Fabric inventory optimization method and system

Abstract

The invention discloses a fabric inventory optimization method and system, and the method comprises the steps: collecting market, sales and production data; predicting a fabric demand according to the collected data; planning fabric purchase and production plans according to the predicted demands, and making inventory space planning in advance; and monitoring the fabric inventory level in real time, and adjusting the purchase and production plan according to the inventory level. According to the invention, market demands and trends can be reflected in real time, fabric demands can be effectively predicted and planned, the inventory level can be dynamically adjusted, and optimal management of fabric inventory is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory optimization, and in particular to a method and system for optimizing fabric inventory. Background Art

[0002] In traditional fabric management, inventory optimization has always been a challenge. Due to the ever-changing market demand, the updating of fashion trends, and the complexity of production plans, it is difficult to accurately predict and plan fabric demand. This often leads to a backlog or shortage of fabric inventory, which affects production plans and sales performance. Therefore, developing a system and method that can reflect market demand and trends in real time, effectively predict and plan fabric demand, and dynamically adjust inventory levels is of great value to improving the operational efficiency and reducing costs of enterprises. Summary of the invention

[0003] To solve the above problems, the purpose of the embodiments of the present invention is to provide a method and system for optimizing fabric inventory, which can not only reflect market demand and trends in real time, effectively predict and plan fabric demand, but also dynamically adjust inventory levels to achieve optimized management of fabric inventory. This can not only avoid backlogs or shortages of fabric inventory, improve the production efficiency and sales performance of enterprises, but also reduce inventory costs and improve the competitiveness of enterprises.

[0004] To achieve the above object, the present invention adopts the following technical solution: a method for optimizing fabric inventory, characterized in that it includes: Collect market, sales and production data; Forecast fabric demand based on collected data; Plan fabric purchases and production plans based on forecasted demand, and plan inventory space in advance; Monitor fabric inventory levels in real time and adjust purchasing and production plans based on inventory levels.

[0005] Preferably, the collected data includes: Market data includes: popular trends, competitor sales, expected sales volume, etc.; Sales data include: actual sales volume, sales speed, return rate, etc.; Production data includes: production progress, quality inspection results, production costs, etc.

[0006] Preferably, the forecast demand can use various machine learning and statistical methods, including time series analysis, regression analysis, neural network, etc. The method uses the weighted moving average in the time series analysis method, and the specific function is: ,i=1,2,3...,n=1,2,3... Among them, E represents the prediction result, Represents monthly data, n represents weight, and generally the prediction results of data from recent months will be relatively more accurate.

[0007] Preferably, the purchase plan includes the time, quantity, price, etc. of purchase; the production plan includes the quantity, progress, quality, etc. of production. The planned inventory space is stored according to the principle of classification and zoning management of different fabrics; the fabrics in the warehouse must not be placed randomly, the storage location must be designed, and the storage rules of the storage location must be set. The first-in-first-out principle must be observed when fabric products enter and exit.

[0008] Preferably, the fabric inventory level can be monitored by deploying various sensors inside the warehouse, such as temperature and humidity sensors, light sensors, gas sensors, etc., to collect environmental parameters and inventory data in real time. These sensors transmit the collected data to the backend system through the Internet of Things platform for real-time processing and analysis. Through the digital twin engine technology, the temperature, humidity and inventory conditions of the real warehouse can be accurately restored in the virtual space, thereby realizing real-time monitoring of the warehouse status.

[0009] Preferably, when the inventory level is lower than a preset warning line, it is necessary to increase purchases and production; when the inventory level is higher than the preset warning line, it is necessary to reduce purchases and production, so as to achieve dynamic adjustment of the inventory level.

[0010] A system for optimizing fabric inventory, characterized by comprising: Data collection module: used to collect market data, sales data and production data; Forecasting module: used to predict fabric demand based on collected data; Planning module: used to plan fabric procurement and production plans based on forecasted demand, and plan inventory space in advance; Monitoring module: used to monitor fabric inventory levels in real time and adjust procurement and production plans based on inventory levels.

[0011] Preferably, it is characterized in that the main functions of the monitoring module include: Data visualization: Visualize and present data to users to help them understand and analyze the data; Charts and Graphs: Use visualization elements such as charts and graphs to transform data into a form that is easy to understand and analyze; Interactive visualization: Use interactive visualizations to allow users to freely explore and analyze data to gain deeper insights; Data dashboard: Use data dashboards to visualize key indicators and data to help users better monitor and manage data.

[0012] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a method for optimizing fabric inventory provided by the present invention.

[0014] Figure 2 This is a structural diagram of a fabric inventory optimization system provided by the present invention. Implementation

[0015] In order to more clearly explain the purpose, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. The example implementation methods can be implemented in various forms and should not be understood as being limited to the examples described herein. On the contrary, these implementation methods are provided to make the present invention more comprehensive and complete, and to fully convey the concepts of the example implementation methods to those skilled in the art.

