A rolling analysis method for spot inventory costs oriented towards end-to-end tracking

Abstract

This invention relates to the field of inventory cost analysis, addressing the problems of lagging information updates and lack of predictive warnings in cost accounting, which prevent the automatic triggering of targeted inventory optimization strategies. Specifically, it is a rolling analysis method for spot inventory costs with full-process tracking, including a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost prediction statistics module, and an inventory optimization decision-making module. Based on complete inventory cost accounting, this invention endows cost management with real-time and forward-looking capabilities through rolling analysis and dynamic prediction. Ultimately, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it achieves an intelligent closed loop from data to action, transforming inventory cost management from traditional recording into a core decision-making tool for profit protection and risk prevention, effectively improving the enterprise's inventory operation efficiency and overall profitability.

Description

Technical Field

[0001] This invention relates to the field of inventory cost analysis, specifically a rolling analysis method for spot inventory costs oriented towards end-to-end tracking. Background Technology

[0002] In the field of supply chain and warehousing management, accurate accounting and effective control of spot inventory costs are key to enterprises to reduce costs, increase efficiency and enhance competitiveness. At present, inventory cost management usually adopts static or segmented accounting methods.

[0003] In existing technologies, most methods focus on the explicit costs during the inventory holding period and a series of necessary expenditures before the goods are put into storage, such as storage fees, handling fees, purchase order processing, long-distance transportation, tariffs and insurance costs. These costs are then summarized on a monthly or quarterly basis to obtain an overall cost accounting, which can clearly show the total cost and cost composition of the goods.

[0004] However, the lack of forward-looking forecasting capabilities in cost analysis leads to a lag in cost statistics. This means that the data is mainly recorded and reported after the fact, failing to build predictive models based on historical cost data. It is difficult to provide early warnings of abnormal growth trends in inventory costs, and it is also impossible to establish a real-time linkage analysis model between costs and profits. As a result, the best time for cost intervention is missed, leading to slow response from enterprises when inventory is piling up and profits are being eroded. The enterprise is unable to automatically generate or trigger targeted inventory optimization strategies. Therefore, a solution is proposed. Summary of the Invention

[0005] This invention, based on complete inventory cost accounting, endows cost management with real-time and forward-looking capabilities through rolling analysis and dynamic forecasting. Ultimately, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it achieves an intelligent closed loop from data to action, transforming inventory cost management from traditional recording into a core decision-making tool for profit protection and risk prevention and control. This effectively improves the inventory operation efficiency and overall profitability of enterprises, and proposes a rolling analysis method for spot inventory costs oriented towards full-process tracking.

[0006] The objective of this invention can be achieved through the following technical solution: a rolling analysis method for spot inventory costs oriented towards end-to-end tracking, comprising the following steps:

[0007] Step 1: Calculate the costs incurred before goods are put into storage to obtain the total cost of the pre-storage process;

[0008] Step 2: Calculate the periodic in-warehouse costs incurred after the goods are put into storage, according to the set period.

[0009] Step 3: At the end of each statistical period, based on the total cost of the pre-inventory process, and by adding the periodic in-inventory costs of the current period and all historical periods, the latest cumulative inventory cost total as of the current moment is generated.

[0010] Step 4: Based on the cumulative inventory cost totals arranged in time series, perform dynamic tracking and trend analysis to predict the future growth rate of inventory costs and the total cost growth.

[0011] Step 5: Obtain the profit data of the goods, perform a linkage analysis with the predicted inventory cost growth, and generate inventory optimization decision suggestions based on the preset decision rules.

[0012] As a preferred embodiment of the present invention, it also includes a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost prediction statistics module, and an inventory optimization decision module;

[0013] The pre-warehousing process cost statistics module is used to calculate the costs incurred before goods are put into storage and send them to the rolling analysis module.

[0014] The warehouse cost statistics module calculates the cost incurred after goods enter the warehouse according to the set period and sends the calculation results to the warehouse cost statistics module.

[0015] The rolling analysis module performs rolling analysis based on the cost of goods before and after warehousing, generates the latest total cost, and sends it to the inventory cost prediction and statistics module.

[0016] The inventory cost prediction and statistics module performs dynamic tracking and analysis based on the acquired total cost from multiple times to obtain inventory cost dynamics, which include the predicted results of the growth rate of product inventory costs and the total cost growth.

[0017] The inventory optimization decision module performs a linked analysis based on the growth of product profits and inventory costs to optimize decision-making.

