CN111325490A - Replenishment method and device - Google Patents

Replenishment method and device Download PDF

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CN111325490A
CN111325490A CN201811531470.5A CN201811531470A CN111325490A CN 111325490 A CN111325490 A CN 111325490A CN 201811531470 A CN201811531470 A CN 201811531470A CN 111325490 A CN111325490 A CN 111325490A
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sku
replenishment
sales
cost
stock
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CN111325490B (en
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马新朝
张颖芳
王本玉
陈佳琦
王野
金晶
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SF Technology Co Ltd
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Abstract

The invention relates to a replenishment method and a replenishment device, wherein the replenishment method comprises the following steps: collecting inventory data, historical sales and replenishment period of SKUs; giving different weights to historical sales according to the time sequence, and calculating a sales cumulative density function of the SKU in the next replenishment period; according to the accumulated density function of the sales volume of the SKU in the next replenishment period, calculating the corresponding holding volume of the SKU when the expected value of the inventory cost is minimum based on a preset constraint condition; and comparing the stock data of the SKU with the holding quantity to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity, and the shortage cost and the inventory cost of the SKU are comprehensively considered, so that the holding quantity calculated by a model is optimal on the total cost of the warehouse, and the inventory management can be scientifically and systematically performed on the warehouse.

Description

Replenishment method and device
Technical Field
The invention relates to the field of inventory management, in particular to a replenishment method and device.
Background
With the rise of online shopping, the demand for perfecting the mechanism and technology of the corresponding logistics and supply chain is more and more urgent. The warehouse management is in a critical position of the supply chain, and the inventory management directly influences the commodity order satisfaction rate, the warehouse inventory management cost and the like.
For the determination of the replenishment point R in the prior art solution,
the static inventory (the holding capacity R) is simply specified according to the average value and the variance of the sales volume of the SKU history, and the sales volume of each SKU fluctuates greatly, so that the scheme cannot adapt to the variation trend of the sales volume, and the holding capacity R is prone to being inaccurate to obtain;
and secondly, predicting the sales volume of the sku by using a machine learning method so as to determine a lower replenishment limit (a holding volume R), wherein the sales volume of the sku is comprehensively influenced by various complex factors, so that the prediction error is large, and the lower replenishment limit of the scheme only considers the stock shortage cost (the temporary replenishment cost is paid because of the temporary replenishment behavior generated by the stock shortage, and the temporary replenishment cost is generally greater than the conventional replenishment cost) and does not consider the inventory cost.
The shortage cost of the Shunfeng warehouse can not be directly measured by the market price of sku, which represents the labor work cost of temporary replenishment after shortage, so the shortage cost considered by the replenishment model is difficult to estimate.
The following technical problems exist in the estimation of the labor work cost:
1. the lower limit of replenishment cannot capture the change of sku sales in real time, so that the order satisfaction rate is low on certain days;
2. the lower limit of the replenishment model cannot comprehensively consider inventory cost and backorder cost;
3. the warehouse stock shortage cost considered by the replenishment model is difficult to estimate, the stock shortage cost cannot be directly measured by the market price or the cost of the sku, and the manual cost of temporary replenishment can be selected to replace the stock shortage cost, however, two difficulties exist in estimating the value: the sku with temporary replenishment is few on the one hand, the temporary replenishment manpower of all skus in the warehouse cannot be directly calculated, and on the other hand, the temporary replenishment manpower is difficult to convert into a specific money cost.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a replenishment method and apparatus.
According to an aspect of the present invention, there is provided a restocking method including:
collecting inventory data, historical sales and replenishment period of SKUs;
giving different weights to historical sales according to the time sequence, and calculating a sales cumulative density function of the SKU in the next replenishment period;
according to the accumulated density function of the sales volume of the SKU in the next replenishment period, calculating the corresponding holding volume R of the SKU when the expected value of the inventory cost is minimum based on a preset constraint condition;
and comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
Further, calculating a cumulative density function of sales of the SKU in the next replenishment cycle by giving different weights to historical sales according to the time sequence, comprising:
giving a weight from small to large to the historical sales data of the SKU based on the time sequence;
accumulating and removing the weight of the historical sales, and integrating into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
calculating the probability of each sales occurrence to form a numerical distribution data set, and obtaining the cumulative density function of historical sales by the weighted sum of each sales and its probability product
Figure RE-GDA0001988356320000021
Further, according to the accumulated density function of the sales volume of the SKU in the next replenishment period, calculating the corresponding holding volume R of the SKU when the expected value of the inventory cost is minimum based on a preset constraint condition, and including:
calculating the unit stock shortage cost k of the SKU and collecting the unit inventory cost h of the SKU;
according to the unit gapCalculating the stock cost and the unit stock cost of each historical sales volume of the SKU in the cumulative density function respectively
Figure RE-GDA0001988356320000022
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to K/K + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
Further, calculating the unit stock out cost of the SKU comprises:
randomly selecting the maximum temporary replenishment quantity in the SKU unit time from the inventory data and the historical sales quantity data;
fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category, and estimating the maximum temporary replenishment quantity in other SKUs in unit time;
and calculating the unit stock shortage cost of the SKU based on the human cost according to the maximum temporary replenishment quantity of the SKU in unit time.
