Intelligent inventory control system based on real-time data analysis and automation technology

Abstract

The invention discloses an intelligent inventory control system based on a real-time data analysis and automation technology, and relates to the technical field of inventory control, and the system comprises an inventory tracking module which tracks the inventory in real time, updates the inventory data, and records the basic information of the inventory materials through the radio frequency identification technology and the bar code scanning technology; the inventory demand prediction module constructs and trains an inventory demand prediction model based on historical data of a certain type of people, uses the model to determine the material type of the current needed inventory, predicts the material inventory quantity based on customer demands, outputs a prediction result, dynamically adjusts the safety inventory level based on the prediction result, and formulates a replenishment plan. Abnormal detection and early warning are carried out; and the inventory decision optimization module is used for comprehensively analyzing historical data and real-time inventory data of a certain type of people, filtering and controlling the inventory data according to an analysis result, and optimizing inventory management. According to the invention, inventory management decisions can be optimized, and risks of inventory overstock and shortage can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory control, and more particularly to an intelligent inventory control system based on real-time data analysis and automation technology. Background Art

[0002] In various industries, inventory control plays a crucial role, which is directly related to a company's operating costs, capital turnover, and customer satisfaction. However, traditional inventory control methods and technologies have many defects that cannot be ignored, greatly limiting the efficiency and effectiveness of inventory management, and thus increasing the company's operating costs and risks.

[0003] First of all, traditional inventory control lacks real-time data support. In today's rapidly changing market environment, it is crucial to have real-time access to inventory levels, sales trends, and information on all links of the supply chain. However, the traditional model often relies on manual data collection and updating at regular intervals, resulting in a significant lag in the data obtained by enterprises and unable to provide strong evidence for timely and accurate decision-making.

[0004] Secondly, the static replenishment strategy is a major drawback of traditional inventory control. Enterprises usually set replenishment points and quantities based on experience or fixed formulas, without fully considering the dynamic changes in market demand, seasonal factors, promotional activities, and the uncertainties of the supply chain. This "one-size-fits-all" approach is likely to lead to overstocking or shortages, increasing inventory holding costs while also potentially missing sales opportunities.

[0005] Moreover, traditional inventory control highly relies on manual intervention. Key processes such as inventory data recording, inventory counting, procurement plan formulation, and replenishment timing judgment are all manually completed. Manual operations are not only inefficient but also easily affected by subjective factors such as fatigue, negligence, and lack of professional knowledge, resulting in data recording errors, decision-making mistakes, etc., further reducing the accuracy and reliability of inventory control.

[0006] In addition, the phenomenon of data silos is widespread in traditional inventory management. Different departments within an enterprise (such as procurement, sales, warehousing, finance, etc.) use their own independent information systems, and there is a lack of effective data sharing and integration mechanisms between these systems. This makes inventory-related data scattered in various systems, unable to form a comprehensive and coherent inventory view, seriously hindering the company's overall control and collaborative management of inventory.

[0007] At the same time, traditional inventory control methods lack accurate forecasting capabilities. Market demand is highly dynamic and uncertain, being comprehensively affected by various factors such as changes in consumer preferences, market competition situation, and macroeconomic environment. However, traditional models mainly rely on manual experience and simple statistical analysis to forecast demand, making it difficult to accurately capture the subtle signals of market changes and unable to formulate reasonable inventory strategies in advance to cope with demand fluctuations.

[0008] Low automation is also a major shortcoming of traditional inventory control. In modern logistics and supply chain management, automation technology has become a key means to improve efficiency and reduce costs. However, traditional inventory management still relies heavily on manual operations in links such as goods in and out, inventory counting, and replenishment execution. This not only has a high labor intensity and low efficiency but also is prone to human errors, making it difficult to meet the increasing demand for efficient operation of enterprises.

[0009] In addition, the lack of visualization tools makes it difficult for inventory managers to intuitively and clearly understand the overall situation of inventory. Traditional inventory data is usually presented in the form of tables or reports, with complex and unintuitive information. Managers need to spend a lot of time and effort analyzing and interpreting the data, making it difficult to quickly insight into the key information and potential problems of inventory and being unfavorable for making scientific and reasonable decisions in a timely manner.

