The Delphi technique is a qualitative forecasting method that allows experts to create accurate forecasts in uncertain conditions. It is based on estimates and opinions, making it a subjective approach. On the other hand, time series forecasting is a quantitative technique that uses statistical analysis of past sales to predict future outcomes. However, this method may be limited in uncertain conditions. Regardless, business forecasting can be applied in various contexts by different types of businesses (Chase, 2003, p. 364).
The text on Business and Economic Forecasting, page 1, discusses the importance of effective forecasting in determining sales. It emphasizes that forecasters rely on the assumption that the past is a reliable indicator of the future. However, it stresses the need for forecasters to consider new information like changing economic conditions and globalization when developing business forecasts based on past sales. The convergence of globalization and an economic slowdown has resulted in heightened uncertainty for businesses.
In the fast-paced world of today, economies worldwide are constantly evolving, with new markets emerging and existing ones transforming. As a result, the demand for products is often unpredictable. To navigate these uncertainties, businesses require flexibility and adaptability in their forecasting approaches (Chase, 2003, p. 472). In the dynamic global marketplace, organizations frequently face unique and unprecedented circumstances. During such periods, modern business forecasting methods can be especially valuable. These methods can be broadly categorized into two groups: qualitative methods and quantitative methods.
Qualitative analysis involves using an intuitive and knowledge-based approach to make estimated forecasts. When qualitative information is lacking, quantitative techniques are utilized. On the other hand, qualitative techniques rely on data analysis (Nava, 2000, p. 8). The Delphi forecasting method encompasses various techniques such as the executive committee, the Delphi method itself, surveys of the sales force and customers, historical analogy, and market research.
The main goal of the majority of Delphi applications is to reliably and creatively explore ideas or generate appropriate information for decision-making. The Delphi Method is built on a structured process that aims to gather and refine knowledge from a group of experts through a series of questionnaires, with controlled opinion feedback interspersed (Chase, 2003, IPPP). The Delphi method is a modified version of the executive committee approach, characterized by indirect, iterative, and structured interaction.
The Delphi method involves selecting a panel of experts who are each provided with a set of questions or issues to answer. Once the experts have responded, their answers are sent to a coordinator or monitoring group who did not participate in the initial stages of the process. This group provides feedback to the other experts without revealing the source of each response. The experts then review the feedback and provide their responses again. This cycle can continue until a consensus is reached among the group.
The group may come together to reach a final consensus (Nava, 2000, p. 8). The most commonly used method for forecasting is time series techniques. These techniques utilize statistical methods to predict future outcomes based on past data. Quantitative techniques are preferred when relevant data is accessible. The underlying assumption is that the historical pattern will persist in the future. The primary types of time series forecasting are average smoothing and exponential smoothing. Moving average, on the other hand, is a sequence of arithmetic averages.
There are multiple methods for forecasting sales for the next year. One method is calculating the moving average, where the actual sales from a specific number of previous years are added up and divided by the number of years considered. Another method is the weighted moving average, which assigns each previous year a weight that should sum up to one. In this approach, more recent years have a higher weight due to their perceived significance (Nava, 2000, p. 13). Additionally, there is a subtype called exponential smoothing within the weighted moving average technique. Exponential smoothing combines the actual variables (such as sales) from the current year with the weighted forecast of that variable for that particular period to determine the forecast for a new year.
Despite its ease of computation, the moving average method is limited in its consideration of ratios based on seasonal effects and cycles (Nava, 2000, p. 14). However, both the Delphi technique and Time series forecasting are valuable tools for forecasting in specific situations. The Delphi technique is especially beneficial for short-term forecasts that depend on experts familiar with specific issues (Nava, 2000, p. 8).
One of the challenges with the Delphi technique, along with other qualitative techniques, is finding competent employees who can offer expert opinions and judgments, and then to get these experts to reach a consensus on a prediction (Nava, 2000, p. 9). When past data is sufficient, quantitative forecasting is typically favored due to the limitations of qualitative techniques (Nava, 2000, p. 12). However, in uncertain circumstances, the Delphi technique offers significant flexibility. By employing the Delphi technique, professionals in a specific field can often achieve an innovative and perceptive agreement.
The Delphi technique is a valuable tool for business forecasting in uncertain conditions, compared to time series forecasting. Time series forecasting may be less useful when uncertainty is high due to its qualitative nature. Predicting future sales based on past sales can be challenging when conditions were more certain. Firstlings Inc., a company that manufactures information quality and postal automation software, assists companies in ensuring clean, accurate, and reliable data for their corporate databases.
Firstlings products (www.firstlings.com) are utilized by over 6,000 customers worldwide. Due to the current global economic downturn and increased business uncertainty, organizations are reassessing their goals and strategies. Like other companies, my company is also compelled to anticipate the future in order to allocate investments, introduce new offerings, explore innovative approaches to developing and utilizing human resources, and more. These critical decisions rely on a sales forecast, which presents the most crucial and challenging aspect of management.
Forecasting is crucial for businesses as it helps predict sales and manage expenditures. At Firstlings, we heavily rely on quantitative methods for business forecasting. These methods take into account various factors such as accuracy level, investment sections, time horizon, capital investment decisions, product changes, style, quality, price fluctuations, labor issues, available data and information, and product life cycle position to accurately forecast future sales.
The company Firstlings relies on past sales and times to forecast future demand for products and services. They use quantitative forecasting tools to effectively determine which specific products or services will be released at a particular time. However, these methods have not been as successful in short-term forecasts. To improve their predictions in a volatile marketplace, Firstlings could consider using qualitative forecasting techniques like the Delphi technique.