[0018] In a preferred embodiment of the present invention, the pre-warehouse process cost statistics module records the purchase price of goods and records the transportation costs, insurance costs and customs declaration fees during transportation in real time, and adds up all the costs to obtain the total pre-warehouse process cost.

[0019] In a preferred embodiment of the present invention, the warehouse cost statistics module calculates the cost of goods after they are put into storage on a daily basis.

[0020] The warehouse cost statistics module automatically collects and calculates the costs incurred after goods are put into storage, including: warehousing rental costs, warehouse operation costs, inventory holding capital costs, goods loss costs, and insurance costs, forming periodic warehouse cost data.

[0021] In a preferred embodiment of the present invention, the rolling analysis module is specifically used for:

[0022] At the end of each set statistical period, the system receives the total pre-inventory cost from the pre-inventory process cost statistics module, and adds the inventory cost data reported by the inventory cost statistics module for the current period and all historical periods to generate the latest cumulative inventory cost total as of the current moment.

[0023] In a preferred embodiment of the present invention, the inventory cost forecasting and statistics module is specifically used for:

[0024] Receive multiple cost totals generated by the rolling analysis module, arranged in time series;

[0025] Based on this time series data, a statistical forecasting model is used to analyze the changing trend of inventory costs, and output the forecast of the future growth rate of inventory costs and the range of total cost growth.

[0026] In a preferred embodiment of the present invention, the inventory optimization decision module is specifically used for:

[0027] Obtain real-time or predicted sales profit data for goods and perform comparative analysis with the inventory cost growth forecast provided by the inventory cost forecast and statistics module.

[0028] When the predicted rate of increase in inventory costs exceeds a preset threshold, or when the proportion of inventory costs eroding profits reaches a critical point, optimization decision recommendations are generated. These recommendations include at least adjusting safety stock levels, initiating promotional programs, or suspending replenishment.

[0029] In a preferred embodiment of the present invention, the inventory cost prediction and statistics module performs the following steps when conducting dynamic tracking analysis:

[0030] S1: Obtain total cost data for N consecutive statistical periods to form a cost time series;

[0031] S2: The time series is fitted using a preset algorithm to calculate the baseline trend of cost changes;

[0032] S3: Identify and quantify the impact of seasonal fluctuations and promotional activities on costs through big data algorithms, and construct a cost disturbance model;

[0033] S4: Combining the baseline trend and the cost disturbance model, predict the inventory cost growth rate and total cost growth for the next M periods. Specifically, obtain the seasonal fluctuations or promotional activities that will occur in the next M periods through pre-stored empirical algorithms, obtain the predicted disturbance amount through the cost disturbance model, and then fit the predicted disturbance amount to the baseline trend to obtain the corrected inventory cost growth rate and total cost growth.

[0034] In a preferred embodiment of the present invention, the inventory optimization decision module performs linkage analysis and decision optimization, including the following steps:

[0035] A1: Calculate the current profit contribution rate of the goods: The current profit contribution rate is obtained by calculating the difference between the expected selling price of the goods and the total cost incurred by the current goods;

[0036] A2: Obtain the inventory cost growth rate predicted by the inventory cost forecasting and statistics module;

[0037] A3: Calculate the dynamic inventory holding profit margin: Establish a profit-cost linkage analysis model to calculate the dynamic profit margin of goods at multiple time points in the future, where the interval between each time point is i periods;

[0038] A4: Set multi-level decision trigger thresholds and compare the dynamic profit margins of goods at multiple future time points to obtain the trigger threshold corresponding to each time point. Based on the triggered thresholds, issue early warnings and generate optimization decision schemes at different levels.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. This invention covers the entire process of cost accounting from order placement to goods entering the warehouse, as well as the warehousing and circulation process after entry, through pre-warehousing process cost statistics and in-warehousing cost statistics. It includes all relevant cost items such as purchase price, transportation cost, warehouse rental fee, capital occupation cost, and goods loss in the statistical scope, overcoming the defects of incomplete accounting scope, generating real and comprehensive cumulative inventory cost data, laying a solid foundation for accurate cost analysis, and realizing full-process and refined accounting of inventory costs.