[0010] Finally, traditional inventory control methods seem inadequate when dealing with complex environments. With the acceleration of globalization and the increasing intensity of market competition, the supply chain environment faced by enterprises has become increasingly complex, involving suppliers, distributors in multiple countries and regions, and complex logistics networks. Traditional inventory control methods are difficult to adapt to this complex and changeable environment and unable to effectively cope with risks such as supply chain disruptions, transportation delays, and exchange rate fluctuations, increasing the uncertainty of enterprise operations.

[0011] Specifically in actual operations, the existing inventory control heavily relies on manual intervention, which is not only error-prone but also extremely inefficient. Inventory managers need to spend a lot of time and energy collecting, sorting, and analyzing various inventory-related information. When formulating procurement plans and replenishment decisions, due to the limitations of manual data processing, they are often unable to quickly respond to changes in market demand. This directly leads to frequent problems of overstock or shortage of inventory, having a negative impact on the operation of enterprises. At the same time, a large amount of historical data accumulated by enterprises and various types of data generated in real time have not been fully and effectively utilized, unable to provide strong support for optimizing inventory control, further exacerbating the dilemma of inventory management and affecting customer satisfaction.

[0012] To effectively solve the above problems, enterprises are urgently in need of adopting advanced technologies to optimize inventory control. Internet of Things technology can achieve real-time monitoring and tracking of inventory items. By deploying sensors in links such as goods, shelves, and transportation equipment, it can collect information such as inventory location, quantity, and status in real time, and transmit this data to the management system in a timely manner, providing enterprises with accurate real-time inventory data. Artificial intelligence and big data analysis technologies can deeply mine and analyze massive historical data and real-time data, predict market demand trends, optimize replenishment strategies, and achieve precise inventory control. Automation technologies such as automated warehousing equipment, automatic sorting systems, and robotic process automation (RPA) can significantly improve the operation efficiency of each link in inventory management, reduce manual intervention, and lower the error rate. In addition, in terms of the use of inventory materials, enterprises should give priority to using inventory materials according to the expiration time and follow the first-in, first-out principle for the inventory of the same material, so as to further optimize inventory management, improve operation efficiency and enterprise competitiveness, and thus gain an advantageous position in the fierce market competition. Summary of the Invention

[0013] In view of the current needs and deficiencies in the development of technology, the present invention provides an intelligent inventory control system based on real-time data analysis and automation technology, which improves the accuracy and response speed of inventory management through real-time data analysis and automation technology, and reduces the operating costs of enterprises.

[0014] The technical solution adopted by the intelligent inventory control system based on real-time data analysis and automation technology of the present invention to solve the above technical problems is as follows:

[0015] An intelligent inventory control system based on real-time data analysis and automation technology, which includes:

[0016] An inventory tracking module, which uses radio frequency identification technology and barcode scanning technology to track inventory in real time, update inventory data, record the basic information of inventory materials, and manage inventory according to the expiration time and the first-in, first-out principle;

[0017] An inventory demand forecasting module, which constructs and trains an inventory demand forecasting model based on the historical data of a certain group of people, uses the inventory demand forecasting model to determine the types of materials required for the current inventory, forecasts the quantity of material inventory based on customer demand, outputs the results including the list of required inventory material types and the predicted inventory quantity of each material, and dynamically adjusts the safety inventory level and formulates a replenishment plan based on the forecasting results, and conducts anomaly detection and early warning;

[0018] An inventory decision optimization module, which comprehensively analyzes the historical data and real-time inventory data of a certain group of people, filters and controls the inventory data according to the analysis results, provides data-driven decision-making basis for users, and optimizes inventory management.

[0019] Optionally, the inventory tracking module involved includes a radio frequency identification unit and a barcode scanning unit, where:

[0020] The radio frequency identification unit uses RFID tags and readers, enables non-line-of-sight scanning, supports batch reading, and updates inventory data in real time;

[0021] The barcode scanning unit records inventory movements by scanning barcodes, collaborates with the radio frequency identification unit to complete the collection and update of inventory data, and records the basic information of inventory materials.

