INDEPENDENT STUDY THE APPLICATION OF FORECASTING TECHNIQUES FOR LUBRICANT MANUFACTURER MONTHIAN PUTPHAN

INDEPENDENT STUDY THE APPLICATION OF FORECASTING TECHNIQUES FOR LUBRICANT MANUFACTURER MONTHIAN PUTPHAN An Independent Study Submitted in Partial Fulfillment of The Requirement for the Degree of Master of Engineering (Industrial Engineering) Graduate School, Kasetsart University 2007

Monthian Putphan 2007: The application of Forecasting Technique for Lubricant Manufacturer. Master of Engineering (Industrial Engineering), Major Field: Industrial Engineering, International Graduate Program. Project Advisor: Professor Pornthep Anusornnitisarn.,PhD total 68 pages Forecasting technique is important for every business, government agency, non-profit organization and is used for planning decisions in various domains of management. Lubricant manufacturer is one industry that must have the ability to forecast as precisely as possible the demands of their customers. Inaccuracy in customer demands or price forecasts may result in significantly increased operating costs, loss of opportunities to sell excess capacity to lubricant markets. In this study, we applied time series and regression to forecast the monthly demands of lubricant products. The optimal forecast techniques have been used for production planning (purchasing raw materials). The forecasting technique applications posted in this study include simple exponential smoothing, Holt s trend exponential smoothing and Holt-Winters exponential smoothing and multiple linear regression. The objectives of this study are to reduce the inaccuracy of company s forecasting and to find the most appropriate and optimized forecasting methodology which can be applied to encourage company to reduce inventory amount and shortage of imported raw materials. To achieve the above objectives, forecasting techniques was applied in excel spreadsheets by using VBA application and SSP package to data computation. By this method, we can improve the imported raw materials stock out 4 times a year, until no stock out. Improved transport expense of the urgent order by airfreight from 2.5 millions baht until urgent order will not be occurred. Included inventory amount in stock was reduced... / /.. Student signature Project Advisor s signature

ACKNOWLEDGEMENT Upon the completion of this project, I wish to express my gratitude and sincere appreciation to all committees especially Professor Pornthep Anusornnitisarn,PhD. for his valuable guidance, encouragement, and support throughout the course of my Master of Engineering at Kasetsart University My recognition also includes all instructors of International Graduate Program in Industrial Engineering, supporting staffs, and friends at Kasetsart University for their sincere help, companionship, and encouragement to accomplish academic goals during studying. Without them, this study would not have been fruitful and enjoyable. Finally, I would like to thank my family for their love, patience, support, and encouragement while studying in this program. Monthian Putphan September 2007

I TABLE OF CONTENTS Page TABLE OF CONTENTS I LIST OF TABLE II LIST OF FIGURES III LIST OF ABBREVIATIONS V INTRODUCTION 1 PROBLEM STATEMENT 5 OBJECTIVE 6 SCOPE 6 LITERATURE REVIEW 8 PROJECT FRAME WORK 12 HISTORICAL DATA 13 CHOOSING FORECASTING TECHNIQUE 16 TIME SERIES FORECASTING TECHNIQUES 18 Simple exponential smoothing 18 Holt s Trend Exponential smoothing 19 Holt-Winters exponential smoothing 20 MULTIPLE LINEAR REGRESSION TECHNIQUE 21 Least-Squares Method 22 Estimation of parameters 22 Coefficient of Determination (R 2 ) 23 Null hypothesis testing 24 VBA APPLIED TO THE FORCASTING MODEL 25 EVALUATION OF THE FORECASTING MODEL 26 FORCASTS JUDGEMENT 27 STEP OF BUILDING LINEAR REGRESSION MODEL 28 THE EXPERIMENT OF FORECASTING RESULTS 41 FORECASTING MODEL AND COMPANY MODEL COMPARISON 44 CONCLUSION 64 REFERENCES 66

II LIST OF TABLE Table Page Table 1 Correlation matrix of sales quantity f product A with predictor variable 29 computed by SPSS Table 2 Correlation summary of products A-H with predictor variable computed by SPSS 30 Table 3 Computed the VIF value by SPSS of the regression model 38 Table 4 The summary of regression model of lubricant products A H 38 Table 5 Selected the appropriate forecasting techniques applied to the forecasting model 43 Table 6 Inventory in stock during the forecasting model and the company s model performed during to January 06- September 06 61 Table 7 Forecasting summary results of lubricant products A-H during January 2006 September 2006 65

III LIST OF FIGURES Figure Page Figure 1 The production flow process of lubricant products 3 Figure 2 The demand forecasting and actual sales amount of product A 6 Figure 3 Project frame work 12 Figure 4 Plot of historical data of lubricant product A to product H 13 Figure 5 Plot of historical data of predictor variable 15 Figure 6 Linear regression lines 22 Figure 7 Plot residual against sales quantity of product F with normal probability plot 36 Figure 8 Plot residual against sales quantity of product F with scatter plot 37 Figure 9 The plot of forecasting results of product A compared with actual sales 45 quantity during January 2006 to September 2006 Figure 10 The plot of forecasting results of product B compared with actual sales 46 quantity during January 2006 to September 2006 Figure 11 The plot of forecasting results of product C compared with actual sales 47 quantity from January 2006 to September 2006 Figure 12 The plot of forecasting results of product D compared with actual sales 48 quantity during January 2006 to September 2006 Figure 13 The plot of forecasting results of product E compared with actual sales 49 quantity during January 2006 to September 2006 Figure 14 The plot of forecasting results of product F compared with actual sales 50 quantity during January 2006 to September 2006 Figure 15 The plot of forecasting results of product G compared with actual sales 51 quantity during January 2006 to September 2006 Figure 16 The plot of forecasting results of product H compared with actual sales 52 quantity during January 2006 to September 2006 Figure 17 The plot of forecasting results of product A compared with actual sales 53 quantity during September 2003- September 06 Figure 18 The plot of forecasting results of product B compared with actual sales 54 quantity during September 2003- September 2006 Figure 19 The plot of forecasting results of product C compared with actual sales 55 quantity during September 2003- September 2006

IV Figure 20 The plot of forecasting results of product D compared with actual sales 56 quantity during September 2003- September 2006 Figure 21 The plot of forecasting results of product E compared with actual sales 57 quantity during September 2003- September 2006 Figure 22 The plot of forecasting results of product F compared with actual sales 58 quantity during September 2003- September 2006 Figure 23 The plot of forecasting results of product G compared with actual sales 59 quantity during September 2003- September 2006 Figure 24 The plot of forecasting results of product H compared with actual sales 60 quantity during September 2003- September 2006 Figure 25 Comparison raw materials (SN-500) use in each period of forecasting activity 62 Figure 26 Comparison raw materials (RC-9204) use in each period of forecasting activity 63 APENDIX FIGURES Appendix A Forecasting model operation flow 66

V LIST OF ABBREVIATIONS GDP = Gross Domestic Product HEI = Headline Inflation VEP = Vehicle Production IMP = Imported Crude Oil Y t = The estimate of the level made in time period t Y t-1 = The estimate of the level made in time period t-1 y t = Actual demand for time period t α, γ, β = The smoothing constant, 0 to 1 g t = The estimate of the growth rate in time period t g t -1 = The estimate in time period t-1 for growth rate sn t = The estimate of the seasonal factor for the season corresponding to time period t sn t-1 = The estimate in time period t-l for seasonal factor N = The number of seasons in a year y t+ m = The most recent estimate of seasonal factor corresponding to time period t+ m Y = The value of the response variable. X 1. X k = Predictor variable. β0 = The y intercept when x equals o. β 1.β k = The slope. e = the error term R 2 = Coefficient of Determination MAD = Mean Absolute Deviation MSE = Mean Square Error FTS = Forecasting Techniques Selected H-T = Holt s trend exponential smoothing H-W = Holt Winters exponential smoothing R-S = Multiple linear regression

1 THE APPLICATION OF FORECASTING TECHNIQUES FOR LUBRICANT MANUFACTURER INTRODUCTION Forecasting is widely used for decisions planning in various domains of any management. (resource management, personnel management, finance management, organizational management) Every business, government agency, non-profit organization need timely forecasting. Forecasting involves making projections about future performance on the basis of historical and current data. When the result of an action is consequent, but cannot be known precisely in advance forecasting may reduce the risk in decision marking by supplying additional information about the possible outcome. This study will focus on MORESCO (Thailand) Co., Ltd, established in June 1995 in Amata Nakorn industrial estate, Chonburi province. The company produces lubricant products which enhance various industries; for example, machines, chemicals, medicines, food and automobile. The lubricant products are sold in Thailand and overseas. In today s competitive environment, lubricants industries must have the ability to forecast as precisely as possible the demands of their customers. Inaccuracy in customer demands or price forecasts may result in significantly increased operating costs, loss of opportunities to sell excess capacity to lubricant markets. Various forecasting approaches have been developed to support these business processes with varying degrees of success. The forecasting approaches in this study include time series and regression to forecast the monthly demands of lubricant products. The optimal forecasting techniques have been used for production planning (purchasing raw materials). The forecasting techniques applications used in this study are : 1. Simple exponential smoothing 2. Holt s trend exponential smoothing 3. Holt Winters exponential smoothing 4. Linear Regression Technique

2 Time series models will be the basis for any study of a behavior of process or metrics over a period of time. The applications of time series models are various, including sales forecasting, weather forecasting, inventory studies etc. In any decision that involves factors of future s uncertainty, time series models have been found one of the most effective methods for forecasting. Future course of actions and decisions for such processes will depend mostly on what would be an anticipated result. The need for these anticipated results has encouraged organizations to develop forecasting techniques to be better prepared to face the seemingly uncertain future (Desikan and Srivastava, 2003). Linear Regression Model is a forecasting model in which the future demand is predicted on the basis of known and quantifiable factors that affect the demand. The models are simple linear regression used with single variable and multiple linear regression used with more than two variables. In this study, we used the data of dependent variable and predictor variable existing in Thailand. Y (dependent variable or response variable) = Monthly sale quantity of lubricant product X (Independent variable or predictor variable) Gross Domestic Product (GDP) Headline Inflation (HEI) Vehicle Production (VEP) Import the Crude Oil (IMP)

