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 222.89 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.

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Instituting a Forecasting Model for Purchasing Jute Bales in the Bangladesh Context:
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2. World Review of Business Research
Vol. 8. No. 1. March 2018 Issue. Pp. 207 – 220
207
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
222.89 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, Email: alaminuddin65@gmail.com
†
Sultanul Nahian Hasnat, Assistant Professor, Operations Management Department, Faculty of
Business Administration, American International University-Bangladesh (AIUB), Bangladesh,
Email: hasnat1983@gmail.com

3. Bhuiyan & Hasnat
208
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 2001-10. 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 2005-09, 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 2013. 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

4. Bhuiyan & Hasnat
209
Collection of
raw jute bales
Selection of
quality jute
bales
Softening
process
Carding
(Breaker, Inter
& Finisher)
Piling /
Conditionin
g
Packaging
Precision
winding
Twisting Spinning
1st
spool
winding & 2nd
cope winding
Drawing
(Three levels)
& Doubling
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
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.

5. Bhuiyan & Hasnat
210
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.

6. Bhuiyan & Hasnat
211
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)
2010
1 January 153.30
2013
37 January 218.40
2 February 186.90 38 February 195.30
3 March 228.90 39 March 183.75
4 April 223.65 40 April 252.00
5 May 215.25 41 May 205.80
6 June 257.25 42 June 181.65
7 July 228.90 43 July 405.00
8 August 327.60 44 August 200.55
9 September 247.80 45 September 305.55
10 October 283.50 46 October 131.25
11 November 256.20 47 November 162.75
12 December 306.60 48 December 196.35
2011
13 January 327.60
2014
49 January 152.25
14 February 195.30 50 February 228.90
15 March 290.85 51 March 218.40
16 April 225.75 52 April 279.30
17 May 182.70 53 May 223.65
18 June 262.50 54 June 211.05
19 July 304.50
20 August 425.25
21 September 210.00
22 October 290.85
23 November 94.50
24 December 94.50
2012
25 January 201.60
26 February 244.65
27 March 320.25
28 April 231.00
29 May 273.00
30 June 160.65
31 July 253.05
32 August 214.20
33 September 194.25
34 October 255.15
35 November 157.50
36 December 148.05

7. Bhuiyan & Hasnat
212
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
Regression Statistics
Multiple R 0.55
R Square 0.31
Adjusted R Square 0.10
Standard Error 62.33
Observations 54.00
ANOVA
df SS MS F Significance F
Regression 12 69924.65744 5827.055 1.499660831 0.163735574
Residual 41 159308.8526 3885.582
Total 53 229233.51
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 203.6979167 35.23105888 5.781771 8.83969E-07 132.5473506 274.8484827 132.5473506 274.8484827
Period -0.577430556 0.547552197 -1.05457 0.297798414 -1.683234638 0.528373527 -1.683234638 0.528373527
Jan 21.36784722 41.90473877 0.509915 0.612845269 -63.26048866 105.9961831 -63.26048866 105.9961831
Feb 21.52527778 41.87253051 0.514067 0.609965066 -63.03801219 106.0885677 -63.03801219 106.0885677
Mar 60.32270833 41.84746249 1.44149 0.157041115 -24.18995575 144.8353724 -24.18995575 144.8353724
Apr 54.81013889 41.82954757 1.310321 0.197380747 -29.66634528 139.2866231 -29.66634528 139.2866231
May 33.12756944 41.81879494 0.792169 0.432821647 -51.32719933 117.5823382 -51.32719933 117.5823382
June 28.245 41.81521011 0.675472 0.503168096 -56.20252907 112.6925291 -56.20252907 112.6925291
July 108.6003472 44.16204502 2.459133 0.018236458 19.41328895 197.7874055 19.41328895 197.7874055
Aug 103.2152778 44.13148422 2.338813 0.0243000 14.08993829 192.3406173 14.08993829 192.3406173
Sep 51.29270833 44.10770007 1.162897 0.251595268 -37.78459809 140.3700148 -37.78459809 140.3700148
Oct 52.65763889 44.09070354 1.194303 0.239223527 -36.38534235 141.7006201 -36.38534235 141.7006201
Nov -19.21493056 44.08050247 -0.43591 0.665191948 -108.2373103 69.80744921 -108.2373103 69.80744921
SUMMARY OUTPUT
Table 2 indicates that the coefficient of determination, R Square, is 0.31.
Subsequently, the adjusted value of R Square is 0.10. The outcome specifies that

8. Bhuiyan & Hasnat
213
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
62.33 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:
H0 = Trend and seasonality pattern is not involved.
H1 = 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 H0 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 H0 is not true.
• .05, we have strong evidence that H0 is not true.
• .01, we have very strong evidence that H0 is not true.
• .0001 we have extremely strong evidence that H0 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 0.29. 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 (H0) 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)

9. Bhuiyan & Hasnat
214
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:
Ft = Ft-1 + α (At-1 - Ft-1)
Where,
Ft = Forecast for period t
T = Specified number of time periods
Ft-1 = Forecast for previous period
At-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 0.45. The forecast using simple exponential smoothing is as
follows:

10. Bhuiyan & Hasnat
215
Table 3: Forecast using Simple Exponential Smoothing
Year Month
Jute
Bales (M.
Ton)
Forecast
(M. Ton)
Year Month
Jute Bales
(M. Ton)
Forecast
(M. Ton)
2010
January 153.30 -
2013
January 218.40 175.36
February 186.90 153.30 February 195.30 194.73
March 228.90 168.42 March 183.75 194.98
April 223.65 195.64 April 252.00 189.93
May 215.25 208.24 May 205.80 217.86
June 257.25 211.40 June 181.65 212.43
July 228.90 232.03 July 405.00 198.58
August 327.60 230.62 August 200.55 291.47
September 247.80 274.26 September 305.55 250.56
October 283.50 262.35 October 131.25 275.30
November 256.20 271.87 November 162.75 210.48
December 306.60 264.82 December 196.35 189.00
2011
January 327.60 283.62
2014
January 152.25 192.31
February 195.30 303.41 February 228.90 174.28
March 290.85 254.76 March 218.40 198.86
April 225.75 271.00 April 279.30 207.65
May 182.70 250.64 May 223.65 239.89
June 262.50 220.07 June 211.05 232.58
July 304.50 239.16 July 222.89
August 425.25 268.56
September 210.00 339.07
October 290.85 280.99
November 94.50 285.43
December 94.50 199.51
2012
January 201.60 152.26
February 244.65 174.46
March 320.25 206.05
April 231.00 257.44
May 273.00 245.54
June 160.65 257.90
July 253.05 214.14
August 214.20 231.65
September 194.25 223.80
October 255.15 210.50
November 157.50 230.59
December 148.05 197.70

11. Bhuiyan & Hasnat
216
Table 3 shows the forecast for the historical data and the upcoming month of July
2014. 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

12. Bhuiyan & Hasnat
217
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 153.30 - - - - -
February 186.90 153.30 33.60 33.60 1128.96 17.98
March 228.90 168.42 60.48 60.48 3657.83 26.42
April 223.65 195.64 28.01 28.01 784.78 12.53
May 215.25 208.24 7.01 7.01 49.11 3.26
June 257.25 211.40 45.85 45.85 2102.61 17.82
July 228.90 232.03 -3.13 3.13 9.80 1.37
August 327.60 230.62 96.98 96.98 9404.81 29.60
September 247.80 274.26 -26.46 26.46 700.23 10.68
October 283.50 262.35 21.15 21.15 447.15 7.46
November 256.20 271.87 -15.67 15.67 245.54 6.12
December 306.60 264.82 41.78 41.78 1745.71 13.63
January 327.60 283.62 43.98 43.98 1934.23 13.42
February 195.30 303.41 -108.11 108.11 11688.00 55.36
March 290.85 254.76 36.09 36.09 1302.41 12.41
April 225.75 271.00 -45.25 45.25 2047.66 20.04
May 182.70 250.64 -67.94 67.94 4615.59 37.19
June 262.50 220.07 42.43 42.43 1800.65 16.17
July 304.50 239.16 65.34 65.34 4269.15 21.46
August 425.25 268.56 156.69 156.69 24550.60 36.85
September 210.00 339.07 -129.07 129.07 16659.72 61.46
October 290.85 280.99 9.86 9.86 97.22 3.39
November 94.50 285.43 -190.93 190.93 36453.10 202.04
December 94.50 199.51 -105.01 105.01 11027.06 111.12
January 201.60 152.26 49.34 49.34 2434.89 24.48
February 244.65 174.46 70.19 70.19 4926.57 28.69
March 320.25 206.05 114.20 114.20 13042.61 35.66
April 231.00 257.44 -26.44 26.44 698.95 11.44
May 273.00 245.54 27.46 27.46 754.01 10.06
June 160.65 257.90 -97.25 97.25 9457.06 60.53
July 253.05 214.14 38.91 38.91 1514.29 15.38
August 214.20 231.65 -17.45 17.45 304.41 8.15
September 194.25 223.80 -29.55 29.55 872.97 15.21
October 255.15 210.50 44.65 44.65 1993.59 17.50
November 157.50 230.59 -73.09 73.09 5342.54 46.41
December 148.05 197.70 -49.65 49.65 2465.22 33.54
January 218.40 175.36 43.04 43.04 1852.61 19.71
February 195.30 194.73 0.57 0.57 0.33 0.29
March 183.75 194.98 -11.23 11.23 126.22 6.11
April 252.00 189.93 62.07 62.07 3852.79 24.63
May 205.80 217.86 -12.06 12.06 145.47 5.86
June 181.65 212.43 -30.78 30.78 947.63 16.95
July 405.00 198.58 206.42 206.42 42608.82 50.97
August 200.55 291.47 -90.92 90.92 8266.36 45.34
September 305.55 250.56 54.99 54.99 3024.37 18.00
October 131.25 275.30 -144.05 144.05 20751.31 109.75
November 162.75 210.48 -47.73 47.73 2278.08 29.33
December 196.35 189.00 7.35 7.35 54.01 3.74
January 152.25 192.31 -40.06 40.06 1604.65 26.31
February 228.90 174.28 54.62 54.62 2983.13 23.86
mrach 218.40 198.86 19.54 19.54 381.81 8.95
April 279.30 207.65 71.65 71.65 5133.29 25.65
May 223.65 239.89 -16.24 16.24 263.87 7.26
June 211.05 232.58 -21.53 21.53 463.73 10.20
2953.88 275267.50 1477.72
MAD MSE MAPE
55.73 5193.73 27.88
TOTAL
2010
2011
2012
2013
2014

13. Bhuiyan & Hasnat
218
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
55.73 MT; that means the average absolute deviation from the mean is 55.73 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 5193.73 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 = 5193.73
Upper Control Limit, UCL = 216
Lower Control Limit, LCL = -216
Where, z = 3 standard deviation

14. Bhuiyan & Hasnat
219
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

15. Bhuiyan & Hasnat
220
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.
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