[0041] 2. This invention also enables the periodic rolling analysis to accumulate costs according to a set period, generating the latest total cost and achieving dynamic and visual management of inventory costs. Furthermore, based on time-series total cost data, it can use statistical prediction models and combine big data algorithms to quantify disturbances such as seasonality and promotions, effectively predicting the future growth rate of inventory costs and the total cost increase. This allows managers to proactively issue early warnings, anticipate cost risks, reserve response time for decision-making, and provide dynamic and real-time cost tracking and forward-looking prediction capabilities.

[0042] 3. In this invention, a decision support mechanism that links costs and profits is constructed. Through the inventory optimization decision module, the growth forecast of inventory costs and the profit contribution rate of goods are analyzed in real time to calculate the dynamically changing inventory holding profit rate. By preset multi-level decision trigger thresholds, the system can automatically generate and recommend different levels of optimization strategies when the profit rate drops to different risk levels. This realizes intelligent decision support based on real-time data, prevents profits from being eroded by unreasonable inventory costs, thereby optimizing the overall inventory structure and improving supply chain response efficiency and profitability. Attached Figure Description

[0043] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 This is a system flowchart of the present invention;

[0045] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1: Please refer to Figure 1 - Figure 2 As shown, the method for rolling analysis of spot inventory costs for end-to-end tracking includes the following steps:

[0048] Step 1: Compile statistics on the purchase price, transportation costs, insurance costs, and customs declaration fees incurred before the goods are put into storage to obtain the total cost of the pre-warehousing process;

[0049] Step 2: Calculate the periodic warehouse costs incurred after the goods are put into storage according to the set period, including warehouse rental costs, warehouse operation costs, inventory holding capital costs, goods loss costs and insurance costs.

[0050] Step 3: At the end of each statistical period, based on the total cost of the pre-inventory process, and by adding the periodic in-inventory costs of the current period and all historical periods, the latest cumulative inventory cost total as of the current moment is generated.

[0051] Step 4: Based on the cumulative inventory cost totals arranged in time series, perform dynamic tracking and trend analysis to predict the future growth rate of inventory costs and the total cost growth.

[0052] Step 5: Obtain the profit data of the goods, link it with the predicted inventory cost growth, and generate inventory optimization decision suggestions based on the preset decision rules.

[0053] Example 2: Please refer to Figure 1 - Figure 2 As shown, the rolling analysis method for spot inventory costs for end-to-end tracking includes a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost forecasting and statistics module, and an inventory optimization decision module.

[0054] The pre-warehousing process cost statistics module records the purchase price of goods before they enter the warehouse, and records the transportation costs, insurance costs, and customs fees during transportation in real time. All costs are added up to obtain the total pre-warehousing process cost, which is then sent to the rolling analysis module.

[0055] The warehouse cost statistics module calculates the costs incurred after goods enter the warehouse on a daily basis. Through high-frequency daily calculations, this module achieves refined and real-time measurement of warehouse costs, enabling the accurate capture and quantification of costs accumulated over time, such as warehousing rental and capital occupation. This avoids data lag and distortion caused by monthly or quarterly calculations, providing an accurate data foundation for real-time perception of inventory holding pressure. The costs incurred after goods enter the warehouse include: warehousing rental costs, warehouse operation costs, inventory holding capital costs, goods loss costs, and insurance costs, forming periodic warehouse cost data. The warehouse cost statistics module sends the calculation results to the warehouse cost statistics module.

[0056] By employing both pre-warehouse process cost statistics and in-warehouse cost statistics modules, the accuracy and comprehensive coverage of inventory cost calculations at the starting point are ensured. This provides a true and complete initial cost base value for subsequent rolling accumulation, thereby improving the reliability of overall cost data from the source.

[0057] At the end of each set statistical period, the rolling analysis module receives the total pre-warehouse cost from the pre-warehouse process cost statistics module, and adds the in-warehouse cost data reported by the in-warehouse cost statistics module for the current period and all historical periods to generate the latest cumulative inventory cost total as of the current moment, thereby completing the rolling analysis of goods cost and sending it to the inventory cost prediction statistics module.

[0058] The rolling analysis module achieves dynamic visualization and continuous updating of total inventory cost through periodic accumulation. It can reflect the true inventory cost at any given moment, changing the lag of traditional static reports and enabling managers to grasp the cost accumulation trajectory in real time, providing core data support for timely cost monitoring and process management.

[0059] The inventory cost forecasting and statistics module receives multiple total costs generated by the rolling analysis module and arranged in time series. Based on this time series data, it uses a statistical forecasting model to perform dynamic tracking analysis, analyzes the changing trend of inventory costs, and outputs the future inventory cost dynamics of goods. The inventory cost dynamics include the forecast of future growth rate and the forecast of the range of total cost growth.

