CN117494907A - Factory production plan scheduling optimization method and system based on sales data analysis - Google Patents
Factory production plan scheduling optimization method and system based on sales data analysis Download PDFInfo
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Abstract
The invention discloses a factory production plan scheduling optimization method and a factory production plan scheduling optimization system based on sales data analysis, which belong to the field of data processing systems specially applied to management.
Description
Technical Field
The invention belongs to the field of data processing systems specially suitable for management, and particularly relates to a factory production plan scheduling optimization method and system based on sales data analysis.
Background
The factory production plan scheduling optimization based on sales data analysis refers to that sales data of factories are utilized for analysis and mining to better understand market demands and sales trends, so that production plans and scheduling are optimized to adapt to the market demands and maximize production benefits, for example, a medicine enterprise estimates various medicine demands in the next period according to the sales data of medicines to allocate medicine production resources, but sales amounts of all production products, environmental data and conditions that the medicine demands can be replaced by other products cannot be accurately estimated when calculation of the medicine demands in future time is carried out, so that scheduling optimization effects are poor, and the problems exist in the prior art;
a method for scheduling production by a tire manufacturing enterprise production planning is disclosed, for example, in chinese patent application publication No. CN109102191 a. The method comprises the following steps: step 1, setting basic data; step 2, compiling a monthly production plan; step 3, making a temporary schedule of monthly production plans of each production factory; step 4, scheduling and scheduling production by a tire building production plan; step 5, scheduling and scheduling tire vulcanization production planning; step 6, compiling a scheduling plan of a monthly production plan; and 7, preparing a tire production operation day plan. The method for scheduling and scheduling the tire manufacturing enterprise production plan can solve the defect that the manual planning cannot comprehensively balance the technological parameters and various constraint conditions, and realize the automatic scheduling integrated information management of the tire molding vulcanization production plan.
The problems proposed in the background art exist in the above patents: in the prior art, when the medicine requirement is calculated, sales quantity, environmental data of each production product and the condition that the sales quantity and the environmental data can be replaced by other products cannot be considered, so that the scheduling optimization effect is poor.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a factory production plan dispatching optimization method and a system based on sales data analysis, the invention obtains the product data of factory production, simultaneously obtains the historical sales data, the historical environment data and the inventory data of each product of factory production, simultaneously obtains the customer use data of the production product, imports the historical sales data and the historical environment data of the factory production product into a future sales data prediction model construction strategy to construct a future sales data prediction model, imports the collected environmental data at the future time into the constructed sales data prediction model to estimate the sales data of the production product in the next sales period, imports the sales data and the inventory data of the production product in the future time into a demand calculation strategy for the production product of factory production, simultaneously imports the demand of each production product into a demand satisfaction calculation formula for each production product, simultaneously extracts the customer use data of the production product into the demand importance calculation strategy for each production product, carries out the calculation of the demand importance degree of each production product, obtains the demand satisfaction degree of each production product obtained by calculation and the importance degree of each production product into the estimated value of each production product in the future sales data prediction model construction model, carries out the estimation of the sales data, and the market demand of each production product can be allocated to the production resource of the production plan production resource according to the whole production plan production resource, and the situation of the sales data can be allocated to the sales data of the sales data can be accurately calculated, and the sales resource can be allocated to the production resource of the production plan production resource is calculated.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the factory production plan scheduling optimization method based on sales data analysis comprises the following specific steps:
s1, acquiring product data produced by a factory, simultaneously acquiring historical sales data, historical environment data and inventory data of products produced by the factory, and simultaneously acquiring customer use data of products;
s2, importing historical sales data and historical environmental data of the factory production products into a future sales data prediction model construction strategy to construct a future sales data prediction model;
s3, importing the collected environmental data at the future time into a constructed sales data prediction model to estimate sales data of products produced in the next sales period;
s4, importing the sales data and the inventory data of the product produced in the next sales period into a demand quantity calculation strategy at the future moment to estimate the demand quantity of each product produced in the factory, and substituting the estimated demand quantity of each product into a demand satisfaction calculation formula to calculate the demand satisfaction of each product;
s5, simultaneously extracting customer usage data of the produced products, importing the customer usage data into a demand importance calculation strategy, and calculating the demand importance of each produced product;
S6, obtaining the calculated demand satisfaction degree of each production product and the calculated demand importance degree of each production product, substituting the demand satisfaction degree and the calculated demand importance degree of each production product into a production planning value calculation strategy to calculate the production planning value of each production product, and distributing and scheduling production resources according to the proportion of the production planning value of each production product to the production planning value of the whole production product.
