Tahoe Salt

We now describe one method for estimating the three parameters L, T, and 5. As an example, consider the demand for rock salt used primarily to melt snow. This salt is produced by a firm called Tahoe Salt, which sells its salt through a variety of independent retailers around the Lake Tahoe area of the Sierra Nevada Mountains. In the past, Tahoe Salt has relied on estimates of demand from a sample of its retailers, but the company has noticed that these retailers always overestimate their purchases, leaving Tahoe (and even some retailers) stuck with excess inventory. After meeting with its retailers, Tahoe has decided to produce a collaborative forecast. Tahoe Salt wants to woik with the retailers to create a more accurate forecast based on the actual retail sales of their salt. Quarterly retail demand data for the past three years are shown in Table 7-1 and charted in Figure 7-1. 

ESTIMATING LEVEL AND TREND The objective of this Step is to estimate the level at Period 0 and the trend. We start by deseasonalizing the demand data. Deseasonalized demand represents the demand that would have been observed in the absence of seasonal fluctuations. The periodicity (p) is the number of periods after which the seasonal cycle repeats. For Tahoe Salt s demand, the pattern repeats every year. Given that we are measuring demand on a quarterly basis, the periodicity for the demand in Table 7-1 is j = 4. 

With this procedure, we can obtain deseasonalized demand between Periods 3 and 10 as shown in Figures 7-2 and 7-3 (all details are available in the accompanying spreadsheet Chapter 7-Tahoe-salt). 

FIGURE 7-2 Excel Workbook with Deseasonalized Demand for Tahoe Salt... 

For the Tahoe Salt example, the deseasonalized danand estimated using Equation 7.4 and the seasonal factors estimated using Equation 7.5 are shown in Figure 7-4 (see worksheet Figure 7-4). 

For the Tahoe Salt example, a total of 12 periods and a periodicity ofp = 4 imply that there are r = 3 seasonal cycles in the data. We obtain seasonal factors using Equation 7.6 as... 

Tahoe Salt and its retailers now have a more accurate forecast of demand. Without the sharing of sell-through information between the retailers and the manufacturer, this supply chain would have a less accurate forecast, and a variety of production and inventory inefficiencies would result... 

Consider the Tahoe Salt demand data in Table 7-1. Forecast demand for Period 1 using trend- and seasonality-corrected exponential smoothing with a = 0.1, /3 = 0.2, y = 0.1. 

If Tahoe Salt uses an adaptive forecasting method for the sell-through data obtained from its retailers. Winter s model is the best choice, because its demand experiences both a trend and seasonality. 

If we do not know that Tahoe Salt experiences both trend and seasonality, how can we find out Forecast error helps identify instances in which the forecasting method being used is inappropriate. In the next section, we describe how a manager can estimate and use forecast error. 

FIGURE 7-7 Tahoe Salt Forecasts Using Four-Period Moving Average... 

FIGURE 7-8 Tahoe Salt Forecasts Using Simple Exponential Smoothing... 

Error Estimates for Tahoe Salt Forecasting Forecasting Method MAD MAPE (%) TS Range... 

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