An explainable machine learning model to optimize demand forecasting in Company DEOS

Nowadays, having an accurate demand forecast is extremely important as it allows the company to manage resources in an optimal way and thus achieve greater productivity. There is a large demand for accurate forecasting, and utilizing artificial intelligence can help companies gain a better understanding of their market. In this research presentation, Machine Learning (ML) is used to optimize demand forecasting. The data collected was trained and due to the available data rate, the Cross-Validation technique was used to avoid overfitting. Using time-series, it will be possible to predict future sales for the first trimester of 2021. Finally, the impact of the ML tool on the deviation of the company's demand forecast was evaluated using indicators of accuracy (forecast accuracy) and bias (forecast bias)

Forecasting and Clustering of Cassava Price by Machine Learning (A study of Cassava prices in Thailand)

Forecasting and Clustering the price of cassava is essential for agriculture, but the difficult part of forecasting is price fluctuation, in which the fact of prices is going up and down and be changed monthly. The paper proposes to forecast  the price of Cassava by machine learning. The process had been calculated by the price of Cassava from January 2005 to February 2022, which has been collected for 17 years by the Office of Agricultural Economics, Ministry of Agriculture, and Cooperatives. The research on forecasting found that the method of Support Vector Machine including using add-on feature with Garlic Price and Potato Price showing the Root Mean Squared Error (RMSE) with the lowest point as of 0.10. If comparing to the Conventional method with the equal database. The result shows that the proposed method demonstrates the value of the Mean Absolute Percentage Error (MAPE) as 3.35%, it displays more effectively as 0.61%. For the final process of clustering the price by analyzing with K-mean, the result came up with a peak pricing period in December of 14.08%. Subsequently, the agricultures would apply the research result to implement their planting plan for profit-making

Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

Product placement of top-selling items in highly visible aisles inside supermarkets plays a crucial role in enhancing customer shopping experience. Moreover, it is important for retailers to assure that their customers can effortlessly navigate the store and locate the items they are searching for in a timely manner. The research proposes a novel and effective approach that combines two methods; the SARIMAX model for forecasting sales of each product based on historical data; by using the predicted result, placing the most demanding item in highly visible aisles. And the use of Graph Database Management Systems (GDBMS) such as Neo4j to find the shortest path for consumers to navigate throughout the store to finish the shopping as per their shopping list. By leveraging the power of data analytics and machine learning, retailers can make data-driven decisions that result in improved sales andcustomer satisfaction. Retailers investing in these technologies and strategies will likely see a significant increase in customer satisfaction and sales

Forecasting Accessory Demand in the Automotive Industry

The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales

Demand Forecasting for Alcoholic Beverage Distribution

Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable resource to a company. While difficult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting demand, which are time of year and customer. For each of the two products, models were built to predict demand for three major customers. For each product/customer combination, this paper compares time series and deep learning models to a naive model to see if the prediction accuracy can be improved. For five out of six products, the time series models reduced error by 2.5–66.7% compared to the naive models. Also, for one product, a hybrid CNN model developed for this paper outperformed the time series models by 3–10% and reduced error by 49% compared to the naive models

Time series segmentation based on stationarity analysis to improve new samples prediction

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation

Prediksi dan visualisasi penyakit COVID-19 menggunakan kombinasi Prophet dan GeoPandas

Covid-19 is spreading very rapidly. Indonesia is one of the countries with the highest cases in Southeast Asia. The purpose of this research is to use machine learning models with the help of tools such as Prophet to predict the trend of the Covid-19 outbreak in Indonesia. Obtained data will be visualized using a Geographic Information System (GIS) with Geopandas, which is used to visualize the spread of Covid-19 in Indonesia. Predictions with three tuning methods using Prophet with trend flexibility and holiday effects scored the best, with 0.68 for RMSLE and 1070 for MAE. Based on the use of Geopandas for Covid-19 cases in Indonesia, Geopandas can be used to visualize geospatial data effectively.COVID-19 menyebar dengan sangat pesat. Indonesia menjadi salah satu negara dengan kasus tertinggi di Asia tenggara. Tujuan penelitian ini adalah dengan memanfaatkan model dalam machine learning menggunakan bantuan tools seperti Prophet untuk memprediksi tren wabah COVID-19 di Indonesia. Data yang diperoleh divisualisasikan menggunakan Sistem Informasi Geografis (SIG) dengan Geopandas untuk memvisualisasikan persebaran COVID-19 di Indonesia. Prediksi dengan tiga metode tuning yang dilakukan Prophet dengan trend flexibility dan holiday effect mendapat skor yang paling baik 0.68 untuk RMSLE dan 1070 untuk MAE. Berdasarkan penggunaan Geopandas untuk kasus COVID-19 di Indonesia, Geopandas dapat digunakan untuk memvisualisasikan data geospasial dengan efektif