3 month moving

3 month weighted

exponential smoothing

If M&M follows your recommendations identified above, what financial and service benefits can the company achieve? Discuss two additional initiatives or strategies that M&M could pursue to improve their ability to match supply with demand. M&M can use a dynamic regression analysis that would help the company to predict the sales of the company through which each of these parameters would help to create a strong differentiation factor through which the company can keep changing the parameters and their weightage to predict the demand accurately and match the actual demand with the actual supply. Alternatively, the company can also use just in time policy where the demand that is observed within the market is immediately being matched through the actual supply that could be produced and generated by the company which would Help them to ensure that the right elements of production are followed and the company can reduce inventories to match this demand and the supply. Case Synopsis: Mahindra & Mahindra Ltd., a $23 billion company in 2021, has been the number one tractor manufacturer in India for the last 30 years. The agriculture tractor sale market in India is seasonal in nature and growing. To meet demand, the company has four manufacturing plants and 26 sales offices across the country; their main job is to coordinate supplies between its 800 dealers and the company. The sales offices provide a rolling demand forecast of tractors for the current month plus two months in the future. The forecast is used to determine the number and models of tractors to manufacture and to support advance ordering of key modules and parts. The deputy general manager of sales in the company's Farm Division has been receiving an increasing number of complaints from irate dealers about the supply of tractors from the company's stockyards. Case Demand Information Month & Fiscal Year Year 2018 2019 2020 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 Jan-19 Feb-19 Mar-19 Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 Mar-21 Industry Tractor Demand 24,644 27,949 32,737 21,654 22,718 28,855 33,205 20,983 17,751 22,114 20,025 31,286 27,039 31,113 39,396 27,219 24,434 41,338 43,171 27,743 27,424 35,941 32,140 43,245 22,713 24,268 23,055 23,532 22,572 28,687 38,422 26,843 25,623 29,066 31,840 36,353 M&M Tractor Demand 10,684 11,693 14,439 9,081 9,781 10,307 14,800 7,960 6,858 9,438 8,487 10,609 11,058 12,588 16,811 11,518 10,217 15,247 15,935 10,304 11,097 15,925 13,201 13,858 8,631 9,465 8,991 8,707 8,352 12,140 13,448 8,584 8,968 11,134 11,059 14,167 M&M Percent of Market 43.4% 41.8% 44.1% 41.9% 43.1% 35.7% 44.6% 37.9% 38.6% 42.7% 42.4% 33.9% 40.9% 40.5% 42.7% 42.3% 41.8% 36.9% 36.9% 37.1% 40.5% 44.3% 41.1% 32.0% 38.0% 39.0% 39.0% 37.0% 37.0% 42.3% 35.0% 32.0% 35.0% 38.3% 34.7% 39.0% A Month Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 16 Mar-21 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 B Demand 15925 13201 13858 8631 9465 8991 8707 8352 12140 13448 8584 8968 11134 11059 14167 Forecast 14328.00 11896.67 10651.33 9029.00 9054.33 8683.33 9733.00 11313.33 11390.67 10333.33 9562.00 10387.00 12120 D Error -5697.00 -2431.67 -1660.33 -322.00 -702.33 3456.67 3715.00 -2729.33 -2422.67 800.67 1497.00 3780.00 -2716.00 -226.33 E Absolute Deviation 5697.00 