I. WHAT IS BUSINESS FORECASTING?

The business forecasting considers the long term. It focuses on product lines.

The aggregate forecasting considers the aggregated (in terms of products)

demand for each of the 12 -24 coming months. The item forecasting is an

estimation of the demand for each item in the coming weeks. The need for

spares is also required for the MRP.

BUSINESS FORECASTING is an estimate or prediction of future developments

in business such as sales, expenditures, and profits. Given the wide swings in

economic activity and the drastic effects these fluctuations can have on profit

margins, it is not surprising that business forecasting has emerged as one of the

most important aspects of corporate planning. Forecasting has become an

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invaluable tool for businesspeople to anticipate economic trends and prepare

themselves either to benefit from or to counteract them.

II. FORECASTING ACCURACY AND ERRORS

Forecast accuracy decreases as the time period covered by the forecast

increases

The forecast error is the difference between the actual value and the forecast

value for the corresponding period.

where E is the forecast error at period t, Y is the actual value at period t, and F is

the forecast for period t.

Measures of aggregate error:

Mean absolute error (MAE)

Mean Absolute Percentage Error (MAPE)

Mean Absolute Deviation (MAD)

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Percent Mean Absolute Deviation (PMAD)

Mean squared error (MSE) or Mean squared

prediction error (MSPE)

Root Mean squared error (RMSE)

Forecast skill (SS)

Average of Errors (E)

Business forecasters and practitioners sometimes use different terminology in

the industry. They refer to the PMAD as the MAPE, although they compute this

as a volume weighted MAPE. For more information see Calculating demand

forecast accuracy.

III. ELEMENTS OF FORECASTING

1. The forecast horizon must cover the time necessary to implement possible

changes.

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2. The degree of accuracy should be stated.

3. The forecast should be reliable; it should work consistently.

4. The forecast should be expressed in meaningful units.

5. The forecast should be in writing.

6. The forecast should be simply to understand and use, or consistent with

historical data intuitively

IV. PROCESS OF FORECASTING

There is a lot of variation on a practical level when it comes to business

forecasting. However, on a conceptual level, all forecasts follow the same

process.

1. A problem or data point is chosen. This can be something like "will people buy

a high-end coffee maker?" or "what will our sales be in March next year?"

2. Theoretical variables and an ideal data set are chosen. This is where the

forecaster identifies the relevant variables that need to be considered and

decides how to collect the data.

3. Assumption time. To cut down the time and data needed to make a forecast,

the forecaster makes some explicit assumptions to simplify the process.

4. A model is chosen. The forecaster picks the model that fits the data set,

selected variables and assumptions.

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5. Analysis. Using the model, the data is analyzed and a forecast made from the

analysis.

6. Verification. The forecaster compares the forecast to what actually happens to

tweak the process, identify problems or in the rare case of an absolutely accurate

forecast, pat himself on the back.

V. APPROACHES ON FORECASTING

Forecasts can be obtained in different ways.

Qualitative Method These approaches are based on judgments and opinions.

Qualitative models have generally been successful with short-term predictions,

where the scope of the forecast is limited. Qualitative forecasts can be thought of

as expert-driven, in that they depend on market mavens or the market as a whole

to weigh in with an informed consensus. Qualitative models can be useful in

predicting the short-term success of companies, products and services, but

meets limitations due to its reliance on opinion over measurable data. Qualitative

models include:

Market Research polling a large number of people on a specific product or

service to predict how many people will buy or use it once launched.

Delphi Method Asking field experts for general opinions and then

compiling them into a forecast.

Scenario Writing. Under this approach, the forecaster starts with different

sets of assumptions. For each set of assumptions, a likely scenario of the

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business outcome is charted out. Thus, the forecaster would be able to

generate many different future scenarios (corresponding to the different

sets of assumptions). The decision maker or businessperson is presented

with the different scenarios, and has to decide which scenario is most

likely to prevail.

Subjective Approach. The subjective approach allows individuals

participating in the forecasting decision to arrive at a forecast based on

their subjective feelings and ideas. This approach is based on the premise

that a human mind can arrive at a decision based on factors that are often

very difficult to quantify. "Brainstorming sessions" are frequently used as a

way to develop new ideas or to solve complex problems. In loosely

organized sessions, participants feel free from peer pressure and, more

importantly, can express their views and ideas without fear of criticism.

Many corporations in the United States have started to increasingly use

the subjective approach.

Quantitative Method Quantitative models discount the expert factor and try to

take the human element out of the analysis. These approaches are concerned

solely with data and avoid the fickleness of the people underlying the numbers.

They also try to predict where variables like sales, gross domestic product,

housing prices and so on, will be in the long-term, measured in months or years.

Quantitative models include:

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The Indicator Approach: The indicator approach depends on the

relationship between certain indicators, for example GDP and

unemployment rates, remaining relatively unchanged over time. By

following the relationships and then following indicators that are leading,

you can estimate the performance of the lagging indicators, by using the

leading indicator data.

Econometric Modeling: This is a more mathematically rigorous version of

the indicator approach. Instead of assuming that relationships stay the

same, econometric modeling tests the internal consistency of data sets

over time and the significance or strength of the relationship between data

sets. Econometric modeling is sometimes used to create custom

indicators that can be used for a more accurate indicator approach.

