DEMAND FORECASTING FOR PERISHABLE ASSET IN IMPORTER COMPANY (PT. TMM)

Transcription

1 Available online at Procedia ISCLO 00 (2014) The ISCLO Conference 2014 DEMAND FORECASTING FOR PERISHABLE ASSET IN IMPORTER COMPANY (PT. TMM) Mahaning Indrawaty Wijaya, Ratih Hendayani, Sri Widiyanesti Economics and Business Schools, Telkom University JL. Telekomunikasi no.1terusanbuahbatu Bandung 40257, West Java, Indonesia Economics and Business School of TELKOM University Abstract Gap between the sales target and realization in perishable assets cause overstock and understock. Both of them, in the end will potentially decrease corporate profitability. In the last two years, there was significant gap between sales target and realization in PT. Tunas Maju Mandiri (PT.TMM) as a company which has focus in importing horticultural product. Therefore, it is needed to make forecasting to know the number of demand in the future, so that appropriate target can be made. Data was collected by doing a direct interview to PT.TMM and by collecting historical data on PT.TMM s perishable asset target and demand. Based on collected data, three types of product that had highest sales rates were selected, those are China apples, Thailand longans, and China oranges. After that, the data were analyzed by Microsoft Excel, Minitab, and Zaitun Time Series with various kinds of methods such as Naïve Approach, Moving Average, Single Exponential Smoothing, Double Exponential Smoothing (Holt), Triple Exponential Smoothing (Winter), and Trend Projection. One best method was chosen to forecast the future demand. The selection of the best method is based on the lowest error rate that will be shown by MAPE (Mean Absolute Percent Error). The result shows that Triple Exponential Smoothing has the lowest error rate for three types of product, which means this is the best method to forecast future demand of PT.TMM s perishable asset The Authors.Published by Telkom Pub.Ltd. Selection and peer-review under responsibility of The ISCLO, Department of Communication, University of Telkom, Bandung, Indonesia Keywords : demand, forecast, horticulture, importer, perishable asset 1. Introduction PT. Tunas Maju Mandiri (hereinafter referred to PT. TMM) is a global company which has focus in importing horticulture product. Economics and politics are external factors that often affect PT. TMM business as global company which company must be able to avoid the threat and utilize the chance [1]. Economical and political instability led to change in regulations that are PT. TMM obeyed. For example, as a result from the Permendagri 7 in 2013 establishment about opening thirteen horticulture taps as a way to stabilize horticulture product, many parties registered themselves to be horticulture importer, that the number of horticulture importer increase more than 100%, from 79 importer in first semester in 2013, become 169 importer in next semester [2]. Increasing number of competitor can be a threat to the accomplishment of PT. TMM business objectives, which basically, goal of business is to get profit. This happen because costumers will have more choices in selecting horticulture importers, and overall this can reduce profitability of the company as a result of reduced number of customer due to movement of consumers to competitors. In an attempt to gain the advantage, PT. TMM plans sales targets every month. That target planning often was not achieved, which as a whole, in the last two years there was 14% gap between target and actual sales, where the biggest gap occurred in China oranges (22%). That gap caused overstock which led the company losing the money due to unsold goods that were stacked in warehouses, because the nature of horticultural products are perishable (easily decomposed) [3] [4] [5]. It also causes understock, namely the

