system

Abstract

The system according to this embodiment aims to perform demand forecasting, inventory management, and work schedule optimization for warehouse logistics. [Solution] The system according to the embodiment comprises a forecasting unit, a tracking unit, an optimization unit, and a staffing unit. The forecasting unit performs demand forecasting. The tracking unit tracks inventory status in real time based on the forecasting results obtained by the forecasting unit. The optimization unit optimizes picking routes and work schedules based on the inventory status obtained by the tracking unit. The staffing unit allocates staff based on the optimization results obtained by the optimization unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, demand forecasting, inventory management, and optimization of work schedules in warehouse logistics have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform demand forecasting, inventory management, and optimization of work schedules in warehouse logistics.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a forecasting unit, a tracking unit, an optimization unit, and a staffing unit. The forecasting unit performs demand forecasting. The tracking unit tracks inventory status in real time based on the forecasting results obtained by the forecasting unit. The optimization unit optimizes picking routes and work schedules based on the inventory status obtained by the tracking unit. The staffing unit allocates staff based on the optimization results obtained by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform demand forecasting, inventory management, and work schedule optimization for warehouse logistics. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system is provided with a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 is provided with a computer 22, a database, and a communication I / F 26. The computer 22 is provided with a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The warehouse logistics forecasting and business improvement solution according to an embodiment of the present invention is a system that uses an AI agent to improve the efficiency of logistics sites and enable future-oriented operations. This system uses an AI agent to make highly accurate volume forecasts based on historical data and external factors, and tracks the inventory status in the warehouse in real time using IoT sensors. Furthermore, the AI ​​agent optimizes picking routes and work schedules, enabling flexible staff allocation. This results in improved efficiency in logistics sites and future-oriented operations. For example, the AI ​​agent makes highly accurate volume forecasts based on historical data and external factors. In this process, machine learning is used to make demand forecasts with minimal errors. For example, past shipping data, seasonal factors, and economic indicators are considered to make forecasts that can respond to peak seasons and sudden increases in demand. Next, IoT sensors are used to track the inventory status in the warehouse in real time. This eliminates inefficiencies in inventory management and enables real-time understanding of inventory status. For example, the number of items in stock and their location information can be immediately grasped, preventing excess or shortages of inventory. Furthermore, the AI ​​agent optimizes picking routes and work schedules. This reduces variability in work efficiency and improves overall work efficiency. For example, it can suggest the optimal picking route and shorten working time. Furthermore, the AI ​​agent enables flexible staffing. By creating staffing plans based on AI predictions, it strengthens handling of peak seasons and reduces variations in work efficiency due to lack of experience or skill differences. For instance, it ensures the necessary number of staff are available during peak seasons, allowing for efficient work. This system enables increased efficiency and future-oriented operations in logistics. Specifically, it can improve annual cost reductions by up to 25% and work efficiency by up to 30%. Additionally, it improves accuracy and customer satisfaction through reduced human error and improved inventory precision. For example, it improves on-time delivery rates and increases customer trust. Moreover, its ease of implementation and measurable design allow for seamless integration with existing warehouse systems, and the effects of implementation can be visualized using KPIs (cost reduction rate, work efficiency improvement rate). For example, it offers comprehensive support, including troubleshooting and improvement suggestions after implementation.In the future, further efficiency can be pursued through the introduction of digital twins, their deployment across the entire supply chain, and the adoption of new technologies. For example, 3D simulations of warehouses can be performed to aim for seamless collaboration with logistics providers and business partners. This will enable warehouse logistics forecasting and operational improvement solutions to achieve increased efficiency and future-oriented operations in logistics operations.

[0029] The warehouse logistics forecasting and business improvement solution according to this embodiment comprises a forecasting unit, a tracking unit, an optimization unit, and a placement unit. The forecasting unit performs demand forecasting. The forecasting unit makes forecasts to respond to peak seasons and sudden increases in demand, taking into account, for example, past shipping data, seasonal factors, and economic indicators. The forecasting unit can perform demand forecasting with minimal error by utilizing machine learning. For example, the forecasting unit analyzes demand patterns based on past shipping data and predicts future demand. The forecasting unit can also predict fluctuations in demand by taking seasonal factors into consideration. Furthermore, the forecasting unit can also predict increases and decreases in demand based on economic indicators. The tracking unit tracks inventory status in real time based on the forecast results obtained by the forecasting unit. The tracking unit tracks inventory status in the warehouse in real time, for example, using IoT sensors. The tracking unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the tracking unit grasps the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The tracking unit can also grasp the location information of each product in real time and optimize inventory placement. Furthermore, the tracking unit can record the movement history of inventory and ensure traceability. The optimization unit optimizes picking routes and work schedules based on the inventory status obtained by the tracking unit. For example, the optimization unit can suggest the optimal picking route and reduce work time. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit can suggest the optimal picking route and reduce work time. The optimization unit can also optimize work schedules and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. The staffing unit allocates staff based on the optimization results obtained by the optimization unit. For example, the staffing unit can create a personnel plan based on AI predictions. The staffing unit can secure the necessary number of people during peak seasons and proceed with work efficiently. For example, the staffing unit can secure the necessary number of people during peak seasons and reduce variability in work efficiency. The staffing unit can also reduce variability in work efficiency due to lack of experience or skill differences. Furthermore, the staffing unit can make optimal allocations based on the skills and experience of the staff.As a result, the warehouse logistics forecasting and business improvement solution according to this embodiment can achieve increased efficiency in logistics operations and future-oriented operations.

[0030] The forecasting unit performs demand forecasting. For example, it considers historical shipment data, seasonal factors, and economic indicators to make forecasts to respond to peak seasons and sudden increases in demand. Specifically, it analyzes historical shipment data in detail to extract demand patterns. This includes time series analysis to understand fluctuations in shipment volume and increases / decreases in demand for specific products. Furthermore, to account for seasonal factors, it models seasonal demand fluctuations based on historical data. For example, it incorporates seasonality to predict increases / decreases in demand for specific products in summer and winter. It also uses economic indicators to reflect consumer purchasing intent and market trends. This utilizes economic data such as GDP growth rate, unemployment rate, and consumer confidence index. The forecasting unit integrates this data and builds a demand forecasting model using machine learning algorithms. For example, it uses regression analysis and time series forecasting models to predict future demand. Furthermore, the forecasting unit optimizes model parameters to minimize errors and improve forecast accuracy. This allows the forecasting unit to respond quickly to peak seasons and sudden increases in demand, supporting the optimization of inventory management and production planning.

