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Topic:
Meaning of Forecasting
Need for Forecasting
How Forecasting helps in
Decision Making
Introduction:
Forecasting is the estimation of the value of a variable (or set of variables) at
some future point in time. In this note we will consider some methods for forecasting. A
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forecasting exercise is usually carried out in order to provide an aid to decision-making
and in planning the future. Typically all such exercises work on the premise that if we
can predict what the future will be like we can modify our behaviour now to be in a better
position, than we otherwise would have been, when the future arrives. Applications for
forecasting include:
Inventory control/production planning - forecasting the demand for a product
enables us to control the stock of raw materials and finished goods, plan the
production schedule, etc
Investment policy - forecasting financial information such as interest rates,
exchange rates, share prices, the price of gold, etc. This is an area in which no one
has yet developed a reliable (consistently accurate) forecasting technique (or at
least if they have they haven't told anybody!)
Economic policy - forecasting economic information such as the growth in the
economy, unemployment, the inflation rate, etc is vital both to government and
business in planning for the future.
Why Forecast?
“Forecasting is an attempt to foresee the future by examining the past.”
Forecasts require judgment.
Lead times require that decisions be made in advance of uncertain events.
Forecasting is an important for all strategic and planning decisions in a supply
chain.
Forecasts of product demand, materials, labor, financing are an important inputs
to scheduling, acquiring resources, and determining resource requirements.
Most estimates obtained in quality forecasting are derived in an objective and
systematic fashion and do not depend solely on subjective guesses and hunches of
the analyst.
Thus, Statistical forecasting concentrates on using the past to predict the future by
identifying trends, patterns and business drives within the data to develop a forecast. This
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forecast is referred to as a statistical forecast because it uses mathematical formulas to
identify the patterns and trends while testing the results for mathematical reasonableness
and confidence. In many Forecasting Processes, statistical forecasting forms the baseline
that is adjusted throughout the process.
Meaning of Forecasting:
Forecasting is the process of estimation in unknown situations. Prediction is a
similar, but more general term. Both can refer to estimation of time series, cross-sectional
or longitudinal data. 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. Risk
and uncertainty are central to forecasting and prediction. Forecasting is used in the
practice of Customer Demand Planning in every day business forecasting for
manufacturing companies. The discipline of demand planning, also sometimes referred to
as supply chain forecasting, embraces both statistical forecasting and a consensus
process.
Forecasting is apart of human conduct. Whatever an individual does at present is
in the expectation of that certain events will take place in future. This expectation is
generally based on the past experience. Forecast made in this fashion may or may not be
true always. In a world, where the future is not taken (known) with certainty, virtually
every business and economic decision rests upon a forecast of future condition.
Thus, planning which is the backbone of any business activity requires the
forecasting of future events, whereas forecasting helps in viewing those events in their
proper perspective .Objectivity is the corer stone of forecasting. It thus helps in reducing
risks associated with uncertain future events. Thus forecasting reduces the areas of
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uncertainty that surround management decision- making with respect to costs, profits,
sales, production pricing capital investment and so forth.
In Statistics, “the term (forecasting) refers to extending or projecting time-series
into the future based on the past behaviour of the quantitative data”. In Other words
“business forecasting refers to the statistical analysis of the past and current movements
in a given time series, so as to obtain clues about the future pattern of movement.”
Business forecasting involves a wide range of tools, including simple electronic
spreadsheets; enterprise resource planning (ERP) and electronic data interchange (EDI)
networks, advanced supply chain management systems, and other Web-enabled
technologies. The practice attempts to pinpoint key factors in business production and
extrapolate from given data sets to produce accurate projections for future costs,
revenues, and opportunities. This normally is done with an eye toward adjusting current
and near-future business practices to take maximum advantage of expectations.
Business forecasting systems often work hand-in-hand with supply chain
management systems. In such systems, all partners in the supply chain can electronically
oversee all movement of components within that supply chain and gear the chain toward
maximum efficiency. With business relationships and supply chains growing increasingly
complex particularly in the world of e-commerce, with heavy reliance on logistics
outsourcing and just-in-time delivery such forecasting systems become crucial for
companies and networks to remain efficient.
Need for Forecasting:
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 invaluable tool for businesspeople to
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anticipate economic trends and prepare themselves either to benefit from or to counteract
them. If, for instance, businesspeople envision an economic downturn, they can cut back
on their inventories, production quotas, and hiring’s. If, on the contrary, an economic
boom seems probable, those same businesspeople can take necessary measures to attain
the maximum benefit from it. Good business forecasts can help business owners and
managers adapt to a changing economy.
