Instituting a Forecasting Model for Purchasing Jute Bales in the Bangladesh Context: A Case Study on Sharif Jute Mills Limited

Transcription

1 World Review of Business Research Vol. 8. No. 1. March 2018 Issue. Pp Instituting a Forecasting Model for Purchasing Jute Bales in the Bangladesh Context: A Case Study on Sharif Jute Mills Limited Md Al-Amin Uddin Bhuiyan * and Sultanul Nahian Hasnat In this paper, we focus on preparing a forecasting model for the demand of jute bales to justify the use of modern forecasting concepts in the Bangladesh context. We study 54 months data from Sharif Jute Mills Limited to determine the core data pattern of jute bales requirements for yarn production. Based on our study, we classify the data with stationary patterns including the minimal presence of seasonality using regression analysis and graphical representation. Following the classification, we prepare a forecasting system for upcoming periods with Simple Exponential Smoothing Model. The model predicts that the production process will require MT of jute bales in the upcoming month. Two different methods, (a) MAD, MSE, MAPE calculation, and (b) Control Chart, justify the accuracy of the model with acceptable results. Finally, we discuss research finding and future prospects so that Sharif Jute Mills Limited and similar companies may perform forecasting smoothly and improve the skill level of the procurement system to stay competitive in the global market. Field of Research: Industrial Engineering, Operations Management 1. Introduction Jute is a natural vegetable fiber with golden and silky shine. This golden fiber is cheap and procured from the skin of the plant's stem. It has high tensile potency, low extensibility, and ensures better breathability of fabrics. The eco-friendly and recyclable features make it one of the most versatile natural fibers used as raw material for packaging, textiles, non-textile, construction, and agricultural sectors. The flexibility of blending with other fibers makes jute the second most important vegetable fiber after cotton in terms of availability, usage, global utilization and fabrication (Wikipedia 2018). Jute regained prominence in the international market since the celebration of International Year of Natural Fibers in 2009 by the United Nations. Worldwide consumers preference for ecological products has amplified and opened new opportunities for the jute industries. Also, the global desire for sustainable development and finding alternatives to synthetic products has made the jute items a favorable substitute. According to the Statistics Division of FAO (2013), Bangladesh is the second largest jute bale producer in the world. It is a bold prospect for the local industry to produce diversified jute products and sell them overseas, taking * Muhammad Al-Amin Uddin Bhuiyan, Alumni, Faculty of Business Administration, American International University-Bangladesh (AIUB), Bangladesh, alaminuddin65@gmail.com Sultanul Nahian Hasnat, Assistant Professor, Operations Management Department, Faculty of Business Administration, American International University-Bangladesh (AIUB), Bangladesh, hasnat1983@gmail.com 207

2 advantage of the massive production. Disappointingly, the industry is failing to capitalize on the opportunity and concentrating more on exporting raw jute bales. The growth in export performance by Bangladesh (Rahman & Khaled 2011) indicates a rise of 13.3% in the raw jute bale and 6.4% in the jute merchandises export during However, the raw jute and jute products exported in the world market increased by 39.5% and 57.6% in turn during the same period. China inhabited the uppermost position among the global jute product exporters with 58.1% of the worldwide export. When compared with China, Bangladesh contributed only 6% of the world market. During , jute goods exported from China increased by 181.1%, at the same time that of Bangladesh declined by 11.1%. Over time, Bangladesh has mislaid the competitive advantages in producing and exporting jute products. Rahman and Khaled (2011, pp. 1-6) clearly indicates that the lack of significant investment in product development and diversification as well as incapability to undertake the industrial transformation undermined jute s trade prospects. The failure to achieve cost competitiveness and improve production efficiency are among the major reasons behind the decline in exports. With the golden days of jute sector about to reappear, the government and the private sector of Bangladesh need to make a concerted effort to increase domestic production of quality jute goods and boost exports. In this case, the operations management concepts can play a vital role in improving the jute production system. This applied study concentrates on preparing a forecasting model for jute bales under the Bangladesh business context for Sharif Jute Mills Limited. The study is aimed at preparing a forecast model mainly on procuring jute bales for yarn production after intensely examining the process flow system. It is possible to optimize the ordering time, lead-time, transportation system, seasonal effects and related costs using forecasting as the company makes a profit through their operations. In a broader context, this study can be a learning curve for the jute industry to set a base to order optimum quantity of jute bales from the domestic market and improve the procurement system performance. This paper is as follows: in Section 2, the paper reviews the literature on predicting the jute requirements using forecasting models. Section 3 covers the research methodology. Section 4 explains the data analysis process. Section 5 develops the forecasting model and computes the accuracy in section 6 to validate the research. Finally, Section 7 discusses future research opportunities. 2. Literature Review This research focused on two broad intentions. The first aim was to develop an appropriate forecasting model to estimate the jute bale requirement for an upcoming month. Secondly, to justify the accuracy of the forecasting model using two measuring techniques. The literature review focuses on both features of the study. Hossain and Abdulla (2015) developed a forecasting model to estimate the jute production in Bangladesh. This study considered the data of yearly jute production in Bangladesh from 1972 to The research used three standards to identify the best-fitted model for jute production estimation. The standards revealed that the Box- Jenkins method based Autoregressive Integrated Moving Average (ARIMA - 1,1,1) is the appropriate model for this research objective. This paper estimated the jute 208