Further, the inventory cost expected value is an inventory cost or stock out cost of the SKU;
and/or
The replenishment cycle comprises at least one of: day, n days, week, month, year;
and/or
The inventory data includes at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU.
According to another aspect of the present invention, there is provided a restocking apparatus including:
the data acquisition module is configured for acquiring stock data, historical sales and replenishment periods of SKUs;
the sales forecasting module is configured for giving different weights to historical sales according to the time sequence and calculating a sales cumulative density function of the SKU in the next replenishment period;
the calculation module is configured for calculating the corresponding cargo holding quantity R of the SKU when the expected value of the inventory cost is minimum according to the accumulated density function of the sales quantity of the SKU in the next replenishment period based on a preset constraint condition;
and comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
Further, the calculation module comprises:
the weight distribution unit is configured for giving weights from small to large to the historical sales data of the SKU based on the time sequence;
and the integration unit is configured for accumulating and de-duplicating the historical sales and integrating the historical sales into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
the first calculation unit: calculating the probability of each sales occurrence to form a numerical distribution data set, and calculating the weighted sum of each sales and its probability product to obtain the cumulative density function of historical sales
Figure RE-GDA0001988356320000041
Further, a data collection module configured to collect a unit inventory cost for the SKU;
the calculation module further comprises:
the second calculation unit is used for calculating and calculating the unit stock shortage cost k of the SKU and collecting the unit stock inventory cost h of the SKU;
calculating the cumulative density function of each historical sales volume of the SKU according to the unit stock shortage cost and the unit stock inventory cost
Figure RE-GDA0001988356320000042
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to K/K + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
Further, calculating the unit stock out cost of the SKU comprises:
randomly selecting the maximum temporary replenishment quantity in the unit time of a SKU from the inventory data and the historical sales data;
fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category, and estimating the maximum temporary replenishment quantity in other SKUs in unit time;
according to the maximum temporary replenishment quantity in the SKU unit time,
calculating a unit out-of-stock cost for the SKU based on human cost.
Further, the inventory cost expected value is an inventory cost or stock out cost of the SKU;
and/or
The replenishment cycle comprises at least one of: day, n days, week, month, year;
and/or
The inventory data includes at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU.
According to another aspect of the invention, there is provided an apparatus comprising
One or more processors;
a memory for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of the above.
According to another aspect of the invention, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a method as defined in any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the replenishment method disclosed by the invention, the corresponding holding quantity R of the SKU when the expected value of the inventory cost is minimum is calculated based on the preset constraint condition, and the stock cost comprehensively considers the stock shortage cost and the inventory cost of SKU, so that the holding quantity R calculated by a model is optimal on the total cost of a warehouse, and the inventory management can be scientifically and systematically carried out on the warehouse.
2. According to the replenishment device disclosed by the invention, the sales forecasting module gives different weights to data points according to the sales date, so that the holding quantity R can capture the change trend of the sales, and the inventory management can be scientifically and systematically carried out on the warehouse.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to better understand the technical scheme of the invention, the invention is further explained by combining the specific embodiment and the attached drawings of the specification.
Example 1:
the replenishment device of this embodiment includes:
a data collection module configured to collect stock data, historical sales, replenishment cycles, and unit inventory costs for SKUs; the inventory data includes at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU.