[0022] Optionally, the basic information of inventory materials recorded by the inventory tracking module involved covers material name, specification, warehousing time, and expiration time;

[0023] The inventory tracking module manages the inventory according to the expiration time and the first-in, first-out principle.

[0024] Optionally, the inventory demand forecasting module involved includes a model construction and training unit, a demand forecasting unit, and a strategy adjustment and warning unit, where:

[0025] The model construction and training unit uses machine learning algorithms to construct and train an inventory demand forecasting model based on the historical data of a certain group of people;

[0026] The demand forecasting unit uses the trained inventory demand forecasting model to determine the types of materials required for the current inventory, forecasts the inventory quantity of materials based on customer demand, and outputs a result including a list of the types of materials required for inventory and the forecast inventory quantity of each material;

[0027] The strategy adjustment and warning unit dynamically adjusts the safety inventory level based on the results of the demand forecasting unit, formulates a replenishment plan, and performs anomaly detection and warning by setting thresholds for key indicators.

[0028] Further optionally, when training the inventory demand forecasting model, the model construction and training unit involved combines real-time sales data for in-depth AI learning, and regularly retraces the difference between the forecast and the actual demand to adjust the model parameters.

[0029] Further optionally, when dynamically adjusting the safety inventory level, the strategy adjustment and warning unit involved comprehensively considers the inventory risk threshold, the service level target, and the forecast inventory quantity of materials.

[0030] Optionally, the inventory decision optimization module involved includes a data comprehensive analysis unit and a decision support unit, where:

[0031] The data comprehensive analysis unit integrates the historical data and real-time inventory data of a certain group of people, uses data mining techniques to analyze the association rules and trend patterns in the data, and filters and controls the inventory data based on key indicators such as turnover rate and slow-moving products;

[0032] The decision support unit generates an inventory analysis report based on the results of the data comprehensive analysis unit, providing a data-driven decision-making basis for users and optimizing inventory management.

[0033] Further optionally, the involved inventory analysis report includes inventory structure analysis, inventory turnover analysis, and inventory cost analysis, providing multi-dimensional data support for user decision-making.

[0034] Further optionally, the involved data comprehensive analysis unit accesses market trend data and supplier data to improve the comprehensiveness and accuracy of inventory data comprehensive analysis.

[0035] Further optionally, the involved inventory decision optimization module communicates with the inventory demand forecasting module, and analyzes and makes decisions on inventory data based on the forecasting results of the inventory demand forecasting module.

[0036] A kind of intelligent inventory control system based on real-time data analysis and automation technology of the present invention has the beneficial effects compared with the prior art as follows:

[0037] 1. The present invention can improve the forecasting accuracy, reduce the risks of inventory backlog and shortage; can improve the inventory tracking efficiency, reduce manual intervention, and improve the accuracy and input speed of data; can optimize inventory management decisions, improve the scientificity and precision of inventory management; can enhance the transparency of the supply chain and promote the healthy development of the supply chain.

[0038] 2. The present invention uses radio frequency identification technology with RFID tags and readers, which can read inventory information in batches without line-of-sight scanning, greatly shortening the inventory count time; the inventory data is updated in real time through the inventory tracking module, and the basic information such as material name, specification, warehousing time, and expiration time is recorded in detail, avoiding errors caused by manual recording, strictly managing the inventory according to the expiration time and the first-in, first-out principle, reducing the loss of expired materials, ensuring the quality of the issued materials, and avoiding product quality problems caused by using expired materials.

[0039] 3. The inventory demand forecasting module of the present invention trains a model through machine learning algorithms, and continuously optimizes the forecasting accuracy in combination with real-time sales data. In the case of rapid changes in market demand, it can timely capture the changes in sales data, adjust the forecasting model, and accurately forecast the material demand, avoiding inventory backlog or out-of-stock caused by forecasting errors.

