Forecasting and multi-criteria optimization

Maximize your organisation’s efficiency with forecasting and multi-criteria optimization

Forecasting is the process of making predictions about future events or outcomes based on past data and trends. It is used in a variety of fields, including finance, economics, and marketing, to make informed decisions about the future.

Multi-criteria optimization is the process of finding the best solution to a problem when there are multiple conflicting objectives or criteria that need to be considered. This can be done using various optimization algorithms and techniques, such as linear programming, goal programming, and decision analysis. Multi-criteria optimization is often used in areas such as resource allocation, transportation planning, and risk assessment, where multiple objectives must be considered simultaneously.

What Is Business Forecasting?

Business forecasting entails making educated assumptions about certain business measures, whether they represent a company’s details, such as sales growth, or expectations for the economy as a whole. Financial and operational decisions are made based on economic realities and an uncertain future.

Forecasting is used by businesses to help them build business plans. Data from the past is collected and analyzed to identify patterns. Big data and artificial intelligence have revolutionized commercial forecasting approaches. A company forecast can be created using a variety of ways. All of the methodologies fall into one of two broad categories: qualitative or quantitative.

Types of Business Forecasting

In business forecasting, two types of models are used: qualitative models and quantitative models.

Qualitative Models

When the scope of the forecast was limited, qualitative models have often been successful with short-term forecasts. Qualitative predictions are expert-driven in the sense that they rely on market experts or the market as a whole to reach an educated agreement.
Although qualitative models can be effective in forecasting the short-term performance of businesses, goods, and services, they have limits owing to their dependence on opinion rather than measurable evidence. Among the qualitative models are:

  • Market research—a process of polling a large number of individuals about a given product or service to anticipate how many people will buy or use it once it is released.
  • Delphi method—gathering broad opinions from field specialists and putting them into a prediction.
Quantitative Models

Quantitative models ignore the expert aspect and attempt to exclude the human dimension from the study. These techniques are simply focused with statistics and avoid the erratic behavior of the people behind the numbers. These methodologies also attempt to forecast where variables such as sales, GDP, housing prices, and so on will be in the long run, measured in months or years.
Quantitative models include:

  • The indicator approach. An indicator method is predicated on the link between particular measures, such as GDP and unemployment rate, being relatively constant over time. By following the relationships and then the leading indicators, you may use the leading indicator data to estimate the performance of the lagging indicators.
  • Econometric modeling. Econometric modeling is a more rigorous mathematical approach to indicator strategy. It analyzes the consistency and significance of relationships between datasets to create custom indicators for a targeted approach. Econometric models are often used in academia to analyze economic policy.
  • Time series methods. Time series forecasting involves analyzing past data to predict future occurrences. Different techniques may weight recent data more heavily or exclude outlier points. Time series forecasting is a common and cost-effective form of business forecasting with no inherent advantage over other approaches.

What is Multi-criteria optimization?

Multi-criteria optimization (also known as multicriteria decision-making or multiobjective optimization) is the process of finding the best solution to a problem when there are multiple conflicting objectives or criteria that need to be considered. This can be a complex process because each objective or criterion may have different weights or priorities, and finding a solution that optimally balances all of these objectives can be challenging.

There are several approaches to multi-criteria optimization, including:

Linear programming. This approach involves finding the optimal solution to a problem by maximizing or minimizing a linear objective function subject to a set of linear constraints.

Goal programming. This approach involves finding the optimal solution to a problem by setting goals for each of the objectives and then trying to achieve those goals as closely as possible.

Decision analysis. This approach involves analyzing the trade-offs between different alternatives and choosing the one that provides the best overall balance of benefits and costs.

Analytic hierarchy process (AHP). This approach involves breaking down the problem into a hierarchy of objectives and criteria, and then using a systematic process to compare and evaluate the different alternatives.

There are also many other techniques that can be used in multi-criteria optimization, such as evolutionary algorithms, fuzzy logic, and neural networks. The choice of technique will depend on the specific characteristics of the problem and the resources available to solve it.

Why businesses need multi-criteria optimization?

Businesses may need to use multi-criteria optimization in order to make informed decisions when there are multiple conflicting objectives or criteria that need to be considered. For example, a company may need to decide which suppliers to use for raw materials. The company may have several criteria that are important to consider, such as cost, delivery time, quality, and environmental sustainability. Using multi-criteria optimization techniques can help the company find the best overall solution that optimally balances all of these conflicting objectives.

Another example is in marketing, where a company may need to decide which marketing channels to use to reach potential customers. The company may have several objectives, such as maximizing reach, increasing brand awareness, and driving sales. Multi-criteria optimization techniques can help the company identify the marketing channels that will be most effective at achieving these objectives.

Overall, multi-criteria optimization can help businesses make more informed, data-driven decisions that take into account all of the relevant objectives and criteria. This can help businesses make better use of their resources and achieve their goals more effectively.