Forecasting is making predictions about future events or trends based on historical and present data. There are qualitative and quantitative forecasting methods. Qualitative methods include executive judgement, sales force opinions, and the Delphi method. Quantitative methods analyze past numerical data to identify trends and patterns using techniques like moving averages, exponential smoothing, and econometric models. Accurate forecasting allows businesses to effectively plan production and operations to meet demand.
Factors effecting selection of distribution channelsShubhanjali -
introduction to distribution
distribution channel
market intermediaries
factors affecting selection of distribution channels:-
1. Nature of product
2. Nature of market
3. Nature of middle men
4. Nature of manufacturing units
5. Competition
6. Govt. rules & policies
conclusion
references
Introduction to Consumer Behaviour; Consumer Behaviour
and Marketing Strategy; Consumer Involvement – Levels
of involvement, and Decision Making
Consumer Decision Process – Stages in Decision Process,
Information Search Process; Evaluative Criteria and
Decision Rules, Consumer Motivation – Types of Consumer
Needs, Ways of Motivating Consumers. Information
Processing and Consumer Perception.
Consumer Attitudes and Attitude Change; Influence of
Personality and Self Concept on Buying Behaviour,
Psychographics and Lifestyles, Impuse Buying.
Diffusion of Innovation and Opinion Leadership, Family
Decision Making, Influence of Reference Group
Industrial Buying Behaviour– Process and factors, Models
of Consumer Behaviour – Harward Seth, Nicosia, E& D,
Economic Model; Introduction to Consumer Behaviour
Audit; Consumer Behaviour Studies in India
Factors effecting selection of distribution channelsShubhanjali -
introduction to distribution
distribution channel
market intermediaries
factors affecting selection of distribution channels:-
1. Nature of product
2. Nature of market
3. Nature of middle men
4. Nature of manufacturing units
5. Competition
6. Govt. rules & policies
conclusion
references
Introduction to Consumer Behaviour; Consumer Behaviour
and Marketing Strategy; Consumer Involvement – Levels
of involvement, and Decision Making
Consumer Decision Process – Stages in Decision Process,
Information Search Process; Evaluative Criteria and
Decision Rules, Consumer Motivation – Types of Consumer
Needs, Ways of Motivating Consumers. Information
Processing and Consumer Perception.
Consumer Attitudes and Attitude Change; Influence of
Personality and Self Concept on Buying Behaviour,
Psychographics and Lifestyles, Impuse Buying.
Diffusion of Innovation and Opinion Leadership, Family
Decision Making, Influence of Reference Group
Industrial Buying Behaviour– Process and factors, Models
of Consumer Behaviour – Harward Seth, Nicosia, E& D,
Economic Model; Introduction to Consumer Behaviour
Audit; Consumer Behaviour Studies in India
This is a presentation covering the concepts of demand forecasting. it includes the meaning of demand forecasting, purpose, scope and factors affecting demand forecasting. It also covers the methods of forecasting for both new and existing products.
price discounting strategy. As a result, the hotel decided to focu.docxChantellPantoja184
price discounting strategy. As a result, the hotel decided to focus on the government market because of the hotel's location.
The government market is price-sensitive (there is an allowable per diem) and not as quality-conscious, and the hotel could selectively discount to this large-volume market. Once again, it is important to point out that marketing planning is a continuous process. Marketing managers must evaluate the situation and adapt to changes that occur. Evaluating the success of the marketing plan is the moment of truth. Managers develop a plan to increase the probability of success, and once the plan is implemented, it is important for management to monitor the results. Any variance from the predicted results should be identified, evaluated, and corrected.
As the environment changes or the results vary, management may need to return to the appropriate step to reformulate marketing strategy or the action plans. The marketing planning process continues as a dynamic procedure, with sufficient flexibility allowing for changes in strategies, action plans, or implementation schedules.
SALES FORECASTING
190 CHAPTER 5 DEVELOPING A MARKETING PLAN
SALES FORECASTING 189
One of the most critical components of a marketing plan is the forecast for sales. Sales forecasting is the process for determining current sales and estimating future sales for a product or service. The success of the firm often results from the accuracy of forecasts. The decisions about the elements of the marketing mix—product-service mix, price, promotion, and distribution— that are made during the situation analysis are based on sales forecasts.
Sales forecasting The process for determining current sales and estimating future sales for a product or service.