3 LUBRICANT PRODUCTION PROCESS The primary purpose of lubrication is to reduce wear and heat between contacting surfaces in relative motion. While wear and heat cannot be completely eliminated, they can be reduced to negligible or acceptable levels. Because heat and wear are associated with friction, both effects can be minimized by reducing the co-efficiency of friction between contacting surfaces. Lubrication is also used to reduce oxidation and prevent rust, to provide insulation in transformer applications, to transmit mechanical power in hydraulic fluid power applications, and lastly to seal against dust, dirt, and water (OECD Environmental Health and safety Publications, 2004). The production processes of lubricant products started from chemical, oil and water are mixed in reactor and heated up to a certain required temperature. In this time, all chemicals mixed and homogenized become emulsion 1. Then, it is necessary to reduce temperature by cooling water and packing the finished products to packaging unit, then keeping them in the warehouse. The overall lubricant processes are under processing control and quality control to meet the quality target and company specification. Figure 1 Production flow process of lubricant products Heating Quality control Raw Materials Emulsification & Chemical reaction Lubricants product Cooling Process control 1 Emulsion is the mixture of two immiscible liquids, one being dispersed throughout the other in small droplets; a colloid system in which both the dispersed phase and the dispersion medium are liquids.

4 The lubricant products and application Die casting lubricant used for lubrication of zinc alloy and aluminum alloy die casting. Cutting fluid used for machining of aluminum and steel. Hydraulic fluid used for hydraulic equipments. Vacuum pump oil applicable to all rotary vacuum pumps. High temperature oil lubrication of sliding surfaces, heat setters in the textile industry.

5 PROBLEM STATEMENT Thailand s economy has grown for several years with investment and movement of industry in this region. There has been high competition in every industry such as product cost, service, quality and delivery etc. Lubricant industries have also been affected in this circumstance. Hence, they have to speed up the development and launch new and competitive products to the market. In the year 2005, MORESCO (Thailand) Co., Ltd. had produced several kinds of products and import many kinds of raw materials of chemicals and oil. We still encounter some problems regarding inventory and production that influence customer satisfaction, production cost and delivery scheduling. Cause arises from several things, but the main cause is inaccurate sales forecasting. The problem was repeated several times in a year because the demands are more fluctuated than the company s forecasting abilities. The company s forecasting is performed every six months (two times a year). The methodology applied by combining sale staffs opinion and average sales quantity for the past six months. Then, sales forecasting results will be applied for the next six months. If demand various changing during overtimes period, the company s forecasting can not respond quickly in time. For example, there is a problem such as loss of big customer or demands grow up too fast. This problem was occurred in last year (2005). Therefore, Moresco (Thailand) Co., Ltd had got problems as follows; 1. Due to raw materials shortage, we have to purchase them urgently 4 times from overseas. In this case, the problem of airfreight cost, including other expenses amount to 2.5 millions baht. 2. The capacity of warehouse is not sufficient enough to hold a lot of inventory because we followed up sales forecasting by producing and importing raw materials to support sales quantity, but actual sales did not exceed the sales target. 3. Delivery schedule to customer was not on time because raw materials in stock were stock out and unable to meet production schedule. Delivery schedule had to be postponed and customer would be dissatisfied. Figure 2 below shows that demand is fluctuated and forecasting is not appropriate.

6 Figure 2 The demand forecasting and actual sale amount of product A. Comparision between actual amount and demand forecasting of product A on 2002-2004 35,000 30,000 25,000 Quantity amount 20,000 15,000 10,000 Actual amount forecasting 5,000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 month OBJECTIVE Objectives are as follows: 1. To reduce the inaccuracy of company s forecasting by using forecasting technique. 2. To find the most appropriate and optimized forecasting methodology which can be applied to encourage company to reduce inventory amount and shortage of imported raw materials. SCOPES 1. Forecasting methodology involved time series technique; simple exponential smoothing, Holt s trend exponential smoothing, Holt - Winters exponential smoothing and regression technique will be used. 2. The historical data for study used sale quantity of lubricant products during January 2002 to December 2005. Eight lubricant products are produced in MORESCO (Thailand) Co., Ltd. 3. The statistic package program used SPSS package for regression analysis and forecasting model has performed with excel visual basic application of Microsoft office 2003 with excel spread sheet.

4. The real name of lubricant products is not applied in this case study because it will affect and is sensitive to company s business. Therefore, the names of eight lubricant products have been replaced by product A, B, C, D, E, F, G and H. 5. The forecasting methodology will be applied to monthly forecasting of production and purchase planning. 7

8 LITERATURE REVIEW There are two general forecasting approaches: qualitative and quantitative. Quantitative methods involve either the extension of historical data or the development of associative models that attempt to utilize causal (explanatory) variables to make a forecast. Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinions) in the forecasting process. Quantitative techniques consist mainly of analyzing objective and data. They usually avoid personal biases that sometimes contaminate qualitative methods. For accurate forecast, both approaches might be integrated for forecasting model. There are 3 forecasting methodologies: 1. Forecasts based on judgment and opinion: Judgmental forecasts depend on analysis of subjective inputs obtained from various sources, such as consumer surveys, sales staffs, managers and executives, and panels of experts (Delurgio and Stephen, 1998). 2. Forecast based on time series: These techniques use historical or time series, data with the assumption that the future will be the same as the past. Some models attempt to smooth out random variations in historical data; others attempt to identify specific patterns in the data and project or extrapolate those patterns into the future, without trying to identify causes of the patterns. (Kedia and Thummala, 2005).Time series forecasting or time series prediction, takes an existing series of data X i-n X i-2, X i-1, X i. The goal is to observe or model the existing data series to enable future unknown data values to be forecasted accurately. The patterns of data for time series component are: Trend: refers to the upward or downward movement that characterizes a time series over a period of time. Thus trend reflects the long-run growth or decline in the time series. Cycle: refers to recurring up and down movements around trend levels. These fluctuations can have a duration from two to ten years or even longer measured from peak to peak. Seasonal: variations are periodic patterns in a time series that complete themselves within a calendar year and are then repeated on a yearly basis. Seasonal variations are usually caused by such factors as weather and customs. Irregular fluctuations are erratic movements in a time series that follow no recognizable or regular fluctuations in a time series are caused by unusual events that cannot be forecasted such as earthquakes, accidents, hurricanes (Bowerman,Connell and Koehler 2005).

9 3. Associative forecasts: These techniques use equations that consist of one or more explanatory variables that can be used to predict future demand. For example, demand for paint might be related to variables such as the price per gallon and the amount spent on advertising as well as specific characteristics of the paint (e.g., drying time, ease of cleanup). In 1980, Arshad and Mohamed attempted to examine the forward pricing efficiency of the local crude palm oil (CPO) future market. In an efficient market, the relevant signal to be used by - the producers, traders and processors is simply the future price. The forward pricing efficiency is measured in terms of the forecasting ability of Malaysian crude palm oil future price on physical price. The relative predictive power of future price is compared with the various forecasts estimated from proven forecasting techniques like moving average, exponential smoothing, Box Jenkins and econometric. The relative predictability of futures price as a forecast for spot prices during various months before delivery is also measured. In 2005, Chen attempted to use forecasting technique with time series and related variables (e.g. fuel costs, interest rates, economic growth) to make accurate forecasting for decision making in power plant fleet management. Electric power plants require accurate forecasting data at each step of the decision making process because they need to plan for an uncertain future. Therefore, most power plants operators spend considerable time and effort in forecasting. The inaccuracy in forecasting significantly affects the decision making process. For example, inaccuracy in customer demand forecasting may result in overbuilding of supply facilities and unprofitable operation in case of overly optimistic forecasting, or it might reduce customer demand and cause poor system reliability in the case of overly inaccurate forecasting. Both cases are unacceptable because they affect profitability. In the latter situation, the penalty for not supplying customer demand is very high in the unregulated electricity market. The study of improving accurate forecasting can help power plants reduce life cycle cost, increase customer satisfaction for electric demand and improve the decision making process. The research of Armstrong and Collopy in 1998 had integrated the statistical methods and judgment for time series forecasting by reviewing judgment, combining forecasts, revising extrapolations, rule-based forecasting; and econometric forecasting from 47 published empirical