Specifically, the step S1 comprises the following specific steps:
s11, setting a sales period, acquiring application data of products produced by a factory in the sales period, and simultaneously acquiring sales data of historical sales periods of products produced by the factory and environmental data of the historical sales periods, wherein the sales periods can be set to be one day, three days and the like, and flexibly set according to the transfer period of the products, and the application data are applications of labels for producing the products, wherein the application data of vitamin C chewable tablets are as follows: the method is used for preventing scurvy and also can be used for auxiliary treatment of various acute and chronic diseases, wherein the sales data of the historical sales period of the factory production products is sales amount of each production product in each historical sales period, and the environmental data of the historical sales period is day and night temperature difference data, average temperature data and environmental pollution index data of each historical sales period;
S12, acquiring inventory data of each production product stored in a factory, and simultaneously acquiring customer usage data of the production products of the factory, wherein the customer usage data of the production products of the factory is usage data of specified medicines by customers, for example, for vitamin C chewable tablets, the usage of the customers is scurvy prevention, and the customer usage data is scurvy prevention;
it should be noted that, the method acquires the product data produced by the factory, acquires the historical sales data, the historical environmental data and the inventory data of each product produced by the factory, and acquires the customer usage data of each product, wherein the customer usage data is used only in the system calculation process, the leakage mode such as hacking is not performed, the product data produced by the factory cannot be leaked to the outside, and the historical sales data, the historical environmental data and the inventory data of each product produced by the factory are acquired, and the customer usage data of each product is acquired, so that the specific confidentiality problem is not considered;
specifically, the future sales data prediction model construction strategy in S2 includes the following specific contents:
S21, extracting a day and night temperature difference data change curve, an average temperature data change curve, an environmental pollution index data curve and sales data of a historical sales period of each historical sales period, constructing sales data which are input into the day and night temperature difference data change curve, the average temperature data change curve, the environmental pollution index data curve and products produced in an upper sales period, outputting a deep learning neural network model which is sales data of products produced in a lower sales period, and setting the deep learning neural network model as a future sales data prediction model;
s22, setting a parameter training set and a parameter testing set according to the extracted diurnal temperature difference data change curve, average temperature data change curve, environment pollution index data curve and sales data of the historical sales period in a ratio of 9:1; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using a 10% parameter test set, and outputting an optimal initial deep learning neural network model meeting the sales data accuracy of products produced in a preset sales period as the deep learning neural network model, wherein an output strategy formula of an m+1th-item neuron in the deep learning neural network model is as follows: Wherein->For the deep learning neural network model m+1 layer output of the s-th neuron, +.>For the connection weight of the (m) th item neuron of the deep learning neural network model and the (m+1) th item neuron of the deep learning neural network model, < ->Input representing the jth neuron of the mth layer of the deep learning neural network model, +.>M-th and j-th items representing deep learning neural network modelBias of linear relation of neurons and deep learning neural network model m+1 layer s th item neurons,/L>Representing the Sigmoid activation function, w is the number of m-th layer neurons of the deep learning neural network model.
Specifically, the content of S3 includes the following specific steps:
s31, acquiring a day and night temperature difference data change curve, an average temperature data change curve and an environmental pollution index data curve at a future moment from weather forecast;
s32, importing the acquired day and night temperature difference data change curve, the average temperature data change curve and the environmental pollution index data curve at the future time into a constructed sales data prediction model to estimate sales data at the future time, and outputting sales data of products produced in the next sales periodWherein n is the number of kinds of products to be produced, Sales data of the i-th product of the lower sales cycle, i being any one of 1 to n.