Some of the important needs of forecasting are listed below:
i. Helps in Production Planning:
The rate of producing the products must be matched with the demand which
may be fluctuating over the time period in the future. Since its time consuming to
change the rate of output of the production processes, so production manager needs
medium range demand forecasts to enable them to arrange for the production
capacities to meet the monthly demands which are varying.
ii. Helps in Financial Planning:
Sales forecasts are driving force in budgeting. Sales forecasts provide the timing
of cash inflows and also provide a basis for budging the requirements of cash
outflows for purchasing materials, payments to employees and to meet other expenses
of power and utilize etc. Hence forecasting helps finance manager to prepare budgets
taking into consideration the cash inflow and cash out flows.
iii. Helps in Economic Planning:
Forecasting helps in the study of macroeconomic variables like population, total
income, employment, savings, investment, general price-level, public revenue, public
expenditure, balance of trade, balance of payments and a host of other macro aspects
at national or regional levels. The forecasts of these variables are generally for a long
period of time ranging between one year to ten or twenty years ahead. Much would
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depend on the perspective of planning, longer the perspective longer would be period
of forecasting. Such forecasts are often called as projections. These are helpful not
only for planning and public policy making, they also include likely economic
environment and aid formulation of business policies as well.
iv. Helps in Workforce Scheduling:
The forecast of monthly demand may further be broken down to weekly
demands and the workforce may have to be adjusted to meet these weekly demands.
Hence, forecasts are needed to enable managers to get tuned with the workforce
changes to meet the weekly production demands.
v. Helps in Decisions Making:
The goal of the forecaster is to provide information for decision making. The
purpose is to reduce the range of uncertainty about the future. Businessmen make
forecasts for the purpose of making profits. In business forecast has to be done at
every stage. A business man may dislike statistics or statistical theories of forecasting,
but he can not do without making forecasts. Business plans of production, sales and
investment requires predictions regarding demand for the product, price at which the
product can be soled and the availability of inputs. The forecast about demand is the
most crucial. Operating budgets of various departments of a company have to be
based upon the expected sales. Efficient production schedules, minimization of
operating cost and investment in fixed assets is when accurate forecasts recording
sales and availability of inputs are available.
vi. Helps in Controlling Business Cycles:
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It is commonly believed that business cycles are always very harmful in their
effects. Abrupt rise and fall in the price level injurious not only to businessmen, but to
all types of persons, industries, trade, agriculture. All suffer from the painful effects
of depression. Trade cycle increase the risk f business; create unemployment; induce
speculation and discourage capital formation. Their effects are not confined to one
country only. Business forecasting reduces the risk associated with business cycles.
Prior knowledge of a phase of a trade cycle with its intensity and expected period of
happening may help businessmen, industrialist, and economists to plan accordingly to
reduce the harmful effects of trade cycle’s .statistics is thus needed for the purpose of
controlling the business-cycles.
Many experts agree that precise business forecasting is as much an art as a
science. Because business cycles are not repetitious, a good forecast results as much from
experience, sound instincts, and good judgment as from an established formula. Business
forecasters can be, and have often been, completely off the mark in their predictions. If
nothing else, business forecasts can be used as blueprint to better understand the nature
and causes of economic fluctuations.
How Forecasting helps in Decision
Making?
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Decision making is to choose best alternative from available alternatives to meet
the objective of a company. Implementing forecasting forces a business has to base
decisions on facts rather than hunches. Poor decision management might lead to failure of
business. To succeed in business today, companies need forecasting that can help
managers and business professionals in decision making. Almost all managerial decisions
are based on forecasts. Every decision becomes operational at some point in the future, so
it should be based on forecasts of future conditions.
Forecasts are needed throughout an organization -- and they should certainly not
be produced by an isolated group of forecasters. Neither is forecasting ever "finished".
Forecasts are needed continually, and as time moves on, the impact of the forecasts on
actual performance is measured; original forecasts are updated; and decisions are
modified, and so on. For example, many inventory systems cater for uncertain demand.
The inventory parameters in these systems require estimates of the demand and forecast
error distributions. The two stages of these systems, forecasting and inventory control,
are often examined independently. Most studies tend to look at demand forecasting as if
this were an end in itself or at stock control models as if there were no preceding stages
of computation. Nevertheless, it is important to understand the interaction between
demand forecasting and inventory control since this influences the performance of the
inventory system. This integrated process is shown in the following figure:
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The decision-maker uses forecasting models to assist him or her in decision-
making process. The decision-making often uses the modeling process to investigate the
impact of different courses of action retrospectively; that is, "as if" the decision has
already been made under a course of action. That is why the sequence of steps in the
modeling process, in the above figure must be considered in reverse order. For example,
the output (which is the result of the action) must be considered first. For Example:
Decision to “Whether to build a new factory” requires forecasts on future demand,
technological innovations, cost, prices, competitor’s plan, labor, legislation, etc.
Most forecasting required for decision making is handled judgmentally in an
intuitive fashion, often without separating the task of forecasting from that of decision
making. Systematic, explicit approaches to forecasting can be used to supplement the
common sense and management ability of decision makers. All types and forms of
forecasting techniques are extrapolation that is, predicting within the existing data.
Quantitative forecasting techniques should be used in with analysis, judgment, common
sense, and business experience in order to produce an effective forecasting outcome.
Decision-making involves the selection of a course of action (means) in pursue of
the decision maker's objective (ends). The way that our course of action affects the
outcome of a decision depends on how the forecasts and other inputs are interrelated and
how they relate to the outcome.