3 production for the upcoming 10 years and a graphical representation verified the accuracy of the system. The graphical comparison of the original series and the forecasted series observed that the forecasted series experienced minimal fluctuation from the original series and concluded that the forecasted series is a better illustration of the original jute production in Bangladesh. However, this research did not conduct any trend identification analysis in selecting the forecasting method. Also, the paper did not use any standard methodology to justify the performance of the forecasting model. A study (Karmaker, Halder & Sarker 2017) estimated the upcoming sale of jute yarn for Akij Jute Mills Limited. This study used sales data from 208 weeks (from 2010 to 2013) to compare the accuracy of the 8 forecasting models including: simple moving average; single exponential smoothing; Holt s-winters exponential smoothing; and classical decomposition model. Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percent Error (MAPE) measures were also used to check the accurateness of the forecasting models. The analysis revealed that a multiplicative decomposition model with trend and seasonal effects has minimal errors. In addition, this study divided the data set into multiple sections and performed the analysis based on the visible data pattern. As a result, this study failed to follow a standard procedure in identifying the data pattern and selecting the appropriate forecasting model for the data set. Furthermore, this paper justified the forecasting models using only one method, which may falsify the research findings. 3. Methodology Business forecasting helps to estimate the future demand using business data. It is important to understand the production process in detail and decide on the appropriate segment for data collection purposes. The study carefully examined the jute yarn production process in Sharif Jute Mills Limited and outlined a process flow diagram for the fabrication system: Figure 1: Process Flow Diagram of Yarn Production System Collection of raw jute bales Selection of quality jute bales Softening process Piling / Conditionin g Carding (Breaker, Inter & Finisher) Drawing (Three levels) & Doubling Packaging Precision winding Twisting 1 st spool winding & 2 nd cope winding Spinning After carefully examining the process flow diagram, the study collected data from Selection of quality jute bales section of the system. This division determines the actual number of jute bales required for production as well as procurement purposes after considering the wastage. Hence, a forecasting based on the actual jute bales volume used for yarn production can improve the procurement system. 209

