CN113505908A - Dynamic inventory optimization method - Google Patents

Dynamic inventory optimization method Download PDF

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CN113505908A
CN113505908A CN202110486711.4A CN202110486711A CN113505908A CN 113505908 A CN113505908 A CN 113505908A CN 202110486711 A CN202110486711 A CN 202110486711A CN 113505908 A CN113505908 A CN 113505908A
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宗理科
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Hefei Shili Tiaoyi Network Technology Co ltd
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    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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Abstract

The invention discloses a dynamic inventory optimization method, which mainly comprises the following steps: (1) constructing a dynamic inventory optimization model, which comprises a static model and a dynamic model; (2) acquiring input data, sales data, warehouse data, order data, goods input data and goods data; (3) importing the data and the information into an inventory optimization model to form a corresponding conversion function or constraint condition; (4) and constructing a model algorithm, and solving the optimal stock quantity of the goods and the corresponding optimal lease area size of the warehouse in the static model and the dynamic model respectively. According to the invention, a dynamic inventory optimization model of standard food materials is constructed by combining actual market demands and enterprise operation conditions, and the optimal stock quantity of goods and the optimal warehouse size are determined from static and dynamic angles, so that the management problem of the inventory in the standardized food material supply process is solved, the turnover speed of goods in the warehouse is increased, and the capital turnover effect of a company is improved.

Description

Dynamic inventory optimization method
Technical Field
The invention relates to the field of inventory management systems, in particular to a dynamic inventory optimization method.
Background
With the continuous development of the domestic economic level, the scale of the catering market is larger and larger, and the total number of hotel restaurants keeps two-figure growth for years. In the whole background of the upgrading of consumption, the requirements of catering food materials are greatly changed. The variety and types of food materials are more and more, the requirements of catering enterprises such as hotels and restaurants on the food materials become more complex, and the food safety management becomes more severe and urgent. According to the daily operation cost structure of a common catering unit, manpower, lease and food materials are three main cost expenses all the time, and the cost is generally 85%, so that the purchasing, storage and inventory management of the food materials are very important for one catering unit, how to optimize inventory and ensure safe and efficient turnover of the food materials by a scientific method is very urgent and important based on dynamic inventory optimization of standard food materials of hotels in a complex demand environment.
The rise of new consumer groups has led to the demand for gourmet food which is more than just satiating, popular with characteristic food materials, interesting food materials with novel appearance and shape, and most of them are standardized food materials. Hotels are more and more fashionable, and various types of catering terminals are more and more, so that the food material requirements are pursued to be diversified, various, interesting, nutritional, green, healthy and the like. The packaged semi-finished standardized food materials can save cooking time of a cook, accelerate dish serving speed of the kitchen and meet the requirement of the market on innovative food materials. Therefore, the procurement and inventory management of standardized food materials is one of the hot issues of current catering industry concern.
On the other hand, for food research and development and production enterprises, accurate analysis and prediction of market demands are required to continuously keep the leading of the industry. Particularly, under the background of emerging mobile internet, many local gourmets and specialties have the opportunity of being widely spread through the internet and are well known to consumers. Therefore, many suppliers of food material research and production are beginning to focus on standardized food materials. More and more traditional food material factories are beginning to research and develop standardized food materials with local characteristics. Under the complicated and changeable demand environment, the variety of food materials is more and more, which brings much trouble to the catering industry and food material suppliers, and the storage and turnover of goods in factories are a great challenge to the production management of food materials.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a dynamic inventory optimization method, constructs a dynamic inventory optimization model of standard food materials by combining actual market demands and enterprise operation conditions, and determines the optimal stock quantity of goods and the optimal warehouse size from static and dynamic angles so as to solve the management problem of the inventory in the standardized food material supply process, improve the turnover speed of goods in a warehouse and improve the capital turnover efficiency of a company.
In order to realize the effect, the invention adopts the technical scheme that:
a dynamic inventory optimization method is used for dynamically determining the optimal stock quantity and the optimal warehouse size of standard food materials, and mainly comprises the following steps:
(1) constructing a dynamic inventory optimization model, which comprises a static model and a dynamic model:
in the static model, the inventory optimization of enterprises is based on the change of the demand of the existing customer volume to carry out daily operation adjustment, the storage size and the stock quantity of products are adjusted according to the seasonality and the macroscopic economy, and the expansion or contraction strategy of the operation enterprises is not considered;
in the dynamic model, the daily operation strategy is adjusted based on enterprise market expansion or macroscopic demand mutation in the inventory optimization of an enterprise, seasonal periodic fluctuation exists, the demand changes along with the increase or decrease of the amount of customers, the size of a warehouse and the stock quantity of products are continuously adjusted according to the number of the customers, the sales and the order quantity, and the market demand is responded according to the change of platform data;
(2) acquiring input data, including the following:
macroscopic data-including demand fluctuation coefficients of seasons, festivals and holidays, and occurrence coefficients of emergencies;
sales data-including statistical analysis of public data on the online platform, mined effective information of the online platform, and enterprise-owned sales data;
warehouse data, including the size of the warehouse at cold normal temperature, the adjustable rental area of the warehouse, and the rent and operation and maintenance cost of the warehouse;
order data-including order batch and order cycle, some basic information of the order;
shipment data-including preferential discount tables for shipments, single shipment volume, and shipment period;
cargo data-including price, shelf life, storage means and volume of the cargo;
(3) importing the data and the information into an inventory optimization model to form corresponding conversion functions or constraint conditions, wherein the constraint conditions comprise adjustable units of a warehouse, a loss minimum function, a goods value-volume ratio, a goods shelf life, a supplier preferential strategy and a demand fluctuation function;
(4) and (3) constructing a model algorithm, adjusting the inventory turnover amount by calculating the inventory turnover amount of the goods by days and predicting by weeks and months in a static model and a dynamic model respectively, calculating the turnover cost of the goods by years, and finally solving the optimal stock preparation amount of the goods and the corresponding optimal lease area size of the warehouse to complete the optimization process of inventory management.
