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CN-122023009-A - Intelligent period present fusion sales execution system and method

CN122023009ACN 122023009 ACN122023009 ACN 122023009ACN-122023009-A

Abstract

The invention relates to an intelligent period and current fusion sales execution system and method, wherein the system receives a customer order or automatically generates a sales plan, performs period and current analysis when an inventory monitoring module inquires that enough inventory meets the order requirement, judges whether the price difference rate exceeds a threshold delta, marks the price difference rate as priority futures sales if the price difference rate exceeds the threshold delta, starts multi-objective optimization calculation to obtain a comprehensive benefit index K, selects futures sales and spot repair warehouse strategies if the K is greater than the threshold theta, selects a pure spot sales strategy if the K is greater than the threshold theta, otherwise selects a mixed strategy, performs risk inspection on the generated sales scheme, executes sales instructions and monitors the order execution state in real time after all risk inspection is passed, and tracks spot shipping progress, futures bargain conditions and customer payment states. The method comprehensively considers a plurality of dimensions such as benefits, inventory, risks and the like, and avoids the problem of local optimization possibly caused by single-objective optimization.

Inventors

  • Yao Aijia
  • Peng Shuangwen

Assignees

  • 普杉科技发展(四川)有限公司

Dates

Publication Date
20260512
Application Date
20260316

Claims (9)

  1. 1. The intelligent period present fusion sales execution system is characterized by comprising an order management module, an inventory monitoring module, a period present analysis module, an AI decision engine, a risk assessment module and an automatic execution module; The order management module is configured to receive and process customer information; the inventory monitoring module is configured to track the inventory state of an enterprise in real time; the period present analysis module is configured to collect market quotations and spot market quotations of futures in real time, calculate period present price difference, base difference and arbitrage space, and analyze market trend and price trend; The AI decision engine is configured to generate an optimal sales scheme by using an optimization algorithm based on the information of order demand, inventory condition, current price relationship and market expectation; The risk assessment module is configured to conduct multi-dimensional risk assessment on sales schemes; The automatic execution module is configured to automatically execute spot shipment instructions, futures transaction instructions and inventory allocation instructions according to the sales scheme generated by the decision engine and monitor the execution state in real time.
  2. 2. The intelligent phase-out fusion sales execution system of claim 1, wherein the system further comprises a client management module and a data center; the client management module is configured to maintain a client information database comprising client credit registration, historical transaction records, payment habits and preferred products, and provide client portraits for sales decisions; The data center is configured to store historical market data, order data, inventory data and transaction records in a manner of combining a time sequence database and a relational database so as to support big data analysis and machine learning model training.
  3. 3. The intelligent period present fusion sales execution system of claim 1, wherein the generating the optimal sales plan using the optimization algorithm comprises: Acquiring order quantity Q, current inventory I, complete inventory S and expected demand D; Calculating an inventory adequacy r= (I-S)/D, if R >1.5, indicating that the inventory is abundant, tending to sell off the inventory quickly, and if R is less than 0.8, indicating that the inventory is tense, tending to delay sales or to raise prices; calculating futures sales revenue E futures = Q x P futures-Q x (trade cost + delivery cost); Calculating spot sales revenue E spot = Q x P spot-Q x (logistic cost + warehouse cost); Calculating a comprehensive benefit index K=alpha× (E futures-E spot) +beta×R-gamma×risk coefficient, wherein alpha, beta and gamma are weight parameters; And if K is greater than the threshold value theta, selecting a futures sales + spot replenishment library and futures are preferentially sold strategy, if K is less than-theta, selecting a pure spot sales strategy, otherwise, selecting a mixed strategy.
  4. 4. The smart off-the-shelf fusion sales execution system of claim 1, wherein the multi-dimensional risk assessment comprises: Checking the credit of the client, namely inquiring the credit registration of the client, and if the requirements are not met, pre-payment is needed or a guarantee is provided; calculating historical price fluctuation rate sigma, and if sigma > sets a threshold value, reducing the future sales proportion or increasing the sleeve protection degree; inventory risk, namely judging whether the inventory is lower than the safety inventory after price difference sales, and if so, triggering an emergency purchasing plan; checking the sleeve protection proportion, namely calculating the proportion of the sleeve protection position to the total sales volume, and ensuring that the sleeve protection position is within a set range; And (3) fund risk, namely checking whether the future deposit reserve occupies a safety proportion exceeding the available fund.
  5. 5. The method of any one of claims 1-4, wherein the smart installment fusion sales execution system is based on: the method comprises the following steps: S1, receiving a customer order or automatically generating a sales plan by a system, performing a date analysis when an inventory monitoring module inquires that enough inventory meets the order requirement, judging whether the price difference rate exceeds a threshold delta, and marking the price difference rate as priority futures sales if the price difference rate exceeds the threshold delta; S2, starting multi-objective optimization calculation by an AI decision engine to obtain a comprehensive gain index K, if K is greater than a threshold value theta, selecting a futures sales + spot-sales supplementary warehouse and futures priority sales strategy, if K < -theta, selecting a pure spot-sales strategy, otherwise, selecting a mixed strategy; S3, performing risk inspection on the generated sales scheme, executing sales instructions after all risk inspection passes, monitoring order execution states in real time, and tracking spot shipment progress, future transaction conditions and customer payment states.
  6. 6. The method based on the intelligent life fusion sales execution system according to claim 5, wherein: the method further comprises the steps of: S4, automatically performing profit accounting after the spot shipment is completed or the position of the futures is put down, comparing actual profits with expected profits, and analyzing deviation reasons; s5, recording the complete data of the sales to a data center, and judging whether to continue to monitor the market and process new orders.
  7. 7. The method for implementing a system for integrated sales based on smart installment of claim 5, wherein the AI decision engine starts a multi-objective optimization calculation to obtain a comprehensive benefit index K comprising: Acquiring order quantity Q, current inventory I, complete inventory S and expected demand D; Calculating an inventory adequacy r= (I-S)/D, if R >1.5, indicating that the inventory is abundant, tending to sell off the inventory quickly, and if R is less than 0.8, indicating that the inventory is tense, tending to delay sales or to raise prices; calculating futures sales revenue E futures = Q x P futures-Q x (trade cost + delivery cost); Calculating spot sales revenue E spot = Q x P spot-Q x (logistic cost + warehouse cost); The comprehensive benefit index k=α× (E futures-E spot) +β×r- γ×risk coefficient is calculated, where α, β, γ are weight parameters.
  8. 8. The method based on the intelligent life fusion sales execution system according to claim 5, wherein: the risk test includes: Checking the credit of the client, namely inquiring the credit registration of the client, and if the requirements are not met, pre-payment is needed or a guarantee is provided; calculating historical price fluctuation rate sigma, and if sigma > sets a threshold value, reducing the future sales proportion or increasing the sleeve protection degree; inventory risk, namely judging whether the inventory is lower than the safety inventory after price difference sales, and if so, triggering an emergency purchasing plan; checking the sleeve protection proportion, namely calculating the proportion of the sleeve protection position to the total sales volume, and ensuring that the sleeve protection position is within a set range; And (3) fund risk, namely checking whether the future deposit reserve occupies a safety proportion exceeding the available fund.
  9. 9. The method based on the intelligent life fusion sales execution system according to claim 5, wherein: the executing sales instructions includes: If spot sales are selected, sending a delivery instruction to the WMS system, generating a bill of lading, and informing a logistics department to schedule delivery; If the futures are selected for sale, sending a sell-out and warehouse-opening instruction to a futures trading system, and establishing a blank cap position; If the strategy is a mixed strategy, spot and futures operations are respectively executed in proportion; updating the stock system, locking the corresponding stock, and preventing re-sales.

