AI-Based B2B Enterprise Data Analysis and Automation System

Abstract

The present invention relates to an AI-based B2B enterprise data analysis and automation system that utilizes AI and machine learning to automate data analysis for B2B enterprises and to support or perform management and decision-making. The system collects B2B transaction data, customer behavior data, and market trend data in real time, automatically analyzes the collected data based on machine learning, and uses the analysis results to predict sales volume and market changes and derive business strategies. Furthermore, the system performs automated business decisions based on analysis or prediction results, automates B2B business processes such as sales, marketing, or supply chain management, and predicts and manages business risks through real-time data monitoring.

Description

Technology Field

[0001] The present invention relates to AI-based data analysis and automation technology, and more specifically, to an AI-based B2B corporate data analysis and automation system capable of collecting and analyzing corporate B2B transaction data, customer behavior data, and market trend data in real time, and using machine learning-based analysis results to perform B2B business process automation and risk management, such as demand forecasting, business decision-making, and sales, marketing, and supply chain management. Background Technology

[0002] In the business-to-business (B2B) environment, trading partners, terms and conditions, market conditions, customer responses, demand fluctuations, and supply chain situations are constantly changing. These changes have a direct impact on a company's sales strategies, marketing strategies, supply chain management, customer service, and management decision-making.

[0003] B2B companies hold or generate large amounts of data in the course of sales, marketing, supply chain management, customer service, and business management. This data may include data related to business-to-business transactions, data related to customer behavior or reactions, and data related to market changes or trends.

[0004] However, conventional B2B data analysis methods often relied on manual data entry or the classification and aggregation of data based on simple, predetermined criteria or fixed algorithms. Such methods struggle to provide sufficient analysis speed and accuracy when data volumes increase or market conditions change rapidly.

[0005] Particularly in B2B operations, due to the large transaction volumes and diverse factors influencing decision-making, it is difficult to quickly identify the relationships between transaction data, customer behavior data, and market trend data through simple manual analysis alone. As a result, companies faced the problem of failing to fully utilize their available data and relying on empirical judgment or retrospective analysis.

[0006] Conventional data analysis systems often generate results based on data at a specific point in time, making it difficult to immediately reflect situations where new data emerges or is updated. Consequently, companies frequently recognize current transaction conditions, customer reactions, or market changes only after they have already occurred, which can lead to delays in responding to them.

[0007] Furthermore, existing B2B data analysis systems are often operated individually by business area, such as sales, marketing, supply chain management, and customer service. In this case, there is a problem in that data generated from each business area cannot be utilized integrally and is used restrictively only within specific departments or business stages.

[0008] For example, if transaction-related changes identified during the sales process, customer responses identified during the marketing process, and demand or market changes identified during the supply chain management process are not linked to each other, it is difficult for a company to make consistent decisions from the perspective of the entire B2B business process.

[0009] B2B companies need to formulate appropriate business strategies by forecasting sales volume, market changes, and demand fluctuations. However, in conventional methods, predicting sales volume or market changes often involved humans manually reviewing past data and experience or performing simple forecasts based on fixed analytical criteria.

[0010] These conventional methods may experience reduced prediction accuracy when data variability is significant or when customer behavior and market trends interact in a complex manner. Furthermore, they have limitations in that it is difficult to quickly implement prediction results into actual business processes, such as sales, marketing, or supply chain management.

[0011] Meanwhile, in B2B transactions, it is sometimes difficult to fully grasp the overall transaction flow or market changes using only internal corporate data. If data exchange between companies is delayed or limited, changes in trading partners, demand, or the market may not be reflected in decision-making in a timely manner.

[0012] Conventional methods of inter-company data exchange often involve human verification and transmission of data, or rely on periodic reports or retrospective settlement data. Such methods lack real-time capability and have limitations in efficiently carrying out inter-company collaboration.

[0013] Furthermore, various business risks can arise in B2B business processes due to market changes, shifts in demand, and changes in transaction flows. While it is crucial to identify and manage these risks early, conventional approaches often involved responding reactively after the risks had already occurred.