Specifically, the future time demand calculation strategy of S4 includes the following specific steps:
s41, importing the sales data and the inventory data of the products produced in the next sales period into a future time demand quantity calculation formula to estimate the demand quantity of each product produced in the factory, wherein the future time demand quantity calculation formula is as follows:wherein->Demand for production of product for item i of the lower sales cycle,/->Inventory data for item i production productWherein Z () is a negative number 0, and if the number in brackets is a negative number ++>Directly take 0, if the number in brackets is positive or 0, then +.>；
S42, substituting the calculated demand quantity of each production product into a demand satisfaction calculation formula to calculate the demand satisfaction of each production product, wherein the demand satisfaction calculation formula of the ith production product is as follows:。
specifically, the specific content of the demand importance calculation policy in S5 is:
s51, extracting the usage data of the specified drugs from the clients producing the products, and extracting the number of drug types of the same usage data in the market according to the usage data Wherein->The number of drug types in the ith market for producing drugs is 5 types of drug types, such as vitamin C oral liquid and vitamin C reagent, in which the number of drug types in the same usage data is 5;
s52, obtaining the same medicine type number of the application data in the market, substituting the medicine type number into a demand importance calculation formula to calculate the demand importance, wherein the demand importance calculation formula of the ith production medicine is as follows:。
specifically, the specific steps of the production plan value calculation strategy in S6 are as follows:
s61, obtaining the calculated demand satisfaction degree of each production product and the demand importance degree of each production product, and substituting the demand satisfaction degree and the demand importance degree into a production product production planning value calculation formula to calculate a production product production planning value, wherein the calculation formula of the ith production product production planning value is as follows:；
s62, substituting the production plan values of the production products obtained through extraction and calculation into a production resource allocation amount calculation formula, and carrying out allocation calculation of production resources, wherein the calculation formula of the production resource allocation amount of the ith production product is as follows: Where P is the total resources produced.
The factory production plan scheduling optimization system based on sales data analysis is realized based on the factory production plan scheduling optimization method based on sales data analysis, and comprises a data acquisition module, a future sales data model construction module, a production product sales prediction module, a product demand satisfaction calculation module, a product demand importance calculation module, a resource allocation module and a control module, wherein the data acquisition module is used for acquiring product data produced by a factory, simultaneously acquiring historical sales data of production products of the factory, historical environmental data and inventory data of production products, simultaneously acquiring customer usage data of the production products, the future sales data model construction module is used for importing the historical sales data and the historical environmental data of the production products of the factory into a future sales data prediction model construction strategy to construct a future sales data prediction model, and the production product sales prediction module is used for importing the acquired environmental data at the future time into the constructed sales data prediction model to predict sales data of production products in the next sales cycle.
The system comprises a product demand satisfaction calculation module, a resource allocation module, a data acquisition module, a future sales data model construction module, a product sales prediction module, a product demand importance calculation module and a resource allocation module, wherein the product demand satisfaction calculation module is used for extracting sales data and inventory data of a product produced in a next sales period, introducing the sales data and inventory data into a demand calculation strategy at a future moment to predict the demand of each production product of a factory, substituting the predicted demand of each production product into a demand satisfaction calculation formula to calculate the demand satisfaction of each production product, the product demand importance calculation module is used for extracting customer usage data of the production product, introducing the data into the demand importance calculation strategy to calculate the demand importance of each production product, and the resource allocation module is used for acquiring the calculated demand satisfaction of each production product and substituting the demand importance of each production product into a production planning calculation strategy to calculate the production planning value of each production product, and allocating and scheduling production resources according to the proportion of the production planning value of each production product to the whole production planning value of the production product.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the factory production plan scheduling optimization method based on sales data analysis by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a factory production plan scheduling optimization method based on sales data analysis as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, product data produced by a factory are obtained, historical sales data, historical environmental data and inventory data of products produced by the factory are obtained, customer use data of the products are obtained, the historical sales data and the historical environmental data of the products produced by the factory are imported into a future sales data prediction model construction strategy to construct a future sales data prediction model, the collected environmental data at the future time is imported into the constructed sales data prediction model to estimate sales data of the products produced in the next sales cycle, the obtained sales data and inventory data of the products produced in the next sales cycle are imported into a future time demand calculation strategy to estimate the demand of the products produced by the factory, the estimated demand of the products is substituted into a demand satisfaction calculation formula to calculate the demand satisfaction of the products, the customer use data of the products is imported into a demand importance calculation strategy to calculate the demand importance of the products, the obtained demand satisfaction of the products and the demand importance of the products are substituted into the production plan calculation strategy to calculate the production plan value of the products, the production resources are estimated according to the overall production plan value of the products, and the demand of the products can be allocated accurately according to the demand of the sales plan and the sales demand of the products is increased.