[0016] like Figure 1 As shown, this embodiment provides a method for optimizing fabric inventory, and the specific steps of the method are as follows: The first step is to collect market, sales and production data; In this embodiment, preferably, the collected market data includes but is not limited to fashion trends, competitor sales, expected sales, etc.; sales data includes but is not limited to actual sales, sales speed, return rate, etc.; production data includes but is not limited to production progress, quality inspection results, production costs, etc. Collect as much data as possible to ensure that the data samples are sufficiently diverse and representative.

[0017] The second step is to predict fabric demand based on the collected data; In this embodiment, preferably, various machine learning and statistical methods can be used to predict fabric demand, including time series analysis, regression analysis, neural network, etc. For example, in this embodiment, the weighted moving average in the time analysis series method is used to implement this step, firstly, the collected market, sales and production data are preprocessed to facilitate subsequent feature extraction, including data cleaning, normalization, denoising, etc.; Substitute the prepared data sample into the formula for calculation. The specific formula is as follows: ,i=1,2,3...,n=1,2,3... Among them, E represents the prediction result, Represents monthly data, n represents weight, and generally the prediction results of data from recent months will be relatively more accurate.

[0018] The third step is to plan fabric procurement and production plans based on forecasted demand and plan inventory space in advance; In this embodiment, the purchase plan includes but is not limited to the time, quantity, price, etc. of the purchase; the production plan includes but is not limited to the quantity, progress, quality, etc. of the production. The purchase and production plan of the fabric is planned by the predicted fabric demand. After the fabric purchase and production plan is obtained, the inventory space can be planned in advance, and the warehouse is planned with the goal of minimizing the total warehouse capacity occupancy rate and the minimum material moving distance.

[0019] Step 4: Monitor fabric inventory levels in real time and adjust procurement and production plans based on inventory levels; Among them, monitoring fabric inventory includes the following functions: Data visualization: Visualize and present data to users to help them understand and analyze the data; Charts and Graphs: Use visualization elements such as charts and graphs to transform data into a form that is easy to understand and analyze; Interactive visualization: Use interactive visualizations to allow users to freely explore and analyze data to gain deeper insights; Data dashboard: Use data dashboards to visualize key indicators and data to help users better monitor and manage data.

[0020] By monitoring fabric inventory, you can effectively understand information such as inventory quantity, quantity to be entered, quantity entered, quantity exited, and waiting time, and you can view the warehouse capacity occupancy rate of different fabrics.

[0021] When fabrics are put into storage, they will be given priority to the warehouse with the lowest storage capacity occupancy rate, so that the total storage capacity occupancy rate of the warehouse is the lowest; when fabrics are shipped out, they will be shipped out first according to the first-in-first-out principle, so that the total time the fabrics are in the warehouse is minimized and the backlog of fabric inventory is avoided.

[0022] If the number of fabrics to be received in the warehouse is less than or equal to the available storage capacity, then these fabrics can be directly received. However, if the number of fabrics to be received exceeds the available storage capacity, then it is necessary to further compare the relative size of the number of fabrics to be received and the sum of the available storage capacity and the number of fabrics to be shipped out. If the number of fabrics to be received is less than or equal to the sum of the available storage capacity and the number of fabrics to be shipped out, then the fabrics can be arranged for storage. However, if the number of fabrics to be received exceeds the sum of the available storage capacity and the number of fabrics to be shipped out, it means that the warehouse is full and the receipt of fabrics needs to be postponed.

[0023] In this embodiment, taking a day as a cycle, when the number of fabrics to be stored in the warehouse is less than or equal to the sum of the number of fabrics out of the warehouse and the free storage capacity every day within the limited number of days of a cycle:

[0024] in, is the quantity of fabric n to be put into storage on a certain day in the next cycle, is the quantity of fabric n in warehouse i before a certain day in the next cycle, is the maximum storage quantity of material n in the warehouse, is the number of materials n in warehouse i waiting to be shipped out before a certain day in the next cycle; if the number of fabrics to be stored is greater than the available storage capacity, the cycle can be continuously shortened to optimize within a smaller cycle to meet the warehouse storage requirements.

[0025] When the fabric is delayed in storage, it is necessary to optimize and adjust the time by time. The dynamic inventory is calculated with hours as the minimum unit time:

[0026]

[0027]

[0028] in, is the maximum storage quantity of material n in warehouse i, is the real-time quantity of material n in warehouse i at time t, is the real-time available storage capacity of material n in warehouse i at time t, is the real-time available storage capacity of material n in warehouse i at time t-1, is the quantity of material n shipped out of warehouse i during the period (t-1, t), is the real-time available storage capacity of material n at time t.

