CN114282872A - Jewelry store inventory management method, device, equipment and storage medium - Google Patents

Abstract

The invention discloses a jewelry store inventory management method, device, computer equipment and storage medium. The jewelry store inventory management method includes: obtaining historical sales data of commodities in the jewelry store; predicting unconditionally predicted sales volume based on the historical sales data without limiting the inventory; establishing the unconditional predicted sales based on different inventory conditions The first association relationship between the conditional expected sales volume and the inventory amount; the second association relationship between the inventory amount, the conditional expected sales volume and the predicted profit is established according to the first association relationship and the inventory amount; according to The second association relationship determines the optimal stock quantity; and the optimal purchase quantity is determined according to the optimal stock quantity. Through the above method, the present invention can greatly improve the profit of the store, and at the same time adopts full quantitative management, ensuring scientificity and iterability.

Description

Jewelry store inventory management method, device, equipment and storage medium

technical field

The present invention relates to the technical field of commodity inventory management, in particular to a method, device, equipment and storage medium for inventory management of a jewelry store.

Background technique

The jewelry industry has small size, high value per piece, and low degree of standardization. The number of SKUs (StockKeeping Uni, stock keeping unit) at the supply chain end exceeds 10 million, which is much higher than other industries. The current mature inventory management model is difficult to produce in the jewelry industry. . From the perspective of procurement, taking the economic bulk ordering model as an example, the capital occupation cost of jewelry procurement is huge. In contrast, the fixed labor cost and logistics cost of procurement execution account for less than 1% of the total cost. The model yields basically no revenue. From the perspective of the sales side, due to the large number of SKUs, most of the SKUs have only a few or even one, which makes it difficult for many quantitative models to estimate parameters.

At present, the inventory management of jewelry retail stores is mainly based on inventory turnover, and the inventory is managed through the historical data of the past year combined with the understanding of the future market development trend. There are two major defects in this method: 1. The management target is set incorrectly, and the inventory turnover days are mistakenly used as the target of inventory management. For example, a typical jewelry retail store has 5 categories, 800 styles, and 2,000 items of inventory. The average inventory turnover days of each of the 5 categories is the management goal. The problem is that the model is oversimplified and fails to take into account Different styles of goods within the same category have completely different cost structures and gross profit margins. For example, some ancient gold styles have much higher gross profit margins than others. Second, the average input-output replaces the marginal input-output as the basis for decision-making, which leads to misguided decision-making. 2. The management is not scientific enough, staying in the semi-quantitative stage, simply observing the historical performance of each product through statistics, and issuing management goals based on the judgment of future market trends, lacking fully quantitative decision-making management methods.

Based on this, there is an urgent need in the art for a new method, device, device and storage medium for inventory management of jewelry stores to solve the technical problems existing in the background art.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problems, a technical solution adopted in the present invention is: a method for managing inventory of jewelry stores is provided, including:

Get historical sales data of jewelry store merchandise;

Predict unconditionally forecast sales according to the historical sales data without limiting the inventory;

According to the unconditional predicted sales volume, a first association relationship between the conditional expected sales volume and the inventory volume under the condition of different inventory quantities is established;

establishing a second association relationship between the inventory amount, the conditional expected sales volume and the predicted profit according to the first association relationship and the inventory amount;

Determine the optimal inventory quantity according to the second association relationship;

The optimal purchase quantity is determined according to the optimal inventory quantity.

Preferably, the unconditional prediction of sales volume based on the historical sales data without limiting the inventory includes:

establishing a seasonal time series model according to the historical sales data;

Fitting the seasonal time series model according to the historical sales data to obtain an optimal seasonal time series model;

The unconditional predicted sales volume is predicted based on the optimal seasonal time series model under the condition that the inventory quantity is not limited.

Preferably, fitting the seasonal time series model according to the historical sales data to obtain the optimal seasonal time series model includes:

Fitting the seasonal time series model according to the historical sales data based on a conditional maximum likelihood algorithm to obtain a parameter set;

The optimal parameter is obtained by calculating according to the parameter set based on the Akaike information criterion;

The seasonal time series model corresponding to the optimal parameter is used as the optimal seasonal time series model.

