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 organization from either our week two assignment or the University material. This paper will show the data in an index using the time series data to forecast inventory for the next year. The Winter Historical Inventory 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 month increments.Methods This breakdown of data allows for quantitative analysis. This approach is objective in nature compared to qualitative analysis which is developed using the judgment of experts. Results The data was plotted 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. University of Phoenix Material Winter Histor ical Inventory Data | Typical Seasonal Demand 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 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 t he other two winter months at an average level. This would allow for the businesses minimal risk of inventory shortage and overage based on the data.