Commodity supply chain management method based on Internet of Things

Abstract

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a commodity supply chain management method based on the Internet of Things. Background Art

[0002] With the development of the global economy and the rapid growth of e-commerce, the complexity of commodity supply chain management is increasing day by day. Existing technologies mainly rely on traditional information systems and data management methods. By integrating ERP (Enterprise Resource Planning) systems, WMS (Warehouse Management System) and TMS (Transportation Management System), enterprises can achieve real-time monitoring and management of inventory, logistics, sales and other links. This integrated management method helps to improve the transparency and efficiency of the supply chain, reduce operating costs, enable enterprises to respond to changes in market demand more promptly, optimize inventory levels, and improve customer satisfaction.

[0003] However, traditional systems mainly rely on manual data input and regular updates, which leads to information lag and affects the accuracy of decision-making. In addition, the lack of accurate selection methods for goods suppliers and real-time monitoring of logistics status makes the supply chain inflexible when facing emergencies, which easily leads to problems such as inventory backlogs or shortages, transportation delays, etc. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a commodity supply chain management method based on the Internet of Things. Through time series analysis and data filtering processing on a cloud analysis management platform, errors in manual data entry are avoided. Enterprises can accurately predict future market demand and dynamically calculate inventory turnover, optimal inventory levels, and transportation and warehousing costs, thereby optimizing inventory management and avoiding inventory backlogs or shortages. In addition, the method ensures that enterprises can select reliable suppliers and improve the stability and efficiency of the supply chain by comprehensively evaluating the supplier's delivery timeliness rate and goods quality defect rate. Real-time logistics monitoring and scheduling capabilities enable goods to be delivered to customers quickly and efficiently, comprehensively improving customer satisfaction, promoting the company's competitiveness and market adaptability, and solving the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a commodity supply chain management method based on the Internet of Things, comprising the following steps:

[0008] S1. Use sensors and IoT devices to collect commodity supply chain data in real time. Commodity supply chain data includes commodity inventory data, logistics data, commodity storage environment data, sales data, and supplier information, and send it to the cloud analysis management platform for data storage and analysis;

[0009] S2. The cloud analysis management platform filters the commodity supply chain data and uses time series analysis to predict future market demand, calculate inventory turnover, optimal inventory level, transportation cost, warehousing cost, and logistics transportation time forecast;

[0010] S3. The cloud analysis management platform calculates the supplier's delivery timeliness rate and the quality defect rate of the supplied goods through supplier information, and comprehensively evaluates and selects reliable suppliers based on future market demand, inventory turnover rate, optimal inventory level, transportation cost, warehousing cost and logistics transportation time forecast;

[0011] S4. After the customer places an order, the cloud analysis management platform receives the order, generates processing instructions, specifies the supplier selected after the preliminary analysis to supply the goods, and updates the inventory information of the goods;

[0012] S5. After the goods are shipped, the cloud analysis management platform monitors and schedules the goods logistics until the goods are delivered to the customers, forming a complete goods supply chain management process.

[0013] Preferably, the formula for filtering the commodity supply chain data is as follows:

[0014]

[0015] In the formula, represents the filtered commodity supply chain data, represents a counting subscript, Indicates at a point in time The original commodity supply chain data, Indicates the size of the filter window, Represents the original commodity supply chain data at time forward The data points are summed.

[0016] Preferably, the future market demand forecasting formula is as follows:

[0017]

[0018] In the formula, Predicting the future Market demand at a given time, represents a constant term, Indicates the past The degree of influence of an observation on the current value, which is automatically obtained through the supply chain system. Indicates at a point in time The original product inventory data, logistics data, product storage environment data and sales data, Indicates the past The impact of the error term on the current value, represents the white noise error term, Indicates that the current forecast value is based on the past The weighted sum of the observations at each time point is composed of is multiplied by the corresponding autoregressive coefficient , and sum them up, Indicates that the current forecast value is also affected by the past The impact of the error term at each time point, each error term is multiplied by the corresponding sliding average coefficient , and sum, observe the value Obtained through the system.

