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\title{Prediction and Judgmental Adjustments of Supply-Chain Planning in Festive Season}
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\begin{document}

             \author[1]{Megha  Chhabra}

             \author[2]{Deepti  Sahu}

             \author[3]{Gunjan  Agarwal}

             \affil[1]{  Sharda University}

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\date{\small \em Received: 14 December 2016 Accepted: 1 January 2017 Published: 15 January 2017}

\maketitle


\begin{abstract}
        


For a robust performance, Shipping costs planning in festive seasons is given the input data as free from trends, season-of-year effects etc. Seasonal forecasting for supplychain planning with past few years of similar data impact shipping costs. Additionally, during a festive season of the year, unbiased and accurate prediction of shipment load plays a major role in bringing up sales. Time-series forecasting methods can be useful to remove traditional fluctuations due to gap in months-of-year of festivals. We describe exponential smoothing techniques and trend fitting methods and compare the predictive accuracy. The accuracy is compared using rootmean square error and median absolute deviation. The exponential smoothing shows changing behavior with increased data size and data item values. The data is compared with and without tuning the seasonal effects due to festive season.

\end{abstract}


\keywords{supply-chain planning, shipment load, forecasting.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
upply-chain holds a huge planning propaganda as a baseline to project sales and revenue generation based on it. Specially a case of modern era, where the comfort to customer can help an organization to retain the customer and thereby increase more sales and generate more business of the products. In addition to it, the planned resource production prediction helps generating less of the cost of production and more of the effort on quality productivity. Supply chain managements involve huge planning horizon for demand forecasting. For the purpose they use forecast systems for initial forecast followed by judgmental adjustment by the company experts to adjust exceptional events in the planning process. The manual adjustments made raise questions related to improvement of accuracy and type of adjustments made. Effective Short-term forecasting is important for improving supply chain management \hyperref[b22]{[26]}, irrespective of the type of business. Multiple applications of the prediction analysis and adjustment behavior in prediction accuracy can be seen in past few years \hyperref[b10]{[14]}- \hyperref[b12]{[16]}, \hyperref[b14]{[18]}- \hyperref[b16]{[20]}. According to the literature of economic forecasting, accuracy of the statistical decisions can be improved when experts consider the changes in the statistical models according to the changes coming from occurrence of special events \hyperref[b0]{[1]}- \hyperref[b5]{[8]}, \hyperref[b7]{[10]}, \hyperref[b8]{[11]} and \hyperref[b18]{[22]}- \hyperref[b19]{[23]} showed that the suggested judgmental adjustments tend to improved accuracy marginally but may also introduce bias. Since it's a human added knowledge as a judgement factor, it is more likely to make error in level of adjustment and make room for error as experimental evidences suggest \hyperref[b20]{[24]}- \hyperref[b22]{[26]}. Forecasters make decisions on the basis of noisy and randomly fluctuating events in time series \hyperref[b6]{[9]}.\par
Several methods, techniques have been used in literature to forecast load demands. We used exponential smoothing technique and trend fitting for prediction.\par
This study presents effect of seasonal demand on prediction methodology of above mentioned models using reference data of handlooms business sector for predicting shipment load for four different Handlooms companies. The proposed methods are used to predict one month's demand. The outcome of both models is analyzed and accuracy is compared. 
\section[{II. Time Series Models}]{II. Time Series Models}\par
A time series is sequential nature of data produced during a certain period of time. Assuming no major disrupting to critical parameters of a recurring event, the future prediction is always related to past data. Two time-series analysis models, namely, multiplicative decomposition and the smoothing technique use the dependency of future data to the past events, and model the behavior as follows: 
\section[{a) Smoothing Techniques}]{a) Smoothing Techniques}\par
Smoothing techniques are used to smoothen out random variations in the data due to irregular components of the time series. They provide a clearer and better view of data and it is easy to understand. 1) Moving averages: A moving average (MA) is an average of the data provided for certain number of time period. The method is called "moving" because it is obtained using summing and averaging the values from a given number of periods say n, each time deleting the oldest value and adding the new one. The moving average is calculated as:MA t+1 =? ???1 ??=1\textbf{(1)}\par
Where t= current period.  