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TOPIC:
Role of Managers in
Forecasting Techniques
Objectives of Forecasting
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ROLE OF A MANAGER IN
FORECASTING TECHNIQUES:
The increasing complexities of the business environment together with the
changing demands and expectations, implies that every organization needs to know the
future values of their key decision variables. In virtually every decision they make,
executives today consider some kind of forecast. In any organization, managers play a
significant role in implementing Forecasting techniques. Forecasting takes the historical
data and project them into the future to predict the occurrence of uncertain events.
Forecasting serves as a self-assessment tool for the company. To handle the increasing
variety and complexity of managerial forecasting problems, many forecasting techniques
have been developed in recent years. Each has its special use, and care must be taken to
select the correct technique for a particular application.
The manager as the forecaster has a role to play in technique selection; and the
better he understands the range of forecasting possibilities, the more likely it is that a
company’s forecasting efforts will bear fruit .Sound predictions of demands and trends
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are no longer luxury items, but a necessity, if managers are to cope with seasonality,
sudden changes in demand levels, and price-cutting maneuvers of the competition,
strikes, and large swings of the economy. Forecasting can help them deal with these
troubles; but it can help them more, the more they know about the general principles of
forecasting, what it can and cannot do for them currently, and which techniques are suited
to their needs of the moment.
The selection of a method depends on many factors—the context of the forecast,
the relevance and availability of historical data, the degree of accuracy desirable, the time
period to be forecast, the cost/ benefit (or value) of the forecast to the company, and the
time available for making the analysis. These factors must be weighed constantly, and on
a variety of levels. In general, for example, the forecasting manager should choose a
technique that makes the best use of available data.
A manager forecasts by going through the reports, graphs and analyzes the pulse
of the business .It can make a huge difference between just surviving and being highly
successful in business. The future direction of the company may rest on the accuracy of
forecasting done by a manager.
The forecasting methodology emphasis on the knowledge and judgment of the
manager. This is unavoidable given the nature of the market, but it follows that
developing a good forecast is a labor-intensive process.
When an objective is set in a company, the external and internal factors have to
observed and listed by the manager. Studying cultural, political and international
environment is necessary as these factors are uncontrollable. Internal factors such
company’s internal policies and their effects on demand changes, technology changes,
sales changes also need to considered .After gathering information various forecasting
techniques which are needed are applied.
The forecast should be operationally applied. This can be done by breaking it
down on the basis of number of product lines, the type of customers, the various
management policies. The various scenarios derived earlier must be compared in light of
the operational feasibility. The idea here is to determine what is feasible and what is
profitable from total internal business environment. After studying various feasibilities
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the forecast becomes really useful. After using the forecast, the forecast errors and
reasons for deviation are monitored regularly.
Managers who implement accurate sales forecasting processes realize important
benefits to company such as:
1. Ability to determine the expected return on investment
2. The ability to plan for production and capacity
3. Determine the value of a business above the value of its current assets
4. Knowing when and how much to buy
5. The ability to identify the pattern or trend of sales
6. Enhanced cash flow
7. In-depth knowledge of customers and the products they order.
The combination of these benefits may result in:
Increased efficiency
Increased customer retention
Increased revenue
Decreased costs
MANAGER AS A FORECASTING MANAGER:
The Forecasting Manager serves as the lead of a forecasting working group. The
primary responsibility of this individual is to implement the forecasting process and
provide objective short-term and long-range forecasting models, standards and guidelines
to the Product Team. This includes the design, construction and implementation of
forecasting models for specific brands. When working with the group, the Forecasting
Manager must have the ability to quickly assess the major issues surrounding each
forecasting problem, understand the decisions the forecast will impact, and recommend a
forecasting approach. In addition, the manager should be able to identify the
information/data required, and to articulate any secondary and/or primary research
required to support the forecasting process.
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The Forecasting Manager may be required to hire external consultants to
complete forecasting projects. Also, this position will require the continuous evaluation
of new forecasting techniques, and technologies through internal/external networking,
and attending forecasting and industry seminars.
Major Responsibilities are:
Understand and implement forecasting and business planning processes;
-Identify key business issues that impact short-term and long-range product and
market forecasts;
Design primary research techniques to understand business drivers and how
research results should be integrated into a forecast model;
Create product-specific long-range forecasts models (incremental and absolute);
Maintain up-to-date forecast models, reflecting current
data/information/assumptions;
Understand any promotional response models developed for their specific
product;
Educate others in the appropriate use of forecasting models;
Compile and analyze secondary marketing and sales data;
Create long-range forecasting models and benchmarks;
Educate others on the appropriate use of these technologies;
For forecasting to be valuable to a company or a business, it must not be treated
as an isolated exercise. Rather, it must be integrated into all facets of an organization.
Objectives of forecasting:Forecasting has few objectives. Some the few important objectives of forecasting
are as follows:
1. To estimate the amount of error in forecast by using probability theory.
2. To assist in managerial decision making in uncertainties.
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3. To acquaint businessmen and economists about the future probable
circumstances.
4. To clarify the differences of actual data of the future by comparing it with pre-
forecasted data by using theory of probable error.
5. To provide basis for determination of future policy.
6. To indicate the probability of happenings in the future.
7. The forecast provides a warning system of critical functions to be monitored
regularly because they might drastically affect the performance of the plan.
