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Example research essay topic: Juvenile Justice System Decision Making Process - 2,554 words

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Forecasting I should start by saying that forecasting is a systematic effort to anticipate future events or conditions. The most well known type of forecast may be that of the meteorologist who prepares daily weather forecasts that help us decide how to dress each day and whether to take an umbrella when we leave for work in the morning. Other common forecasts are those that anticipate future economic conditions, traffic patterns, and even the size and number of classrooms that will be needed in local schools. In the following essay I will comment on various approaches to forecasting. I will present various educated findings as well as my personal opinion on the given matter. At the same time in the justice or any business related system, forecasting usually refers to an effort to anticipate the size of future correctional populations or to the future sales.

Every state and local government (or business and company) must have enough secure confinement space available to house accused and convicted offenders who cannot be trusted to remain in the community. Building and operating jails and prisons as well as conducing any other business activity, however, is very expensive. Jurisdictions try to provide just enough space, but not too much (Henry, 34). Nearly every facility administrator and decision maker in the juvenile justice system has faced the same question, "how many beds will we need?" This question is asked for a variety of reasons. Demographic trends may indicate coming growth in the juvenile population. Juvenile crime may seem to be getting more or less frequent, more or less severe.

A jurisdiction may be facing a financial crisis. Deteriorating buildings may be forcing new construction, or a change in political leadership may be bringing new policies to the juvenile justice system. No matter what compels State and local officials or companies and business organizations to ask about future bed-space, their interest in the answer is usually urgent and intense (Franses, 187). Juvenile justice professionals who must respond to questions about space needs are often tempted to answer with simple statistical predictions based upon recent trends in juvenile arrests or court commitments, or even recent changes in detention and corrections populations themselves. An analyst may find, for example, that juvenile arrests have increased 20 percent in the past five years and conclude that detention beds likewise should be increased 20 percent (Henry, 36). One must remember that imple answers like this one are often appreciated by policymakers because they allow agencies to proceed with budgeting and construction plans.

In the long term, however, simple answers always turn out to be problematic (Henry, 37). Repeated experience with estimating future space needs teaches policymakers and practitioners alike that there are no simple answers, or more accurately, there are no simple and reliable answers. Statistical predictions are only as good as the data elements that go into statistical models and the assumptions used to build the models. The actual demand for corrections and detention space has a funny way of proving statistical models wrong, whether in one year, five years, or ten (Franses, 189). The readers should be aware that for these reasons, some jurisdictions have elected to stop "predicting" future needs for bed space. Instead, they have begun to incorporate a routine "forecasting" process into their decisions about future detention and corrections space (Seifert, 110).

At the same time I would like to note that forecasting is different from predicting, although both strategies involve statistical projections. In a prediction framework, the results of a statistical analysis are used to make decisions. At the same time under a forecasting framework, statistical projections are seen as merely the beginning of a more involved decision-making process. In an attempt to predict the context of any activity, researchers use data about the past to speculate about the future and they encourage policymakers to act on that statistical vision of the future.

In a forecasting context, researchers combine the outcomes of prior forecasts with the newest data about actual bed space utilization to examine how the recent past differed from expectations. It should be noted that by analyzing the differences between their expectations and subsequent reality, officials learn about factors that influence future demand for bed space but they don't place undue faith in anyone's ability to predict the future of any business or other activity the person or company is interested in at any given time and place (Henry, 39). Forecasting is also more inclusive than prediction. Prediction models can only account for measurable factors, or variables for which data actually exist. Forecasting models are not limited by data availability. The heart of a forecasting process, in fact, is often the discussion that takes place after statistical projections are complete.

