Prediction or forecasting is a common phenomenon for which all human beings are always eager to know. The pre-knowledge about unknown and uncertain future prepare them to cope up in an efficient way. Since the dawn of civilization, this desire has been satisfied by priests, astrologers, fortune tellers, etc. In the present scenario, the necessity of predicting future is fulfilled in ample ways. There are several forecasting methods available from simplest to some of the most complicated; from judgmental to quantitative. Forecasting, in true essence, is a branch of the anticipatory sciences used for identifying and projecting alternative possible future. It plays vital role in most of our activities and in all we do concerning the future. Weather prediction, staff scheduling, business, production planning and multistage management decision analysis are among distinctive examples of forecasting areas. In such fields people want to foresee as closely as possible and plan for the future. In broad terms, a forecast is simply a statement, based upon some criteria, concerning the future condition of a system. It opens menu windows onto future. It is a medium guiding towards plans for the development of a better future as the forecasted visions give an alternative to plan, design, shape, and cope with future. To make a forecast with 100% accuracy may not be possible, but efforts are made to reduce the forecasting errors or increase the speed of the forecasting process. For these forecasts, to be accurate, either no major change should occur from conditions that have prevailed during the past or such changes must be canceled out. Otherwise, forecasting errors are possible, unless some appropriate prudence about the direction of the forthcoming changes is developed. In the fast-paced and rapidly changing world, the future will be vastly different from the present in a number of ways. Furthermore, because of constant development of knowledge and advances in various arenas, the global society demands an increasing ability to shape the future for better. As a result, society and each institution recognize the need of knowledge about possible future which is basically the consequences of decisions and actions taken in present. Thus, it is increasingly necessary that one should have better forecasting tools that can be applied in an efficient way. It is more and more important to forecast the possible future, implied by the changes created by this knowledge. Hence, forecasting has become an essential tool for society to decide, plan, design, steer, manage, implement, and control changes by identifying preferable future with forecasts. In past few decades of research and development many methodologies and tools have emerged to deal with the forecasting processes. These methods can be broadly categorized as: a.Time series methods: Time series methods use historical data as the basis of estimating future outcomes. Such methods try to estimate how the sequence of observation will continue into the future. b.Causal / econometric methods: Some forecasting methods use the assumption that it is possible to identify the underlying factors which influence the variable that is being forecast. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast. c.Judgmental methods: Judgmental forecasting methods incorporate intuitive judgments, opinions and subjective probability estimates. Some other methods like Simulation, Prediction market, Probabilistic forecasting and Ensemble forecasting, Reference class forecasting are also being employed for forecasting purposes. The time series analysis has been emerged as one of the useful tool to predict the future behavior of various systems. 1.1 :Time series analysis

A time series is basically, a collection of observations made sequentially in time. It is a chronological...

...Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. 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 judgemental 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.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.[1]
Although quantitative analysis can be very precise, it is not always appropriate. Some experts in the field of forecasting have advised against the use of mean square error to compare forecasting methods.[2]
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Categories of forecasting methods
[edit]Qualitative vs. quantitative methods
Qualitative...

...R
ForecastingModels
5
TEACHING SUGGESTIONS
Teaching Suggestion 5.1: Wide Use of Forecasting. Forecasting is one of the most important tools a student can master because every ﬁrm needs to conduct forecasts. It’s useful to motivate students with the idea that obscure sounding techniques such as exponential smoothing are actually widely used in business, and a good manager is expected to understand forecasting. Regression is commonly accepted as a tool in economic and legal cases. Teaching Suggestion 5.2: Forecasting as an Art and a Science. Forecasting is as much an art as a science. Students should understand that qualitative analysis (judgmental modeling) plays an important role in predicting the future since not every factor can be quantiﬁed. Sometimes the best forecast is done by seat-of-thepants methods. Teaching Suggestion 5.3: Use of Simple Models. Many managers want to know what goes on behind the forecast. They may feel uncomfortable with complex statistical models with too many variables. They also need to feel a part of the process. Teaching Suggestion 5.4: Management Input to the Exponential Smoothing Model. One of the strengths of exponential smoothing is that it allows decision makers to input constants that give weight to recent data. Most managers want to feel a part of the modeling process and appreciate the...

...CHAPTER 1
INTRODUCTION
Demand forecasting refers to the prediction or estimation of a future situation under given constraints. Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market. Pricing is one of the four Ps of the marketing mix. The other three aspects are product, promotion, and place. It is also a key variable in microeconomic price allocation theory. Price is the only revenue generating element amongst the 4ps, the rest being cost centers. Pricing is the manual or automatic process of applying prices to purchase and sales orders, based on factors such as: a fixed amount, quantity break, promotion or sales campaign, specific vendor quote, priceprevailing on entry, shipment or invoice date, combination of multiple orders or lines, and many others. Automated systems require more setup and maintenance but may prevent pricing errors.
A market entry strategy is the planned method of delivering goods orservices to a target market and distributing them there. When importing or...