2431.67 1660.33 322.00 702.33 3456.67 3715.00 2729.33 2422.67 800.67 1497.00 3780.00 29214.67 2434.56 F MAPE 66.01 25.69 18.47 3.70 8.41 28.47 27.62 31.80 27.01 7.19 13.54 26.68 23.72 A B Month Demand Jan-20 15925 Feb-20 13201 Mar-20 13858 Apr-20 8631 May-20 9465 Jun-20 8991 Jul-20 8707 Aug-20 8352 Sep-20 12140 Oct-20 13448 Nov-20 8584 Dec-20 8968 Jan-21 11134 Feb-21 11059 Mar-21 14167 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Forecast 13834.75 10917.45 9612.40 9120.90 8882.20 8540.15 10470.90 12480.60 10642.00 9281.60 10120.90 10876.15 12775.90 D Error -5203.75 -1452.45 -621.40 -413.90 -530.20 3599.85 2977.10 -3896.60 -1674.00 1852.40 938.10 3290.85 -1134.00 -94.50 E Absolute Deviation 5203.75 1452.45 621.40 413.90 530.20 3599.85 2977.10 3896.60 1674.00 1852.40 938.10 3290.85 26450.60 2204.22 F MAPE 60.29 15.35 6.91 4.75 6.35 29.65 22.14 45.39 18.67 16.64 8.48 23.23 21.49 B Demand 15925 13201 13858 8631 9465 8991 8707 8352 12140 13448 8584 8968 11134 11059 14167 1 2 A Month Jan-20 3 Feb-20 4 Mar-20 5 Apr-20 6 May-20 7 Jun-20 8 Jul-20 9 Aug-20 10 Sep-20 11 Oct-20 12 Nov-20 13 Dec-20 14 Jan-21 15 Feb-21 16 Mar-21 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error Forecast 13154 13717.20 9648.24 9501.65 9093.13 8784.23 8438.45 11399.69 13038.34 9474.87 9069.37 10721.07 10991.41 13531.88 D Error -5086.20 -183.24 -510.65 -386.13 -432.23 3701.55 2048.31 -4454.34 -506.87 2064.63 337.93 3175.59 -231.65 -19.30 E Absolute Deviation 5086.20 183.24 510.65 386.13 432.23 3701.55 2048.31 4454.34 506.87 2064.63 337.93 3175.59 22887.65 1907.30 F MAPE 58.93 1.94 5.68 4.43 5.18 30.49 15.23 51.89 5.65 18.54 3.06 22.42 18.62 If M&M follows your recommendations identified above, what financial and service benefits can the company achieve? Discuss two additional initiatives or strategies that M&M could pursue to improve their ability to match supply with demand. M&M can use a dynamic regression analysis that would help the company to predict the sales of the company through which each of these parameters would help to create a strong differentiation factor through which the company can keep changing the parameters and their weightage to predict the demand accurately and match the actual demand with the actual supply. Alternatively, the company can also use just in time policy where the demand that is observed within the market is immediately being matched through the actual supply that could be produced and generated by the company which would Help them to ensure that the right elements of production are followed and the company can reduce inventories to match this demand and the supply. Case Synopsis: Mahindra & Mahindra Ltd., a $23 billion company in 2021, has been the number one tractor manufacturer in India for the last 30 years. The agriculture tractor sale market in India is seasonal in nature and growing. To meet demand, the company has four manufacturing plants and 26 sales offices across the country; their main job is to coordinate supplies between its 800 dealers and the company. The sales offices provide a rolling demand forecast of tractors for the current month plus two months in the future. The forecast is used to determine the number and models of tractors to manufacture and to support advance ordering of key modules and parts. The deputy general manager of sales in the company's Farm Division