Further, in step (4), the inventory management optimization process includes the following steps:
(41) extracting seasonal, periodic and trend characteristics according to the fluctuation of macroscopic data of the catering industry to form a monthly correction function:
f(α,β,γ)＝g(α)*h(β)*y(γ) (1)
wherein, alpha, beta and gamma are seasonal, periodic and trend factors respectively, the factor value is determined by historical data training, g (alpha), h (beta) and y (gamma) are a seasonal correction function, a periodic correction function and a trend function respectively, and are used for the prediction and adjustment of monthly and quarterly;
(42) extracting fluctuation factors including single-day, single-week, single-month, holiday and seasonal characteristics according to direct fluctuation of day, week and month of the dish sales data of the platform on the catering line, and forming a daily correction function:
φ(δ)＝δt (2)
phi (delta) is a correction function taking days as a unit and is calculated according to 365-day annual sales data acquired from the platform;
(43) the product demand is obtained through the total demand change of the catering industry and the actual retail sales forecast of the catering enterprise, and the formula is as follows:
Figure RE-GDA0003258091350000031
Qi(t)＝Θ(f,φ,S) (4)
wherein: s is the actual retail amount of the ith product, qj(t) t time sales of jth dish, DijDemand for ith product for jth dish, Qi(t) represents the cumulative predicted total demand for all dishes for the ith product at time t;
(44) the size of the warehouse is set: the warehouse is divided into a freezing warehouse and a normal temperature warehouse, the holding volume of the warehouse is adjusted according to the quantity of stored goods, and the adjustable size units of the freezing warehouse and the normal temperature warehouse are S1And S2The number of leased units is K1And K2The lease size of the warehouse meets the following conditions:
Figure RE-GDA0003258091350000032
Figure RE-GDA0003258091350000033
S1*K1≥Wcold＞S1*(K1-1) (7)
S2*K2≥Wnor＞S2*(K2-1) (8)
wherein, ViUnit memory cell, R, required for the ith productiStock turnaround period for ith product, WcoldSize of stock occupied for all products requiring refrigeration, WnorSize of stock occupied for all regular stored products, K1And K2The adjustment value of (2) can only change according to the month;
(45) determining inventory constraints:
the constraint of unit value volume on the set of inventory turnaround is epsiloniThe higher the unit value volume ratio, the larger the product inventory turnaround cycle setting:
Figure RE-GDA0003258091350000041
wherein: piIs the price of the ith product, ViVolume of ith product;
the constraint that market demand sets the turnaround cycle of product inventory is thetaiAnd the turnover period of the product stock with frequent market demand is set to be smaller:
Figure RE-GDA0003258091350000042
wherein: qiIs the demand in the ith product turnaround cycle, FiThe frequency of monthly turnover for the ith product;
the constraint of shelf life on the product inventory turnover period is eiThe product inventory turnover period with shorter shelf life is set to be smaller:
ei＝f(edi) (11)
wherein: ediDays of shelf life for the ith product;
the influence of the supplier discount on the product inventory turnaround is set to tauiThen stock turnaround period RiThe setting of (a) is as follows:
Ri＝L(εi,θi,ei,msi,τi) (12)
(46) optimizing an objective function according to the following formula:
W＝Wnor+Wcold (13)
Np＝∑iDi*rif(Qi) (14)
Tr＝∑i360/Ri*Tp (15)
Lo＝∑if(edi)*Qli*Pi (16)
Rev＝Np-W-Tr-Lo (17)
wherein: w is storage charge, NpPreferential total for ordering, TrFor the total freight, LoTotal loss for inventory, Rev total profit, f (Q)i) As a function of the relation between the order preference and the batch of single orders, riIs the gross interest rate, T, of a single productpFor a single transportation charge, QliThe loss amount of the product over-preservation.
Further, in the step (4), the dynamic inventory optimization model selects order data of a plurality of months based on the enterprise operation data, and the size, the stock quantity and the goods input quantity of the warehouse are calculated by taking the week as a calculation unit.
Further, the dynamic inventory optimization model is divided into two types and ten contrast solution models according to different stocking strategies and different stocking amounts.
Further, the stocking strategies comprise a fixed stocking strategy and a rolling stocking strategy.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, a dynamic inventory optimization model of standard food materials is constructed by combining actual market demands and enterprise operation conditions, inventory turnover quantity is adjusted by calculating inventory turnover quantity of goods by days and predicting by weeks and months from static and dynamic angles, turnover cost of goods is calculated by years, and finally the optimal stock preparation quantity of the goods and the corresponding optimal lease area size of a warehouse are solved, so that turnover speed of the goods in the warehouse is improved, and capital turnover efficiency of a company is effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a static model inventory management method of the present invention;
FIG. 2 is a schematic flow diagram of a dynamic model inventory management damper of the present invention;
FIG. 3 is a schematic diagram of a solution flow framework of a dynamic inventory optimization theoretical model of the present invention;
FIG. 4 is a schematic diagram of the entities and information flow of the dynamic inventory optimization theoretical model of the present invention;
FIG. 5 is a schematic diagram of the input and output structure of the dynamic inventory optimization theoretical model of the present invention;
FIG. 6 is a schematic diagram of a framework of two types of ten contrast solution models according to the present invention;
FIG. 7 is a schematic diagram illustrating a solution flow of the dynamic inventory criteria model of the present invention;
FIG. 8 is a graph of the amount of orders for a company in the example during 10 months;
FIG. 9 is a graph of the total amount of product demanded for a company over 10 months in the example;
FIG. 10 is a graph of the total volume demand for two products for a single product over 10 months for a company in the example;
FIG. 11 is a graph of warehouse space size over time for different stocking strategy models in an embodiment;
FIG. 12 is a histogram of the number of times of stock changes over time under different stock strategy models in the embodiment;
FIG. 13 is a histogram of monthly inventory loss under different stocking strategy models in the example.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Referring to fig. 1 to 13, a dynamic inventory optimization method for dynamically determining the optimal stock quantity and the optimal warehouse size of standard food materials is provided. The standard food material is a food material product with fixed weight, stable quality and determined product appearance and ingredient standard, and is usually a pre-packaged semi-finished product, a pre-prepared dish product, a special dish, a pastry product, a quick-frozen meat product and the like. Compared with non-standard food materials, the standard food materials are fixed in packaging form and convenient to transport, manage and store, and the standard food materials are popularized more conveniently compared with the non-standard food materials such as live seafood, vegetable rice and the like.