\section[{c) Trend and seasonal component}]{c) Trend and seasonal component}\par
To occupy the Seasonal festive pattern, time series decomposition model breaks down, analyzes and forecasts the seasonal and the trend components. The method is often referred as Time series decomposition, since the technique is analyzing seasonal indexes after decomposing the series in order to identify seasonal components called as seasonal indexes. These helps deseasonalize the series. This deseasonalized series helps in projecting trend projection line. Lastly, seasonal indexes are used to seasonalize the trend projection. \hyperref[b23]{[27]}.The steps involved are as follows:\par
1. Identify the quarters, months etc. and calculate centered moving averages (CMA).  
\section[{III. Experimentation}]{III. Experimentation}\par
The data is input to both the methods with and without tuning the seasonal effects. In order to fit the seasonal component, extent of seasons is fixed for a month's duration. For example, Ludhiana manufacturers tend to see a huge impact on sale during Diwali, Baisakhi etc. Data is selected and analyzed from four Ludhiana-based handloom manufacturers. For a better accuracy rate, last three years data is analyzed. In the given market trend of last three years, Each festive  
\section[{a) Data}]{a) Data}\par
The data is collected for the festive season's months of Punjab for Ludhiana based four Handlooms manufacturers for the last three years. Table \hyperref[tab_2]{1} is organized structure of observed shipment load for the festive season of Lohri (Jan), Holi (March), Vaisakhi (April), Rakhsha-Bandhan (August), Krwachauth(Sep-Oct), Diwali and E-id (Oct-Nov), Guru-Nanak Jayanti (Nov) and finally Christmas(Dec) for all four handlooms. Along with these values, table1 also contains one last entry as observed value of Lohri (Jan  {\ref '17)} .\par
The graphical representation of the observed data along with its linear trend fitting is shown in graph1. The graph shows observed shipment lad of all four handlooms over the seasonal period of last three years along with one last entry as observed shipment load of Jan'17 which is value of interest here. 
\section[{b) Results}]{b) Results}\par
All three Smoothing averages and trend fitting with and without tuning the trend effect are applied on the data collected and outcome is predicted for festive season of Lohri (Jan  {\ref '17)}. The results are compared with already observed value for Lohri (Jan  {\ref '17)}. 
\section[{i. Smoothing Technique}]{i. Smoothing Technique}\par
? Moving averages: Here the moving average (MA) is an average of the data provided for observed shipment load of all four Handlooms for festive seasons of past three years. Graph 2 shows Observed Vs Forecasted shipment load with 3period -moving average for the festive season of past three years for Ludhiana based four Handlooms supply-chain companies. The graph illustrates that with 3 period moving average the next forecasted value that is Jan'17 reduced than observed value. Where as in graph 3 shows with the 4-period moving average the forecast increases. handlooms in the market hence, adjusted exponential smoothening (F t ) adj is obtained as a result. Therefore using eq 3, trend adjusted forecast is calculated with ? = 2/(n+1) i.e.= 2/(27+1) = 0.1, and initial T t = 0 and ?= 0.1. The adjusted forecast in table4 gives final (F t ) adj =2280.922 which is close to simple exponential smoothing without any tuning for trend effects F t =2312.219 Accuracy of forecast is better judged by finding mean square error for different values of smoothing constant ? (0< ??1). In order to get which smoothing factor gives better result, comparison between forecasts for ?=0.1 and ?=0.8 is shown in table \hyperref[tab_6]{5}. Result shows for ?=0.8 is relatively gives more root mean square error hence less accurate forecast for large data set. It is observed that the data with larger fluctuations over the period of time more than a year does not predict accurate using exponential smoothing. ii. Trend Fitting: Using eq6 the model can be fitted using table1 data. To occupy the Seasonal festive pattern, time series decomposition model breaks down, analyzes and forecasts the seasonal and the trend components. The given data set has distinct nine seasons hence the forecast is effected by the trend and seasonal component. Table  {\ref 6} shows before and after seasonal and trend decomposition effect comparison of trend fitting. The forecasted value comes out to be T = 2378and 2322 resp. for Jan'17.\par
The accuracy so measure for all the methods applied are shown in table \hyperref[tab_7]{7}. Accuracy is compared by calculating MSE and RMSE of all the forecasts so far applied in this work. The lesser the RMSE better is the forecast. As shown in table 7, Trend fitting after trend deseasonalization gives least RMSE and hence is the best forecast seen. 
\section[{IV. Conclusion}]{IV. Conclusion}\par
The techniques used in this case study shows following results based on forecast and the measure of error based on MSE and RMSE:  
\section[{SEASONS IN ALL THREE YEARS}]{SEASONS IN ALL THREE YEARS} 
\section[{O B S E R V E D S H I P M E N T L O A D ( U N I T S P E R P A C K O F H A N D L O O M S ) ( Y )}]{O B S E R V E D S H I P M E N T L O A D ( U N I T S P E R P A C K O F H A N D L O O M S ) ( Y )}\par
Prediction and Judgmental Adjustments of supply-Chain Planning in Festive Season\par
? Using Weighted moving averages, given the understanding of the owner of the sales-market head, the weights assigned to the various months of certain period 'n' hugely impacts the forecast value accuracy level. In the given data set, the weights assigned by the stakeholder proves to give best outcome of all forecasts. WMA is the best suited outcome for the dataset.  