10 researches. The research suggested that integration forecast can improve accuracy when the experts have domain knowledge and when significant trends are involved. They found that the integration forecast is advantage when judgment based on statistic method and good information of additional relevant. On the contrary, integration affects accuracy when judgment is biased or its use is unstructured. The weight of combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data are involved with high uncertainty or high instability, they recommend revising judgment, revising extrapolations or combining. The research of Marko in 2003 had attempted to examine simple and advanced forecasting methods performance to modeling linerboard price behavior. The forecast technique of Holt-Winters exponential smoothing, Vector autoregressive model (VAR) and Autoregressive integrated moving average model (ARIMA) are performed in the model. The result of forecast performance comparison shows that Holt-Winters exponential smoothing make sufficient performance in the short-term forecast. In the long-term forecasting, the Vector autoregressive model (VAR) is better than all other techniques. The Autoregressive integrated moving average model (ARIMA) forecasts are also quite similar to those of Holt-Winters method and Vector autoregressive model (VAR). Inconclusive results of the study could be explained by the haphazard pattern of linerboard price behavior. Due to the complicated nature of price movement, abrupt price changes are followed by prolonged periods of no change, it is not feasible to agree on one best model. Under these circumstances, forecasted performance strongly depends on particular time periods, for which forecasts are produced as well as on forecasting horizon. Hence, mixed forecasts, combined different techniques are likely to render better result in price forecasting in containerboard industry. In 2004, Singhrattna1 and Rajagopalan had attempted to develop statistical forecasting method for summer monsoon rainfall in Thailand. Predictors are identified from the large-scale ocean-atmospheric circulation variables are sea surface temperature (SST), sea level pressure (SLP), surface air temperature (SAT) and El Niño Southern Oscillation (ENSO) in the Indo-Pacific region. The identified predictors are part of the broader El Niño Southern Oscillation (ENSO) phenomenon. The predictors exhibit significant relationship to the summer rainfall only during the latter half of 1980 when Thailand s summer rainfall also showed a method relationship with ENSO. Two methods

for generating ensemble forecasts are adapted. The first is the traditional linear regression, and the second method is the local polynomial based non-parametric method. The predictors are consistent in terms of their physical mechanistic links to the monsoon. The result from the set of predictors based on SST, SAT, SLP fields and ENSO, the optimal subset was found by the combination that gave the best forecasting skill. Several formal methods are available for subset selection such as stepwise regression or cross-validation metrics etc. Since the number of significant predictors is small, almost all combinations were tried out to find the optimal predictor set. For summer monsoon rainfall, the best sets of predictors were found to be the ones based on SLP and SST. The land temperatures (SAT) did not seem to improve the skill very much. 11

12 Project Frame Work Figure 3 Project frame work. 1. Historical Data 2. Choosing Forecasting Techniques 3. Time series forecasting techniques Simple Exponential smoothing Holt s trend Exponential smoothing Holt-Winters Exponential smoothing 4. Regression forecasting techniques Response variable =monthly Qty sale lubricants Predictor variable = GDP, Headline Inflation, Vehicle production, Import crude oil 5. Model applied by Excel VBA Application 6. Forecasting evaluation MAD MSE 7. Forecasting Judgment MAD values MSE values Graphical & opinion 8. Forecasting comparison (Forecast model & company model)

13 1. Historical data We used historical data from actual sale quantity amount of lubricant products including monthly observation from January 2002 to December 2005. The data has compiled from accounting program. The historical data of lubricant products will be described in figure 4. The graph shows that demands have an upward trend and seasonal pattern. Figure 4 Plot of historical data of lubricant product A to product H Historical data of product A from January 2002 - December 2005 Historical data of product B from January 2002 - December 2005 30000 35000 Sale V o lu m e ( Li 25000 20000 15000 10000 5000 Sale V o lu m e ( Li 30000 25000 20000 15000 10000 5000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period (Product A) (Product B) Historical data of product C from January 2002 - December 2005 Historical data of product D from January 2002 - December 2005 Sale V o lu m e ( Li 50000 40000 30000 20000 10000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period S ale V o lu m e ( Li 12000 10000 8000 6000 4000 2000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period (Product C) (Product D)

14 Sale V o lu m e ( Li 14000 12000 10000 8000 6000 4000 2000 0 Historical data of product E from January 2002 - December 2005 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period Sale V o lu m e ( Li 10000 8000 6000 4000 2000 0 Historical data of product F from January 2002 - December 2005 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period (Product E) (Product F) Historical data of product G from January 2002 - December 2005 Historical data of product H from January 2002 - December 2005 Sale Vo lu m e ( Li 12000 10000 8000 6000 4000 2000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period Sale V o lu m e ( Li 10000 8000 6000 4000 2000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Period (Product G) (Product H)

15 The historical data of the predictor variable of regression model from the year 2002 2006 indicated by experience of salesmen who are working in lubricant business more than 10 years. They are recommended that the quantity of sales lubricant relationship with 4 factors include: 1. Headline Inflation (HEI) 2. Gross Domestic Product (GDP) 3. Vehicle Production (VEP) 4. Import the crude oil (IMP) The historical data of predictors will be posted in this study which is indicated by salesmen recommendation. The data plotted over time series from January 2002 December 2006 will be shown in figure 5. Figure 5 Plot of historical data of predictor variable over time period. 7 6 5 4 3 2 1 0 Historical data Head line inflation from January 2002 -September 2006 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Period (Head line inflation) 1,200,000.00 1,000,000.00 800,000.00 600,000.00 400,000.00 200,000.00 0.00 Historical data Gross domestic product from January 2002 -September 2006 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 Period (Gross domestic production)

16 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 Historical data Vihicle production January 2002 -September 2006 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 Period (Vehicle production) 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 Historical data Import crude oil from January 2002 -September 2006 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 Period (Import crude oil) 2. Choosing forecasting techniques Many different kinds of forecasting techniques are available and no single technique works best in every situation (Delurgio and Stephen, 1998). When selecting forecasting techniques, we need to consider that the forecasting techniques can be served as the decision making, managing plan and company situation. It is important to select forecasting techniques. In this study, we are indicated that the appropriate forecasting techniques should be served the following items: 2.1 Cost and customer-service sensitivity The inventory cost and customer-service sensitivity of lubricant products are important factors that impact Moresco (Thailand) because customer service is company s first priority. Therefore, we have to avoid inventory stock out and the delivery schedule must be on time. To encourage this point, forecasting must be more accurate and inventory must be less. The more inaccuracy forecasting is impacted by the higher inventory cost.higher customer sensitivity, the higher the customer-service cost of stock outs due to accurate forecasting (Mentzer and Bienstock, 1998). For example, raw materials stock out must be ordered urgently by airfreight (high costing).

17 2.2 Accuracy of forecasting techniques. The accuracy requirement has an impact on product shelf life, raw material lead time, production schedule lead time and forecasting time horizon (Mentzer and Bienstock, 1998). The shelf life of lubricant product is 4-6 months, raw material lead time approximately 25-30 days and forecasting horizon suitable for monthly. So, the forecasting techniques should be short range period of time. 2.3 Availability of historical data. We have historical data of sales quantity lubricant products from January 2002 December 2005 associated with time series forecasting and regression, the predictors variable data available to update from website: 1. http://www.bot.or.th for the data of GDP. 2. http://www.thaiauto.or.th for the data of VEP. 3. http://www.bot.or.thfor the data of HEI. 4. http://www.eppo.go.th for the data of IMP. 2.4 The forecasting horizon. The forecasting horizon is important because some techniques are more suited to long-range forecasts while others work best for the short range. Moving averages and exponential smoothing are essentially short-range techniques, since they produce forecasts for the next period. Several qualitative techniques are well suited to long-range forecasts because they do not require historical data (Delurgio and Stephen, 1998). In this study, we will forecast monthly, the appropriated forecasting technique is short range period of time. As the result, the most effective forecasting technique should be combined with the time series, regression and qualitative analysis (Mentzer and Bienstock, 1998). Therefore, in this study we used time series techniques and causal technique to improve the sales forecasting in Moresco (Thailand) Co., Ltd.The forecasting techniques consist of:

18 1. Simple Exponential Smoothing. 2. Holt s trend Exponential smoothing. 3. Holt-Winter Exponential smoothing. 4. Linear Regression technique. 3. Time series forecasting technique Forecasting techniques based on time series data are made on the assumption that future values of the series can be estimated from the past values. Although no attempt is made to identify variables that influence the series, these methods are widely used and with quite satisfactory results. Analysis of time series data requires the analyst to identify the underlying behavior of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, and variations around an average. In addition, there can be random or irregular variations (Kalekar, 2004). The time series techniques in this study performed as follows: 3.1 Simple exponential smoothing Exponential smoothing technique is the weighted average of past observations with heavier weights given to recent values and exponentially decreasing weights to earlier values (Marko, 2003). This technique has been successfully employed in practice to predict the future values of many types of time series such as price, sales, or inventory data. Exponential smoothing methods under some circumstances may be more feasible, more accurate, cheaper, and easier to use than more complicated forecasting techniques. (Kalekar, 2004). Simple exponential smoothing is used for shortrange forecasting, usually just one month into the future. The model assumes that the data fluctuates around a reasonably stable mean and there is no trend or no consistent pattern of growth (Taylor, 2003).For exponential smoothing, to create the new forecast is to simply use the old forecast plus an adjustment for the error that occurred in the last forecast. When α has a value close to 1, the new forecast will include a substantial adjustment for the error in the previous forecast. Conversely, when α is close to o, the new forecast will include very little adjustment.