The method mainly comprises the following steps:
(1) constructing a dynamic inventory optimization model, which comprises a static model and a dynamic model:
in the static model, the inventory optimization of enterprises is based on the change of the demand of the existing customer volume to carry out daily operation adjustment, the storage size and the stock quantity of products are adjusted according to the seasonality and the macro economy, and the expansion or contraction strategy of the operation enterprises is not considered. The size of the inventory space of the enterprise is designed according to the maximum goods input quantity of the product, the static model assumes the goods input quantity of the enterprise every time, the monthly demand setting is kept unchanged, and then the size of the inventory space is kept unchanged. Counting the products from the warehouse entry, counting the remaining inventory and the date of the products in the warehouse according to the shipment volume of the order, and when the inventory is lower than the minimum amount or exceeds the specified storage period, cleaning and continuing replenishment are needed, and the storage cost of the products in the mode is basically kept unchanged.
As shown in fig. 1, in the static inventory management method, an enterprise sets a fixed product stocking amount for a certain period according to the actual demand of market products, and arranges the space size of the inventory, in which case the space size of the warehouse is fixed and unchangeable, and the rental fee of the warehouse remains unchanged. In order to compare the set quantity of the inventory properly, the stock lot under different periods can be given, and the reasonable product inventory under the static model is determined through calculation and comparison. Different incoming lots affect the order bargaining capability of the enterprise, and corresponding order discounts may be different. After the order is put in storage, the stock number of the product is counted in real time, the shelf life of the product is recorded, if the stock number is lower than the set minimum stock or the shelf life of the stock product exceeds the specification, the product is arranged to be put in, and the overdue product is cleared. The stock data is updated in real time along with the change of the order, and the transportation cost is calculated according to the number of times of goods entering and leaving.
In the dynamic model, the daily operation strategy is adjusted based on enterprise market expansion or macroscopic demand mutation in the inventory optimization of the enterprise, seasonal periodic fluctuation exists, the demand changes along with the increase or decrease of the customer quantity, the warehouse size and the product stock quantity are continuously adjusted according to the quantity, sales and order quantity of the customers and the market demand response according to the change of platform data. The size of the inventory space of an enterprise is designed according to the maximum goods input quantity of products, the dynamic model assumes the goods input quantity of the enterprise every time, the goods input quantity can be set according to the monthly rolling change, and then the inventory space changes along with the change. Counting the products from the warehouse entry, counting the remaining inventory and the date of the products in the warehouse according to the shipment volume of the order, and when the inventory is lower than the minimum amount or exceeds the specified storage period, cleaning and continuing replenishment are needed, and the storage cost of the products in the mode can dynamically change along with the demand.
As shown in fig. 2, in the dynamic inventory management method, an enterprise sets a certain period of product rolling adjustment for the inventory amount according to the actual demand of the products in the market, and arranges the space size of the inventory, in this situation, the space size of the warehouse is set to be capable of being adjusted properly monthly, and the rental cost of the warehouse can be changed according to the demand. Similarly, in order to compare the set quantity of the inventory properly, the stock lot under different periods can be given, and the reasonable product inventory under the dynamic model is determined through calculation and comparison. Different incoming lots affect the order bargaining capability of the enterprise, and corresponding order discounts may be different. After the order is put in storage, the stock number of the product is counted in real time, the shelf life of the product is recorded, if the stock number is lower than the set minimum stock or the shelf life of the stock product exceeds the specification, the product is arranged to be put in, and the overdue product is cleared. The stock data is updated in real time along with the change of the order, and the transportation cost is calculated according to the number of times of goods entering and leaving.
The construction of the dynamic inventory optimization model of the standard food material based on the dynamic demand environment is divided into the dynamic model and the static model, and the optimization solving process is shown in fig. 3. Under the constraint conditions of product and ingredient demand, warehouse configuration, product and ingredient purchase and the like, the optimization model aims at the minimum operation and maintenance cost and the maximum operation profit, and the optimal reserve capacity of various products and ingredients and the optimal configuration size of the warehouse can be obtained by solving. The fundamental purpose of model construction is to solve the optimal stock quantity of various products and ingredients and the optimal allocation size of the warehouse, so that the operation and maintenance cost of the warehouse storage of the company is minimized, and the operation profit is maximized.
(2) Acquiring input data, including the following:
macroscopic data-including the demand fluctuation coefficient of seasons, festivals and holidays, and the occurrence coefficient of emergencies. The data is derived from data jointly issued by local cooking associations and local restaurant industry associations, for example, 3.6 ten thousand catering units exist in Anhui province fat market, and 78 standardized food materials such as dried egg, bean curd with thousand leaves, honey lotus roots, sweet potato balls, lotus root soaking strips and the like exist when the annual demand of each standardized food material exceeds 1000 ten thousand.
Sales data-including statistical analysis of public data on the online platform, effective information of the mined online platform, and enterprise-owned sales data (basic information of sales of an enterprise for a certain period and annual growth data of the enterprise). The data of the background order mainly comes from enterprise management data, and generally comprises basic information such as a customer name, an order placing date, an order placing product name, a product specification, an order quantity, a product unit price, an order total amount, a customer contact telephone number, customer address information and a settlement mode. The client names are hotels, restaurants and restaurant canteens in the business area of the company, and are part of B-end clients such as agents and distributors of the company.
Warehouse data-including the cold and normal temperature of the warehouse, the adjustable rental area of the warehouse, and the rent and operation and maintenance cost of the warehouse. Information of the warehouse: the warehouse is a three-dimensional standard warehouse which is divided into a normal-temperature warehouse and a freezing warehouse, the normal-temperature warehouse is of a steel structure, a standardized factory building structure with the floor height of 12.5 meters is arranged in a 2500-square warehouse, 20 groups of standardized grid type reinforced goods shelves are arranged in the warehouse, and 1 row is formed by combining every 2 groups. The distance between rows is 4 m. Each row of layers is 8 meters high, and divided into 4 layers, and each layer is 2 meters high; each row is 20 meters in length and is divided into 10 cabins, and each cargo space is 2 meters; each row is 4 meters, and the volume of each three-dimensional warehouse is 2 meters high, 1.8 meters in the bank, 2 meters long, and 7.2 cubic meters in total. The warehouse has a group of 4 layers, wherein each layer has 10 single goods positions, each layer has 40 single goods positions, one group has 80 goods positions, and the full-warehouse full-load capacity of the normal-temperature warehouse is 20 groups of 80 goods positions by 7.2 cubic meters of each goods position 11520 cubic meters, namely, the warehouse can contain food and food goods stored at normal temperature of 11520 cubic meters at most.