[0019] Preferably, the calculation formula of the inventory turnover rate is as follows:

[0020]

[0021] In the formula, represents the inventory turnover rate, represents the cost of goods sold during the time period, represents the beginning inventory, represents the ending inventory, Represents a constant used to calculate average inventory.

[0022] Preferably, the calculation formula of the optimal inventory level is as follows:

[0023]

[0024] In the formula, represents the optimal inventory level, represents the quantity of goods required each year, represents the fixed cost of each order, Represents the cost of storing each item.

[0025] Preferably, the calculation formula of the transportation cost is as follows:

[0026]

[0027] In the formula, Indicates the transportation cost, Indicates the transportation cost per kilometer, Indicates the total distance of cargo transportation. represents the cost of shipping each item, Indicates the total weight of the goods transported. Indicates other miscellaneous costs incurred during transportation.

[0028] Preferably, the storage cost calculation formula is as follows:

[0029]

[0030] In the formula, represents the storage cost, represents the cost of storing each item, represents the optimal inventory level, represents the fixed cost of each order, Indicates the quantity of goods required each year.

[0031] Preferably, the formula for predicting the logistics transportation time is as follows:

[0032]

[0033] In the formula, Indicates the logistics transportation forecast time, It indicates the basic transportation time when there are no other factors affecting it. represents the influence coefficient of distance on transportation time, Indicates the degree of influence of weight on transportation time. represents the impact coefficient of traffic congestion on transportation time, It represents the traffic congestion coefficient. The impact coefficient and traffic congestion coefficient are obtained through the expert experience library built into the cloud analysis management platform. represents the error term, Indicates the total distance of cargo transportation. Indicates the total weight of the goods transported.

[0034] Preferably, the calculation formula for the supplier's on-time delivery rate is as follows:

[0035]

[0036] In the formula, Indicates The on-time delivery rate of each supplier, Indicates The number of orders that a supplier delivered on time within a time period, Indicates The total number of orders from a supplier during the same period of time, including the number of orders delivered on time and the number of orders not delivered on time, represents the aggregated on-time delivery performance of all suppliers. Represents a measure of overall goods deliveries.

[0037] Preferably, the calculation formula for the quality defect rate of the supplied goods is as follows:

[0038]

[0039] In the formula, Indicates The quality of the goods supplied by the supplier is defective. Indicates The number of products identified as unqualified by a supplier during the statistical period, Indicates The total number of inspected products provided by each supplier, including qualified and unqualified products, It means summing the number of defective products from all suppliers. Indicates the sum of the total number of inspected products provided by all suppliers.

[0040] Compared with the prior art, the present invention provides a commodity supply chain management method based on the Internet of Things, which has the following beneficial effects:

[0041] The present invention avoids errors in manual data entry through time series analysis and data filtering processing on the cloud analysis management platform. Enterprises can accurately predict future market demand and dynamically calculate inventory turnover, optimal inventory levels, and transportation and warehousing costs, thereby optimizing inventory management and avoiding inventory backlogs or shortages. In addition, the method ensures that enterprises can select reliable suppliers and improve the stability and efficiency of the supply chain by comprehensively evaluating the supplier's delivery timeliness rate and goods quality defect rate. Real-time logistics monitoring and scheduling capabilities enable goods to be delivered to customers quickly and efficiently, comprehensively improving customer satisfaction and promoting the company's competitiveness and market adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The traditional system mainly relies on manual data input and regular updates, which leads to information lag and affects the accuracy of decision-making. In addition, the lack of accurate selection methods for goods suppliers and real-time monitoring of logistics status makes the supply chain inflexible when facing emergencies, which easily leads to inventory backlogs or shortages, transportation delays and other problems. Therefore, a commodity supply chain management method based on the Internet of Things is proposed. Please refer to Figure 1, the method comprises the following steps:

[0045] S1. Use sensors and IoT devices to collect commodity supply chain data in real time. Commodity supply chain data includes commodity inventory data, logistics data, commodity storage environment data, sales data, and supplier information, and send it to the cloud analysis management platform for data storage and analysis;

[0046] By deploying various types of sensors, such as RFID tags, temperature and humidity monitors, and dynamic weighing equipment, inventory data of goods can be collected in real time. These sensors can not only accurately track the real-time inventory levels of goods, but also monitor the environmental conditions of goods during the logistics process, ensuring that parameters such as temperature and humidity are within a safe range, and preventing products from being damaged by environmental factors;

[0047] In terms of logistics, by installing GPS and other positioning systems in transport vehicles, real-time logistics data such as transport routes, estimated arrival times and transport status can be obtained. This information is automatically aggregated and transmitted to the cloud analysis platform through IoT devices. The cloud platform has powerful data processing capabilities and can store data from different sources, including sales data and supplier information, so that companies can better understand market demand and supply capabilities.

[0048] In addition, by using big data analysis and artificial intelligence algorithms, the cloud platform can conduct in-depth analysis of the collected data and discover potential trends and patterns. By analyzing sales data, companies can predict future procurement needs and optimize inventory management. At the same time, the integration of supplier information provides important support for supply chain transparency and collaboration, helping companies make more informed decisions when evaluating supplier performance.

[0049] S2. The cloud analysis management platform filters the commodity supply chain data and uses time series analysis to predict future market demand, calculate inventory turnover, optimal inventory level, transportation cost, warehousing cost, and logistics transportation time forecast;

[0050] The formula for data filtering is as follows:

[0051]

[0052] In the IoT environment, sensors and devices generate a large amount of data, which may contain erroneous or irrelevant information. Filtering can ensure that the data used in the analysis process is more accurate, thereby improving the effectiveness of decision-making. In the formula, represents the filtered commodity supply chain data, represents a counting subscript, Indicates at a point in time The original commodity supply chain data, Indicates the size of the filter window, Represents the original commodity supply chain data at time forward The data points are summed up, and the filtered and cleaned data set is smaller and processed faster, making real-time analysis and response more efficient, which is crucial for responding to rapidly changing market environments.

[0053] Through this filtering formula, the logistics data, commodity storage environment data and sales data can be filtered one by one;

[0054] The formula for forecasting future market demand is as follows:

[0055]

[0056] By understanding changes in customer demand, companies can adjust production and distribution plans in a timely manner to ensure that products are available when customers need them, thereby improving customer satisfaction and loyalty. In the formula, Predicting the future Market demand at a given time, represents a constant term, Indicates the past The influence of the observed value on the current value. Indicates the observation period, which is used to predict market demand. and The values ​​are automatically obtained through the supply chain system. Indicates at a point in time The original commodity inventory data, logistics data, commodity storage environment data and sales data can be used for data fusion through normalization. Indicates the past The impact of the error term on the current value, represents the white noise error term, Indicates that the current forecast value is based on the past The weighted sum of the observations at each time point is composed of is multiplied by the corresponding autoregressive coefficient , and sum them up, Indicates that the current forecast value is also affected by the past The impact of the error term at each time point, each error term is multiplied by the corresponding sliding average coefficient , and sum, observe the value Through the system, accurate demand forecasts can help companies arrange inventory reasonably, avoid excess or shortage of inventory, and reduce inventory costs;

[0057] The formula for calculating inventory turnover ratio is as follows:

[0058]

[0059] A high turnover rate indicates good inventory management and fast sales of goods. Conversely, it may mean inventory backlog or poor sales. By improving inventory turnover, companies can use funds more effectively, reduce capital occupation, and improve capital liquidity. In the formula, represents the inventory turnover rate, represents the cost of goods sold during the time period, represents the beginning inventory, represents the ending inventory, Represents a constant used to calculate average inventory. Fast-turnover inventory reduces the risk of obsolescence, which can effectively reduce losses, especially in a rapidly changing market.