Drawings
FIG. 1 is a schematic flow chart of a factory production plan scheduling optimization method based on sales data analysis;
FIG. 2 is a schematic diagram of the overall framework of the plant production planning scheduling optimization system based on sales data analysis of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: the factory production plan scheduling optimization method based on sales data analysis comprises the following specific steps:
s1, acquiring product data produced by a factory, simultaneously acquiring historical sales data, historical environment data and inventory data of products produced by the factory, and simultaneously acquiring customer use data of products;
it should be noted that, S1 includes the following specific steps:
s11, setting a sales period, acquiring application data of products produced by a factory in the sales period, and simultaneously acquiring sales data of historical sales periods of products produced by the factory and environmental data of the historical sales periods, wherein the sales periods can be set to be one day, three days and the like, and flexibly set according to the transfer period of the products, and the application data are applications of labels for producing the products, wherein the application data of vitamin C chewable tablets are as follows: the method is used for preventing scurvy and also can be used for auxiliary treatment of various acute and chronic diseases, wherein the sales data of the historical sales period of the factory production products is sales amount of each production product in each historical sales period, and the environmental data of the historical sales period is day-night temperature difference data, average temperature data and environmental pollution index data of each historical sales period;
S12, acquiring inventory data of each production product stored in a factory, and simultaneously acquiring customer usage data of the production products of the factory, wherein the customer usage data of the production products of the factory is usage data of specified medicines by customers, for example, for vitamin C chewable tablets, the usage of the customers is scurvy prevention, and the customer usage data is scurvy prevention;
it should be noted that, the method acquires the product data produced by the factory, acquires the historical sales data, the historical environmental data and the inventory data of each product produced by the factory, and acquires the customer usage data of each product, wherein the customer usage data is used only in the system calculation process, the leakage mode such as hacking is not performed, the product data produced by the factory cannot be leaked to the outside, and the historical sales data, the historical environmental data and the inventory data of each product produced by the factory are acquired, and the customer usage data of each product is acquired, so that the specific confidentiality problem is not considered;
s2, importing historical sales data and historical environmental data of the factory production products into a future sales data prediction model construction strategy to construct a future sales data prediction model;
The future sales data prediction model construction strategy in the S2 comprises the following specific contents:
s21, extracting a day and night temperature difference data change curve, an average temperature data change curve, an environmental pollution index data curve and sales data of a historical sales period of each historical sales period, constructing sales data which are input into the day and night temperature difference data change curve, the average temperature data change curve, the environmental pollution index data curve and products produced in an upper sales period, outputting a deep learning neural network model which is sales data of products produced in a lower sales period, and setting the deep learning neural network model as a future sales data prediction model;
s22, setting a parameter training set and a parameter testing set according to the extracted diurnal temperature difference data change curve, average temperature data change curve, environment pollution index data curve and sales data of the historical sales period in a ratio of 9:1; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using a 10% parameter test set, and outputting an optimal initial deep learning neural network model meeting the sales data accuracy of products produced in a preset sales period as the deep learning neural network model, wherein an output strategy formula of an m+1th-item neuron in the deep learning neural network model is as follows: Wherein->For the deep learning neural network model m+1 layer output of the s-th neuron, +.>For the connection weight of the (m) th item neuron of the deep learning neural network model and the (m+1) th item neuron of the deep learning neural network model, < ->Input representing the jth neuron of the mth layer of the deep learning neural network model, +.>Bias representing linear relationship of the mth item neuron of the deep learning neural network model and the (m+1) th item neuron of the deep learning neural network model, < ->Representing a Sigmoid activation function, wherein w is the number of m-th layer neurons of the deep learning neural network model;
the following is a code example of a Python-based deep learning neural network model for extracting a diurnal temperature difference data variation curve, an average temperature data variation curve, an environmental pollution index data curve, and sales data of a history sales period for each history sales period, and constructing sales data inputted as a diurnal temperature difference data variation curve, an average temperature data variation curve, an environmental pollution index data curve, and an upper sales period production product, outputting a neural network model for sales data of a lower sales period production product,
