FORECASTING AND DEMAND PLANNING. CHAPTER 11. DAVID A. COLLIER AND JAMES R. EVANS. 11-1 Describe the importance of forecasting to the value chain. 11-2 Explain basic concepts of forecasting and time series. 11-3 Explain how to apply simple moving average and exponential smoothing models.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
DAVID A. COLLIER AND JAMES R. EVANS
11-2Explain basic concepts of forecasting and time series.
11-3Explain how to apply simple moving average and exponential smoothing models.
11-4Describe how to apply regression as a forecasting approach.
11-5 Explain the role of judgment in forecasting.
11-6 Describe how statistical and judgmental forecasting techniques are applied in practice.
he demand for rental cars in Florida and other warm climates peaks during college spring break season. Call centers and rental offices are flooded with customerswanting to rent a vehicle. National Car Rental took a unique approach by developing a customer-identification forecasting model, by which it identifies all customers who are young and rent cars only once or twice a year. These demand analysis models allow National to call this target market segment in February, when call volumes are lower, to sign them up again. The proactive strategy is designed to both boost repeat rentals and smooth out the peaks and valleys in call center volumes.
Think of a pizza delivery franchise located near a college campus. What factors that influence demand do you think should be included in trying to forecast demand for pizzas?
Trend and Business Cycle Characteristics (each data point is 1 year apart)
Call Center Volume
Example of a time series with trend and seasonal components
Chart of Call Volume
Σ׀(At – Ft ) ׀
Σ׀(At – Ft )/At ׀
Develop three-period and four-period moving-average forecasts and single exponential smoothing forecasts with α= 0.5. Compute the MAD, MAPE, and MSE for each. Which method provides a better forecast?
Using the Excel Moving Average and Exponential
Smoothing templates, we find that the MSE for a
three-period moving average is 5.98, the MSE for a
four-period moving average is 6.21, and the MSE for
the exponential-smoothing model is 9.65.
Based on these error metrics (MAD, MSE, MAPE), the 3-month moving average is the best method among the three.
Single Exponential Smoothing (SES)is a forecasting technique that uses a weighted average of past time-series values to forecast the value of the time series in the next period.
Ft+1 = At + (1 – )Ft = Ft + (At – Ft) [11.5]
Exhibit 11.9 Summary of Single Exponential Smoothing Milk-Sales Forecasts with
α = 0.2
Chart of Sales versus Time
A tracking signal provides a method for monitoring a forecast by quantifying bias—the tendency of forecasts to consistently be larger or smaller than the actual values of the time series.
Tracking signal = Σ(At – Ft)/MAD [11.8]
Tracking signals between plus and minus 4 indicate an adequate forecasting model.Forecasting in Practice
1. What are the service management characteristics of the CSR job?
2. Define the mission statement and strategy of the Help Desk contact center. Why is the Help Desk important? Who are its customers?
3. How would you handle the customer affected by the inaccurate stock price in the banks trust account system? Would you take a passive or proactive approach? Justify your answer.
4. Using the data on Call Volume in the accompanying table, how would you forecast short-term demand?