FORECASTING(overview)

The history of forecasting

• The development of business forecasting in the 17th century was a major innovation [Bernstein P. (1996)]

• Along with the development of data-based methods, forecasting has grown significantly over the last 50 years

• Without any data history, human judgement may be the only way to make predictions about the future

• If data are available, forecasts can be produced by quantitative techniques

• Lack of managerial oversight and improper use of forecasting techniques can lead costly decisions

Is forecasting neccessary?

• How can an operations manager realistically set production scheduleo without some estimates of future sales..

• How can a company determine staffing for its call centerso without some guess of future demand for service

• Everyone requires forecasts• The need for forecasts cuts across all

functional lines as well as types of organizations

Types of forecasts-quantitative or

qualitative• A purely qualitative technique is one requiring no

manipulation of datao judgement of the forecaster is used

• A purely quantitative technique need no judgemento mechanical procedures that produce quantitative results are used

• Effective management of modern organizations needo judgement and common sense along with mechanical and data-

manipulative procedures

Choosing a forecasting method

• Rarely (practically never)does one method work for all cases• While choosing the forecasting method, organization’s

manager must consider:o Different products

• New versus establishedo Characteristics of data

• Stationary or unstationary• Has trend / seasonality /or wavelike fluctuation

o Goals• simple prediction versus need for future values

o Constraints• cost, required expertise, immediacy

• Several methods can be tried in a given situation• The methodology producing the most accurate forecasts in

one case may not be the best methodology in another situation

Forecasting steps1. Problem formulation and data collection

1. The problem determines the appropriate data2. Often accessing and assembling appropriate data is challenging and time-

consuming task3. If appropriae data are not available, the problem may have to be redefined

2. Data manipulation and cleaning1. Some data may not be relevant to problem2. Some data may have missing values that must be estimated

3. Model building and evaluation1. Involves fitting the collected data into a forecasting model that minimizes

forecasting error

4. Model implementation1. Generation of the actual model once the appropriate data have collected

5. Forecast evaluation1. Involves comparing forecast values with actual historical values

Exploring time series data pattern

• Horizontal patterno When data collected over time fluctuate around a constant level or

mean, horizontal pattern exists.o This type of series is said to be stationary in meano Ex. Monthly sales for a food product that do not increase or decrease

consistently over an extended period

• Trend patterno When data grow or decline over several time periods, trend pattern

existso Ex. Population growth, price, inflation, technological change, consumer

preferences, productivity increases..

Exploring time series data pattern

• Cyclical patterno Is the wavelike fluctuation around the trendo Are usually affected by economic conditionso Cyclical peak in figure shows economic expansiono Cyclical vallye shows economic contradiction

• Seasonal patterno When observations are influenced by seasonal factors a seasonal

pattern existso It refers to a pattern of change that repeats itself year after yearo Water power residential customers is highest in the first quarter, winter

monthso Seasonal variation may reflect weather conditions, school schedules,

holidays

Measuring Forecast Error

• Basic Forecasting Notation

• A residual• Is the difference between on actual observed

value and its forecast value

Measuring Forecast Error

• Mean absolute deviation:

• Mean Squared error

• Root mean squared error

• Mean absolute percentage error

• Mean percentage error

Naive models• Often young businesses face the dilemma of trying to

forecast with very small data sets• This situation creates a real problem, since many

forecasting techniques require large amounts of data• These methods assume that recent periods are the

best predictors of futureo Ex. Tomorrow’s weather will be much likely today’s weather

• When data values increase over time, they are said to be non-stationary in level or to have a trendo If the above equation is used the projections-estimations will be consistently

low.o Technique can be adjusted to take trend into the consideration by adding the

differences between last two periods. Then the adjusted equation is:

Methods Based on Averaging

• Some quick, inexpensive, very simple short-term forecasting tools are needed when management faces with hundreds or thousands of items

• Simple Averages• Decision is made to use the first t data points as

the initialization parta and remaining data point as the test part

Moving Averages• What if the analyst is more concerned with recent

observations• We use moving averages.

• whereo : the forecast value for the next periodo : the actual value at period to k: the number of terms in the moving average

Weighted Moving Average

• Takes only the most recent observation into account,

• But the weights of observations are not equal.• Gives higher weights most recent data.

ExerciseThe yield on a general obligation bond for the city of Davenport fluctuates with the market. The monthly quotations for 2014 are given as:

month yield

January 9.29

February 9.99

March 10.16

April 10.25

May 10.61

June 11.07

July 11.52

August 11.09

September

10.80

October 10.50

November 10.96

December 9.97

Exercisea) Find the forecast value of the yield for the obligation

bonds for each month, starting with April, using a trend adjusted naive method

b) Find the forecast value of the yield for the obligation bonds for each month, starting with April, using a two-month moving average

c) Find the forecast value of the yield for the obligation bonds for each month, starting with June, using a five-month moving average

d) Evaluate the performance of the methods using MADe) Evaluate the performance of the methods using MSEf) Forecast the yield for January 2007 using the better

technique