[0060] The formula for calculating the optimal inventory level is as follows:

[0061]

[0062] By analyzing and calculating the optimal inventory level, companies can reduce inventory holding costs, including storage costs and insurance costs. In the formula, represents the optimal inventory level, represents the quantity of goods required each year, represents the fixed cost of each order, Indicates the cost of storing each item. The calculation of the optimal inventory level helps companies reduce unnecessary inventory backlogs while meeting customer needs and improving overall operational efficiency.

[0063] The formula for calculating transportation cost is as follows:

[0064]

[0065] By accurately calculating transportation costs, companies can identify and control unnecessary expenses during transportation and optimize transportation routes and methods. In the formula, Indicates the transportation cost, Indicates the transportation cost per kilometer, Indicates the total distance of cargo transportation. represents the cost of shipping each item, Indicates the total weight of the goods transported. Represents other miscellaneous expenses incurred during transportation. Accurate transportation cost calculation enables enterprises to have a clearer understanding of each link of the supply chain and promote transparency and collaboration in the supply chain.

[0066] The formula for calculating storage cost is as follows:

[0067]

[0068] By calculating warehousing costs, companies can identify high-cost areas, optimize warehouse layout and management processes, and improve warehousing efficiency. In the formula, represents the storage cost, represents the cost of storing each item, represents the optimal inventory level, represents the fixed cost of each order, Indicates the number of goods needed each year. After understanding the composition of storage costs, companies can take measures to reduce holding costs, such as reducing unnecessary inventory or improving the utilization of storage space;

[0069] The formula for logistics transportation time prediction is as follows:

[0070]

[0071] By predicting transportation time, enterprises can arrange transportation plans and dispatches more reasonably, improve transportation efficiency, and reduce delays. Accurate logistics transportation time prediction can help enterprises provide customers with more reliable delivery commitments and enhance customer trust. In the formula, Indicates the logistics transportation forecast time, It indicates the basic transportation time when there are no other factors affecting it. represents the influence coefficient of distance on transportation time, Indicates the degree of influence of weight on transportation time. represents the impact coefficient of traffic congestion on transportation time, It represents the traffic congestion coefficient. The impact coefficient and traffic congestion coefficient are obtained through the expert experience library built into the cloud analysis management platform. represents the error term, Indicates the total distance of cargo transportation. Indicates the total weight of the goods being transported. Understanding the logistics transportation time can help companies better plan their inventory and ensure that there is no shortage or excess of inventory during transportation;

[0072] S3. The cloud analysis management platform calculates the supplier's delivery timeliness rate and the quality defect rate of the supplied goods through supplier information, and comprehensively evaluates and selects reliable suppliers based on future market demand, inventory turnover rate, optimal inventory level, transportation cost, warehousing cost and logistics transportation time forecast;

[0073] The formula for calculating the supplier's on-time delivery rate is as follows:

[0074]

[0075] By analyzing the on-time delivery rate of suppliers, enterprises can scientifically evaluate and compare the performance of different suppliers and make more informed supplier selections, which helps improve the overall supplier quality and performance. In the formula, Indicates The on-time delivery rate of each supplier, Indicates The number of orders that a supplier delivered on time within a time period, Indicates The total number of orders from a supplier during the same period of time, including the number of orders delivered on time and the number of orders not delivered on time, represents the aggregated on-time delivery performance of all suppliers. It measures the overall delivery volume of goods. By understanding the delivery capacity of suppliers, companies can effectively avoid production interruptions caused by delayed arrival of raw materials or goods, thereby ensuring the continuity and stability of the production process;

[0076] The calculation formula for the quality defect rate of supplied goods is as follows:

[0077]