2 number of stock is under the number of demand. That causes PT. TMM lose the opportunity to sell or even lose the customers. These indicate PT.TMM has not made optimal capacity planning. Therefore, PT. TMM need establish better capacity planning in the future. This can be done by forecasting the future demand [6] [7]. There are many methods of forecasting. Yet, in general, forecasting can be done both quantitatively and qualitatively. Yet quantitative forecasting is more often used in business world because this approach uses the set of numbers to forecast, which the numbers are usually the events in the past, such as the product sales in previous years, market surveys, or other research [8]. The most common quantitative method that is used in demand forecasting is time series method [9]. In time series method, there are several models that are often used, such as moving average, exponential smoothing, and trend projection [9]. Of these methods, the best method will be selected. The best forecasting method is a method that has smallest deviation from the actual values [10]. An amount of research has been done previously by a number of researchers to determine which forecasting method was the best in predicting a wide range of variables or objects. For instance, research which was conducted by Kumar and Sahu [11] which foresees demand for milk products (paneer) in Chhattisgarh, showed that the most suitable method was single exponential smoothing. Then research that was written by Akkurt, et al [12], regarding forecasting of natural gas usage in Turkey showed that the most suitable method is the double exponential smoothing for annually forecast and SARIMA for monthly forecast. Further research, Sahu [13] showed that the naive method and semi-average method is the best method to predict sweet curd in Chhattisgarh, India. Research conducted by Candra and Sarjono [14] showed that the most appropriate method for predicting spring beds are linear regression. Research that was conducted by Daniel [15] showed that moving average method is the most appropriate method to predict the yield production of plastic in manufacturing companies. The previous research indicates that the best forecasting method differs for each item or variable which was forecasted. So that, this research aims to find out the best forecasting method in foreseeing PT.TMMs products demand in the future. That PT. TMM could sell as many as the target and avoid loss that is caused by surplus or shortage of goods. 2. Methodology This research is descriptive quantitative research which aims to describe object characteristics [16] with using a set of numbers [17]. Both of primary data and secondary data were used. Primary data was obtained by conducting unstructured interview, that researchers did not use the guidelines that have been arranged, but only use the questions outline to be asked [18] to general manager of PT. TMM to know about the perishable products. Secondary data was obtained with documenter study method, that was done by collecting chosen documents that appropriate with the goal and focus of problem [19], such as historical data of PT.TMMs perishable asset demand and sales from February 2012 until January 201, and also all information that is related to this research. Then collected data was analyzed with using time series as a research variable. Six time series methods were used as variable attributes of this research, namely Naïve Approach, Moving Average, Single Exponential Smoothing, Double Exponential Smoothing (Holt), Triple Exponential Smoothing (Winter), and Trend Projection. The data processed with Microsoft Excel, Minitab, and Zaitun Time Series. Microsoft excel was used to count error rate of naïve approach method. Minitab was used to count error value of moving average, exponential smoothing, and trend projection methods. Meanwhile Zaitun Time Series [20] was used to determine optimum value of alpha, beta and gamma for triple exponential smoothing. After data was processed, one best method was chosen based on smallest error rate that would be shown by MAPE to forecast future demand of PT. TMMs perishable asset. The model could be seen in Figure 1.1

3 Perishable Asset of PT. TMM Time series : 1. Naïve Approach 2. Moving Average 3. Single Exponential Smoothing 4. Double Exponential Smoothing (Holt) 5. Triple Exponential Smoothing (Winter) 6. Trend Projection Demand Forecasting Quantitative Forecasting Best Forecasting Method Forecasting result for next year Historical data of PT.TMMs products demand MAPE Fig. 1.1 Methodology 2.1. Operational Variables Table 1.1 Operational Variable Variable Attribute Scale Time Series Method Naïve Approach Moving Average Single Exponential Smoothing Double Exponential Smoothing (Holt) Triple Exponential Smoothing (Winter) Trend Projection Forecasting Error MAPE (mean absolute percent error) Naïve Approach Naïve approach method can be done by assuming demand in the future will be equal with demand in the last period [9] Moving Average Moving average method is a method that uses average of n last period demand to forecast next period demand. Mathematically, simple moving average is expressed as follows [9] : Moving Average = (n = total periods in moving average) (1) Single Exponential Smoothing Single Exponential smoothing is method which data plots are weighted by exponential function. The equation of single exponential smoothing is written down as follow [9] F t = F t-1 + α (A t-1 F t-1 ) (2)

4 Which is : F t = new forecast F t-1 = previous forecast α = smoothing constant A t-1 = previous actual demand Double Exponential Smoothing (Holt) Exponential smoothing should be changed when there a trend is detected. It can be done by calculating the average of exponential smoothing data, then adjust it with positive or negative delay (lag) in the trend, so it is also called exponential smoothing method with trend adjustment or double exponential smoothing (Holt). Here is the formula of exponential smoothing with trend adjustment [9] Ft = α (A t-1 ) + (1- α) (F t-1 + T t-1 ) (3) Whereas, for trend adjustment can be done by using following equation [9]. Tt = β (F t - F t-1 ) + (1-β) T t-1 (4) Which is : F t = forecasting with exponential which is smoothed from time series data at period of t T t =trend with exponential which is smoothed at period of t A t = actual demand in period of t α = smoothing constant for average (0 α 1) β = smoothing constant for trend (0 β 1) Triple Exponential Smoothing (Winter) When seasonal pattern is detected, equation needs to be modified to adjust the seasonal pattern. This method is also named exponential smoothing with seasonal adjustment or triple exponential smoothing (winter). Mathematical equation for exponential smoothing with seasonal adjustment is written as follows [10] Ft = α + (1- α) (F t-1 + T t-1 ) (5) Which is : F t = forecasting with exponential which is smoothed from time series data at period of t T t = trend with exponential which is smoothed at period of t = seasonal estimate p = forecasted periods s = length of seasonality Trend adjustment (Tt) can be done by using this following equation [10] Tt = β (F t - F t-1 ) + (1-β) T t-1 (6) Whereas seasonal adjustment (S t ) can be smoothed by. Mathematic equation from seasonal adjustment can be written as follow [10] St = + (1- (7) (Which is smoothing constant for seasonal, 0 1)