Topic:
Requirements of Good
Forecast
Types of Forecasts
Demand Forecast
Environmental Forecast
Technological Forecast
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REQUIREMENTS OF A GOOD
FORECAST :
A good forecast should satisfy the following criteria:
1. Time frame
2. Pattern of the data
3. Cost of forecasting/ Economy
4. Accuracy
5. Availability of data
6. Durability
7. Plausibility/Ease of operation and understanding
8. Flexibility
1. Time frame:
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The first factor that can influence the choice of forecasting is the time frame of the
forecasting situation. Forecasts are generally for points in time that may be a number
of days, weeks, months, quarters, or years in the future. This length of time is called
the time frame or time horizon. The length of the time frame is usually categorized as
follows:
Immediate: less than one month
Short term: one to three months
Medium: more than three months to less than two years.
Long term: two years or more
In general, the length of the time frame will influence the choice of the forecasting
technique. Typically a longer time frame makes accurate forecasting more difficult
with qualitative forecasting techniques becoming more useful as the time frame
lengthens.
2. Pattern of the data:
The pattern of the data must also be considered when choosing a forecasting model.
The components present i.e.’ trend, cycle, seasonal or some combination of these will
help determine the model that will be used. Thus it is extremely important to identify
the existing data pattern.
3. Cost of forecasting/ Economy:
Though the firm is interested in accurate forecasts, the benefits of accurate results
must be weighed against the cost of the method. While choosing a forecasting
technique, several costs are relevant. First, the cost of developing the model must be
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considered. Second the cost of storing the necessary data must be considered. Some
forecasting methods require the storage of a relatively small amount of data, while
other methods require the storage of large amounts of data. Last, the cost of the actual
operation of the forecasting technique is obviously very important. Some forecasting
methods are operationally simple, while others are very complex. The degree of
complexity can have a definite influence on the total cost of forecasting.
4. Accuracy desired:
Accuracy in forecasting is very important. The previous method must be checked for
want of accuracy by observing that the predictions made in the past are accurate or
not. The accuracy of past forecasting can be checked against present performance and
of present forecasts against future performance. In some situations a forecast that is in
error by as much as 20% may be acceptable. In other situations a forecast that is in
error by 1% might be disastrous. The accuracy that can be obtained using any
particular forecasting method is always an important consideration.
5. Availability of data:
Immediate availability of data is an important requirement and the method employed
should be able to produce good results quickly. The technique which takes much time
to produce useful information is of no use. Historical data on the variable of interest
are used when quantitative forecasting methods are employed. The availability of this
information is a factor that may determine the forecasting method to be used. Since
various forecasting methods require different amounts of historical data, the quantity
of data available is important. Beyond this, the accuracy and the timeliness of the data
that are available must be examined, since the use of inaccurate or outdated historical
data will obviously yield inaccurate predictions. If the needed historical data are not
available, special data-collection procedures may be necessary.
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6. Plausibility/Ease of operation and understanding:
The ease with the forecasting method is operated and understood is important.
Management must be able to understand and have confidence in the technique used.
It has to understand clearly how the estimate was made. Mathematical and statistical
techniques should be avoided if the management cannot understand what the
forecaster does.
Managers are held responsible for the decisions they make and if they are to be
expected to base their decisions on predictions, they must be able to understand the
techniques used to obtain these predictions. A manager simply will not have
confidence in the predictions obtained from a forecasting technique he or she does not
understand, and if the manager does not have confidence in these predictions, they
will not be used in the decision-making process. Thus, the managers understanding of
the forecasting system is of crucial importance.
7. Durability:
The forecast should be durable and should not be changed frequently. The durability
of the forecasts depends on the simplicity and ease of comprehension as well as on
continuous link between the past and the present and between present and the future.
8. Flexibility:
The technique used in forecasting must be able to accommodate and absorb frequent
changes occurring in the economy.
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TYPES OF FORECAST:
A forecast is a prediction or an estimation of a future situation .The objectives of an
organization are facilitated by a number of different types of forecast. These may be
related to cash flows, operating budgets, personnel requirement, inventory levels, and so
on. However a broad classification of the types of forecasts is as follows.
1. Demand forecasts
2. Environmental forecasts
3. Technological forecasts
1. Demand forecasts:
Demand forecasting means an estimation of the level of demand that might be
realized in future under given circumstances. These are concerned with the
predictions of demand for products or services to minimize the uncertainties of the
unknown future. These forecasts facilitate in formulating material and capacity plans
and serves as inputs to financial, marketing and personnel planning. The forecast
itself may be generated in a number of ways, many of which depend heavily upon
sales and marketing information.
Objectives of demand forecasting:
The objectives of forecasting are different in case of short run and long run forecasts.
Short run forecasting:
Short run forecasting is usually a period not exceeding one year. The following
are the objectives of short run demand forecasting:
1. To evolve a suitable production policy:
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Short term forecasts help the firm to plan the production so as to avoid the
problems of over production and short supply.
2. To plan the purchase pf raw materials:
The firm’s can plan the purchase of raw materials at appropriate time to
reduce the cost and control inventories.
3. To arrange for short term financial requirements:
The firms require not only short term funds for purchase of raw materials
and payment of wages, but also medium term funds for replacement and
renewal to maintain productive efficiency.