[0078] The quality defect rate can provide companies with objective supplier evaluation criteria, help companies to have more basis in negotiations and cooperation with suppliers, and enhance the transparency and fairness of management. In the formula, Indicates The quality of the goods supplied by the supplier is defective. Indicates The number of products identified as unqualified by a supplier during the statistical period, Indicates The total number of inspected products provided by each supplier, including qualified and unqualified products, It means summing the number of defective products from all suppliers. It means to sum up the total number of inspected products provided by all suppliers. Based on the quality data, enterprises can classify and manage key suppliers, concentrate resources and cooperate with suppliers with excellent performance, and gradually improve the quality level of the entire supply chain;

[0079] S4. After the customer places an order, the cloud analysis management platform receives the order, generates processing instructions, specifies the supplier selected after the preliminary analysis to supply the goods, and updates the inventory information of the goods;

[0080] When a customer places an order, the cloud analysis management platform plays a vital role. The platform receives order data from various sales channels in real time through an integrated API interface. These order information are stored in a distributed database to ensure data security and fast access. On this basis, the platform uses advanced data analysis algorithms to conduct in-depth analysis of historical sales data, seasonal trends, and current inventory status, thereby generating accurate processing instructions.

[0081] Based on the analysis results, the platform will automatically designate the best supplier who has undergone preliminary screening and performance evaluation to supply goods. This process not only takes into account the supplier's delivery timeliness and quality defect rate, but also integrates real-time logistics information and product availability to ensure that the selected supplier can meet the customer's delivery requirements and product quality standards. In addition, the cloud platform will dynamically update the product inventory information to ensure the real-time and accuracy of inventory data. Through the embedded data visualization tool, relevant personnel can instantly view the inventory level and understand which products are about to be out of stock, so as to make timely replenishment decisions;

[0082] Through the integration of cloud computing and Internet of Things (IoT) technologies, the entire process has achieved a high degree of automation and intelligence, improving the efficiency and accuracy of order processing. This data-driven approach not only optimizes supply chain management and reduces the need for manual intervention, but also significantly shortens the order processing cycle, thereby improving customer satisfaction and enhancing the company's market responsiveness. Ultimately, through the linkage of real-time data analysis and supplier management systems, companies can better plan resources and reduce operating costs, while maintaining sensitivity to market changes and achieving efficient supply chain operations;

[0083] S5. After the goods are shipped, the cloud analysis management platform monitors and dispatches the goods logistics until the goods are delivered to the customers, forming a complete goods supply chain management process;

[0084] After the goods are shipped, the cloud analysis management platform uses advanced Internet of Things (IoT) technology and real-time data collection methods to conduct comprehensive monitoring and scheduling of commodity logistics. This platform uses embedded sensors and RFID tags to track the location, temperature, humidity and other key environmental parameters of the goods in real time during transportation to ensure the safety and quality of the goods during transportation. At the same time, the platform integrates big data analysis and artificial intelligence algorithms to conduct in-depth analysis of the massive data collected, predict potential logistics bottlenecks and risks, and automatically optimize the distribution route through the intelligent scheduling system to improve transportation efficiency.

[0085] In addition, the cloud analysis management platform also seamlessly connects with various logistics nodes such as warehouses, transportation companies and distribution centers to build a multi-party collaborative logistics network. Through the API interface, the platform can obtain the status information of each node in real time, conduct dynamic scheduling and resource allocation, and ensure that the goods can be delivered to customers on time. At the same time, with the help of blockchain technology, the platform can provide transparent and tamper-proof records for each transaction and logistics link, enhancing the trust of the supply chain;

[0086] When the goods are about to arrive at the customer, the platform will also notify the customer through mobile applications or text messages, providing real-time logistics status updates so that customers can keep abreast of goods information at any time. This comprehensive commodity supply chain management process not only improves logistics efficiency and transparency, but also optimizes customer experience, ensuring that customers can receive high-quality goods within the expected time.

