Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Modified chap003 by Durrah Abdulaziz 287 views
- IBF conference, 20-22 Amsterdam Nov... by Humberto Galasso 1390 views
- Forecasting by Soim Ahmad 2006 views
- Alex Albertini from Pacific Sunwear... by eyefortransport 1047 views
- 130131 sbi sop offering in saas by Luckasz Vanherrew... 600 views
- Forecasting by Rukunuddin Aslam 5045 views

741 views

Published on

Forecasting

No Downloads

Total views

741

On SlideShare

0

From Embeds

0

Number of Embeds

9

Shares

0

Downloads

31

Comments

0

Likes

3

No embeds

No notes for slide

- 1. Student ID 5671020 Master of science (Management) Email: wichiasr@hotmail.com, Tel.+66810716437 Quantitative Analysis for Management Forecasting By Wichian Srichaipanya
- 2. Contents • Measure of Forecast Accuracy • Monitoring and Controlling Forecast • Moving Average • Exponential Smoothing 2 • Introduction
- 3. FORECAST: • A statement about the future value of a variable of interest such as demand. • Forecasting is used to make informed decisions. Introduction
- 4. Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services 3-4
- 5. Features of Forecasts • Assumes causal system past ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate for groups vs. individuals • Forecast accuracy decreases as time horizon increases 3-5
- 6. Steps in the Forecasting Process 3-6 Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Obtain, clean and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast”
- 7. Types of Forecasts • Judgmental - uses subjective inputs • Time series - uses historical data assuming the future will be like the past • Associative models - uses explanatory variables to predict the future 3-7
- 8. Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method – Opinions of managers and staff – Achieves a consensus forecast 3-8
- 9. Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance 3-9
- 10. Forecast Variations 3-10 Trend Irregular variation Seasonal variations 90 89 88 Cycles
- 11. Moving average – A technique that averages a number of recent actual values, updated as new values become available. where Ft+1 = Forecast for time period t+1 Yt = actual value in time period t n = number of periods to average 3-11 Ft+1 = n Yt+Yt-1+ …+Yt-n+1 Moving Average
- 12. • Weighted moving average – More recent values in a series are given more weight in computing the forecast. 3-12 Ft+1 = n w1Yt+w2Yt-1+ …+wnYt-n+1 where wi = weight for ith observation
- 13. Simple Moving Average 3-13 35 37 39 41 43 45 47 1 2 3 4 5 6 7 8 9 10 11 12 Actual MA3 MA5
- 14. 3-14 Example : Moving Average A 3-month moving average for some company according to actual sales volume and estimated sales volume.
- 15. 3-15 Example : Weighted Moving Average Forecast with weights of 3 for the most recent observation, 2 for the next observation, and 1 for the most distant observation.
- 16. • Premise--The most recent observations might have the highest predictive value. – Therefore, we should give more weight to the more recent time periods when forecasting. 3-16 Ft+1 = Ft + α(Yt - Ft) Exponential Smoothing • Weighted averaging method based on previous forecast plus a percentage of the forecast error • Y-F is the error term, α is the % feedback
- 17. 3-17 Period Actual Alpha = 0.1 Error Alpha = 0.4 Error 1 42 2 40 42 -2.00 42 -2 3 43 41.8 1.20 41.2 1.8 4 40 41.92 -1.92 41.92 -1.92 5 41 41.73 -0.73 41.15 -0.15 6 39 41.66 -2.66 41.09 -2.09 7 46 41.39 4.61 40.25 5.75 8 44 41.85 2.15 42.55 1.45 9 45 42.07 2.93 43.13 1.87 10 38 42.36 -4.36 43.88 -5.88 11 40 41.92 -1.92 41.53 -1.53 12 41.73 40.92 Example - Exponential SmoothingExample - Exponential Smoothing
- 18. Example : Exponential Smoothing graph 3-18 35 40 45 50 1 2 3 4 5 6 7 8 9 10 11 12 Period Demand α = .1 α = .4 Actual
- 19. 3-19 Measure of Forecast Accuracy • Error - difference between actual value and predicted value • Mean Absolute Deviation (MAD) – Average absolute error • Mean Squared Error (MSE) – Average of squared error • Mean Absolute Percent Error (MAPE) – Average absolute percent error
- 20. MAD, MSE and MAPE • MAD – Easy to compute – Weights errors linearly • MSE – Squares error – More weight to large errors • MAPE – Puts errors in perspective 3-20
- 21. MAD, MSE, and MAPE 3-21 MAD = Actual forecast−∑ n MSE = Actual forecast) -1 2 −∑ n ( MAPE = Actual forecast− n / Actual*100)∑(
- 22. Example : MAD, MSE, MAPE 3-22 Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100 1 217 215 2 2 4 0.92 2 213 216 -3 3 9 1.41 3 216 215 1 1 1 0.46 4 210 214 -4 4 16 1.90 5 213 211 2 2 4 0.94 6 219 214 5 5 25 2.28 7 216 217 -1 1 1 0.46 8 212 216 -4 4 16 1.89 -2 22 76 10.26 MAD= 2.75 MSE= 10.86 MAPE= 1.28
- 23. 3-23 Monitoring and Controlling Forecast After a forecast has been completed, it is important that it is not be forgotten. No manager wants to be reminded when his or her forecast is horribly in accurate, but the firm needs to determine why the actual demand differed from that projected.
- 24. Tracking Signal 3-24 Tracking signal = (Actual- forecast) MAD ∑ • Running sum of the forecast error(RSFE) divided by the mean absolute deviation (MAD) Upper and Lower limit– Persistent tendency for forecasts to be Greater or less than actual values. One way to monitor forecasts to ensure that they are performing well
- 25. Upper and Lower Limit 3-25 There is no single answer, but they try to find reasonable values.
- 26. Example : Tracking signals 3-26 The objective is to compute the tracking signal and determine whether forecasts are performing adequately. In period 6, this tracking signal is within acceptable limits from -2.0 MADs to +2.5 MADs.
- 27. Any Question? Thank You

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment