Ordering processing system and method for franchise merchant

Abstract

The invention discloses a franchise order processing system and method, the system comprises a franchise terminal module, a headquarter management module, a block chain module and an intelligent contract module, and the franchise terminal module comprehensively considers historical order records, payment timeliness and sales performance factors of franchise. Orders can be processed more quickly, and the operation efficiency of enterprises is improved. The order accuracy of the method is obviously higher than that of a traditional method. According to the method, the inventory can be managed more effectively, and the utilization efficiency of the inventory is improved. Therefore, the method provided by the invention can better meet the requirements of the franchise and improve the satisfaction degree of the franchise.

Description

Technical Field

[0001] The present invention relates to the field of commercial operation management, and in particular to a franchisee order processing system and method. Background Art

[0002] With the widespread application of chain operation model, the order process management between franchisees and headquarters has become crucial. The traditional franchisee order processing method has many problems, which affects the order efficiency and accuracy and increases the operating cost.

[0003] Traditional methods usually rely on manual operations, such as franchisees sending orders to the headquarters via phone, fax or email, and the headquarters manually collecting, sorting and processing the orders. This method is prone to information transmission errors, order loss, untimely processing and other problems. Moreover, manual processing is difficult to conduct effective data analysis and management of a large number of orders, and cannot meet the needs of rapid enterprise development and market changes.

[0004] In addition, the traditional ordering system lacks effective integration of franchisee credit assessment and inventory management, which may lead to some franchisees over-ordering and resulting in inventory backlogs, or franchisees with poor credit defaulting on payments, causing economic losses to the company. Summary of the invention

[0005] The purpose of the present invention is to provide a franchisee order processing system and method to solve the problems existing in the existing methods.

[0006] The present invention provides a franchisee order processing system, including a franchisee order processing system and method based on blockchain and smart contracts. The system includes a franchisee terminal module, a headquarters management module, a blockchain module and a smart contract module. The franchisee terminal module comprehensively considers the franchisee's historical order records, payment timeliness, and sales performance factors, and assigns a credit score to each franchisee. The weight of the historical order record is set to w1, the weight of the payment timeliness is set to w2, and the weight of the sales performance is set to w3; for the historical order record, quantitative scoring is performed according to indicators such as order completion rate and order accuracy, and is set to S1; for payment timeliness, quantitative scoring is performed according to the deviation between the actual payment time and the specified payment time, and is set to S2; for sales performance, quantitative scoring is performed according to indicators such as sales volume and sales growth rate, and is set to S3; then the franchisee's credit score, C = ω 1 S 1 +ω 2 S 2 +ω 3 S 3 ,When processing orders, the smart contract will handle them differently according to the franchisee’s credit score.

[0007] Furthermore, the franchisee terminal module should have an encryption function to ensure the security of the order information during transmission.

[0008] Furthermore, the headquarters management module should have order review, inventory allocation, delivery arrangement and franchisee information management and maintenance functions.

[0009] Furthermore, the blockchain module should adopt distributed ledger technology to ensure the immutability and security of order information and franchisee information.

[0010] Furthermore, the smart contract module should automatically execute the order processing process according to the preset rules and the franchisee credit evaluation results. By analyzing the ordering preferences and sales trends of franchisees in different regions, the headquarters can adjust the product production and marketing strategies. Suppose there are k different regions. For region l (l = 1, 2, ..., k), the franchisee order preference vector is P l =(p l1 , p l2 ,…,p ln ), where p li It represents the preference of franchisees in a region for products, and the sales trend vector is T l =(t l1 , t l2 ,…,t ln ), where t li Represents the sales growth rate of commodity i in region l. By calculating vector inner product and other methods, the correlation between ordering preferences and sales trends in different regions is analyzed to provide a basis for decision-making.

[0011] On the other hand, the invention also provides a franchisee order processing system, including a franchisee order processing system and method based on artificial intelligence and machine learning. The system includes a franchisee input module, a headquarters processing module, an artificial intelligence analysis module and a machine learning prediction module. The machine learning prediction module uses a machine learning algorithm to establish an order demand prediction model, comprehensively considers historical order data, seasonal factors, and market promotion factors, and predicts the future order demand of franchisees. Suppose the historical order data is X=(x 1 , x 2 , …, x m ), where x represents the relevant feature vector of the i-th order, and the seasonal factor vector is S = (s 1 ,s 2 ,…,s n ), the marketing promotion activity vector is M=(m 1 , m 2 ,…,m p ), a prediction model is constructed through a neural network algorithm, and the prediction model is set to be Y=f(X, S, M), where Y is the predicted order demand vector.

[0012] Furthermore, the franchisee input module should have prompting and error correction functions to facilitate the franchisee to correctly input the order information.

[0013] Furthermore, the headquarters processing module should have order processing, franchisee management, inventory management, and data statistics and analysis functions.

[0014] Furthermore, the artificial intelligence analysis module should use natural language processing and image recognition technology to assist in order review, and analyze order data, franchisee data and market data.

[0015] Furthermore, the machine learning prediction module should predict franchisee order demand, market trends and inventory changes through machine learning algorithms to provide decision support for inventory management and delivery arrangements.

[0016] Beneficial technical effects:

[0017] In terms of order processing efficiency, orders can be processed more quickly, improving the operational efficiency of the enterprise. The order accuracy of the inventive method is significantly higher than that of the traditional method. The method of the present invention can manage inventory more effectively and improve the utilization efficiency of inventory. This shows that the method of the present invention can better meet the needs of franchisees and improve the satisfaction of franchisees. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 System principle flow chart; DETAILED DESCRIPTION

[0019] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0020] Embodiment 1:

[0021] Franchisee order processing system and method based on blockchain and smart contracts

[0022] (I) System composition

[0023] Franchisee terminal module: Franchisees can input order information, including product name, specification, quantity, delivery time, etc. through this module, and can view order status and historical order records.

[0024] Headquarters management module: Headquarters management personnel receive and process franchisee orders through this module, including order review, inventory allocation, delivery arrangement and other functions, and can also manage and maintain franchisee information.

[0025] Blockchain module: used to store order information, franchisee information and related transaction records to ensure data is tamper-proof and secure.

[0026] Smart contract module: Automatically execute order processing according to preset rules and conditions, such as automatically triggering inventory deduction and delivery notification after order review.

[0027] (II) Invention

[0028] Invention point 1: Blockchain technology ensures data security and transparency

[0029] ·Technical application: Using the distributed ledger technology of blockchain, order information and franchisee information are stored on multiple nodes, and each node saves a complete copy of the data. Any modification to the data needs to be verified and agreed upon by multiple nodes to ensure that the data cannot be tampered with.

[0030] Advantages: It ensures the authenticity and integrity of order data, prevents data from being tampered with or lost, and improves the trust between franchisees and headquarters.

[0031] Invention point 2: Smart contracts realize automated processes

[0032] ·Algorithm mechanism: The rules and processes for order processing are defined through smart contracts. For example, when a franchisee submits an order and meets the credit and inventory conditions, the smart contract automatically executes the order review and approval operation, and triggers the inventory management module to deduct inventory and the shipping department to prepare for shipment.

[0033] Advantages: Reduced manual intervention, improved efficiency and accuracy of order processing, and avoided human errors and delays.

[0034] Invention point 3: Franchisee credit assessment integration

[0035] Evaluation model: Establish a franchisee credit evaluation model, comprehensively consider factors such as franchisee's historical order records, payment timeliness, sales performance, etc., and assign a credit score to each franchisee. Let the weight of historical order records be w 1 , the payment timeliness weight is w 2 , sales performance weight is ω 3 , and w 1 +w 2 +w 3 = 1. For historical order records, quantitative scoring is performed based on indicators such as order completion rate and order accuracy, and is set as S 1 ; For payment timeliness, a quantitative score is given based on the deviation between the actual payment time and the stipulated payment time, which is set as S 2 ; For sales performance, quantitative scoring is performed based on indicators such as sales volume and sales growth rate, and is set as S 3 Then the franchisee’s credit score C = w 1 S 1 +w2 S 2 +w 3 S 3 When processing orders, the smart contract will handle them differently based on the franchisee’s credit score. For example, franchisees with good credit can enjoy priority shipment and more relaxed payment terms.

[0036] Advantages: It encourages franchisees to maintain a good credit record, optimizes resource allocation, and reduces the company's operating risks.