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
# definition neural network structure
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.weights_input_hidden = np.random.uniform(-1, 1, (input_size, hidden_size))
self.weights_hidden_output = np.random.uniform(-1, 1, (hidden_size, output_size))
self.biases_hidden = np.random.uniform(-1, 1, (hidden_size))
self.biases_output = np.random.uniform(-1, 1, (output_size))
def forward(self, input):
hidden = np.tanh(np.dot(input, self.weights_input_hidden) + self.biases_hidden)
output = np.dot(hidden, self.weights_hidden_output) + self.biases_output
return output
# initializing neural network structure
nn = NeuralNetwork(input_size=4, hidden_size=10, output_size=1)
# load data
data = np.load('data.npy')
# extract data
temperature_data = data[:, 0]
day_night_temp_data = data[:, 1]
pollution_data = data[:, 2]
sales_data = data[:, 3]
Data normalization
scaler = MinMaxScaler()
temperature_data = scaler.fit_transform(temperature_data.reshape(-1, 1))
day_night_temp_data = scaler.fit_transform(day_night_temp_data.reshape(-1, 1))
pollution_data = scaler.fit_transform(pollution_data.reshape(-1, 1))
sales_data = scaler.fit_transform(sales_data.reshape(-1, 1))
Training neural network model
epochs = 1000
learning_rate = 0.01
for epoch in range(epochs):
input = np.array([temperature_data[0], day_night_temp_data[0], pollution_data[0], sales_data[0]])
target = sales_data[1]
output = nn.forward(input)
error = target - output
nn.weights_input_hidden += learning_rate * np.dot(input.T, error)
nn.weights_hidden_output += learning_rate * np.dot(hidden.T, error)
nn.biases_hidden += learning_rate * np.sum(error, axis=0, keepdims=True)
nn.biases_output += learning_rate * np.sum(error, axis=0, keepdims=True)
Sales data for sales cycle production product under # forecast
input = np.array([temperature_data[1], day_night_temp_data[1], pollution_data[1], sales_data[1]])
output = nn.forward(input)
Inverse normalization
predicted_sales = scaler.inverse_transform(output)[0]
# rendering data
plt.figure()
plt.plot(temperature_data, label='Temperature')
plt.plot(day_night_temp_data, label='Day-Night Temperature')
plt.plot(pollution_data, label='Pollution')
plt.plot(sales_data, label='Sales')
plt.plot(predicted_sales, label='Predicted Sales')
plt.legend()
plt.show()
It should be noted here that the influence of the environment on the sales of the medicine mainly appears in the following aspects:
1. climate change: climate change may lead to an increased incidence of certain diseases, thereby affecting sales of related drugs, for example, as global climate warms, the incidence of asthma and allergic diseases may increase, resulting in increased demand for related drugs;
2. environmental pollution: environmental pollution may have an impact on human health, leading to an increase in the incidence of certain diseases, for example, air pollution may increase the incidence of cardiovascular and respiratory diseases, thus affecting the sales of related drugs;
3. ecosystem changes: changes in the ecosystem may lead to changes in the transmission pathways of certain diseases, thereby affecting sales of related drugs; for example, global warming may lead to an expansion of the range of motion of vector organisms such as mosquitoes, thereby increasing the risk of transmission of diseases such as malaria, and affecting sales of related drugs;
4. Social and economic factors: social and economic factors may affect the life style and health condition of people, thereby affecting sales of related drugs, for example, as the living standard of people increases, the incidence of chronic diseases such as obesity and diabetes may increase, thereby affecting sales of related drugs;
in summary, the environmental impact on drug sales is versatile;
s3, importing the collected environmental data at the future time into a constructed sales data prediction model to estimate sales data of products produced in the next sales period;
the content of S3 comprises the following specific steps:
s31, acquiring a day and night temperature difference data change curve, an average temperature data change curve and an environmental pollution index data curve at a future moment from weather forecast;
s32, importing the acquired day and night temperature difference data change curve, the average temperature data change curve and the environmental pollution index data curve at the future time into a constructed sales data prediction model to estimate sales data at the future time, and outputting sales data of products produced in the next sales periodWherein n is the number of kinds of products to be produced,sales data of the ith production product of the lower sales cycle, i being any one of 1 to n;
S4, importing the sales data and the inventory data of the product produced in the next sales period into a demand quantity calculation strategy at the future moment to estimate the demand quantity of each product produced in the factory, and substituting the estimated demand quantity of each product into a demand satisfaction calculation formula to calculate the demand satisfaction of each product;
the future time demand calculation strategy of the S4 comprises the following specific steps:
s41, importing the sales data and the inventory data of the products produced in the next sales period into a future time demand quantity calculation formula to estimate the demand quantity of each product produced in the factory, wherein the future time demand quantity calculation formula is as follows:wherein->Demand for production of product for item i of the lower sales cycle,/->Inventory data for the ith production item, wherein Z () is a negative number 0, and +_if negative in brackets>Directly take 0, if the number in brackets is positive or 0, then +.>；
