Scribd
Upload
515 views2 pages

Forecast Problems-Practice

Demand forecasting is important for effective supply chain management. A mismatch between supply and demand can impact companies, so strategies are adopted to influence demand. Qualitative forecasting techniques use judgment-based approaches and are suitable when historical data is limited. Time series models and associative models make forecasts based on historical data under different conditions. Measures like mean squared error, mean absolute deviation, and mean absolute percentage error evaluate forecast accuracy. Tracking signals provide information to improve forecast quality. CPFR (collaborative planning, forecasting and replenishment) involves data sharing between companies to benefit supply chain forecasting.

Uploaded by

Muhammed Husain Musani
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as RTF, PDF, TXT or read online on Scribd
515 views2 pages

Forecast Problems-Practice

Demand forecasting is important for effective supply chain management. A mismatch between supply and demand can impact companies, so strategies are adopted to influence demand. Qualitative forecasting techniques use judgment-based approaches and are suitable when historical data is limited. Time series models and associative models make forecasts based on historical data under different conditions. Measures like mean squared error, mean absolute deviation, and mean absolute percentage error evaluate forecast accuracy. Tracking signals provide information to improve forecast quality. CPFR (collaborative planning, forecasting and replenishment) involves data sharing between companies to benefit supply chain forecasting.

Uploaded by

Muhammed Husain Musani
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as RTF, PDF, TXT or read online on Scribd
You are on page 1/ 2

Chapter 5 Demand Forecasting 163

3. Why is demand forecasting important for effective supply chain management?


4. Explain the impact of a mismatch in supply and demand. What strategies can com-
panies adopt to influence demand?
5. What are qualitative forecasting techniques? When are these methods more suitable?
6. What are the main components of a time series?
7. Explain the difference between a time series model and an associative model. Under
what conditions would one model be preferred to the other?
8. What is the impact of the smoothing constant value on the simple exponential
smoothing forecast?
9. Compare and contrast the jury of executive opinion and the Delphi techniques.
10. Explain the key differences between the weighted moving average and the simple
exponential smoothing forecasting methods.
11. What are three measures of forecasting accuracy?
12. What is a tracking signal? What information does the tracking signal provide that
managers can use to improve the quality of forecasts?
13. What are the key features of CPFR? Why would a company consider adopting CPFR?
14. What are the eight tasks associated with the CPFR model? Why is sharing data
important in CPFR implementation? What are the benefits of sharing information?
15. West Marine identified the ten performance improvement steps in their successful
implementation of CPFR. Is West Marine’s approach unique, or can their experience
be duplicated at another company? What are the key challenges that other companies
will face in implementing CPFR?
16. Why is widespread adoption of CPFR below expectations?
17. What is cloud computing, and how can companies benefit from this technology in
solving their supply chain forecasting problems?

SPREADSHEET PROBLEMS
1. Ms. Winnie Lin’s company sells computers. Monthly sales for a six-month period are
as follows:

MONTH SALES
Jan 18,000
Feb 22,000
Mar 16,000
Apr 18,000
May 20,000
Jun 24,000

a. Plot the monthly data on a sheet of graph paper.


b. Compute the sales forecast for July using the following approaches: (1) a four-month
moving average; (2) a weighted three-month moving average using .50 for June, .30
for May and .20 for April; (3) a linear trend equation (4) exponential smoothing
with α (smoothing constant) equal to .40, assuming a February forecast of 18,000
c. Which method do you think is the least appropriate? Why?
1
6
4
Part 3

Operations Issues in Supply Chain Management

2. The owner of the Chocolate Outlet Store wants to forecast chocolate demand.
Demand for the preceding four years is shown in the following table:

YEAR DEMAND (POUNDS)


1 68,800
2 71,000
3 75,500
4 71,200

Forecast demand for Year 5 using the following approaches: (1) a three-year moving
average; (2) a three-year weighted moving average using .40 for Year 4, .20 for Year 3
and .40 for Year 2; (3) exponential smoothing with α = .30, and assuming the fore-
cast for Period 1 = 68,000.
3. The forecasts generated by two forecasting methods and actual sales are as follows:

MONTH SALES FORECAST 1 FORECAST 2


1 269 275 268
2 289 266 287
3 294 290 292
4 278 284 298
5 268 270 274
6 269 268 270
7 260 261 259
8 275 271 275

Compute the MSE, the MAD, the MAPE, the RSFE and the tracking signal for each
forecasting method. Which method is better? Why?

You might also like