[0037] Invention point 4: Real-time inventory management and synchronization

[0038] Inventory algorithm: Inventory information is updated in real time through blockchain technology, and both the headquarters and franchisees can view the inventory status in real time. Suppose the number of inventory items is n, and the inventory quantity of each item is Q i (i=1,2,…,n), when the order is submitted, the smart contract automatically checks whether the inventory meets the order requirements. Suppose the demand for item j in the order is D j ,if (m is the number of product types in the order), the inventory meets the order requirements; otherwise, if the inventory is insufficient, it will automatically trigger a replenishment reminder or adjust the delivery plan.

[0039] Advantages: Improves the efficiency and accuracy of inventory management, avoids inventory backlogs or out-of-stock situations, and reduces inventory costs.

[0040] Invention point 5: Data analysis and decision support

[0041] Data analysis algorithm: Use big data analysis technology to analyze order data, franchisee data, and inventory data to provide decision support for the headquarters. For example, by analyzing the ordering preferences and sales trends of franchisees in different regions, the headquarters can adjust product production and marketing strategies. Suppose there are k different regions, and for region l (l = 1, 2, ..., k), the franchisee order preference vector is P l =(p l1 , p l2 ,…,p ln ), where p li It represents the preference of franchisees in region l for product i (which can be quantified by order frequency, order quantity and other indicators). The sales trend vector is T l =(t l1 , t l2 ,…,t ln ), where t li Represents the sales growth rate of commodity i in region l. By calculating vector inner product and other methods, the correlation between ordering preferences and sales trends in different regions is analyzed to provide a basis for decision-making.

[0042] Advantages: Help enterprises better understand market demand and franchisee situation, formulate more reasonable economic strategies, and improve the competitiveness of enterprises.

[0043] (III) Treatment methods

[0044] 1. The franchisee enters the order information through the franchisee terminal module, and the information is encrypted and uploaded to the blockchain module.

[0045] 2. The smart contract module reviews the order based on preset rules and franchisee credit assessment results.

[0046] 3. If the order is approved, the smart contract automatically triggers the inventory management module to deduct inventory and notify the shipping department to arrange shipment. At the same time, the shipping information and order status updates will be recorded in real time on the blockchain module.

[0047] 4. Franchisees can view order status and delivery information in real time through the franchisee terminal module.

[0048] Embodiment 2:

[0049] Franchise order processing system and method based on artificial intelligence and machine learning

[0050] (I) System composition

[0051] Franchisee input module: Franchisees enter order information in this module, including product name, specifications, quantity, delivery time, etc.

[0052] Headquarters processing module: responsible for receiving and processing franchisee orders, including order review, inventory allocation, delivery arrangement and other functions, while managing and maintaining franchisee information.

[0053] Artificial intelligence analysis module: Uses artificial intelligence algorithms to analyze order data, franchisee data, and market data to provide decision support for order processing.

[0054] Machine learning prediction module: Use machine learning algorithms to predict franchisee order demand, market trends, inventory changes, etc., and prepare in advance.

[0055] (II) Invention

[0056] Invention point 1: Artificial intelligence assisted order review

[0057] · Algorithm application: Natural language processing and image recognition technology are used to review the order information entered by franchisees. For example, if the product name entered by the franchisee is not standardized or there is an error, the artificial intelligence system can automatically identify it and prompt the franchisee to make corrections.

[0058] Advantages: Improves the accuracy and efficiency of order review and reduces the workload of manual review.

[0059] Invention 2: Machine learning predicts order demand

[0060] Prediction model: Use machine learning algorithms to establish an order demand prediction model, taking into account historical order data, seasonal factors, market promotion activities and other factors, to predict the future order demand of franchisees. Suppose the historical order data is X = (x 1 , x 2 , …, x m ), where x i represents the relevant feature vector of the i-th order (including order time, order quantity, commodity type, etc.), and the seasonal factor vector is S = (s 1 ,s 2 ,…,s n ), the marketing promotion activity vector is M=(m 1 , m 2 ,…,m p ). The prediction model is constructed by the neural network algorithm, and the prediction model is set to Y = f (X, S, M), where Y is the predicted order demand vector.

[0061] Advantages: The headquarters can arrange production and inventory in advance based on the forecast results to avoid out-of-stock or inventory backlogs, thereby improving the company's operational efficiency.