Preferably, the time series period of the seasonal time series model is an annual period, and the change period is a monthly series.

Preferably, the first association relationship between the conditional expected sales volume and the inventory volume established under the condition of different inventory quantities according to the unconditional predicted sales volume includes:

Based on the Poisson distribution, a first correlation relationship between the conditional expected sales volume and the inventory volume is established according to the unconditional predicted sales volume under the condition of different inventory volumes.

Preferably, the determining the optimal inventory quantity according to the second association relationship includes:

The method based on mathematical programming obtains the inventory corresponding to the maximum predicted profit according to the second association relationship as the optimal inventory.

Preferably, the determining the optimal purchase quantity according to the optimal inventory quantity includes:

Obtain purchase lead time, on-hand inventory;

The optimal purchase quantity is calculated according to the optimal inventory quantity based on the purchase lead time, the on-hand inventory quantity.

In order to solve the above-mentioned technical problems, another technical solution adopted by the present invention is to provide a jewelry store inventory management device, including:

The historical data acquisition module is used to acquire the historical sales data of various commodities in the jewelry store;

The unconditional forecast sales forecast module is used to predict the unconditional forecast sales according to the historical sales data without limiting the inventory;

a conditional expected sales volume calculation module, configured to establish a first association relationship between the conditional expected sales volume and the inventory volume under the condition of different inventory quantities according to the unconditional predicted sales volume;

a predicted profit calculation module, configured to establish a second association relationship between the inventory amount, the conditional expected sales volume and the predicted profit according to the first association relationship and the inventory amount;

an optimal inventory calculation module, configured to determine the optimal inventory according to the second association relationship;

A decision-making module, configured to determine the optimal purchase quantity according to the optimal inventory quantity.

In order to solve the above technical problem, another technical solution adopted by the present invention is: a computer device, comprising: a memory, a processor and a computer program stored in the memory and running on the processor, the processor executes the The computer program implements the jewelry store inventory management method as described above.

In order to solve the above-mentioned technical problem, another technical solution adopted by the present invention is to provide a computer storage medium on which program files are stored, and when the program files are executed by a processor, the above-mentioned jewelry store inventory management method is implemented.

The beneficial effects of the present invention are as follows: in the jewelry store inventory management method of the present invention, the unconditional predicted sales volume is first predicted by historical sales data, and then the relationship between the inventory amount, the conditional expected sales volume and the predicted profit is obtained, and the time when the predicted profit is the largest is directly obtained. corresponding inventory. The method of the invention directly sets the inventory management target as profit maximization, completely avoids the phenomenon that the more management is strengthened due to improper target setting (such as turnover), the profit may be lower, and the profit of the store is greatly improved. At the same time, full quantitative management is adopted, empiricism is eliminated, and scientificity and iterability are guaranteed.

Description of drawings

1 is a schematic flowchart of a jewelry store inventory management method according to an embodiment of the present invention;

Fig. 2 is a schematic flowchart of step S102 in Fig. 1;

FIG. 3 is a schematic flowchart of step S202 in FIG. 2;

4 is a schematic structural diagram of a jewelry store inventory management device according to an embodiment of the present invention;

5 is a schematic structural diagram of a computer device according to an embodiment of the present invention;

6 is a schematic structural diagram of a computer storage medium according to an embodiment of the present invention;

Fig. 7 is the sales index comparison diagram that adopts the jewelry store inventory management method of the embodiment of the present invention;

FIG. 8 is a comparison diagram of the return on assets index of the inventory management method of the jewelry store according to the embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The terms "comprising" and "having" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor a separate or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

FIG. 1 is a schematic flowchart of a method for managing inventory in a jewelry store according to an embodiment of the present invention. It should be noted that, if there is substantially the same result, the method of the present invention is not limited to the sequence of the processes shown in FIG. 1 . As shown in Figure 1, the method includes the steps:

Step S101: Obtain historical sales data of commodities in a jewelry store.

In step S101, the historical sales data corresponding to a certain commodity in the jewelry store is obtained, and the following descriptions are all based on the prediction of a single commodity. In this embodiment, the acquired historical sales data is monthly sales.

