1 6078 45000 132000 Feb 2 3898 36000 99000 Mar 3 3020 72000 132000 April 4 5607 40000 66000 May 5 5990 33000 132000 June 6 4501 33000 90000 July 7 5304 35000 66000 Aug 8 6302 66000 219000 Sept 9 5731 60000 138000 Oct 10 3224 54000 231000 Nov 11 3438 72000 132000 Dec 12 5607 60000 99000 2011 Jan 13 408 41000 6600 Feb 14 687 32000 143000 Mar 15 890 33000 99000 April 16 3408 28000 231000 May 17 4878 32000 99000 June 18 3178 37000 132000 July 19 3716 31000 99000 Aug 20 3839 42000 232000 Sept 21 4321 55000 99000 Oct 22 6342 60000 68000 Nov 23 6890 48000 132000 Dec 24 7300 32000 165000 243
2012 Jan 25 3400 21000 132000 Feb 26 2707 27000 198000 Mar 27 3878 24000 240000 April 28 4982 18000 99000 May 29 4934 22000 240000 June 30 3699 27000 66000 July 31 3776 26000 132000 Aug 32 4101 35000 198000 Sept 33 4434 32000 231000 Oct 34 3762 29700 99000 Nov 35 3708 28900 0 Dec 36 4283 21600 0 Modeling and Analysis of Monthly Yield of the Product Sales Table 2: Forecasting Results using Three Period Moving Average Year Month M. Code KEROSINE DIESEL PETROLEUM 2013 Jan 37 3917.667 26733.33 33000 Feb 38 3969.556 25744.44 11000 Mar 39 4056.741 24692.59 14666.67 April 40 3981.321 25723.46 19555.56 May 41 4002.539 25386.83 15074.07 June 42 4013.534 25267.63 16432.1 July 43 3999.131 25459.3 17020.58 Aug 44 4005.068 25371.25 16175.58 Sept 45 4005.911 25366.06 16542.75 Oct 46 4003.37 25398.87 16579.64 Nov 47 4004.783 25378.73 16432.66 Dec 48 4004.688 25381.22 16518.35 2014 Jan 49 4004.28 25386.28 16510.21 Feb 50 4004.584 25382.08 16487.07 Mar 51 4004.517 25383.19 16505.21 April 52 4004.46 25383.85 16500.83 May 53 4004.521 25383.04 16497.71 June 54 4004.499 25383.36 16501.25 July 55 4004.494 25383.41 16499.93 Aug 56 4004.505 25383.27 16499.63 Sept 57 4004.499 25383.35 16500.27 Oct 58 4004.499 25383.34 16499.94 Nov 59 4004.501 25383.32 16499.95 Dec 60 4004.5 25383.34 16500.05 2015 Jan 61 4004.5 25383.33 16499.98 Feb 62 4004.5 25383.33 16499.99 244
Mar 63 4004.5 25383.33 16500.01 April 64 4004.5 25383.33 16500 May 65 4004.5 25383.33 16500 June 66 4004.5 25383.33 16500 July 67 4004.5 25383.33 16500 Aug 68 4004.5 25383.33 16500 Sept 69 4004.5 25383.33 16500 Oct 70 4004.5 25383.33 16500 Nov 71 4004.5 25383.33 16500 Dec 72 4004.5 25383.33 16500 Moving Average Methods (1) Where, = an index that corresponds to time periods = Number of periods (data points) in the moving average = Actual value in period =Moving average = Forecast for time period t Table 3: Forecasting Results using Weighted Moving Average (0.25, 0.25 and 0.5) Year Month M. Code KEROSINE DIESEL PETROLEUM 2013 Jan 37 4009 25450 24750 Feb 38 4002.25 25350 12375 Mar 39 4074.125 24437.5 12375 April 40 4039.875 24918.75 15468.75 May 41 4039.031 24906.25 13921.88 June 42 4048.016 24792.19 13921.88 July 43 4043.734 24852.34 14308.59 Aug 44 4043.629 24850.78 14115.23 Sept 45 4044.752 24836.52 14115.23 Oct 46 4044.217 24844.04 14163.57 Nov 47 4044.204 24843.85 14139.4 Dec 48 4044.344 24842.07 14139.4 2014 Jan 49 4044.277 24843.01 14145.45 Feb 50 4044.275 24842.98 14142.43 Mar 51 4044.293 24842.76 14142.43 245