[0062] Invention point 3: Franchise behavior analysis and personalized services

[0063] Analysis algorithm: Use machine learning algorithms to analyze franchisee historical order behaviors, including order frequency, order time, product preferences, etc., to provide personalized services for each franchisee. Assume that the number of franchisees is N, for franchisee j (j = 1, 2, ..., N), its historical order behavior vector is B j =(b j1 , b j2 , …, b jn ), where b ji represents the value of franchisee j on the ith behavior feature (e.g., order frequency is b j1 , order time is b j2 , product preference is b j3 Classify franchisees through clustering algorithms and other methods, and provide personalized services based on the classification results.

[0064] Advantages: Improved franchisee satisfaction and loyalty, and promoted good cooperative relations between the company and franchisees.

[0065] Invention point 4: Market trend analysis and strategy adjustment

[0066] Trend analysis algorithm: Use artificial intelligence algorithms to analyze market data, including competitor product information, market price fluctuations, consumer demand changes, etc., to provide companies with market trend analysis reports. Suppose the market data is Z = (z 1 , z 2 , …, z q ), build a trend analysis model through deep learning algorithm, and set the analysis model as R = g(Z), where R is the market trend analysis result (including market growth trend, competition situation, etc.). The headquarters can adjust product strategy and pricing strategy in time according to the report.

[0067] Advantages: Help enterprises grasp market trends in a timely manner, formulate more reasonable business strategies, and improve their competitiveness.

[0068] Invention point 5: Dynamic inventory management optimization

[0069] Inventory optimization algorithm: Based on the inventory changes predicted by machine learning and combined with real-time order data, the inventory is dynamically managed and optimized. Suppose the number of inventory items is n, and the inventory quantity of each item is Q i (i=1,2,…,n), the inventory change predicted by machine learning is △Q i When insufficient inventory is predicted, (m is the number of product types in the order, D j is the demand for item j in the order), timely arrange replenishment; when there is excess inventory, that is, When the demand increases, take promotional measures or adjust production plans.

[0070] Advantages: Improves the efficiency and accuracy of inventory management, reduces inventory costs, and improves the economic benefits of the enterprise.

[0071] (III) Treatment methods

[0072] 1. Franchisees enter order information through the franchisee input module.

[0073] 2. The artificial intelligence analysis module reviews the order information, while the machine learning prediction module predicts the franchisee’s order demand and inventory changes.

[0074] 3. If the order is approved, the headquarters processing module will make inventory allocation and delivery arrangements based on the inventory situation and forecast results. At the same time, the delivery information and order status will be updated in real time in the system.

[0075] 4. Franchisees can check order status and delivery information in real time through the Franchisee Input Module.

[0076] Embodiment 3

[0077] Comparative synergistic test

[0078] (I) Experimental setup

[0079] 1. Selection of test area

[0080] 1. Select franchise stores in multiple different regions as test subjects, including regions with different economic development levels and market sizes.

[0081] 2. Experimental Grouping

[0082] 1. The franchise stores are divided into three groups, and the two methods of the present invention (based on blockchain and smart contracts, and based on artificial intelligence and machine learning) and the traditional order processing method are used for order processing.

[0083] 3. Test cycle

[0084] 1. Set the test period to 6 months, and observe and record the order processing during the test period.

[0085] 2. Evaluation indicators

[0086] 1. Order processing efficiency: Calculate the time from when the franchisee submits the order to when the shipment is completed, in hours. Set t as the time when the franchisee submits the order and t as the time when the shipment is completed, then the order processing efficiency.

[0087] 2. Order accuracy: Calculate the percentage of orders with errors during order processing to the total number of orders. Suppose the number of error orders is and the total number of orders is, then the order accuracy is.

[0088] 3. Inventory turnover rate: Calculate the number of times inventory goods are turned over within a certain period of time, reflecting the efficiency of inventory management. Suppose the beginning inventory amount is, the ending inventory amount is, and the sales cost during the period is, then the inventory turnover rate.

[0089] 4. Franchisee satisfaction: Collect franchisee satisfaction scores on the order processing process through questionnaires or telephone interviews, with a full score of 100.

[0090] The test results are shown in the following table:

[0091]

[0092] in conclusion:

[0093] 1. In terms of order processing efficiency, the method based on blockchain and smart contracts and the method based on artificial intelligence and machine learning are significantly better than the traditional method. This shows that the two methods of the present invention can process orders more quickly and improve the operational efficiency of enterprises.

[0094] 2. In terms of order accuracy, the order accuracy of the two inventive methods is significantly higher than that of the traditional method. This shows that the method of the present invention can more accurately process orders and reduce the occurrence of errors.