S42, substituting the calculated demand quantity of each production product into a demand satisfaction calculation formula to calculate the demand satisfaction of each production product, wherein the demand satisfaction calculation formula of the ith production product is as follows:；
s5, simultaneously extracting customer usage data of the produced products, importing the customer usage data into a demand importance calculation strategy, and calculating the demand importance of each produced product;
The specific content of the demand importance calculation strategy in S5 is:
s51, extracting the usage data of the specified drugs from the clients producing the products, and extracting the number of drug types of the same usage data in the market according to the usage dataWherein->The number of drug types in the ith market for producing drugs is 5 types of drug types, such as vitamin C oral liquid and vitamin C reagent, in which the number of drug types in the same usage data is 5;
s52, obtaining the same medicine type number of the application data in the market, substituting the medicine type number into a demand importance calculation formula to calculate the demand importance, wherein the demand importance calculation formula of the ith production medicine is as follows:；
6. obtaining the demand satisfaction degree of each production product and the demand importance degree of each production product obtained through calculation, substituting the demand satisfaction degree and the demand importance degree of each production product into a production planning value calculation strategy to calculate the production planning value of each production product, and distributing and scheduling production resources according to the proportion of the production planning value of each production product to the production planning value of the whole production product;
it should be noted that, the case where the product can be replaced by another product is used as a parameter of sales analysis in the next stage, and the case where the product can be replaced by another product has the following possible effects on sales of the product:
1. The sales of products is reduced: when more competitive alternative products are presented on the market, consumers may prefer to purchase alternative products, which will result in a decrease in sales of the original product;
2. price competition is aggravated: in the face of competition for alternative products, businesses may be forced to lower the price of the product to attract consumer purchases. This may lead to a reduced sales of the product and may also lead to a reduced profit margin for the enterprise;
3. and (3) demand transfer: when a consumer chooses to purchase an alternative product, the demand for the original product may be transferred to the alternative product. Such transfer may result in a decrease in sales of the original product, while sales of the replacement product may increase;
the specific steps of the production plan value calculation strategy in the S6 are as follows:
s61, obtaining the calculated demand satisfaction degree of each production product and the demand importance degree of each production product, and substituting the demand satisfaction degree and the demand importance degree into a production product production planning value calculation formula to calculate a production product production planning value, wherein the calculation formula of the ith production product production planning value is as follows:；
s62, substituting the production plan values of the production products obtained through extraction and calculation into a production resource allocation amount calculation formula, and carrying out allocation calculation of production resources, wherein the calculation formula of the production resource allocation amount of the ith production product is as follows: Wherein P is the total resources produced;
according to the invention, product data produced by a factory are obtained, historical sales data, historical environmental data and inventory data of products produced by the factory are obtained, customer use data of the products are obtained, the historical sales data and the historical environmental data of the products produced by the factory are imported into a future sales data prediction model construction strategy to construct a future sales data prediction model, the collected environmental data at the future time is imported into the constructed sales data prediction model to estimate sales data of the products produced in the next sales cycle, the obtained sales data and inventory data of the products produced in the next sales cycle are imported into a future time demand calculation strategy to estimate the demand of the products produced by the factory, the estimated demand of the products is substituted into a demand satisfaction calculation formula to calculate the demand satisfaction of the products, the customer use data of the products is imported into a demand importance calculation strategy to calculate the demand importance of the products, the obtained demand satisfaction of the products and the demand importance of the products are substituted into the production plan calculation strategy to calculate the production plan value of the products, the production resources are estimated according to the overall production plan value of the products, and the demand of the products can be allocated accurately according to the demand of the sales plan and the sales demand of the products is increased.