4 According to Stevenson (2005, p. 72), there are two common approaches to forecasting the qualitative approach and the quantitative approach. Qualitative methods consist of subjective inputs, which often defy specific numerical description. On the other hand, quantitative methods involve historical data projection to make a forecast. It usually evades individual biases that sometimes infect qualitative methods. Also, the data pattern is a significant factor in understanding how the time series behaved in the past. If such behavior continues in the future, the past pattern works as a guide in selecting a suitable forecasting method. After performing the forecast, accuracy and control of the forecast is a vital aspect. It is essential to include an indication of the extent to which the forecast may deviate from the value of the variable that occurs. Stevenson has also mentioned that it is vital to monitor forecast errors during periodic forecasts to determine if the errors are within reasonable bounds. If they are not, it is necessary to take corrective action. The study principally focused on quantitative approach rather than the qualitative approach, used by the organization, to prepare the forecast. As historical data is available, the quantitative method can provide a better result in a forecasting model. Based on that, the study collected monthly jute bales usage data from a secondary source at Sharif Jute Mills Limited. Regression analysis and graphical method assisted to systematically analyze the data and identify the underlying series pattern. Based on the analysis, the study selected an appropriate time series forecasting model to prepare a forecast for upcoming periods. The forecasted data was justified using various methods to ensure the accuracy and control of forecast. According to Russell and Taylor (2011, p. 502) the summary of the methodology as follows: Figure 2: Forecasting Process 4. Data Analysis The study gathered 54 months (From January 2010 to June 2014) of information about actual jute bales used for production purpose from a secondary source. The analysis process explored the information from different viewpoints to understand the data series pattern and to find an appropriate forecasting model to perform the forecasting. 210

5 Table 1: Actual Demand of Jute Bales in Metric Ton (MT) Year Period Month Jute Bales (M. Ton) Year Period Month Jute Bales (M. Ton) January January February February March March April April May May June June July July August August September September October October November November December December January January February February March March April April May May June June July August September October November December January February March April May June July August September October November December

6 Figure 3: Jute Bales Used for Production Figure 3 represents the information regarding actual jute bales used to produce yarn for four and half years in Sharif Jute Mills Limited. From the chart, it is visible that the jute bales demand remained steady over the production time frame. There is no hint of trend and seasonal pattern in the data series. To be more precise, the study conducted a regression analysis to clarify the presence of a trend and a seasonal pattern in the demand outline: Table 2: Regression Analysis for Trend and Seasonality SUMMARY OUTPUT Regression Statistics Multiple R 0.55 R Square 0.31 Adjusted R Square 0.10 Standard Error Observations ANOVA df SS MS F Significance F Regression Residual Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept E Period Jan Feb Mar Apr May June July Aug Sep Oct Nov Table 2 indicates that the coefficient of determination, R Square, is Subsequently, the adjusted value of R Square is The outcome specifies that 212

7 the independent variables are accountable for only 10% of the variation in the dependent variable, the jute bales demand. The standard error of the regression is MT, which is an estimate of the variation of the observed demand about the regression line. Also, the analysis generated p-values based on the dummy variables in consideration of the following hypothesis: H 0 = Trend and seasonality pattern is not involved. H 1 = Trend and seasonality pattern is involved. According to Lind, Marchal and Wathen (2010, p. 339), determining the p-value not only results in a decision regarding H 0 but also it gives additional insight into the strength of the decision. In statistical significance testing, the p-value is the probability of obtaining a test statistic result at least as extreme as the one that is observed, assuming that the null hypothesis is true. An informal interpretation of a p- value, based on a significance level of 5% or 10%, if the p-value is less than:.10, we have some evidence that H 0 is not true..05, we have strong evidence that H 0 is not true..01, we have very strong evidence that H 0 is not true we have extremely strong evidence that H 0 is not true. From Table 2, it is noticeable that the F-statistic value is reasonably high. Comparing this value with 5%, it indicates the acceptance of the null hypothesis. Also under the estimated regression line, the p-value for the period is The higher value of p rejects the presence of any trend pattern within the data series. The analysis further reveals that the relationship between the months and demand of jute bale is negative, except for the months July and August. The discrepancy factor underlies in the jute cultivation process in Bangladesh. The jute cultivation takes place at different times throughout the country because of the environment and earth features. In the southern region of Bangladesh, jute is cultivated in the months of April-May, and harvested in July-August. The nation produces a majority of the portion of the jute during this season taking advantage of the suitable condition. On the other hand, the northern region plants jute in August-September and harvests them during November-December. The dealers and whole sellers buy raw jute from the farmers and sell them all over the country by their distribution channels. Sharif Jute Mills Limited purchases major shares of the required jute bales from the wholesale market. They acquire limited quantity directly from the farmers during the July-August period. Adding managerial judgment with the quantitative forecasting can assist to overcome the minor seasonal impact for the July-August period. As a result, we can conclude that the null hypothesis (H 0 ) of Trend and seasonality pattern is not involved in the data series is accepted. The jute bales demand for Sharif Jute Mills Limited falls under the stationary pattern and Delurgio (1999, p. 48) prescribes simple exponential smoothing as the most suitable forecasting method for this pattern. 5. Forecasting Model In preparing the forecasting model for jute bales, the study considered a simple exponential smoothing forecast model. According to Stevenson (2005, p. 84) 213