[0031] The tracking unit tracks inventory status in real time based on prediction results obtained by the forecasting unit. For example, the tracking unit uses IoT sensors to track inventory status within the warehouse in real time. Specifically, it uses RFID tags and barcode scanners to instantly grasp the inventory quantity and location information of each product. This allows for accurate recording of each product's inbound / outbound information and movement history, ensuring inventory traceability. Furthermore, the tracking unit utilizes warehouse layout information to optimize inventory placement. For example, it determines the optimal placement location based on product demand and shipping frequency, improving the efficiency of picking operations. The tracking unit also monitors inventory levels in real time to prevent excesses and shortages, replenishing or adjusting as needed. This eliminates inefficiencies in inventory management and reduces inventory costs. Additionally, the tracking unit records inventory movement history, ensuring product traceability, which can be used for quality control and recall responses. In this way, the tracking unit improves the accuracy of inventory management and supports the efficiency of logistics operations.

[0032] The optimization unit optimizes picking routes and work schedules based on inventory information obtained by the tracking unit. Specifically, it utilizes warehouse layout information and product placement information to propose optimal picking routes and reduce work time. For example, by optimizing product placement and picking order, it minimizes the distance workers travel and improves work efficiency. The optimization unit also sets work priorities to optimize work schedules and achieve efficient operations. This includes determining work priorities based on product shipping deadlines and importance, and reducing work variability. Furthermore, the optimization unit assigns optimal tasks based on workers' skills and experience to reduce variability in work efficiency. This enables simultaneous improvement of work efficiency and quality. In addition, the optimization unit monitors work progress in real time and adjusts schedules and reallocates resources as needed. In this way, the optimization unit supports work efficiency and flexibility, improving the performance of the logistics site.

[0033] The staffing department allocates staff based on the optimization results obtained by the optimization department. Specifically, it utilizes past work data and staff skill information to create personnel plans based on AI predictions. For example, to ensure the necessary number of people are available during peak seasons and to ensure efficient work, it analyzes past peak season data to predict the required number of personnel. The staffing department also reduces variability in work efficiency by making optimal allocations based on staff skills and experience. This includes assigning appropriate tasks considering each staff member's areas of expertise and years of experience. Furthermore, the staffing department aims to improve work efficiency by developing staff skill development and training plans. For example, it conducts regular training sessions to promote the acquisition of new work procedures and techniques, thereby improving staff skills. The staffing department also monitors staff work status in real time and adjusts allocations and provides support as needed. This enables the staffing department to achieve efficient staff allocation and work optimization, improving the performance of the logistics site. In addition, the staffing department can collect staff feedback and use it to improve allocation plans and the work environment. This allows the staffing department to boost staff motivation and support improvements in work efficiency.

[0034] The data collection unit collects data. The data collection unit tracks the inventory status in the warehouse in real time, for example, using IoT sensors. The data collection unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the data collection unit can grasp the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The data collection unit can also grasp the location information of each product in real time, optimizing inventory placement. Furthermore, the data collection unit can record the inventory movement history, ensuring traceability. This allows the data collection unit to provide the forecasting unit with the necessary data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input inventory data acquired by IoT sensors into a generating AI, and have the generating AI perform analysis of the inventory data.

[0035] The data collection unit can provide data to the forecasting unit. For example, the data collection unit can track the inventory status in the warehouse in real time using IoT sensors and provide the data to the forecasting unit. The data collection unit can instantly grasp the inventory quantity and location information of each product and provide it to the forecasting unit. For example, the data collection unit can grasp the inventory quantity of each product in real time and provide it to the forecasting unit. The data collection unit can also grasp the location information of each product in real time and provide it to the forecasting unit. Furthermore, the data collection unit can record the inventory movement history and provide it to the forecasting unit. This allows the forecasting unit to obtain the necessary data from the data collection unit. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input inventory data acquired by IoT sensors into a generating AI and have the generating AI perform analysis of the inventory data.

[0036] The optimization unit can propose the optimal picking route. For example, the optimization unit proposes the optimal picking route based on the inventory status in the warehouse. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit proposes the optimal picking route and shortens work time. The optimization unit can also optimize the work schedule and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. As a result, work efficiency is improved by proposing the optimal picking route. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input inventory data into a generating AI and have the generating AI propose the optimal picking route.

[0037] The staffing department can create staffing plans based on AI predictions. For example, the staffing department can secure the necessary number of people during peak seasons based on AI predictions, thereby ensuring efficient work. The staffing department can reduce variations in work efficiency due to inexperience or skill differences. For example, the staffing department can secure the necessary number of people during peak seasons based on AI predictions, thereby reducing variations in work efficiency. The staffing department can also make optimal assignments based on the skills and experience of the staff. As a result, efficient staffing becomes possible by creating staffing plans based on AI predictions. Some or all of the above processes in the staffing department may be performed using AI, for example, or not using AI. For example, the staffing department can input AI prediction data into a generating AI and have the generating AI create a staffing plan.

[0038] The staffing department can secure the necessary number of personnel during peak seasons. For example, the staffing department can secure the necessary number of personnel during peak seasons and proceed with work efficiently. The staffing department can reduce variations in work efficiency due to lack of experience or skill differences. For example, the staffing department can secure the necessary number of personnel during peak seasons and reduce variations in work efficiency. The staffing department can also make optimal assignments based on the skills and experience of the staff. This reduces variations in work efficiency by securing the necessary number of personnel during peak seasons. Some or all of the above processes in the staffing department may be performed using AI, for example, or not using AI. For example, the staffing department can input personnel data for peak seasons into a generating AI and have the generating AI secure the necessary number of personnel.

[0039] The tracking unit can instantly grasp the inventory quantity and location information of each product. The tracking unit can, for example, use IoT sensors to track the inventory status in the warehouse in real time. The tracking unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the tracking unit can grasp the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The tracking unit can also grasp the location information of each product in real time, optimizing inventory placement. Furthermore, the tracking unit can record the movement history of inventory, ensuring traceability. This eliminates inefficiencies in inventory management by instantly grasping inventory quantities and location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input inventory data acquired by IoT sensors into a generating AI, and have the generating AI perform the task of determining inventory quantities and location information.