\section[{SEASONS IN ALL THREE YEARS O B S E R V E D S H I P M E N T L O A D ( U N I T S P E R P A C K O F H A N D L O O M S ) ( Y )}]{SEASONS IN ALL THREE YEARS O B S E R V E D S H I P M E N T L O A D ( U N I T S P E R P A C K O F H A N D L O O M S ) ( Y )}\par
Prediction   \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.85\textwidth}}
shows 3-month\end{longtable} \par
 
\caption{\label{tab_1}Table 2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.2683647798742138\textwidth}P{0.02459119496855346\textwidth}P{0.09729559748427673\textwidth}P{0.11119496855345912\textwidth}P{0.022452830188679246\textwidth}P{0.09515723270440252\textwidth}P{0.11974842767295597\textwidth}P{0.011761006289308176\textwidth}P{0.09943396226415094\textwidth}}
Year\tabcellsep \tabcellsep 1\tabcellsep \tabcellsep 2\tabcellsep \tabcellsep \tabcellsep 3\tabcellsep \\
\tabcellsep \tabcellsep Observed\tabcellsep \tabcellsep \tabcellsep Observed\tabcellsep \tabcellsep \tabcellsep Observed\\
Festive Month\tabcellsep Season (t)\tabcellsep Shipment Load (Units per pack of\tabcellsep Festive Month\tabcellsep Season (t)\tabcellsep Shipment Load (Units per pack of\tabcellsep Festive Month\tabcellsep \tabcellsep Shipment Load (Units per pack of\\
\tabcellsep \tabcellsep Handlooms)(Y )\tabcellsep \tabcellsep \tabcellsep Handlooms)(Y)\tabcellsep \tabcellsep \tabcellsep Handlooms)(Y)\\
(Jan'14)\tabcellsep 1\tabcellsep 2500\tabcellsep (Jan'15)\tabcellsep 1\tabcellsep 2200\tabcellsep (Jan'16)\tabcellsep 1\tabcellsep 2300\\
(March'14)\tabcellsep 2\tabcellsep 1130\tabcellsep (March'15)\tabcellsep 2\tabcellsep 1145\tabcellsep (March'16)\tabcellsep 2\tabcellsep 1130\\
(April'14)\tabcellsep 3\tabcellsep 2200\tabcellsep (April'15)\tabcellsep 3\tabcellsep 2500\tabcellsep (April'16)\tabcellsep 3\tabcellsep 2400\\
(August'14)\tabcellsep 4\tabcellsep 2250\tabcellsep (August'15)\tabcellsep 4\tabcellsep 2300\tabcellsep (August'16)\tabcellsep 4\tabcellsep 2250\\
(Sep-Oct'14)\tabcellsep 5\tabcellsep 3450\tabcellsep (Sep-Oct'15)\tabcellsep 5\tabcellsep 3400\tabcellsep (Sep-Oct'16)\tabcellsep 5\tabcellsep 3350\\
(Oct-Nov'14)\tabcellsep 6\tabcellsep 3000\tabcellsep (Oct-Nov'15)\tabcellsep 6\tabcellsep 2800\tabcellsep (Oct-Nov'16)\tabcellsep 6\tabcellsep 3000\\
(Oct-Nov'14)\tabcellsep 7\tabcellsep 3330\tabcellsep (Oct-Nov'15)\tabcellsep 7\tabcellsep 2850\tabcellsep (Oct-Nov'16)\tabcellsep 7\tabcellsep 3150\\
(Nov'14)\tabcellsep 8\tabcellsep 1100\tabcellsep (Nov'15)\tabcellsep 8\tabcellsep 1200\tabcellsep (Nov'16)\tabcellsep 8\tabcellsep 1400\\
(Dec'14)\tabcellsep 9\tabcellsep 1700\tabcellsep (Dec'15)\tabcellsep 9\tabcellsep 1950\tabcellsep (Dec'16)\tabcellsep 9\tabcellsep 1900\\
\tabcellsep Year\tabcellsep \tabcellsep \tabcellsep 4\tabcellsep \tabcellsep (Jan'17)\tabcellsep 1\tabcellsep 2250\\
\multicolumn{5}{l}{? Weighted moving Average (WMA): The expert}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{planner/ analysts of the companies decide to weigh}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{the past three month's sales. WMA calculated using}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: average weightage given to past values for the combined data is shown in table 3. Using observed Shipment load for the last three months from table1, WMA is calculated for festive season of Lohri (Jan'17) as follows: WMA for Lohri'17= 2233.33.]} 