5 Trend Projection Trend projection is the method in accordance with trend line of the data plots in the past, and it is projected into the future forecasting. In mathematics, the trend equation is written as follow [9] ŷ = a + bx (8) Which is : ŷ = calculated value from variable which will be predicted (dependent variable) a = y-axis intersection b = regression slope x = independent variable Regression slope can be known by following equation [9] b = (9) Which is : b = regression slope x = known independent variable value y = known dependent variable value = x value average = y value average n = number of data or observation Once the value of b is known, then the value of a (y-axis intersect) can also be determined by the following equation [9] a= (10) MAPE Each forecasting method certainly contains errors, namely the difference between the results of forecasting with actual situation. Calculating forecast error aims to show how well the forecasting method works by using historical data. There are some calculations that are commonly used to calculate total forecast error. Yet, the calculations will produce consistent result when those are used to evaluate different forecasting methods [10]. Thus, to obtain information about what forecasting method is the best method, one sort of calculation can be chosen. One error calculation that is used is MAPE. MAPE (mean absolute percent error) is the average of absolute differentiation between the predicted value and the actual value, expressed as a percentage of the actual value. Equation of MAPE is written as follows [9] : MAPE = (11) 3. Result and Discussion 3.1. Testing Demand Forecasting Method of PT. Tunas Maju Mandiri s Perishable Asset Testing forecasting method was done to know what forecasting method is the most appropriate to forecast the perishable asset. The best method is the method that has lowest error rate. Forecasting is made for the products that dominate company product s sales, namely; China apples, Thailand longans and China oranges. Other products

6 were not forecasted because the proportion was small and there are so many variations, that classification process is difficult to do. Figure 4.1 Percentage of PT Tunas Maju Mandiri s Import Fruit Sales February 2012 January 2014 Period Figure 4.1 shows that the sales are dominated with China apples, followed by Thailand longans and China oranges. The rest are wide range of products which has 20% of total sales. From the various fruits that are sold, there is a gap between the target and actual sales. The target, sales and gap can be seen in Table 4.1 Table 4.1 Target and Actual Sales PT. TMM period February 2012-January 2014 Product name Target Sales Gap China apples 52,535 49,288 6% Thailand longans 41,820 36,918 12% China oranges 35,935 28,041 22% Others 35,820 28,114 21% Total 166, ,361 14% Source: PT. TMM document Based on Table 4.1 it can be seen that during the last two years, there is 14% gap between target and actual sales. So better capacity planning had to be made in the future to minimize the gap. Based on the calculation using Microsoft Excel, Minitab 16, and Zaitun Time Series, forecasting error rate of the each method can be determined. The rates can be seen in table 4.2. Table 4.2 Comparison of Forecasting Methods Error Rate (MAPE) Forecasting Method China apples Thailand longans China oranges Naïve Approach 38% 25% 47% Moving Average 21% 21% 43% Single Exponential Smoothing 31% 24% 48% Double Exponential Smoothing (Holt) 35% 23% 46% Triple Exponential Smoothing (Winter) 11% 11% 17% Trend Projection 29% 23% 48% Based on table 4.2 it can be seen that in predicting demand for PT.TMMs China apples, Thailand longans, and China oranges, triple exponential smoothing (winter) is the method that has lowest error rate (MAPE). Thus, this method is the most appropriate method to forecast the products in the future. However, the error rate of the method in predicting China apples (11%) was greater than the gap between the target and the sales of China apples (6%). This means this method is not better than the capacity planning effort that had been done by PT. TMM previously, namely by intuition. However, the results is still given to be consideration of company's decisionmaking. The results of demand forecasting by using a triple exponential smoothing (winter) can be seen in Table 4.3