[0040] The optimization unit can reduce work time. For example, the optimization unit can propose the optimal picking route based on the inventory status in the warehouse, thereby reducing work time. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit can propose the optimal picking route, thereby reducing work time. The optimization unit can also optimize the work schedule and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. As a result, overall work efficiency is improved by reducing work time. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input inventory data into a generating AI and have the generating AI perform the work time reduction.

[0041] The staffing unit can reduce variations in work efficiency due to lack of experience or skill differences. For example, the staffing unit can secure the necessary number of people during peak seasons based on AI predictions, thereby enabling efficient work. The staffing unit can also optimize staffing based on the skills and experience of the staff. This improves overall work efficiency by reducing variations in work efficiency due to lack of experience or skill differences. Some or all of the above-described processes in the staffing unit may be performed using AI, for example, or without AI. For example, the staffing unit can input AI prediction data into a generating AI and have the generating AI execute staffing to reduce variations in work efficiency due to lack of experience or skill differences.

[0042] The forecasting unit can improve the accuracy of its forecasts by combining historical demand data with current market trends. For example, the forecasting unit can analyze seasonal demand patterns based on historical demand data and make forecasts by combining them with current market trends. The forecasting unit can improve the accuracy of its demand forecasts by incorporating external data such as economic indicators and consumer confidence indices. The forecasting unit can analyze social media trends in real time and reflect them in its demand forecasts. This improves the accuracy of the forecasts by combining historical demand data with current market trends. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical demand data and current market trend data into a generating AI and have the generating AI perform the task of improving the accuracy of the forecasts.

[0043] The prediction unit can automatically detect outliers and anomalies and incorporate them into the prediction model. For example, the prediction unit can analyze data using statistical methods to detect outliers and anomalies. The prediction unit can use machine learning algorithms to automatically identify outliers and anomalies and incorporate them into the prediction model. When the prediction unit detects outliers or anomalies, it can adjust the prediction model to minimize their impact. This improves the accuracy of the prediction model by automatically detecting outliers and anomalies. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on outliers and anomalies into a generating AI and have the generating AI incorporate them into the prediction model.

[0044] The forecasting unit can customize the forecasting model by considering the demand characteristics of each region. For example, the forecasting unit can analyze the demand characteristics of each region and create different forecasting models for each region. The forecasting unit can customize the forecasting model by considering the consumer behavior and purchasing patterns of each region. The forecasting unit can create forecasting models that reflect the economic conditions and market trends of each region. This improves the accuracy of the forecasting model by considering the demand characteristics of each region. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input regional demand data into a generating AI and have the generating AI perform the customization of the forecasting model.

[0045] The forecasting unit can analyze the trends of competitors and reflect them in the forecast results. For example, the forecasting unit can analyze the sales data and market share of competitors and reflect them in the forecast results. The forecasting unit can collect information on new products and campaigns of competitors and reflect it in the demand forecast. The forecasting unit can monitor the trends of competitors in real time and reflect it in the forecast results. As a result, the accuracy of the forecast results is improved by analyzing the trends of competitors. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input competitor trend data into a generating AI and have the generating AI perform the task of reflecting it in the forecast results.

[0046] The tracking unit can analyze the rate of inventory consumption in real time and optimize the timing of inventory replenishment. For example, the tracking unit can monitor the rate of inventory consumption in real time and predict the appropriate timing for replenishment. The tracking unit can automatically adjust the timing of replenishment based on the rate of inventory consumption. The tracking unit can analyze the rate of inventory consumption and optimize the timing of replenishment in accordance with fluctuations in demand. In this way, the timing of inventory replenishment can be optimized by analyzing the rate of inventory consumption in real time. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input inventory consumption rate data into a generating AI and have the generating AI perform the optimization of the timing of inventory replenishment.

[0047] The tracking unit can automatically detect inventory deterioration or damage and issue alerts. For example, the tracking unit can use IoT sensors to detect inventory deterioration or damage in real time. When deterioration or damage is detected, the tracking unit can automatically issue an alert to prompt action. The tracking unit can analyze the deterioration or damage data and take preventative measures. This enables a rapid response by automatically detecting inventory deterioration or damage. Some or all of the above processes in the tracking unit may be performed using AI, or not. For example, the tracking unit can input inventory deterioration or damage data into a generating AI and have the generating AI issue alerts.

[0048] The tracking unit can manage inventory while taking into account environmental data such as temperature and humidity in the warehouse. For example, the tracking unit can monitor the temperature and humidity in the warehouse in real time and reflect this in inventory management. The tracking unit can optimize the storage conditions of inventory based on the environmental data. The tracking unit can adjust the inventory management method in response to fluctuations in temperature and humidity. This improves the accuracy of inventory management by taking into account the environmental data in the warehouse. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input warehouse temperature and humidity data into a generating AI and have the generating AI perform adjustments to inventory management.

[0049] The tracking unit can record the inventory movement history and ensure traceability. For example, the tracking unit can record the inventory movement history in detail and ensure traceability. The tracking unit can analyze the inventory movement history and identify the cause if a problem occurs. Based on the inventory movement history, the tracking unit can propose an efficient inventory management method. In this way, traceability can be ensured by recording the inventory movement history. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input inventory movement history data into a generating AI and have the generating AI perform the task of ensuring traceability.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect environmental data such as temperature and humidity in the warehouse in real time and reflect it in inventory management. For example, the data collection unit can optimize inventory storage conditions in response to fluctuations in temperature and humidity. Furthermore, the data collection unit can predict inventory deterioration and damage based on environmental data and take preventative measures. In addition, the data collection unit can provide environmental data to other elements to improve the overall accuracy of inventory management. Thus, considering environmental data improves the accuracy of inventory management.

[0052] The staffing department can make optimal assignments based on staff skills and experience. For example, it can analyze each staff member's skill set and assign them to the most suitable tasks. It can also consider staff experience and assign less experienced staff to support them. Furthermore, it can assign staff to promote skill development. This enables efficient work assignments based on staff skills and experience.