The sales forecasting module is configured for giving different weights to historical sales according to the time sequence and calculating a sales cumulative density function of the SKU in the next replenishment period; the replenishment cycle comprises at least one of: day, n days, week, month, year. Specifically, the sales prediction module comprises:
the frequency allocation unit is configured to assign different weights to the historical sales data of the SKU based on time sequence;
the weight distribution unit is configured for giving weights from small to large to the historical sales data of the SKU based on the time sequence;
and the integration unit is configured for accumulating and de-duplicating the historical sales and integrating the historical sales into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
the first calculation unit: calculating the probability of each sales occurrence to form a numerical distribution data set, and calculating the weighted sum of each sales and its probability product to obtain the cumulative density function of historical sales
Figure RE-GDA0001988356320000061
The second calculation unit is used for calculating and calculating the unit stock shortage cost k of the SKU and collecting the unit stock inventory cost h of the SKU;
calculating the cumulative density function of each historical sales volume of the SKU according to the unit stock shortage cost and the unit stock inventory cost
Figure RE-GDA0001988356320000062
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to K/K + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
Further, calculating the unit stock out cost of the SKU comprises: randomly selecting the maximum temporary replenishment quantity in the unit time of a SKU from the inventory data and the historical sales data;
fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category, and estimating the maximum temporary replenishment quantity in other SKUs in unit time;
according to the maximum temporary replenishment quantity in the SKU unit time,
calculating a unit out-of-stock cost for the SKU based on human cost.
The replenishment method corresponding to the replenishment device in the embodiment comprises the following steps:
s1: collecting inventory data, historical sales and replenishment cycles for SKUs, the inventory data including at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU; the replenishment cycle is as follows: days, n days, weeks, months and years, and the replenishment period is the time period from one replenishment to the next replenishment.
S2: giving different weights to historical sales according to the time sequence, and calculating a cumulative density function of sales of the SKU in the next replenishment period, wherein the cumulative density function specifically comprises the following steps:
s2-1: giving weights from small to large to historical sales data of the SKU based on time sequence, and in order to consider real-time performance of sales change, the more recent sales weight is larger, and through discretization processing of a cumulative distribution function of the historical sales data, errors caused by inaccurate distribution fitting of the historical data are avoided, so that the solution of the child-reporting model is more accurate;
s2-2: accumulating and removing the weight of the historical sales, and integrating into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
s2-3: calculating the probability of each sales occurrence to form a numerical distribution data set, and obtaining the cumulative density function of historical sales by the weighted sum of each sales and its probability product
Figure RE-GDA0001988356320000071
S3: calculating the corresponding inventory quantity R of the SKU when the expected value of the inventory cost is minimum according to the accumulated density function of the sales quantity of the SKU in the next replenishment period based on preset constraint conditions, wherein the method comprises the following steps:
s3-1: calculating the unit stock shortage cost k of the SKU and collecting the unit inventory cost h of the SKU; calculating a unit out-of-stock cost k for the SKU, comprising:
s3-1-1: randomly selecting the maximum temporary replenishment quantity in the unit time of a SKU from the inventory data and the historical sales data;
s3-1-2: fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category to estimate the maximum temporary replenishment quantity in other SKU unit time,
s3-1-3: calculating the unit stock shortage cost of the SKU based on the human cost according to the maximum temporary replenishment quantity of the SKU in unit time;
s3-2: calculating the cumulative density function of each historical sales volume of the SKU according to the unit stock shortage cost and the unit stock inventory cost
Figure RE-GDA0001988356320000072
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to K/K + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
And comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
The inventory R is calculated for all SKUs in the warehouse in turn, so that the overall replenishment cost of the warehouse is minimized.
The replenishment method is further described by way of example below:
step 1: data acquisition: collecting inventory data and historical sales data (sales data in a certain time, in this embodiment, sales data of nearly 60 days) of all SKUs in the warehouse, wherein the replenishment cycle is day;
the cost per unit of inventory for the SKU is obtained from the rate table for each SKU by storing information with the warehouse (which may also be obtained by communicating with warehouse personnel).
Step 2: sales data processing
Discretizing the probability density distribution of the historical sales, and integrating the probability density distribution into a sales distribution queue according to the sales change real-time property and the sales sequence (from small to large): r1, r2 … … rn;
weighting discrete sales distribution, wherein the sales weight is inversely proportional to the time sequence, the sales weight of the more recent sales is larger, the sales weight of the latest day is 60, the sales weight of the farthest day is 1, the replenishment period is day, the probability p (rn) of each sales occurrence is calculated, and a numerical distribution data set { r 1: count 1; r 2: count 2; … …. rn: countn; }
Calculating a cumulative density function of sales of the SKU in a next replenishment cycle
Figure RE-GDA0001988356320000081
Figure RE-GDA0001988356320000082
And 3, step 3: calculating out the cost of goods shortage
When an order of a sku is out of stock, a temporary replenishment behavior is triggered, namely after a certain time, warehouse staff can acquire the sku from a storage area and replenish the sku to a picking area, and due to the fact that the out-of-stock cost cannot be directly measured by the market price or the cost of the sku in the scene, the manual cost of temporary replenishment is selected for replacing the sku, and the specific processing method is as follows:
(1) screening out the SKU temporary replenishment behavior from the inventory data, and selecting a SKU (recorded as SKU)i) And (5) temporary replenishment with the largest replenishment quantity.
(2) Fitting linear regression model
Suppose QiThe SKUiAmount of temporary restocking
Fitting Linear model regression model Qi＝a1*Vi+a2*Wi+a3*Typei
Wherein, ViThe SKUiVolume, WiThe SKUiWeight, TypeiThe SKUiA category.
(3) Predicting Q of all skus in warehouse by using linear model regression modeli(default a for each SKU1、a2、a3Same), according to Q in said SKU unit of timeiCalculating the unit stock out cost of the SKU based on human cost, and estimating Q according to the modeliAccording to the reciprocal ratios, if the replenishment quantity of the replenishment staff to SKU1 is 8 and the replenishment quantity of SKU2 is 2 within one day, the replenishment cost of SKU1 is 1/4 of SKU2, the emergency replenishment cost of SKU1 and SKU2 can be finally obtained according to the wages of the replenishment staff in one day, and the emergency replenishment cost of all SKUs, namely the final stock shortage cost k of each SKU, can be obtained by analogy in turn. I.e. Q, under the condition that the working capacity of the replenishment personnel is constantiThe higher the temporary restocking cost, the lower the temporary restocking cost representing 1 such sku per restocking. In specific application, the labor cost can be analyzed from the wages of the replenishment staff in one day, the unit stock shortage cost k of sku1 and sku2 can be obtained according to the one-day replenishment capacity of the replenishment staff, and the unit stock shortage cost k of all skus can be obtained by analogy.
And 3, step 3: calculating the inventory R of said SKU
Assuming r is the expected sales volume on the day, taking the sales volume distribution queue from the SKU; and R is the today holding quantity R, namely the optimal replenishment point.
Should stock be prepared today, i.e. the best replenishment point
Figure RE-GDA0001988356320000091
And r1<r2<……<rn，
k is unit cost of goods shortage
h is unit inventory cost
If the stock quantity of the SKU exceeds the expected sales volume interval (r)<R), product occupancy into the warehouse results in inventory costs:
Figure RE-GDA0001988356320000092
if the stock quantity of the SKU is smaller than the expected sales volume interval (R is larger than R), temporary replenishment needs to be carried out, and the stock cost is generated:
Figure RE-GDA0001988356320000093
in summary, the expected inventory cost TC (R) for a replenishment cycle is:
Figure RE-GDA0001988356320000094
the objective function is Min (TC (R)), and the cumulative density function of each historical sales volume of the SKU is calculated according to the unit stock shortage cost and the unit stock cost
Figure RE-GDA0001988356320000095
The following stock cost expectation values: TC (r1), TC (r2) … … TC (rn), which satisfies the following three constraints: K/K + h is more than or equal to TC (rn-1) and less than or equal to TC (rn); the output rn is the hold R for the SKU.
And comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
The inventory R is calculated for all SKUs in the warehouse in turn, so that the overall replenishment cost of the warehouse is minimized.
According to the method, a probability accumulation function of the sales volume is changed, the sales volume interval changes along with time change, and change information considering the sales volume is captured through the weight of the historical sales volume value, so that the optimal goods holding quantity R of the SKU changes every day instead of the traditional static goods holding quantity R, and only the sum of the goods holding quantities R of the SKU is smaller than the upper limit of the inventory of a warehouse, and finally, the improved newsstand model is applied to a plurality of regular replenishment behaviors.
The device of the embodiment can scientifically and systematically perform inventory management on the warehouse by executing the replenishment method through the processor.
The readable storage medium of the embodiment stores the replenishment method implemented when being executed by the processor, so that the replenishment device is convenient to use and popularize.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the features described above have similar functions to (but are not limited to) those disclosed in this application.

