Forecasting MethodsJoin now to read essay Forecasting MethodsIntroductionAll businesses are confronted with the general problem of having to make decisions under conditions of uncertainty. Management must understand the nature of demand and competition in order to develop realistic business plans, determine a strategic vision for the organization, and determine technology and infrastructure needs. To address these challenges, forecasting is used. According to Makridakis (1989), forecasting future events can be characterized as the search for answers to one or more of the following questions:
What new economic, technical, or sociological forces is the organization likely to face in both the near and long term?When might these forces impact the firmЎ¦s objective environment?Who is likely to be first to adapt to each competitive challenge?How much change should the firm anticipate both in the short run and the long run?In this paper, I will provide an overview of forecasting methods and compare and contrast these various methods. The paper will then focus on how Mattel, one of the nations largest toy manufacturers, uses demand forecasting under conditions of uncertainty ЎV most specifically those relating to the pattern and rate at which customers demand products.
What is Forecasting?In Operations Management, demand forecasting is defined as Ў§the business process that attempts to estimate sales and the use of products so that they can be purchased, stocked, or manufactured in appropriate quantities in advance to support the firmЎ¦s value adding activities.ЎЁ(Ross, 1995). Forecasting is a process that transforms historical time-series data and/or qualitative assessments into statements about future events. This process can produce either qualitative or subjective projections. Note that no forecasting process can consistently provide perfect forecasts. Any forecast that perfectly estimates subsequent events should raise cause for alarm, as this is probably indicative of improprieties such as Ў§cooking the booksЎ¦ or reporting performance data that shows conformance with plans versus actual events (Makridakis, 1989).
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1.1.2: A Case for Proposing a Report on Forecasts in the Future (SCE)§1) was published in the Proceedings of the ACM Standard for Evaluation of Business Systems in 2006.
1.1.3: SCE’s Statement on the Future of Forecasting (SCEV2)§1) refers to forecasts based on a broad range of historical and present data and is available on the SCE website.SCE has previously provided predictions of future business activities on its website (see https://www.seda.org/SCEV.htm and http://www.seda.org/CPSV.shtml for more information). In addition, SCE’s website also includes forecasts on a variety of other systems as well. For example, SCE’s website includes forecast predictions of a company’s future activities in various data sets. SCEV2 also includes predictions on a range of future business activities, including a variety of SCE’s own projections. The CACME®-SCE®® report was presented at CTS on 9 July 2015. To read more about the SCE’s CTS presentation and the SCE in general topics, go to http://sedai.org/scea/ and view SCE’s CTS page. For an overview of these and other programs where the SCE focuses, see https://sedai.org/SCE.htm .
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1.2: For forecasting as a series and for forecasts to be accurate to a specified length, the SCE has a number of requirements. The SCE has to provide estimates of future business activities. However, there is an important requirement that forecasts be produced in a format that reflects the future. In order to produce forecasts that reflect a certain set of assumptions, forecasts are required to be written in a format that supports future information about such important business activities. Most forecast data are available online. Also there is a requirement that forecasts be produced with at least 7-12 additional lines of analysis to reflect a business needs for a particular business activity. A business is not the only segment of a market subject to the SCE. Therefore, business plans can contain any number of lines including market activities, changes in business conditions, developments in the business, and changes in demand and supply as well as other business facts and market changes. Some forecasts based on market assumptions were also published for the purpose so that users can interpret and analyze these forecast projections. SCE’s reports can be read at http://sedai.org/SCEV . The program of forecaster to forecast the future has a long history. Although the SCE has adopted similar programings in its various operations, the focus and the accuracy with which an SCE will be operated is different. While SCE is the source for all business plans and forecasts, the SCE is not the only source for actual business forecast data. A forecast that is based on forecast information on a network may not be completely accurate. For example, there may be a change in operating mode, conditions, economic conditions, future earnings per share that is not entirely accurate because forecast information is incomplete. Therefore, future forecasters may fail to take into account business changes and future events that could force the SCE to revise its forecasts, but they may also fail to
Forecasting MethodsThere are four basic types of forecasting methods: qualitative, time series analysis, causal relationships, and simulation.Qualitative TechniquesQualitative techniques are subjective or judgmental and based on estimates and opinions (Chase, 2005). These forecasts reflect peopleЎ¦s judgments or opinions and suggest likely conditions, such as peopleЎ¦s opinion about whether it will rain today. These forecasts are preferred when there is a desire to engage individuals within the organization with a key business process. A potential pitfall of this technique is that some individuals base their judgments of future events on historical data, which may not provide relevant demand patterns that are stable enough to warrant their use to forecast future events. Additionally, emerging demand patterns may be too unstable for a numeric approach. Consequently, intimate knowledge of the market should be the data source of choice.
There are numerous qualitative approaches to demand forecasting, following are some of the more common approaches:Grass-Roots Forecasting seeks input from people at the level of the organization that gives them the best contact with the event under study (Chase, 2005). This technique may consist of conducting a marketing study of sales representatives for their readings on current market conditions. The potential fault with this tool is that it is subject to the short-term perspectives of the sources. The source of the data may be unduly influenced by recent events. For example, a sales person who has had a good day may provide an overly-optimistic forecast for the future that does not accurately represent market conditions on the whole.
The Method
Grass-Roots Foretelling relies on a number of approaches. Many of these can be performed in one of three different ways. For example, the method may be performed by presenting a “sample” data set, or by presenting a series of “trend lines.” The samples are then subjected to a series of “trendings” to indicate new sales patterns. The tables presented are based on the “point demand” for and demand for a given item (such as prices). The trend lines are then presented at different times from each such point demand, thus being similar and reflecting the time the data was presented. By presenting the point demand data in several ways, the method can produce the following information or output:
The amount of sales the target market needs. The market’s “market need” of any specific product or service.
The specific category of the subject selling the product or service,
The subject’s target audience–a sample of sales representatives, for instance–(for the same subject),
the current market, and
the specific target target target market.
Grass-Roots Forecasting’s “Trend Line” method is a method that uses information gathered from different points in the market cycle by presenting a series of “trendlines” (defined as lines that appear in the data over time during the “trend line” data point selection process). The “Trend Line” data points consist of a series such as sales of a product or service, the number of units sold by the market, the source of a sales call, or the target market for a product or service. The “Trend Line” results represent the most market-changing event or product for each subject.
How to Use
For the purposes of this approach, I will use the following data collection techniques:
The field is a sample of sales representative sales representatives from a large, well performing industry. I will use the phrase “target audience” to show that the target audiences are large and that there are many sales representatives with whom the sales representatives are interacting in business daily. However the data will be subject to variation from one week to the next and will be different by topic.
To measure a sales representative’s target market for a given product, I will show how the sales representative will respond to a request regarding a product by noting the product’s name (for example, to provide a call back); if the call back is successful with the product’s name, or the product is accepted by the sales representative without any problem; and if the product meets a low sales target target, within the last 24 hours or less.
To measure sales representative’s target market for a given title, I will show how the Sales Representatives will respond to a sales call involving the subject by stating the subject’s name, the subject’s age, and the subject’s location. To measure total sales of all specific titles, I will use the
Historical Analogy: Forecasting based on historical analogy explores the possibility that past events can provide insights into the prediction of future related events. This method ties what is currently being forecasted to a similar item (Chase, 2005). For example, utilizing the sales pattern of black and white television sets to forecast color television sales. Economists relay on this type of forecasting model to forecast business cycles and related developments. This method could prove inaccurate if the forces that drove past events are no longer present.
Market Research Forecasting: This forecasting method collects data in a variety of ways such as surveys, interviews and focus groups to evaluate the