[0053] The tracking unit can analyze the rate of inventory consumption in real time and optimize the timing of inventory replenishment. For example, the tracking unit can monitor the rate of inventory consumption and predict the appropriate timing for replenishment. Furthermore, the tracking unit can automatically adjust the replenishment timing based on the consumption rate. In addition, the tracking unit can optimize the replenishment timing in response to fluctuations in demand. This allows for the optimization of inventory replenishment timing by analyzing the rate of inventory consumption in real time.

[0054] The tracking unit can automatically detect inventory deterioration or damage and issue alerts. For example, the tracking unit can use IoT sensors to detect inventory deterioration or damage in real time. Furthermore, if deterioration or damage is detected, it can automatically issue an alert to prompt action. In addition, the tracking unit can analyze the deterioration and damage data and take preventative measures. This enables rapid response by automatically detecting inventory deterioration and damage.

[0055] The prediction unit can automatically detect outliers and anomalies and incorporate them into the prediction model. For example, the prediction unit can analyze data using statistical methods to detect outliers and anomalies. It can also use machine learning algorithms to automatically identify outliers and anomalies and incorporate them into the prediction model. Furthermore, when outliers or anomalies are detected, the prediction model can be adjusted to minimize their impact. This improves the accuracy of the prediction model by automatically detecting outliers and anomalies.

[0056] The forecasting unit can customize forecasting models by considering regional demand characteristics. For example, the forecasting unit can analyze regional demand characteristics and create different forecasting models for each region. It can also customize forecasting models by considering regional consumer behavior and purchasing patterns. Furthermore, it can create forecasting models that reflect regional economic conditions and market trends. As a result, the accuracy of the forecasting model is improved by considering regional demand characteristics.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The forecasting unit performs demand forecasting. The forecasting unit considers past shipment data, seasonal factors, economic indicators, etc., to make forecasts to respond to peak seasons and sudden increases in demand. The forecasting unit can use machine learning to perform demand forecasts with minimal error. Step 2: The tracking unit tracks inventory status in real time based on the prediction results obtained by the prediction unit. The tracking unit uses IoT sensors to track inventory status in the warehouse in real time and instantly grasps the inventory quantity and location information of each product. Step 3: The optimization unit optimizes picking routes and work schedules based on inventory information obtained by the tracking unit. The optimization unit proposes the optimal picking route and reduces work time. It also optimizes the work schedule and improves work efficiency. Step 4: The staffing department allocates staff based on the optimization results obtained by the optimization department. The staffing department creates a personnel plan based on AI predictions, secures the necessary number of people during peak seasons, and proceeds with work efficiently.

[0059] (Example of form 2) The warehouse logistics forecasting and business improvement solution according to an embodiment of the present invention is a system that uses an AI agent to improve the efficiency of logistics sites and enable future-oriented operations. This system uses an AI agent to make highly accurate volume forecasts based on historical data and external factors, and tracks the inventory status in the warehouse in real time using IoT sensors. Furthermore, the AI ​​agent optimizes picking routes and work schedules, enabling flexible staff allocation. This results in improved efficiency in logistics sites and future-oriented operations. For example, the AI ​​agent makes highly accurate volume forecasts based on historical data and external factors. In this process, machine learning is used to make demand forecasts with minimal errors. For example, past shipping data, seasonal factors, and economic indicators are considered to make forecasts that can respond to peak seasons and sudden increases in demand. Next, IoT sensors are used to track the inventory status in the warehouse in real time. This eliminates inefficiencies in inventory management and enables real-time understanding of inventory status. For example, the number of items in stock and their location information can be immediately grasped, preventing excess or shortages of inventory. Furthermore, the AI ​​agent optimizes picking routes and work schedules. This reduces variability in work efficiency and improves overall work efficiency. For example, it can suggest the optimal picking route and shorten working time. Furthermore, the AI ​​agent enables flexible staffing. By creating staffing plans based on AI predictions, it strengthens handling of peak seasons and reduces variations in work efficiency due to lack of experience or skill differences. For instance, it ensures the necessary number of staff are available during peak seasons, allowing for efficient work. This system enables increased efficiency and future-oriented operations in logistics. Specifically, it can improve annual cost reductions by up to 25% and work efficiency by up to 30%. Additionally, it improves accuracy and customer satisfaction through reduced human error and improved inventory precision. For example, it improves on-time delivery rates and increases customer trust. Moreover, its ease of implementation and measurable design allow for seamless integration with existing warehouse systems, and the effects of implementation can be visualized using KPIs (cost reduction rate, work efficiency improvement rate). For example, it offers comprehensive support, including troubleshooting and improvement suggestions after implementation.In the future, further efficiency can be pursued through the introduction of digital twins, their deployment across the entire supply chain, and the adoption of new technologies. For example, 3D simulations of warehouses can be performed to aim for seamless collaboration with logistics providers and business partners. This will enable warehouse logistics forecasting and operational improvement solutions to achieve increased efficiency and future-oriented operations in logistics operations.

[0060] The warehouse logistics forecasting and business improvement solution according to this embodiment comprises a forecasting unit, a tracking unit, an optimization unit, and a placement unit. The forecasting unit performs demand forecasting. The forecasting unit makes forecasts to respond to peak seasons and sudden increases in demand, taking into account, for example, past shipping data, seasonal factors, and economic indicators. The forecasting unit can perform demand forecasting with minimal error by utilizing machine learning. For example, the forecasting unit analyzes demand patterns based on past shipping data and predicts future demand. The forecasting unit can also predict fluctuations in demand by taking seasonal factors into consideration. Furthermore, the forecasting unit can also predict increases and decreases in demand based on economic indicators. The tracking unit tracks inventory status in real time based on the forecast results obtained by the forecasting unit. The tracking unit tracks inventory status in the warehouse in real time, for example, using IoT sensors. The tracking unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the tracking unit grasps the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The tracking unit can also grasp the location information of each product in real time and optimize inventory placement. Furthermore, the tracking unit can record the movement history of inventory and ensure traceability. The optimization unit optimizes picking routes and work schedules based on the inventory status obtained by the tracking unit. For example, the optimization unit can suggest the optimal picking route and reduce work time. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit can suggest the optimal picking route and reduce work time. The optimization unit can also optimize work schedules and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. The staffing unit allocates staff based on the optimization results obtained by the optimization unit. For example, the staffing unit can create a personnel plan based on AI predictions. The staffing unit can secure the necessary number of people during peak seasons and proceed with work efficiently. For example, the staffing unit can secure the necessary number of people during peak seasons and reduce variability in work efficiency. The staffing unit can also reduce variability in work efficiency due to lack of experience or skill differences. Furthermore, the staffing unit can make optimal allocations based on the skills and experience of the staff.As a result, the warehouse logistics forecasting and business improvement solution according to this embodiment can achieve increased efficiency in logistics operations and future-oriented operations.

