The document brushes the topics of Forecasting, Fashion forecasting, Fundamental principles of forecasting, Methods of forecasting (qualitative, quantitative, casual, judgemental, time series), Categories of fashion forecast (long term, short term), Forecast errors, and Causes of fashion forecast errors.
3. “
Basically, forecasting is a decision-making
tool that helps businesses cope with the
impact of the future’s uncertainty by
examining historical data and trends.
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4. Fashion Forecasting
▷ Essentially, fashion
forecasters predict the
collection of silhouettes,
colors, textures, fabrics,
graphics, prints, footwear,
accessories that will be the
forthcoming trends on the
runway and in retail stores
from season to season.
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5. Fashion Forecasting
▷ By examining new, emerging
trends across all industries
and meticulous
considerations they arrive at
conclusions to see how they
may influence future fashion
trends. This includes new
developments across the
creative industries.
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8. Fundamental Principles of Forecasting
Forecasts are almostalways incorrect. It’s almost
never a question of whether a forecast is correct or
not, but it’s almost never a question of whether a
forecast is correct or not. Instead, the attention should
be on the question of “how wrong do we estimate it to
be?” and “how do we plan to handle the probable
forecast error?” Much of the discussion about the
firm’s buffer capacity or buffer stock is based on the
amount of the prediction error.
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9. Fundamental Principles of Forecasting
Forecasts for groups or families of goods are more
accurate. A decent forecast for a product line is usually
easier to develop than one for a single product. As
individual product forecasting errors are accumulated,
they tend to cancel each other out. Forecasting
demand for all family sedans, for example, is often
more accurate than forecasting demand for a single
model of sedan.
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10. Fundamental Principles of Forecasting
Forecasts over shorter time periods are more
accurate. There are less probable interruptions in the
near future in general. In the foreseeable future, there
are fewer possible disruptions that could affect
product demand. Demand over long periods of time in
the future is often unreliable.
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11. Fundamental Principles of Forecasting
Every projection should contain a margin of error
estimate. The first principle emphasised the
significance of responding to the query. “How far off
the mark is the forecast?” As a result, an estimate of
the forecast error is a crucial metric to include with the
forecast. To be complete, a good forecast must include
both the forecast estimate and the error estimate.
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12. Fundamental Principles of Forecasting
Forecasts aren’t a replacement for calculateddemand.
When you have real demand data for a certain time
period, you should never rely your calculations on the
prediction for that same time period. When possible,
always use real data.
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14. Qualitative Methods
▷ These methods are based on emotions,
intuitions, judgments, personal
experiences, and opinions. This means that
there is no math involved in qualitative
forecasting methods. Delphi Method,
Market Survey, Executive Opinion,
SalesForce Composite are part of this type
of forecasting.
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15. Quantitative Methods
▷ These methods depend wholly on
mathematical or quantitative models. The
outcome of this method relies entirely on
mathematical calculations. Time Series and
Associative Models are a part of this type
of forecasting.
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16. Casual Methods
▷ Regression analysis and autoregressive moving
average with exogenous inputs are causal
forecasting methods that predict a variable using
underlying factors. These methods assume that a
mathematical function using known current
variables can be used to forecast the future value
of a variable.
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17. Judgemental Methods
▷ The Delphi method, scenario building, statistical
surveys and composite forecasts each are
judgmental forecasting methods based on
intuition and subjective estimates. The methods
produce a prediction based on a collection of
opinions made by managers and panels of experts
or represented in a survey.
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18. Time Series Methods
▷ The time series type of forecasting methods, such
as exponential smoothing, moving average and
trend analysis, employ historical data to estimate
future outcomes. A time series is a group of data
that’s recorded over a specified period, such as a
company’s sales by quarter since the year 2000 or
the annual production of Coca Cola since 1975.
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20. ▷ Major changes in international domestic
demographics,
▷ Shifts in the fashion industry along with market
structures, consume expectations,
▷ Values, and impulsion to buy,
▷ New developments in technology, and
Long Term Forecasting
Long-term forecasting seeks to identify:
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21. Long Term Forecasting
▷ Shifts in the economic, political, and cultural
alliances between certain countries.
There are many specialized marketing consultants
that focus on long-term forecasting and attend trade
shows and other events that notify the industry on
what is to come.
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22. Short Term Forecasting
▷ Short-term forecasting focuses on current events
both domestically and internationally as well as
pop culture in order to identify possible trends
that can be communicated to the customer
through the seasonal color palette, fabric, and
silhouette stories.
▷ It gives fashion a modern twist to a classic look
that intrigues our eyes.
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23. Short Term Forecasting
▷ Some important areas to follow when scanning
the environment are: current events, art, sports,
science and technology.
▷ Short-term forecasting can also be considered fad
forecasting.
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26. Forecast Errors
Mean Forecast
Error (MFE). As
the name implies,
this term is
calculated as the
mathematical
average forecast
error over a
specified time
period.
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The formula is:
27. Forecast Errors
Mean Absolute
Deviation(MAD).
The formula is again
given as the name
of the term. It
literally means the
average of the
mathematical
absolute deviations
of the forecast
errors (deviations). 27
The formula is:
28. Forecast Errors
Tracking Signal. Similar to the concept of control limits
for statistical process control charts, the tracking signal
provides a somewhat subjective limit for the
forecasting method to go subjective limit for the
forecasting method to go off track before “off track”
before some action is taken. It is calculated from the
MFE and the MAD:
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29. Causes of fashion
forecast errors
In the absence of good
data, forecasts are set
by whoever is most
vocal, persuasive or
authoritative.
Fashion items are new.
New items, by
definition, have no
sales history. You have
to base your forecast
on the sales history of
similar items.
Tastes are fickle. A
color that sold well
last year may bomb
this year. You have to
judge trends. This
involves guesswork.
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30. Because life cycles are short, you
have little opportunity to correct
for error. If lead times are longer
than the life of an item, you have
no opportunity to re-order from
your supplier.
Fashion merchandise has short life
cycles. You can rarely accumulate
enough sales history to generate a
statistically accurate forecast before
the item’s season has ended.
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Causes of fashion
forecast errors