Step S102: Predict unconditionally predicted sales volume based on historical sales data without limiting inventory.

Further, as shown in Figure 2, step S102 includes the following steps:

Step S201: Establish a seasonal time series model according to historical sales data.

In step S201, since the historical sales data of jewelry is seasonal, that is, it has the characteristics of high sales in peak season months and low sales in off-season months, a seasonal time series model is selected to predict future sales, specifically SARIMA (Seasonal Autoregressive Integrated Moving Average) model. It should be understood that the condition of not limiting the stock quantity in this embodiment refers to ignoring the sales forecast under the stock condition, that is, assuming that the stock quantity is infinite.

The SARIMA model form of this embodiment is SARIMA(p,d,q)(P,D,Q) _s , where (p,d,q) is the non-seasonal part, and (P,D,Q) _s is the seasonal part Section, the meaning of each parameter in the model is explained as follows:

p: the number of autoregressive coefficients in the non-seasonal part;

d: the number of differences in the non-seasonal part;

q: the number of moving average coefficients of the non-seasonal part;

P: the number of autoregressive coefficients in the seasonal part;

D: The number of differences in the seasonal part;

Q: The number of moving average coefficients in the seasonal part;

s: the change period of the seasonal time series. In this embodiment, since the predicted sales volume is monthly sales volume, the time series period is an annual period, and the change period is a monthly series. Therefore, s=12, which means that 12 months are a sequence period.

Use the SARIMA model to model the monthly sales sequence {y _t } of the product as follows:

Φ _p (L)A _P (L)(1-L) ^d (1-L ^s ) ^D y _t =Θ _q (L)B _Q (L)ε _t ......(1).

为ε_t的方差，假设有n个月的历史销售数据，则

Among them, y _t is the monthly sales volume of the t month, L is the non-seasonal lag operator of y _t , there are Ly _t =y _t-1 , L ^s y _t =y _ts ; ε _t is the white noise process, let

is the variance of ε _t , assuming there are n months of historical sales data, then

Φ _p (L) and A _P (L) are non-seasonal and seasonal autoregressive operators, respectively; Θ _q (L) and B _Q (L) are non-seasonal and seasonal moving average operators, respectively, and the calculation formulas are as follows:

Φ _p (L)=1-Φ ₁ L-Φ ₂ L ² ...-Φ _p L ^p ;

A _P (L)=1-A ₁ LA ₂ L ² ...-A _P L ^P ;

Θ _q (L)=1+Θ ₁ L+Θ ₂ L ² ...+Θ _q L ^q ;

B _Q (L)=1+B ₁ L+B ₂ L ² …+B _Q L ^Q .

Step S202: Fitting a seasonal time series model according to historical sales data to obtain an optimal seasonal time series model.

Further, as shown in Figure 3, step S202 includes the following steps:

Step S301: Fit the seasonal time series model according to the historical sales data based on the conditional maximum likelihood algorithm, and obtain a parameter set.

In step S301, the parameters of the SARIMA model established in step S201 are abbreviated as:

ξ=(p,d,q,P,D,Q,s);

ψ(ξ)=(Φ ₁ ,Φ ₂ ...Φ _p ,A ₁ ,A ₂ ...A _P ,Θ ₁ ,Θ ₂ ...Θ _q ,B ₁ ,B ₂ ...B _Q ).

Fit the established SARIMA model according to historical sales data. Specifically, the conditional maximum likelihood algorithm (Conditional Maximum Likelihood) was used to fit the SARIMA model.

Transformed by equation (1) in step S201, there are:

则似然函数

and

then the likelihood function

得到ψ_max(ξ)和

使得对于任意ψ(ξ),

有

通过上述步骤得出一定数量的参数集合。一般来说，参数个数增加，会使得

变大。但参数个数过多，会导致过拟合现象，使得模型的预测未来的能力降低，所以需要对参数个数和最大似然值取得一个折中。For a certain ξ, write the log-likelihood function of base e

get ψ _max (ξ) and

such that for any ψ(ξ),

Have

A certain number of parameter sets are obtained through the above steps. Generally speaking, increasing the number of parameters will make

get bigger. However, if the number of parameters is too large, it will lead to over-fitting and reduce the ability of the model to predict the future. Therefore, it is necessary to obtain a compromise between the number of parameters and the maximum likelihood value.

