Nina Industries has continued to have problems with their forecasting and noted
ID: 392992 • Letter: N
Question
Nina Industries has continued to have problems with their forecasting and noted that their demand may be seasonal. Given the information provided below, determine the seasonal index for each month based on sales for the past two years. Use the seasonal index for each month to "de-seasonalize" the data. What is the de-seasonalized value for March of 2017? Enter your answer to two (2) decimal places. Month 2016 2017 January 697 729 February 927 989 March 1195 988 Apri 1103 985 May 954 1123 June July1165 1179 August 925 903 September 963 1143 October 850 1023 November 602 972 December 969 554 1216 1298Explanation / Answer
Answer:
The monthly seasonal index is calculated as below
The formula used for calculating the seasonal index = Demand of the actual month / Average of the yearly demand.
With this formula, we have calculated the seasonal index for 2016 and 2017 year separately as shown in the below table.
Forthe common seasonal index for each month, we have taken the average of each month from 2016 & 2017 data. Then we have taken the average for all the month data together and then for calculating the common seasonal index i.e.Seasonal Index based on 2016 & 2017 = Monthly Average/ Average of 2016&2017.
Refer the table for the full calculations as below
Deseasonalized value for March month = monthly seasonalized demand / seasonal index = 1091.5 / 1.12 =977.17
Month 2016 2017 Seasonal Index2016 = Month value/Average of 2016 Seasonal Index
2017= Month value/Average of 2016 Monthly Average = (Jan16+Jan17) /2 Seasonal Index based on 2016 & 2017 = Monthly Average/ Average of 2016&2017 Jan 697 729 0.72 0.74 713.00 0.73 Feb 927 989 0.96 1.00 958.00 0.98 Mar 1195 988 1.24 1.00 1091.50 1.12 Apr 1103 985 1.14 0.99 1044.00 1.07 May 954 1123 0.99 1.13 1038.50 1.06 Jun 1216 1298 1.26 1.31 1257.00 1.29 Jul 1165 1179 1.21 1.19 1172.00 1.20 Aug 925 903 0.96 0.91 914.00 0.94 Sep 963 1143 1.00 1.15 1053.00 1.08 Oct 850 1023 0.88 1.03 936.50 0.96 Nov 602 972 0.62 0.98 787.00 0.81 Dec 969 554 1.01 0.56 761.50 0.78 Average 963.83 990.50 Average of 2016 & 2017 Both year 977.17
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