\caption{\label{tab_2}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.11195121951219512\textwidth}P{0.21042682926829268\textwidth}P{0.017621951219512197\textwidth}P{0.019695121951219512\textwidth}P{0.13475609756097562\textwidth}P{0.13682926829268294\textwidth}P{0.09951219512195122\textwidth}P{0.11920731707317074\textwidth}}
\multicolumn{5}{l}{Year 2017 ) ? Exponential smoothing Technique: Since trend is expected out of festive season demands of the}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Observed Shipment Load\tabcellsep \tabcellsep \\
(\tabcellsep Festive Month\tabcellsep \multicolumn{2}{l}{Year Season(t)}\tabcellsep (Units per pack of Handlooms)\tabcellsep MA 3\tabcellsep MA 4\tabcellsep CMA 4\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep (y)\tabcellsep \tabcellsep \\
\tabcellsep (Jan'14)\tabcellsep \tabcellsep 1\tabcellsep 2500\tabcellsep \tabcellsep \\
\tabcellsep (March'14)\tabcellsep \tabcellsep 2\tabcellsep 1130\tabcellsep 1943.333\tabcellsep \\
\tabcellsep (April'14)\tabcellsep \tabcellsep 3\tabcellsep 2200\tabcellsep 1860\tabcellsep 2020\tabcellsep 2138.75\\
\tabcellsep (August'14)\tabcellsep \tabcellsep 4\tabcellsep 2250\tabcellsep 2633.333\tabcellsep 2257.5\tabcellsep 2491.25\\
\tabcellsep (Sep-Oct'14)\tabcellsep 1\tabcellsep 5\tabcellsep 3450\tabcellsep 2900\tabcellsep 2725\tabcellsep 2866.25\\
\tabcellsep (Oct-Nov'14)\tabcellsep \tabcellsep 6\tabcellsep 3000\tabcellsep 3260\tabcellsep 3007.5\tabcellsep 2863.75\\
\tabcellsep (Oct-Nov'14)\tabcellsep \tabcellsep 7\tabcellsep 3330\tabcellsep 2476.667\tabcellsep 2720\tabcellsep 2501.25\\
\tabcellsep (Nov'14)\tabcellsep \tabcellsep 8\tabcellsep 1100\tabcellsep 2043.333\tabcellsep 2282.5\tabcellsep 2182.5\\
\tabcellsep (Dec'14)\tabcellsep \tabcellsep 9\tabcellsep 1700\tabcellsep 1666.667\tabcellsep 2082.5\tabcellsep 1809.375\\
\tabcellsep (Jan'15)\tabcellsep \tabcellsep 1\tabcellsep 2200\tabcellsep 1681.667\tabcellsep 1536.25\tabcellsep 1711.25\\
\tabcellsep (March'15)\tabcellsep \tabcellsep 2\tabcellsep 1145\tabcellsep 1948.333\tabcellsep 1886.25\tabcellsep 1961.25\\
\tabcellsep (April'15)\tabcellsep \tabcellsep 3\tabcellsep 2500\tabcellsep 1981.667\tabcellsep 2036.25\tabcellsep 2186.25\\
\tabcellsep (August'15)\tabcellsep \tabcellsep 4\tabcellsep 2300\tabcellsep 2733.333\tabcellsep 2336.25\tabcellsep 2543.125\\
\tabcellsep (Sep-Oct'15)\tabcellsep 2\tabcellsep 5\tabcellsep 3400\tabcellsep 2833.333\tabcellsep 2750\tabcellsep 2793.75\\
\tabcellsep (Oct-Nov'15)\tabcellsep \tabcellsep 6\tabcellsep 2800\tabcellsep 3016.667\tabcellsep 2837.5\tabcellsep 2700\\
\tabcellsep (Oct-Nov'15)\tabcellsep \tabcellsep 7\tabcellsep 2850\tabcellsep 2283.333\tabcellsep 2562.5\tabcellsep 2381.25\\
\tabcellsep (Nov'15)\tabcellsep \tabcellsep 8\tabcellsep 1200\tabcellsep 2000\tabcellsep 2200\tabcellsep 2137.5\\
\tabcellsep (Dec'15)\tabcellsep \tabcellsep 9\tabcellsep 1950\tabcellsep 1816.667\tabcellsep 2075\tabcellsep 1860\\
\tabcellsep (Jan'16)\tabcellsep 3\tabcellsep 1\tabcellsep 2300\tabcellsep 1793.333\tabcellsep 1645\tabcellsep 1795\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.24332061068702288\textwidth}P{0.043256997455470736\textwidth}P{0.025954198473282442\textwidth}P{0.06704834605597965\textwidth}P{0.04974554707379135\textwidth}P{0.011895674300254453\textwidth}P{0.022709923664122136\textwidth}P{0.012977099236641221\textwidth}P{0.012977099236641221\textwidth}P{0.008651399491094149\textwidth}P{0.17951653944020357\textwidth}P{0.021628498727735368\textwidth}P{0.010814249363867684\textwidth}P{0.010814249363867684\textwidth}P{0.030279898218829517\textwidth}P{0.012977099236641221\textwidth}P{0.03676844783715012\textwidth}P{0.021628498727735368\textwidth}P{0.011895674300254453\textwidth}P{0.008651399491094149\textwidth}P{0.0064885496183206106\textwidth}}