Step S302: Calculate and obtain the optimal parameter according to the parameter set based on the Akaike information criterion.

In step S302 , an Akaike information criterion (AIC criterion; Akaike information criterion) is used to achieve the purpose of step 302 . where AIC is defined as:

并计算得到相应的AIC_ξ。最后找出使得AIC_ξ最小的ξ_min。即对于任意ξ，AIC_ξmin≤AIC_ξ。For any ξ, the calculation yields

And calculate the corresponding AIC _ξ . Finally find the ξ _min that minimizes the AIC _ξ . That is, for any ξ, AIC _ξmin ≤ AIC _ξ .

In practice, ξ usually has a range, such as:

0≤p≤3, 1≤d≤3, 0≤q≤3, 0≤P≤2, 0≤D≤1, 0≤Q≤2. After adding the range limit, the calculation amount of ξ _min is greatly reduced.

得到了SARIMA模型的最优参数，为了表示方便，依然记为：Finally, according to ξ _min ,ψ _max (ξ _min ),

The optimal parameters of the SARIMA model are obtained. For convenience of expression, they are still recorded as:

_p ,d, _q ,P,D, _Q , _s ,Φ1, _Φ2 … _Φp ,A1,A2… _{A P} , _Θ1 , _Θ2 … _Θq ,B1,B2… _BQ .

Step S303: Use the seasonal time series model corresponding to the optimal parameter as the optimal seasonal time series model.

In step S303, the obtained optimal parameters are substituted into the formula (1) to obtain the optimal SARIMA model.

Step S203: Predict unconditionally predicted sales volume based on the optimal seasonal time series model under the condition that the inventory quantity is not limited.

In step S203, according to the optimal SARIMA model obtained in the above steps, the unconditional predicted sales volume in the future can be predicted. Since the month is used as the change period in this embodiment, the future monthly sales volume is predicted. It should be understood that the unconditional sales forecast in this embodiment refers to the sales forecast under the condition of ignoring the inventory, that is, it is assumed that the inventory is infinite.

Specifically, the calculation steps are as follows:

First, let w _t =(1-L) ^d (1-L ^s ) ^D y _t , then from equation (1) we have:

Φ _p (L)A _P (L)w _t =Θ _q (L)B _Q (L)ε _t .

Let ρ=Max(p,P), η=Max(q,Q), then we have:

Φ _p (L)A _P (L)=1-α ₁ L-α ₂ L…-α _ρ L and Θ _q (L)B _Q (L)=1+β ₁ L+β ₂ L…+β _η L.

Substitute into formula (1):

(1-α ₁ L-α ₂ L...-α _ρ L)w _t =(1+β ₁ L+β ₂ L...+β _η L)ε _t .

Arranged:

Then the forecast for the next month is:

From w _t =(1-L) ^d (1-L ^s ) ^D y _t =(1-γ ₁ L ¹ -γ ₂ L ² ... -γ _d+sD L ^d+sD )y _t =y _t -γ ₁ y _t-1 -γ ₂ y _t-2 …-γ _d+sD y _{td-sD get} :

Unconditional forecast sales for the next month: y _t (1) = γ ₁ y _t + γ ₂ y _t-1 …+γ _d+sD y _td-sD+1 +w _t (1).

其中当m-i>0，ε_t+m-i＝0；当m-i≤0，w_t(m-i)＝w_t+m-i。By analogy, the forecast for the next m months is:

Wherein, when mi>0, ε _t+mi =0; when mi≤0, w _t (mi)=w _t+mi .

Unconditional forecast sales for the next m months: y _t (m)=γ ₁ y _t (m-1)+γ ₂ y _t (m-2)…+γ _d+sD y _t (md-sD+1)+ w _t (m). Wherein when x≤0, y _t (x)=y _t+x .

The unconditional forecast sales volume of m months in the future can be calculated by the above calculation process.