19 The specific formula for simple exponential smoothing is: Y t = α * y t + (1 - α) * Y t-1.. 3.1 Where Y t = The estimate of the level made in time period t Y t-1 = The estimate of the level made in time period t-1 y t = Actual demand for time period t α = The smoothing constant, 0 to 1 3.2 Holt s Trend Exponential smoothing Exponential smoothing with a trend works much like simple smoothing except that two components must be updated in each period - level and trend (Kalekar, 2004). The level is a smooth estimate of the value of the data at the end of each period. The trend is a smooth estimate of average growth at the end of each period. (Bowerman,Connell and Koehler 2005). Holt s Trend exponential smoothing is appropriate when both the level and the growth rate are changing with no seasonal pattern. The smoothing equations are The estimate of the level of the time series in time period T is Y t = α y t + (1- α) (Y t-1 + g t-1 ).. 3.2 The estimate of the growth rate of the time series in time period T is g t = γ (Y t Y t-1 ) + (1 - γ ) g t-1 3.3 A point forecast made in time period T for y T+ m is y t+ m (t) = Y t + mg t 3.4 m = 1,2,..n Where Y t = the estimate of the level in time period t g t = The estimate of the growth rate in time period t

20 Y t-1 = The estimate in time period t-1 for level g t-1 = The estimate in time period t-1 for growth rate α, γ = The smoothing constant, 0 to 1 3.3 Holt-Winters exponential smoothing The Holt Winters method is appropriate when time series has a linear trend with an additive seasonal pattern with the level, the growth rate, and the seasonal pattern which may be changed (Bowerman,Connell and Koehler 2005). The equation for additive Holt Winters method described by: The estimate of the level of the time series in time period t is Y t = Y t-1 + g t-1 + α [(y t (Y t-1 + g t-1 sn t-l )]. 3.5 The estimate of the growth rate of the time series in time period T is g t = g t-1 + α γ [(y t ( L t-1 + g t-1 + sn t-l )].. 3.6 The estimate of the seasonal factor SN T in time period T is sn t = sn t-l + (1- α )β [(y t - (Y t-1 + g t-1 + sn t-l )] 3.7 A point forecast made in time period T for y T+ m is y t+ m (T) = Y t + mg t + sn t+ m-n.. 3.8 m = 1,2,..n Where Y t = The estimate of the level in time period t g t = The estimate of the growth rate in time period t sn t = The estimate of the seasonal factor for the season corresponding to time period t Y t-1 = The estimate in time period t-1 for level g t-1 = The estimate in time period t-1 for growth rate sn t-1 = The estimate in time period t-l for seasonal factor N = The number of seasons in a year y t+ m = The most recent estimate of seasonal factor for season corresponding to time period t + m

21 α, γ, β = Smoothing constant, 0 to1 4. Multiple linear regression technique Regression involves a linear relationship between two or more variables. The objective in linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations of data points from the line (Kutner,Nachtsheim and Neter, 2004). This least squares line has the equation: Simple linear regression model Y = β0 + β1x+ ε. 4.1 Multiple linear regression model Y = β0 + β1 x 1 + β2b 2 x 2 +. + β K x k + ε.. 4.2 Where: Y = the value of the response variable. x 1. x k = predictor variable. β0 = the y intercept when x equals o. β1. βk = the slope. ε = the error term In this study, we determine the response variable by monthly quantity of sales lubricant products Predictor variables are: 1. Gross Domestic Product in Thailand (GDP) the data pick up from http://www.bot.or.th 2. Headline Inflation in Thailand (HEI) the data pick up from http://www.bot.or.th 3. Vehicle Production in Thailand (VEP)the data pick up from http://www.thaiauto.or.th 4. Import Crude Oil in Thailand (IMP)the data pick up from http://www.eppo.go.th The parameters β0 and β 1 in regression model are called regression co-efficient. β 1 is the slop of regression line. It indicates the change in mean of the probability distribution of y per unit increased in x. The parameter β 0 is the y intercept of the regression line. When the scope of the model include x =0, β 0 gives the mean of probability distribution of y at x =0. When the scope of the model does not cover x=0, β 0 does not have any particular meaning as a separate term in regression model. The regression model is shown in figure 6

22 Figure 6 Linear regression lines. An observed value of y when x equal 0 Error term y-intercept Y= β 0 + β 1 x β 0 β 1 Mean value of y when x equal 0 One unit change in x X 4.1 Least-Squares Method The mean y-value for a given x-value which line passes through the mean value of both x and y and which minimizes the sum of the distance between each point and the predictive line. Such an approach should result in a line which we can call a "best fit" to the sample data. The least-squares method achieves this result by calculating the minimum average squared deviations between the sample Y points and the estimated line. A procedure is used for finding the values of β 0 and β 1. or b 0 and b 1 which reduces the solution of simultaneous linear equations. The method of the least square requires that the sum of the n squared deviation: denoted by Q n Q = Σ(yi- β 0 β 1 xi) 2... 4.3 i=1 4.2 Estimation of parameters Let, b 0, b 1,, bp are estimators of β 0, β 1, βp The estimator of β 0 and β 1 that minimizes the Q for given sample observation (x 1, y 1 ).(x n,y n) The least square estimators of b 0 and b 1 equation are:

23 b1 = Σ ( xi- ) (yi- ).. 4.4 Σ(xi- ) 2 b 0 = 1/n (Σyi- b1σxi) = - b1 4.5 The estimate of Variance σ 2 = S 2 Single population is estimated by n S 2 = Σi=1( yi - ) 2... 4.6 n-1 For regression model we need to compute the point estimator of variance σ 2. The point estimates of variance are we called mean square error that follows the equation below: Sum square error, denoted by SSE n n SSE = Σ e 2 i = Σ (yi - ŷi) 2... 4.7 i=1 i=1 Mean square error denoted by MSE MSE = Σ e 2 i. 4.8 n-2 4.3 Coefficient of Determination (R 2 ) For linear regression model, Total variation Σ (yi - ) 2 = Explained variation Σ (ŷi -y) 2 Unexplained variation Σ (yi - ŷi ) 2 R 2 = Explained variation 4.9 Total variation R 2 is the proportion of total variation in n observed values of the dependent variable that is explained by the overall regression model is called coefficient of determination, Sine 0 SSE SST, it follows that : 0 R 2 1

24 In fact, R 2 values closer to 1 indicating better fits. R 2 is not likely to be 0 or 1 but somewhere between these limits. 4.5 Null hypothesis test The regression model is used for the significance of the relationship between y and x with F-test for test the hold regression model Ho: β 1 =β 2 =..= β k-1 = 0 which says that none of the independent variable x 1,x 2, x k is significantly related to Y (model does not significant). Ha: At least one of β 1, β 2,.., β k doest not equal 0 which says that at least one of the independent variables is significantly related to Y (model is significant). F* = MSR where, MSR = SSR, MSE = SSE MSE p -1 n-p The decision rule control the type I error at α is: If F* F (1- α; p-1, n-p), conclude Ho If F* > F (1- α; p-1, n-p), conclude Ha T-test for testing the significance of the independent variable x k Ho: β 1 = 0 Ha: β 1 0 t* = b1 S {b1} b1 = Σ (xi )) (yi - ) 4.10 Σ(xi ) 2 S 2 {b1} = MSE. 4.1 Σ(xi ) 2 If / t*/ t (1- α/2 ) ; n-2 ),conclude H0 If / t*/ > t (1- α/2); n-2), conclude Ha

25 5. VBA applied to forecasting model Forecasting model in this study is applied to excel spread sheet and excel visual basic for application in Microsoft office 2003. Excel visual basic application is a programming application that uses Visual Basic code to run many features of the Excel package. VBA has a built in recorder that has the ability to record your actions in your spreadsheet or word processor and translate them into VBA. This means that you can actually start programming without knowing a single command or keyword in the programming language (Hansen, 2002). Program written in VBA within the Visual Basic Editor (VBE) may be called macros. The excel visual basic for application also provides applicants the advantage of saving time in order to find out the target sheet in excel spread sheet, calculate, and is easy to use by creating custom commands, menus, dialog boxes, and even fullfeatured applications. For example: According to workbook if you want to go to spreadsheet no. 35 (graphical result; product G) you can create control bottom and use macro recorder to take action in your spreadsheets as below: Graphical result;produt G The VBA code applied in control bottom as: Private Sub CommandButton12_Click() ' ActiveWindow.ScrollWorkbookTabs Sheets:=35 Sheets("Graphical result; product G").Select End Sub The macro recorder can record basically any task in Excel, but VBA has lots of other abilities that you cannot do in Excel. Some of them are automating tasks (looping), creating dialog boxes and custom menus. Therefore, in this study, VBA code has been written for excel spreadsheets application.

26 6. Evaluation of forecasting model We used the performance measures to assess the quality of the forecasting method. We are able to measure the forecasting model which is appropriate by looking at the forecasting error. The error in forecasting represents the combined effects of the irregular component and the accuracy which is the appropriate forecasting technique (Bowerman,Connell and Koehler, 2005). If the numbers of forecasting error are too large, it may indicate poor forecasting or inappropriate technique. 1. MAD and MSE MAD and MSE are two measures to evaluate the forecasting error that MAD weighs all errors evenly, while MSE weighs errors according to their squared values (Delurgio and Stephen, 1998). The mean squared error is a common measure for forecasting error and exercise a mathematical advantage to the more easily interpreted mean absolute deviation (Janssen,2006). When using either MAD or MSE, we are comparing the results of forecasting technique which is the lowest MAD or MSE. The equation is: MAD = Actual - forecast N MSE = {Actual forecast} 2 N-1 Note that MAD = Mean Absolute Deviation MSE = Mean Square Error N = Number of observation

27 7. Forecasting Judgment We consider two ways that judgment can be integrated into time series and regression. 7.1 MAD and MSE have been selected with lowest value. If two measures convert when compared to another forecasting model, we will use graphical and opinion to decision making for the forecasting model. 7.2 Graphic & forecaster s opinion The graphic of time series plots actual sales quantity and forecasting value. The forecaster can view to see the feature of time series. This method will be used when the forecasting value is not satisfied and the forecaster would like to improve the decision making. We use graphic and opinion to observe the feature of future demand that we can use to consider for future demands (Armstrong and Collopy, 1998).