The freezer, namely a refrigerated food storage warehouse with the temperature of minus eighteen degrees, is smaller than a normal temperature warehouse. The layer height is only 7 meters, the whole area is only 900 squares, and the shelf space is also standardized. Each group is composed of 2 rows of shelves, and there are 6 groups in total. Each group is directly spaced by 3.5 meters, the total height of each group is 5.8 meters, each group is divided into layers, the height of the cabin is 1.8 meters, one row is 10 meters long, the cabin is divided into 5 cabins, and the length of each cabin is 1.8 meters and the cost is 1.6 meters. That is, a group of compartments has 2 × 5 × 20 compartments, each having a volume of 1.8 × 1.6 × 1.8 × 5.18 cubes. The full freezer capacity is 6 x 20 x 5.18 to 621.6 cubic meters, i.e., the maximum capacity to hold 621.6 cubes of frozen food.
Order data-including order batch and order cycle, some basic information for the order. Mainly comprises the dish sales volume: the data comes from each major network ordering platform, and the catering consumption big data is read, collected and sorted by multiple platforms, and is arranged and classified according to the popularity and sales volume. And simultaneously carrying out secondary analysis on the standardized food materials and ingredients required in each dish. The sales volume of each dish for each day can be obtained, so that the standard food material consumption required by the dish is deduced reversely.
Preparing dishes: the ingredients of one dish usually comprise three main ingredients, auxiliary ingredients and seasonings. The main material is the main raw material of a dish, such as the dried pan thousand-leaf bean curd, the main raw material is the thousand-leaf bean curd, the auxiliary material is a small amount of streaky pork slices, and the seasoning is edible salt and chicken essence required by the dried pan thousand-leaf bean curd. The ingredients of the dish can be quantitatively measured, for example, the dried pan bean curd with thousand pages is prepared by trying a bag of 400 g bean curd with thousand pages, 3 g edible salt and 5 g chicken essence. For example, the ingredients of the dish are the taro sweet potato balls, and the dish can be prepared by only opening a bag of standardized taro sweet potato balls and frying the balls in oil. If 5 ten thousand of the canteens of each hotel, restaurant and restaurant in a certain city are tasted by customers every day, namely 5 ten thousand bags of bean curd are needed in the market every day, the specification of the bean curd is basically 400 g by 30 bags, namely 1666 thousand pages of bean curd are eaten.
Shipment data-including preferential discount tables for shipments, single shipment volume, and shipment period;
cargo data-including price, shelf life, storage means and volume of the cargo;
(3) and importing the data and the information into an inventory optimization model to form a corresponding conversion function or constraint condition, wherein the constraint condition comprises an adjustable unit of the warehouse, a loss minimum function, a goods value-to-volume ratio, a goods shelf life, a supplier preferential strategy and a demand fluctuation function.
The standard food material storage is divided into normal temperature storage and freezing storage, the storage unit is in an adjustable leasing mode, the warehouse area can be adjusted flexibly according to the inventory turnover, the cost can be controlled in an optimized and timely manner, and the dynamic adjustment of the warehouse can be realized by modifying parameters configured in the warehouse in the model. The current-stage demand of the standard food material is directly determined by the order quantity of a customer, and the next-stage demand of the standard food material can be obtained by predicting the sales volume of a catering enterprise and macroscopic information of the catering industry, so that the demand of standard food material products and ingredients can be estimated according to the daily order quantity of the standard food material circulation supply enterprise and the sales volume and retail volume of the catering enterprise and industry, data and information are imported into an inventory optimization model, a corresponding conversion function or constraint condition is formed, and the model can perform optimization solution on a target.
Other inputs of the model also comprise purchasing information of standard food materials, and batches, goods quantity, goods price and preferential modes of goods input from suppliers, the purchasing period and batches can also be dynamically changed according to the turnover of product inventory, the purchasing frequency and quantity influence the purchasing cost of the product, and the quantity of the product inventory is restricted, so that the inventory optimization model is restrained.
In addition, the value, size and shelf life of the standard food material products and ingredients also determine the reasonable storage period and storage size. Generally speaking, under the constraint conditions of product and ingredient demand, warehouse configuration, product and ingredient purchase and the like, the optimization model aims at the minimum operation and maintenance cost and the maximum enterprise profit, the optimal inventory of various products and ingredients and the optimal configuration size of the warehouse can be obtained by solving, and the input and output of the dynamic inventory optimization theoretical model are shown in fig. 4.
(4) And (3) constructing a model algorithm, adjusting the inventory turnover amount by calculating the inventory turnover amount of the goods by days and predicting by weeks and months in a static model and a dynamic model respectively, calculating the turnover cost of the goods by years, and finally solving the optimal stock preparation amount of the goods and the corresponding optimal lease area size of the warehouse to complete the optimization process of inventory management. According to actually collected data, a dynamic inventory standard model is established, and the solution of the model is divided into a fixed goods feeding strategy and a rolling goods feeding strategy, namely a static model and a dynamic model. The model aims at maximizing enterprise profit, and the constraint conditions comprise: reduce the storage cost, improve the bargaining capability, reduce the transportation cost and reduce the storage loss. And finally, optimizing and solving the product inventory and the warehouse capacity by using an integer programming method through a statistical software R language.