April 52 4044.285 24842.88 14143.18 May 53 4044.284 24842.87 14142.8 June 54 4044.287 24842.84 14142.8 July 55 4044.286 24842.86 14142.9 Aug 56 4044.286 24842.86 14142.85 Sept 57 4044.286 24842.86 14142.85 Oct 58 4044.286 24842.86 14142.86 Nov 59 4044.286 24842.86 14142.86 Dec 60 4044.286 24842.86 14142.86 2015 Jan 61 4044.286 24842.86 14142.86 Feb 62 4044.286 24842.86 14142.86 Mar 63 4044.286 24842.86 14142.86 April 64 4044.286 24842.86 14142.86 May 65 4044.286 24842.86 14142.86 June 66 4044.286 24842.86 14142.86 July 67 4044.286 24842.86 14142.86 Aug 68 4044.286 24842.86 14142.86 Sept 69 4044.286 24842.86 14142.86 Oct 70 4044.286 24842.86 14142.86 Nov 71 4044.286 24842.86 14142.86 Dec 72 4044.286 24842.86 14142.86 Weighted Moving Average Method In general, (2) Table 4: Forecasting Results using Simple Exponential Smoothing Method Year Month M. Code KEROSINE DIESEL PETROLEUM 2013 Jan 37 4168 29060 19800 Feb 38 4191 23060 0 Mar 39 4186.4 27568 15840 April 40 4187.32 24260 3960 May 41 4187.136 26666.4 12672 June 42 4187.173 24921.6 6336 July 43 4187.165 26185.12 10929.6 Aug 44 4187.167 25270.56 7603.2 Sept 45 4187.167 25932.42 10010.88 Oct 46 4187.167 25453.47 8268.48 246
Nov 47 4187.167 25800.04 9529.344 Dec 48 4187.167 25549.26 8616.96 2014 Jan 49 4187.167 25730.73 9277.171 Feb 50 4187.167 25599.42 8799.437 Mar 51 4187.167 25694.44 9145.129 April 52 4187.167 25625.68 8894.984 May 53 4187.167 25675.43 9075.991 June 54 4187.167 25639.43 8945.013 July 55 4187.167 25665.48 9039.789 Aug 56 4187.167 25646.63 8971.208 Sept 57 4187.167 25660.27 9020.834 Oct 58 4187.167 25650.4 8984.924 Nov 59 4187.167 25657.54 9010.909 Dec 60 4187.167 25652.38 8992.106 2015 Jan 61 4187.167 25656.12 9005.712 Feb 62 4187.167 25653.41 8995.867 Mar 63 4187.167 25655.37 9002.991 April 64 4187.167 25653.95 8997.836 May 65 4187.167 25654.98 9001.566 June 66 4187.167 25654.23 8998.867 July 67 4187.167 25654.77 9000.82 Aug 68 4187.167 25654.38 8999.407 Sept 69 4187.167 25654.66 9000.429 Oct 70 4187.167 25654.46 8999.689 Nov 71 4187.167 25654.61 9000.225 Dec 72 4187.167 25654.5 8999.837 Exponential Smoothing: (3) (4) = Forecast for period t = Forecast for the previous period = Smoothing constant (represents the percentage of the forecast error) = Actual demand or sales for the previous period 247
Table 5: Forecasting Results for Kerosene Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan 37 3917.667 4009 4168 Feb 38 3969.556 4002.25 4191 Mar 39 4056.741 4074.125 4186.4 April 40 3981.321 4039.875 4187.32 May 41 4002.539 4039.031 4187.136 June 42 4013.534 4048.016 4187.173 July 43 3999.131 4043.734 4187.165 Aug 44 4005.068 4043.629 4187.167 Sept 45 4005.911 4044.752 4187.167 Oct 46 4003.37 4044.217 4187.167 Nov 47 4004.783 4044.204 4187.167 Dec 48 4004.688 4044.344 4187.167 2014 Jan 49 4004.28 4044.277 4187.167 Feb 50 4004.584 4044.275 4187.167 Mar 51 4004.517 4044.293 4187.167 April 52 4004.46 4044.285 4187.167 May 53 4004.521 4044.284 4187.167 June 54 4004.499 4044.287 4187.167 July 55 4004.494 4044.286 4187.167 Aug 56 4004.505 4044.286 4187.167 Sept 57 4004.499 4044.286 4187.167 