[0040] 4. The inventory decision optimization module of the present invention integrates multi-source data and uses data mining technology to deeply analyze inventory data; inventory structure analysis enables enterprises to clearly understand the proportion of various materials and judge whether the inventory structure is reasonable; inventory turnover analysis reflects the inventory turnover speed and provides a basis for evaluating the efficiency of inventory management; inventory cost analysis helps enterprises master the cost composition and change trend and provides a reference for cost control; the generated inventory analysis report is presented in intuitive charts and data, providing comprehensive and accurate inventory information for the enterprise management layer, supporting them to make scientific inventory management decisions, realizing the refinement and intelligence of inventory management, and enhancing the competitiveness of the enterprise;

[0041] 5. Data intercommunication is achieved among the inventory tracking module, inventory demand forecasting module, and inventory decision optimization module of the present invention, and each module works collaboratively based on a unified data foundation; the forecasting result of the inventory demand forecasting module provides an analysis basis for the inventory decision optimization module, and the inventory decision optimization module guides the adjustment of the management strategy of the inventory tracking module according to the analysis result, forming an efficient internal collaboration mechanism;

[0042] 6. The present invention can also access market trend data and supplier data to timely understand market dynamics and supplier situations, enabling enterprises to better adapt to external environmental changes, expand business cooperation, and build an intelligent inventory management ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] APPENDIX Figure 1 is the system module connection diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] To make the technical solutions, technical problems to be solved, and technical effects of the present invention clearer and more understandable, the following combines specific embodiments to clearly and completely describe the technical solutions of the present invention.

[0045] Embodiment:

[0046] Referring to APPENDIX Figure 1 , this embodiment proposes an intelligent inventory control system based on real-time data analysis and automation technology, which includes:

[0047] An inventory tracking module, which uses radio frequency identification technology and barcode scanning technology to track inventory in real time, update inventory data, record the basic information of inventory materials, and manage inventory according to the expiration time and the first-in, first-out principle;

[0048] Inventory demand forecasting module, which constructs and trains an inventory demand forecasting model based on the historical data of a certain group of people, uses the inventory demand forecasting model to determine the types of materials required for the current inventory, forecasts the inventory quantity of materials based on customer demand, outputs the results including the list of required inventory material types and the forecast inventory quantity of each material, and dynamically adjusts the safety inventory level and formulates a replenishment plan based on the forecast results, and conducts anomaly detection and warning;

[0049] Inventory decision-making optimization module, which comprehensively analyzes the historical data and real-time inventory data of a certain group of people, filters and controls the inventory data according to the analysis results, provides data-driven decision-making basis for users, and optimizes inventory management.

[0050] In this embodiment, the inventory tracking module includes a radio frequency identification unit and a barcode scanning unit, where:

[0051] Radio frequency identification unit, which uses RFID tags and readers to achieve fast and batch collection of inventory information. The RFID tags are attached to the inventory materials and store information such as the unique identification code of the materials; the reader communicates with the RFID tags through radio frequency signals, without the need for line-of-sight scanning, and can not only quickly obtain the information of a single material, but also batch-read the data of multiple tags in a short time, greatly improving the efficiency and accuracy of inventory data collection; whether it is in the warehouse shelf inventory or in the material inbound and outbound links, the radio frequency identification unit can update the inventory data in real time to ensure that the inventory information obtained by the system always remains the latest state;

[0052] Barcode scanning unit, which records the movement track of inventory materials by scanning barcodes; in the daily management of inventory materials, whether it is shelving, unshelving or transfer operations, the staff can use barcode scanning equipment to scan the barcodes on the materials, and the system records the changes in inventory by identifying the barcode information.

[0053] The barcode scanning unit and the radio frequency identification unit cooperate with each other. The former is suitable for accurately recording the operations of single or small amounts of materials, while the latter plays an advantage in batch operations. The two jointly complete the collection and update of inventory data; at the same time, the barcode scanning unit is also responsible for recording the basic information of inventory materials, including material name, specification, inbound time, expiration time, etc., providing data support for subsequent inventory management strategies.

[0054] The inventory tracking module manages the inventory strictly in accordance with the expiration time and the first-in, first-out principle based on the recorded basic information of inventory materials. When materials are out of the warehouse, the system preferentially selects the materials with an approaching expiration time to avoid waste caused by expired materials; for materials of the same batch, the outbound operations are carried out in the order of inbound time to ensure the scientificity and rationality of inventory management.