28 STEP OF BUILDING LINEAR REGRESSION MODEL Step 1 : Select variable The predictor variable of regression model selected by recommendation of salesmen experiences who are working in lubricant business for more than 10 years. They recommended that the quantity of sales lubricant relationship with 4 factors includes : 1. Headline Inflation (HEI) 2. Gross Domestic Product (GDP) 3. Vehicle Production (VEP) 4. Import the crude oil (IMP) Step 2 : Correlation matrix Correlation matrix is the important preliminary step because independent variables should not be correlated with one another. Correlation coefficient is a simple statistical measure of relationship between one dependent and one or more independent variables. It is used as a measure of the goodness-of-fit for the model (Sorana and Daniela, 2006). If independent variables are correlated, this might affect the robustness of our results. When employing multiple regression analysis, we attach the most weight to any of the independent variables in order to explain the variation in Y. The correlation matrix computed with sales quantity of lubricant products A to H and predictor variable (Gross Domestic Product in Thailand (GDP), Headline inflation (HEI), Vehicle production (VEP) and Import crude oil (IMP)) have been computed by SPSS with Pearson Correlation. The correlation matrix shown in table 4 can be interpreted that the vehicle production (VEP) is strongly correlated with sales quantity of lubricant products A with a Pearson Coefficient of 0.625, Headline inflation (HEI) 2 nd strongly correlated with sales quantity of lubricant products A with a Pearson Coefficient of 0.523, Gross Domestic Product in Thailand (GDP) 3 rd strongly correlated with sales quantity of lubricant products A with a Pearson Coefficient of 0.491 and imported crude oil (IMP) is weakly correlated with sales quantity of lubricant product A with a Pearson Coefficient of 0.190. The strong correlation as respectively

29 as the number of a Pearson Coefficient of product A to product H with predictor variable is shown in Table 5. Table 1 Correlation matrix of sales quantity of product A with predictor variable computed by SPSS A HEI GDP VEP IMP 2 Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Correlations A HEI GDP VEP IMP 1.523**.491**.625**.190.000.000.000.196 48 48 48 48 48.523** 1.828**.884**.341*.000.000.000.018 48 48 48 48 48.491**.828** 1.872**.369**.000.000.000.010 48 48 48 48 48.625**.884**.872** 1.446**.000.000.000.001 48 48 48 48 48.190.341*.369**.446** 1.196.018.010.001 48 48 48 48 48 2 Correlation significant at the 0.01 means that there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis p-value associated to 3 tprs, df less than 0.01. In other words there is a statistically significant linear relationship between the variables (Sorana and Daniela,. 3 rprs2 = The squared of Pearson correlation coefficient or Pearson coefficient of determination

30 The summary of correlation between sales quantities of product A to product H with predictor variable as described in table 2 Table 2 Correlation summary of product A-H with predictor variable computed by SPSS Product name Correlation by Pearson correlation 1 st 2 nd 3 rd 4 th A VEP HEI GDP IMP Pearson Coefficient 0.625 0.523 0.491 0.19 B VEP HEI GDP IMP Pearson Coefficient 0.901 0.891 0.822 0.473 C VEP GDP HEI IMP Pearson Coefficient 0.742 0.721 0.686 0.479 D VEP GDP HEI IMP Pearson Coefficient 0.677 0.584 0.531 0.453 E VEP GDP HEI IMP Pearson Coefficient 0.901 0.88 0.872 0.477 F GDP VEP HEI IMP Pearson Coefficient 0.902 0.865 0.793 0.445 G VEP GDP HEI IMP Pearson Coefficient 0.889 0.861 0.857 0.483 H VEP GDP HEI IMP Pearson Coefficient 0.897 0.889 0.841 0.471 Step 3 Create regression model and hypothesis testing. We created regression model and tested the hypothesis of the Y (dependent variable) and x (predictor variable). We follow the correlation matrix result which was the predictor variable is strongest first and also hypothesis test for regression slope perform by t test for single predictor (H 0 : β1 = 0, H a : β1 0), then adding other predictors to the model. For t-test, if probability is below 0.05 or α < 0.05 (we are determine α = 0.05) we can decide that the slope is significant at 0.05 significant level, then we have to conclude that x is significantly related to Y or model strong evidence the regression relationship is significant(chistensen,2003). F-statistic test is use with more than one predictor (H 0 : β1, β2, βk = 0, H a : not all (β1, β2, βk 0). By computing this statistic, we test the hypothesis that none of the explanatory variables helps

31 explain variation in Y about its mean. The information to notice here is the probability Sig. If this probability is below 0.05, we conclude that the F-statistic is large enough so that we can reject Ho that means we have the strong evidence that Ho is false. Computed sale lubricant quantity of product F (dependent variable, Y) against predictor variable (See in table 5) that is gross domestic product (GDP, x) first, and then follow the strong correlated as respectively. (See in item 2.1) Note: The variables used in regression model are: Ŷ = sale quantity of lubricant products X 1 = Headline inflation (HEI) X 2 = Gross Domestic Production (GDP) X 3 = Vehicle production (VEP) X 4 = Import crude oil (IMP) Create the Model and hypothesis testing. Fitted Y (sale quantity) against GDP(x 2 ) Unstandardized Coefficients Coefficients a Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -37549.7 2962.474-12.675.000 GDP.047.003.902 14.129.000 a. Dependent Variable: F *t-test H 0 : β2 = 0 H a : β2 0 t * = 14.129 P- value = 0.000 < 0.05 Fitted regression line Ŷ = -37549.7 + 0.047 x 2 Conclusion: The model is significant

32 Add x 3 in the model Fitted Y(sale quantity) against GDP(x 2 ), VEP(x 3 ) Model 1 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig. 5E+008 2 237239017.1 116.783.000 a 91415474 45 2031454.975 6E+008 47 a. Predictors: (Constant), VEP, GDP b. Dependent Variable: F Model 1 (Constant) GDP VEP a. Dependent Variable: F Unstandardized Coefficients Coefficients a Standardized Coefficients B Std. Error Beta t Sig. -28485.6 4381.792-6.501.000.032.006.616 5.029.000.059.022.328 2.677.010 F-test H 0 : β2, β3 = 0 H a : β2, β3 0 F* = 116.783 P-value = 0.000 Conclusion: The model significant Test for drop X 2 from model *t-test H 0 : β2 = 0 H a : β2 0 t * = 5.029 P- value = 0.000 < 0.05 Conclude Ha: β2 0, x 2 obtain in the model. Test for drop X 3 from model *t-test H 0 : β3 = 0 H a : β3 0 t * = 2.677 P- value = 0.010 < 0.05 Conclusion: Ha: β3 0, x 3 obtain in the model. Fitted regression line Y = -28485.6 + 0.032 x 2 + 0.059 x 3

33 Add x 1 in the model Fitted Y (sale quantity) against GDP(x 2 ), VEP(x 3 ), HEI(x 1 ) Model 1 Regression Residual Total ANOVA b Sum of Squares df Mean Square F Sig. 5E+008 3 158197063.9 76.238.000 a 91302316 44 2075052.642 6E+008 47 a. Predictors: (Constant), HEI, GDP, VEP b. Dependent Variable: F Model 1 (Constant) GDP VEP HEI a. Dependent Variable: F Unstandardized Coefficients Coefficients a Standardized Coefficients B Std. Error Beta t Sig. -28941.5 4839.771-5.980.000.032.007.623 4.876.000.063.028.349 2.276.028-65.722 281.438 -.031 -.234.816 F-test H 0 : β1, β2, β3 = 0 H a : β1, β2, β3 0 F* = 76.237 P-value = 0.000 Conclusion: the model is significant Test for drop X 2 from model *t-test H 0 : β2 = 0 H a : β2 0 t * = 4.876 P- value = 0.000 < 0.05 Conclusion Ha: β2 0, x 2 obtain is in the model. Test for drop X 3 from model *t-test H 0 : β3 = 0 H a : β3 0 t * = 2.276 P- value = 0.028 < 0.05 Conclusion: Ha: β3 0, x 3 obtain is in the model.

34 Test for drop X 1 from model *t-test H 0 : β1 = 0 H a : β1 0 t * = -0.234 P- value = 0.816 > 0.05 Conclusion: H 0 : β1= 0, It is not significant, should drop x 1 Fitted regression line Ŷ = -28941.5 + 0.032 x 2 + 0.063 x 3 Add x 4 in the model Fitted Y (sale quantity) against GDP(x 2 ), VEP(x 3 ), IMP(x 4 ) ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 5E+008 3 159364055.6 79.862.000 a Residual 87801341 44 1995485.030 Total 6E+008 47 a. Predictors: (Constant), IMP, GDP, VEP b. Dependent Variable: F Unstandardized Coefficients Coefficients a Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -30758.9 4659.784-6.601.000 GDP.032.006.623 5.130.000 VEP.051.023.281 2.232.031 IMP.652.484.089 1.346.185 a. Dependent Variable: F F-test H 0 : β2, β3, β4 = 0 H a : β2, β3, β4 0 F* = 79.862 P-value = 0.000 Conclusion: the model is significant Test for drop X 2 from model *t-test H 0 : β2 = 0 H a : β2 0 t * = 5.130 P- value = 0.000 < 0.05 Conclusion: Ha: β2 0, x 2 obtain is in the model.

