# I. Introduction 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 [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 [14]- [16], [18]- [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 [1]- [8], [10], [11] and [22]- [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 [24]- [26]. Forecasters make decisions on the basis of noisy and randomly fluctuating events in time series [9]. Several methods, techniques have been used in literature to forecast load demands. We used exponential smoothing technique and trend fitting for prediction. 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. # II. Time Series Models 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: # a) Smoothing Techniques 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(1) Where t= current period. # c) Trend and seasonal component 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. [27].The steps involved are as follows: 1. Identify the quarters, months etc. and calculate centered moving averages (CMA). # III. Experimentation 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 # a) Data The data is collected for the festive season's months of Punjab for Ludhiana based four Handlooms manufacturers for the last three years. Table 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 '17) . 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. # b) Results 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 '17). The results are compared with already observed value for Lohri (Jan '17). # i. Smoothing Technique ? 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 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 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. The accuracy so measure for all the methods applied are shown in table 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. # IV. Conclusion The techniques used in this case study shows following results based on forecast and the measure of error based on MSE and RMSE: # 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 ) Prediction and Judgmental Adjustments of supply-Chain Planning in Festive Season ? 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. # 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 ) Prediction 2shows 3-month 1Year123ObservedObservedObservedFestive MonthSeason (t)Shipment Load (Units per pack ofFestive MonthSeason (t)Shipment Load (Units per pack ofFestive MonthShipment Load (Units per pack ofHandlooms)(Y )Handlooms)(Y)Handlooms)(Y)(Jan'14)12500(Jan'15)12200(Jan'16)12300(March'14)21130(March'15)21145(March'16)21130(April'14)32200(April'15)32500(April'16)32400(August'14)42250(August'15)42300(August'16)42250(Sep-Oct'14)53450(Sep-Oct'15)53400(Sep-Oct'16)53350(Oct-Nov'14)63000(Oct-Nov'15)62800(Oct-Nov'16)63000(Oct-Nov'14)73330(Oct-Nov'15)72850(Oct-Nov'16)73150(Nov'14)81100(Nov'15)81200(Nov'16)81400(Dec'14)91700(Dec'15)91950(Dec'16)91900Year4(Jan'17)12250? Weighted moving Average (WMA): The expertplanner/ analysts of the companies decide to weighthe past three month's sales. WMA calculated usingaverage 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. 2Year 2017 ) ? Exponential smoothing Technique: Since trend is expected out of festive season demands of theObserved Shipment Load(Festive MonthYear Season(t)(Units per pack of Handlooms)MA 3MA 4CMA 4(y)(Jan'14)12500(March'14)211301943.333(April'14)32200186020202138.75(August'14)422502633.3332257.52491.25(Sep-Oct'14)153450290027252866.25(Oct-Nov'14)6300032603007.52863.75(Oct-Nov'14)733302476.66727202501.25(Nov'14)811002043.3332282.52182.5(Dec'14)917001666.6672082.51809.375(Jan'15)122001681.6671536.251711.25(March'15)211451948.3331886.251961.25(April'15)325001981.6672036.252186.25(August'15)423002733.3332336.252543.125(Sep-Oct'15)2534002833.33327502793.75(Oct-Nov'15)628003016.6672837.52700(Oct-Nov'15)728502283.3332562.52381.25(Nov'15)81200200022002137.5(Dec'15)919501816.66720751860(Jan'16)3123001793.33316451795 3.Season (t)Weights (w) Values (y) weights*valueFestivalLast Month1/21900950ChristmasTwo months ago1/61400233.33Gurunanak JayantiThree Months ago1/331501050EidForecasted value2233.33Lohri(Jan'17)Observed shipment load for the festive season of 2016 for Ludhiana based four Handlooms supply-chaincompanies.OBSERVED SHIPMENT LOAD (UNITS PER PACK OF HANDLOOMS)1000 2000 3000 4000 Graph 2: 0 Observed Shipment load(Jan'14)(March'14)(April'14)(August'14)(Sep-Oct'14)(Oct-Nov'14)(Oct-Nov'14)(Nov'14)(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)(March'16)(April'16)(August'16)(Sep-Oct'16)(Oct-Nov'16)(Oct-Nov'16)(Nov'16)(Dec'16)Jan'17SHIPMENT LOAD0 500 1000 1500 2000 2500 3000 3500 4000OBSERVED 4SHIPMENT LOAD0 1000 2000 3000 4000OBSERVEDwith such huge variation. Setting ?=0.8 gives poorforecast of the observed shipment load of Lohri(Jan'17).? The trend adjustments made in data due toseasonal effect of festivals dramatically improvesprojection using trend line projection. RMSEcomparison between other techniques and trendprojection shows trend projection with trend? Due to large variation of shipment load in variousdeseasonalization gives best of all results andsequentialmonths,Exponentialsmoothingclosest forecast to actual observation.technique could not predict better results for dataFestive Month Year Season(t)Observed Shipment Load (Units per pack of Handlooms) (y)Old Forecast F t-1New Forecast (F t = F t-1 + 0.1(y t-1 -F t-1 ))Adjusted forecast (F t ) adj = F t + (1-?)/? * T t(Jan'14)12500250025002500(March'14)21130250023632239.7(April'14)3220023632346.72221.06(August'14)422502346.72337.032215.251(Sep-Oct'14)534502337.032448.3272438.893(Oct-Nov'14)630002448.3272503.4942544.654(Oct-Nov'14)733302503.4942586.1452697.575(Nov'14)811002586.1452437.532404.064(Dec'14)1917002437.532363.7772267.28(Jan'15)122002363.7772347.42245.812(March'15)211452347.42227.162027.515(April'15)325002227.162254.4442099.319(August'15)423002254.4442258.9992123.487(Sep-Oct'15)534002258.9992373.0992353.828(Oct-Nov'15)628002373.0992415.7892436.867(Oct-Nov'15)728502415.7892459.2112517.259(Nov'15)2812002459.2112333.2892272.204 5Year 201728)( 7Error measureSmoothing techniques Exponential smoothing MA4 Moving averages MA3 ?= 0.1 ?= 0.8NormalTrend fitting DeseasonalizedSSE21470355.5615913144.5316949382.1322536138.72 15230927.116568052.846MSE795198.3539589375.7234627754.8936834671.8046 564108.4115243261.2165RMSE891.7389494767.7080978792.309847913.6037459 751.0715089493.2151828Forecasted value215025442358174523782322 © 20 7 Global Journa ls Inc. 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