Oct 58 4004.499 4044.286 4187.167 Nov 59 4004.501 4044.286 4187.167 Dec 60 4004.5 4044.286 4187.167 2015 Jan 61 4004.5 4044.286 4187.167 Feb 62 4004.5 4044.286 4187.167 Mar 63 4004.5 4044.286 4187.167 April 64 4004.5 4044.286 4187.167 May 65 4004.5 4044.286 4187.167 June 66 4004.5 4044.286 4187.167 July 67 4004.5 4044.286 4187.167 Aug 68 4004.5 4044.286 4187.167 Sept 69 4004.5 4044.286 4187.167 Oct 70 4004.5 4044.286 4187.167 Nov 71 4004.5 4044.286 4187.167 Dec 72 4004.5 4044.286 4187.167 Table 5 above is the Forecasting Results for Kerosene using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 248
Table 6: Forecasting Sales Result for Diesel Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan 37 26733.33 25450 29060 Feb 38 25744.44 25350 23060 Mar 39 24692.59 24437.5 27568 April 40 25723.46 24918.75 24260 May 41 25386.83 24906.25 26666.4 June 42 25267.63 24792.19 24921.6 July 43 25459.3 24852.34 26185.12 Aug 44 25371.25 24850.78 25270.56 Sept 45 25366.06 24836.52 25932.42 Oct 46 25398.87 24844.04 25453.47 Nov 47 25378.73 24843.85 25800.04 Dec 48 25381.22 24842.07 25549.26 2014 Jan 49 25386.28 24843.01 25730.73 Feb 50 25382.08 24842.98 25599.42 Mar 51 25383.19 24842.76 25694.44 April 52 25383.85 24842.88 25625.68 May 53 25383.04 24842.87 25675.43 June 54 25383.36 24842.84 25639.43 July 55 25383.41 24842.86 25665.48 Aug 56 25383.27 24842.86 25646.63 Sept 57 25383.35 24842.86 25660.27 Oct 58 25383.34 24842.86 25650.4 Nov 59 25383.32 24842.86 25657.54 Dec 60 25383.34 24842.86 25652.38 2015 Jan 61 25383.33 24842.86 25656.12 Feb 62 25383.33 24842.86 25653.41 Mar 63 25383.33 24842.86 25655.37 April 64 25383.33 24842.86 25653.95 May 65 25383.33 24842.86 25654.98 June 66 25383.33 24842.86 25654.23 July 67 25383.33 24842.86 25654.77 Aug 68 25383.33 24842.86 25654.38 Sept 69 25383.33 24842.86 25654.66 Oct 70 25383.33 24842.86 25654.46 Nov 71 25383.33 24842.86 25654.61 Dec 72 25383.33 24842.86 25654.5 Table 6 above is the Forecasting Results for Diesel using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 249
Table 7: Forecasting Sales Result for Petroleum Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan 37 33000 24750 19800 Feb 38 11000 12375 0 Mar 39 14666.67 12375 15840 April 40 19555.56 15468.75 3960 May 41 15074.07 13921.88 12672 June 42 16432.1 13921.88 6336 July 43 17020.58 14308.59 10929.6 Aug 44 16175.58 14115.23 7603.2 Sept 45 16542.75 14115.23 10010.88 Oct 46 16579.64 14163.57 8268.48 Nov 47 16432.66 14139.4 9529.344 Dec 48 16518.35 14139.4 8616.96 2014 Jan 49 16510.21 14145.45 9277.171 Feb 50 16487.07 14142.43 8799.437 Mar 51 16505.21 14142.43 9145.129 April 52 16500.83 14143.18 8894.984 May 53 16497.71 14142.8 9075.991 June 54 16501.25 14142.8 8945.013 July 55 16499.93 14142.9 9039.789 Aug 56 16499.63 14142.85 8971.208 Sept 57 16500.27 14142.85 9020.834 Oct 58 16499.94 14142.86 8984.924 Nov 59 16499.95 14142.86 9010.909 Dec 60 16500.05 14142.86 8992.106 2015 Jan 61 16499.98 14142.86 9005.712 Feb 62 16499.99 14142.86 8995.867 Mar 63 16500.01 14142.86 9002.991 April 64 16500 14142.86 8997.836 May 65 16500 14142.86 9001.566 June 66 16500 14142.86 8998.867 July 67 16500 14142.86 9000.82 Aug 68 16500 14142.86 8999.407 Sept 69 16500 14142.86 9000.429 Oct 70 16500 14142.86 8999.689 Nov 71 16500 14142.86 9000.225 Dec 72 16500 14142.86 8999.837 Table 7 above is the Forecasting Results for Petroleum using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 250