[0061] The forecasting unit performs demand forecasting. For example, it considers historical shipment data, seasonal factors, and economic indicators to make forecasts to respond to peak seasons and sudden increases in demand. Specifically, it analyzes historical shipment data in detail to extract demand patterns. This includes time series analysis to understand fluctuations in shipment volume and increases / decreases in demand for specific products. Furthermore, to account for seasonal factors, it models seasonal demand fluctuations based on historical data. For example, it incorporates seasonality to predict increases / decreases in demand for specific products in summer and winter. It also uses economic indicators to reflect consumer purchasing intent and market trends. This utilizes economic data such as GDP growth rate, unemployment rate, and consumer confidence index. The forecasting unit integrates this data and builds a demand forecasting model using machine learning algorithms. For example, it uses regression analysis and time series forecasting models to predict future demand. Furthermore, the forecasting unit optimizes model parameters to minimize errors and improve forecast accuracy. This allows the forecasting unit to respond quickly to peak seasons and sudden increases in demand, supporting the optimization of inventory management and production planning.

[0062] The tracking unit tracks inventory status in real time based on prediction results obtained by the forecasting unit. For example, the tracking unit uses IoT sensors to track inventory status within the warehouse in real time. Specifically, it uses RFID tags and barcode scanners to instantly grasp the inventory quantity and location information of each product. This allows for accurate recording of each product's inbound / outbound information and movement history, ensuring inventory traceability. Furthermore, the tracking unit utilizes warehouse layout information to optimize inventory placement. For example, it determines the optimal placement location based on product demand and shipping frequency, improving the efficiency of picking operations. The tracking unit also monitors inventory levels in real time to prevent excesses and shortages, replenishing or adjusting as needed. This eliminates inefficiencies in inventory management and reduces inventory costs. Additionally, the tracking unit records inventory movement history, ensuring product traceability, which can be used for quality control and recall responses. In this way, the tracking unit improves the accuracy of inventory management and supports the efficiency of logistics operations.

[0063] The optimization unit optimizes picking routes and work schedules based on inventory information obtained by the tracking unit. Specifically, it utilizes warehouse layout information and product placement information to propose optimal picking routes and reduce work time. For example, by optimizing product placement and picking order, it minimizes the distance workers travel and improves work efficiency. The optimization unit also sets work priorities to optimize work schedules and achieve efficient operations. This includes determining work priorities based on product shipping deadlines and importance, and reducing work variability. Furthermore, the optimization unit assigns optimal tasks based on workers' skills and experience to reduce variability in work efficiency. This enables simultaneous improvement of work efficiency and quality. In addition, the optimization unit monitors work progress in real time and adjusts schedules and reallocates resources as needed. In this way, the optimization unit supports work efficiency and flexibility, improving the performance of the logistics site.

[0064] The staffing department allocates staff based on the optimization results obtained by the optimization department. Specifically, it utilizes past work data and staff skill information to create personnel plans based on AI predictions. For example, to ensure the necessary number of people are available during peak seasons and to ensure efficient work, it analyzes past peak season data to predict the required number of personnel. The staffing department also reduces variability in work efficiency by making optimal allocations based on staff skills and experience. This includes assigning appropriate tasks considering each staff member's areas of expertise and years of experience. Furthermore, the staffing department aims to improve work efficiency by developing staff skill development and training plans. For example, it conducts regular training sessions to promote the acquisition of new work procedures and techniques, thereby improving staff skills. The staffing department also monitors staff work status in real time and adjusts allocations and provides support as needed. This enables the staffing department to achieve efficient staff allocation and work optimization, improving the performance of the logistics site. In addition, the staffing department can collect staff feedback and use it to improve allocation plans and the work environment. This allows the staffing department to boost staff motivation and support improvements in work efficiency.

[0065] The data collection unit collects data. The data collection unit tracks the inventory status in the warehouse in real time, for example, using IoT sensors. The data collection unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the data collection unit can grasp the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The data collection unit can also grasp the location information of each product in real time, optimizing inventory placement. Furthermore, the data collection unit can record the inventory movement history, ensuring traceability. This allows the data collection unit to provide the forecasting unit with the necessary data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input inventory data acquired by IoT sensors into a generating AI, and have the generating AI perform analysis of the inventory data.

[0066] The data collection unit can provide data to the forecasting unit. For example, the data collection unit can track the inventory status in the warehouse in real time using IoT sensors and provide the data to the forecasting unit. The data collection unit can instantly grasp the inventory quantity and location information of each product and provide it to the forecasting unit. For example, the data collection unit can grasp the inventory quantity of each product in real time and provide it to the forecasting unit. The data collection unit can also grasp the location information of each product in real time and provide it to the forecasting unit. Furthermore, the data collection unit can record the inventory movement history and provide it to the forecasting unit. This allows the forecasting unit to obtain the necessary data from the data collection unit. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input inventory data acquired by IoT sensors into a generating AI and have the generating AI perform analysis of the inventory data.

[0067] The optimization unit can propose the optimal picking route. For example, the optimization unit proposes the optimal picking route based on the inventory status in the warehouse. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit proposes the optimal picking route and shortens work time. The optimization unit can also optimize the work schedule and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. As a result, work efficiency is improved by proposing the optimal picking route. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input inventory data into a generating AI and have the generating AI propose the optimal picking route.

[0068] The staffing department can create staffing plans based on AI predictions. For example, the staffing department can secure the necessary number of people during peak seasons based on AI predictions, thereby ensuring efficient work. The staffing department can reduce variations in work efficiency due to inexperience or skill differences. For example, the staffing department can secure the necessary number of people during peak seasons based on AI predictions, thereby reducing variations in work efficiency. The staffing department can also make optimal assignments based on the skills and experience of the staff. As a result, efficient staffing becomes possible by creating staffing plans based on AI predictions. Some or all of the above processes in the staffing department may be performed using AI, for example, or not using AI. For example, the staffing department can input AI prediction data into a generating AI and have the generating AI create a staffing plan.