35 Test for drop X 3 from model *t-test H 0 : β3 = 0 H a : β3 0 t * = 2.232 P- value = 0.031 < 0.05 Conclusion: Ha: β3 0, x 3 obtain is in the model. Test for drop X4 from model *t-test H 0 : β4 = 0 H a : β4 0 t * = 1.346 P- value = 0.185 > 0.05 Conclusion: H 0 : β4= 0, It is not significant, should drop x 4 Fitted regression line Y = -30759.9 + 0.032 x 2 + 0.051 x 3 So, we can conclude that the appropriate regression line is Ŷ = -28485.6 + 0.032 x 2 + 0.059 x 3 Step 4 Testing the fitted regression model. 4.1 Plot residual against the dependent variable with normal probability plot. Figure 7 plots the residual of the regression with sales of lubricant product F with normal probability. The plot is nearly linear suggesting that error distribution is normal. 4.2 Plot the residual against the dependent variable Y. Figure 8 plots the residual of the regression with sales quantity lubricant product F by scatter plot the shape that centers a round 0 with 0 slopes indicates the function is linear. 4.3 Check R 2, coefficient of determination. R 2 = Explained variation Total variation R 2 computed below by SPSS of regression model is 0.838 means that the sales quantity of lubricant product F with two predictor variables are Vehicle production (VEP) and Gross domestic production (GDP) explains 83.8 % of total variation in 48 observed sales quantity. The range is 0 R 2 1, if values closer to1 indicating better fits (Julian J. Faraway, 2002). Model Summary Model 1 a. Adjusted Std. Error of R R Square R Square the Estimate.916 a.838.831 1425.29119 Predictors: (Constant), VEP, GDP

36 Testing the fitted of regression model. Plot residual against dependent variable (Y) with normal probability plot. Figure 7 Plot residual against sales quantity of product F with normal probability plot. Normal P-P Plot of Regression Standardized Residual 1.0 Dependent Variable: F 0.8 Expected Cum Prob 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 Observed Cum Prob 1.0

37 Figure 8 Plot residual against sale quantity of product F with scatter plot. Plot the residual against the dependent variable Y. 4000.00000 3000.00000 Unstandardized Residual 2000.00000 1000.00000 0.00000-1000.00000-2000.00000-3000.00000 0.00 2000.00 4000.00 F 6000.00 8000.00 10000.00 Check R 2, coefficient of determination. Model Summary Model 1 a. Adjusted Std. Error of R R Square R Square the Estimate.916 a.838.831 1425.29119 Predictors: (Constant), VEP, GDP Step 5 Checking multicollinearity Multicollinearity occurs when the predictor variables are highly correlated among themselves. We can measure them by using the Variance Inflation Factor(VIF) (Kutner,Nachtsheim and Neter, 2004). This factors measure how much the variances of estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related if largest VIF value among all x variables is used as an indicator of the severity of multicollinearity. The maximum VIF value in excess of 10 is frequently taken as an indication that multicollinearity may influence the least squares estimates. In table 6, we computed the VIF value of regression model. The results of both predictor variables are 4.175 < 10. We can conclude that this model is not the multicolinearity.

38 5.1 Check multicollinearity. Table 3 Computed the VIF value by SPSS of the regression model Model 1 Coefficients a Unstandardized Standardized Coefficients Coefficients Collinearity Statistics B Std. Error Beta t Sig. Tolerance VIF (Constant)-28485.6 4381.792-6.501.000 GDP.032.006.616 5.029.000.240 4.175 VEP.059.022.328 2.677.010.240 4.175 a. Dependent Variable: F We conclude that the fitted regression line applied to regression model of product F is : Ŷ = -28485.6 + 0.032 x 2 + 0.059 x 3 The summary of linear regression models that computed as methodology above is described in table 4 Table 4 : The summary of the regression model of lubricant product A H Product name Fit regression line R 2 VIF value A Ŷ = 1167.909+0.130x 3 0.390 1.00 B Ŷ = -17323.4+3168.509 x 1 +0.329 x 3 0.853 4.584,4.584 C Ŷ = 1536.894+0.349 x 3 0.551 1.00 D Ŷ = 701.122+0.073 x 3 0.458 1.00 E Ŷ = -17199.8+580.087 x 1 +0.019 x 2 +0.074 x 3 0.862 4.890, 4.454, 6.410 F Ŷ = -28485.6+0.032 x 2 + 0.059 x 3 0.838 4.175,4.175 G Ŷ = -21122.9+0.020 x 2 + 0.110 x 3 0.821 4.175,4.175 H Ŷ = -16008.4 +0.018 x 2 + 0.070 x 3 0.852 4.175,4.175 Remark: Ŷ = sales quantity of lubricant products X 1 = Headline inflation (HEI) X 2 = Gross Domestic Production (GDP) X 3 = Vehicle production (VEP) X 4 = Import crude oil (IMP)

39 According to table 4, we can interpret that product A fitted regression line applied to regression model is simple linear regression Ŷ = 1167.909+0.130x 3, R 2 = 0.390 mean that sales quantity of product A with vehicle production explain 39.0 % of total variation in 48 observed sales quantity of product A and VIF = 1.00 means that the model does not have multicollinearity. Product B fitted regression line applied to regression model is multiple linear regression Ŷ = -17323.4+3168.509 x 1 +0.329 x 3, R 2 = 0.853 means that the sales quantity of product B with Headline inflation (HEI) and vehicle production explain 85.3 % of total variation in 48 observed sale quantity of product B and VIF = 4.584,4.584 means that the model does not have multicollinearity. Product C fitted regression line applied to regression model is simple linear regression Ŷ = 1536.894+0.349 x 3, R 2 = 0.551 means that the sales quantity of product C with vehicle production explain 55.1 % of total variation in 48 observed sales quantity of product C and VIF = 1.00 means that the model does not have multicollinearity. Product D fitted regression line applied to regression model is simple linear regression Ŷ = 701.122+0.073 x 3, R 2 = 0.458 means that the sales quantity of product D with vehicle production explain 45.8 % of total variation in 48 observed the sales quantity of product D and VIF = 1.00 means that the model does not have multicollinearity. Product E fitted regression line applied to regression model is multiple linear regression Ŷ = -17199.8+580.087 x 1 +0.019 x 2 +0.074 x 3, R 2 = 0.862 mean that the sales quantity of product E with Headline inflation (HEI), Gross Domestic Production (GDP) and vehicle production explain 86.2 % of total variation in 48 observed the sales quantity of product E and VIF = 4.890,4.454,6.410 means that the model does not have multicollinearity. Product F fitted regression line applied to regression model is multiple linear regression Ŷ = -28485.6+0.032 x 2 + 0.059 x 3, R 2 = 0.838 mean that the sales quantity of product F with Gross Domestic Production (GDP) and vehicle production explain 83.8 % of total variation in 48 observed the sales quantity of product F and VIF = 4.175,4.175 means that the model does not have multicollinearity.

40 Product G fitted regression line applied to regression model is multiple linear regression Ŷ = -21122.9+0.020 x 2 + 0.110 x 3, R 2 = 0.821 mean that the sales quantity of product G with Gross Domestic Production (GDP) and vehicle production explain 82.1 % of total variation in 48 observed the sales quantity of product G and VIF = 4.175,4.175 mean that the model does not have multicollinearity. Product H the fitted regression line applied to regression model is multiple linear regression Ŷ = -16008.4 +0.018 x 2 + 0.070 x 3, R 2 = 0.852 mean that sale quantity of product H with Gross Domestic Production (GDP) and vehicle production explain 85.2 % of total variation in 48 observed sale quantity of product H and VIF = 4.175,4.175 mean that model no occurred of multicollinearity.

41 THE EXPERIMENT OF FORECASTING RESULTS Forecasting model has been trialed in Excel spreadsheet by using VBA application. Four forecasting techniques were compared in forecasting model including simple exponential smoothing, Holt s trend exponential smoothing, Holt-Winters exponential smoothing and linear regression technique. The evaluation of forecasting techniques used forecasting error or MAD and MSE values. The appropriate forecasting techniques must have lowest value of forecasting error or lowest MAD and MSE values. The forecasting model also has the graphic of historical data plotted over time series between actual demands and forecasting. The forecaster can consider the demands of the next forecasting period. Then, the forecaster can decide the appropriate forecasting techniques that should be applied in the forecasting model by using MAD and MSE values and the opinion of the forecaster. The experiment results will be described in table 5. According to table 5, we can indicate that the appropriate forecasting technique applied to the forecasting model of lubricant product A in January 2006 to September 2006 is Holt s trend exponential smoothing. It comes from the combination between MAD, MSE and the forecaster s opinion. For lubricant product B, the Holt s trend exponential smoothing was selected for demands forecasting during January 2006 to September 2006 by using the lowest value of MAD and MSE. For lubricant product C, the Holt-Winters exponential smoothing was selected for forecasting the demand during January 2006 to September 2006 by measuring from the lowest value of MAD and MSE. For lubricant product D, linear regression technique was selected for demands forecasting during January 2006 to September 2006 by measuring from lowest value of MAD and MSE. For lubricant product E, Holt-Winters exponential smoothing was selected for forecasting the demand during January 2006 to September 2006 by using the lowest value of MAD and MSE. For lubricant product F, Holt-Winters exponential smoothing was selected for demands forecasting during January 2006 to September 2006 by evaluating the lowest values of MAD and MSE. For lubricant product G, Holt s trend exponential smoothing and Holt-Winters exponential smoothing were selected for forecasting the demands during January 2006 to September 2006 by evaluating the lowest values of MAD and MSE. The Holt s trend exponential smoothing was suitable for demands forecasting during January to September 2006, except in April, the appropriate forecasting technique was Holt-Winters exponential smoothing.