Claims (10)

1. A method of restocking, comprising:
collecting inventory data, historical sales and replenishment period of SKUs;
giving different weights to historical sales according to the time sequence, and calculating a sales cumulative density function of the SKU in the next replenishment period;
according to the accumulated density function of the sales volume of the SKU in the next replenishment period, calculating the corresponding holding volume R of the SKU when the expected value of the inventory cost is minimum based on a preset constraint condition;
and comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
2. The replenishment method of claim 1, wherein calculating the cumulative density function of sales of the SKU for the next replenishment cycle based on different weights assigned to historical sales in chronological order comprises:
giving a weight from small to large to the historical sales data of the SKU based on the time sequence;
accumulating and removing the weight of the historical sales, and integrating into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
calculating the probability of each sales occurrence to form a numerical distribution data set, and obtaining the cumulative density function of historical sales by the weighted sum of each sales and its probability product
Figure FDA0001905757870000011
3. The replenishment method according to claim 2, wherein calculating the holding quantity R of the SKU corresponding to the smallest expected value of the stock cost based on a preset constraint according to the accumulated density function of the sales quantity of the SKU in the next replenishment period comprises:
calculating the unit stock shortage cost k of the SKU and collecting the unit inventory cost h of the SKU;
calculating the cumulative density function of each historical sales volume of the SKU according to the unit stock shortage cost and the unit stock inventory cost
Figure FDA0001905757870000012
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to k/k + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
4. A method of restocking according to claim 3, wherein calculating the unit out-of-stock cost for the SKU comprises:
randomly selecting the maximum temporary replenishment quantity in the SKU unit time from the inventory data and the historical sales quantity data;
fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category, and estimating the maximum temporary replenishment quantity in other SKUs in unit time;
and calculating the unit stock shortage cost of the SKU based on the human cost according to the maximum temporary replenishment quantity of the SKU in unit time.
5. A restocking method according to claims 1 to 4,
the inventory cost expected value is an inventory cost or stock out cost for the SKU;
and/or
The replenishment cycle comprises at least one of: day, n days, week, month, year;
and/or
The inventory data includes at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU.
6. A replenishment device, comprising:
the data acquisition module is configured for acquiring stock data, historical sales and replenishment periods of SKUs;
the sales forecasting module is configured for giving different weights to historical sales according to the time sequence and calculating a sales cumulative density function of the SKU in the next replenishment period;
the calculation module is configured for calculating the corresponding cargo holding quantity R of the SKU when the expected value of the inventory cost is minimum according to the accumulated density function of the sales quantity of the SKU in the next replenishment period based on preset constraint conditions;
and comparing the stock data of the SKU with the holding quantity R to obtain the replenishment quantity, so that the stock data of the SKU is not less than the holding quantity R.
7. The replenishment device according to claim 6, wherein the calculation module comprises:
the weight distribution unit is configured for giving weights from small to large to the historical sales data of the SKU based on the time sequence;
and the integration unit is configured for accumulating and de-duplicating the historical sales and integrating the historical sales into a sales distribution queue according to the sales sequence: r1, r2 … … rn;
the first calculation unit: calculating the probability of each sales occurrence to form a numerical distribution data set, and calculating the weighted sum of each sales and its probability product to obtain the cumulative density function of historical sales
Figure FDA0001905757870000031
8. A restocking device according to claim 7,
a data collection module further configured to collect a unit inventory cost of the SKU;
the calculation module further comprises:
the second calculation unit is used for calculating and calculating the unit stock shortage cost k of the SKU and collecting the unit stock inventory cost h of the SKU;
calculating the cumulative density function of each historical sales volume of the SKU according to the unit stock shortage cost and the unit stock inventory cost
Figure FDA0001905757870000032
The following stock cost expectation values: TC (r1), TC (r2) … …, TC (rn), and the condition that the preset constraint condition TC (rn-1) is more than or equal to k/k + h is more than or equal to TC (rn) is met;
the output rn is the hold R for the SKU.
9. The replenishment device according to claim 8, wherein calculating the unit backorder cost of the SKU comprises:
randomly selecting the maximum temporary replenishment quantity in the unit time of a SKU from the inventory data and the historical sales data;
fitting a linear model regression model according to the SKU maximum temporary replenishment quantity, the volume, the weight and the category, and estimating the maximum temporary replenishment quantity in other SKUs in unit time;
according to the maximum temporary replenishment quantity in the SKU unit time,
calculating a unit out-of-stock cost for the SKU based on human cost.
10. A replenishment device according to claims 6-9 wherein the inventory cost expected value is the cost of stock or stock out of stock of the SKU;
and/or
The replenishment cycle comprises at least one of: day, n days, week, month, year;
and/or
The inventory data includes at least one of: stock quantity, volume, weight, stock space volume and temporary replenishment data of the SKU.
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CN113947341A (en) * 2020-07-17 2022-01-18 上海顺如丰来技术有限公司 Supply chain replenishment method and device, computer equipment and storage medium
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CN115630899A (en) * 2022-11-01 2023-01-20 中国外运股份有限公司 Multi-platform inventory optimization method and system
CN115630899B (en) * 2022-11-01 2023-08-11 中国外运股份有限公司 Multi-platform inventory optimization method and system

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