[0069] The staffing department can secure the necessary number of personnel during peak seasons. For example, the staffing department can secure the necessary number of personnel during peak seasons and proceed with work efficiently. The staffing department can reduce variations in work efficiency due to lack of experience or skill differences. For example, the staffing department can secure the necessary number of personnel during peak seasons and reduce variations in work efficiency. The staffing department can also make optimal assignments based on the skills and experience of the staff. This reduces variations in work efficiency by securing the necessary number of personnel during peak seasons. Some or all of the above processes in the staffing department may be performed using AI, for example, or not using AI. For example, the staffing department can input personnel data for peak seasons into a generating AI and have the generating AI secure the necessary number of personnel.

[0070] The tracking unit can instantly grasp the inventory quantity and location information of each product. The tracking unit can, for example, use IoT sensors to track the inventory status in the warehouse in real time. The tracking unit can instantly grasp the inventory quantity and location information of each product, preventing inventory surpluses and shortages. For example, the tracking unit can grasp the inventory quantity of each product in real time, eliminating inefficiencies in inventory management. The tracking unit can also grasp the location information of each product in real time, optimizing inventory placement. Furthermore, the tracking unit can record the movement history of inventory, ensuring traceability. This eliminates inefficiencies in inventory management by instantly grasping inventory quantities and location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input inventory data acquired by IoT sensors into a generating AI, and have the generating AI perform the task of determining inventory quantities and location information.

[0071] The optimization unit can reduce work time. For example, the optimization unit can propose the optimal picking route based on the inventory status in the warehouse, thereby reducing work time. The optimization unit can reduce variability in work efficiency and improve overall work efficiency. For example, the optimization unit can propose the optimal picking route, thereby reducing work time. The optimization unit can also optimize the work schedule and improve work efficiency. Furthermore, the optimization unit can set work priorities and achieve efficient work. As a result, overall work efficiency is improved by reducing work time. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input inventory data into a generating AI and have the generating AI perform the work time reduction.

[0072] The staffing unit can reduce variations in work efficiency due to lack of experience or skill differences. For example, the staffing unit can secure the necessary number of people during peak seasons based on AI predictions, thereby enabling efficient work. The staffing unit can also optimize staffing based on the skills and experience of the staff. This improves overall work efficiency by reducing variations in work efficiency due to lack of experience or skill differences. Some or all of the above-described processes in the staffing unit may be performed using AI, for example, or without AI. For example, the staffing unit can input AI prediction data into a generating AI and have the generating AI execute staffing to reduce variations in work efficiency due to lack of experience or skill differences.

[0073] The forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated emotions. For example, if the user is stressed, the forecasting unit will place more emphasis on historical data to improve the accuracy of the forecast. If the user is relaxed, the forecasting unit can make forecasts that place more emphasis on current market trends. If the user is in a hurry, the forecasting unit can use a simplified forecasting model to provide forecast results quickly. This allows for more accurate forecasts by adjusting the accuracy of the demand forecast based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the forecasting unit may be performed using AI or not using AI. For example, the forecasting unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy of the demand forecast.

[0074] The forecasting unit can improve the accuracy of its forecasts by combining historical demand data with current market trends. For example, the forecasting unit can analyze seasonal demand patterns based on historical demand data and make forecasts by combining them with current market trends. The forecasting unit can improve the accuracy of its demand forecasts by incorporating external data such as economic indicators and consumer confidence indices. The forecasting unit can analyze social media trends in real time and reflect them in its demand forecasts. This improves the accuracy of the forecasts by combining historical demand data with current market trends. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input historical demand data and current market trend data into a generating AI and have the generating AI perform the task of improving the accuracy of the forecasts.

[0075] The prediction unit can automatically detect outliers and anomalies and incorporate them into the prediction model. For example, the prediction unit can analyze data using statistical methods to detect outliers and anomalies. The prediction unit can use machine learning algorithms to automatically identify outliers and anomalies and incorporate them into the prediction model. When the prediction unit detects outliers or anomalies, it can adjust the prediction model to minimize their impact. This improves the accuracy of the prediction model by automatically detecting outliers and anomalies. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on outliers and anomalies into a generating AI and have the generating AI incorporate them into the prediction model.

[0076] The forecasting unit can estimate the user's emotions and adjust how the demand forecast results are displayed based on the estimated emotions. For example, if the user is stressed, the forecasting unit can provide a simple and easy-to-read display. If the user is relaxed, the forecasting unit can provide a display that includes detailed information. If the user is in a hurry, the forecasting unit can provide a display that gets straight to the point. By adjusting how the demand forecast results are displayed based on the user's emotions, a user-friendly display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the forecasting unit may be performed using AI or not using AI. For example, the forecasting unit can input user emotion data into the generative AI and have the generative AI adjust how the demand forecast results are displayed.

[0077] The forecasting unit can customize the forecasting model by considering the demand characteristics of each region. For example, the forecasting unit can analyze the demand characteristics of each region and create different forecasting models for each region. The forecasting unit can customize the forecasting model by considering the consumer behavior and purchasing patterns of each region. The forecasting unit can create forecasting models that reflect the economic conditions and market trends of each region. This improves the accuracy of the forecasting model by considering the demand characteristics of each region. Some or all of the above processes in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input regional demand data into a generating AI and have the generating AI perform the customization of the forecasting model.

[0078] The forecasting unit can analyze the trends of competitors and reflect them in the forecast results. For example, the forecasting unit can analyze the sales data and market share of competitors and reflect them in the forecast results. The forecasting unit can collect information on new products and campaigns of competitors and reflect it in the demand forecast. The forecasting unit can monitor the trends of competitors in real time and reflect it in the forecast results. As a result, the accuracy of the forecast results is improved by analyzing the trends of competitors. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input competitor trend data into a generating AI and have the generating AI perform the task of reflecting it in the forecast results.