[0029] By monitoring inventory levels in real time, staff can adjust purchasing and production plans based on total inventory, inventory turnover days and inventory coverage period to keep inventory levels within a reasonable range.

[0030] The total inventory refers to the inventory quantity of an enterprise at a specific point in time; the inventory turnover days refers to the number of days required for an enterprise to complete an inventory turnover; the inventory coverage period refers to the number of days that the enterprise estimates that the inventory can support based on the current inventory level and sales speed.

[0031] In addition, the present invention also supports setting warning values. When the inventory level is lower than the preset warning line, it is necessary to increase procurement and production; when the inventory level is higher than the preset warning line, it is necessary to reduce procurement and production to achieve dynamic adjustment of inventory levels and avoid inventory backlogs or shortages.

[0032] Figure 2 This is a structural diagram of a fabric optimization system provided by the present invention, which is described in detail as follows: A fabric optimization system comprises: Data collection module: used to collect market, sales and production data; Forecasting module: used to predict future fabric demand based on the collected data using various machine learning and statistical methods.

[0033] Planning module: used to formulate procurement plans and production plans based on predicted demand and make adjustments to inventory space in advance.

[0034] Monitoring module: used to monitor fabric inventory levels in real time and adjust procurement and production plans based on inventory levels.

[0035] Through the above system and method, not only can the market demand and trend be reflected in real time, and fabric demand can be effectively predicted and planned, but also the inventory level can be adjusted dynamically to achieve optimized management of fabric inventory. This can not only avoid the backlog or shortage of fabric inventory, improve the production efficiency and sales performance of the enterprise, but also reduce inventory costs and improve the competitiveness of the enterprise.

[0036] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technical solution that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing fabric inventory, characterized in that: include: Collect market, sales and production data; Forecast fabric demand based on collected data; Plan fabric purchases and production plans based on forecasted demand, and plan inventory space in advance; Monitor fabric inventory levels in real time and adjust purchasing and production plans based on inventory levels.

2. The method for optimizing fabric inventory according to claim 1, characterized in that: The collected data includes: Market data includes: popular trends, competitor sales, expected sales volume, etc.; Sales data include: actual sales volume, sales speed, return rate, etc.; Production data includes: production progress, quality inspection results, production costs, etc.

3. The method for optimizing fabric inventory according to claim 1, characterized in that: The forecast demand can use various machine learning and statistical methods, including time series analysis, regression analysis, neural networks, etc.

4. A method for optimizing fabric inventory according to claim 3, characterized in that: The method adopts the weighted moving average in the time series analysis method, and the specific function is: ,i=1,2,3...,n=1,2,3... Among them, E represents the prediction result, Represents monthly data, n represents weight, and generally the prediction results of data from recent months will be relatively more accurate.

5. The method for optimizing fabric inventory according to claim 1, characterized in that: The procurement plan includes the time, quantity, price, etc. of procurement; the production plan includes the quantity, progress, quality, etc. of production.

6. A method for optimizing fabric inventory according to claim 5, characterized in that: The planned inventory space is based on the principle of classification and zoning management for different fabrics. The fabrics in the warehouse must not be placed randomly. Storage locations must be designed and storage rules must be set. The first-in-first-out principle must be followed when fabric products enter and exit.

7. The method for optimizing fabric inventory according to claim 1, characterized in that: The monitoring of fabric inventory levels can collect environmental parameters and inventory data in real time by deploying various sensors inside the warehouse, such as temperature and humidity sensors, light sensors, gas sensors, etc. These sensors transmit the collected data to the backend system through the Internet of Things platform for real-time processing and analysis. Through the digital twin engine technology, the temperature, humidity and inventory conditions of the real warehouse can be accurately restored in the virtual space, thereby realizing real-time monitoring of the warehouse status.

8. The method for optimizing fabric inventory according to claim 1, characterized in that: Real-time fabric monitoring level: when the inventory level is lower than the preset warning line, it is necessary to increase procurement and production; when the inventory level is higher than the preset warning line, it is necessary to reduce procurement and production to achieve dynamic adjustment of inventory levels.

9. A system for optimizing fabric inventory, characterized in that: include: Data collection module: used to collect market data, sales data and production data; Forecasting module: used to predict fabric demand based on collected data; Planning module: used to plan fabric procurement and production plans based on forecasted demand, and plan inventory space in advance; Monitoring module: used to monitor fabric inventory levels in real time and adjust procurement and production plans based on inventory levels.

10. A system for optimizing fabric inventory according to claim 9, characterized in that: The main functions of the monitoring module include: Data visualization: Visualize and present data to users to help them understand and analyze the data; Charts and Graphs: Use visualization elements such as charts and graphs to transform data into a form that is easy to understand and analyze; Interactive visualization: Use interactive visualizations to allow users to freely explore and analyze data to gain deeper insights; Data dashboard: Use data dashboards to visualize key indicators and data to help users better monitor and manage data.

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