8 exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at the point. That is: Next Period Forecast = Previous forecast + α (Actual - Previous Forecast) Where, (Actual - Previous Forecast) signify the forecast error and α is the smoothing constant represents a percentage of forecast error. The simple exponential smoothing equation is as follows: Where, F t = F t-1 + α (A t-1 - F t-1 ) F t = Forecast for period t T = Specified number of time periods F t-1 = Forecast for previous period A t-1 = Actual demand for previous period α = Smoothing constant In preparing the forecast, determining the value of the smoothing constant (α) is an important issue. When the α value is low the forecasting curve becomes smoother but less adjusting to forecasting error. In contrast, when α value is high the smoothness of the forecast curve goes away and becomes more adjusting to forecast error. The study used Microsoft Excel Solver to calculate the optimum value of α and the result was The forecast using simple exponential smoothing is as follows: 214

9 Table 3: Forecast using Simple Exponential Smoothing Year Month Jute Bales (M. Ton) Forecast (M. Ton) January Year Month Jute Bales (M. Ton) Forecast (M. Ton) January February February March March April April May May June June July July August August September September October October November November December December January January February February March March April April May May June June July July August September October November December January February March April May June July August September October November December

10 Table 3 shows the forecast for the historical data and the upcoming month of July To visualize the performance of the model the study plotted the forecasted values along with the actual demand of jute bales in the following graph: Figure 4: Forecast with Actual Demand of Jute Bales. In Figure 4, the blue line represents the actual demand of previous periods and the red line represents the forecast of previous and coming periods for jute bales. From the figure, it is visible that the forecast line is adjusting to the actual demand value. The error level seems to be in range. To be more precise about the performance of the forecasting model it is necessary to conduct an accuracy test. 6. Accuracy Test To judge the performance of the forecasting model it is required to perform the accuracy test using one or more measures. The goal is to minimize the forecast error, as the complex nature of most real-world variables makes it hard to correctly predict the future value of the demand on a regular basis. Consequently, it is important to include an indication of the extent to which the forecast might deviate from the value of the demand that occurs. Stevenson (2005, p. 93) describes that the commonly used measures for summarizing historical errors are the MAD, MSE and MAPE: MAD = Σ Actual - Forecast / No. of periods MSE = Σ (Actual Forecast)² / No. of periods MAPE = Σ (( Actual - Forecast / Actual) 100) / No. of periods 216

11 Table 4: Calculation of MAD, MSE and MAPE Year Month Jute Bales (M. Ton) Forecast (M. Ton) (Actual - Forecast) Actual - Forecast (Actual - Forecast)² ( Actual - Forecast /Actual)*100 January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December January February March April May June July August September October November December January February mrach April May June TOTAL MAD MSE MAPE

