CRM-driven intelligent supply chain management system
CRM-driven intelligent supply chain management systems solve the problem of market uncertainty in supply chain management through data mining and predictive models, enabling scientific analysis support and personalized services, and improving the flexibility and production efficiency of the supply chain.
Patent Information
- Application Number
- CN202511092732.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-05
- Publication Date
- 2025-11-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing technologies lack scientific analytical methods and technical support in supply chain management, leading to sales risks for enterprises due to market uncertainties, such as stockouts, inventory backlogs, and product styles failing to keep up with market demand.
A CRM-driven intelligent supply chain management system, combining operational and analytical CRM, uses data mining and predictive models to forecast future demand, enabling convenient access and sharing of customer information, customer segmentation and differentiated services, reducing service costs, and increasing unit output.
Through data mining and predictive models, we provide scientific analytical support, reduce enterprise service costs, improve production efficiency, enhance market forecast accuracy, enable personalized services, and strengthen the flexibility and adaptability of the supply chain.
Smart Images
Figure CN120975734A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of logistics supply chain, in particular to a CRM-driven intelligent supply chain management system. BACKGROUND
[0002] The supply chain exists as a form of entity enterprise chain, and can also be regarded as a virtual information flow, a network chain of mixed logistics and fund flow. The supply chain management is a new management concept, is a whole process of commodity production and circulation from the perspective of integration, is a comprehensive management of materials, information and funds, and promotes the maximum value-added of the supply chain. The midstream enterprises in the supply chain are both for customers and suppliers, and adjacent enterprises are transaction counterparts. The enterprises in the supply chain need to consider the purchasing demand as customers, and analyze the demand of downstream customers as suppliers.
[0003] Under the condition of relying on experience to judge the trend, the enterprises in the supply chain will face the sales risks brought by market uncertainty, such as shortage, inventory accumulation, product style attribute not following market demand, and not meeting the changes of consumer behavior. Under the background of popularization of information system, the enterprise informatization has been basically completed, and the data basically meet the demand of analysis. What is lacking is a scientific analysis method and technical support. Therefore, the CRM-driven intelligent supply chain management system is proposed. SUMMARY
[0004] The main purpose of the present application is to provide a CRM-driven intelligent supply chain management system, which can effectively solve the problems mentioned in the background art.
[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: The CRM-driven intelligent supply chain management system comprises a supply chain basic structure model, a CRM customer relationship management system and a supply chain management system based on an analytical CRM. The supply chain basic structure model comprises supply chain participants and a supply chain process system. The supply chain participants comprise suppliers, manufacturers, distributors and consumers. The suppliers provide raw materials or parts to the manufacturers. The manufacturers transform the raw materials or parts into finished products. The distributors transport the finished products from the manufacturers to the retailers or end users. The supply chain process system predicts future demand based on historical data, market trends and other factors, purchases the required raw materials or parts according to the demand prediction, and uses the purchased raw materials or parts to carry out production activities to transform them into finished products.
[0006] Further, the CRM customer relationship management system can be divided into operational and analytical CRM from the implementation level, the purpose of operational CRM is to achieve customer information acquisition more convenient, sales and marketing automation, and to achieve enterprise departments customer information sharing, operational CRM mainly includes marketing automation, sales automation and customer service and support, on the one hand, from the enterprise business process on the fusion of customer demand, build new enterprise and customer relationship management mechanism, on the other hand, from the technology to achieve the information collection, analysis, tracking of customers, to achieve accurate demand forecasting, analysis of customer behavior, market changes, etc.
[0007] Further, the analysis of CRM includes customer interaction, enterprise internal operation and analysis layer, the interaction level is the interface for the enterprise to understand the customer, collect customer basic data, the actual application includes call center, mailbox, etc.; the operation layer provides background support for the front-end customer interaction layer, including sales, marketing and customer service automation, among which customer service automation is connected with the call center, providing background customer information identification and reasonable information push for the call center, and providing technical support for the reply accuracy of the call center; the analysis layer undertakes deeper knowledge reasoning and realizes sales and marketing automation, makes the optimal decision according to customer data analysis and market situation.
[0008] Further, the supply chain management system of the analysis of CRM includes market prediction function module, basic information module, customer relationship management and production and marketing system, the basic information function module includes basic material module, product information module and customer information management module, the customer information management module records the identity information and all purchase traces of the customer, and prepares for customer behavior analysis.
[0009] Further, the supply and marketing system provides traditional enterprise internal production and marketing product logistics data and corresponding customer order data, and the customer relationship management module includes customer segmentation, market prediction and customer satisfaction analysis module.
[0010] Further, the market prediction function module comprehensively predicts the design trend of products and the purchasing power of customers according to the basic data of each node enterprise of the supply chain system platform, provides reference for enterprise production, and builds the following prediction model: Set the total demand of the customers of the enterprise as Q, W and E represent the demand of large customers and the demand of general customers respectively, and set the demand of p customers as 80% of the orders of the enterprise, It represents the demand of large customer i ( i= 1.2.3.....P) in a certain period, and the large customer cooperates with the enterprise stably, and the demand changes little, so the large customer demand can be determined by the actual investigation of the relevant departments of the enterprise, and the prediction formula is represented as follows: ; The general customer demand changes with the market situation, and the uncertainty is large, the historical data of the customer in the past t periods in the enterprise database has been set, the demand is predicted by using the exponential smoothing method, the historical data is weighted according to the consistency with the prediction target to predict, and the prediction formula can be as follows: ; In the above formula, the historical actual data of the general customer except the large customer in the t-i+1 period is represented, and λ∈ [0, 1] represents a smoothing coefficient.
[0011] Compared with the prior art, the present application has the following beneficial effects: In the present application, the analysis type CRM is introduced in the face of huge data, effective data mining analysis can be carried out, a complete set of data analysis is provided, knowledge output is generated, the most intuitive reference opinion is given for enterprise decision, and the basic supply chain framework is reformed based on the analysis type CRM, and an analysis type supply chain management system framework is constructed; meanwhile, the customers are subdivided according to certain indexes, so that differentiated services are implemented for customers in different levels, the service cost of the enterprise is reduced, and the unit output is improved. BRIEF DESCRIPTION OF DRAWINGS
[0012] Figure 1 It is the system framework of the analysis type CRM of the present application; Figure 2 It is the analysis type supply chain management system module schematic view of the present application. DETAILED DESCRIPTION
[0013] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application is further described below in combination with specific embodiments.
[0014] For example, Figure 1 Figure 2 As shown, the CRM-driven intelligent supply chain management system includes a supply chain basic structure model, a CRM customer relationship management system, and an analysis-based CRM supply chain management system. The supply chain basic structure model includes supply chain participants and a supply chain process system. The supply chain participants include suppliers, manufacturers, distributors, and consumers. The suppliers provide raw materials or parts to the manufacturers. The manufacturers transform the raw materials or parts into finished products. The distributors transport the finished products from the manufacturers to retailers or end users. The supply chain process system predicts future demand based on historical data, market trends, and other factors, procures raw materials or parts needed according to the demand prediction, and uses the procured raw materials or parts to perform production activities to transform them into finished products.
[0015] The CRM customer relationship management system can be divided into operational and analytical CRM from an implementation level. The purpose of the operational CRM is to achieve more convenient customer information acquisition, sales and marketing automation, and customer information sharing among departments of an enterprise. The operational CRM mainly includes marketing automation, sales automation, and customer service and support. On the one hand, it integrates customer demand from the enterprise business process to build a new enterprise and customer relationship management mechanism. On the other hand, it realizes information collection, analysis, tracking, and accurate demand prediction of customers from a technical point of view. The analytical CRM explores the internal laws of data, analyzes customer behavior, market changes, and the like based on big data mining. The analytical customer relationship management focuses on the management of information, which is divided into enterprise internal customer transaction data and enterprise external related information. The purpose of information analysis and mining is to analyze customer types, perform customer stratification, and thus provide differentiated treatment and personalized services, so as to improve marketing effectiveness.
[0016] The analytical CRM includes customer interaction, enterprise internal operation, and analysis layer. The interaction layer is an interface for an enterprise to understand customers and collect customer basic data. In actual application, it includes a call center, an email box, and the like. The operation layer provides background support for the front-end customer interaction layer and includes sales, marketing, and customer service automation. The customer service automation is connected with the call center to provide background customer information identification and reasonable information push for the call center, and to provide technical support for the reply accuracy of the call center. The analysis layer undertakes deeper knowledge reasoning and realizes sales and marketing automation. According to customer data analysis and in combination with market conditions, the analysis layer makes optimal decisions. Data flow is a large collection of various standardized data. After storage in a data warehouse and processing by a data mining technology, the data flow achieves business goals, such as customer segmentation, value analysis, loss analysis, timely service remediation, analysis of customer transaction correlation, and provision of cross-selling suggestions.
[0017] The supply chain management system of the analytic CRM comprises a market prediction function module, a basic information module, a customer relationship management and production and marketing system. The basic information function module comprises a basic material module, a product information module and a customer information management module. The customer information management module records the identity information and all purchase traces of customers, and prepares for customer behavior analysis. The production and marketing system provides traditional enterprise internal production and marketing product logistics data and corresponding customer order data. The customer relationship management module comprises a customer segmentation, market prediction and customer satisfaction analysis module. Generally, there is a core enterprise in the supply chain, which dominates the operation of the entire supply chain. The suppliers determine their production and inventory levels according to the procurement of the core enterprise, and the customer enterprises of the core enterprise determine the inventory and marketing customer groups according to the production and supply of the core enterprise. Because the core enterprise often influences market trends, on the other hand, the enterprises selling to the middle end need to feed back the market and customer demand to the core enterprise along the supply chain, and must have products that can meet customer demand to improve sales levels.
[0018] The market prediction function module comprehensively predicts the product design trend and customer purchasing power according to the basic data of each node enterprise of the supply chain system platform, provides a reference for enterprise production, and constructs the following prediction model: let the total demand of customers of an enterprise be Q, W and E represent the demand of large customers and general customers respectively, and let the demand of p customers constitute 80% of the orders of the enterprise, represent the expected demand of large customer i (i = 1, 2, 3,..., P) in a certain period. The demand of large customers does not change much because the cooperation between large customers and the enterprise is relatively stable, so the demand of large customers can be determined by the actual investigation of the relevant departments of the enterprise. The prediction formula is represented by the following formula: ; The demand of general customers changes with the market situation, and the uncertainty is large. Let the historical data of customers in the past t periods be in the database of the enterprise. The demand is predicted by using the exponential smoothing method. The historical data is given different weights according to the consistency with the prediction target to predict. The prediction can be performed by the following prediction formula: ; In the above formula, represents the historical actual data of large customers and other general customers in the t-i+1 period, and λ ∈ [0, 1] represents the smoothing coefficient.
[0019] Compared with general supply chain system, in addition to information system technology, the analysis type supply chain management system also needs to introduce the related technology of CRM, and the data collection, storage and processing technology considered for providing big data for CRM, mainly including data collection technology, WEB database, data warehouse technology; The data collection technology uses sensors, signal collection, computer interface and other engineering technologies, which are also commonly used in the acquisition of basic information of goods in the Internet of Things data, the start of data analysis, WEB database is a database technology introduced to support the sharing of online supply chain platform, support online interaction between enterprises and customers, synchronize customer information with production and marketing, production follows effective demand, through WEB self-service function can solve part of the customer demand collection problem, collect the implementation demand, effectively support production, data warehouse technology provides a big data storage environment, and provides standardized data set for data mining.
[0020] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for limiting the present application, although the foregoing embodiments of the present application are described in detail, for those skilled in the art, it still can be modified, or part of the technical features of the equivalent replacement. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A CRM-driven intelligent supply chain management system, characterized by: It includes a basic supply chain structure model, a CRM customer relationship management system, and a supply chain management system based on analytical CRM. The basic supply chain structure model includes supply chain participants and a supply chain process system. Supply chain participants include suppliers, manufacturers, distributors, and consumers. Suppliers provide raw materials or components to manufacturers, manufacturers transform raw materials or components into finished products, and distributors transport finished products from manufacturers to retailers or end users. The supply chain process system predicts future demand based on historical data, market trends, and other factors. Based on the demand forecast, it procures the necessary raw materials or components and uses the procured raw materials or components to carry out production activities, transforming them into finished products.
2. The CRM-driven intelligent supply chain management system according to claim 1, characterized in that: From an implementation perspective, the CRM (Customer Relationship Management) system can be divided into two types: operational and analytical. Operational CRM aims to make customer information acquisition more convenient, automate sales and marketing, and enable customer information sharing among various departments within the enterprise. Operational CRM mainly includes marketing automation, sales automation, and customer service and support. On the one hand, it integrates customer needs into the enterprise's business processes to build a new enterprise-customer relationship management mechanism. On the other hand, it uses technology to collect, analyze, and track customer information to accurately predict demand. Analytical CRM, on the other hand, explores the inherent patterns in data and analyzes customer behavior and market changes based on big data mining.
3. The CRM-driven intelligent supply chain management system according to claim 2, characterized in that: Analytical CRM includes customer interaction, internal operations, and analysis layers. The interaction layer is the interface for enterprises to understand customers and collect basic customer data. In practical applications, it includes call centers, email, etc. The operation layer provides backend support for the front-end customer interaction layer, and includes three parts: sales, marketing and customer service automation. Among them, customer service automation is connected to the call center, providing the call center with backend customer information identification and reasonable information push, and providing technical support for the accuracy of call center responses. The analysis layer is responsible for deeper knowledge reasoning and realizing sales and marketing automation. Based on customer data analysis and combined with market conditions, it makes optimal decisions.
4. The CRM-driven intelligent supply chain management system according to claim 3, characterized in that: The analytical CRM supply chain management system includes a market forecasting module, a basic information module, a customer relationship management and production, supply and sales system. The basic information module includes a basic materials module, a product information module and a customer information management module. The customer information management module records customer identity information and all purchase history to prepare for customer behavior analysis.
5. The CRM-driven intelligent supply chain management system according to claim 4, characterized in that: The supply and sales system provides traditional internal enterprise production and sales product logistics data, as well as corresponding customer order data. The customer relationship management module includes customer segmentation, market forecasting, and customer satisfaction analysis modules.
6. The CRM-driven intelligent supply chain management system according to claim 5, characterized in that: The market forecasting module uses basic data from enterprises at each node of the supply chain system platform to comprehensively predict product design trends and customer purchasing power, providing a reference for enterprise production and constructing the following forecasting model: Let Q be the total customer demand of the company, and W and E represent the demand from major customers and general customers, respectively. Let p be the customers whose demand constitutes 80% of the company's orders. Let i represent the expected demand of a major customer (i = 1, 2, 3, ..., P) over a certain period. Since the cooperation between major customers and the company is relatively stable and their demand does not fluctuate significantly, the demand of major customers can be determined through actual investigation by relevant departments of the company. The forecasting formula is as follows: ； Customer demand generally fluctuates with market conditions and is highly uncertain. Assuming the company's database contains historical customer data for the past t periods, exponential smoothing can be used to predict demand. Different weights are assigned to historical data based on their consistency with the prediction target. The following prediction formula can be used: ； In the above formula This represents the historical actual data of general customers other than major customers during the t-i+1 period, where λ∈[0,1] represents the smoothing coefficient.