4. To determine appropriate price policy:
Short run forecasting helps the firm to evolve a suitable price policy
depending upon the expected market conditions to maintain consistent
sales.
5. To fix sales targets:
Realistic sales targets can be fixed for the salesmen on the basis of short
term demand forecasting. If the targets are too high the salesmen may fail
to achieve them and they will get discouraged. If the targets are too low
the salesmen will achieve the targets so easily that the incentives will
prove meaningless.
Long run forecasting:
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Long run forecasting is generally for a period exceeding 3 years. The following
are the objectives of long run demand forecasting.
1. To plan the establishment of a new unit or expansion of an existing
unit:
Planning of a new unit or expansion of an existing unit requires an
analysis of the long term demand potential of the products. The
competitive strength of the firm will be greater if it has better knowledge
than the rivals of the growth trends in the economy.
2. To plan long term financial requirements:
Long run forecasts are essential to assess long term financial
requirements. When the funds required for expansion, modernization and
diversification are large, it takes time to make necessary arrangements for
raising sufficient resources through long term loans and the issue of shares
and debentures.
3. To plan manpower requirements:
Long term demand forecasting is useful for manpower planning. Training
and personnel development can be started well in advance on the basis of
estimates of manpower requirements assessed according to long term
demand forecasts.
2. Environmental forecasts:
Environmental forecasting is attempting to predict the nature and intensity of the micro
environmental and macro environmental forces that are likely to affect a firm's decision
making and have an impact upon its performance in a given period. Environmental
concerns such as pollution control, are much better managed from an anticipatory rather
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than an after the fact standpoint. Environmental forecasts are concerned with the social,
political and economic environment of the state or the country.
Social Forecast:
It provides a better understanding of the forces shaping the environment. It should
provide confidence to manager that his decisions reflect assessment of these issues. The
use of social forecasting stems from recognition that social pressures are becoming an
increasing determinant for the success of any organization. The various indicators
indicate that the society will be experiencing a total change in next few years. Some of
these changes have to be anticipated and must be incorporated in any long-range plans of
an organization.
The purpose of social forecasting is to provide an analytical framework for
helping the corporate decision-maker to make his own judgment based on analysis.
The social forecasting may not guarantee that correct decisions will be taken. Nor
will it ensure that forecasts will be obtained from the emerging trends. It provides a
better understanding of the forces shaping the environment. In social forecasting we
include all those environmental factors that are not currently embraced by economic or
technological forecasting. Primarily it involves individual as customer, supplier, manager
or employee. It concerns people in-groups both inside as well as outside organizations.
It further unfolds to government, society in general and to transnational organizations.
Economic forecast:
Economic forecasting is essentially concerned with modeling how people behave using
financial criteria as a means for maximizing welfare. It is dependent on certain
assumption of people behaviour. If the behaviour changes the forecast is likely to
change. Economic forecasts are valuable because they help in predicting inflation rates,
money supplies, operating budget and so on.
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The Techniques Available for Environmental Forecasting:
a. Brainstorming
b. Delphi
c. Checklists
d. Forecasting of issues in isolation
e. Simple extrapolation
f. Fitting curves of a known characteristic
g. Analogies
h. Substitution curves
i. Monitoring
j. Value profiles
k. Cross relationships between factors
l. Trend impact analysis
m. Cross impact analysis
n. Scenarios
3. Technological forecasts:
It is forecasting the future characteristics of useful technological machines, procedures or
techniques. These are concerned with new developments in existing technologies as well
as the development of new technologies. They have become increasingly important to
major firms in the computer, aerospace, nuclear and many other technologically advanced
industries.
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Important aspects:
Primarily, a technological forecast deals with the characteristics of technology, such as
levels of technical performance, like speed of a military aircraft, the power in watts of a
particular future engine, the accuracy or precision of a measuring instrument, the number
of transistors in a chip in the year 2015, etc. The forecast does not have to state how these
characteristics will be achieved.
Secondly, technological forecasting usually deals with only useful machines, procedures
or techniques. This is to exclude from the domain of technological forecasting those
commodities, services or techniques intended for luxury or amusement.
Methods of technology forecasting:
Commonly adopted methods of technology forecasting include
The Delphi method
Forecast by analogy
Growth curves
Extrapolation
Normative methods of technology forecasting commonly used include — like the
Relevance trees
Morphological models
Mission flow diagrams
Thus technological forecasting is not mere astrology or palmistry, but a scientific and
well defined procedure adopted by a technological forecaster or a consultancy for the
forecasting of a particular technology. Even though technological forecasting is a
scientific discipline, some experts are of the view that "the only certainty of a particular
forecast is that it is wrong to some degree."
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Topic: Timing of Forecasts
Short Range ForecastMedium Range ForecastLong Range Forecast
Forecasting Methods- Qualitative Forecasting
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TIMING OF FORECASTS:
Forecasts are classified according to period, time and use. The three divisions of
forecast are short range forecast, medium range forecast and long range forecast.
1. Short range forecast:
It is typically less than 3 months but has a time span of upto 1 year. It is used in planning,
purchasing for job schedules, job assignments, work force levels, product levels.