[0095] 3. In terms of inventory turnover rate, the inventory turnover rates of the two inventive methods are higher than those of the traditional method, which shows that the method of the present invention can manage inventory more effectively and improve the utilization efficiency of inventory.

[0096] 4. In terms of franchisee satisfaction, the franchisee satisfaction of the two inventive methods is higher than that of the traditional method, which shows that the method of the present invention can better meet the needs of franchisees and improve franchisee satisfaction.

[0097] 5. Overall, the methods based on blockchain and smart contracts performed better in terms of order accuracy and inventory turnover, and the methods based on artificial intelligence and machine learning performed slightly better in terms of order processing efficiency, but both methods were significantly better than traditional methods.

Claims

1. A franchisee order processing system, characterized in that The invention comprises a franchisee order processing system and method based on blockchain and smart contracts, the system comprising a franchisee terminal module, a headquarters management module, a blockchain module and a smart contract module, the franchisee terminal module comprehensively considers the franchisee's historical order records, payment timeliness, and sales performance factors, and assigns a credit score to each franchisee, assuming that the weight of the historical order record is w1, the weight of the payment timeliness is w2, and the weight of the sales performance is w3; for the historical order record, quantitative scoring is performed according to indicators such as order completion rate and order accuracy, and is set as S1; for payment timeliness, quantitative scoring is performed according to the deviation between the actual payment time and the prescribed payment time, and is set as S2; for sales performance, quantitative scoring is performed according to indicators such as sales volume and sales growth rate, and is set as S3; then the franchisee's credit score, C = w1S1+w2S2+w3S3, and the smart contract will perform different processing according to the franchisee's credit score when processing an order.

2. The franchisee order processing system according to claim 1, characterized in that The franchisee terminal module should have encryption function to ensure the security of order information during transmission.

3. The franchisee order processing system according to claim 1, characterized in that The headquarters management module should have the functions of order review, inventory allocation, delivery arrangement, and franchisee information management and maintenance.

4. The franchisee order processing system according to claim 1, characterized in that The blockchain module should adopt distributed ledger technology to ensure the immutability and security of order information and franchisee information.

5. The franchisee order processing system according to claim 1, characterized in that The smart contract module should automatically execute the order processing process according to the preset rules and the franchisee credit assessment results. By analyzing the ordering preferences and sales trends of franchisees in different regions, the headquarters can adjust the product production and marketing strategies. Suppose there are k different regions. For region l (l = 1, 2, ..., k), the franchisee order preference vector is P l =(p l1 , p l2 ,…,p ln ), where p li It represents the preference of franchisees in a region for products, and the sales trend vector is T l =(t l1 , t l2 ,…,t ln ), where t li It represents the sales growth rate of commodity i in region I. By calculating the inner product of vectors and other methods, the correlation between ordering preferences and sales trends in different regions is analyzed to provide a basis for decision making.

6. A franchisee order processing system, characterized in that The system and method for processing franchisee orders based on artificial intelligence and machine learning include a franchisee input module, a headquarters processing module, an artificial intelligence analysis module, and a machine learning prediction module. The machine learning prediction module uses a machine learning algorithm to establish an order demand prediction model, comprehensively considers historical order data, seasonal factors, and market promotion factors, and predicts the future order demand of franchisees. Assume that the historical order data is X=(x1, x2, ..., x m ), where x represents the relevant feature vector of the i-th order, and the seasonal factor vector is S = (s1, s2, ..., s n ), the marketing promotion activity vector is M = (m1, m2, ..., m p ), a prediction model is constructed through a neural network algorithm, and the prediction model is set to be Y=f(X, S, M), where Y is the predicted order demand vector.

7. The franchisee order processing system according to claim 6, characterized in that The franchisee input module should have prompt and error correction functions to facilitate franchisees to correctly enter order information.

8. The franchisee order processing system according to claim 6, characterized in that The headquarters processing module should have order processing, franchisee management, inventory management, and data statistics and analysis functions.

9. The franchisee order processing system according to claim 6, characterized in that The artificial intelligence analysis module should use natural language processing and image recognition technology to assist in order review, and analyze order data, franchisee data, and market data.

10. The franchisee order processing system according to claim 6, characterized in that The machine learning prediction module should use machine learning algorithms to predict franchisee order demand, market trends, and inventory changes, and provide decision support for inventory management and delivery arrangements.

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