Modeling and Analysis of Quarterly Yield of the Product Sales Models were developed and analyzed for Quarterly Yield of the Product Sales. This is to show and to know what their product sales will be in quarters using different forecasting methods. Table 8: Quarter Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2010:1 1 12996 153000 363000 2010:2 2 16098 106000 288000 2010:3 3 17337 161000 423000 2010:4 4 12269 186000 462000 2011:1 5 1985 106000 248600 2011:2 6 11464 97000 462000 2011:3 7 11876 128000 430000 2011:4 8 20532 140000 365000 2012:1 9 9985 72000 570000 2012:2 10 13615 67000 405000 2012:3 11 12311 93000 561000 2012:4 12 11753 80200 99000 Table 9: Three Period Moving Average Quarterly Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2013:1 13 2013:2 14 2013:3 15 2013:4 16 2014:1 17 2014:2 18 2014:3 19 12559.67 80066.67 355000 12207.89 84422.22 338333.3 12173.52 81562.96 264111.1 12313.69 82017.28 319148.1 12231.7 82667.49 307197.5 12239.64 82082.58 296818.9 12261.68 82255.78 307721.5 2014:4 20 12244.34 82335.28 303912.7 2015:1 21 2015:2 22 2015:3 23 2015:4 24 12248.55 82224.55 302817.7 12251.52 82271.87 304817.3 12248.14 82277.24 303849.2 12249.4 82257.89 303828.1 Table 9 above shows the forecast of quarterly yield of the product sales. The forecasting method used was three period moving averages. 251
Table 10: Weighted Moving Average Quarterly Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2013:1 13 12358 80100 291000 2013:2 14 12195 83350 310500 2013:3 15 12125.25 81750 252750 2013:4 16 12200.88 81737.5 276750 2014:1 17 12180.5 82143.75 279187.5 2014:2 18 12171.78 81943.75 271968.8 2014:3 19 12181.23 81942.19 274968.8 2014:4 20 12178.69 81992.97 275273.4 2015:1 21 12177.6 81967.97 274371.1 2015:2 22 12178.78 81967.77 274746.1 2015:3 23 12178.46 81974.12 274784.2 2015:4 24 12178.32 81971 274671.4 Table 10 above shows the forecast of quarterly yield of the product sales. The forecasting method used was weighted moving average method. Table 11: Simple Exponential Smoothing Quarterly Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2013:1 13 2013:2 14 2013:3 15 2013:4 16 2014:1 17 2014:2 18 2014:3 19 2014:4 20 2015:1 21 2015:2 22 2015:3 23 2015:4 24 11864.6 82760 191400 11842.28 82248 172920 11846.74 82350.4 176616 11845.85 82329.92 175876.8 11846.03 82334.02 176024.6 11845.99 82333.2 175995.1 11846 82333.36 176001 11846 82333.33 175999.8 11846 82333.33 176000 11846 82333.33 176000 11846 82333.33 176000 11846 82333.33 176000 Table 11 above shows the forecast of quarterly yield of the product sales. The forecasting method used was simple exponential smoothing method. 252