Example 2
As shown in fig. 2, the factory production plan scheduling optimization system based on sales data analysis is realized based on the factory production plan scheduling optimization method based on sales data analysis, and comprises a data acquisition module, a future sales data model construction module, a production product sales prediction module, a product demand satisfaction calculation module, a product demand importance calculation module, a resource allocation module and a control module, wherein the data acquisition module is used for acquiring product data of factory production, simultaneously acquiring historical sales data of factory production products, historical environmental data and inventory data of production products, simultaneously acquiring customer usage data of production products, the future sales data model construction module is used for introducing the historical sales data and the historical environmental data of factory production products into a future sales data prediction model construction strategy to construct a future sales data prediction model, and the production product sales prediction module is used for introducing the acquired environmental data at the future time into the constructed sales data prediction model to predict sales data of production products in the next sales cycle;
in this embodiment, the product demand satisfaction calculation module is configured to extract sales data and inventory data of the product produced in the next sales cycle, import the sales data and inventory data into a future time demand calculation policy, estimate a demand of each product produced in a factory, and simultaneously, replace the estimated demand of each product produced into a demand satisfaction calculation formula to calculate a demand satisfaction of each product produced, the product demand importance calculation module is configured to extract customer usage data of the product to import the demand importance calculation policy to calculate a demand importance of each product produced, the resource allocation module is configured to obtain the demand satisfaction of each product produced obtained by calculation and the demand importance of each product produced into a production plan calculation policy to calculate a production plan of each product produced, and allocate and schedule production resources according to a proportion of the production plan of each product produced to the production plan of the whole product, where the control module is configured to control operations of the data acquisition module, the future sales data model construction module, the product sales prediction module, the product demand satisfaction calculation module, and the resource allocation module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs the plant production plan scheduling optimization method based on sales data analysis described above by calling a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memories have at least one computer program stored therein, and the computer program is loaded and executed by the processors to implement the factory production plan scheduling optimization method based on sales data analysis provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
The computer program, when run on the computer device, causes the computer device to perform the plant production planning scheduling optimization method based on sales data analysis described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely a way of partitioning, and there may be additional ways of partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The factory production plan scheduling optimization method based on sales data analysis is characterized by comprising the following specific steps of:
s1, acquiring product data produced by a factory, simultaneously acquiring historical sales data, historical environment data and inventory data of products produced by the factory, and simultaneously acquiring customer use data of products;
s2, importing historical sales data and historical environmental data of the factory production products into a future sales data prediction model construction strategy to construct a future sales data prediction model;
s3, importing the collected environmental data at the future time into a constructed sales data prediction model to estimate sales data of products produced in the next sales period;
S4, importing the sales data and the inventory data of the product produced in the next sales period into a demand quantity calculation strategy at the future moment to estimate the demand quantity of each product produced in the factory, and substituting the estimated demand quantity of each product into a demand satisfaction calculation formula to calculate the demand satisfaction of each product;
s5, simultaneously extracting customer usage data of the produced products, importing the customer usage data into a demand importance calculation strategy, and calculating the demand importance of each produced product;
s6, obtaining the calculated demand satisfaction degree of each production product and the calculated demand importance degree of each production product, substituting the demand satisfaction degree and the calculated demand importance degree of each production product into a production planning value calculation strategy to calculate the production planning value of each production product, and distributing and scheduling production resources according to the proportion of the production planning value of each production product to the production planning value of the whole production product.
2. The method for optimizing plant production plan scheduling based on sales data analysis according to claim 1, wherein S1 comprises the specific steps of:
s11, setting a sales period, acquiring application data of products produced by a factory in the sales period, and simultaneously acquiring sales data of historical sales periods and environmental data of the historical sales periods of the products produced by the factory, wherein the environmental data of the historical sales periods are day-night temperature difference data, average temperature data and environmental pollution index data of each historical sales period;
S12, acquiring inventory data of each production product stored in the factory, and simultaneously acquiring customer use data of the production products of the factory.
3. The plant production plan scheduling optimization method based on sales data analysis according to claim 2, wherein the future sales data prediction model construction strategy in S2 includes the following specific contents:
s21, extracting a day and night temperature difference data change curve, an average temperature data change curve, an environmental pollution index data curve and sales data of a historical sales period of each historical sales period, constructing sales data which are input into the day and night temperature difference data change curve, the average temperature data change curve, the environmental pollution index data curve and products produced in an upper sales period, outputting a deep learning neural network model which is sales data of products produced in a lower sales period, and setting the deep learning neural network model as a future sales data prediction model;
s22, extracting day and night temperature difference data change curves, average temperature data change curves and environmental pollution index data of each historical sales periodSetting a parameter training set and a parameter testing set according to the sales data of the curve and the historical sales period in a ratio of 9:1; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using a 10% parameter test set, and outputting an optimal initial deep learning neural network model meeting the sales data accuracy of products produced in a preset sales period as the deep learning neural network model, wherein an output strategy formula of an m+1th-item neuron in the deep learning neural network model is as follows: Wherein->For the deep learning neural network model m+1 layer output of the s-th neuron, +.>For the connection weight of the (m) th item neuron of the deep learning neural network model and the (m+1) th item neuron of the deep learning neural network model, < ->Input representing the jth neuron of the mth layer of the deep learning neural network model, +.>Bias representing linear relationship of the mth item neuron of the deep learning neural network model and the (m+1) th item neuron of the deep learning neural network model, < ->Representing the Sigmoid activation function, w is the number of m-th layer neurons of the deep learning neural network model.