For lubricant product H, Holt s trend exponential smoothing and Holt-Winters exponential smoothing were selected for forecasting the demands during January 2006 to September 2006 by evaluating the lowest values of MAD and MSE. The Holt-Winters exponential smoothing was suitable for forecasting in January, February, March, April, July and September. The Holt s trend exponential smoothing was appropriated for forecasting in May and June 2006. 42

43 Table 5 The appropriate forecasting techniques applied to forecast model Forecasting period Product A Product B Product C Product D Product E Product F Product G Product H MAD MSE FTS MAD MSE FTS MAD MSE FTS MAD MSE FTS MAD MSE FTS MAD MSE FTS MAD MSE FTS MAD MSE FTS Jan-06 2,467 8,559,134 H-T 1,513 5,947,270 H-T 4,570 33,522,988 H-W 1,194 2,214,298 R-S 589 554,608 H-W 626 875,938 H-W 463 425,140 H-T 683 722,409 H-W Feb-06 2,430 8,395,896 H-T 1,519 5,900,432 H-T 4,551 33,119,863 H-W 1,182 2,176,230 R-S 587 549,038 H-W 651 929,258 H-W 454 416,638 H-T 703 765,813 H-W Apr-06 2,399 8,244,812 H-T 1,539 5,900,020 H-T 4,551 32,873,543 H-W 1,163 2,134,680 R-S 587 548,155 H-W 642 911,701 H-W 490 508,233 H-T 709 774,101 H-W Apr-06 2,362 8,091,452 H-T 1,565 5,959,183 H-T 4,480 32,255,135 H-W 1,194 2,238,193 R-S 595 562,002 H-W 645 905,917 H-W 532 521,668 H-W 709 765,435 H-W May-06 2,319 7,938,903 H-T 1,555 5,864,568 H-T 4,396 31,646,492 H-W 1,184 2,204,758 R-S 606 586,540 H-W 657 919,262 H-W 486 494,147 H-T 704 757,978 H-T Jun-06 2,289 7,800,835 H-T 1,565 5,840,217 H-T 4,352 31,140,210 H-W 1,163 2,163,942 R-S 609 588,122 H-W 650 904,391 H-W 477 486,119 H-T 693 744,035 H-T Jul-06 2,273 7,695,126 H-T 1,552 5,746,413 H-T 4,317 30,678,427 H-W 1,170 2,169,678 R-S 610 582,560 H-W 641 888,465 H-W 475 479,671 H-T 632 642,136 H-W Aug-06 2,266 7,619,337 H-T 1,552 5,691,918 H-T 4,351 30,850,502 H-W 1,169 2,152,028 R-S 599 572,157 H-W 649 891,060 H-W 467 471,125 H-T 651 680,366 H-W Sep-06 2,274 7,617,711 H-T 1,544 5,613,880 H-T 4,310 30,381,323 H-W 1,172 2,146,248 R-S 589 562,119 H-W 653 888,379 H-W 475 478,028 H-T 660 684,107 H-W

44 FORECASTING MODEL AND COMPANY MODEL COMPARISON We compare forecasting model and company model to indicate which one is more appropriate to forecast the demand of lubricant products. The optimized forecasting is monitored by More accuracy Low inventory No inventory stock out a. Comparison of forecasting accuracy The results of accurate forecasting of lubricant products evaluate by graphical visualization and % difference values by forecasting result compare to actual sales quantity. Good forecasting % difference values must be less. There were described in figure 9 to 16.

45 Figure 9 plots the forecasting model (by Holt s trend) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product A. The results show that the forecasting model is better than the company s model. It will be described below: % difference (average) of forecast model by Holt s trend is -0.9438 % % difference (average) of company model is 21.5845 % Figure 9 : The plot of forecasting results of product A compared with actual sales quantity during January to September 2006 40,000 Forecast model % Diff (Avg) = -0.9438 Min = -8.7088 Max = 5.3409 Company model % Diff( Av g) = 21.5845 Min = 11.0406 Max = 29.0171 Actual Company model Forecast model Sale Q'ty 30,000 20,000 10,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product A)

46 Figure 10 plots the forecasting model (by Holt s trend) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product B. The results show that the forecasting model is better than the company model. It will be described below: % difference (average) of forecast model by Holt s trend is -2.6076 % % difference (average) of company model is 13.5590 % Figure 10 : The plot of forecasting results of product B compared with actual sales quantity during January to September 2006 Forecast model % Diff (Av g) = -2.607 Min = -9.0396 Max = 8.2300 Company model % Diff (Av g) = 13.5590 Min = 4.2524 Max = 26.6667 Actual Company model Forecast model S a l e Q ' ty 50,000 40,000 30,000 20,000 10,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product B)

47 Figure 11 plot the forecasting model (by Holt-Winters) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product C. The results show that the forecasting model is better than the company s model. It will be described below: % difference average of forecast model by Holt-Winters is 0.2964 % % difference average of company model is 10.9223 % Figure 11 : The plot of forecasting results of product C compared with actual sales quantity during January to September 2006 Forecast model % Diff (Avg) =0.2964 Min = 14.6414 Max = 16.1166 Company model % Diff (Av g) = 10.9223 Min = 1.2146 Max = 23.1527 Actual Company model Forecast model S a l e Q 'ty 50,000 40,000 30,000 20,000 10,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product C)

48 Figure 12 plots the forecasting model (by multiple linear regressions) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product D. The results show that the forecasting model is better than the company s model. It will be described below: % difference average of forecast model by multiple linear regressions is 1.1118 % % difference average of company model is 19.5316 % Figure 12 : The plot of forecasting results of product D compared with actual sales quantity during January to September 2006 S a l e Q ' ty 12,000 10,000 8,000 6,000 4,000 2,000 0 Forecast model % Diff (Avg) =1.1118 Min = -26.8466 Max = 47.4516 Company model % Diff (Avg) = 19.5316 Min = -5.4622 Max =45.1613 Actual Company model Forecast model Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product D)

49 Figure 13 plots the forecasting model (by Holt-Winters) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product E. The results show that the forecasting model is better than the company s model. It will be described below: % difference average of forecast model by Holt-Winters is 3.0744 % % difference average of company model is -15.4463 % Figure 13 : The plot of forecasting results of product E compared with actual sales quantity during January to September 2006 20,000 Forecast model % Diff (Avg) =-3.0744 Min = -17.9358 Max = 11.0485 Company model % Diff (Avg) = -15.4463 Min = -29.4872 Max = 3.0303 Actual Company model Forecast model S a l e Q ' ty 15,000 10,000 5,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product E)

50 Figure 14 plot the forecasting model (by Holt-Winters) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product F. The results show that the forecasting model is better than the company s model. It will be described below: % difference average of forecast model by Holt-Winters is 3.7806 % % difference average of company model is 8.0444 % Figure 14 : The plot of forecasting results of product F compared with actual sales quantity during January to September 2006 Forecast model % Diff (Avg) =3.7806 Min =-9.2857 Max = 25.2875 Company model % Diff (Avg) = 8.0444 Min = -11.0672 Max = 35.9833 Actual Company model Forecast model 14,000 12,000 S a l e Q ' ty 10,000 8,000 6,000 4,000 2,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product F)

51 Figure 15 plot the forecasting model (by Holt s trend) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product G. The results show that the forecasting model and the company s model look very closely. It will be described below: % difference (average) of the forecasting model by Holt s trend is 4.1043 % % difference (average) of the company s model is 2.3035 % Figure 15 : The plot of forecasting results of product G compared with actual sales quantity during January to September 2006 Forecast model % Diff (Av g) = 4.1043 Min = -2.5458 Max = 27.5122 Company model % Diff (Av g) = 2.3035 Min =-11.0672 Max = 20.7983 Actual Company model Forecast model 14,000 12,000 Sal e Q 't y 10,000 8,000 6,000 4,000 2,000 0 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product G)

52 Figure 16 plots the forecasting model (by Holt-Winters& Holt s trend) against the company s model. Both are compared to actual sales quantity during January 2006 to September 2006 of product H. The results show that the forecasting model is better than the company s model. It will be described below: % difference average of the forecasting model by Holt-Winters& Holt s trend is 6.0144 % % difference average of the company s model is 6.7962 % Figure 16 : The plot of forecasting results of product H compared with actual sales quantity during January to September 2006 S a l e Q ' t y 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Forecast model % Diff (Avg) = 6.0144 Min = -15.9289 Max = 24.0254 Company model % Diff (Avg) = 6.7962 Min = - 13.5602 Max = 43.7500 Actual Forecast model Company model Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Month (Product H) How ever, the forecasting accuracy will be measured by R 2 value. R 2 is the proportion of total variation in n observed values of the dependent variable that is explained by the overall

53 regression model is called coefficient of determination. The R 2 values close to 1 indicating that the forecasting model is more appropriate (Julian J. Faraway, 2002). Figure 17 shows the results of the accurate forecasting of lubricant products A evaluated by R 2 value, between the forecasting model and the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.8012 R 2 Company s model =0.4763 Figure 17 : The plot of forecasting results of product A compared with actual sales quantity during September 2003- September 2006 Sale Q 'ty 40000 35000 30000 25000 20000 15000 10000 5000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product A) Actual Company model Forecast model

54 Figure 18 shows the results of the accurate forecasting of lubricant products B evaluated by R 2 value, between the forecasting model & the company s model. Boht are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company model. It will be described below: R 2 Forecasting model =0.9990 R 2 Company s model =0.7393 Figure 18 : The plot of forecasting results of product B compared with actual sales quantity during September 2003- September 2006 50000 Actual Company model Forecast model 40000 Sale Q'ty 30000 20000 10000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product B)

55 Figure 19 shows that the results of accurate forecasting of lubricant products C evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.5436 R 2 Company s model =0.3540 Figure 19 : The plot of forecasting results of product C compared with actual sales quantity during September 2003- September 2006 50000 Actual Company model Forecast model 40000 Sale Q 'ty 30000 20000 10000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product C)

56 Figure 20 shows that the results of the accurate forecasting of lubricant products D evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model and the company s model look very closely. It will be described below: R 2 Forecasting model =0.1759 R 2 Company s model =0.2031 Figure 20 : The plot of forecasting results of product D compared with actual sales quantity during September 2003- September 2006 S a l e Q ' ty 12000 10000 8000 6000 4000 2000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product D) Actual Company model Forecast model

57 Figure 21 shows that the results of the accurate forecasting of lubricant products E evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.9822 R 2 Company s model =0.7962 Figure 21 : The plot of forecasting results of product E compared with actual sales quantity during September 2003- September 2006 20000 Actual Company model Forecast model 15000 Sale Q 'ty 10000 5000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product E)

58 Figure 22 shows that the results of the accurate forecasting of lubricant products F evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.9095 R 2 Company s model =0.6354 Figure 22 : The plot of forecasting results of product F compared with actual sales quantity during September 2003- September 2006 Sale Q 'ty 14000 12000 10000 8000 6000 4000 2000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product F) Actual Company model Forecast model