The process of inventory management optimization includes the steps of:
(41) extracting seasonal, periodic and trend characteristics according to the fluctuation of macroscopic data of the catering industry to form a monthly correction function:
f(α,β,γ)＝g(α)*h(β)*y(γ) (1)
wherein, alpha, beta and gamma are seasonal, periodic and trend factors respectively, the factor value is determined by historical data training, g (alpha), h (beta) and y (gamma) are a seasonal correction function, a periodic correction function and a trend function respectively, and are used for the prediction and adjustment of monthly and quarterly;
(42) extracting fluctuation factors including single-day, single-week, single-month, holiday and seasonal characteristics according to direct fluctuation of day, week and month of the dish sales data of the platform on the catering line, and forming a daily correction function:
φ(δ)＝δt (2)
phi (delta) is a correction function taking days as a unit and is calculated according to 365-day annual sales data acquired from the platform;
(43) the product demand is obtained through the total demand change of the catering industry and the actual retail sales forecast of the catering enterprise, and the formula is as follows:
Figure RE-GDA0003258091350000101
Qi(t)＝Θ(f,φ,S) (4)
wherein: s is the actual retail amount of the ith product, qj(t) t time sales of jth dish, DijDemand for ith product for jth dish, Qi(t) represents the cumulative predicted total demand for all dishes for the ith product at time t;
(44) the size of the warehouse is set: the warehouse is divided into a freezing warehouse and a normal temperature warehouse, the holding volume of the warehouse is adjusted according to the quantity of stored goods, and the adjustable size units of the freezing warehouse and the normal temperature warehouse are S1And S2The number of leased units is K1And K2The lease size of the warehouse meets the following conditions:
Figure RE-GDA0003258091350000102
Figure RE-GDA0003258091350000103
S1*K1≥Wcold＞S1*(K1-1) (7)
S2*K2≥Wnor＞S2*(K2-1) (8)
wherein, ViUnit memory cell, R, required for the ith productiStock turnaround period for ith product, WcoldSize of stock occupied for all products requiring refrigeration, WnorSize of stock occupied for all regular stored products, K1And K2The adjustment value of (2) can only change according to the month;
(45) determining inventory constraints:
the constraint of unit value volume on the set of inventory turnaround is epsiloniThe higher the unit value volume ratio, the larger the product inventory turnaround cycle setting:
Figure RE-GDA0003258091350000111
wherein: piIs the price of the ith product, ViVolume of ith product;
the constraint that market demand sets the turnaround cycle of product inventory is thetaiAnd the turnover period of the product stock with frequent market demand is set to be smaller:
Figure RE-GDA0003258091350000112
wherein: qiIs the demand in the ith product turnaround cycle, FiThe frequency of monthly turnover for the ith product;
the constraint of shelf life on the product inventory turnover period is eiThe product inventory turnover period with shorter shelf life is set to be smaller:
ei＝f(edi) (11)
wherein: ediDays of shelf life for the ith product;
the influence of the supplier discount on the product inventory turnaround is set to tauiThen stock turnaround period RiThe setting of (a) is as follows:
Ri＝L(εi,θi,ei,msi,τi) (12)
(46) optimizing an objective function according to the following formula:
W＝Wnor+Wcold (13)
Np＝∑iDi*rif(Qi) (14)
Tr＝∑i360/Ri*Tp (15)
Lo＝∑if(edi)*Qli*Pi (16)
Rev＝Np-W-Tr-Lo (17)
wherein: w is storage charge, NpPreferential total for ordering, TrFor the total freight, LoTotal loss for inventory, Rev total profit, f (Q)i) As a function of the relation between the order preference and the batch of single orders, riIs the gross interest rate, T, of a single productpFor a single transportation charge, QliThe loss amount of the product over-preservation.
The main bodies related to the inventory optimization model comprise a macro market, a production enterprise, a platform service provider, a terminal demand provider and a warehouse leasing provider. The macro market refers to the retail and consumption confidence index of provincial and national catering industries, from which dynamic changes in industry demand are obtained, including periodic, seasonal and trending information. The manufacturing enterprise indexes the manufacturers or suppliers of food materials. Platform merchants refer to supply chain enterprises that offer standard food distribution services. The terminal demand business refers to an operation enterprise of the catering service. The warehousing businessman refers to a warehousing enterprise providing warehouse leasing service for standard food material supply chain enterprises. The entity and information flow of the dynamic inventory optimization theoretical model is shown in fig. 5.
The demand side dynamic inventory optimization model established in the method selects 10-month order data based on enterprise operation data, and accounts the size, inventory amount, inventory quantity and the like of a warehouse by taking weeks as a calculation unit. The model is divided into two types of ten contrast solution models according to different strategies of goods feeding and different sizes of goods feeding, as shown in fig. 6. The fixed stocking strategy represents that the stocking amount of each product in a single year is kept unchanged in the same period of the year, and the stocking strategy is only related to seasonal factors and is not related to market environment and enterprise growth, so that the fixed stocking strategy is a completely static stocking strategy model. The rolling stock strategy represents that the single stock quantity of each product is highly related to the demand of the first three weeks, can respond to the demand change of the market in real time, can respond to the growth and the contraction of the market in time, and is a dynamic stock strategy model. As shown in the figure, the method is divided into five grades according to the size of the goods input, and the goods are prepared according to the demand of one week (F1 and S1), the goods are prepared according to the demand of two weeks (F2 and S2), the goods are prepared according to the demand of three weeks (F3 and S3), the goods are prepared according to the demand of four weeks (F4 and S4), the goods are prepared according to the demand of five weeks (F5 and S5), wherein the goods preparation amount is the maximum stock quantity of the products.
The constraint conditions of the model mainly comprise the following four aspects: (1) inventory cost: the products need different storage conditions and warehouse sizes according to the specification and the size, and the warehouse size can be dynamically adjusted according to the business condition of an enterprise; (2) bargaining cost: the bargaining capability of the enterprise is determined by the quantity of the single product, and the higher the single product quantity is, the higher the preferential price is; (3) transportation cost: the product feeding frequency of each product determines the transportation cost of the product, and the lower the product feeding amount is, the higher the product feeding frequency is, the higher the uniformly shared cost is; (4) loss cost: the longer the product is in stock, the higher the risk that the product will exceed the shelf life, and the amount of goods in stock determines the length of time the product is in stock.
Ten inventory optimization models are established through R language statistical analysis software, the final business benefits of the models are compared, the reasonable inventory of a single product and the reasonable size of the warehouse space are determined, and the calculation process is shown in figure 7. Firstly, preprocessing twenty thousand pieces of order data, sequencing according to time and classifying according to products, and measuring and calculating the sales volume data of the week degree and the month degree of a single product. Secondly, determining the single input quantity of each product according to the input mode and the input quantity, and bringing the preprocessed order data into a model for simulation statistics. Then, the storage amount of each product in each week is calculated in a gathering mode, the total inventory space requirement is determined, and the inventory data and the size of the storage space of the products are obtained. And finally, under the given product price information and the given supplier preference information, respectively calculating the inventory cost, the transportation cost, the preference price and the inventory loss of the enterprise, measuring and calculating the operating benefit of the enterprise, and finally determining the optimal inventory strategy.
The practical application process and effect of the dynamic inventory optimization method are specifically described in the following with the practical operation data of the company where the applicant is located.
