Assume that this is the beginning of year 2014 and you have joined a retail giant in France, a few days ago, as a marketing manager. You have been tasked with forecasting the sales for year 2014. Just to make yourself aware of the past sales figures, you request your secretary to provide you with the sales figures for the past ten years. She complies with your request and provides data in an excel sheet. While looking at the sheet, you notice that food sales are categorized into 1) Super market and grocery stores, 2) Drink sales, and 3) Other specialized food retailing items. Each of the above-mentioned data is given in a separate column (i.e., columns C-E). The last column contains the total of the
three categories.

Your manager has specifically requested to provide a report that contains all important observations, your analysis, decisions made along with justifications, forecasts, and interpretations.
 
Part B: After following above-mentioned steps, you were very happy with your progress and decided to write a report and present it to your boss. One of your friends came to visit  you while you were writing your report. He asked a very interesting question that made you think to do further investigation. His asked, “Is it necessary to estimate year 11 values by using the “Total Food retailing” data? Why can’t you forecast the values for each of the categories individually and then add them together to come up with final forecasts?” You ponder upon his question and then decide to make forecast for each of the categories and then combine forecasts. That means that now,  

a) for each of the categories of retails sales (supermarket, drink and specialized categories), you need to split data and follow the steps (a, c, d).  

b) pick the best models, combine their forecasts for year 9 & 10, and

c) compare it with the best model that you obtained in Part A.   You should finally provide in your analysis which method is a good one i.e., using “Total
food retailing” data or “Combining individual forecasts of each category”.  

Excel Data is below: 