[0014] In particular, if real-time changing data is not continuously monitored, it is difficult to predict the potential for business risks at an early stage. Consequently, companies may experience problems such as transaction delays, failures in demand forecasting, delays in market response, and decreased operational efficiency.

[0015] AI and machine learning technologies can be utilized to analyze large volumes of data and identify relationships, changing trends, and recurring patterns. By applying these technologies to B2B data analysis, companies can perform faster and more flexible data analysis compared to manual analysis or fixed algorithms.

[0016] However, simply applying AI or machine learning to data analysis alone is insufficient to fully achieve the automation of B2B business processes. In the actual operations of B2B companies, data collection, analysis, demand forecasting, decision-making, business automation, and risk management need to operate in an interconnected manner.

[0017] Therefore, there is a need for a system capable of collecting B2B transaction data, customer behavior data, and market trend data in real time, analyzing them based on machine learning, utilizing the analysis results for demand forecasting and automated business decision-making, and further automating B2B business processes such as sales, marketing, and supply chain management.

[0018] In addition, there is a need for a system capable of improving inter-company collaboration and automated operations by exchanging B2B data in real time and reflecting the exchanged data in analysis, prediction, and decision-making.

[0019] Furthermore, there is a need for an AI-based B2B enterprise data analysis and automation system that can predict and manage business risks through real-time data monitoring and enhance corporate operational efficiency by linking data-driven decision-making with business automation. The problem to be solved

[0020] The problem that the present invention aims to solve is to provide an AI-based B2B corporate data analysis and automation system that collects corporate B2B transaction data, customer behavior data, and market trend data in real time, and automatically analyzes the collected data based on machine learning to provide analysis results applicable to the corporate B2B business processes.

[0021] Another problem that the present invention aims to solve is to provide an AI-based B2B corporate data analysis and automation system capable of predicting sales volume and market changes using collected and analyzed data, and deriving business strategies based on the prediction results.

[0022] Another problem that the present invention aims to solve is to provide a system capable of performing automated business decisions based on data analysis results or demand forecast results, and automating at least one B2B business process among sales, marketing, or supply chain management based on said decisions.

[0023] Another problem that the present invention aims to solve is to provide an AI-based B2B corporate data analysis and automation system that predicts and manages business risks through real-time data monitoring and utilizes the risk prediction results for corporate decision-making.

[0024] Another problem that the present invention aims to solve is to provide a system capable of improving inter-company collaboration and automated operations by exchanging B2B data between companies in real time and utilizing the exchanged data for analysis, demand forecasting, or automated business decision-making.

[0025] Another problem that the present invention aims to solve is to improve conventional, limited analysis methods that rely on manual data input or fixed algorithms, and to provide a system that can enhance corporate operational efficiency through AI and machine learning-based data analysis and automated decision-making. means of solving the problem

[0026] An AI-based B2B corporate data analysis and automation system according to one embodiment of the present invention for solving the above problem is implemented by a computer device and includes a data collection unit that collects corporate B2B transaction data, customer behavior data, and market trend data in real time.

[0027] The above system includes a data analysis unit that automatically analyzes B2B transaction data, customer behavior data, and market trend data collected by the data collection unit based on machine learning.

[0028] The above system includes a demand forecasting unit that predicts sales volume and market changes using the analysis results of the data analysis unit and derives business strategies based on the prediction results.

[0029] The above system includes a decision automation unit that performs automated business decisions based on the analysis results of the data analysis unit or the forecast results of the demand forecasting unit.

[0030] The above system includes a business automation unit that automates at least one B2B business process among sales, marketing, or supply chain management based on the decision of the decision automation unit.

[0031] The above system includes a risk management unit that predicts and manages business risks through real-time data monitoring.

[0032] The above data collection unit can collect B2B transaction data through B2B data exchange between companies. In this case, the B2B data exchanged between companies can be utilized for analysis by the data analysis unit, forecasting by the demand forecasting unit, or decision-making by the decision automation unit.

[0033] The above data analysis unit can analyze B2B transaction data, customer behavior data, and market trend data together to generate analysis results that can be utilized for decision-making related to a company's sales, marketing, supply chain management, or customer service.

[0034] The above decision automation unit can perform automated business decisions based on at least one of the analysis results of the data analysis unit, the forecast results of the demand forecasting unit, and the risk forecast results of the risk management unit.

[0035] The above business automation unit can automate at least one business process among sales, marketing, or supply chain management based on data.

[0036] The above risk management department can predict business risks based on data changes identified by real-time data monitoring and support decision-making for managing the predicted business risks.

[0037] The above system can improve or optimize inter-company collaboration through real-time B2B data exchange and automated operations. Effects of the invention

[0038] According to the present invention, since B2B transaction data, customer behavior data, and market trend data of a company can be collected and analyzed in real time, data held or acquired by the company can be utilized more quickly for business decision-making.

[0039] According to the present invention, compared to methods where a person manually inputs data or analyzes data restrictively according to a fixed algorithm, the operational efficiency of a company can be increased through automatic analysis based on AI and machine learning.

[0040] According to the present invention, since sales volume and market changes can be predicted, companies can formulate or adjust business strategies based on the prediction results.

[0041] According to the present invention, automated business decision-making can be performed based on data analysis results or demand forecast results, thereby reducing manpower and time required in the decision-making process.

[0042] According to the present invention, at least one B2B business process among sales, marketing, or supply chain management can be automated, thereby improving the speed and efficiency of business processing for the company.

[0043] According to the present invention, since business risks can be predicted and managed through real-time data monitoring, companies can respond more quickly to the possibility of risk occurrence.

[0044] According to the present invention, B2B data between companies can be exchanged in real time and utilized for analysis, prediction, and decision-making, thereby improving inter-company collaboration and automated operations.

[0045] According to the present invention, data collection, data analysis, demand forecasting, automated decision-making, business process automation, and risk management can be performed as a single interconnected flow, thereby increasing the data usability of B2B companies and improving the efficiency of the entire business process. Brief explanation of the drawing

[0046] Figure 1 is a configuration diagram of an AI-based B2B corporate data analysis and automation system according to one embodiment of the present invention. FIG. 2 is a flowchart of an AI-based B2B corporate data analysis and automation method according to one embodiment of the present invention. Specific details for implementing the invention

[0047] Hereinafter, embodiments of the present invention will be described in detail. These embodiments are intended to aid in understanding the present invention, and the scope of the present invention is not limited by the following embodiments.

[0048] In this specification, “B2B” refers to an area related to transactions, collaboration, business processing, or data exchange between companies. The present invention may target data generated or utilized in a B2B business environment that includes transactions between companies.

[0049] In this specification, “B2B transaction data” refers to data generated or utilized in the course of business transactions or business processing between companies. The said B2B transaction data may be data that can be used for decision-making related to a company’s sales, marketing, supply chain management, or customer service.

[0050] In this specification, “customer behavior data” refers to data related to the behavior, reaction, or change of a customer or trading partner in the course of a B2B business. Customer behavior data can be analyzed together with B2B transaction data and utilized for decision-making that takes into account the reactions of the customer or trading partner.

[0051] In this specification, “market trend data” refers to data related to changes in the market, changes in demand, or external or environmental changes that may influence the formulation of a company’s business strategy. Market trend data can be analyzed together with B2B transaction data and customer behavior data to be utilized for predicting sales volume or market changes.

[0052] In this specification, “real-time” means that data is collected, analyzed, or reflected within a time range after it is generated or updated, within which it can be utilized for corporate decision-making or business processing. Therefore, “real-time” in this invention is not necessarily limited to immediate processing in a physical sense without any time delay.

[0053] In this specification, “automatic analysis” means that a computer device performs machine learning-based analysis on collected data to generate analysis results that can be utilized for decision-making, without requiring a human to manually judge each piece of data.

[0054] In this specification, “automated business decision-making” refers to the systematic calculation or execution of a company’s business processing direction, business strategy, or B2B business process processing content based on data analysis results, demand forecast results, or risk forecast results. Such automated business decision-making may include forms that support human decision-making or forms in which a system performs decision-making according to a defined processing flow.

[0055] In this specification, “optimization” is not necessarily limited to calculating a mathematically unique global optimal solution, but may mean deriving or applying a processing direction that is more suitable in terms of work efficiency, timeliness of decision-making, business process connectivity, or risk management compared to conventional manual processing or fixed processing methods, based on collected and analyzed data and prediction results.

[0056] The AI-based B2B corporate data analysis and automation system of the present invention may be implemented by a computer device. The computer device refers to an information processing device capable of collecting, analyzing, predicting, making decisions, automating business operations, and managing risks regarding B2B data.

[0057] The system of the present invention may functionally include a data collection unit, a data analysis unit, a demand forecasting unit, a decision automation unit, a business automation unit, and a risk management unit. Each of the above components is functionally separated for convenience of explanation, and in actual implementation, they may be implemented integrally within a single computer device or distributed across two or more computer devices.

[0058] The Data Collection Department collects the company's B2B transaction data, customer behavior data, and market trend data in real time. When key corporate data is generated or updated, the Data Collection Department provides it to the Data Analysis Department for use.

[0059] The data collected by the data collection unit is not limited to B2B transaction data but may include customer behavior data and market trend data. Accordingly, the data analysis unit can perform analysis that reflects the company's transaction situation, customer responses, and market changes, rather than analyzing based on a single type of data alone.

[0060] The data collection unit can collect B2B transaction data through B2B data exchange between companies. Data collected through such exchange can be analyzed in conjunction with internally held data, and the results of this analysis can be utilized for demand forecasting or automated business decision-making.

[0061] The Data Analysis Department automatically analyzes B2B transaction data, customer behavior data, and market trend data collected by the Data Collection Department using machine learning. By analyzing relationships, trends of change, or recurring patterns among the collected data, the Data Analysis Department can generate analysis results that can be utilized for corporate business processes.

[0062] The data analytics department is not limited to independently analyzing only B2B transaction data but can also consider customer behavior data and market trend data. Through this, the analysis results can be utilized for decision-making related to sales, marketing, supply chain management, or customer service.

[0063] For example, the Data Analysis Department can generate analysis results that can influence the company's business strategy or operational direction by considering changes in transaction-related data, customer behavior-related data, and market trend-related data together. In this case, the analysis results can be utilized by the Demand Forecasting Department, Decision Automation Department, Business Automation Department, or Risk Management Department.

[0064] The Demand Forecasting Department predicts sales volume and market changes using the analysis results of the Data Analysis Department. Based on machine learning-based analysis results, the Demand Forecasting Department predicts potential future changes in sales volume or market trends and utilizes these prediction results to derive business strategies.

[0065] The forecast results generated by the demand forecasting unit are not limited to simple data display or reporting but can be utilized to formulate strategies related to a company's sales, marketing, or supply chain management. As a result, companies can respond more quickly to changes in demand or the market.

[0066] The Demand Forecasting Department can derive business strategies using the analysis results generated by the Data Analysis Department. Here, business strategies refer to strategies that can be utilized to determine the direction of B2B business processes, such as sales, marketing, supply chain management, or customer service.

[0067] The Decision Automation Department performs automated business decisions based on the analysis results of the Data Analysis Department or the forecast results of the Demand Forecasting Department. Based on the analysis or forecast results, the Decision Automation Department can determine the direction of corporate business processing or make decisions for the execution or coordination of business processes.

[0068] The decision automation unit can perform automated business decisions based on at least one of the analysis results of the data analysis unit, the forecast results of the demand forecasting unit, and the risk forecast results of the risk management unit. Accordingly, decision-making can be made by considering transaction data analysis, demand forecasting, and risk forecasting together, rather than relying on a single piece of information.

[0069] The Business Automation Department automates at least one B2B business process among sales, marketing, or supply chain management based on decisions made by the Decision Automation Department. By ensuring that data-driven decision results are reflected in actual B2B business processes, the Business Automation Department can reduce the disconnect between data analysis and business execution.

[0070] B2B business processes automated by the business automation unit may include business processing steps related to sales, marketing, or supply chain management. The present invention is not limited to a specific single business area and can be applied to data-driven business automation of B2B enterprises.

[0071] The Business Automation Department can reduce the repetitive data verification, judgment, and task processing previously performed by humans by automating at least one business process in sales, marketing, or supply chain management based on data. As a result, it can reduce the company's labor and time costs and improve the consistency of business processes.

[0072] The Risk Management Department predicts and manages business risks through real-time data monitoring. Based on changes in collected data or the analysis results of the Data Analysis Department, the Risk Management Department determines the likelihood of business risks occurring and supports decision-making for risk management.

[0073] Business risks predicted and managed by the Risk Management Department may be related to B2B transactions, customer behavior, market trends, or changes in B2B business processes. By monitoring these data changes in real time, the Risk Management Department enables the company to respond to risks more quickly.

[0074] The risk forecast results from the Risk Management Department are provided to the Decision Automation Department and can be reflected in automated business decision-making. Accordingly, the system can make decisions by considering not only demand forecasting or the derivation of business strategies, but also risk management perspectives.

[0075] The system of the present invention exchanges B2B data between companies in real time, and the exchanged data can be utilized by a data analysis unit, a demand forecasting unit, or a decision automation unit. Accordingly, transaction flows, customer responses, or market changes that are difficult to identify using only internal company data can be reflected more quickly.

[0076] Data collected through B2B data exchange between companies can be analyzed by the Data Analysis Department as B2B transaction data, and the analysis results from the Data Analysis Department can be utilized by the Demand Forecasting Department to predict sales volume and market changes. Additionally, the aforementioned analysis or forecast results can be utilized by the Decision Automation Department for automated business decision-making.

[0077] The system of the present invention can improve or optimize inter-company collaboration through real-time B2B data exchange and automated operations. In this case, the optimization of inter-company collaboration may include analyzing, predicting, and reflecting the exchanged data in decision-making to more appropriately determine the processing direction of B2B business processes.

[0078] In one embodiment of the present invention, the system first collects corporate B2B transaction data, customer behavior data, and market trend data in real time through a data collection unit. Subsequently, a data analysis unit automatically analyzes the collected data based on machine learning.

[0079] Next, the Demand Forecasting Department predicts sales volume and market changes using the analysis results of the Data Analysis Department, and derives business strategies based on the prediction results. The Decision Automation Department performs automated business decisions based on the aforementioned analysis or prediction results.

[0080] Subsequently, the Business Automation Department automates at least one B2B business process among sales, marketing, or supply chain management based on the decisions of the Decision Automation Department. The Risk Management Department predicts and manages business risks through real-time data monitoring and, if necessary, can incorporate the risk prediction results into the decision-making of the Decision Automation Department.

[0081] In another embodiment of the present invention, the system exchanges B2B data between companies in real time and can utilize the exchanged data for automatic analysis, prediction of sales volume and market changes, or automated business decision-making. This can improve collaboration between companies and automated operations.

[0082] In the system of the present invention, input data such as B2B transaction data, customer behavior data, and market trend data can be used as input for machine learning-based data analysis. Output data such as analysis results, demand forecast results, risk forecast results, or automated business decision results can be utilized in B2B business processes such as sales, marketing, supply chain management, and customer service.

[0083] The system of the present invention can be configured so that data collection, analysis, prediction, decision-making, business automation, and risk management are not performed independently or in isolation, but rather as an interconnected flow. Accordingly, this reduces the problem of data analysis results not being reflected in actual business processes and enables data-driven B2B business operations.

[0084] The system of the present invention improves upon conventional, limited analysis methods that rely on manual input or fixed algorithms. In particular, through machine learning-based data analysis, it analyzes B2B transaction data, customer behavior data, and market trend data together, and utilizes the analysis results for demand forecasting, business strategy formulation, automated decision-making, and risk management.

[0085] The system of the present invention can increase the operational efficiency of a company. This is because, as data collection, analysis, prediction, decision-making, and business automation are performed within a single interconnected structure, the time and cost required for humans to manually verify and judge data can be reduced.

[0086] The system of the present invention can support companies in establishing more appropriate business strategies by predicting sales volume and market changes. Furthermore, by predicting and managing business risks through real-time data monitoring, companies can respond more quickly to market changes or changes in transaction conditions.

[0087] The system of the present invention can improve inter-company collaboration by utilizing B2B data exchange for analysis and decision-making. Accordingly, B2B companies can perform automated business operations that reflect the flow of data between companies, rather than relying solely on internal data from a single company.

[0088] Each embodiment and configuration described above is not limited to being mutually exclusive and may be combined or modified within the scope of the technical spirit and claims of the present invention. Industrial applicability

[0089] The present invention can be used for sales, marketing, supply chain management, customer service, data analysis, demand forecasting, market trend analysis, automated decision-making, and risk management tasks of B2B companies.

[0090] In addition, the present invention can be used for collaborative work based on B2B data exchange between companies, transaction-related data analysis work, demand forecasting work, and business process automation work.

[0091] Therefore, the present invention can be applied to industrial fields that utilize AI and machine learning to improve the data usability, business efficiency, market responsiveness, and risk management capabilities of B2B companies.

Claims

Claim 1 An AI-based B2B enterprise data analysis and automation system implemented by a computer device, comprising: a data collection unit that collects B2B transaction data, customer behavior data, and market trend data of an enterprise in real time; a data analysis unit that automatically analyzes the B2B transaction data, customer behavior data, and market trend data collected by the data collection unit based on machine learning; a demand forecasting unit that predicts sales volume and market changes using the analysis results of the data analysis unit and derives business strategies based on the prediction results; a decision automation unit that performs automated business decisions based on the analysis results of the data analysis unit or the prediction results of the demand forecasting unit; a business automation unit that automates at least one B2B business process among sales, marketing, or supply chain management based on the decision of the decision automation unit; and a risk management unit that predicts and manages business risks through real-time data monitoring. Claim 2 The AI-based B2B corporate data analysis and automation system according to claim 1, wherein the data collection unit collects B2B transaction data through B2B data exchange between companies. Claim 3 The AI-based B2B corporate data analysis and automation system according to claim 1, wherein the data analysis unit analyzes the B2B transaction data, the customer behavior data, and the market trend data together to generate analysis results that can be utilized for decision-making related to corporate sales, marketing, supply chain management, or customer service. Claim 4 The AI-based B2B corporate data analysis and automation system according to claim 1, wherein the demand forecasting unit predicts sales volume and market changes using the machine learning-based analysis results and derives a business strategy based on the prediction results. Claim 5 The AI-based B2B corporate data analysis and automation system according to claim 1, wherein the decision automation unit performs automated business decision-making based on at least one of the analysis result of the data analysis unit, the forecast result of the demand forecasting unit, and the risk forecast result of the risk management unit. Claim 6 The AI-based B2B enterprise data analysis and automation system according to claim 1, wherein the business automation unit automates at least one business processing process among sales, marketing, or supply chain management based on data. Claim 7 The AI-based B2B corporate data analysis and automation system according to claim 1, wherein the risk management unit predicts business risks based on data changes identified by real-time data monitoring and supports decision-making for managing the predicted business risks. Claim 8 A method for AI-based B2B corporate data analysis and automation, comprising: a step in which a computer device collects B2B transaction data, customer behavior data, and market trend data of a company in real time; a step in which the computer device automatically analyzes the collected B2B transaction data, customer behavior data, and market trend data based on machine learning; a step in which the computer device predicts sales volume and market changes using the results of the automatic analysis and derives a business strategy based on the prediction results; a step in which the computer device performs an automated business decision based on the results of the automatic analysis or the prediction results; a step in which the computer device automates at least one B2B business process among sales, marketing, or supply chain management based on the automated business decision; and a step in which the computer device predicts and manages business risks through real-time data monitoring.

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