360° Retail Business Analytics by Adopting Hybrid Machine Learning and a Business Intelligence Approach

Abstract

Business owners and managers need strategic information to plan and execute their decisions regarding business operations. They work in a cyclic plan of execution and evaluation. In order to run this cycle smoothly, they need a mechanism that should access the entire business performance. The sole purpose of this study is to assist them through applied research framework-based analysis to obtain effective results. The backbone of the purposed framework is a hybrid mechanism that comprises business intelligence (BI) and machine learning (ML) to support 360-degree organization-wide analysis. BI modeling gives descriptive and diagnostic analysis via interactive reports with quick ad hoc analysis which can be performed by executives and managers. ML modeling predicts the performance and highlights the potential customers, products, and time intervals. The whole mechanism is resource-efficient and automated once it binds with the operational data pipeline and presented results in a highly efficient manner. Data analysis is far more efficient when it is applied to the right data at the right time and presents the insights to the right stakeholders in a friendly, usable environment. The results are beneficial to viewing the past, current, and future performance with self-explanatory graphical interpretation. In the proposed system, a clear performance view is possible by utilizing the sales transaction data. By exploring the hidden patterns of sales facts, the impact of the business dimensions is evaluated and presented on a dynamically filtered dashboard.

Industry 4.0 technologies for cultivated meat manufacturing

“…AI, ML and BI can be used to track key performance indicators, identify trends and patterns in the data, and generate insights that can help inform regulatory decisions and strategies (Alqhatani et al, 2022; Kler et al, 2022). BC can enable regulatory agencies and other stakeholders to verify that CM products are being produced and distributed in compliance with all relevant regulations easily (Xu et al, 2022).…”

Enhancing Operational Efficiency: Integrating Machine Learning Predictive Capabilities in Business Intellgence for Informed Decision-Making

“…Predictive analysis relies on historical data and models to forecast future trends and events. Predictive analysis methods include time series analysis, regression analysis, and machine learning algorithms [5] .…”

Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility

“…A major retail chain adopted the suggested framework as a way to improve their demand forecasting accuracy as well as to optimize inventory management to solve the difficulties caused by demand volatility. For the retail industry to avoid overstocking, understocking, and the ensuing financial losses, it is crucial to estimate consumer demand appropriately [45,46]. The retail chain understood that conventional demand forecasting techniques frequently fell short of accurately reflecting the complex interactions between consumer behavior, outside variables, and supply chain performance.…”