File Name: demand forecasting and estimation .zip
Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term.
Recent history is filled with stories of companies and sometimes even entire industries that have made grave strategic errors because of inaccurate industrywide demand forecasts. For example: In , U. Such forecasts are crucial […]. For example:. The inaccurate suppositions did not stem from a lack of forecasting techniques; regression analysis, historical trend smoothing, and others were available to all the players.
Recent history is filled with stories of companies and sometimes even entire industries that have made grave strategic errors because of inaccurate industrywide demand forecasts. For example: In , U. Such forecasts are crucial […].
For example:. The inaccurate suppositions did not stem from a lack of forecasting techniques; regression analysis, historical trend smoothing, and others were available to all the players.
Instead, they shared a mistaken fundamental assumption: that relationships driving demand in the past would continue unaltered. None realized that history can be an unreliable guide as domestic economies become more international, new technologies emerge, and industries evolve. As a result of changes like these, many managers have come to distrust traditional techniques.
Some even throw up their hands and assume that business planning must proceed without good demand forecasts. I disagree. It is possible to develop valuable insights into future market conditions and demand levels based on a deep understanding of the forces behind total-market demand.
These insights can sometimes make the difference between a winning strategy and one that flounders. Merely going through the process has merit for a management team. Instead of just coming out with pat answers, numbers, and targets, the team is forced to rethink the competitive environment. Total-market forecasting is only the first stage in creating a strategy. Conduct sensitivity analyses to understand the most critical assumptions and to gauge risks to the baseline forecast.
Define it broadly enough to include all potential end users so that you can both identify the appropriate drivers of demand and reduce the risk of surprise product substitutions. In defining the market, an understanding of product substitution is critical. Customers might behave differently if the price or performance of potential substitute products changes.
One company studying total demand for industrial paper tubes had to consider closely related uses of metal and plastic tubes to prevent customer switching among tubes from biasing the results. Understand, too, that a completely new product could displace one that hitherto had comprised the entire market—like the electronic calculator, which eliminated the slide rule.
Naturally, these forecasts grew more inaccurate with time as end users were presented with new choices. The companies are now broadening their market definitions to take account of heightened competition from other long-distance carriers.
There are several ways you can make sure you include all important substitute products both current and potential. From interviews with industrial customers you can learn about substitutes they are studying or about product usage patterns that imply future switching opportunities.
Moreover, market research can lead to insights about consumer products. Speaking with experts in the relevant technologies or reviewing technological literature can help you identify potential developments that could threaten your industry.
Analyses like these can lead to the construction of industry demand curves—graphs representing the relationship between price and volume. With an appropriate definition, the total-industry demand curves will often be steeper than demand curves for individual products in the industry. In some cases, managers can make quick judgments about market definition. A total-market forecast may not be critical to business strategy if market definition is very difficult or the products under study have small market shares.
Instead, your principal challenge may be to understand product substitution and competitiveness. One company analyzed the potential market for new consumer food cans, and it concluded that growth trends in food product markets were not critical to the strategy question.
What was critical was knowing the value positions of the new packages relative to metal cans, glass jars, and composite cans. So the company spent time on that subject. The second step in forecasting is to divide total demand into its main components for separate analysis.
There are two criteria to keep in mind when choosing segments: make each category small and homogeneous enough so that the drivers of demand will apply consistently across its various elements; make each large enough so that the analysis will be worth the effort.
Of course, this is a matter of judgment. You may find it useful in making this judgment to imagine alternative segmentations based on end-use customer groups, for example, or type of purchase.
Then hypothesize their key drivers of demand discussed later and decide how much detail is required to capture the true situation. As the assessment continues, managers can return to this stage and reexamine whether the initial decisions still stand up.
In this disguised example, industry data permitted the division of demand into 12 end-use categories. Some categories, like business forms and reprographic paper, were big contributors to total consumption; others, such as labels, were not. One other converting was fairly large but too diverse for deep analysis.