Step S103: Establish a first association relationship between the conditional expected sales volume and the inventory volume under the condition of different inventory volumes according to the unconditional predicted sales volume.

In step S103, based on the Poisson distribution, a first association relationship between the conditional expected sales volume and the inventory volume under the condition of different inventory volumes is established according to the unconditional predicted sales volume.

Specifically, in the aforementioned steps, the unconditional forecast sales volume in the next m months is obtained. But the future sales volume is a random variable, generally assumed to be Poisson distribution Poission(y,λ), where y is the sales volume and λ is the sales expectation (mean). The y _t (m) obtained from the sales forecast under the condition of unrestricted inventory can be regarded as the mean and variance of the Poisson distribution. The goal of managing inventory _is to make Profit maximization.

In reality, the amount of inventory cannot be infinite. Based on the Poisson distribution, the first correlation relationship between the conditional expected sales volume and the inventory quantity under the condition of different inventory quantities can be obtained, specifically:

Among them, P(k) is the conditional expected sales volume, and k is the inventory quantity. It is easy to see that the conditional expected sales volume is the predicted average sales volume under the condition of a specific inventory quantity. It is the average result of a combination of various factors, which can reflect the influence of the inventory quantity k on the sales volume. Generally, the larger the k, the greater the conditional expected sales volume.

Step S104: Establish a second correlation relationship between the stock quantity, the conditional expected sales volume and the predicted profit according to the first correlation relation and the stock quantity.

In step S104, the predicted profit refers to the income realized by selling the commodity minus the total cost, and the predicted profit is the profit of the total sales volume of a certain commodity. The total cost includes: commodity cost, sales cost and capital cost. Since the forecast profit is affected by the conditional expected sales volume and the stock quantity, there is a second correlation between the stock quantity, the conditional expected sales volume and the forecast profit. Specifically, the second association relationship is:

Inc(P(k),k)=P(k)*C*E-k*C*R=C*(P(k)*E-k*R).

Among them, Inc(P(k),k) is the predicted profit, C is the purchase price of the unit commodity, and C*E is (the unit commodity selling price - unit commodity storage cost - unit commodity preparation fee - unit commodity mall deduction point - Employee commission per unit commodity - unit commodity sales expense), R is the capital interest rate.

Step S105: Determine the optimal inventory quantity according to the second association relationship.

In step S105, the optimal inventory refers to the calculated optimal inventory for a certain period of time in the future, in this embodiment, the optimal inventory in a certain month in the future. Since the purchase cost of the unit commodity is the same, maximizing the predicted profit is equivalent to maximizing P(k)*E-k*R. In step S103, we know that the larger the k, the larger the conditional expected sales volume P(k), and the larger the P(k)*E, that is, the increase of the inventory will increase the conditional expected sales volume, but the k*R will also become larger. . When inventory is small, adding one unit of inventory can improve forecast profit. When the inventory is large, an increase of one unit will reduce the forecast profit. The relationship between forecast profit and inventory is a concave function.

In this embodiment, the method based on mathematical programming obtains the inventory corresponding to the maximum predicted profit according to the second association relationship as the optimal inventory, and the optimal inventory is km _max .

First, define KS to represent the existing inventory, define RS to represent the purchase amount (this is the variable that needs to be requested), and the upper limit of replenishment funds is Fund; then the inventory after purchase is KKS=KS+RS; then there are:

max(Inc(P(KKS),KKS))

Through the above method, the corresponding RS can be obtained when P(KKS) is the largest, and then the corresponding KKS can be obtained, at this time km _max =KKS=KS+RS.

Step S106: Determine the optimal purchase quantity according to the optimal inventory quantity.

In the actual process, there is a procurement lead time, that is, the period from order placement to delivery time. For example, on August 1st today, the procurement lead time is 1 month, that is, the purchase order placed today will arrive on September 1st.

In step S106, it is necessary to obtain the procurement lead time and the on-hand inventory first, and the on-hand inventory includes the existing inventory of the store and the inventory in transit (that is, the inventory in the transportation process). Then, based on the procurement lead time and the existing inventory, the optimal purchase quantity is calculated according to the optimal inventory quantity. The optimal purchase quantity refers to the current optimal purchase quantity.

