1. Forecasting

2. Learning Objectives List the elements of a good forecast.
Outline the steps in the forecasting process.
Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.
Compare and contrast qualitative and quantitative approaches to forecasting.

3. Learning Objectives
Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems.
Describe two measures of forecast accuracy.
Describe two ways of evaluating and controlling forecasts.
Identify the major factors to consider when choosing a forecasting technique.

4. FORECAST:
The art and science of predicting future (It may involve using statistics and mathematical model, or may be a subjective prediction).
Forecasting is used to make informed decisions.
Short-range (up to 1 Yr): planning purchasing, job scheduling, workforce levels, job assignment.
Medium-rang (3 Mth – 3 Yr): sales planning, production planning and budgeting.
Long-range (more than 3 Yr): planning for new products, facility location or expansion, and R&D.
Medium and longer-range forecasts are distinguished from short-range forecasts by three features:
First, intermediate and long-run forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants, and processes. Implementing some facility decisions, such as GM’s decision to open a new Thai manufacturing plant in Rayong province can take 5 to 8 years from inception (begging) to completion.
Second, short-term forecasting usually employs (uses) different methodologies than longer-term forecasting. Mathematical techniques, such as moving averages, exponential smoothing and trend extrapolation are common to short-run projections. Broader, less quantitative methods are useful in predicting such issues as whether a new product, like the optical disk recorder, should be introduced into a company’s product line.
Finally, short-range forecasts tend to be more accurate than longer-range forecasts. Factors that influence demand change every day. Thus, as the time horizon lengthens, it is likely that forecast accuracy will diminish. It almost goes without saying, then, that sales forecasts must be updated regularly to maintain their value and integrity. After each sales period, forecasts should be reviewed and revised.

5. Forecasts
Forecasts affect decisions and activities throughout an organization
Accounting, finance
Human resources
Marketing
MIS
Operations
Product / service design

6. 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

7. Features of Forecasts Assumes causal system past ==> future
I see that you will get an A this semester.
Assumes causal system past ==> future
Forecasts rarely perfect because of randomness
Forecasts more accurate for groups cf. (compared to) individuals
Forecast accuracy decreases as time horizon increases
Features common to all forecasts
Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.
Forecasts are rarely perfect; actual results usually differ from predicted values. This means that outside factors that we cannot predict and control often impact the forecast. No one can predict precisely how an often large number of these related factors will impinge (affect) upon the variable in question; this, along with the presence of randomness, precludes a perfect forecast. Allowance should be made for forecast errors.
Forecast for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a cancelling effect. For example, Tupperware company aggregates (sums) product forecasts by both family (e.g. mixing bowls vs. cups vs. storage containers) and region (countries). This approach helps balance the over- and underpredictions of each product and country.
Forecast accuracy decreases as the time horizon (the time period covered by the forecast) increases. Generally speaking, short-range forecasts must contend with fewer uncertainties than longer-range forecasts, so they tend to be more accurate.

8. Elements of a Good Forecast
Timely
Accurate
Reliable
Be Written
Easy to use
Meaningful Units
Cost-effective
Timely: Usually, a certain amount of time is needed to respond to the information contained in a forecast. For example, capacity cannot be expanded overnight, nor can inventory levels be changed immediately. Hence, the forecasting horizon must cover the time necessary to implement possible changes.
Accurate: the degree of accuracy should be stated. This will enable users to plan for possible errors and will provide a basis for comparing alternative forecasts.
Reliable: It should work well and consistently. A technique that sometimes provides a good forecast and sometimes a poor one will leave users with the uneasy feeling that they may get burned every tine a new forecast is issued.
Meaningful: The forecast should be expressed in meaningful units. Financial planners need to know how many dollars will be needed, production planners need to know how many units of raw materials or components will be needed, and schedulers need to know what machines and skills will be required. The choice of units depends on user needs.
The forecast should be in writing and presented in an understandable format.
The forecasting technique should be simple to understand and use. Users often lack confidence in forecasts based on sophisticated techniques; they do not understand either the circumstances in which the techniques are appropriate or the limitations of the techniques. Misuse of techniques is an obvious consequence (result).
The forecast should be cost-effective: the benefits should outweigh (surpass, or exceed) the costs.

9. 6 Steps in the Forecasting Process
Step 6 Monitor the forecast (modify, revise)
Determine the purpose of the forecast. How will it be used and when will it be needed? This step will provide an indication of the level of detail required in the forecast, the amount of resources (i.e. personnel, computer, time, dollars) that can be justified, and the level of accuracy necessary.
Establish a time horizon. The forecast must indicate a time interval, keeping in mind that accuracy necessary.
Select a forecasting technique.
Obtain, clean and analyse appropriate data. Obtaining the data can involve significant effort. Once obtained, the data may need to be “clean” to get rid of outliers (values far from most others in a set of data) and obviously incorrect data before analysis.
Make the forecast
Monitor the forecast. A forecast has to be monitored to determine whether it is performing in a satisfactory manner. If it is not, reexamine the method, assumptions, validity of data, and so on; modify as needed; and prepare a revised forecast.
Step 5 Make the forecast
Step 4 Obtain, clean and analyze data (eliminate outliers, incorrect data)
Step 3 Select a forecasting technique (Moving AVG, Weighted AVG, etc)
Step 2 Establish a time horizon (How long?)
Step 1 Determine purpose of forecast (How/when it will be used?, Resources)

10. 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

11. MAD, MSE, and MAPE  Actual  forecast MAD = n MSE = Actual forecast)- 1 2   n (
MAPE =
Actual
forecast  n
/ Actual*100) (

12. MAD, MSE and MAPE MAD MSE MAPE Easy to compute Weights errors linearly
Squares error
More weight to large errors
MAPE
Puts errors in perspective (the errors are presented as percentage)