Description

Intelligent period present fusion sales execution system and method Technical Field The invention relates to the field of big data processing, in particular to an intelligent period present fusion sales execution system and method. Background In the field of bulk commodity transactions, enterprises need to sell physical products through spot markets, and also need to use futures markets for risk management and hedging. In a traditional sales model, an enterprise typically views spot sales and futures operations as two independent business processes, managed separately by different departments. The spot sales department focuses on daily business such as customer orders, delivery plans, and goods money recovery, while the futures department focuses on establishing a set of position and managing the risk of holding a warehouse. With the increasing market competition and frequent price fluctuations, enterprises are increasingly demanding an AI sales system that can comprehensively consider both futures and spot markets. The system not only needs to process the traditional order management function, but also can formulate a sales strategy according to factors such as the current price relationship, the stock condition, the market expectation and the like, and select the optimal sales channel and opportunity, thereby maximizing the enterprise income. However, there is currently a lack of truly life-prolonging converged marketing systems on the market. The existing ERP system mainly processes the spot sales flow, futures trading software is focused on trading execution, and effective data interaction and strategy coordination are lacked between the two. The problem that 1, sales decision lacks AI and supports, sales personnel mainly depend on experience to judge when to sell through spot channel and when to sell through futures market, and lack quantitative analysis tools, so that the best sales opportunity is easily missed or suboptimal sales channel is selected, and sales income is not ideal; 2, after a sales department signs a spot contract, the sales department needs to manually inform the future department of establishing corresponding position of the spot, the information transfer is lagged and easy to make mistakes, which may lead to untimely spot or mismatching of spot quantity, and the enterprise is exposed in price risk, 3, the change of the spot price in the period of unable real-time monitoring is difficult for the enterprise to find the opportunity of the spot or avoid unfavorable price relationship in time, when the price of the spot is significantly higher than the price of the spot, the prior system should preferentially sell and supplement the spot, but the prior system cannot automatically recognize and execute such strategies, 4, the inventory management and sales strategy lacks linkage, the sales decision fails to fully consider the factors such as inventory cost, inventory capability, inventory turnover rate, etc., the conditions of stock backlog or insufficient inventory may occur, which affect the fund efficiency and customer satisfaction, 5, the sales execution process is more manually operated, the manual intervention is required from receiving orders, confirming inventory, arranging to placing the spot, the operation efficiency is low, and the error is easy to be caused by negligence, the price of the prior system is increased, 6, the inventory management and sales system has high cost and control performance and high risk can not be provided in real-time, the prior system has high risk monitoring performance, once a risk event occurs, the enterprise is often overwhelmed. Disclosure of Invention The invention aims to overcome the defects of the prior art, provides an intelligent period current fusion sales execution system and method, and solves the defects of the prior art. The intelligent period present fusion sales execution system comprises an order management module, an inventory monitoring module, a period present analysis module, an AI decision engine, a risk assessment module and an automatic execution module; The order management module is configured to receive and process customer information; the inventory monitoring module is configured to track the inventory state of an enterprise in real time; the period present analysis module is configured to collect market quotations and spot market quotations of futures in real time, calculate period present price difference, base difference and arbitrage space, and analyze market trend and price trend; The AI decision engine is configured to generate an optimal sales scheme by using an optimization algorithm based on the information of order demand, inventory condition, current price relationship and market expectation; The risk assessment module is configured to conduct multi-dimensional risk assessment on sales schemes; The automatic execution module is configured to automatically execute spot shipment instructions, futures transactio