[0079] The tracking unit can estimate the user's emotions and adjust the frequency of inventory tracking based on the estimated emotions. For example, if the user is stressed, the tracking unit can increase the frequency of inventory tracking to provide reassurance. If the user is relaxed, the tracking unit can decrease the frequency of inventory tracking to reduce the workload. If the user is in a hurry, the tracking unit can adjust the tracking frequency to allow for quick understanding of the inventory status. In this way, adjusting the frequency of inventory tracking based on the user's emotions can provide reassurance to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of inventory tracking.

[0080] The tracking unit can analyze the rate of inventory consumption in real time and optimize the timing of inventory replenishment. For example, the tracking unit can monitor the rate of inventory consumption in real time and predict the appropriate timing for replenishment. The tracking unit can automatically adjust the timing of replenishment based on the rate of inventory consumption. The tracking unit can analyze the rate of inventory consumption and optimize the timing of replenishment in accordance with fluctuations in demand. In this way, the timing of inventory replenishment can be optimized by analyzing the rate of inventory consumption in real time. Some or all of the above processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input inventory consumption rate data into a generating AI and have the generating AI perform the optimization of the timing of inventory replenishment.

[0081] The tracking unit can automatically detect inventory deterioration or damage and issue alerts. For example, the tracking unit can use IoT sensors to detect inventory deterioration or damage in real time. When deterioration or damage is detected, the tracking unit can automatically issue an alert to prompt action. The tracking unit can analyze the deterioration or damage data and take preventative measures. This enables a rapid response by automatically detecting inventory deterioration or damage. Some or all of the above processes in the tracking unit may be performed using AI, or not. For example, the tracking unit can input inventory deterioration or damage data into a generating AI and have the generating AI issue alerts.

[0082] The tracking unit can estimate the user's emotions and adjust the display method of inventory tracking based on the estimated user emotions. For example, if the user is stressed, the tracking unit can provide a simple and highly visible display method. If the user is relaxed, the tracking unit can provide a display method that includes detailed information. If the user is in a hurry, the tracking unit can provide a display method that gets straight to the point. By adjusting the display method of inventory tracking based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method of inventory tracking.

[0083] The tracking unit can manage inventory while taking into account environmental data such as temperature and humidity in the warehouse. For example, the tracking unit can monitor the temperature and humidity in the warehouse in real time and reflect this in inventory management. The tracking unit can optimize the storage conditions of inventory based on the environmental data. The tracking unit can adjust the inventory management method in response to fluctuations in temperature and humidity. This improves the accuracy of inventory management by taking into account the environmental data in the warehouse. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input warehouse temperature and humidity data into a generating AI and have the generating AI perform adjustments to inventory management.

[0084] The tracking unit can record the inventory movement history and ensure traceability. For example, the tracking unit can record the inventory movement history in detail and ensure traceability. The tracking unit can analyze the inventory movement history and identify the cause if a problem occurs. Based on the inventory movement history, the tracking unit can propose an efficient inventory management method. In this way, traceability can be ensured by recording the inventory movement history. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input inventory movement history data into a generating AI and have the generating AI perform the task of ensuring traceability.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The forecasting unit can estimate user emotions when forecasting demand and dynamically adjust the forecasting model based on those emotions. For example, if a user is stressed, the forecasting unit can place more emphasis on historical data to improve forecast accuracy. If a user is relaxed, the forecasting unit can focus on current market trends. Furthermore, if a user is in a hurry, a simplified forecasting model can be used to provide forecast results quickly. By adjusting the accuracy of demand forecasting based on user emotions, more accurate forecasts become possible.

[0087] The data collection unit can collect environmental data such as temperature and humidity in the warehouse in real time and reflect it in inventory management. For example, the data collection unit can optimize inventory storage conditions in response to fluctuations in temperature and humidity. Furthermore, the data collection unit can predict inventory deterioration and damage based on environmental data and take preventative measures. In addition, the data collection unit can provide environmental data to other elements to improve the overall accuracy of inventory management. Thus, considering environmental data improves the accuracy of inventory management.

[0088] The optimization unit can estimate the user's emotions and adjust picking routes and work schedules based on those estimates. For example, if the user is stressed, the optimization unit can suggest a more efficient picking route to reduce the workload. If the user is relaxed, it can provide a detailed work schedule. Furthermore, if the user is in a hurry, it can set priorities to complete tasks quickly. This allows for improved work efficiency based on the user's emotions.

[0089] The staffing department can make optimal assignments based on staff skills and experience. For example, it can analyze each staff member's skill set and assign them to the most suitable tasks. It can also consider staff experience and assign less experienced staff to support them. Furthermore, it can assign staff to promote skill development. This enables efficient work assignments based on staff skills and experience.

[0090] The tracking unit can analyze the rate of inventory consumption in real time and optimize the timing of inventory replenishment. For example, the tracking unit can monitor the rate of inventory consumption and predict the appropriate timing for replenishment. Furthermore, the tracking unit can automatically adjust the replenishment timing based on the consumption rate. In addition, the tracking unit can optimize the replenishment timing in response to fluctuations in demand. This allows for the optimization of inventory replenishment timing by analyzing the rate of inventory consumption in real time.

[0091] The forecasting unit can estimate the user's emotions and adjust how it displays the demand forecast results based on those emotions. For example, if the user is stressed, the forecasting unit can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how the demand forecast results are displayed based on the user's emotions, it becomes possible to create a display that is easy for the user to understand.

[0092] The tracking unit can automatically detect inventory deterioration or damage and issue alerts. For example, the tracking unit can use IoT sensors to detect inventory deterioration or damage in real time. Furthermore, if deterioration or damage is detected, it can automatically issue an alert to prompt action. In addition, the tracking unit can analyze the deterioration and damage data and take preventative measures. This enables rapid response by automatically detecting inventory deterioration and damage.

[0093] The prediction unit can automatically detect outliers and anomalies and incorporate them into the prediction model. For example, the prediction unit can analyze data using statistical methods to detect outliers and anomalies. It can also use machine learning algorithms to automatically identify outliers and anomalies and incorporate them into the prediction model. Furthermore, when outliers or anomalies are detected, the prediction model can be adjusted to minimize their impact. This improves the accuracy of the prediction model by automatically detecting outliers and anomalies.