59 Figure 23 shows that the results of the accurate forecasting of lubricant products G evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.9928 R 2 Company s model =0.6462 Figure 23 : The plot of forecasting results of product G compared with actual sales quantity during September 2003- September 2006 Sale Q ' ty 14000 12000 10000 8000 6000 4000 2000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product G) Actual Company model Forecast model

60 Figure 24 shows that the results of the accurate forecasting of lubricant products H evaluated by R 2 value, between the forecasting model & the company s model. Both are compared with actual sales quantity. The forecasting was performed during September 2003 - September 2006(3 years). The results show that the forecasting model is better than the company s model. It will be described below: R 2 Forecasting model =0.8195 R 2 Company s model =0.4270 Figure 24 The plot of forecasting results of product H compared with actual sales quantity during September 2003- September 2006 14000 12000 10000 Actual Company model Forecast model Sale Q 'ty 8000 6000 4000 2000 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 Period (Product H)

61 b. Inventory in stock and raw materials stock out Inventory amount in stock and stock out are two factors that indicate the performance of forecasting effectiveness. The good forecasting must have low inventory and no stock out. Forecasting has an impact on inventory level and number of stock out because if forecasting gives information that in the next period the demand will grow up to 15 % but actually is opposite. It will make higher inventory. On the contrary, if the forecasting gives information that in the next period, the demand it will grow up to 15 % but actually the demand grow up to 30 %, there will be stock out. Table 13 shows that inventory in stock of lubricant product A,B,C,D,E and H was conducted with the forecasting model in January 2006 September 2006. It perform low inventory when compared with the company s model. However, in overall view of inventory was conducted with the forecasting model is better than the company s model by inventory amount lower 21,822 liters. For stock out, the forecasting model show no stock out occurrence due to forecasting performed in January 2006 September 2006 but the company s model shows three products stock out (product E, G, H). Table 6 Inventory in stock during the forecasting model and the company s model performed during January 2006- September 2006 Product Name Inventory (liter) Forecast model Company model Diff Forecast model Stock out Company model A 160,429 214,828-54,399 NO NO B 192,724 246,118-53,394 NO NO C 267,070 310,359-43,289 NO NO D 156,974 185,573-28,599 NO NO E 83,865 88,914-5,049 NO YES F 87,415 85,485 1,930 NO NO G 85,068 75,078 9,990 NO YES H 83,284 85,051-1,767 NO YES Average 139,604 161,426-21,822 Raw materials shortage also important to monitoring of forecasting performance the best one must be none of stock shortage that is important for lubricant manufactures because if there is

62 stock shortage, products can not be sent to customer and this problem will impact to customer, it may stop production line or they will have another choice to purchase lubricant products from other competitor. Figure 25 indicates that raw material SN-500 (that one raw material for producing product E, G, H) was stock out in May, June and July 2006 which forecasting is performed by the company s model. It is impacted on product s delivery schedule (delay) and transportation cost (urgent order by airfreight). On the contrary, the forecasting model is so good that there is no stock out. Figure 25: Comparison of raw materials (SN-500) used in each period of the forecasting activity Q ua tity use (liter) 15,000 12,000 9,000 6,000 3,000 0-3,000-6,000 Raw material(sn-500) use in each period R/ M stock out Demand Company forecast Forecast model Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06

63 Figure 26 Indicate that raw material name is RC-9204 (that one raw material for produce product E) was stock out in May, June and July 2006 which is forecasting perform by company model. It is impact to products delivery schedule and transportation cost. Figure 26 Comparison raw materials (RC-9204) use in each period of forecasting activity 150 Raw material ( RC-9204) use in each period R/ M stock out Demand Company forecast Forecast model Q uantity use (liter) 100 50 0-50 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06

64 CONCLUSION The purpose of this study is to find the appropriate forecasting techniques to forecast more accurate the demands of lubricant products. We select forecasting techniques which depend on customer sensitivity, forecasting accuracy, forecasting horizontal and historical data. Two forecasting techniques selected are time series forecasting (simple exponential smoothing, Holt s trend exponential smoothing, and Holt-Winter exponential smoothing) and regression technique with predictor variable that includes Gross Domestic Product (GDP), Headline inflation (HEI), Vehicle production (VEP) and import crude oil (IMP). The forecasting techniques were evaluated by MAD and MSE. The evaluated value results and opinion of forecaster were used for forecasting judgment. The appropriateness of forecasting techniques has been compared with existing company s forecasting technique by looking at the pending amount of inventory and number of raw materials stock out. The appropriate technique should provide fewer inventories and no stock out. The results of this study are the appropriate forecasting techniques used for lubricant products during time period: January 2006 September 2006 described below Product A is Holt s trend exponential smoothing Product B is Holt s trend exponential smoothing Product C is Holt- Winter exponential smoothing Product D is Regression Product E is Holt- Winter exponential smoothing Product F is Holt- Winter exponential smoothing Product G is Holt s trend exponential smoothing Product H is Holt- Winter exponential smoothing The forecasting techniques mentioned above have been applied for purchase planning and production planning. The results of forecasting performance when compared with forecasting techniques (forecasting model) and existing forecasting (company s model) in January 2006 September 2006 found that the performance of the forecasting model is better than the company s model as described in table 7 for accuracy by R 2 (R 2 closed to 1 mean that it better fit)

65 Forecasting model; average R 2 value = 0.7785 mean that forecasting by the forecasting model of 8 lubricant products able explains 77.85 % of the total variation. Company s model; average R 2 value = 0.5847 mean that forecasting by the company s model of 8 lubricant products can explain 58.47 % of the total variation. For inventory amount: The good forecasting must produce low inventory in stock; Forecasting model; inventory pending in stock average is 139,604 liters Company s model; inventory pending in stock average is 161,426 liters For inventory stock out The good forecasting must perform no stock out Forecasting model; no stock out Company s model; stock out 6 times Table 7 Forecasting summary results of lubricant product A-H during to January 2006 September 2006 Product Name Technique Selected Forecast model Accuracy (R 2 ) Pending Inventory (liter) Stock out Company Forecast Company Forecast Company model Diff model model Diff model model A H-T 0.8012 0.4763 0.3249 160,429 214,828-54,399 NO NO B H-T 0.9990 0.7393 0.2597 192,724 246,118-53,394 NO NO C H-W 0.5430 0.3540 0.1890 267,070 310,359-43,289 NO NO D R-S 0.1759 0.2031-0.0272 156,974 185,573-28,599 NO NO E H-W 0.9822 0.7962 0.1860 83,865 88,914-5,049 NO YES F H-W 0.9095 0.6354 0.2741 87,415 85,485 1,930 NO NO G H-T 0.9980 0.6462 0.3518 85,068 75,078 9,990 NO YES H H-W 0.8195 0.4270 0.3925 83,284 85,051-1,767 NO YES Average 0.7785 0.5347 0.2439 139,604 161,426-21,822

66 REFERENCES Bruce L. Bowerman, Richard T. O Connell and Anne B. Koehler, 2005. Forecasting, Time series and regression and applied approach, Miami University, Ohio. Delurgio and Stephen. Forecasting Principles and Applications, 1998. Burr Ridge, IL: Irwin / McGraw-Hill. Erik Jansson, 2006. Forecast design at GE healthcare Europe, Design of the process for the unit Production forecast. Lulea University of Technology, Germany. Fatimah Mohd. Arshad and Zainalabidin Mohamed, 1980. Price Discovery through Crude Palm Oil Futures: An Economic Evaluation. Faculty of Economics and Management University Putra Malaysia. Hongmei Chen, 2005. A multi-scale forecasting methodology for power plant fleet management, Georgia Institute of technology. James W. Taylor, 2003. Exponential smoothing with a damped multiplicative trend, University of Oxford, UK. J. Scott Armstrong and Fred Collopy, 1998. Integration of statistical methods and judgment for time series forecasting: principles from Empirical research. University of Pennsylvania, Philadelphia. Jonh T. Mentzer and earol C. Bienstock, 1998. The seven principle of sale forecasting system. Leslic A. Christensen, 2003. Introduction to building a linear regression model. Gooyear Tire & Rubber Company. Lidia S.Marko, 2003. Inventory and price forecasting: Evidence from US containerboard industry.

67 Georgia Institute of technology. Michael H. Kutner, Christopher J. Nachtsheim and John Neter, 2004. Applied linear regression models, Emory University, published by McGraw Hill Education (Asia), Singapore. Michael Schacht Hansen, 2002. Visual basic for application Programming Excel. Nkrintra Singhrattna and Balaji Rajagopalan, 2004. Seasonal forecasting of Thailand summer monsoonrainfall, Department of Civil, Environmental and Architectural Engineering, University of Colorado at Boulder, Boulder, USA and Thailand Public Works Department, Bangkok, Thailand. OECD Environmental Health and Safety Publications, 2004. Emission scenario document on lubricants and lubricant additive.located in Paris, France (http://www.oecd.org/ehs/). Prajakta S. Kalekar, 2004. Time series forecasting using Holt-Winters Exponential smoothing. Kanwal -Rekhi school of Information Technology, India. Prasanna Desilkan and Jaideep Srivastava, 2003. Time series analysis and forecasting method for temporal mining of interlinked document. Department of computer science University of Minnesota, USA. Sorana Daniela,2006.Pearson versus Spearman, Kendall's Tau Correlation Analysis on Structure-Activity Relationships of Biologic Active Compounds. University of Medicine and Pharmacy, Cluj-Napoca, Romania Vipul Kedia and Vamsidhar Thummala, 2005. Time series forecasting through clustering. International institute of information technology Hyderabad, India.

68 APPENDIX A Forecasting model operation flow 1 Data Update - Actual demand - Inventory - Variable 2 Adjust smoothing constant - Solver 3 Select forecasting requirement - product name - Time period - Enter 4 Consider parameter - MAD - MSE - Graphical 5 Select & fill the appropriate Forecasting numbers 6 Consider inventory control