2. Medium range forecast:
It is typically 3 months to 1 year but has a time span from one to three years. It is used for
sales planning, production planning, cash budgeting and so on.
3. Long range forecast:
This has a time span of three or more years. It is used for designing and installing new
plants, facility location, capital expenditures, research and development, etc.
There are long term forecasts as well as short term forecasts. Operation managers
need long range forecasts to make strategic-decisions about products, processes and
facilities. They also need short term forecasts to assist them in making decisions about
production issues that span, only the next few weeks.
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Forecasting forms an integral part of planning and decision making, production
managers must be clear about the horizon of forecasts-month or year.
For Example, they must be clear about the Methods of Forecasting and the units
of Forecasting (gross rupee sales, individual product, demand etc).
Application of Short Range forecasts:
Short Range sales Forecasts provide operations managers with the information to
make important decisions such as the following
How much inventory of a particular product (Ex Finished goods)
should be carried next month?
How much of each product should be scheduled for production
next week
How much of each raw material should be ordered for delivery
next week?
How much workers should be scheduled to work on regular time
basis and on overtime basis next week?
How many maintenance workers should be scheduled to work next
week?
Application of Long Range forecasts:
Long Range Forecasts provide, operations managers with information to
make important decisions such as the Following:
Selecting a product design. The final design is dependent on
Expected sales volume. If the Demand is high, the Design should
be such that the product can be mass –produced ton ensuring low
costs manufacture.
Selecting a production processing scheme
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Selecting a plan to supply scarce materials
Selecting a long range production capacity plan
Selecting a long range Financial Plan for acquiring funds for
capital investment
To build new buildings and to purchase new materials
To develop new sources of materials and new source of capital
funds(finance)
Difference between these Forecasts:
Medium and Long range Forecasts deal with more comprehensive issues and
support management decisions regarding design and the development of new
products ,plants and processes. And the Short range forecasts tend to be more accurate
then the long range forecasts .Ex sales forecasts need to be updated regularly in order to
maintain their value after each sale period, the forecasts should be reviewed and revised.
QUALITATIVE METHODS OF FORECASTING:
Qualitative forecasting methods consist of collecting the opinions and judgments of
individuals who are expected to have the best knowledge of current activities or future plans of
the organization. For example, knowledge of demand trends and consumer plans are often
known to marketing and sales personnel are presumably familiar with individual customers or
retail market segment. Management usually maintains broader market information on trends by
product line, geographic area, customer groups, etc.
Qualitative forecasting methods have the advantage that they can incorporate subjective
experience as inputs along with objectives data. It is a human brain that permits assimilation of
all types of information and the ultimate issuance of a prediction.
Since each human being has different knowledge, experience, and perceptive of reality,
intuitive forecasts are likely to differ from one individual to another. Furthermore, the less they
are based upon fact and quantified data, the less they lend themselves to analyses and resolution
of differences of opinion. The quantification of data gives them a more precise meaning than
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words which are in exact and are capable of being misunderstood. Also, if the forecast prove to
be inaccurate there is an objective bases for improvement the next time around.
A number of approaches fall under qualitative methods and these are as follows:
PERSONAL OPINION- In this approach of forecasting, an individual does some
forecast of the future based on his or her own judgement or opinion without using a
formal quantitative model. Such an assessment can be relatively reliable and accurate.
This approach is usually recommended when condition in the past are not likely to hold
in the future. For instance, getting an assessment of whether inventory levels are likely to
last until the next replenishment; whether a machine will require, repair in the next month
and so on.
Advantages:
It is fast and efficient.
It is timely and based on good information content.
It uses the collective knowledge of experts.
Disadvantages:
Experts can make mistakes.
Subjectivity and bias of experts and vitiate the forecast.
The group dynamics of the experts could be greatly influenced by the degree of
dominance of a particular person. He who could shout loudest might get his way.
MARKET SURVEY:
This method is used to collect data on well defined objectives and assumptions of about
the future value of a variable. A carefully designed questionnaire is administered to the
selected target audience of customers. Customers are selected independently using a
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representative random sample. This method is very popular and if carefully implemented will
give you good results.
This is the apt technique to use, particularly if you want to forecast sales for a new
product or new brand.
This method of forecasting requires the active cooperation of the target audience.
The sample size must be reasonably large. Larger the sample size smaller will be the
standard error and sampling error.
Larger the sample size the more time consuming and costly the survey will be. Swo, you
have to strike a balance between sample size and cost.
DELPHI METHOD:
It is a quantitative forecasting method that obtains forecasts through group consensus. In
the expert opinion method of forecasting a consensus forecast is arrived at after eliciting the
opinions and views of experts with diverse background. Certainly this method is subject to
group dynamics. At times, judgements may be highly influenced by persuasions of some
group members who have strong likes and dislikes. Delphi method attempts to retain the
wisdom and accumulated knowledge of a group while simultaneously attempting to reduce
the group effects.
In this method, group members are asked to make individual assessment about a
forecast. These assessments are complied and then fed back to the members, so that they get
the opportunity to compare their judgement with others. They are then given an option to
revise their forecasts. After three or four replications, group members reach their final
conclusion.