Sales Forecasting Techniques
Sales forecasting techniques are separated" into two broad categories: quantitative techniques and qualitative techniques. Quantitative techniques use past data values and employ a set of rules to obtain estimates of future sales. Qualitative techniques rely on judgment or intuition and tend to be used when data are not readily available. Quantitative methods can be further classified as either causal or time series. Both types of quantitative methods use trends in historical data to predict future sales; however, causal analysis techniques establish a cause and effect relationship between variables and the results. Using historical data to establish the relationship between sales and other factors that are believed to influence sales. These techniques model the relationships
Expert opinion
Marketers look to a panel of experts with knowledge of the industry and the marketplace to provide a forecast.
Delphi technique The Delphi technique involves collecting forecasts, developing composites, and sending the data to those participating several times until a consensus results.
Sales force forecast
This technique aggregates the sales forecast of each .
ForecastingDiscuss the different types of forecasts to include tim.pdfamolmahale23
Forecasting
Discuss the different types of forecasts to include time-series, causal, and qualitative models.
When might a researcher or project manager utilize exponential smoothing?
What benefit does a Delphi technique provide when working with qualitative-based decision
making?
Solution
Forecasting is basically the process of estimating or predicting the future trend, based on the
trend and information of the past and the present.Forecasting is a calculated assumption of how
the trend is going to be in a future date based on what we saw in the past and what we are
observing in the present scenario.
Time series methods:
These methods use historical data to assume future trends.
There are various time series methods such as,
1)Simple Moving Average Method: it is commonly used in technical analysis of financial data
such as stock prices,trading volumes or returns.Among the most popular technical indicators,
moving averages are used to gauge the direction of the current trend.It is calculated by averaging
a number of past data points. Once determined, the resulting average is then plotted onto a chart
in order to allow traders to look at smoothed data rather than focusing on the day-to-day price
fluctuations that are inherent in all financial markets.
As new values become available, the oldest data points must be dropped from the set and new
data points must come in to replace them. Thus, the data set is constantly \"moving\" to account
for new data as it becomes available. This method of calculation ensures that only the current
information is being accounted for.
for example, to calculate a basic 10-day moving average you would add up the closing prices
from the past 10 days and then divide the result by 10. The average thus obtained is plotted on a
chart. As the time progresses, we replace the first variable with the latest variable available ie.
latest closing price of 11th day, therefore getting a new avaerage. We plot this one too in the
chart. The chart thus formed gives a trend which is used for forecasting future movements.
2)Exponentially smoothed moving average:
Over the years, technicians have found two problems with the simple moving average. The first
problem lies in the time frame of the moving average (MA). Most technical analysts believe that
price action, the opening or closing stock price, is not enough on which to depend for properly
predicting buy or sell signals of the MA\'s crossover action. To solve this problem, analysts now
assign more weight to the most recent price data by using the exponentially smoothed moving
average (EMA).It is a type of infinite impulse response filter that applies weighting factors
which decrease exponentially. The weighting for each older datum decreases exponentially,
never reaching zero.
The exponentially smoothed moving average addresses both of the problems associated with the
simple moving average. First, the exponentially smoothed average assigns a greater weight to the
more recent data..
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The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
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𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
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2. Forecasting is the process of making predictions of the
future based on past and present data.
This is most commonly by analysis of trends.- TIME SERIES
ANALYSIS
A commonplace example might be estimation of some
variable of interest at some specified future date.
Prediction is a similar, but more general term.
Both might refer to formal statistical methods
employing time series, cross-
sectional or longitudinal data, or alternatively to less
formal judgmental methods.
Usage can differ between areas of application: for
example, in hydrology, the terms “forecast” and
“forecasting” are sometimes reserved for estimates of
values at certain specific future times, while the term
“prediction” is used for more general estimates, such as
the number of times floods will occur over a long period.
3. Risk and uncertainty are central to forecasting
and prediction;
it is generally considered good practice to
indicate the degree of uncertainty attached to
specific forecasts.
In any case, the data must be up to date in
order for the forecast to be as accurate as
possible.
In some cases, the data used to predict the
variable of interest is itself forecasted.[1]
4. When the strategy is implemented, the rest of
the company must be poised to deal with the
consequences.
An important component in this
implementation is the sales forecast, which is
the estimate of how much the company will
actually sell.
The rest of the company must then be geared
up (or down) to meet that demand. In this
module, we explore forecasting in more
detail, as there are many choices that can be
made in developing a forecast.
5. Accuracy is important when it comes to
forecasts.
If executives overestimate the demand for a
product, the company could end up spending
money on manufacturing, distribution, and
servicing activities it won’t need.
Data Impact, a software developer, recently
overestimated the demand for one of its new
products. Because the sales of the product
didn’t meet projections, Data Impact lacked
the cash available to pay its vendors, utility
providers, and others. Employees had to be
terminated in many areas of the firm to trim
costs.