Specifically, in the foregoing steps, the optimal inventory quantity in a certain month in the future has been determined, and the current optimal purchase quantity is calculated according to the procurement lead time and the existing inventory quantity. Optimal Purchase Quantity = Optimal Inventory Quantity in a Future Month + Conditional Expected Sales Volume during Purchase Lead Time - On-hand Inventory Quantity. Among them, if the result is a positive number, it means the missing quantity, that is, the quantity to be purchased; if the result is a negative number or zero, it means that the inventory is wasted, and it is not necessary to purchase for the time being.

For example: today, August 1st, the purchase lead time of product A is 1 month, and the purchase amount for today needs to be determined according to the optimal inventory amount on September 1st. Then there are:

Purchase volume of product A on August 1 = optimal inventory volume on September 1 + conditional expected sales in August - product A's in-store inventory on August 1 - in-transit inventory. Assuming that the optimal inventory on September 1 is 30, the conditional expected sales volume in August is 20, the inventory of product A in the store on August 1 is 25, and the inventory in transit is 10, then the purchase volume of product A on August 1 = 30+ 20-25-10=15, that is, 15 units of A commodity need to be purchased on August 1.

The jewelry store inventory management method of the invention first predicts unconditionally predicted sales volume through historical sales data, and then obtains the relational expression between inventory quantity, conditional expected sales volume and predicted profit, and directly obtains the inventory corresponding to the maximum predicted profit. The method of the invention directly sets the inventory management target as profit maximization, completely avoids the phenomenon that the more management is strengthened due to improper target setting (such as turnover), the profit may be lower, and the profit of the store is greatly improved. At the same time, full quantitative management is adopted, empiricism is eliminated, and scientificity and iterability are guaranteed.

As shown in FIG. 7 and FIG. 8 , it shows the changes in the sales index and the return on assets index after a pilot jewelry store adopts the jewelry store inventory management method of the present invention in January 2021. It can be clearly seen from the figure that the sales indicators and the return on assets have improved compared with those before this method was adopted.

FIG. 4 is a schematic structural diagram of a jewelry store inventory management device 40 according to an embodiment of the present invention. As shown in FIG. 4 , the device 40 includes a historical data acquisition module 41 , an unconditional forecast sales volume forecast module 42 , a conditional expected sales volume calculation module 43 , a forecast profit calculation module 44 , an optimal inventory calculation module 45 and a decision module 46 .

The historical data acquisition module 41 is used to acquire historical sales data of various commodities in the jewelry store;

The unconditional forecast sales volume forecasting module 42 is used to predict the unconditional forecast sales volume according to the historical sales data without limiting the inventory;

The conditional expected sales volume calculation module 43 is configured to establish a first association relationship between the conditional expected sales volume and the inventory quantity under the condition of different inventory quantities according to the unconditional predicted sales volume;

The predicted profit calculation module 44 is configured to establish a second association relationship between the inventory amount, the conditional expected sales volume and the predicted profit according to the first association relationship and the inventory amount;

The optimal inventory calculation module 45 is configured to determine the optimal inventory quantity according to the second association relationship;

The decision module 46 is used to determine the optimal purchase quantity according to the optimal inventory quantity.

For the specific limitations of the jewelry store inventory management device 40, reference may be made to the limitations on the jewelry store inventory management method above, which will not be repeated here. Each module in the above-mentioned jewelry store inventory management device 40 may be implemented in whole or in part by software, hardware, and combinations thereof.

Please refer to FIG. 5 , which is a schematic structural diagram of a computer device 50 according to an embodiment of the present invention. As shown in FIG. 5 , the computer device 50 includes a processor 52 and a memory 51 coupled to the processor 52 . The memory 51 stores a computer program for implementing the inventory management method for a jewelry store described in any of the above embodiments. The processor is used to execute the computer program stored in the memory.