HISTORICAL ANALOGY:
This method is applied when a new product is about to be introduced by a company.
Forecasting sales for new products are difficult in view of lack of proper historical data.
Historical analogy method attempts to forecast sales for a new product based on the
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performance of related or similar products in the market place. The database of sales of these
products forms the basis for forecasting.
Disadvantages:
You cannot precisely say how your new product is similar or related to a particular
product.
Suppose you have a number of products that you feel are similar to yours. Which of these
will you consider as most similar to yours?
Products that are similar to yours could have failed in the past for a variety of reasons.
Let us say a similar product failed in the past because whenever there was an
advertisement about this product, it was not available on the shelf. So, the consumers
developed a negative perception about this product and became skeptical about its
availability. You may not know all these and simply conclude your product will also fail!
PANEL CONSENSES:
To reduce the prejudices and ignorance that may arise in the individual judgement , it is
possible to develop consensus among group to individuals. Such a panel of individuals is
encouraged to share information, opinion, and assumptions to predict future value of some
variable.
Disadvantages:
It is dependent on group dynamics and frequently requires a facilitator or convenor to
coordinate the process of developing a consensus.
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Topic:
Forecasting Methods- Quantitative Forecast
Steps of Forecasting
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QUANTITATIVE FORECASTING METHODS :
Time series Forecasting Methods:
Time series forecasting methods are based on analysis of historical data (time series: a set of observations measured at successive times or over successive periods). They make the assumption that past patterns in data can be used to forecast future data points.
1. Moving averages (simple moving average, weighted moving average): forecast is based on arithmetic average of a given number of past data points
2. Exponential smoothing (single exponential smoothing, double exponential smoothing): a type of weighted moving average that allows inclusion of trends, etc.
3. Mathematical models (trend lines, log-linear models, Fourier series, etc.): linear or non-linear models fitted to time-series data, usually by regression methods
4. Box-Jenkins methods: autocorrelation methods used to identify underlying time series and to fit the "best" model
Components of Time Series Demand:
1. Average: the mean of the observations over time
2. Trend: a gradual increase or decrease in the average over time
3. Seasonal Influence: predictable short-term cycling behaviour due to time of day, week, month, season, year, etc.
4. Cyclical Movement: unpredictable long-term cycling behaviour due to business cycle or product/service life cycle
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5. Random Error: remaining variation that cannot be explained by the other four components
Simple Moving Average:
Moving average techniques forecast demand by calculating an average of actual demands from a specified number of prior periods
each new forecast drops the demand in the oldest period and replaces it with the demand in the most recent period; thus, the data in the calculation "moves" over time
Simple moving average: At = Dt + Dt-1 + Dt-2 + ... + Dt-N+1
N
Where N = total number of periods in the average
Forecast for period: t+1: Ft+1 = At
Key Decision: N - How many periods should be considered in the forecast
Tradeoff: Higher value of N - greater smoothing, lower responsiveness
Lower value of N - less smoothing, more responsiveness
The more periods (N) over which the moving average is calculated, the less susceptible the forecast is to random variations, but the less responsive it is to changes.
A large value of N is appropriate if the underlying pattern of demand is stable. A smaller value of N is appropriate if the underlying pattern is changing or if it is
important to identify short-term fluctuations
Weighted Moving Average:
A weighted moving average is a moving average where each historical demand may be weighted differently
Average: At = W1 Dt + W2 Dt-1 + W3 Dt-2 + ... + WN Dt-N+1
Where:
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N = total number of periods in the average
Wt = weight applied to period t's demand
Sum of all the weights = 1
Forecast: Ft+1 = At = forecast for period t+1
Exponential Smoothing:
Exponential smoothing gives greater weight to demand in more recent periods, and less weight to demand in earlier periods
Average: At = a Dt + (1 - a) At-1 = a Dt + (1 - a) Ft
Forecast for period t+1: Ft+1 = At
Where:
At-1 = "series average" calculated by the exponential smoothing model to period t-1
a = smoothing parameter between 0 and 1
The larger the smoothing parameter , the greater the weight given to the most recent demand
Double Exponential Smoothing:
(TREND-ADJUSTED EXPONENTIAL SMOOTHING)
When a trend exists, the forecasting technique must consider the trend as well as the series average ignoring the trend will cause the forecast to always be below (with an increasing trend) or above (with a decreasing trend) actual demand
Double exponential smoothing smoothes (averages) both the series average and the trend
Forecast for period t+1: Ft+1 = At + Tt
Average: At = aDt + (1 - a) (At-1 + Tt-1) = aDt + (1 - a) Ft
Average trend: Tt = B CTt + (1 - B) Tt-1
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Current trend: CTt = At - At-1
Forecast for p periods into the future: Ft+p = At + p Tt
Where:
At = exponentially smoothed average of the series in period t
Tt = exponentially smoothed average of the trend in period t
CTt = current estimate of the trend in period t
a = smoothing parameter between 0 and 1 for smoothing the averages
B = smoothing parameter between 0 and 1 for smoothing the trend
Multiplicative Seasonal Method:
What happens when the patterns you are trying to predict display seasonal effects?
What is seasonality? - It can range from true variation between seasons, to variation between months, weeks, days in the week and even variation during a single day or hour.