These discussions can address a much wider range of factors, including practice and policy concerns for which there may never be objective data (Franses, 191). In a forecasting process, juvenile justice officials are encouraged to review the results of each statistical projection with important stakeholders, including lawmakers, practitioners, service providers, neighborhood groups, and youth from the communities most affected by the juvenile justice system. In these discussions, agency officials may consider a variety of hypotheses about why their previous expectations were or were not realized. They can examine the values and beliefs of important actors in the juvenile justice system, unofficial agency practices, subtle changes in legislative priorities, and even broad social and economic conditions (Henry, 42). The breadth and quality of the data used in statistical projections is important in population forecasting, but knowledge about management and policy influences is just as important. One must also never forget that jurisdictions seeking to improve their ability to anticipate future demands for secure bed space need to design a decision-making process that is statistically informed, but not statistically dependent or statistically controlled (Barber, 38).

There is no magical way to predict the future. Population projections use information about the past to project the future, but projections are never completely reliable, especially in a world where conditions change constantly. Here I would like to note that as Seifert has observed, "[u]sing the past to 'see' the future is like driving a car by looking into the rear view mirror. As long as the road is straight or curving in wide arcs, the driver can stay on the road by looking backward. However, if a sharp turn occurs or a bridge is out, the driver will crash (Seifert, 112). The basic common sense definition is that forecasting is the process of predicting a future event.

It often involves taking historical data and projecting them into the future with some kind of mathematical model. At the same time forecasting can be either intuitive or subjective. The initiative approach means that you use your common good sense with the model to are judgement. Whereas the subjective model Means that the judgement would be made solely on the model itself. Forecasts are seldom prefect, they are costly, time consuming, and are a pain to prepare and to monitor. Forecast have three time horizons, and the answer to each is, it certainly all depends on numerous other facts! ! ! ! !

Usually the short range forecast is anywhere from tomorrow up to a year. The medium range is from one year up to about three years, and the long term is about three years till when ever you desire (Henry, 44). There are three basic types of forecasts. The first is the economic forecast. The economic forecast deals with thing like the money supply and inflation rates.

The second type of forecast is the technological forecast. The technological forecast is more concerned with technological processes, results in new products, new plants, and new equipment. The last type of forecast is the demand forecast. The demand forecast can be called the sales forecast of the company's production, and capacity (Mentzer, 93).

Which triggers financial decisions, marketing and personel planning as well. There are also two forecasting approaches. They are qualitative and quantitative. Qualitative is with little information, like new products. Can this be done? Is a qualitative question, this could be said of the new flat screen T.

V. On the other hand is a quantitative. Quantitative is used with vast amounts of data. This can be called the historical model. Usually this is with math models, like the sale of cars (Barber, 41). Good forecasts are critically important, they influence all levels of business, and drive a business in many ways (Henry, 49).

Forecasting is the science and art of predicting the future. In the domain of commerce, forecasting techniques are used to: Predict future demand for a product or service. Predict the effect of investment or purchasing decisions. Maintain effective inventory levels. Reduce uncertainty and manage risk for any future business situation. I would like to add here that forecasting techniques are used to give a business or organization a clear view of their future situation.

The use of forecasting tools to provide a clear understanding of the effect decisions will have is a very effective method to ensure an organization's continued success (Seifert, 116). Predictability Just talking about things, without really doing it, theory Forecasting The REAL thing, telling whats going to happen (Mentzer, 99). One must not forget that forecasting techniques involve the formulation of models about the world, and the manipulation of the models to form predictions about the future state of the world as it relates to the products or services we are interested in with respect to our company (Barber, 44). It should be noted that there are two families of forecasting methods: quantitative and qualitative.

Quantitative methods are those which rely on quantitative data such as previous sales figures or inventory levels to make their predictions about the future, and include Time-Series and Causal models. Qualitative methods mainly rely on subjective data gathered from executives, salespeople or consumers to try and predict future economic conditions for a business (Henry, 54). I would like to note that time-series models are based on a series of discrete and equal time increments. That is, predictions for the next {week, month, quarter, year} are based on, and only on, the past values of the last N periods{weeks, months, years} of the variable we wish to forecast.