...estimated elasticities of demand are computed as
ˆ ˆP
E=b
Q
3.
M
ˆ
ˆ
EM = c
Q
ˆP
ˆ
E XR = d R
Q
When demand is specified in log-linear form, the demand function can be written
as
Q = aP b M c PRd
Chapter 7: Demand Estimation and Forecasting
144
To estimate a log-linear demand function, the above equation must be converted to
logarithms:
ln Q = ln a + b ln P + c ln M + d ln PR
ˆˆˆ
ˆ
In this log-linear form, the elasticities of demand are constant: E = b , E = c , and
M
ˆ
E XR
4.
ˆ
=d
When a firm possesses some degree of market power, which makes it a
price-setting firm, the demand curve for the firm can be estimated using the
method of least-squares estimation set forth in Chapter 4. The following
steps can be followed to estimate the demand function for a price-setting
firm:
Step 1: Specify the price-setting firm’s demand function.
Step 2: Collect data for the variables in demand function.
Step 3: Estimate the firm’s demand using least-squares regression.
5.
A time-series model shows how a time-ordered sequence of observations on a
variable, such as price or output, is generated. The simplest form of time-series
forecasting is linear trend forecasting. In a linear trend model, sales in each time
period (Qt) are assumed to be linearly related to time (t):
Qt = a + bt
and regression analysis is used to...

...
Demand Estimation
Demand Curve Estimation
■ Simple Linear Demand Curves
■ The best estimation method balances marginal costs and marginal benefits.
■ Simple linear relations are useful for demand estimation.
■ Using Simple Linear Demand Curves
■ Straight-line relations give useful approximations.
Identification Problem
■ Changing Nature of Demand Relations
■ Demand relations are dynamic.
■ Interplay of Supply and Demand
■ Economic conditions affect demand and supply.
■ Shifts in Demand and Supply
■ Curve shifts can be estimated.
Simultaneous Relations
[pic]
Interview and Experimental Methods
■ Consumer Interviews
■ Interviews can solicit useful information when market data is scarce.
■ Interview opinions often differ from actual market transaction data.
■ Market Experiments
■ Controlled experiments can generate useful insight.
Experiments can become expensive
Regression Analysis
■ What Is a Statistical Relation?
■ A statistical relation exists when averages are related.
■ A deterministic relation is true by definition.
■ Specifying the Regression Model
■ Dependent variable Y is caused by X.
■ X variables are independently determined from Y.
■ Least Squares Method
■ Minimize sum of squared residuals.
[pic]...

... |50000 |
|Others |2000 |3000 |
2.Assume that in past years,a firm sold an average of 1000 units of a particular product line each year.On the average,200 units were sold in the spring,350 in the summer,300 in the fall and 150 in the winter.Compute the seasonal relatives for each season.If the expected demand in the subsequent year is 1100 units,use the seasonal relatives to forecast the seasonal demand.
3.A specific forecastingmodel was used to forecast the demand for a product.The forecast and the corresponding demand that subsequentlyy occurred are shown below.Use the MAD and tacking signal to evaluate the accuracy of the model.
|Month |Actual |Forecast |
|October |700 |660 |
|November |760 |840 |
|December |780 |850...

...Businesses use forecasting to predict future, trends, patterns, and business with data to develop a forecast. This data is used to predict future sales. In forecasting we use testing and reasonableness to predict future events. Companies use this method to compare their sales with other companies. Forecasting has many benefits to include; what is the popular product customers are purchasing, and it enhances cash flow, and identifies patterns and trends inside a corporation. Using this method is popular and is quite achieving when done effectively.
Forecasting can result in decrease in product cost, increase company efficiency, and increase revenue. This method has to be administered at it entirely to reap the best benefits. Forecasting also requires a company to keep record of inventory, sales, and customer satisfaction. Many items are needed such as; financial statements, accounting records. In order to be successful you have to know what the customers want and why they want it.
Something’s that can affect the benefits of forecasting is weather, consumer income for example a recession, changes in population, and product changes. I have notice with some businesses, for example Chik Fila years ago changed their chicken. Something like this could cause changes in forecasting and profits.
Eight Steps to Forecasting
Determine the use of the forecast-...

...Forecasting
BUS446: Production Control (CFM1316A)
Monday, April 29, 2013
Forecasting
In the business world today, companies use forecasting methods to implement processes and strategies in order to meet organizational goals. Forecasting will allow a company to plan for possible outcomes, making adjustments to inventory levels and staff. Through forecasting, companies will attempt to keep operating costs at a manageable level without sacrificing production and quality. The are several different types of forecasting available to companies today, each with advantages and disadvantages. The goal of any organization is to implement the forecasting method which best fits the needs of that organization. The forecasting needs and processes are different for each individual organization. Some companies will chose to maintain low inventory levels, opting for forecasting which focuses on shorter time periods; while other companies will need longer range forecasting due to maintaining higher inventory levels. Regardless of the needs, forecasting can be a useful tool for any company. We will look at objective and subjective forecasting methods, how these methods are implemented, and their effectiveness.
Objective forecasting is commonly used for short term...