Forecasting_with_Indices

QRB/501 Dr. Julianne Manchester August 22, 2010 Forecasting With Indices According to Business: The Ultimate Resource (2009), “forecasting is the prediction of outcomes, trends, or expected future behavior of a business, industry sector, or the economy through the use of statistics. Forecasting is an operational research technique used as a basis for management planning and decision making. Common types of forecasting include trend analysis, regression analysis, Delphi technique, time series analysis, correlation, exponential smoothing, and input-output analysis.” Simply stated, forecasting is a way of predicting future results or expectations based on assumptions or historical data. An index is a statistical measure of changes in a portfolio of stocks representing a portion of the overall market (Investopedia, 2010). Quantitative indexes and rating systems are used to give information about general trends and to allow us to make comparisons and judgments (Sevilla & Somers, 2007). An index or index number measures the change in a particular item (typically a product or service) between two time periods (Lind, Marchal, & Wathen, 2008, p. 570). Furthermore, an index number is a number that expresses the relative change in price, quantity, or value compared to a base period (Lind et al., 2008). The following time series analysis will discuss the inventory forecast methodology for Company XYZ. An inventory system tracks products for a company and provides important information in strategic planning. Managers can track inventory by factors such as units in demand, number of items received, items on order, items on hand, or any number of factors important to the company using the system. The inventory system that Company XYZ uses (see Table 1) tracks the units in demand and indicates the typical demands for summer highs over a four year period. Historical data is used by companies to forecast future outcomes through Time Series Analysis. Time series forecasting models try to predict the future based on past data. For example, sales figures collected for the past six weeks can be used to forecast sales for the seventh week (Chase, Jacobs, & Aquilano, 2005, p. 518). Seasonal variation refers to the patterns of change in a time series within a year (Lind et al., 2008). These patterns tend to repeat themselves each year (Lind et al., 2008, p. 605). A seasonal factor is the amount of correction needed in a time series to adjust for the season of the year (Chase et al., 2005, p. 532). Table 1 indicates a significant increase in demand during April, May, June, July, and August. By contrast, it indicates a decrease in demand during September through March. Therefore, late spring through summer appears to be the peak period of unit demand for products sold by Company XYZ. The data from Table 1 is used to create an inventory demand index which provides the basis for the summer seasonal demand forecast (See Index 1). Meanwhile, Table 2 illustrates the forecast for year five. The forecast for year five will assist Company XYZ in determining material and production needs and help to manage costs appropriately. Seasonal forecasting prepares a company for market demands. Forecasting methods assist companies in planning for industry demands throughout the year. Although a perfect forecast is unlikely, planning is an important aspect of meeting industry supply and demand. Index 1 –Seasonal Demand – Summer Summer Demand Index Month Base Year Year 2 Year 3 Year 4 Index Y2 Index Y3 Index Y4 Index Y5 1 18,000 45,100 59,800 35,500 2.51 3.32 1.97 2.60 2 19,800 46,530 30,740 51,250 2.35 1.55 2.59 2.16 3 15,700 22,100 47,800 34,400 1.41 3.04 2.19 2.21 4 53,600 41,350 73,890 68,000 0.77 1.38 1.27 1.14 5 83,200 46,000 60,200 68,100 0.55 0.72 0.82 0.70 6 72,900 41,800 55,200 61,100 0.57 0.76 0.84 0.72 7 55,200 39,800 32,180 62,300 0.72 0.58 1.13 0.81 8 57,350 64,100 38,600 66,500 1.12 0.67 1.16 0.98 9 15,400 47,600 25,020 31,400 3.09 1.62 2.04 2.25 10 27,700 43,050 51,300 36,500 1.55 1.85 1.32 1.57 11 21,400 39,300 31,790 16,800 1.84 1.49 0.79 1.37 12 17,100 10,300 31,100 18,900 0.60 1.82 1.11 1.18 Note: Adapted from the University of Phoenix (2010) Week Two Supplement: Summer Historical Data. Table 1 - Typical Seasonal Demand (in units) - Summer Month Year 1 Year 2 Year 3 Year 4 1 18,000 45,100 59,800 35,500 2 19,800 46,530 30,740 51,250 3 15,700 22,100 47,800 34,400 4 53,600 41,350 73,890 68,000 5 83,200 46,000 60,200 68,100 6 72,900 41,800 55,200 61,100 7 55,200 39,800 32,180 62,300 8 57,350 64,100 38,600 66,500 9 15,400 47,600 25,020 31,400 10 27,700 43,050 51,300 36,500 11 21,400 39,300 31,790 16,800 12 17,100 10,300 31,100 18,900 Avg. 38,113 40,586 44,802 45,896 Note: Adapted from the University of Phoenix (2010) Week Two Supplement: Summer Historical Data. Table 2 – Seasonal Forecast - Summer** Seasonal Forecast Month Base Year Year 2 Year 3 Year 4 Year 5 1 18,000 45,100 59,800 35,500 46,800 2 19,800 46,530 30,740 51,250 42,768 3 15,700 22,100 47,800 34,400 34,697 4 53,600 41,350 73,890 68,000 61,104 5 83,200 46,000 60,200 68,100 58,240 6 72,900 41,800 55,200 61,100 52,488 7 55,200 39,800 32,180 62,300 35,880 8 57,350 64,100 38,600 66,500 56,203 9 15,400 47,600 25,020 31,400 34,650 10 27,700 43,050 51,300 36,500 43,489 11 21,400 39,300 31,790 16,800 29,318 12 17,100 10,300 31,100 18,900 20,178 References Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2005). Operations management for competitive advantage (11th ed.). Retrieved from the University of Phoenix eBook Collection database Forecasting. (2009). In Business: The ultimate resource. Retrieved from http://www.credoreference.com/entry/ultimatebusiness/forecasting Index investing: What is an index'. (2010). Retrieved from http://www.investopedia.com/university/indexes/index1.asp Lind, D. A., Marchal, W. G., & Wathen, S. A. (2008). Statistical techniques in business & economics (13th ed.). Retrieved from University of Phoenix eBook Collection database Lind, D. A., Marchal, W. G., & Wathen, S. A. (2010). Basic statistics using excel 2007 (14th ed.). Retrieved from University of Phoenix eBook Collection database Sevilla, A., & Somers, K. (2007). Quantitative reasoning: Tools for today’s informed citizen. Retrieved from University of Phoenix eBook Collection database