4. The method for optimizing plant production plan scheduling based on sales data analysis according to claim 3, wherein the content of S3 comprises the following specific steps:
s31, acquiring a day and night temperature difference data change curve, an average temperature data change curve and an environmental pollution index data curve at a future moment from weather forecast;
s32, importing the acquired day and night temperature difference data change curve, the average temperature data change curve and the environmental pollution index data curve at the future time into a constructed sales data prediction model to estimate sales data at the future time, and outputting sales data of products produced in the next sales period Wherein n is the number of species of the product, < >>Sales data of the i-th product of the lower sales cycle, i being any one of 1 to n.
5. The method for optimizing plant production plan scheduling based on sales data analysis according to claim 4, wherein the future time demand calculation strategy of S4 comprises the specific steps of:
s41, importing the sales data and the inventory data of the products produced in the next sales period into a future time demand quantity calculation formula to estimate the demand quantity of each product produced in the factory, wherein the future time demand quantity calculation formula is as follows:wherein->Demand for production of product for item i of the lower sales cycle,/->Inventory data for the ith production item, wherein Z () is a negative number 0, and +_if negative in brackets>Directly take 0, if the number in brackets is positive or 0, then +.>；
S42, substituting the calculated demand quantity of each production product into a demand satisfaction calculation formula to calculate the demand satisfaction of each production product, wherein the demand satisfaction calculation formula of the ith production product is as follows:。
6. the method for optimizing factory production plan scheduling based on sales data analysis according to claim 5, wherein the specific contents of the demand importance calculation strategy in S5 are:
S51, extracting the usage data of the specified drugs from the clients producing the products, and extracting the number of drug types of the same usage data in the market according to the usage dataWherein->The number of drug types in the ith market for producing drugs is 5 types of drug types, such as vitamin C oral liquid and vitamin C reagent, in which the number of drug types in the same usage data is 5;
s52, obtaining the same medicine type number of the application data in the market, substituting the medicine type number into a demand importance calculation formula to calculate the demand importance, wherein the demand importance calculation formula of the ith production medicine is as follows:。
7. the method for optimizing plant production plan scheduling based on sales data analysis according to claim 6, wherein the specific steps of the production plan value calculation strategy in S6 are as follows:
s61, obtaining the calculated demand satisfaction degree of each production product and the demand importance degree of each production product, and substituting the demand satisfaction degree and the demand importance degree into a production product production planning value calculation formula to calculate a production product production planning value, wherein the calculation formula of the ith production product production planning value is as follows: ；
S62, substituting the production plan values of the production products obtained through extraction and calculation into a production resource allocation amount calculation formula, and carrying out allocation calculation of production resources, wherein the calculation formula of the production resource allocation amount of the ith production product is as follows:where P is the total resources produced.
8. The factory production plan scheduling optimization system based on sales data analysis is realized based on the factory production plan scheduling optimization method based on sales data analysis according to any one of claims 1-7, and is characterized by comprising a data acquisition module, a future sales data model construction module, a production product sales prediction module, a product demand satisfaction degree calculation module, a product demand importance degree calculation module, a resource allocation module and a control module, wherein the data acquisition module is used for acquiring product data of factory production, simultaneously acquiring historical sales data of factory production products, historical environmental data and inventory data of production products, simultaneously acquiring customer usage data of production products, the future sales data model construction module is used for importing the historical sales data and the historical environmental data of factory production products into a future sales data prediction model construction strategy to construct a future sales data prediction model, and the production product sales prediction module is used for importing the acquired environmental data at the future time into the constructed sales data prediction model to predict sales data of production products in a lower sales period.
9. The factory production plan scheduling optimization system based on sales data analysis of claim 8, wherein the product demand satisfaction calculation module is configured to extract sales data and inventory data of the production products in a next sales cycle, import the sales data and inventory data into a future time demand calculation strategy, estimate demand of each production product in the factory, and simultaneously, substitute the estimated demand of each production product into a demand satisfaction calculation formula to calculate demand satisfaction of each production product, the product demand importance calculation module is configured to extract customer usage data of the production products, import the demand importance calculation strategy to calculate demand importance of each production product, and the resource allocation module is configured to obtain the calculated demand satisfaction of each production product and the demand importance of each production product into a production plan calculation strategy to calculate production plan of each production product, and allocate and schedule production resources according to a proportion of the production plan of each production product to the overall production plan.
10. The plant production planning scheduling optimization system based on sales data analysis of claim 9, wherein the control module is configured to control operation of the data acquisition module, the future sales data model construction module, the production product sales prediction module, the product demand satisfaction calculation module, the product demand importance calculation module, and the resource allocation module.
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