Qrb/501 – Week 3 – Forecasting with Indices

Week 3 Forecasting with Indices QRB/501 Week 3 Forecasting with Indices The individual assignment for this week tasked the students to select one cheek from either our week two assignment or the University material. This paper bequeath show the data in an king using the time serial data to forecast inventory for the next year. The Winter Historical history Data from the (University of Phoenix, 2010) shows four years of actual demand of inventory data for the seasonal Winter Highs. Each year is divided into 12 calendar month increments.Methods This breakdown of data allows for quantitative analysis. This approach is objective in spirit 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 notwithstanding the different pass months do not show a clear trend. University of Phoenix temporal Win ter Historical Inventory Data Typical Seasonal train for Winter Highs Actual 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 training 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 allow for the businesses minimal ri skiness of inventory shortage and overage based on the data.