\multicolumn{2}{l}{.Season (t)}\tabcellsep \tabcellsep \tabcellsep \multicolumn{10}{l}{Weights (w) Values (y) weights*value}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Festival}\\
\multicolumn{2}{l}{Last Month}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{1/2}\tabcellsep \tabcellsep \tabcellsep 1900\tabcellsep 950\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Christmas}\\
\multicolumn{3}{l}{Two months ago}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{1/6}\tabcellsep \tabcellsep \tabcellsep 1400\tabcellsep 233.33\tabcellsep \tabcellsep \tabcellsep \multicolumn{6}{l}{Gurunanak Jayanti}\\
\multicolumn{3}{l}{Three Months ago}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{1/3}\tabcellsep \tabcellsep \tabcellsep 3150\tabcellsep 1050\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Eid}\\
\tabcellsep \tabcellsep \multicolumn{7}{l}{Forecasted value}\tabcellsep \tabcellsep \tabcellsep 2233.33\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Lohri(Jan'17)}\\
\multicolumn{20}{l}{Observed shipment load for the festive season of 2016 for Ludhiana based four Handlooms supply-chain}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep companies.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{17}{l}{OBSERVED SHIPMENT LOAD (UNITS PER PACK OF HANDLOOMS)}\\
\multicolumn{2}{l}{1000 2000 3000 4000 Graph 2: 0 Observed Shipment load}\tabcellsep (Jan'14)\tabcellsep (March'14)\tabcellsep (April'14)\tabcellsep (August'14)\tabcellsep (Sep-Oct'14)\tabcellsep (Oct-Nov'14)\tabcellsep (Oct-Nov'14)\tabcellsep (Nov'14)\tabcellsep \multicolumn{2}{l}{(Dec'14) SEASONS IN ALL THREE YEARS (Jan'15) (March'15) (April'15) (August'15) (Sep-Oct'15) (Oct-Nov'15) (Oct-Nov'15) (Nov'15) (Dec'15) (Jan'16)}\tabcellsep (March'16)\tabcellsep (April'16)\tabcellsep (August'16)\tabcellsep (Sep-Oct'16)\tabcellsep (Oct-Nov'16)\tabcellsep (Oct-Nov'16)\tabcellsep (Nov'16)\tabcellsep (Dec'16)\tabcellsep Jan'17\\
SHIPMENT LOAD\tabcellsep 0 500 1000 1500 2000 2500 3000 3500 4000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
OBSERVED\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.18776297335203365\textwidth}P{0.01788218793828892\textwidth}P{0.012517531556802243\textwidth}P{0.016690042075736326\textwidth}P{0.03338008415147265\textwidth}P{0.04589761570827489\textwidth}P{0.3296283309957924\textwidth}P{0.10014025245441795\textwidth}P{0.10610098176718093\textwidth}}
\tabcellsep SHIPMENT LOAD\tabcellsep 0 1000 2000 3000 4000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep OBSERVED\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{with such huge variation. Setting ?=0.8 gives poor}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{forecast of the observed shipment load of Lohri}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{(Jan'17).}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{? The trend adjustments made in data due to}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{seasonal effect of festivals dramatically improves}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{projection using trend line projection. RMSE}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{comparison between other techniques and trend}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{projection shows trend projection with trend}\\