Table 12: Forecasting Sales Results of Kerosene Quarter Q. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013:1 13 2013:2 14 2013:3 15 2013:4 16 2014:1 17 2014:2 18 2014:3 19 2014:4 20 2015:1 21 2015:2 22 2015:3 23 2015:4 24 12559.67 12358 11864.6 12207.89 12195 11842.28 12173.52 12125.25 11846.74 12313.69 12200.88 11845.85 12231.7 12180.5 11846.03 12239.64 12171.78 11845.99 12261.68 12181.23 11846 12244.34 12178.69 11846 12248.55 12177.6 11846 12251.52 12178.78 11846 12248.14 12178.46 11846 12249.4 12178.32 11846 Table 12 above shows the forecast results of quarterly yield for Kerosene product sales using the applied models. Table 13: Forecasting Sales Results of Diesel Quarter Q. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013:1 13 2013:2 14 2013:3 15 2013:4 16 2014:1 17 2014:2 18 2014:3 19 2014:4 20 2015:1 21 2015:2 22 2015:3 23 2015:4 24 80066.67 80100 82760 84422.22 83350 82248 81562.96 81750 82350.4 82017.28 81737.5 82329.92 82667.49 82143.75 82334.02 82082.58 81943.75 82333.2 82255.78 81942.19 82333.36 82335.28 81992.97 82333.33 82224.55 81967.97 82333.33 82271.87 81967.77 82333.33 82277.24 81974.12 82333.33 82257.89 81971 82333.33 Table 13 above shows the forecast results of quarterly yield for Diesel product sales using the applied models. 253
Table 14: Forecasting Sales Result of Petroleum Quarter Q. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013:1 13 355000 291000 191400 2013:2 14 338333.3 310500 172920 2013:3 15 264111.1 252750 176616 2013:4 16 319148.1 276750 175876.8 2014:1 17 307197.5 279187.5 176024.6 2014:2 18 296818.9 271968.8 175995.1 2014:3 19 307721.5 274968.8 176001 2014:4 20 303912.7 275273.4 175999.8 2015:1 21 302817.7 274371.1 176000 2015:2 22 304817.3 274746.1 176000 2015:3 23 303849.2 274784.2 176000 2015:4 24 303828.1 274671.4 176000 Table 14 above shows the forecast results of quarterly yield for Petroleum product sales using the applied models. Discussion of Results The tables above show the forecasting results of Diesel, Kerosene and Petroleum product sales in Masters Energy Oil and Gas. The results show the future monthly and quarterly prediction results for the next three years. In conclusion, it has been noted that forecasting is an estimation of the future. This was shown in the results of the forecast. From the result analysis, it shows that there is a little influence of the environmental factors on the product sales. Reference 1. Allen P. Geoffrey and Fildes Robert, Econometric Forecasting, Department of Resource Economics, University of Massachusetts, Amherst MA 01003, USA;Kluwer Academic Publishers, 2001. 2. Duk Bin Jun and Chul Kim Forecasting Consumer Demand: The Role of Sticker Shock on Quantity and Variety; KAIST Business School (85 Hoegiro, Dongdamoon-gu, Seoul, 130-722, Korea; Feb 2012. 3. Dwaikat Nidal, Forecasting in Production Planning & Inventory Control; Industrial Engineering Department, An Najah National University, 2009. 4. Fischer Martin, Modeling and Forecasting Energy Demand: Principles And Difficulties; Troccoli (ed.), Management of Weather and Climate Risk in the Energy Industry, Springer Science+Business Media B.V. 2010 254
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