7 Table 4.3 Forecasting results for Perishable Asset February 2014-January 2015 Month China apples Thailand longans China oranges February 2,955 2,022 1,744 March 2,177 1,443 1,488 April 1,916 1, May 1,728 1, June 1,599 1, July 2,800 1, August 3,353 2,508 2,199 September 1,362 1, October 1,381 1, November 1,992 1, December 2,642 1,634 1,274 January 2,146 1,616 1,342 Total 26,051 18,712 12,706 Based on Table 4.3, it can be seen that demand of PT. TMM perishable assets during February January 2015 period will fluctuate, where the highest demand for China apples, Thailand longans, and China oranges will occur in August. In this period, China apples sales is predicted to reach 26,051 boxes, 18,712 boxes Thailand longans and 12,706 boxes China oranges. 4. CONCLUSION AND RECOMMENDATION 4.1. CONCLUSION 1) Based on the calculation of error rate from each forecasting method for each product, the result shows that in the case of China apples, the gap already smaller than lowest error rate of the forecasting methods, So the prediction that had been done by PT.TMM with using intuition are the most appropriate method to forecast the demand of Chinese apples. Yet, in other two products, Thailand longans and China orangess, triple exponential smoothing method (winter) has the smallest error rate. So triple exponential smoothing method (winter) is the most appropriate method to forecast Thailand longans and China oranges in February January 2015 period. 2) Based on data processing, it can be seen that during February January 2015 period demand for China apples will reach to 26,051 boxes, Thailand longans will reach to 18,712 boxes and China oranges will reach to 12,706 boxes. During the forecasting period, demand will fluctuate, where the highest demand for China apples, Thailand longans, and China oranges will occur in August, while the lowest demand for those three products will occur in May 4.2. RECOMMENDATION 1) Based on the result of data processing, PT TMM should make demand forecasting using triple exponential smoothing (winter) for the perishable products as a means to minimize forecasting error and risk of loss that are caused by uncertainty in the future. Moreover, that means is easy, fast, and objective to be used, that PT.TMM could make well forecasting of their product 2) Further research should consider other variables that might be affect the sales of PT.TMM perishable assets, such as weather, and macroeconomic factors.

8 REFERENCE [1] David, Fred R. (2013). Strategic Management Concept and Cases. England. Pearson Education Limited [2] Nasib Petani Terancam, Pemohon Jadi Importir Buah Naik 100 Persen. 16 Desember Accessed at March 5 th (2014) [online] [3] Jederman, Reiner. et al. Reducing food losses by intelligent food logistics. Phil. Trans. Royal Society. May [4] Giri, B.C and Chaudhuri, K. S. Deterministic Models of Perishable Inventory with Stock-dependent Demand Rate and Nonlinear Holding Cost. Volume 105, Issue 3, March [5] Feng, Youyi. Perishable Asset Revenue Management with Markovian Time Dependent Demand Intensities. Volume 46, Issue 7 July [6] Tilman, David. Forecasting Agriculturally Driven Global Environmental Change. Science Magazine. Vol. 292 no April [7] Clottey, Toyin. et al. Forecasting Product Returns for Remanufacturing Operations. Decision Sciences Journal. Volume 43, Issue 4, August [8] Kosasih, Sobarsa. (2009). Manajemen Operasi. Jakarta. Mitra Wacana Media. [9] Heizer, Jay dan Barry Render. (2009). Manajemen Operasi. Jakarta. Salemba Empat [10] Hanke, John E. et al. (2001). Business Forecasting Seventh Edition. New Jersey. Prentice-Hall, Inc. [11] Sahu, Pradeep Kumar dan Rajesh Kumar. Demand Forecasting For Sales of Milk Product (Paneer) In Chhattisgarh. International Journal of Inventive Engineering and Sciences (IJIES) Vol.1 No.9 August [12] Akkurt, Mustafa, et al. Forecasting Turkey s Natural Gas Consumption by Using Time Series Methods. European Journal of Economic and Political Studies. Vol.2, [13] Sahu, Pradeep Ku. Use of Time Series Models for Sales Foreecasting of Sweet Curd in Chhattisgarh, India. International Journal of Innovative Research and Studies Vol.2 No.12 [14] Sevenpri Candra dan Haryadi Sarjono. Forecasting for Inventory Control. Journal of Supply Chain Management, Vol.6 June 2012 [15] Ezeliora Chukwuemeka Daniel, et al. Moving Average Analysis of Plastic Production Yield in a Manufacturing Industry. International Journal of Multidiciplinary Sciences and Engineering Vol.4, No.2, February 2013 [16] Zikmund, William G. et al. (2010). Business Research Methods 8th Edition. United States of America. South Western. [17] Nasehudin, Toto Syatori, dan Nanang Gozali. (2012). Metode Penelitian Kuantititatif. Bandung. CV Pustaka Setia [18] Sugiyono. (2012). Metode Penelitian Kuantitatif, Kualitatif, dan Kombinasi (Mixed Mothod). Bandung. Alfabtea, CV [19] Sukmadinata, Nana Syaodih. (2012). Metode Penelitian Pendidikan. Bandung. PT. Remaja Rosdakarya.

9 [20] Badan Pusat Statistik. Zaitun Time Series Mengikuti Ajang Asia Pacific ICT Awards 2009 di Melbourne [Online]. (27 Juni 2014)