6. Operations management is complex:
You have to plan, implement, and supervise
the production of goods and services.
But forecasting can help smooth out the
process by ensuring adequate resources to
meet demand.
7. Organizations use forecasting methods
to predict business outcomes.
Forecasts create estimates that can help
managers develop and implement production
strategies.
Operations managers are responsible for the
processes that deliver the final product. ...
These forecasts are used for budgeting, sales
and demand planning.
8. The method of forecasting will vary according
to available data and industry size and
respective goals.
Forecasts are developed using both
qualitative and quantitative data.
Although they are useful in making educated
predictions, they are not always accurate, so
they should be used with caution.
9. Short range: These forecasts are usually 3
months into the future. They are most often
used for hiring, scheduling, determining
production levels of planning processes.
Medium range: This is usually 3 months to a
year into the future. These forecasts are used
for budgeting, sales and demand planning.
Long range: These forecast 3 years or more
into the future. It's used for capital
expenditures, relocation and expansion, and
research and development.
10. Forecasting Horizons
Long term forecasting tends to be completed at high levels
in the organization. The time frame is generally
considered longer than 2 years into the future. Detailed
knowledge about the products and markets are required
due to the high degree of uncertainty. This is commonly
the case with new products entering the market, emerging
new technologies and opening new facilities. Often no
historical data is available.
Medium term forecasting tends to be several months up to
2 years into the future and is referred to as intermediate
term. Both quantitative and qualitative forecasting may be
used in this time frame.
Short term forecasting is daily up to months in the future.
These forecasts are used for operational decision making
such as inventory planning, ordering and scheduling of the
workforce. Usually quantitative methods such as time
series analysis are used in this time frame.
11. Forecasting is valuable to businesses
because it gives the ability to make informed
business decisions and develop data-driven
strategies.
Financial and operational decisions are made
based on current market conditions and
predictions on how the future looks.
12. Determine what the forecast is for.
Select the items for the forecast.
Select the time horizon.
Select the forecast model type.
Gather data to be input into the model.
Make the forecast.
Verify and implement the results.
13. Economic forecasts: Make predictions related
to inflation, money supplies, and other
economic factors that can affect businesses.
These forecasts often influence medium to
long range planning
Technological forecasts: Keep track of rate of
technological progress. As technologies
mature and become more applicable to
business use cases, these may require new
facilities equipment and processes. These
forecasts inform long range planning
14. Demand forecasts:
Estimate consumer demand for a business'
products or services.
A demand forecast will be used to estimate
production and all relevant inputs.
These forecasts can inform short, medium,
and long term planning.
Also referred to as sales forecasts.
15. The Benefits of Forecasting in Planning and
Production
More effective production scheduling. So
much of contemporary demand planning
strategy can be compared to looking in a
rearview mirror. ...
Inventory management and reduction. ...
Cost reduction. ...
Optimized transport logistics.
16.
17. Qualitative Forecasting
Qualitative forecasting techniques are
subjective, based on the opinion and
judgment of consumers and experts; they are
appropriate when past data are not available.
They are usually applied to intermediate- or
long-range decisions.
In the following, we discuss some examples
of qualitative forecasting techniques:
18. Executive Judgement (Top Down)
Groups of high-level executives will often
assume responsibility for the forecast.
They will collaborate to examine market data
and look at future trends for the business.
Often, they will use statistical models as well
as market experts to arrive at a forecast.
19. Sales Force Opinions (Bottom up)
The sales force in a business are those
persons most close to the customers.
Their opinions are of high value.
Often the sales force personnel are asked to
give their future projections for their area or
territory.
Once all of those are reviewed, they may be
combined to form an overall forecast for
district or region.
20. Delphi Method
This method was created by the Rand Corporation in
the 1950s.
A group of experts are recruited to participate in a
forecast.
The administrator of the forecast will send out a
series of questionnaires and ask for inputs and
justifications.
These responses will be collated and sent out again
to allow respondents to evaluate and adjust their
answers.
A key aspect of the Delphi method is that the
responses are anonymous, respondents do not have
any knowledge about what information has come
from which sources.
That permits all of the opinions to be given equal
consideration.
The set of questionnaires will go back and forth
multiple times until a forecast is agreed upon.