\multicolumn{6}{l}{? Due to large variation of shipment load in various}\tabcellsep \multicolumn{2}{l}{deseasonalization gives best of all results and}\\
sequential\tabcellsep \multicolumn{2}{l}{months,}\tabcellsep \multicolumn{2}{l}{Exponential}\tabcellsep smoothing\tabcellsep \multicolumn{2}{l}{closest forecast to actual observation.}\\
\multicolumn{6}{l}{technique could not predict better results for data}\tabcellsep \\
\multicolumn{4}{l}{Festive Month Year Season(t)}\tabcellsep \multicolumn{2}{l}{Observed Shipment Load (Units per pack of Handlooms) (y)}\tabcellsep Old Forecast F t-1\tabcellsep New Forecast (F t = F t-1 + 0.1(y t-1 -F t-1 ))\tabcellsep Adjusted forecast (F t ) adj = F t + (1-?)/? * T t\\
(Jan'14)\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep 2500\tabcellsep 2500\tabcellsep 2500\tabcellsep 2500\\
(March'14)\tabcellsep \tabcellsep \tabcellsep 2\tabcellsep \tabcellsep 1130\tabcellsep 2500\tabcellsep 2363\tabcellsep 2239.7\\
(April'14)\tabcellsep \tabcellsep \tabcellsep 3\tabcellsep \tabcellsep 2200\tabcellsep 2363\tabcellsep 2346.7\tabcellsep 2221.06\\
(August'14)\tabcellsep \tabcellsep \tabcellsep 4\tabcellsep \tabcellsep 2250\tabcellsep 2346.7\tabcellsep 2337.03\tabcellsep 2215.251\\
(Sep-Oct'14)\tabcellsep \tabcellsep \tabcellsep 5\tabcellsep \tabcellsep 3450\tabcellsep 2337.03\tabcellsep 2448.327\tabcellsep 2438.893\\
(Oct-Nov'14)\tabcellsep \tabcellsep \tabcellsep 6\tabcellsep \tabcellsep 3000\tabcellsep 2448.327\tabcellsep 2503.494\tabcellsep 2544.654\\
(Oct-Nov'14)\tabcellsep \tabcellsep \tabcellsep 7\tabcellsep \tabcellsep 3330\tabcellsep 2503.494\tabcellsep 2586.145\tabcellsep 2697.575\\
(Nov'14)\tabcellsep \tabcellsep \tabcellsep 8\tabcellsep \tabcellsep 1100\tabcellsep 2586.145\tabcellsep 2437.53\tabcellsep 2404.064\\
(Dec'14)\tabcellsep 1\tabcellsep \tabcellsep 9\tabcellsep \tabcellsep 1700\tabcellsep 2437.53\tabcellsep 2363.777\tabcellsep 2267.28\\
(Jan'15)\tabcellsep \tabcellsep \tabcellsep 1\tabcellsep \tabcellsep 2200\tabcellsep 2363.777\tabcellsep 2347.4\tabcellsep 2245.812\\
(March'15)\tabcellsep \tabcellsep \tabcellsep 2\tabcellsep \tabcellsep 1145\tabcellsep 2347.4\tabcellsep 2227.16\tabcellsep 2027.515\\
(April'15)\tabcellsep \tabcellsep \tabcellsep 3\tabcellsep \tabcellsep 2500\tabcellsep 2227.16\tabcellsep 2254.444\tabcellsep 2099.319\\
(August'15)\tabcellsep \tabcellsep \tabcellsep 4\tabcellsep \tabcellsep 2300\tabcellsep 2254.444\tabcellsep 2258.999\tabcellsep 2123.487\\
(Sep-Oct'15)\tabcellsep \tabcellsep \tabcellsep 5\tabcellsep \tabcellsep 3400\tabcellsep 2258.999\tabcellsep 2373.099\tabcellsep 2353.828\\
(Oct-Nov'15)\tabcellsep \tabcellsep \tabcellsep 6\tabcellsep \tabcellsep 2800\tabcellsep 2373.099\tabcellsep 2415.789\tabcellsep 2436.867\\
(Oct-Nov'15)\tabcellsep \tabcellsep \tabcellsep 7\tabcellsep \tabcellsep 2850\tabcellsep 2415.789\tabcellsep 2459.211\tabcellsep 2517.259\\
(Nov'15)\tabcellsep 2\tabcellsep \tabcellsep 8\tabcellsep \tabcellsep 1200\tabcellsep 2459.211\tabcellsep 2333.289\tabcellsep 2272.204\end{longtable} \par
 
\caption{\label{tab_5}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2017\\
28\\
)\\
(\end{longtable} \par
 
\caption{\label{tab_6}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.08793103448275862\textwidth}P{0.2637931034482759\textwidth}P{0.08342175066312997\textwidth}P{0.08116710875331565\textwidth}P{0.16458885941644563\textwidth}P{0.022546419098143238\textwidth}P{0.06312997347480105\textwidth}P{0.08342175066312997\textwidth}}