However, the econometric models are more often used in academic fields

to evaluate economic policies.

Time Series Methods: This refers to a collection of different methodologies

that use past data to predict future events. The difference between the

time series methodologies is usually in fine details, like giving more recent

data more weight or discounting certain outlier points. By tracking what

happened in the past, the forecaster hopes to be able to give a better than

average prediction about the future. This is the most common type of

business forecasting, because it is cheap and really no better or worse

than other methods.

VI. TIME SERIES FORECASTING 7 | P a g e

Time series analysis comprises methods for analyzing time series data in order

to extract meaningful statistics and other characteristics of the data. 

Time series forecasting is the use of a model to predict future values based on

previously observed values.

* * *

The idea is that the evolution in the past will continue into the future.

Data Patterns

Trend: A long term upward or downward movement in data.

Seasonality: Short-term regular variations related to weather, holiday, or

other factors.

Cycle: Wavelike variation lasting more than one year.

Irregular Variation: Caused by unusual circumstances, not reflective of

typical behavior.

Random variation: Residual variation after all other behaviors are

accounted for.

They differ by the shape of the line which best fits the observed data.

Averaging Methods : moving average, regression, exponential smoothing

The methods which can be used are (linear) regressions, moving averages and

exponential smoothings. They differ by the importance they give to the data and

by their complexity.

Forecasts based on time series analysis are based on a 3-step procedure8 | P a g e

1. Select the model

2. Parametrize the model

3. Make a forecast and estimate confidence

Stationary Time Series

A common assumption in many time series techniques is that the data are

stationary.

A stationary process has the property that the mean, variance and

autocorrelation structure do not change over time. Stationarity can be defined in

precise mathematical terms, but for our purpose we mean a flat looking series,

without trend, constant variance over time, a constant autocorrelation structure

over time and no periodic fluctuations (seasonality).

Seasonal Time Series

A common assumption in many time series techniques is that the data are

stationary.

A stationary process has the property that the mean, variance and

autocorrelation structure do not change over time. Stationarity can be defined in

precise mathematical terms, but for our purpose we mean a flat looking series,

without trend, constant variance over time, a constant autocorrelation structure

over time and no periodic fluctuations (seasonality).

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For practical purposes, stationarity can usually be determined from a run

sequence plot.

Cycle Time Series

Cycles are up and down movement similar to seasonal variations but of longer

duration, e.g., two to six years between peaks.

It is difficult to project cycles from past data, because turning points are difficult to

identify. A short moving average or a naive approach may be of some value.

ASSOCIATIVE FORECASTS

High correlation of a forecast with leading variables can be useful in computing

the forecast.

Moving Average

Weighted Average

Exponential Smoothing

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Linear Regression

Y(t) = a + bt

The regression analysis aims at fitting a straight line in the set of points. Since

there are different ways of fitting the curve, an objective must be selected.

VII. WHICH METHOD TO USE?

Depends on: • observation If you have few observations, it will be difficult to

define a detailed model. A linear regression for computing the trends requires at

least 10 observations to be valid. If you have some seasonal variation, 3 or 4

cycles of observations are necessary for your model to start being effective.

Depends on: • time horizon The time horizon is also important. Do you want to

make a forecast for tomorrow, next month, next year or the next 5 years? How

does the demand process vary with respect to your time horizon? Linear

regressions (and causal relationship methods) seem for example more robust for

long term forecast. In any case, the observations on which you base your

forecast is a direct indicator of the horizon range for which your forecast is valid.

Depends on: • the MAD Observed If sufficient data are available, you could use

the 3 fourths of your observations to set the parameters of your model and then,

use the last fourth of observations to compare the forecasts of your model with

what really happened. Comparing the errors (the MAD) made by different

methods (and/or by different models) provides an immediate selection criterion.

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Depends on: • complexity The amount of energy to spend for the forecasting

process plays also a role too.

BIBLIOGRAPHY

The Gale Group Inc. (2003) Business Forecasting. Retrieved from

http://www.encyclopedia.com/topic/Business_forecasting.aspx

NIST/SEMATECH (2003, June 1) e-Handbook of Statistical Methods.

Retrieved from http://www.itl.nist.gov/div898/handbook

Makridakis S. and S.C. Wheelwright, Forecasting Methods for

Management, John Wiley & Sons, 1989.

Anandi P. Sahu , Ph.D. (N/D). Forecasting. Retrieved from

http://www.referenceforbusiness.com/encyclopedia/Fa-For/Forecasting.ht

ml

Andrew Beattie (N/D). The Basics of Business Forecasting. Retrieved

from http://www.investopedia.com/articles/financial-theory/11/basics-

business-forcasting.asp

N/A (2015, October 29). Time Series. Retrieved from

https://en.wikipedia.org/wiki/Time_series

N/A (2015, October 29). Forecasting. Retrieved from

https://en.wikipedia.org/wiki/Forecasting

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Ming Chuan University (N/D). Part 2 Forecasting. Retrieved from

http://mcu.edu.tw/~ychen/op_mgm/notes/part2.html

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