Comparing and Contrasting Forecasting MethodsJoin now to read essay Comparing and Contrasting Forecasting MethodsComparing and Contrasting Forecasting MethodsCompanies use forecasting to help decide how to best spend funds for the next year, to predict if expansion is needed, to plan for how much of each product to produce within a certain period of time, and other decisions that effect the company’s future plans. Qualitative, time series analysis, causal relationships, and simulation are the four basic types of forecasting (Chase, Jacobs, & Aquilano, 2006). The forecasting methods that will be compared and contrasted within this paper are the Delphi method (which is an example of qualitative), time series analysis, seasonal, and causal relationship forecasting.
The Delphi method is a judgmental forecasting method, which uses the evaluation of several experts within the field that is being analyzed to forecast company sales. The process starts by contacting several experts and asking them to participate in the study of the company. When multiple experts agree to join in the research, the company’s material is sent to each specialist to evaluate, and are asked to send the findings and materials back to the company with suggestions and predictions attached. A company coordinator studies all of the information and forecasts, has key company executives make additional comments and reflections, then asks the experts if they want to make any changes to the information they provided. This will happen several times until all parties involved reach a consensus. “Forecasts based on a group of forecasts are better than forecasts of a single forecaster, particularly where no formal forecasting process exists…” (Ahamad & Ismael, 2003, p. 22). For the Delphi method to be used effectively, it is important to keep the identity of the experts used undisclosed, so those that are providing information to the company know it will not be used against any participant at a later time and make the experts feel comfortable to be honest in the review provided. The Delphi method is used when little or no historical data is available, making this method very useful for new companies, but not necessarily the best method if historical data is available for comparison.
“Time Series Forecasting (TSF), the forecast of a chronologically ordered variable, corporals an important tool to model complex systems, where the goal is to predict the system’s behavior and not how it works,” (Cortez, Rocha, & Neves, 2004, p. 415). Several different forecasting methods exist within the time series category, including simple moving average, weighted moving average and simple exponential smoothing, exponential smoothing with trend, and linear regression. To decide which of these methods are best to use for forecasting, time horizon to forecast, data availability, accuracy required, size of forecasting budget, and availability of qualified personnel should be taken into consideration (Chase, Jacobs, & Aquilano, 2006). Time series analysis uses past data to try to predict future events. Therefore, this type of forecasting does not work for newly formed companies, but this type of forecasting method does work for companies that have many years in the industry.
Seasonal forecasting is a form of forecasting that also uses historical data to predict sales within a certain season, such as spring for flowers, fall for harvest, or summer for travel. It can also be used in trying to predict certain weather conditions like hurricanes and tornados, or for companies that produce office supplies the beginning of the school year might be considered a season. “Seasonality is so strong in many industries that losses routinely occur in the off-season. It causes elementary and secondary textbook businesses, for example, to incur operating losses in the first two fiscal quarters,” (Radas & Shugan, 1998, p. 296). After many years of collecting information, future sales can be predicted by quarter, season, or peak of sales that have been established.
Causal relationship happens when one independent variable causes an occurrence of another independent variable. For example, when snow causes the sale of snow shovels and ice scrapers to increase. If it is known ahead of time that it will snow, the increase of sales of snow shovels and ice scrapers can be predicted. “The first step in causal relationship forecasting is to find those occurrences that are really the causes. Often leading indicators are not causal relationships, but in some indirect way, they may suggest that some other things might happen,” (Chase, Jacobs, & Aquilano, 2006).When using causal relationship forecasting, finding each and every relationship between the unknown and the product the business is selling could generate added income. Causal relationship forecasting can be used in conjunction with other methods of forecasting to provide an even more precise prediction.
The Effect of Accurate Predictive Statistics on Business
The effect of accurate data on the business success of an organization can differ between different organizations. The effective effect depends on a number of factors, ranging from the organization’s size to financial performance. In some organizations, a greater level of success can mean greater level of income because more people work in the organization and so are expected to work harder. In addition, accurate statistical data is often used for determining the success of an organization.
The Effect of Accurate Data on Business
A significant part of the value of statistical methods for business is their reliance on the method’s use of historical information to measure the success of a company. This means that, if you use a statistical method that looks at data from one year back to the next, this information is often used to predict which companies have performed better. For example, if you use an industry-specific statistical method that looks at the average business performance, the data can be used to look at sales data that can be sold to customers.
For that reason, most organizations that rely heavily on statistical methods focus more on business growth than they do on the performance of the organization. For example, a large organization that relies primarily on a statistical method can become stagnant, and a small organization can recover from the slump in performance. This may not make for good business trends, but it can serve to minimize the economic stress caused by the downturn (Cohn et al., 2003).
A statistical method’s reliability in predicting success is often measured primarily by how well it identifies each problem (like whether businesses have been successful during the previous year) by assuming that each problem (or number) is a product of its past performance. The business plan could include the following:
Data to identify the business’s history of profitability.
The business plan does not include a past performance.
As with a business plan, statistical methods generally have been used to predict the success of a company since the earliest days of its existence. By keeping the business plan accurate, businesses may be better able to evaluate the company’s ability to achieve their goals and plan for future events.
With effective and accurate methods of forecasting, there is a very clear implication that the business plans of a few business groups—such as many private equity funds and government agencies working within an economy—are more likely to generate income.
The Impact of Statistical Methods on Business Success
If any method of doing business—specifically, any statistical method that provides information on economic fundamentals that has been derived from statistical analysis—were to be used to predict any business group’s ability to successfully conduct a business, the results would suffer. One of the first things businesses are expected to do, on a practical level, when using statistical methods, is to focus on their business and focus on the organization they plan to run as the most profitable. In an effective business strategy, the company needs to take more risks than it is willing to take to achieve its goals. This can sometimes mean running a risky business, which could cause the company to lose revenue.
Using this approach, businesses will often try to plan to compete against a competitor. By building out their strategic plans and gaining a better understanding of business fundamentals, they may be able to maintain their business as effectively as they can and thus be able to expand their business and win more market share by reducing barriers.
There are two major ways to do the same. First, a business based on self-analysis based on data that is available over time (e.g., from the financial statement that an