It then developed secondary branches of the tree to further dissect these categories and to determine their drivers of demand. It analyzed the remaining segments less completely that is, via a regression against broad macroeconomic trends.
Other companies have used similar methods to segment total demand. One company divided demand for maritime satellite terminals by type of ship e. Another divided demand for long-distance telephone service into business and residential customers and then subdivided it by usage level. And a third segmented consumer appliances into three purchase types—appliances used in new home construction, replacement appliance sales in existing homes, and appliance penetration in existing homes.
In thinking about market divisions, managers need to decide whether to use existing data on segment sizes or to commission research to get an independent estimate. Reliable public information on historical demand levels by segment is available for many big U. For some foreign markets and less well-researched industries in the United States, like the labels industry, you may have to get independent estimates. Even with good data sources, however, the readily available information may not be divided into the best categories to support an insightful analysis.
In these cases, managers must decide whether to develop their forecasts based on the available historical data or to undertake their own market research programs, which can be time-consuming and expensive.
Note that while such segmentation is sufficient for forecasting total demand, it may not create categories useful for developing a marketing strategy.
A single product may be driven by entirely different factors. One study of industrial components found that consumer industry categories provided a good basis for projecting total-market demand but gave only limited help in formulating a strategy based on customer preferences: distinguishing those who buy on price from those who buy on service, product quality, or other benefits. Such buying-factor categories generally do not correlate with the customer industry categories used for forecasting.
A strong sales force, however, can identify customer preferences and develop appropriate account tactics for each one. The third step is to understand and forecast the drivers of demand in each category. Here you can make good use of regressions and other statistical techniques to find some causes for changes in historical demand. But this is only a start. The tougher challenge is to look beyond the data on which regressions can easily be based to other factors where data are much harder to find.
Then you need to develop a point of view on how those other factors may themselves change in the future. An end-use analysis from the commodity paper example, reprographic paper, is shown in the accompanying chart. The management team, using available data, divided reprographic paper into two categories: plain-paper copier paper and nonimpact page printer paper. Without this important differentiation, the drivers of demand would have been masked, making it hard to forecast effectively.
In most cases, managers can safely assume that demand is affected both by macroeconomic variables and by industry-specific developments. In looking at plain-paper copier paper, the team used simple and multiple regression analyses to test relationships with macroeconomic factors like white-collar workers, population, and economic performance. Most of the factors had a significant effect on demand.
Intuitively, it also made sense to the team that the level of business activity would relate to paper consumption levels. Demand growth for copy paper, however, had exceeded the real rate of economic growth and the challenge was to find what other factors had been causing this.
The team hypothesized that declining copy costs had caused this increased usage. The relationship was proved by estimating the substantial cost reductions that had occurred, combining those with numbers of tons produced over time, and then fashioning an indicative demand curve for copy paper. Economists sometimes describe this as a downward-shifting supply curve leading to movement down the demand curve.
Further major declines in cost per copy seemed unlikely because paper costs were expected to remain flat, and the data indicated little increase in price elasticity, even if cost per copy fell further.
So the team concluded that usage growth per level of economic performance was likely to continue the flattening trend begun in growth in copy paper consumption would be largely a function of economic growth, not cost declines as in the past.
The team then reviewed several econometric services forecasts to develop a base case economic forecast. Similar studies have been performed in other industries. Here the team divided demand into its consuming industries and then asked experts in each industry for production forecasts. Total demand for components was projected on the assumption that it would move parallel to a weight-averaged forecast of these customer industries. In another example, a team forecasting demand for maritime satellite terminals extrapolated past penetration curves for each of five categories of ships.
These curves were then adjusted for major changes in the shipping industry e. Knowing the drivers of demand is crucial to the success of any total-market demand forecast. In , as I mentioned earlier, most electric utilities used an incomplete total-demand forecast to predict robust demand growth.
The team divided electricity demand into the three traditional categories: residential, commercial, and industrial. It then profiled differences in residential demand because of more efficiency in home appliances and changes in home size and the ratio of multi-unit to single-family dwellings.