[0087] By applying the above method and utilizing the time series analysis and data filtering processing of the cloud analysis management platform, errors in manual data entry can be avoided. Enterprises can accurately predict future market demand and dynamically calculate inventory turnover, optimal inventory levels, and transportation and warehousing costs, thereby optimizing inventory management and avoiding inventory backlogs or shortages. In addition, this method ensures that enterprises can select reliable suppliers and improve the stability and efficiency of the supply chain by comprehensively evaluating the supplier's delivery timeliness and goods quality defect rate.

[0088] Embodiment 1:

[0089] In this experiment, the supplier's delivery timeliness rate and supply quality defect rate were analyzed through the cloud management analysis platform to select suppliers for cooperation. The following data were collected based on the orders:

[0090] Supplier A has five orders, of which Order 1 is delivered on time with a delivery quantity of 50, Order 2 is delivered on time with a delivery quantity of 30, Order 3 is not delivered on time with an undelivered quantity of 20, Order 4 is delivered on time with a delivery quantity of 60, and Order 5 is not delivered on time with an undelivered quantity of 10. Order 2 has 30 quality defects, Order 4 has 60 quality defects, and Order 5 has 10 quality defects;

[0091] At this time, according to the calculation formula of supplier delivery timeliness rate Calculated

[0092] , , i.e. the on-time delivery rate of supplier A ;

[0093] At this time, according to the calculation formula of the quality defect rate of the supplied goods Calculated , , that is, the quality defect rate of goods supplied by supplier A ;

[0094] Real-time example 2:

[0095] In this experiment, the supplier's delivery timeliness rate and supply quality defect rate were analyzed through the cloud management analysis platform to select suppliers for cooperation. The following data were collected based on the orders:

[0096] Supplier B has five orders, of which Order 1 is delivered on time with a delivery quantity of 40, Order 2 is delivered on time with a delivery quantity of 25, Order 3 is delivered on time with a delivery quantity of 30, Order 4 is not delivered on time with a delivery quantity of 15, and Order 5 is delivered on time with an undelivered quantity of 35. Order 2 has 25 quality defects, Order 4 has 15 quality defects, and Order 5 has 35 quality defects;

[0097] At this time, according to the calculation formula of supplier delivery timeliness rate Calculated

[0098] , , i.e. the on-time delivery rate of supplier B ;

[0099] At this time, according to the calculation formula of the quality defect rate of the supplied goods Calculated , , that is, the quality defect rate of goods supplied by supplier B ;

[0100] Considering the goods delivery capabilities and quality defect rates of suppliers A and B, the cloud management and analysis platform will automatically select supplier B for business cooperation to ensure the efficient operation of the enterprise supply chain.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A commodity supply chain management method based on the Internet of Things, characterized in that: The following steps are involved: S1. Use sensors and IoT devices to collect commodity supply chain data in real time. Commodity supply chain data includes commodity inventory data, logistics data, commodity storage environment data, sales data, and supplier information, and send it to the cloud analysis management platform for data storage and analysis; S2. The cloud analysis management platform filters the commodity supply chain data and uses time series analysis to predict future market demand, calculate inventory turnover, optimal inventory level, transportation cost, warehousing cost, and logistics transportation time forecast; S3. The cloud analysis management platform calculates the supplier's delivery timeliness rate and the quality defect rate of the supplied goods through supplier information, and comprehensively evaluates and selects reliable suppliers based on future market demand, inventory turnover rate, optimal inventory level, transportation cost, warehousing cost and logistics transportation time forecast; S4. After the customer places an order, the cloud analysis management platform receives the order, generates processing instructions, specifies the supplier selected after the preliminary analysis to supply the goods, and updates the inventory information of the goods; S5. After the goods are shipped, the cloud analysis management platform monitors and schedules the goods logistics until the goods are delivered to the customers, forming a complete goods supply chain management process.