To deal with seasonal effects in forecasting two tasks must be completed:
1. A forecast for the entire period (ie year) must be made using whatever forecasting technique is appropriate. This forecast will be developed using whatever
2. The forecast must be adjust to reflect the seasonal effects in each period (ie month or quarter)
The multiplicative seasonal method adjusts a given forecast by multiplying the forecast by a seasonal factor
Step 1: calculate the average demand y per period for each year (y) of past data by dividing total demand for the year by the number of periods in the year
Step 2: divide the actual demand Dy,t for each period (t) by the average demand y per period (calculated in Step 1) to get a seasonal factor fy,t for each period; repeat for each year of data
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Step 3: calculate the average seasonal factor t for each period by summing all the seasonal factors fy,t for that period and dividing by the number of seasonal factors
Step 4: determine the forecast for a given period in a future year by multiplying the average seasonal factor t by the forecasted demand in that future year
Seasonal Forecasting (multiplicative method)
Actual Demand
Year Q1 Q2 Q3 Q4 Total Avg 1 100 70 60 90 320 802 120 80 70 110 380 953 134 80 70 100 381 96
Seasonal Factor
Year Q1 Q2 Q3 Q41 1.25 .875 .75 1.1252 1.26 .84 .74 1.163 1.4 .83 .73 1.04Avg. Seasonal Factor 1.30 .85 .74 1.083
Seasonal Factor - the percentage of average quarterly demand that occurs in each quarter.
Annual Forecast for year 4 is predicted to be 400 units.
Average forecast per quarter is 400/4 = 100 units.
Quarterly Forecast = avg. forecast × seasonal factor.
Q1: 1.303(100) = 130 Q2: .85(100) = 85 Q3: .74(100) = 74 Q4: 1.083(100) = 108
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STEPS IN FORECASTING:
Forecasting business change involves more than analysis of statistical data-it also
embodies the prediction of economic change such as secular trend. Seasonal variations.
Cyclical variations and a consideration of cause and effect.
Broadly speaking the forecasting of business fluctuations consists of the following steps:
1. Understanding why changes in the past have occurred:
One of the basic principles of statistical forecasting-indeed of all forecasting
when historical data are available – is that the forecaster should use the data on
past performance to get a “speedometer reading” of the current rate (say, of sales)
and of how fast this rate is increasing or decreasing. The current rate and changes
in the rate- “acceleration” and “deceleration”-constitute the basis of forecasting.
Once they are known, various mathematical techniques can develop projections
from them. If an attempt is made to forecast business fluctuations without
understanding why past changes have taken place, the forecast will be purely
mechanical based solely upon the application of mathetical formulae and subject
to serious error.
Observation and analysis of the past behavior is one of the most vital parts of
forecasting. However, it should be carefully noted that though future may be some
sort of extension of the past. It may not be an exact replica. Changes in business
and economic activity are caused by numerous forces or factors which are often
difficult to discover and measure. Not only this, they may appear in all kinds of
combinations and may be constantly changing. Hence in making forecasts, we
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should not assume that history repeats itself. Rather, we should believe that there
are certain regularities in the past behavior which can be observed and used as a
basis for reducing the uncertainities of the future. It is often said that the past,
imperfect indicator of the future though it is, is the best guide we have in
attempting to make predictions.
2. Determining which phases of business activity must be measured:
After it is known why business fluctuations have occurred, or if there is
a reasonable supposition, it is necessary to measure certain phases of business
activity in order to predict what changes will probably follow the present level of
activity.
3. Selecting and compiling data to be used as measuring devices:
There is an interdependent relationship between the selection of
statistical data and determination of why business fluctuations occur. Statistical
data cannot be collected and analysed in an intelligent manner unless there ia a
sufficient understanding of business fluctuations; likewise, it is important that
reasons for business fluctuations be stated in such a manner that it is possible to
secure data that are related to the reasons.
4. Analyzing the data:
In this last step, the data are analysed in the light of one’s understanding
of the reason why change occurs. For example, if it is reasoned that a certain
combination of forces will result in a given change, the statistical part of the
problem is to measure these forces and from the data available, to draw
conclusions on the future course of action. The methods of drawing conclusions
may be called forecasting techniques and they represent any one of a large
number of analytical devices for summarizing data and drawing inferences from
the summaries.
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Conclusion:
We have given just an overview of the types of forecasting
methods available. The key in forecasting nowadays is to
understand the different forecasting methods and their relative
merits and so be able to choose which method to apply in a
particular situation (for example consider how many time series
forecasting methods the package has available).
All forecasting methods involve tedious repetitive calculations and
so are ideally suited to be done by a computer. Forecasting
packages, many of an interactive kind (for use on pc's) are
available to the forecaster.
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Bibiliography: Business Statistics by J.K. Sharma
www.findarticles.com
www.encyclopedia.co.uk/define/forecasting
www.dictionary.reference.com/browse/forecast
www.eurofound.europa.eu/eiro
www.ieor.verkeley.edu.oliver/book.html
En.wikipedia.org/wiki/technology_forecasting
http://en.wikipedia.org/wiki/Technology_forecasting
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