(1) Order requirement:
fig. 8 shows a total amount of orders calculated in units of weeks based on 2 ten thousand pieces of order data from 2019 to 2020 and 11 months of the company. The company's sales per week during normal business hours are between 10 ten thousand and 14 ten thousand dollars, as shown, but seasonal fluctuations in demand are difficult to consider due to the outbreak of inefficacy events in 2020 and the limited order data collected. In the period from 1 month to 5 months in 2020, orders show severe abnormal fluctuation, the sales in the period from 2 months in 2020 directly drops from 12 ten thousand yuan to more than 1 ten thousand yuan, and the sales in the period from 5 months in 2020 increases to 12 ten thousand yuan. In general, the steep drop and rapid recovery growth of sales represent a significant challenge to the risk response capability and demand real-time response capability of enterprises. Therefore, how to select a reasonable operation strategy according to daily information data is necessary to quickly respond to market changes, reduce the operation risk of food supply chain enterprises and improve the operation benefit of the enterprises.
As shown in fig. 9, the volume of the product in a single week is calculated according to the total demand of the order in a week, the product storage is divided into cold storage and normal temperature storage, the volume fluctuation of the product is similar to the fluctuation of the total sales volume, as can be seen from the figure, the fluctuation of the inventory volume demand of the product is large in a normal period, but in an epidemic situation, long-term uncertainty generates a certain inventory backlog risk to the operation of an enterprise, and unreasonable inventory amount can also increase the inventory cost and the loss of inventory drop price, so that whether a certain necessity exists for dynamically adjusting the size of the warehouse according to the demand change is explored.
From the total sales and product specification volume of the company, fluctuations in different seasons are different, the influence of the adversity events in 2020 on the company is obvious, the seasonal demand of partial products is different due to the large number and variety of products, and for example, the fluctuation of the demand of the product numbered P0026 and the fluctuation of the demand of the product numbered P0049 are different as shown in fig. 10, if the sales of the adversity events factor P0026 is not considered to be in an increasing trend, but P0049 is in a certain falling trend, and the influence of the degree of fire heat in the market on partial products is obvious. Therefore, whether to help the business benefits of the enterprise by exploring the dynamic rolling stock strategy and dynamically adjusting the size of the warehouse is certain.
(2) Product inventory
The product stocking amount of 10 models is measured and calculated according to different stocking strategies of the products and the size division of the stocking amount, and the required warehouse space size is calculated according to the stocking amount and the product volume as shown in fig. 11. The dashed portion represents the amount of warehouse space required for the products of the fixed stocking strategy and the solid portion represents the amount of warehouse space required for the products of the rolling stocking strategy. The fixed stocking strategy corresponds to a fixed warehouse size, and the rolling stocking strategy corresponds to a variable warehouse size. The more the single shipment, the larger the required warehouse space, the higher the warehousing cost per unit, and the lower the single shipment, the smaller the required warehouse space, and the lower the warehousing cost per unit.
The warehouse is given with a fixed size according to the average daily input under normal operation under a fixed strategy, and the required shelf space is 317m by taking the monthly input as an example3Taking five weeks as an example, the required shelf space is 1585m3The rolling strategy saves about 22% shelf space throughout the year compared to the fixed strategy for the same stock cycle, and particularly saves about 66% shelf space during a similar epidemic incident. Therefore, compared with a fixed strategy, the rolling strategy can respond to the change of market demands in time, but the rolling strategy has certain limitation, the warehouse space cannot be changed rapidly and frequently under the condition of self-built warehouse facilities, and the investment of infrastructure facilities is basically invested once. Only in the flexible rentable warehouse space mode, the rolling strategy can achieve good cost throttling effect.
The frequency of the goods input is influenced by different goods input strategies and different sizes of the goods input, the number of the goods input in a single month from 10 months in 2019 to 7 months in 2020 is shown in fig. 12, the research adopts a real-time goods input mode, namely, the product inventory is close to one third of the demand in a single week for immediate replenishment, and the quantity of the single replenishment is determined by different goods input strategies and the goods input quantity. It can be seen from the figure that the more the single-time goods input amount is, the fewer the times of goods input are, the requirement of the customer can be met in time, but the risk of overstocked inventory exists; a rolling goods feeding strategy mode is adopted, the single-month goods feeding times are higher than that of a fixed strategy mode, and the unit transportation cost of goods feeding is slightly higher; during the epidemic situation, the obvious times of goods feeding are reduced, and the risk of overstock of the stock can be effectively avoided by adopting a rolling strategy and a mode of low goods feeding amount.
Through the analysis of the product input amount and the input frequency of the company, the fact that the input frequency of the company is increased due to the excessively low inventory amount of the product can be seen, the transportation cost of a unit is improved, the bargaining capability of an enterprise is reduced due to the excessively low input amount, and the price preference of a supplier is difficult to enjoy; and the excessive stock amount is set, so that the transportation cost is reduced, but the storage cost is increased. The rolling goods feeding strategy can save warehouse space, but can increase the frequency of replenishment, and is particularly obvious in the case of low goods feeding quantity. Therefore, it is considered to adopt a moderate rolling stock approach.
(3) Loss of inventory
Inventory loss is primarily a risk loss of shelf life for product meters that reside in long-term warehouses. Currently, the shelf life of the products stored in the warehouse of the company is between 3 months and 12 months, and the risk loss of stock is counted for the products stored in the warehouse for more than half the shelf life. The monthly inventory loss from 2019 to 2020 and 7 by this company is shown in fig. 13. As can be seen from the figure, the inventory loss is the most serious during the period from 2 to 5 months in 2020, the impact on the catering industry during an inefficacy event is a direct cause of high loss, the maximum monthly loss in the model amounts to 54 ten thousand yuan, and during normal operation, the monthly inventory loss is the highest and is not about 3 ten thousand yuan.
In the period from 10 months in 2019 to 7 months in 2020, the total loss of the ten models S1, F1, S2, F2, S3, F3, S4, F4, S5 and F5 is 2.56 ten thousand yuan, 2.63 ten thousand yuan, 7.29 ten thousand yuan, 11.01 ten thousand yuan, 21.07 ten thousand yuan, 29.03 ten thousand yuan, 46.50 ten thousand yuan, 60.90 ten thousand yuan, 84.90 ten thousand yuan and 100.75 ten thousand yuan respectively. According to measurement and calculation, under the condition of feeding according to a week amount, the rolling feeding strategy can reduce inventory loss by 3% compared with the fixed feeding strategy; in the case of two-week-worth of stock, the rolling stock strategy can reduce inventory loss by 34% over the fixed stock strategy; in the case of three-week-worth of stock, the rolling stock strategy can reduce inventory loss by 27% over the fixed stock strategy; in the case of four week worth of stock, the rolling stock strategy can reduce inventory loss by 24% over the fixed stock strategy; the rolling stock strategy can reduce inventory loss by 16% over the fixed stock strategy in the case of five-week-worth of stock.
