please rephrase the following context:
One of the ways to reduce the pollution resulting from the supply chains of various products and services is to reduce all types of waste in business process management.
Failing to plan supply chain flows leads to the wastage of financial resources, energy, workforce, equipment, and excessive use of substances harmful to nature. They also cause an increase in greenhouse gases and an increase in global temperature. 
By assuming knowing the future, it is possible to produce, transport, and store exactly the amount of goods that are needed. It is also possible to prevent the waste of labor and energy by carefully planning work shifts. Preventing the production of goods or services without demand and reducing waste resulting from them is another benefit of knowing the future and planning for it.
Giunipero et al. and Mentzer indicated that supply chain management (SCM) was initially introduced by "J.W. Forrester" in the 1960s. The author, in the book Industrial Dynamics, demonstrated that the success of companies is inherently dependent on the optimum management of four flows of information, materials/products, labor, and capital".
The profit from planning future flows of the supply chains originates from the elimination of waste and the increase of strategic correlation in managing flows of material/products, information, capital, and labor. Subsequently, optimum planning for the future helps businesses create economic and environmentally competitive advantages, allowing them to contest for more market share.
Businesses need to estimate future conditions using quantitative parameters in planning because forecasting is a critically vital part of an effective planning for the future. Demand (sales) is one of the most significant parameters used in planning for the future. Demand (sales) time-series prediction can be used as an estimator in planning for optimal management of logistics, warehousing, liquidity, energy, workforce, waste disposal plans, etc.
With the emergence of three elements of machine learning methods, big data, and modern high-performance computers, we can imagine that in the near future, more accurate predictor and planner systems will be used in operating businesses. In order to proceed with this purpose, management researchers must utilize and test machine learning theories in practice. Such activities lead to the advancement of the applied theories of machine based autonomous systems in different fields.
Sales prediction, using historical sales values, product characteristics, and environmental parameters provides a short-term or long-term estimate of the future performance of a business, which can be used to prepare financial and business plans suitable for the future. (Jimens 2017)
According to previous comparative research, it is clear that predictions acquired from machine learning methods can be more accurate than classic statistical methods in the presence of abundant data. Therefore, in applications where a large number of measured features of a phenomenon are available, it is optimal to use machine learning methods to exploit the inherent value of big data.
The quality of big data and feature engineering, which provide inputs for machine learning models, is critical. In prediction using machine learning methods, many factors affecting sales can be considered in one model. (Pavlishenko 2019)
Developed machine learning models discover patterns in historical data and use those patterns to generate predictions, predictions based on historical data. (Daridi 2021)
Therefore if we can obtain a prediction accurate enough to be a basis for a secondary supply chain flow optimization, we can gain economic and environmental competitive advantages.
In the meantime, many predictive methods have been developed and examined to make predictions using big data and machine learning, each of which has its advantages and disadvantages. The important matter is that the accuracy of different methods depends significantly on the application. Therefore, in improving predictions, methods can be tested in each specific application and classified in terms of accuracy.
 To achieve this goal, it is necessary to test several predictive models in commercial applications and compare them to establish a decision basis for choosing methods. It is also essential to use the numerous data available in commercial enterprises and compare their predictive ability to draw a conclusion.
The thing that makes the need for such ratings inevitable is that many methods of artificial intelligence do not have the same accuracy in dealing with types of data and also types of applications. For example, it cannot be said with certainty that hypothetical model A has the best accuracy in time series forecasts. Because it has been proven that according to the type of input data, the size of available data, the parameter being predicted, application, data quality, and many other cases, different methods of artificial intelligence, behaviors, and especially different accuracies of They show themselves. Therefore, as mentioned in the background section of the research, there is always a lot of research with the aim of different rankings, in different fields, with the aim of discovering the best data, the best methods, etc., in order to use them can overcome the barrier of ambiguity in finding the best method.

In this research, we use and observe features and selected machine-learning methods in the application of sales (demand) prediction of a retailer to establish a basis for ranking them (both features and methods).
Sales forecasting is an integral part of modern business intelligence. Sales forecasting can be a complex problem, especially in cases where we face a lack of data or the dispersion of existing data. (Pavlishenko, 2019) (Placeholder1; 1, 4)