A time-series has four main components that we wish to use when forecasting. They are its: Trends. The overall direction of the demand over time (Seifert, 118). Seasonality. The fluctuations in demand that occur repeatedly and often (i.

e. every year). Cycles. The fluctuations in a variable that occur repeatedly and in the long-term (i. e. every several years).

Random Variations. The element of chance in all data. As business application consultants, we are frequently asked questions about predicting the future. I would also like to noted that forecasting is a tool to help businesses do that, and we will try to explain what it is and how it can help manage your business (Barber, 58). We all know that by definition, a forecast is an attempt to predict and plan future results or events. However, no forecast is made without looking into the past.

In business, this involves gathering factual historical information, such as last years annual sales or actual gross margin, by product or sales person. Forecasting also requires looking forward and looking for trends in a particular industry or what recent changes have occurred, or could occur, that will affect results. Proper utilization of this information will help a business determine what their goals should be for the years to come. In essence, forecasting is a tool which, if applied correctly, will help people to manage their business and make informed decisions (Seifert, 120). What is the purpose of a forecast?

It is important to identify goals so a business can determine what data needs to be collected. For example, if a business is trying to obtain financing from a bank, owners may need to prepare a forecast projecting total revenues and expenses for the next three years in addition to providing historical information. Or business owners may want to prepare a forecast that will be used internally to set monthly sales and profitability goals for each sales person or each product line. These two examples require very different data needs and displays.

For the bank, business owners need complete financial information at the company level, and for the sales goal forecast, monthly sales and margins by individual or product line are required (Henry, 57). One should start by collecting historical data for the foundation of the projection. This tracking information, such as sales by product, actual gross margin, sales personnel, divisions, locations, becomes crucial to making sound business decisions for the future. If a business does not track details on a day-to-day basis, it is very difficult to pull it together after the fact to make accurate projections.

How can sales goals be set if a business does not know how much was sold last year, or what was the year-end gross margin? I would also like to note that while it may seem like a bookkeeping nightmare to track this information, a good accounting system should certainly provide these answers without extra effort at any time needed (Barber, 56). Another step would be to examine new information or trends that may affect a business and incorporate them into the forecast. For example, if a business wanted to pay its sales people a commission based on sales, it should first forecast the impact on profitability. This could be accomplished by applying the incentive plan a business is considering to forecasted sales and profit margins for next year. The forecast will help devise a plan that will be acceptable for both the sales force and the profitability of the business.

Any of the commonly used spreadsheet packages, such as Microsoft Excel, are excellent in developing forecasts. In addition, most systems have a report writer that can access all of the data stored in the system, or can export the results directly to a spreadsheet package. The key to using one of these tools is to have someone familiar with the tool who understands its functionality and options. Training on most tools is readily available in the area and is well worth the investment (Seifert, 122). Whatever tool is used, it must provide the flexibility to change assumptions quickly and easily. For example, it may be important to update the forecast frequently to reflect more current data and assumptions or try various assumptions based on the same historical data.

The basic conclusion is that when done correctly, forecasting is a tool to help business owners and managers be proactive and make informed business decisions that will carry them successfully into the future. Bibliography: Henry, B. , Forecasting Technological Innovation (Euro courses: Technological Innovation), McGraw Hill, 2002. Seifert, Dirk, Collaborative Planning, Forecasting, and Replenishment: How to Create a Supply Chain Advantage, Prentice Hall, 2001. Barber, Gerry, Call Center Forecasting and Scheduling: The Best of Call Center Management Review, Penguin books, 2000. Franses, Philip, Time Series Models for Business and Economic Forecasting, Oxford University Press, 2000. Mentzer, John, Sales Forecasting Management: Understanding the Techniques, Systems and Management of the Sales Forecasting Process, Oxford University Press, 2000.

Kaplan, Robert, The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, Penguin books, 2000.


Free research essays on topics related to: decision making process, technological innovation, state and local, juvenile justice system, gross margin

Research essay sample on Juvenile Justice System Decision Making Process

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