Through the comparison, the influence of the emergency on the inventory loss of the company is very large, and the loss can be reduced by selecting a proper goods inlet strategy and controlling a proper goods inlet quantity; the amount of single input goods or the size of the stock setting directly influences the size of stock loss, and the control of reasonable stock is necessary; the rolling stock-in strategy has obvious advantages in the aspect of inventory loss control-free control compared with the fixed stock-in strategy, and the rolling strategy has obvious advantages under the condition of stock-in according to two-week quantity.
(4) Analysis of business benefits
Ten simulation models are divided according to order record data of the company from 10 months to 7 months in 2019 to 2020, according to different product stocking strategies and different stocking amounts, and cost benefit results of the ten models are calculated by considering four factors of inventory cost, bargained discount, transportation cost and loss cost and are shown in table 1.
TABLE 1 cost effectiveness accounting results under different stocking strategy models
Cost of inventory Bargaining offers Cost of transportation Loss cost Total up to
S1 15.42 29.95 29.28 2.38 -17.12
S2 26.72 51.05 13.62 6.78 3.93
S3 37.47 74.79 9.99 19.60 7.73
S4 48.98 104.20 7.39 43.25 4.57
S5 61.05 136.93 5.67 78.98 -8.76
F1 18.00 20.77 18.60 2.45 -18.28
F2 30.00 41.97 9.62 10.24 -7.89
F3 48.00 64.81 6.69 27.00 -16.88
F4 57.00 92.15 5.28 56.66 -26.78
F5 75.00 122.48 4.55 93.72 -50.79
Through comparative analysis, it can be seen that in terms of inventory cost, as the stock quantity increases, the larger the required inventory space is, and the inventory cost of the fixed stocking strategy is also obviously higher than that of the rolling stocking strategy; in the aspect of bargaining preferential offer, the more the enterprise goods intake is, the greater the preferential strength given by the supplier, and the preferential offer of the rolling goods intake strategy is higher than that of the fixed goods intake strategy; in the aspect of transportation cost, the more times of transportation, the less the number of single transportation, the higher the cost of unit transportation, and the transportation cost of the rolling stocking strategy is slightly higher than that of the fixed stocking strategy because the transportation times of the rolling stocking strategy is more than that of the fixed stocking strategy; in terms of loss cost, the greater the number of stock products, the greater the risk of stock loss, especially in the face of uncertain market demand, the greater the loss due to lost sales, and in the case of rolling stocking strategy, the better the risk control capability is than that of fixed stocking strategy.
Under a fixed goods-feeding strategy, the loss and the benefit of the five models of the company are respectively-18.28 ten thousand yuan, -7.89 ten thousand yuan, -16.88 ten thousand yuan, -26.78 ten thousand yuan and-8.76 ten thousand yuan, the benefit of an enterprise firstly rises and declines under the condition that the goods-feeding quantity is from low to high, the benefit is the best under the condition that the goods-feeding quantity is two weeks, and the benefit is the worst under the condition that the goods-feeding quantity is five weeks. Under the rolling goods-in strategy, the loss and the benefit of the five models of the company are respectively-17.12 ten thousand yuan, -3.93 ten thousand yuan, 7.73 ten thousand yuan, 4.57 ten thousand yuan and-50.79 ten thousand yuan, the benefit of the enterprise firstly rises and declines under the condition that the goods-in quantity is from low to high, the benefit is the best under the condition that the goods-in quantity is three weeks, and the benefit is the worst under the condition that the goods-in quantity is one week. Comparing the fixed stocking strategy with the rolling stocking strategy, it can be seen that the fixed strategy has negative benefits in all cases, while the rolling stocking strategy can achieve positive business benefits in three cases. And the rolling stock strategy rate is superior to the fixed stock strategy under each stock quantity.
Through the analysis of the operational benefits, the company selects more goods input, can increase the storage cost, promotes the bargaining capability of enterprises, reduces the transportation cost, but increases the risk of loss while increasing the inventory, and is difficult to deal with the market emergency situation. When the rolling stocking strategy is selected, the stocking benefit according to the three-week amount is the best, and when the fixed stocking strategy is selected, the stocking benefit according to the two-week amount is the best. Between the rolling stocking strategy and the fixed stocking strategy, the selection of the rolling stocking strategy can realize positive benefits, and the best situation is 16.6 ten thousand yuan more than the fixed stocking strategy. Therefore, the optimal selection strategy of the enterprise is to carry out rolling stock in two-week amount.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (5)

1. A dynamic inventory optimization method for dynamically determining the optimal stock quantity and the optimal warehouse size of standard food materials is characterized by mainly comprising the following steps:
(1) constructing a dynamic inventory optimization model, which comprises a static model and a dynamic model:
in the static model, the inventory optimization of enterprises is based on the change of the demand of the existing customer volume to carry out daily operation adjustment, the storage size and the stock quantity of products are adjusted according to the seasonality and the macroscopic economy, and the expansion or contraction strategy of the operation enterprises is not considered;
in the dynamic model, the daily operation strategy is adjusted based on enterprise market expansion or macroscopic demand mutation in the inventory optimization of an enterprise, seasonal periodic fluctuation exists, the demand changes along with the increase or decrease of the amount of customers, the size of a warehouse and the stock quantity of products are continuously adjusted according to the number of the customers, the sales and the order quantity, and the market demand is responded according to the change of platform data;
(2) acquiring input data, including the following:
macroscopic data-including demand fluctuation coefficients of seasons, festivals and holidays, and occurrence coefficients of emergencies;
sales data-including statistical analysis of public data on the online platform, mined effective information of the online platform, and enterprise-owned sales data;
warehouse data, including the size of the warehouse at cold normal temperature, the adjustable rental area of the warehouse, and the rent and operation and maintenance cost of the warehouse;
order data-including order batch and order cycle, some basic information of the order;
shipment data-including preferential discount tables for shipments, single shipment volume, and shipment period;
cargo data-including price, shelf life, storage means and volume of the cargo;
(3) importing the data and the information into an inventory optimization model to form corresponding conversion functions or constraint conditions, wherein the constraint conditions comprise adjustable units of a warehouse, a loss minimum function, a goods value-volume ratio, a goods shelf life, a supplier preferential strategy and a demand fluctuation function;
(4) and (3) constructing a model algorithm, adjusting the inventory turnover amount by calculating the inventory turnover amount of the goods by days and predicting by weeks and months in a static model and a dynamic model respectively, calculating the turnover cost of the goods by years, and finally solving the optimal stock preparation amount of the goods and the corresponding optimal lease area size of the warehouse to complete the optimization process of inventory management.
