Forecasting Product Sales for Masters Energy Oil and Gas Using. Different Forecasting Methods

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1 by the authors Licensee IJASER- Under Creative Commons License 3.0 Research article ISSN 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: /ijaser 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 ( cezeliora@gmail.com) Received on May 2013; Accepted on May 2013; Published on June 2013

2 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

3 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 Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec

4 2012 Jan Feb Mar April May June July Aug Sept Oct Nov Dec 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 Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb

5 Mar April May June July Aug Sept Oct Nov Dec 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 Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar

6 April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec 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 Feb Mar April May June July Aug Sept Oct

7 Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec 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

8 Table 5: Forecasting Results for Kerosene Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Table 5 above is the Forecasting Results for Kerosene using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 248

9 Table 6: Forecasting Sales Result for Diesel Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Table 6 above is the Forecasting Results for Diesel using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 249

10 Table 7: Forecasting Sales Result for Petroleum Year Month M. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013 Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Jan Feb Mar April May June July Aug Sept Oct Nov Dec Table 7 above is the Forecasting Results for Petroleum using Moving Average, Weighted Moving Average and Simple Exponential Smoothing 250

11 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: : : : : : : : : : : : Table 9: Three Period Moving Average Quarterly Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2013: : : : : : : : : : : : Table 9 above shows the forecast of quarterly yield of the product sales. The forecasting method used was three period moving averages. 251

12 Table 10: Weighted Moving Average Quarterly Yield of the products Quarter Q. Code KEROSINE DIESEL PETROLEUM 2013: : : : : : : : : : : : 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: : : : : : : : : : : : Table 11 above shows the forecast of quarterly yield of the product sales. The forecasting method used was simple exponential smoothing method. 252

13 Table 12: Forecasting Sales Results of Kerosene Quarter Q. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013: : : : : : : : : : : : 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: : : : : : : : : : : : Table 13 above shows the forecast results of quarterly yield for Diesel product sales using the applied models. 253

14 Table 14: Forecasting Sales Result of Petroleum Quarter Q. Code Moving Average Weighted Moving Average Simple Exponential Smoothing 2013: : : : : : : : : : : : 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, 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, , Korea; Feb Dwaikat Nidal, Forecasting in Production Planning & Inventory Control; Industrial Engineering Department, An Najah National University, 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

15 5. Ibrahim M.S., R.Mansour M.A. and Abed A.M., Improve Six-Sigma Management by Forecasting Production Quantity Using Image Verification Quality Tool; department of Industrial Engineering, Zagazig University, Zagazig City, Egypt. International Journal of Advances in Engineering & Technology, Sept Ihueze CC, Okafor EC; Multivariate Time Series Analysis for Optimum Production Forecast: A Case Study of 7up Soft Drink Company in Nigeria. 7. Ihueze C.C., and Onyechi P.C., Historical Data and the Use of Forecasting for Production Planning; Africa Journal of Basic & Applied Sciences, Latham Andrew, Differences in Forecasting Demand for a Product Versus a Service; Demand Media, Suwan-Achariya Chinasak and Roumtham Benjawan, Forecasting and Appropriate Reservation A Case Study of Agricultural Cooperative Singhanakorn Limited; European Journal of Social Sciences ISSN Vol.28 No.4 (2012), pp EuroJournals Publishing, Inc