21. Market Surveys
Some organizations will employ market
research firms to solicit information from
consumers regarding opinions on products
and future purchasing plans
22. Quantitative forecasting models are used to forecast future data
as a function of past data. They are appropriate to use when past
numerical data is available and when it is reasonable to assume
that some of the patterns in the data are expected to continue
into the future. These methods are usually applied to short- or
intermediate-range decisions. Some examples of quantitative
forecasting methods are causal (econometric) forecasting
methods, last period demand (naïve), simple and weighted N-
Period moving averages and simple exponential smoothing,
which are categorizes as time-series methods. Quantitative
forecasting models are often judged against each other by
comparing their accuracy performance measures. Some of these
measures include Mean Absolute Deviation (MAD), Mean Squared
Error (MSE), and Mean Absolute Percentage Error (MAPE).
We will elaborate on some of these forecasting methods and the
accuracy measure in the following sections.[3]
23. Causal (Econometric) Forecasting Methods
(Degree)
Some forecasting methods try to identify the
underlying factors that might influence the
variable that is being forecast. For example,
including information about climate patterns
might improve the ability of a model to predict
umbrella sales. Forecasting models often take
account of regular seasonal variations. In
addition to climate, such variations can also be
due to holidays and customs: for example, one
might predict that sales of college football
apparel will be higher during the football season
than during the off-season.
24. When we plot our historical product demand, the following patterns can
often be found:
Trend – A trend is consistent upward or downward movement of the
demand. This may be related to the product’s life cycle.
Cycle – A cycle is a pattern in the data that tends to last more than one
year in duration. Often, they are related to events such as interest rates,
the political climate, consumer confidence or other market factors.
Seasonal – Many products have a seasonal pattern, generally predictable
changes in demand that are recurring every year. Fashion products and
sporting goods are heavily influenced by seasonality.
Irregular variations – Often demand can be influenced by an event or
series of events that are not expected to be repeated in the future.
Examples might include an extreme weather event, a strike at a college
campus, or a power outage.
Random variations – Random variations are the unexplained variations in
demand that remain after all other factors are considered. Often this is
referred to as noise.
25. Time series methods use historical data as the basis of
estimating future outcomes. A time series is a series
of data points indexed (or listed or graphed) in time order.
Most commonly, a time series is a sequence taken at
successive equally spaced points in time. Thus, it is a
sequence of discrete-time data. Examples of time series
are heights of ocean tides, counts of sunspots, and the
daily closing value of the Dow Jones Industrial Average.
Time series are very frequently plotted via line charts.
Time series are used in statistics, signal processing,
pattern recognition, econometrics, mathematical finance,
weather forecasting, earthquake prediction,
electroencephalography, control engineering, astronomy,
communications engineering, and largely in any domain of
applied science and engineering which involves temporal
measurements.[
26. Simple Moving Average
In this method, we take the average of the
last “n” periods and use that as the forecast
for the next period. The value of “n” can be
defined by the management in order to
achieve a more accurate forecast. For
example, a manager may decide to use the
demand values from the last four periods
(i.e., n = 4) to calculate the 4-period moving
average forecast for the next period.
27. Weighted Moving Average
This method is the same as the simple
moving average with the addition of a weight
for each one of the last “n” periods. In
practice, these weights need to be
determined in a way to produce the most
accurate forecast. Let’s have a look at the
same example, but this time, with weights:
28. Seasonal Index
Many organizations produce goods whose
demand is related to the seasons, or changes
in weather throughout the year. In these
cases, a seasonal index may be used to assist
in the calculation of a forecast.
29. Depending on the data you have available and
time horizon you are using, you will want to
apply different forecasting models. While
there are a lot of models one could use, these
fall into three broad types:
Judgmental (qualitative): This type of forecast
uses subjective inputs. This includes
the Delphi method, scenario building, and
statistical surveys which are based on
intuition and a collection of opinions from
managers and experts.
30. Time-series (quantitative): Time series forecasts use
historical data to make predictions about the future.
Data corresponds to a specific period, such as
monthly inputs over a span of ten years. These
forecasts rely on the assumption that past patterns
will repeat in the future, so these data inputs are
used to create long term forecasts.
Associative models (quantitative): Associative models
use data corresponding to various underlying factors
to predict the desired variable. These models assume
a relationship between the factors and the variable to
predict more complex patterns. Some methods
include regression analysis and autoregressive
moving averages.
31. Scheduling: Staff and inventory scheduling
are critical functions to meet demand. This
process involves organizing, selecting, and
allocating the necessary resources to
complete the desired outputs over a period of
time.
In a service business, for example, a forecast
could be used to ensure you have enough
front office employees to meet fluctuating
demand that often involves attending to
immediate customer service requests.
32. Material requirements planning (MRP): This
system is used to calculate the materials
needed to manufacture a final product.
MRP requires operations managers to take
inventory, determine if any additional
inventory is needed, and scheduling
production.