Forecasting Methods

Forecasting Methods

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Introduction

All 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:
„X What new economic, technical, or sociological forces is the organization likely to face in both the near and long term?
„X When might these forces impact the firm¡¦s objective environment?
„X Who is likely to be first to adapt to each competitive challenge?
„X 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 ¡V 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 ¡§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 firm¡¦s value adding activities.¡¨(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 ¡§cooking the books¡¦ or reporting performance data that shows conformance with plans versus actual events (Makridakis, 1989).
Forecasting Methods
There are four basic types of forecasting methods: qualitative, time series analysis, causal relationships, and simulation.
Qualitative Techniques
Qualitative techniques are subjective or judgmental and based on estimates and opinions (Chase, 2005). These forecasts reflect people¡¦s judgments or opinions and suggest likely conditions, such as people¡¦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.

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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:
„X 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.
„X 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.
„X Market Research Forecasting: This forecasting method collects data in a variety of ways such as surveys, interviews and focus groups to evaluate the purchase patterns and attitudes of current and potential buyers of a good or service. Designers of goods and services use this method to understand their current customers and the buyers they would like to serve.
„X Dlephi Method: The Delphi method compiles forecasts through sequential, independent responses by a group of experts to a series of questionnaires. The forecaster compiles and analyses the respondents¡¦ input and develops a new questionnaire for the same group of experts. This sequence works towards consensus that reflects input from all of the experts while preventing any one individual from dominating the process (Chase, 2005).

Quantitative Techniques
Quantitative forecasting techniques transform input in the form of numerical data into forecasts using methods in one of three categories. Each category of quantitative forecasting methods assumes that past events provide an excellent basis for enhancing the understanding of likely future outcomes.
„X Time Series Analysis: Time series analysis is based on the premise that data relating to past demand or performance can be used to predict future demand. Examples of this method include:
a. Simple moving average, where a time period containing a number of data points if averaged by dividing the sum of the point values by the number of points.
b. Regression analysis, where the average relationship between a dependent variable, sales for example, and one or more dependent variables, price or advertising for example, is estimated by fitting a straight line to past data to relate the data value to time.
c. Trend projections, a forecasting technique that relies primarily on historical time series data to predict the future. This method involves fitting a mathematical trend line to the data points and then projecting it into the future.
„X Causal Studies: Causal studies look for causal relationships between leading variables and forecasted variables. This method tries to understand the system underlying and surrounding the item being forecast such as the affect of advertising, quality and competition on sales (Chase, 2005).
„X Mathematical or Simulation models: Simulation models are what-if models that attempt to simulate the effects of alternative management policies and assumptions about the firm¡¦s external environment. They try to represent past behavior in a valid mathematical relationship and then alter that data to project future events. Most financial models are simulation models. These models are effective in performing a variety of what-if analyses that assist management in determining the best course of action for the company. Technological advances in computers have allowed more and more companies to build and utilize modeling for planning and decision-making efforts (Chase, 2005).
Mattel and Demand Forecasting
With the holiday season upon us, many children are anxiously awaiting the appearance of this season¡¦s ¡§hot¡¨ new toy under the Christmas tree. Each year, toy makers such as Mattel must make a set of important decisions that will set the course of their firm¡¦s economic performance. Many months prior to the arrival of the Christmas season, Mattel must decide which toys will likely be ¡§hot¡¨ and which will be ¡§dogs¡¨. They must carefully balance their supply of these ¡§hot¡¨ toys with consumer demand or they will be faced with unhappy customers or worse yet, stuck with slow moving merchandise. This is not an easy task given the long manufacturing and distribution lead times of the toy manufacturing industry.
To meet consumer demand for the current holiday season, toy manufacturers must ramp up production by mid-year to ensure that they will have sufficient numbers of best selling toys and few slow movers (Pereira, 2005). A major gap in this equation is presented by the fact that many adults really don¡¦t understand what children will desire by the time Christmas arrives. Speaking from personal experience, my kids want every toy they see advertised on television between September and Christmas to appear under their tree. Unfortunately, what adult¡¦s value has little to do with what children demand. While adults may feel that an illuminated globe that talks too you is a very cool educational toy, the kids tend to lean towards Tickle Me Elmo. To bridge this gap, Mattel utilizes market research to predict which toy will be a winner.
Each year, Mattel submits their new products to evaluation by the ultimate consumers ¡V the kids. Utilizing a two stage process, the toys are evaluated for their appeal to the target market. The first stage of the process involves selecting a specific mix of 100 children from childcare centers to evaluate new toys. The children are divided into focus groups with ¡§equal representation from those who like action figures, board games, construction toys, dolls, and arts and crafts.¡¨ (Pereira, 1997). In June, the children are polled and asked to rate their top three choices from among the toys presented in each category. By the end of June, the focus groups reduce the 380 proposed toys to a collection of 63 finalists ¡V the top three in each of the 21 categories. In the second stage, the finalist toys are shipped to KinderCare Learning Centers around the country where specially trained teachers observe which toys the children prefer and cast secret ballots ranking their favorites (Pereira, 1997). I know a three year old and a five year old who would love to be a part of this focus group!
Conclusion
It is evident that demand forecasting is part art and art science. It begins with an understanding of the organization¡¦s decision making needs and proceeds to a study of data to determine how the best forecasting tool can be developed to serve the organization¡¦s business needs. Each piece of analysis creates new information, adding richness and depth to the overall business thought process.
References
Chase, R. et al. (2005). Operations Management for Competitive Advantage, 11th edition. McGraw-Hill Companies: New York.
Makridakis, S. et al. (1998). Forecasting Methods and Applications, 5th edition. John Wiley & Sons: New York.
Pereira, J. (1997). To these Youngsters, Trying Out the Toys is Hardly Kids Play. Wall Street Journal, 12/17/97.
Ross,D. (1995). Distribution Planning and Control. Chapman & Hall: New York.
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