forecasting.

Questions:

1. What are some of the potential benefits of a more formalized approach to forecasting?

2. What is your weekly forecast for periods F15 to F18 for each of Product 1 and Product 2. Explain your choice of technique for each product. Also, for each product, you must plot the data for the first 14 weeks and then provide another graph showing the plotted data for all 18 weeks – which includes your new forecast period.

(Hint: When you look at the plotted data for your full 18 weeks, do you see any change in the behavior you had observed in weeks 1-14? If the formula you have chosen does not continue the behavior you saw in weeks 1 – 14, your formula might be incorrect. Try another formula. If you still see a change in behavior when you plot the data, you might have to make a management decision to manually adjust the numbers based on what you see). Just ensure you explain what you are doing. Your text allows you to use the Excel templates but I want to see your work and not just the Excel template.

The Print Shop Manufacturing

The Print Shop Manufacturing makes various components for printers and copiers. In addition to supplying these items to a major manufacturer of printers and copiers, the company distributes these and similar items to office supply stores and computer stores as replacement parts for printers and desktop copiers. In all, the company makes about 20 different items. The two markets (the major manufacturer and replacement market) require somewhat different handling. For example, replacement products must be packaged individually whereas products are shipped in bulk to the major manufacturer.

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The company does not use forecasts for production planning. Instead, the operations manager decides which items to produce, and the batch size, based partly on orders, and the amounts in inventory. The products that have the fewest amounts in inventory get the highest priority. Demand is uneven, and the company has experienced being overstocked on some items and out of stock on others. Being understocked has occasionally created tensions with the manger of retail outlets. Another problem is that prices of raw materials have been creeping up, although the operations manager thinks that this might be a temporary condition.

Because of competitive pressures and falling profits, the manager has decided to undertake a number of changes. One change is to introduce more formal forecasting procedures in order to improve production planning and inventory management. With that in mind, the manager wants to begin forecasting for two products. These products are important for several reasons. First, they account for a disproportionately large share of the company’s profits. Second, the manager believes that one of these products will become increasingly important to future growth plans; and third, the other product has experienced periodic out-of-stock instances. The manager has compiled data on product demand for the two products from order records for the previous 14 weeks. These are shown in the following table:

Product Product Product Product

Week 1 2 Week 1 2

1 50 40 8 76 47

2 54 38 9 79 42

3 57 41 10 82 43

4 60 46 11 85 42

5 64 42 12 87 49

6 67 41 13 92 43

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7 90* 41 14 96 44

NSPI Residential Energy Weighted Average Exponential Smoothing Linear Trend Line

Year Actual Sales (GWh) Forecast Sales Forecast Error Forecast Error2 2Y WA Forecast Error Forecast Error2 3Y WA Forecast Error Forecast Error2 a = 0.25 Forecast Error Forecast Error2 a = 0.45 Forecast Error Forecast Error2 Forecast Forecast Error Forecast Error

2004 4,113.50 4,051.80 61.70 3,807.20 4,066.21 47.29 2,236.77

2005 4,112.40 4,026.04 86.36 7,458.12 4,067.22 45.18 2,040.95 4,079.56 32.84 1,078.23 4,100.46 11.94 142.46

2006 3,979.40 4,058.99 -79.59 6,334.25 4,112.95 -133.55 17,835.60 4,047.63 -68.23 4,655.29 4,064.90 -85.50 7,310.55 4,134.72 -155.32 24,125.24

2007 4,218.20 4,281.47 -63.27 4,003.47 4,045.90 172.30 29,687.29 4,068.43 149.77 22,430.05 4,039.09 179.11 32,080.03 4,023.17 195.03 38,035.37 4,168.98 49.22 2,422.43

2008 4,231.90 4,100.71 131.19 17,210.53 4,098.80 133.10 17,715.61 4,103.33 128.57 16,529.39 4,265.65 -33.75 1,139.38 4,253.00 -21.10 445.22 4,203.24 28.66 821.36

2009 4,318.20 4,210.25 107.96 11,654.28 4,225.05 93.15 8,676.92 4,143.17 175.03 30,636.67 4,133.51 184.69 34,111.01 4,159.75 158.45 25,107.64 4,237.50 80.70 6,512.59

2010 4,216.20 4,030.69 185.51 34,415.00 4,275.05 -58.85 3,463.32 4,256.10 -39.90 1,592.01 4,237.23 -21.03 442.42 4,258.82 -42.62 1,816.87 4,271.76 -55.56 3,086.71

2011 4,346.10 4,389.56 -43.46 1,888.86 4,267.20 78.90 6,225.21 4,255.43 90.67 8,220.44 4,077.07 269.03 72,379.62 4,114.17 231.93 53,792.47 4,306.02 40.08 1,606.65

2012 4,231.50 4,176.49 55.01 3,026.05 4,281.15 -49.65 2,465.12 4,293.50 -62.00 3,844.00 4,378.70 -147.20 21,666.59 4,370.00 -138.50 19,183.23 4,340.28 -108.78 11,832.17

2013 4,436.30 4,387.50 48.80 2,381.37 4,288.80 147.50 21,756.25 4,264.60 171.70 29,480.89 4,190.24 246.06 60,544.11 4,201.24 235.06 55,250.96 4,374.53 61.77 3,814.97

2014 4,462.80 4,333.90 4,337.97 4,399.70 4,409.46 4,408.79

2015 4,587.70

2016 4,632.10

92,179.12 107,825.33 112,733.45 229,059.41 202,020.54 56,601.36

MSE 9,217.91 13,478.17 16,104.78 25,451.05 22,446.73 5,660.14

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Linear Trend Line

Year Time Period (x) Acutal Sales (y) x2 xy

2004 1.00 4,113.50 1.00 4,113.50

2005 2.00 4,112.40 4.00 8,224.80

2006 3.00 3,979.40 9.00 11,938.20

2007 4.00 4,218.20 16.00 16,872.80

2008 5.00 4,231.90 25.00 21,159.50

2009 6.00 4,318.20 36.00 25,909.20

2010 7.00 4,216.20 49.00 29,513.40

2011 8.00 4,346.10 64.00 34,768.80

2012 9.00 4,231.50 81.00 38,083.50

2013 10.00 4,436.30 100.00 44,363.00

S 55.00 42,203.70 385.00 234,946.70

n = 10.00 periods in my time series

b = slope of the line = 34.26

a = y-intercept = 4,031.95

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