Error measure\tabcellsep \multicolumn{4}{l}{Smoothing techniques Exponential smoothing MA4 Moving averages MA3 ?= 0.1 ?= 0.8}\tabcellsep Normal\tabcellsep \multicolumn{2}{l}{Trend fitting Deseasonalized}\\
SSE\tabcellsep 21470355.56\tabcellsep 15913144.53\tabcellsep 16949382.13\tabcellsep \multicolumn{3}{l}{22536138.72 15230927.11}\tabcellsep 6568052.846\\
MSE\tabcellsep 795198.3539\tabcellsep 589375.7234\tabcellsep 627754.8936\tabcellsep \multicolumn{3}{l}{834671.8046 564108.4115}\tabcellsep 243261.2165\\
RMSE\tabcellsep 891.7389494\tabcellsep 767.7080978\tabcellsep 792.309847\tabcellsep \multicolumn{3}{l}{913.6037459 751.0715089}\tabcellsep 493.2151828\\
Forecasted value\tabcellsep 2150\tabcellsep 2544\tabcellsep 2358\tabcellsep 1745\tabcellsep 2378\tabcellsep \tabcellsep 2322\end{longtable} \par
 
\caption{\label{tab_7}Table 7 :}\end{figure}
 			\footnote{© 20 7 Global Journa ls Inc. (US) 1} 			\footnote{© 20 7 Global Journa ls Inc. (US)} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
\bibitem[Fildes and Goodwin ()]{b19}\label{b19} 	 		‘Against your better judgment? How organizations can improve their use of management judgment in forecasting’.  		 			R Fildes 		,  		 			P Goodwin 		.  	 	 		\textit{Interfaces}  		2007. 37 p. .  	 
\bibitem[Mathews and Diamantopoulos ()]{b1}\label{b1} 	 		\textit{Alternative indicators of forecast revision and improvement. Marketing Intelligence and Planning},  		 			B P Mathews 		,  		 			A Diamantopoulos 		.  		1987. 5 p. .  	 
\bibitem[Holt (2004)]{b13}\label{b13} 	 		‘Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages’.  		 			Charles C Holt 		.  	 	 		\textit{International Journal of Forecasting}  		January-March 2004. 20  (1)  p. .  	 
\bibitem[Divakar et al. ()]{b16}\label{b16} 	 		\textit{CHAN4CAST: A multichannel, multiregion sales forecasting model and decision support system for},  		 			S Divakar 		,  		 			Ratchford Bt 		,  		 			V Shankar 		.  		2005. p. .  	 
\bibitem[Moon et al. ()]{b12}\label{b12} 	 		‘Conducting a sales forecasting audit’.  		 			M A Moon 		,  		 			J T Mentzer 		,  		 			C D Smith 		.  	 	 		\textit{International Journal of Forecasting}  		2003. 19 p. .  	 
\bibitem[Macgregor ()]{b9}\label{b9} 	 		‘Decomposition for judgemental forecasting and estimation’.  		 			D Macgregor 		.  	 	 		\textit{Principles of forecasting},  				 			J S Armstrong 		 (ed.)  		 (Norwell, MA)  		2001. Kluwer. p. .  	 
\bibitem[Fildes et al. ()]{b21}\label{b21} 	 		‘Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning’.  		 			R Fildes 		,  		 			P Goodwin 		,  		 			Lawrence M Nikolopoulos 		,  		 			K 		.  	 	 		\textit{Int J Forecasting}  		2008. 24.  	 
\bibitem[Fildes et al. ()]{b22}\label{b22} 	 		‘Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning’.  		 			R Fildes 		,  		 			P Goodwin 		,  		 			M Lawrence 		,  		 			K Nikolopoulos 		.  	 	 		\textit{International Journal of Forecasting}  		2009. 25 p. .  	 
\bibitem[Donihue ()]{b5}\label{b5} 	 		‘Evaluating the role judgment plays in forecast accuracy’.  		 			M R Donihue 		.  	 	 		\textit{Journal of Forecasting}  		1993. 12 p. .  	 
\bibitem[Gardner ()]{b17}\label{b17} 	 		‘Exponential smoothing: The state of the art -Part II’.  		 			E S Gardner 		.  	 	 		\textit{International Journal of Forecasting}  		2006. 22 p. .  	 