[0055] In this embodiment, the inventory demand forecasting module includes a model construction and training unit, a demand forecasting unit, and a strategy adjustment and warning unit, where:

[0056] The model construction and training unit uses machine learning algorithms such as linear regression, decision trees, neural networks, etc. to construct and train an inventory demand forecasting model based on the historical data of a certain group of people; during the training process, the model construction and training unit combines real-time sales data to perform in-depth AI learning on the model, enabling it to capture the dynamic changes in market demand; at the same time, the model construction and training unit regularly backtracks the difference between the predicted and actual demands, and adjusts the model parameters through methods such as error analysis to continuously optimize the prediction accuracy of the model; for example, during the peak sales season or promotional activities, the rapid changes in real-time sales data can be promptly fed back into the model training, enabling the model to better adapt to market fluctuations.

[0057] The demand forecasting unit uses the trained inventory demand forecasting model to predict the types and quantities of materials required for the current inventory; the demand forecasting unit determines the list of types of materials required for inventory by analyzing customer demand data, combining market trends and historical sales patterns, and predicts the inventory quantity for each material; these prediction results provide an important basis for the enterprise's inventory procurement and allocation, helping the enterprise make preparations in advance to meet market demand.

[0058] The strategy adjustment and warning unit, based on the results of the demand forecasting unit, comprehensively considers the inventory risk threshold, service level target, and the predicted material inventory quantity to dynamically adjust the safety inventory level and formulate a replenishment plan; the inventory risk threshold is set according to the actual operation situation and risk tolerance of the enterprise, and the service level target reflects the enterprise's pursuit of customer satisfaction; when the predicted material inventory quantity is lower than the safety inventory level, the strategy adjustment and warning unit triggers a replenishment mechanism to promptly remind the enterprise to make a purchase; at the same time, the strategy adjustment and warning unit monitors the inventory status in real time by setting thresholds for key indicators such as inventory turnover rate and out-of-stock rate, and once the indicators exceed the threshold range, the system immediately issues a warning to help the enterprise promptly discover and solve problems in inventory management.

[0059] In this embodiment, the inventory decision optimization module includes a data comprehensive analysis unit and a decision support unit, where:

[0060] The data comprehensive analysis unit integrates the historical data and real-time inventory data of a certain group of people, and uses data mining technologies such as association rule mining and clustering analysis to analyze the association rules and trend patterns in the data; by analyzing key indicators such as inventory turnover rate and slow-moving products, the data comprehensive analysis unit can filter and control inventory data, identify potential problems and optimization opportunities in inventory management; for example, by analyzing that the inventory turnover rate of certain products is relatively low, the enterprise can take corresponding measures such as adjusting the procurement strategy and optimizing the promotion activities to improve the inventory turnover rate; in addition, the data comprehensive analysis unit realizes data interconnection with the inventory demand forecasting module, and based on the forecasting results of the inventory demand forecasting module, conducts more accurate inventory data analysis;

[0061] The decision support unit generates an inventory analysis report containing inventory structure analysis, inventory turnover rate analysis, inventory cost analysis, etc. based on the results of the data comprehensive analysis unit, providing multi-dimensional data support for users; inventory structure analysis helps users understand the proportion of various types of materials in the inventory and judge whether the inventory structure is reasonable; inventory turnover rate analysis reflects the turnover speed of the inventory and provides a basis for the enterprise to evaluate the efficiency of inventory management; inventory cost analysis helps users master the composition and change trend of inventory costs and provides a reference for cost control. These reports are presented in the form of intuitive charts and data, providing users with clear and comprehensive inventory management information, helping users make scientific decisions and optimize inventory management strategies.

[0062] In summary, by adopting the intelligent inventory control system based on real-time data analysis and automation technology of the present invention, the prediction accuracy can be improved, and the risks of inventory backlog and shortage can be reduced; the inventory tracking efficiency can be enhanced, manual intervention can be reduced, and the accuracy and input speed of data can be improved; the inventory management decision-making can be optimized, and the scientificity and accuracy of inventory management can be improved; the supply chain transparency can be enhanced, and the sound development of the supply chain can be promoted.