Industrial demand was analyzed by evaluating the future of several key consuming industries, paying special attention to changes in their total production and electricity use. In , forecasters in the U. One company, however, did a more detailed demand forecast that showed that growth would soon flatten out.
It found that more than two-thirds of white-collar workers either did not require PCs in their jobs—actors and elevator operators, for instance—or were supported mostly by inexpensive terminals linked to large computers, as in the case of many clerical workers. The potential market was not big enough to support the growth rate.
Indeed, the market began to flatten the next year. Forecasting total demand became crucial for another company that was thinking about acquiring a maker of video games. The analysis made clear that the main target market, upper-income families with children, was already well penetrated. This finding convinced management that demand would fall and that the proposed acquisition did not make sense. The dramatic decline in video game sales shortly thereafter confirmed the wisdom of this judgment.
Managers who rely on single-point demand forecasts run dangerous risks.
An organization faces several internal and external risks, such as high competition, failure of technology, labor unrest, inflation, recession, and change in government laws. Therefore, most of the business decisions of an organization are made under the conditions of risk and uncertainty. An organization can lessen the adverse effects of risks by determining the demand or sales prospects for its products and services in future. Demand forecasting is a systematic process that involves anticipating the demand for the product and services of an organization in future under a set of uncontrollable and competitive forces. According to Evan J. Demand forecasting enables an organization to take various business decisions, such as planning the production process, purchasing raw materials, managing funds, and deciding the price of the product.
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. Which estimate is tied to a proposed marketing plan and which assumes a particular set of uncontrollable and competitive forces. Philip Kotler. The company sales forecast is the expected level of company sales based on a chosen marketing plan and assumed marketing environment ". Douglas, "Demand forecasting may be defined as the process of finding values for demand in future time periods.
In Demand forecasting mangers forecast the most likely future demand of a product so that he can make necessary arrangement for the various factor of production.
Demand forecasting models are past the point of academic curiosity, and although they are still in the early stages of their life cycle, they are well beyond the development stage. The modelling of demand phenomena may be viewed as having two main thrusts: the first is a scientific one that leads to a greater understanding of the phenomena.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Demand forecasting in power distribution systems using nonparametric probability density estimation Abstract: Customer demand data are required by power flow programs to accurately simulate the behavior of electric distribution systems.
Managerial Economics pp Cite as. In a commercial environment, the success of the organisation is dependent on its ability to produce the goods customers want, and sell them at a price customers are prepared to pay. New investment decisions, whether to replace equipment, the quantity of labour to employ, are each related to the anticipated level of sales. It is then vital that the decision-maker understands the relationship between the likely level of sales and the internal and external variables that influence sales. Moreover, any decision involves a comparison of the anticipated costs and benefits of following a particular action. In the business context, the benefits from that action are usually expressed in the form of revenues.
In general, an estimation technique can be used to forecast demand but a forecasting technique cannot be used to estimate demand. A manager who wishes to.
Demand forecasting is the process of making estimations about future customer demand over a defined period, using historical data and other information. Proper demand forecasting gives businesses valuable information about their potential in their current market and other markets, so that managers can make informed decisions about pricing, business growth strategies, and market potential. Without demand forecasting, businesses risk making poor decisions about their products and target markets — and ill-informed decisions can have far-reaching negative effects on inventory holding costs , customer satisfaction, supply chain management , and profitability. In this instance, other information such as expert opinions, market research, and comparative analyses are used to form quantitative estimates about demand. This approach is often used in areas like technology, where new products may be unprecedented, and customer interest is difficult to gauge ahead of time. When historical data is available for a product or product line and trends are clear, businesses tend to use the time series analysis approach to demand forecasting. A time series analysis is useful for identifying seasonal fluctuations in demand, cyclical patterns, and key sales trends.
No business owner has a crystal ball showing what customers will want during the upcoming year and how much of it they will buy. Fortunately, you can forecast demand using tools and information.
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Сирена выла не преставая. Сьюзан подбежала к. - Коммандер. Стратмор даже не пошевелился.
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