[0094] The tracking unit can estimate the user's emotions and adjust the frequency of inventory tracking based on those emotions. For example, if the user is stressed, the tracking unit can increase the frequency of inventory tracking to provide reassurance. Conversely, if the user is relaxed, the tracking frequency can be reduced to lessen the workload. Furthermore, if the user is in a hurry, the tracking frequency can be adjusted to allow for quick access to inventory information. In this way, adjusting the frequency of inventory tracking based on the user's emotions can provide reassurance to the user.

[0095] The forecasting unit can customize forecasting models by considering regional demand characteristics. For example, the forecasting unit can analyze regional demand characteristics and create different forecasting models for each region. It can also customize forecasting models by considering regional consumer behavior and purchasing patterns. Furthermore, it can create forecasting models that reflect regional economic conditions and market trends. As a result, the accuracy of the forecasting model is improved by considering regional demand characteristics.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The forecasting unit performs demand forecasting. The forecasting unit considers past shipment data, seasonal factors, economic indicators, etc., to make forecasts to respond to peak seasons and sudden increases in demand. The forecasting unit can use machine learning to perform demand forecasts with minimal error. Step 2: The tracking unit tracks inventory status in real time based on the prediction results obtained by the prediction unit. The tracking unit uses IoT sensors to track inventory status in the warehouse in real time and instantly grasps the inventory quantity and location information of each product. Step 3: The optimization unit optimizes picking routes and work schedules based on inventory information obtained by the tracking unit. The optimization unit proposes the optimal picking route and reduces work time. It also optimizes the work schedule and improves work efficiency. Step 4: The staffing department allocates staff based on the optimization results obtained by the optimization department. The staffing department creates a personnel plan based on AI predictions, secures the necessary number of people during peak seasons, and proceeds with work efficiently.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements described above, including the forecasting unit, tracking unit, optimization unit, and placement unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the forecasting unit is implemented by the control unit 46A of the smart device 14 and performs demand forecasting based on past shipment data, seasonal factors, economic indicators, etc. The tracking unit tracks inventory status in real time using the IoT sensor of the smart device 14. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes picking routes and work schedules. The placement unit is implemented by the control unit 46A of the smart device 14 and creates a personnel plan based on AI forecasts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the prediction unit, tracking unit, optimization unit, and placement unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the prediction unit is implemented by the control unit 46A of the smart glasses 214 and performs demand forecasting based on past shipment data, seasonal factors, economic indicators, etc. The tracking unit tracks inventory status in real time using the IoT sensors of the smart glasses 214, for example. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes picking routes and work schedules. The placement unit is implemented by the control unit 46A of the smart glasses 214 and creates a personnel plan based on AI predictions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the forecasting unit, tracking unit, optimization unit, and placement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the forecasting unit is implemented by the control unit 46A of the headset terminal 314 and performs demand forecasting based on past shipment data, seasonal factors, economic indicators, etc. The tracking unit tracks inventory status in real time using the IoT sensor of the headset terminal 314, for example. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes picking routes and work schedules. The placement unit is implemented by the control unit 46A of the headset terminal 314 and creates a personnel plan based on AI forecasts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the forecasting unit, tracking unit, optimization unit, and placement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the forecasting unit is implemented by the control unit 46A of the robot 414 and performs demand forecasting based on past shipping data, seasonal factors, economic indicators, etc. The tracking unit tracks inventory status in real time using the IoT sensors of the robot 414. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes picking routes and work schedules. The placement unit is implemented by the control unit 46A of the robot 414 and creates a personnel plan based on AI forecasts. The correspondence between each unit and the equipment and control unit is not limited to the examples described above and can be changed in various ways.

[0151] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A forecasting unit that performs demand forecasting, A tracking unit tracks the inventory status in real time based on the prediction results obtained by the prediction unit, An optimization unit optimizes picking routes and work schedules based on inventory status obtained by the tracking unit, A staffing unit that performs staffing based on the optimization results obtained by the optimization unit, Equipped with A system characterized by the following features. (Note 2) It includes a data collection unit for collecting data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Provide data to the prediction unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The optimization unit, We propose the optimal picking route. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned arrangement section is, Develop a workforce plan based on AI predictions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned arrangement section is, We ensure we have enough staff during peak seasons. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned tracking unit is Instantly grasp the inventory level and location information for each product. The system described in Appendix 1, characterized by the features described herein. (Note 8) The optimization unit, Reduce working time The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned arrangement section is, Reduces variations in work efficiency due to lack of experience or skill differences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The prediction unit, It estimates user sentiment and adjusts the accuracy of demand forecasts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The prediction unit, Combining historical demand data with current market trends improves forecast accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, Automatically detect outliers and anomalies and incorporate them into the predictive model. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, Adjust the way we estimate user sentiment and display demand forecast results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, Customize the forecasting model to take into account the demand characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, Analyze the trends of competitors and reflect them in the forecast results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned tracking unit is It estimates user sentiment and adjusts the frequency of inventory tracking based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned tracking unit is Analyze inventory consumption rate in real time and optimize inventory replenishment timing. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned tracking unit is Automatically detects inventory deterioration or damage and issues alerts. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is It estimates the user's emotions and adjusts how inventory tracking is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is Inventory management is carried out while taking into account environmental data such as temperature and humidity inside the warehouse. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is Record the history of inventory movements and ensure traceability. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A forecasting unit that performs demand forecasting, A tracking unit tracks the inventory status in real time based on the prediction results obtained by the prediction unit, An optimization unit optimizes picking routes and work schedules based on inventory status obtained by the tracking unit, A staffing unit that performs staffing based on the optimization results obtained by the optimization unit, Equipped with A system characterized by the following features.

2. It includes a data collection unit for collecting data. The system according to feature 1.

3. The aforementioned collection unit is The prediction unit provides data. The system according to feature 2.

4. The optimization unit, We propose the optimal picking route. The system according to feature 1.

5. The aforementioned arrangement section is, Develop a staffing plan based on AI predictions. The system according to feature 1.

6. The aforementioned arrangement section is, We ensure we have enough staff during peak seasons. The system according to feature 1.

7. The aforementioned tracking unit is Instantly grasp the inventory level and location information for each product. The system according to feature 1.

8. The optimization unit, Reduce working time The system according to feature 1.

Images