has been receiving an increasing number of complaints from irate dealers about the supply of tractors from the company's stockyards. Case Demand Information Month & Fiscal Year Year 2018 2019 2020 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 Jan-19 Feb-19 Mar-19 Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 Mar-21 Industry Tractor Demand 24,644 27,949 32,737 21,654 22,718 28,855 33,205 20,983 17,751 22,114 20,025 31,286 27,039 31,113 39,396 27,219 24,434 41,338 43,171 27,743 27,424 35,941 32,140 43,245 22,713 24,268 23,055 23,532 22,572 28,687 38,422 26,843 25,623 29,066 31,840 36,353 M&M Tractor Demand 10,684 11,693 14,439 9,081 9,781 10,307 14,800 7,960 6,858 9,438 8,487 10,609 11,058 12,588 16,811 11,518 10,217 15,247 15,935 10,304 11,097 15,925 13,201 13,858 8,631 9,465 8,991 8,707 8,352 12,140 13,448 8,584 8,968 11,134 11,059 14,167 M&M Percent of Market 43.4% 41.8% 44.1% 41.9% 43.1% 35.7% 44.6% 37.9% 38.6% 42.7% 42.4% 33.9% 40.9% 40.5% 42.7% 42.3% 41.8% 36.9% 36.9% 37.1% 40.5% 44.3% 41.1% 32.0% 38.0% 39.0% 39.0% 37.0% 37.0% 42.3% 35.0% 32.0% 35.0% 38.3% 34.7% 39.0% A Month Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Dec-20 Jan-21 Feb-21 16 Mar-21 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 B Demand 15925 13201 13858 8631 9465 8991 8707 8352 12140 13448 8584 8968 11134 11059 14167 Forecast 14328.00 11896.67 10651.33 9029.00 9054.33 8683.33 9733.00 11313.33 11390.67 10333.33 9562.00 10387.00 12120 D Error -5697.00 -2431.67 -1660.33 -322.00 -702.33 3456.67 3715.00 -2729.33 -2422.67 800.67 1497.00 3780.00 -2716.00 -226.33 E Absolute Deviation 5697.00 2431.67 1660.33 322.00 702.33 3456.67 3715.00 2729.33 2422.67 800.67 1497.00 3780.00 29214.67 2434.56 F MAPE 66.01 25.69 18.47 3.70 8.41 28.47 27.62 31.80 27.01 7.19 13.54 26.68 23.72 A B Month Demand Jan-20 15925 Feb-20 13201 Mar-20 13858 Apr-20 8631 May-20 9465 Jun-20 8991 Jul-20 8707 Aug-20 8352 Sep-20 12140 Oct-20 13448 Nov-20 8584 Dec-20 8968 Jan-21 11134 Feb-21 11059 Mar-21 14167 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Forecast 13834.75 10917.45 9612.40 9120.90 8882.20 8540.15 10470.90 12480.60 10642.00 9281.60 10120.90 10876.15 12775.90 D Error -5203.75 -1452.45 -621.40 -413.90 -530.20 3599.85 2977.10 -3896.60 -1674.00 1852.40 938.10 3290.85 -1134.00 -94.50 E Absolute Deviation 5203.75 1452.45 621.40 413.90 530.20 3599.85 2977.10 3896.60 1674.00 1852.40 938.10 3290.85 26450.60 2204.22 F MAPE 60.29 15.35 6.91 4.75 6.35 29.65 22.14 45.39 18.67 16.64 8.48 23.23 21.49 B Demand 15925 13201 13858 8631 9465 8991 8707 8352 12140 13448 8584 8968 11134 11059 14167 1 2 A Month Jan-20 3 Feb-20 4 Mar-20 5 Apr-20 6 May-20 7 Jun-20 8 Jul-20 9 Aug-20 10 Sep-20 11 Oct-20 12 Nov-20 13 Dec-20 14 Jan-21 15 Feb-21 16 Mar-21 17 Apr-21 18 19 Cumulative Forecast Error 20 Mean Forecast Error 21 Cumulative Absolute Deviation 22 Mean Absolute Deviation 23 Mean Absolute Percentage Error Forecast 13154 13717.20 9648.24 9501.65 9093.13 8784.23 8438.45 11399.69 13038.34 9474.87 9069.37 10721.07 10991.41 13531.88 D Error -5086.20 -183.24 -510.65 -386.13 -432.23 3701.55 2048.31 -4454.34 -506.87 2064.63 337.93 3175.59 -231.65 -19.30 E Absolute Deviation 5086.20 183.24 510.65 386.13 432.23 3701.55 2048.31 4454.34 506.87 2064.63 337.93 3175.59 22887.65 1907.30 F MAPE 58.93 1.94 5.68 4.43 5.18 30.49 15.23 51.89 5.65 18.54 3.06 22.42 18.62