Qrb/501 – Week 3 – Forecasting with Indices

workweek 3 harbingering with Indices QRB/501 Week 3 Forecasting with Indices The individual assignment for this week tasked the students to select superstar organization from either our week two assignment or the University material. This paper impart show the selective information in an index using the time series data to forecast inventory for the next category. The wintertime Historical Inventory Data from the (University of genus Phoenix, 2010) shows four years of actual demand of inventory data for the seasonal worker Winter Highs. Each year is divided into 12 month increments.Methods This breakdown of data allows for quantitative analysis. This get down is objective in nature compared to qualitative analysis which is developed using the judgment of experts. Results The data was plan and graphed into a chart to show the trend. Based on the chart the index has shown an increase from year to year during December but the other winter months do not show a clear trend. Univ ersity of Phoenix Material Winter Historical Inventory Data Typical Seasonal Demand for Winter Highs true(a) Demands (in units) Month Year 1 Year 2 Year 3 Year 4 Forecast 1 55,200 39,800 32,180 62,300 47,370 2 57,350 64,100 38,600 66,500 56,638 3 15,400 47,600 25,020 31,400 29,855 4 27,700 43,050 51,300 36,500 39,638 5 21,400 39,300 31,790 16,800 27,323 6 17,100 10,300 31,100 18,900 19,350 7 18,000 45,100 59,800 35,500 39,600 8 19,800 46,530 30,740 51,250 37,080 9 15,700 22,100 47,800 34,400 30,000 10 53,600 41,350 73,890 68,000 59,210 1 83,200 46,000 60,200 68,100 64,375 12 72,900 41,800 55,200 61,100 57,750 Avg. 38,113 40,586 44,802 45,896 42,349 Conclusion This inventory provides good information to suggest that forecasting December will show an increase but the other winter months are not clear. My recommendation would be to would be to increase the inventory for December but hold the inventory for the other two winter months at an average level. This would al low for the businesses minimal risk of inventory shortage and overage based on the data.