\bibitem[Diamantopoulos and Mathews ()]{b3}\label{b3} 	 		‘Factors affecting the nature and effectiveness of subjective revision in sales forecasting: An empirical forecast selection’.  		 			A Diamantopoulos 		,  		 			B P Mathews 		.  	 	 		\textit{Journal of Forecasting}  		1989. 9 p. .  	 
\bibitem[Eaves and Kingsman ()]{b14}\label{b14} 	 		‘Forecasting for the ordering and stock-holding of spare parts’.  		 			Ahc Eaves 		,  		 			B G Kingsman 		.  	 	 		\textit{J Opl Res Soc}  		2004. 55 p. .  	 
\bibitem[Lim and Connor ()]{b7}\label{b7} 	 		‘Judgmental adjustment of initial forecasts -its effectiveness and biases’.  		 			J S Lim 		,  		 			M Connor 		.  	 	 		\textit{Journal of Behavioral Decision Making}  		1995. 8 p. .  	 
\bibitem[Lim et al. ()]{b8}\label{b8} 	 		‘Judgmental forecasting with time series and causal information’.  		 			J S Lim 		,  		 			M Connor 		,  		 			R L Goodrich 		.  	 	 		\textit{International Journal of Forecasting}  		1996. 2000. 12 p. .  	 	 (International Journal of Forecasting) 
\bibitem[Lawrence et al. ()]{b18}\label{b18} 	 		‘Judgmental forecasting: A review of progress over the last 25 years’.  		 			M Lawrence 		,  		 			P Goodwin 		,  		 			M O'connor 		,  		 			D \&onkal 		.  	 	 		\textit{International Journal of Forecasting}  		2006. 22 p. .  	 
\bibitem[Mathews and Diamantopoulos ()]{b2}\label{b2} 	 		‘Judgmental revision of sales forecasts -a longitudinal extension’.  		 			B P Mathews 		,  		 			A Diamantopoulos 		.  	 	 		\textit{Journal of Forecasting}  		1989. 8 p. .  	 
\bibitem[Mathews and Diamantopoulos ()]{b0}\label{b0} 	 		‘Managerial intervention in forecasting: An empirical investigation of forecast manipulation’.  		 			B P Mathews 		,  		 			A Diamantopoulos 		.  	 	 		\textit{International Journal of Research in Marketing}  		1986. 3 p. .  	 
\bibitem[Fader and Hardie (ed.) ()]{b10}\label{b10} 	 		\textit{Principles of Forecasting: A Handbook for Researchers and Practitioners},  		 			P S Fader 		,  		 			Bgs Hardie 		.  		Armstrong J.S. (ed.)  		2001. Norwell, MA: Kluwer.  	 	 (Forecasting trial sales of new consumer packaged goods) 
\bibitem[Yaniv ()]{b15}\label{b15} 	 		‘Receiving other people's advice: Influence and benefit’.  		 			I Yaniv 		.  	 	 		\textit{Organizational Behavior and Human Decision Processes}  		2004. 93 p. .  	 
\bibitem[Sharma and Bhatia ()]{b23}\label{b23} 	 		‘Short-Term transaction forecasting using time series analysis: A case study for India’.  		 			S Sharma 		,  		 			M P Bhatia 		.  	 	 		\textit{Advances in Information mining},  				2012. 4 p. .  	 
\bibitem[Fildes ()]{b11}\label{b11} 	 		‘Telecommunications demand forecasting-A review’.  		 			R Fildes 		.  	 	 		\textit{Int J Forecasting}  		2002. 18 p. .  	 
\bibitem[Turner ()]{b4}\label{b4} 	 		‘The role of judgment in macroeconomic forecasting’.  		 			D S Turner 		.  	 	 		\textit{Journal of Forecasting}  		1990. 9 p. .  	 
\bibitem[Goodwin et al. ()]{b20}\label{b20} 	 		\textit{Understanding the use of forecasting systems: An interpretive study in a supply-chain company},  		 			P Goodwin 		,  		 			W Y Lee 		,  		 			R Fildes 		,  		 			K Nikolopoulos 		,  		 			M Lawrence 		.  		2007.  		 			Bath University Management School working paper 		 	 
\bibitem[Harvey ()]{b6}\label{b6} 	 		‘Why are judgments less consistent in less predictable task situations?’.  		 			N Harvey 		.  	 	 		\textit{Organizational Behavior and Human Decision Processes}  		1995. 63 p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