13. Example 1

14. Ans: Example 1

15. 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
Qualitative method
Quantitative method

16. Qualitative method (Judgmental forecast)
Executive opinions (long-range planning, new product development)
Sales force opinions (direct contact with customers; however, sales staff are overly influenced by recent experience)
Consumer surveys (specific information; but money and time-consuming)
Executive opinions: A small group of upper-level managers (e.g. in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range planning and new product development.
Advantage: to combine the considerable knowledge and talents of various managers.
Disadvantage: the view of one person will prevail (influence) over the entire group.
Sales force opinions: Each salesperson estimates what sales will be in his or her region. These forecast are them reviewed to ensure that they are realistic. Then they are combined at the district and national levels to reach an overall forecast.
Advantage: Sales staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans that the customers may be considering for the future.
Disadvantage: Staff members (sales members) may be unable to distinguish between what customers would like to do and what they actually will do. And, these sales members are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic.
Consumer surveys: This method solicits (asks) input from customers or potential customers regarding future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning fore new products.
Advantage: The company or organisation can obtain information that might not be available elsewhere.
Advantage: A considerable amount of knowledge and skill is required to construct a survey, administer it. Survey can be expensive and time consuming.
Delphi method: An iterative process intended to achieve a consensus forecast. This method involves circulating a series of questionnaire among individuals who possess (have) the knowledge and ability to contribute meaningfully. Responses are kept anonymous (an unknown or unacknowledged name), which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail.

17. Quantitative method Naïve approach Moving average
Exponential smoothing
Trend projection
Linear regression
Time series models
Associative model

18. 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
Random variations - caused by chance and unusual circumstances

19. Forecast Variations Trend Cycles Random variation Time Time Year 1
Seasonal variations
Month

20. Naive Forecasts Uh, give me a minute.... We sold 250 wheels last
week.... Now, next week we should sell....
The forecast for any period equals the previous period’s actual value.

21. Naïve Forecasts Simple to use Virtually no cost
Quick and easy to prepare
Data analysis is nonexistent
Easily understandable
Cannot provide high accuracy
Can be a standard for accuracy

22. Uses for Naïve Forecasts
Stable time series data
F(t) = A(t-1)
Seasonal variations
F(t) = A(t-n)
Data with trends
F(t) = A(t-1) + (A(t-1) – A(t-2))

23. Techniques for Averaging
Moving average
Weighted moving average
Exponential smoothing

24. Moving Averages At-n + … At-2 + At-1 Ft = MAn= n
Moving average – A technique that averages a number of recent actual values, updated as new values become available.
Weighted moving average – More recent values in a series are given more weight in computing the forecast.
Ft = MAn= n
At-n + … At-2 + At-1
Ft = WMAn= n
wnAt-n + … wn-1At-2 + w1At-1

25. Simple Moving Average
Actual
MA5
MA3
Ft = MAn= n
At-n + … At-2 + At-1

26. Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
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.

27. Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
Weighted averaging method based on previous forecast plus a percentage of the forecast error
A-F is the error term,  is the % feedback

28. Example 3 - Exponential Smoothing

29. Picking a Smoothing Constant
 .1
.4
Actual

30. Example 3 - Exponential Smoothing

31. Common Nonlinear Trends
Figure 3.5
Parabolic
Exponential
Growth

32. Linear Trend Equation Ft = a + bt Ft = Forecast for period t
t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line

33. Calculating a and b b = n
(ty) - t y 2 ( t) a 

34. Linear Trend Equation Example

35. Linear Trend Calculation
y = t a =
812 -
6.3(15) 5 b
5 (2499)
15(812)
5(55)
225
12495
12180
275
6.3
143.5

36. Techniques for Seasonality
Seasonal variations
Regularly repeating movements in series values that can be tied to recurring events.
Seasonal relative
Percentage of average or trend
Centered moving average
A moving average positioned at the center of the data that were used to compute it.

37. Associative Forecasting
Predictor variables - used to predict values of variable interest
Regression - technique for fitting a line to a set of points
Least squares line - minimizes sum of squared deviations around the line

38. Linear Model Seems Reasonable
Computed relationship
A straight line is fitted to a set of sample points.

39. Linear Regression Assumptions
Variations around the line are random
Deviations around the line normally distributed
Predictions are being made only within the range of observed values
For best results:
Always plot the data to verify linearity
Check for data being time-dependent
Small correlation may imply that other variables are important

40. Controlling the Forecast
Control chart
A visual tool for monitoring forecast errors
Used to detect non-randomness in errors
Forecasting errors are in control if
All errors are within the control limits
No patterns, such as trends or cycles, are present

41. Sources of Forecast errors
Model may be inadequate
Irregular variations
Incorrect use of forecasting technique

42. Tracking Signal  Tracking signal = (Actual - forecast) MAD
Ratio of cumulative error to MAD
Tracking signal =
(Actual -
forecast)
MAD 
Bias – Persistent tendency for forecasts to be
Greater or less than actual values.

43. Choosing a Forecasting Technique
No single technique works in every situation
Two most important factors
Cost
Accuracy
Other factors include the availability of:
Historical data
Computers
Time needed to gather and analyze the data
Forecast horizon

44. Operations Strategy Forecasts are the basis for many decisions
Work to improve short-term forecasts
Accurate short-term forecasts improve
Profits
Lower inventory levels
Reduce inventory shortages
Improve customer service levels
Enhance forecasting credibility

45. Supply Chain Forecasts
Sharing forecasts with supply can
Improve forecast quality in the supply chain
Lower costs
Shorter lead times
Gazing at the Crystal Ball (reading in text)

46. Exponential Smoothing

47. Linear Trend Equation

48. Simple Linear Regression