Series ID	Time	Bitmap Supermarket and grocery stores	Drinks retailing	Other specialised food retailing	Total Food retailing
Jan-2004	1	1533.8	143.0	231.3	1908.1
Feb-2004	2	1413.3	128.7	215.4	1757.4
Mar-2004	3	1472.1	140.8	228.6	1841.5
Apr-2004	4	1469.1	141.5	234.5	1845.1
May-2004	5	1475.3	133.0	221.4	1829.7
Jun-2004	6	1412.5	133.4	218.5	1764.4
Jul-2004	7	1508.5	138.4	224.6	1871.5
Aug-2004	8	1476.3	135.9	228.2	1840.4
Sep-2004	9	1490.9	142.0	226.1	1859.0
Oct-2004	10	1542.3	148.2	236.4	1926.9
Nov-2004	11	1513.4	153.7	248.1	1915.2
Dec-2004	12	1717.9	226.9	296.5	2241.3
Jan-2005	13	1535.2	145.7	239.3	1920.2
Feb-2005	14	1416.9	139.3	225.1	1781.3
Mar-2005	15	1555.3	155.9	247.7	1958.9
Apr-2005	16	1481.8	149.3	244.5	1875.6
May-2005	17	1473.0	143.1	242.9	1859.0
Jun-2005	18	1452.0	142.8	238.6	1833.4
Jul-2005	19	1546.2	144.4	245.9	1936.5
Aug-2005	20	1553.4	150.3	251.9	1955.6
Sep-2005	21	1545.8	157.6	246.8	1950.2
Oct-2005	22	1593.8	177.8	267.2	2038.8
Nov-2005	23	1558.3	194.7	271.7	2024.7
Dec-2005	24	1776.1	279.9	316.0	2372.0
Jan-2006	25	1585.2	171.9	287.0	2044.1
Feb-2006	26	1480.1	157.6	265.7	1903.4
Mar-2006	27	1635.1	174.7	291.5	2101.3
Apr-2006	28	1582.7	172.2	268.2	2023.1
May-2006	29	1572.3	173.0	261.7	2007.0
Jun-2006	30	1557.4	164.7	253.6	1975.7
Jul-2006	31	1591.7	171.1	264.8	2027.6
Aug-2006	32	1636.0	173.6	273.6	2083.2
Sep-2006	33	1614.7	181.7	269.1	2065.5
Oct-2006	34	1677.0	189.3	289.6	2155.9
Nov-2006	35	1683.8	206.5	289.6	2179.9
Dec-2006	36	1873.0	296.5	341.9	2511.4
Jan-2007	37	1684.4	191.2	304.6	2180.2
Feb-2007	38	1557.4	179.7	288.8	2025.9
Mar-2007	39	1740.8	194.9	311.3	2247.0
Apr-2007	40	1649.9	191.4	307.1	2148.4
May-2007	41	1678.0	183.3	304.0	2165.3
Jun-2007	42	1642.0	173.5	279.5	2095.0
Jul-2007	43	1695.7	182.1	300.1	2177.9
Aug-2007	44	1762.9	189.9	308.5	2261.3
Sep-2007	45	1740.8	204.4	296.2	2241.4
Oct-2007	46	1853.8	204.5	276.8	2335.1
Nov-2007	47	1897.1	217.3	272.0	2386.4
Dec-2007	48	2054.7	309.9	322.6	2687.2
Jan-2008	49	1849.9	214.9	239.7	2304.5
Feb-2008	50	1747.1	184.3	229.9	2161.3
Mar-2008	51	1881.1	210.3	241.3	2332.7
Apr-2008	52	1737.5	200.4	223.8	2161.7
May-2008	53	1827.4	196.9	234.7	2259.0
Jun-2008	54	1729.5	195.4	221.4	2146.3
Jul-2008	55	1789.9	179.5	238.0	2207.4
Aug-2008	56	1822.2	176.8	250.0	2249.0
Sep-2008	57	1729.8	179.2	243.7	2152.7
Oct-2008	58	1875.8	214.1	250.6	2340.5
Nov-2008	59	1899.4	222.8	254.7	2376.9
Dec-2008	60	2121.2	310.8	320.6	2752.6
Jan-2009	61	1988.7	242.9	236.1	2467.7
Feb-2009	62	1758.5	194.0	222.5	2175.0
Mar-2009	63	1928.8	215.0	243.3	2387.1
Apr-2009	64	1878.4	213.6	241.6	2333.6
May-2009	65	1896.7	211.3	238.4	2346.4
Jun-2009	66	1806.5	206.0	230.6	2243.1
Jul-2009	67	1876.0	201.4	237.1	2314.5
Aug-2009	68	1905.5	208.7	245.2	2359.4
Sep-2009	69	1871.8	206.1	241.0	2318.9
Oct-2009	70	2013.3	218.5	266.1	2497.9
Nov-2009	71	2111.7	236.1	275.6	2623.4
Dec-2009	72	2265.7	325.9	342.0	2933.6
Jan-2010	73	2053.5	233.7	255.4	2542.6
Feb-2010	74	1817.4	199.0	215.5	2231.9
Mar-2010	75	2017.7	222.6	229.6	2469.9
Apr-2010	76	1951.1	222.2	214.4	2387.7
May-2010	77	1988.6	213.4	224.5	2426.5
Jun-2010	78	1888.2	206.3	205.6	2300.1
Jul-2010	79	2035.8	206.7	198.7	2441.2
Aug-2010	80	2012.8	212.9	203.3	2429.0
Sep-2010	81	1984.2	224.3	203.2	2411.7
Oct-2010	82	2075.9	238.7	214.4	2529.0
Nov-2010	83	2159.7	251.2	201.4	2612.3
Dec-2010	84	2360.1	380.0	251.8	2991.9
Jan-2011	85	2157.3	250.0	205.8	2613.1
Feb-2011	86	1953.8	216.8	194.6	2365.2
Mar-2011	87	2108.1	240.5	200.4	2549.0
Apr-2011	88	2062.3	244.4	196.2	2502.9
May-2011	89	2034.8	232.1	185.9	2452.8
Jun-2011	90	1988.6	223.6	183.2	2395.4
Jul-2011	91	2085.9	239.0	188.5	2513.4
Aug-2011	92	2093.4	245.7	192.4	2531.5
Sep-2011	93	2074.0	251.6	188.5	2514.1
Oct-2011	94	2168.9	264.3	197.6	2630.8
Nov-2011	95	2128.7	282.9	201.4	2613.0
Dec-2011	96	2397.4	409.6	257.2	3064.2
Jan-2012	97	2138.1	265.2	188.1	2591.4
Feb-2012	98	2013.9	239.4	200.1	2453.4
Mar-2012	99	2174.6	259.6	199.7	2633.9
Apr-2012	100	2082.6	247.1	214.5	2544.2
May-2012	101	2109.8	243.3	216.2	2569.3
Jun-2012	102	2064.9	238.2	208.6	2511.7
Jul-2012	103	2097.8	243.2	204.5	2545.5
Aug-2012	104	2167.7	255.7	213.9	2637.3
Sep-2012	105	2111.5	261.9	211.7	2585.1
Oct-2012	106	2200.2	265.8	230.6	2696.6
Nov-2012	107	2188.8	283.4	219.5	2691.7
Dec-2012	108	2425.6	400.3	276.2	3102.1
Jan-2013	109	2231.5	274.1	234.5	2740.1
Feb-2013	110	2021.8	231.7	226.2	2479.7
Mar-2013	111	2267.2	273.4	233.4	2774.0
Apr-2013	112	2117.1	256.1	234.4	2607.6
May-2013	113	2186.5	250.0	243.0	2679.5
Jun-2013	114	2104.6	241.9	228.7	2575.2
Jul-2013	115	2155.7	248.7	235.9	2640.3
Aug-2013	116	2244.2	264.6	247.8	2756.6
Sep-2013	117	2157.0	262.8	240.2	2660.0
Oct-2013	118	2299.5	264.4	244.5	2808.4
Nov-2013	119	2271.3	271.5	232.2	2775.0
Dec-2013	120	2612.8	394.5	270.9	3278.2