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Question Completion Status: The Instant Paper Clip Office Supply Company sells and delivers office supplies to companies, schools, and agencies within a 50-mile radius of its warehouse. The office supply business is competitive, and the ability to deliver orders promptly is a big factor in getting new customers and maintaining old ones. The manager of the company wants to be certain that enough drivers and vehicles are available to deliver orders promptly and that they have adequate inventory in stock. Therefore, the manager wants to be able to forecast the demand for deliveries during the next month. From the records of previous orders, management has accumulated the following data for the past 4 months: Month Jan. Feb. Mar. Apr. Orders 120 90 110 95 Compute the monthly demand forecast for May using exponential smoothing with smoothing parameter alpha=0.2. Month Orders Forecast (exponential smoothing) Jan. 120 Feb. 90 Mar 110 Apr. 95 May Suppose the manager also knows the Trendline and Seasonal relatives for Quarter 1 (Jan Feb Mar) and Quarter 2 (Apr May Jun) as they are given below. Compute the monthly demand forecast for February through May using Multiplicative Seasonality Model. Month Forecast (multiplicative Orders Trendline seasonality) Jan 120 107 Feb. 90 103 Mar. 110 99 Apr. 95 95 May 91 Seasons Seasonal Relatives Q1 1.1 Q2 0.9 Q3 04 For each method, compute the absolute errors of forecasts for Feb, March, and Apr. Which method would you use to forecast demand for May? Absolute error Absolute error Month Orders (Multiplicative exponential smoothing ) Seasonality) Feb 90 Mar 110 Q4 For each method, compute the absolute errors of forecasts for Feb, March, and Apr. Which method would you use to forecast demand for May? Month Orders Absolute error (exponential smoothing) Absolute error (Multiplicative Seasonality) Feb 90 Mar. 110 Apr. 95 From errors in Feb, Mar and Apr, compute the Mean Absolute Deviation for each forecast method. MAD of the exponential smoothing = MAD of the multiplicative seasonality =