[0063] The above applications have elaborated in detail the principle and implementation manner of the present invention through specific examples. These embodiments are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art of this technical field without departing from the principle of the present invention shall fall within the scope of patent protection of the present invention.

Claims

1. An intelligent inventory control system based on real-time data analysis and automation technology, characterized in that, It includes: An inventory tracking module that uses radio frequency identification (RFID) technology and barcode scanning technology to track inventory in real time, update inventory data, record the basic information of inventory materials, and manage inventory based on the expiration time and the first-in, first-out (FIFO) principle; An inventory demand forecasting module that constructs and trains an inventory demand forecasting model based on the historical data of a certain group of people, uses the inventory demand forecasting model to determine the types of materials required for the current inventory, forecasts the inventory quantity of materials based on customer demand, outputs the results including the list of required inventory material types and the forecast inventory quantity of each material, and dynamically adjusts the safety inventory level and formulates a replenishment plan based on the forecast results, and conducts anomaly detection and warning; An inventory decision optimization module that comprehensively analyzes the historical data and real-time inventory data of a certain group of people, filters and controls the inventory data according to the analysis results, provides data-driven decision-making basis for users, and optimizes inventory management.

2. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 1, wherein The inventory tracking module includes an RFID unit and a barcode scanning unit, where: The RFID unit uses RFID tags and readers, does not require line-of-sight scanning, supports batch reading, and updates inventory data in real time; The barcode scanning unit records inventory movements by scanning barcodes, collaborates with the RFID unit to complete the collection and update of inventory data, and records the basic information of inventory materials.

3. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 1, wherein The basic information of inventory materials recorded by the inventory tracking module includes material name, specification, warehousing time, and expiration time; The inventory tracking module manages inventory according to the expiration time and the FIFO principle.

4. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 1, wherein The inventory demand forecasting module includes a model construction and training unit, a demand forecasting unit, and a strategy adjustment and warning unit, where: The model construction and training unit uses machine learning algorithms to construct and train an inventory demand forecasting model based on the historical data of a certain group of people; The demand forecasting unit uses the trained inventory demand forecasting model to determine the types of materials required for the current inventory, forecasts the inventory quantity of materials based on customer demand, and outputs the results including the list of required inventory material types and the forecast inventory quantity of each material; The strategy adjustment and warning unit dynamically adjusts the safety inventory level, formulates a replenishment plan based on the results of the demand forecasting unit, and conducts anomaly detection and warning by setting the thresholds of key indicators.

5. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 4, wherein When training the inventory demand forecasting model, the model construction and training unit conducts in-depth AI learning by combining real-time sales data, and regularly retraces the difference between the forecast and the actual demand to adjust the model parameters.

6. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 4, wherein When dynamically adjusting the safety inventory level, the strategy adjustment and warning unit comprehensively considers the inventory risk threshold, the service level target, and the forecast inventory quantity of materials.

7. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 1, characterized in that, The inventory decision optimization module includes a data comprehensive analysis unit and a decision support unit, where: The data comprehensive analysis unit integrates the historical data and real-time inventory data of a certain group of people, uses data mining technology to analyze the association rules and trend patterns in the data, and filters and controls the inventory data according to the key indicators; The decision support unit generates an inventory analysis report based on the results of the data comprehensive analysis unit, provides data-driven decision-making basis for users, and optimizes inventory management.

8. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 7, characterized in that, The inventory analysis report includes inventory structure analysis, inventory turnover analysis, and inventory cost analysis, providing multi-dimensional data support for user decision-making.

9. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 7, characterized in that, The data comprehensive analysis unit accesses market trend data and supplier data to improve the comprehensiveness and accuracy of the comprehensive analysis of inventory data.

10. The intelligent inventory control system based on real-time data analysis and automation technology according to claim 7, characterized in that, The inventory decision optimization module is data-interconnected with the inventory demand forecasting module, and analyzes and makes decisions on inventory data based on the forecasting results of the inventory demand forecasting module.

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