The processor 52 may also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 52 can be an integrated circuit chip with signal processing capability. Processor 52 may also be a general purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components . A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Referring to FIG. 6, FIG. 6 is a schematic structural diagram of a computer storage medium 60 according to an embodiment of the present invention. The computer storage medium 60 in the embodiment of the present invention stores a program file 61 capable of implementing all the above-mentioned methods, wherein the program file 61 may be stored in the above-mentioned computer storage medium 60 in the form of a software product, and includes several instructions to enable a A computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) executes all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer storage medium 60 includes: a USB flash drive, a removable hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc. that can store program codes media, or terminal devices such as computers, servers, mobile phones, and tablets.

In the several embodiments provided by the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The above is only an embodiment of the present invention, and is not intended to limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, All are similarly included in the scope of patent protection of the present invention.

Claims (10)

1. A jewelry store inventory management method, comprising:

acquiring historical sales data of jewelry shop commodities;

predicting unconditional predicted sales volume according to the historical sales data under the condition of not limiting inventory;

establishing a first association relation between the condition expected sales volume and the stock quantity under the conditions of different stock quantities according to the unconditional predicted sales volume;

establishing a second association relation among the inventory, the conditional expected sales volume and the predicted profit according to the first association relation and the inventory;

determining an optimal inventory according to the second incidence relation;

and determining the optimal purchasing quantity according to the optimal inventory quantity.

2. The jewelry shop inventory management method according to claim 1, wherein said predicting unconditionally predicted sales amounts without limiting inventory amounts based on said historical sales data comprises:

establishing a seasonal time series model according to the historical sales data;

fitting the seasonal time series model according to the historical sales data to obtain an optimal seasonal time series model;

predicting the unconditionally predicted sales based on the optimal seasonal time series model without limiting inventory.

3. The jewelry store inventory management method of claim 2, wherein fitting the seasonal time series model based on the historical sales data to obtain an optimal seasonal time series model comprises:

fitting the seasonal time series model according to the historical sales data based on a conditional maximum likelihood algorithm to obtain a parameter set;

calculating to obtain optimal parameters according to the parameter set based on the Chichi information criterion;

and taking the seasonal time series model corresponding to the optimal parameter as the optimal seasonal time series model.

4. The jewelry shop management method according to claim 2, wherein the time-series period of the seasonal time-series model is a year period, and the variation period is a month period.

5. The jewelry shop inventory management method according to claim 1, wherein establishing a first correlation between the conditional expected sales volume and the stock quantity under the condition of different stock quantities from the unconditionally forecasted sales volume comprises:

and establishing a first correlation between the conditional expected sales volume and the inventory quantity under the condition of different inventory quantities according to the unconditionally predicted sales volume based on the Poisson distribution.

6. The jewelry store inventory management method according to claim 1, wherein said determining an optimal inventory amount according to said second correlation comprises:

and acquiring the corresponding inventory amount when the predicted profit is maximum according to the second incidence relation by a mathematical programming-based method to serve as the optimal inventory amount.

7. The jewelry shop management method of claim 1, wherein said determining an optimal procurement amount from said optimal inventory amount comprises:

acquiring a purchase lead period and the existing stock;

and calculating the optimal purchasing quantity according to the optimal stock quantity based on the purchasing lead period and the existing stock quantity.

8. A jewelry store inventory management apparatus, comprising:

the historical data acquisition module is used for acquiring historical sales data of various commodities of the jewelry shop;

the unconditional forecast sales forecasting module is used for forecasting the unconditional forecast sales under the condition of not limiting the stock according to the historical sales data;

the condition expected sales calculation module is used for establishing a first association relation between the condition expected sales and the stock quantity under the conditions of different stock quantities according to the unconditional predicted sales;

the predicted profit calculation module is used for establishing a second association relation among the inventory quantity, the condition expected sales and the predicted profit according to the first association relation and the inventory quantity;

the optimal inventory calculation module is used for determining the optimal inventory according to the second incidence relation;

and the decision module is used for determining the optimal purchasing quantity according to the optimal inventory quantity.

9. A computer device, comprising: memory, processor and computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a jewelry store inventory management method according to any one of claims 1 to 7.

10. A computer storage medium having stored thereon a program file, wherein the program file, when executed by a processor, implements the jewelry store inventory management method of any one of claims 1 to 7.

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