[0060] The inventory cost forecasting and statistics module performs the following steps during dynamic tracking and analysis:

[0061] S1: Obtain total cost data for N consecutive statistical periods to form a cost time series;

[0062] S2: Use algorithms such as moving average or exponential smoothing to fit the time series and calculate the baseline trend of cost changes;

[0063] S3: Identify and quantify the impact of seasonal fluctuations and promotional activities on costs through big data algorithms, and construct a cost disturbance model;

[0064] S4: Combining the baseline trend and the cost disturbance model, the seasonal fluctuations or promotional activities that will occur in the next M periods are obtained through the pre-stored empirical algorithm, and the predicted disturbance amount is obtained through the cost disturbance model. The predicted disturbance amount is then fitted to the baseline trend to predict the inventory cost growth rate and total cost growth amount in the next M periods.

[0065] The above steps integrate baseline trends with external disturbances to achieve intelligent projection of future cost increases, enabling the management system to have forward-looking early warning capabilities. This allows for the identification of risks before costs accelerate abnormally, reserving valuable response time for proactive intervention and decision-making.

[0066] The inventory optimization decision module obtains real-time or predicted sales profit data of goods and performs comparative analysis with the inventory cost growth forecast provided by the inventory cost forecast and statistics module.

[0067] When the predicted growth rate of inventory costs exceeds a preset threshold, or when the proportion of inventory costs eroding profits reaches a critical point, optimization decision suggestions are generated. These optimization decision suggestions include at least adjusting the safety stock level, launching a promotional plan, or suspending replenishment.

[0068] When the inventory optimization decision module performs linked analysis and decision optimization, it includes the following steps:

[0069] A1: Calculate the current profit contribution rate of the goods: The current profit contribution rate is obtained by calculating the difference between the expected selling price of the goods and the total cost incurred by the current goods;

[0070] A2: Obtain the inventory cost growth rate predicted by the inventory cost forecasting and statistics module;

[0071] A3: Calculate the dynamic inventory holding profit margin: Establish a profit-cost linkage analysis model to calculate the dynamic profit margin of goods at multiple time points in the future, where the interval between each time point is i periods;

[0072] A4: Set multi-level decision trigger thresholds and compare the dynamic profit margins of goods at multiple future time points. If the dynamic profit margin at a certain time point is lower than the set threshold, obtain the trigger threshold corresponding to each time point, issue early warnings based on the triggered thresholds, and generate optimization decision schemes at different levels. The schemes include at least adjusting the reorder point, initiating tiered discounts to clear inventory, or delaying the purchase of goods.

[0073] Through linked analysis and multi-level threshold triggering mechanisms, the inventory optimization decision-making module has achieved automated closed-loop management from cost warning to action recommendations. It dynamically binds cost forecasts with profit targets and can automatically match and recommend strategies with different priorities at different stages of profit margin erosion. This transforms data insights into executable operational instructions, significantly improving the initiative and accuracy of inventory decisions.

[0074] In summary, by constructing a complete cost tracking chain from pre-warehouse to in-warehouse, the system achieves complete and refined inventory cost accounting. Through rolling analysis and dynamic forecasting, it endows cost management with real-time and forward-looking capabilities. Finally, through intelligent linkage between cost and profit and a multi-level decision-making mechanism, it realizes an intelligent closed loop from data to action. This entire approach addresses the pain points of traditional inventory cost management, such as fragmented information, delayed decision-making, and passive control. It transforms inventory cost management from a traditional recording function into a core decision-making tool that supports enterprise supply chain response, profit protection, and risk prevention and control, effectively improving the enterprise's inventory operation efficiency and overall profitability.

[0075] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0076] Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values ​​are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.

[0077] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for rolling analysis of spot inventory costs oriented towards end-to-end tracking, characterized in that, Includes the following steps: Step 1: Calculate the costs incurred before goods are put into storage to obtain the total cost of the pre-storage process; Step 2: Calculate the periodic in-warehouse costs incurred after the goods are put into storage, according to the set period. Step 3: At the end of each statistical period, based on the total cost of the pre-inventory process, and by adding the periodic in-inventory costs of the current period and all historical periods, the latest cumulative inventory cost total as of the current moment is generated. Step 4: Based on the cumulative inventory cost totals arranged in time series, perform dynamic tracking and trend analysis to predict the future growth rate of inventory costs and the total cost growth. Step 5: Obtain the profit data of the goods, link it with the predicted inventory cost growth, and generate inventory optimization decision suggestions based on the preset decision rules.

2. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 1, characterized in that, It also includes a pre-warehouse process cost statistics module, an in-warehouse cost statistics module, a rolling analysis module, an inventory cost forecasting and statistics module, and an inventory optimization decision-making module; The pre-warehousing process cost statistics module is used to calculate the costs incurred before goods are put into storage and send them to the rolling analysis module. The warehouse cost statistics module calculates the cost incurred after goods enter the warehouse according to the set period and sends the calculation results to the warehouse cost statistics module. The rolling analysis module performs rolling analysis based on the cost of goods before and after warehousing, generates the latest total cost, and sends it to the inventory cost prediction and statistics module. The inventory cost prediction and statistics module performs dynamic tracking and analysis based on the acquired total cost from multiple times to obtain inventory cost dynamics, which include the predicted results of the growth rate of product inventory costs and the total cost growth. The inventory optimization decision module performs a linked analysis based on the growth of product profits and inventory costs to optimize decision-making.

3. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, The pre-warehouse process cost statistics module records the purchase price of goods and records transportation costs, insurance costs, and customs fees in real time. All costs are added together to obtain the total pre-warehouse process cost.

4. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, The warehouse cost statistics module calculates the cost of goods after they are put into storage on a daily basis. The warehouse cost statistics module automatically collects and calculates the costs incurred after goods are put into storage, including: warehousing rental costs, warehouse operation costs, inventory holding capital costs, goods loss costs, and insurance costs, forming periodic warehouse cost data.

5. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, The rolling analysis module is specifically used for: At the end of each set statistical period, the system receives the total pre-inventory cost from the pre-inventory process cost statistics module, and adds the inventory cost data reported by the inventory cost statistics module for the current period and all historical periods to generate the latest cumulative inventory cost total as of the current moment.

6. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, The inventory cost forecasting and statistics module is specifically used for: Receive multiple cost totals generated by the rolling analysis module, arranged in time series; Based on this time series data, a statistical forecasting model is used to analyze the changing trend of inventory costs, and output the forecast of the future growth rate of inventory costs and the range of total cost growth.

7. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, The inventory optimization decision module is specifically used for: Obtain real-time or predicted sales profit data for goods and perform comparative analysis with the inventory cost growth forecast provided by the inventory cost forecast and statistics module. When the predicted rate of increase in inventory costs exceeds a preset threshold, or when the proportion of inventory costs eroding profits reaches a critical point, optimization decision recommendations are generated. These recommendations include at least adjusting safety stock levels, initiating promotional programs, or suspending replenishment.

8. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, When the inventory cost forecasting and statistics module performs dynamic tracking and analysis, it specifically executes the following steps: S1: Obtain total cost data for N consecutive statistical periods to form a cost time series; S2: The time series is fitted using a preset algorithm to calculate the baseline trend of cost changes; S3: Identify and quantify the impact of seasonal fluctuations and promotional activities on costs through big data algorithms, and construct a cost disturbance model; S4: Combining the baseline trend and the cost disturbance model, predict the inventory cost growth rate and total cost growth for the next M periods. Specifically, obtain the seasonal fluctuations or promotional activities that will occur in the next M periods through pre-stored empirical algorithms, obtain the predicted disturbance amount through the cost disturbance model, and then fit the predicted disturbance amount to the baseline trend to obtain the corrected inventory cost growth rate and total cost growth.

9. The method for rolling analysis of spot inventory costs oriented towards end-to-end tracking as described in claim 2, characterized in that, When the inventory optimization decision module performs linkage analysis and decision optimization, it includes the following steps: A1: Calculate the current profit contribution rate of the goods: The current profit contribution rate is obtained by calculating the difference between the expected selling price of the goods and the total cost incurred by the current goods; A2: Obtain the inventory cost growth rate predicted by the inventory cost forecasting and statistics module; A3: Calculate the dynamic inventory holding profit margin: Establish a profit-cost linkage analysis model to calculate the dynamic profit margin of goods at multiple time points in the future, where the interval between each time point is i periods; A4: Set multi-level decision trigger thresholds and compare the dynamic profit margins of goods at multiple future time points to obtain the trigger threshold corresponding to each time point. Based on the triggered thresholds, issue early warnings and generate optimization decision schemes at different levels.

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