Advantages and Disadvantages of qualitative and quantitative forecasting techniques

Planning is essential for proper and effective management, and forecasting is an important subset of the planning function (Choi, 1999). Rahmlow and Klimberg (2002) identified some of the most important decision areas as well as the impact that forecasting has on these areas within an organization, the results are displayed in Table 1.
In the food retail industry, a major contributing factor to the successful operation and optimal stock management is forecasting (Arunraj and Ahrens, 2015). Kokkinou (2013) states that, as restaurant operators deal with highly perishable products, overestimation of sales can lead to unnecessary labor costs and stock wastage. Underestimation of sales can lead to unsatisfactory customer service and loss of revenue
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Qualitative forecasting methods are often applied when limited data is available or when time is insufficient. These forecasting methods usually rely on the judgment of experts within a certain field. They usually take less time to construct and can be relatively inexpensive and easy to understand. A major disadvantage of qualitative forecasting is that it can be largely opinionated and as a result be subjective.
Quantitative forecasting methods rely on historical data to predict the future by finding trends and relationships in the historical data. Quantitative methods can further be classified into time series and causal methods. Time series methods are based on the assumption that past occurrences and behavior has some relevance in the future. They do not focus on what caused this behavior but rather assume that whatever caused this behavior will continue doing so in the future. Predictions are made by determining the impact of trends and seasonal factors on past data and extrapolating this behavior into the
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These models are known as Hybrid Models. Hybrid Models are used to improve forecast accuracy by combining two or more forecasting methods with alternative capabilities to accommodate for the limitations that may be present when only one of the methods is used (Arunraj and Ahrens, 2015).
Selecting the appropriate forecasting method(s) is a crucial part of the forecasting process. As made clear in the preceding section, a wide variety of methods is available, each with its own limitations and capabilities. Armstrong (2001) identified some key principles and factors to consider in the selection of appropriate forecasting methods.

The principles are listed as follows:
• Use forecasting methods that contain methodical and detailed steps that can be explained and replicated.
• If sufficient data is available, use quantitative rather than qualitative methods.
• If large changes in the forecasts can be expected, use causal methods instead of time-series methods.
• Unless considerable proof is present that a complex method will improve forecasts, use simple forecasting methods.
Some factors to consider during the decision process of selecting an appropriate method

Forecasting techniques fall into two categories of methods: quantitative and qualitative. Quantitative forecasting relies on data list past volumes -- purchase, sales, traffic, for example. Quantitative techniques do not rely on opinions or imagination. They are purely statistical methods for forecasting.

1

Authority

The main advantage of quantitative techniques is that the forecast has a solid recorded base of actual data. This lends the results a of projection authority. It is hard to dispute a forecast like “we expect to sell 400 widgets in March because we sold 400 last March.” A forecast based on opinion, such as “industry opinion indicates that we will sell 400 widgets in March” is open to dispute. Such a forecast leads the receiver of the projection to question who the experts are and what the foundation for their opinion is.

  • The main advantage of quantitative techniques is that the forecast has a solid recorded base of actual data.
  • A forecast based on opinion, such as “industry opinion indicates that we will sell 400 widgets in March” is open to dispute.

Quantitative techniques for forecasting have more to offer than just copying past data into a projection. Trend analysis provides a modifying factor to bare numbers. For example, sales of 400 widgets last March came after February sales figures of 380 and were followed by April’s figures of 420. If a steady increase, decline or cycle in numbers forms a pattern, quantitative forecast will adjust past data to fit in with the pattern. Again, data manipulation has to be backed up by evidence of actual trends in order to be credible.

  • Quantitative techniques for forecasting have more to offer than just copying past data into a projection.
  • If a steady increase, decline or cycle in numbers forms a pattern, quantitative forecast will adjust past data to fit in with the pattern.

Quantitative methods are usually simpler than qualitative techniques. However, this does not mean that all quantitative forecasts are based on direct application of one or two factors researched from past behaviour. Analysts construct models to perform forecasts and these models may contain many different factors that adjust historical data to produce the projection. These other factors modify the results of the bare historical data and so they are called “modifiers.”

  • Quantitative methods are usually simpler than qualitative techniques.
  • However, this does not mean that all quantitative forecasts are based on direct application of one or two factors researched from past behaviour.

The collection of source data is not a mandatory part of quantitative methods. The analyst undertaking the forecast may use data collected by others, possibly for different purposes. This data, however, should not reduce the authority of the forecast. Information imported into the project from other sources should come from authoritative organisations, like government, or supranational bodies, academic institutions or respected Non-Governmental Organisations. The analyst needs to guarantee that the data upon which the forecast was based is correct. If she did not oversee data collection, there is a risk the data could have been forged, or manipulated to prove someone else’s goals and so it would not be a viable base for any forecasts.

  • The collection of source data is not a mandatory part of quantitative methods.
  • This data, however, should not reduce the authority of the forecast.

Quantitative techniques for forecasting are usually cheaper to implement than qualitative methods. This is because the main resource of the forecast is the data. Beyond the cost of data gathering, there is little extra expense involved. Qualitative methods require the use of surveys, expert opinion and alternative scenarios, which require consultants and paid advisors to compile.

  • Quantitative techniques for forecasting are usually cheaper to implement than qualitative methods.
  • Qualitative methods require the use of surveys, expert opinion and alternative scenarios, which require consultants and paid advisors to compile.

What are the advantages of qualitative forecasting technique vs quantitative techniques?

More Flexibility in Forecasting This can improve the quality of a forecast because quantitative data cannot capture nuances that years of experience can detect. For example, if a small business is planning to open a new store, the quantitative data may show strong historical sales trends for the area.

What are the advantages and disadvantages of quantitative sales forecasting?

Advantages of quantitative forecasting:-Numerical so easy to interpret and easy to analyse for example graphs can be made. -Data can be objectively interpreted and bias is often not an issue. Disadvantages of quantitative forecasting:-May lack detail.

What are the main advantages that quantitative techniques for forecasting have?

Advantages of Quantitative Forecasting Addresses Historic Data When conducting quantitative methods, businesses are able to objectively address the company's history. From revenue and sales to expenses, businesses have the unbiased past data they need to make informed decisions about the company's future.

What are the advantages of quantitative techniques?

Quantitative methods can provide valuable insight to the ordering of reality and the materialized discourses. Furthermore, they can mitigate personal bias. They cannot provide in-depth understanding of the analyzed items due to the inherently reductive nature of classification.