2. The commodity supply chain management method based on the Internet of Things according to claim 1, characterized in that: The formula for filtering the commodity supply chain data is as follows: In the formula, represents the filtered commodity supply chain data, represents a count subscript, Indicates at a point in time The original commodity supply chain data, Indicates the size of the filter window, Represents the original commodity supply chain data at time forward The data points are summed.

3. The method for managing a commodity supply chain based on the Internet of Things according to claim 2, characterized in that: The future market demand forecasting formula is as follows: In the formula, Predicting the future Market demand at a given time, represents a constant term, Indicates the past The degree of influence of an observation on the current value, which is automatically obtained through the supply chain system. Indicates at a point in time The original product inventory data, logistics data, product storage environment data and sales data, Indicates the past The impact of the error term on the current value, represents the white noise error term, Indicates that the current forecast value is based on the past The weighted sum of observations at time points is composed of each observation. is multiplied by the corresponding autoregressive coefficient , and sum them up, Indicates that the current forecast value is also affected by the past The impact of the error term at each time point, each error term is multiplied by the corresponding sliding average coefficient , and sum, observe the value Obtained through the system.

4. The method for managing a commodity supply chain based on the Internet of Things according to claim 3, characterized in that: The calculation formula for the inventory turnover rate is as follows: In the formula, represents the inventory turnover rate, represents the cost of goods sold during the time period, represents the beginning inventory, represents the ending inventory, Represents a constant used to calculate average inventory.

5. The method for managing a commodity supply chain based on the Internet of Things according to claim 4, characterized in that: The calculation formula for the optimal inventory level is as follows: In the formula, represents the optimal inventory level, represents the quantity of goods required each year, represents the fixed cost of each order, Represents the cost of storing each item.

6. The method for managing a commodity supply chain based on the Internet of Things according to claim 5, characterized in that: The calculation formula of the transportation cost is as follows: In the formula, Indicates the transportation cost, Indicates the transportation cost per kilometer, Indicates the total distance of cargo transportation. represents the cost of shipping each item, Indicates the total weight of the goods transported. Indicates other miscellaneous costs incurred during transportation.

7. The method for managing a commodity supply chain based on the Internet of Things according to claim 6, characterized in that: The warehousing cost calculation formula is as follows: In the formula, represents the storage cost, represents the cost of storing each item, represents the optimal inventory level, represents the fixed cost of each order, Indicates the quantity of goods required each year.

8. The method for managing a commodity supply chain based on the Internet of Things according to claim 7, characterized in that: The formula for predicting the logistics transportation time is as follows: In the formula, Indicates the logistics transportation forecast time, It indicates the basic transportation time when there are no other factors affecting it. represents the influence coefficient of distance on transportation time, Indicates the degree of influence of weight on transportation time. represents the impact coefficient of traffic congestion on transportation time, It represents the traffic congestion coefficient. The impact coefficient and traffic congestion coefficient are obtained through the expert experience library built into the cloud analysis management platform. represents the error term, Indicates the total distance of cargo transportation. Indicates the total weight of the goods transported.

9. The method for managing a commodity supply chain based on the Internet of Things according to claim 8, characterized in that: The calculation formula for the supplier's on-time delivery rate is as follows: In the formula, Indicates The on-time delivery rate of each supplier, Indicates The number of orders that a supplier delivered on time within a time period, Indicates The total number of orders from a supplier during the same period of time, including the number of orders delivered on time and the number of orders not delivered on time, represents the aggregated on-time delivery performance of all suppliers. Represents a measure of overall goods deliveries.

10. The method for managing a commodity supply chain based on the Internet of Things according to claim 9, characterized in that: The calculation formula for the quality defect rate of the supplied goods is as follows: In the formula, Indicates The quality of the goods supplied by the supplier is defective. Indicates The number of products identified as unqualified by a supplier during the statistical period, Indicates The total number of inspected products provided by each supplier, including qualified and unqualified products, It means summing the number of defective products from all suppliers. Indicates the sum of the total number of inspected products provided by all suppliers.

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