12 MAD measures the difference between actual demand and average forecast values providing equal weight to all errors. In the above forecasting model, the MAD is MT; that means the average absolute deviation from the mean is MT. MSE measures the average of the squares of the errors. The MSE is the second moment (about the origin) of the error, and thus incorporates both the variance of the estimator and its bias. In this model, the MSE is MT. MAPE provides the measurement of forecast error relative to the actual value. In the forecasting model, the MAPE is 27.88%; that means the average absolute percentage of error is 27.88%. Another useful tool for monitoring forecast errors is the control chart. In this method, errors are plotted on a control chart in the order that they occur. The centerline of the chart represents an error of zero. There are two limits in the control chart named Upper Control Limit (UCL) and Lower Control Limit (LCL). They represent the upper and lower ends of the range of acceptable variation for the errors. Another commonly used method to monitor forecast error is tracking signal, but Stevenson (2005, p. 96) claimed control chart as a better approach than the tracking signal. He mentioned that the main weakness of the tracking signal approach is its use of cumulative errors; individual errors can be obscured so that large positive and negative values cancel each other. Conversely, with control chart every error is judged individually. Therefore, it can be misleading to rely on a tracking signal approach to monitor errors. In the modern age of technology, easy calculation of standard deviation has given the control chart superiority over the tracking signal. Control chart assumes that when errors are random, they will be distributed according to a normal distribution around a mean of zero. Hence, for a standard deviation of 3 approximately 99.74% of the values can be expected to fall within ±3s of zero. Standard Deviation, s = MSE Upper Control Limit, UCL = 0 + z MSE Lower Control Limit, LCL = 0 - z MSE Where, z = Standard deviations from the mean Using the value of MSE from Table 6 the calculation is as follows: Standard Deviation, s = Upper Control Limit, UCL = 216 Lower Control Limit, LCL = -216 Where, z = 3 standard deviation 218

13 Figure 5: Control Chart According to the control chart in Figure 5 all the values are within the range. The values are randomly distributed in the chart, which represents the stability of the process. From the above discussion, it can be concluded that the forecasting model is working suitably. It is notable that forecast accuracy decreases as the time horizon increases. The longer time span allows the environmental factors to fluctuate and creates an impact on the estimation. The leadership can avoid this situation through continuously monitoring the performance of this forecasting model. The procurement department should update the MAD, MSE, and MAPE and control chart for monitoring the accuracy of this forecasting model. This will apprise the leadership about the prevailing condition of this forecasting model. 7. Conclusion The jute bale production is highly dependent upon environment and less predictable than any time before. The industry leaders are constantly monitoring the demand patterns and developing forecasting models to predict the jute bale requirements. This research sensibly analyzes the yarn production process of Sharif Jute Miles Limited and pinpoints Selection of quality jute bales section to develop a forecasting system and consequently, optimize the jute bale purchasing system. The data analysis process follows a standard methodology and includes trend and seasonality identification procedure compared to the studied articles. This study develops a time series forecasting model and predicts the jute bale requirements in Sharif Jute Miles Limited. Two accuracy measures justified the performance of the forecasting models. This specific research finding is more beneficial for the manufacturing companies to improve their procurement system compared to the broad intentions of the reviewed articles. The research finding will assist the leaders 219

14 to make acute decisions in adopting the demand patterns in production strategies. Future models can incorporate qualitative, environmental and economic data into their forecasting models to identify the changes in the factors influencing the demand pattern and develop an early alert system. Furthermore, this research will work as a platform for future experimentation on deploying a complete predictive analysis based forecasting system for the jute product manufacturing industry. References Delurgio, S.A. 1999, Forecasting Principles and Applications, 1st edn. McGraw-Hill, New York. Hossain, M.M. and Abdulla, F 2015, Jute Production in Bangladesh: A Time Series Analysis, Journal of Mathematics and Statistics, Vol. 11, No. 3, Pp Karmaker, C.L., Halder, P.K. and Sarker, E 2017, Jute Production in Bangladesh: A Time Series Analysis, Journal of Industrial Engineering, Vol. 2017, No. 1, Pp. 1-8, viewed 08 March 2017, < Lind, D.A., Marchal, W.G. and Wathen S.A. 2010, Statistical Techniques in Business and Economics, 14th edn, McGraw-Hill, New York. Rahman, M and Khaled, N 2011, Global Market Opportunities in Export of Jute, Occasional Paper: 93, Centre for Policy Dialogue, Dhaka. Russell, R.S. and Taylor, B.W. 2011, Operations Management - Creating Value Along the Supply Chain, 7th edn, John Wiley & Sons, Inc., Hoboken. Stevenson, W.J. 2005, Operations Management, 9th edn, McGraw-Hill, New York. Statistics Division 2013, Production of top five producers, Statistics Division of FAO, viewed 17 September 2015, < Wikipedia 2018, Jute, Wikipedia, viewed 16 January 2018, < 220