2. A method for dynamic inventory optimization as claimed in claim 1, wherein: in the step (4), the inventory management optimization process includes the following steps:
(41) extracting seasonal, periodic and trend characteristics according to the fluctuation of macroscopic data of the catering industry to form a monthly correction function:
f(α,β,γ)＝g(α)*h(β)*y(γ) (1)
wherein, alpha, beta and gamma are seasonal, periodic and trend factors respectively, the factor value is determined by historical data training, g (alpha), h (beta) and y (gamma) are a seasonal correction function, a periodic correction function and a trend function respectively, and are used for the prediction and adjustment of monthly and quarterly;
(42) extracting fluctuation factors including single-day, single-week, single-month, holiday and seasonal characteristics according to direct fluctuation of day, week and month of the dish sales data of the platform on the catering line, and forming a daily correction function:
φ(δ)＝δt (2)
phi (delta) is a correction function taking days as a unit and is calculated according to 365-day annual sales data acquired from the platform;
(43) the product demand is obtained through the total demand change of the catering industry and the actual retail sales forecast of the catering enterprise, and the formula is as follows:
Figure FDA0003050679480000021
Qi(t)＝Θ(f,φ,S) (4)
wherein: s is the actual retail amount of the ith product, qj(t) t time sales of jth dish, DijDemand for ith product for jth dish, Qi(t) represents the cumulative predicted total demand for all dishes for the ith product at time t;
(44) the size of the warehouse is set: the warehouse is divided into a freezing warehouse and a normal temperature warehouse, the holding volume of the warehouse is adjusted according to the quantity of stored goods, and the adjustable size units of the freezing warehouse and the normal temperature warehouse are S1And S2The number of leased units is K1And K2The lease size of the warehouse meets the following conditions:
Figure FDA0003050679480000022
Figure FDA0003050679480000023
S1*K1≥Wcold＞S1*(K1-1) (7)
S2*K2≥Wnor＞S2*(K2-1) (8)
wherein, ViUnit memory cell, R, required for the ith productiStock turnaround period for ith product, WcoldSize of stock occupied for all products requiring refrigeration, WnorSize of stock occupied for all regular stored products, K1And K2The adjustment value of (2) can only change according to the month;
(45) determining inventory constraints:
the constraint of unit value volume on the set of inventory turnaround is epsiloniThe higher the unit value volume ratio, the larger the product inventory turnaround cycle setting:
Figure FDA0003050679480000031
wherein: piIs the price of the ith product, ViVolume of ith product;
the constraint that market demand sets the turnaround cycle of product inventory is thetaiAnd the turnover period of the product stock with frequent market demand is set to be smaller:
Figure FDA0003050679480000032
wherein: qiIs the demand in the ith product turnaround cycle, FiThe frequency of monthly turnover for the ith product;
the constraint of shelf life on the product inventory turnover period is eiThe product inventory turnover period with shorter shelf life is set to be smaller:
ei＝f(edi) (11)
wherein: ediDays of shelf life for the ith product;
the influence of the supplier discount on the product inventory turnaround is set to tauiThen stock turnaround period RiThe setting of (a) is as follows:
Ri＝L(εi,θi,ei,msi,τi) (12)
(46) optimizing an objective function according to the following formula:
W＝Wnor+Wcold (13)
Np＝∑iDi*rif(Qi) (14)
Tr＝∑i360/Ri*Tp (15)
Lo＝∑if(edi)*Qli*Pi (16)
Rev＝Np-W-Tr-Lo (17)
wherein: w is storage charge, NpPreferential total for ordering, TrFor the total freight, LoTotal loss for inventory, Rev total profit, f (Q)i) As a function of the relation between the order preference and the batch of single orders, riIs the gross interest rate, T, of a single productpFor a single transportation charge, QliThe loss amount of the product over-preservation.
3. A method for dynamic inventory optimization as claimed in claim 1, wherein: in the step (4), the dynamic inventory optimization model selects order data of a plurality of months on the basis of enterprise operation data, and the size, the inventory quantity and the inventory quantity of the warehouse are calculated by taking weeks as a calculation unit.
4. A dynamic inventory optimization method as claimed in claim 3, in which: the dynamic inventory optimization model is divided into two types and ten contrast solution models according to different goods feeding strategies and different goods feeding amounts.
5. The dynamic inventory optimization method of claim 4, further comprising: the stocking strategies include a fixed stocking strategy and a rolling stocking strategy.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113823395A (en) * 2021-11-25 2021-12-21 天津医药集团众健康达医疗器械有限公司 Medical consumable stock method, system, terminal equipment and storage medium
CN113996233A (en) * 2021-10-21 2022-02-01 盛和资源(连云港)新材料科技有限公司 Product batching system based on artificial intelligence algorithm
CN115660491A (en) * 2022-11-02 2023-01-31 中国石油天然气股份有限公司 Method for optimizing and evaluating continuous production inventory containing inferior heavy crude oil
CN115965140A (en) * 2022-12-27 2023-04-14 北京航天智造科技发展有限公司 Inventory optimal planning method, system, equipment and storage medium
CN115994779A (en) * 2023-03-17 2023-04-21 联一信息技术(北京)有限公司 ERP management system and method
CN116452243A (en) * 2023-05-11 2023-07-18 长沙麦可思信息科技有限公司 Enterprise order prediction method, system and medium based on big data

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101303588A (en) * 2008-06-04 2008-11-12 东北大学 Method and system for automatically making steel-smelting continuous casting furnace sub batch plan
US20180046971A1 (en) * 2016-08-11 2018-02-15 International Business Machines Corporation Determining a non-optimized inventory system
CN108108994A (en) * 2017-11-10 2018-06-01 浙江中控软件技术有限公司 For the plan optimization method of chemical enterprise supply chain
CN108280538A (en) * 2018-01-05 2018-07-13 广西师范学院 Based on distributed logistics inventory's optimization method under cloud computing environment
CN109543881A (en) * 2018-10-23 2019-03-29 上海大学 Equipment inventory management decision optimization method, system, electronic equipment and storage medium
CN110322203A (en) * 2019-07-05 2019-10-11 江苏云脑数据科技有限公司 Retail business inventory optimization analysis method
CN112633541A (en) * 2019-09-24 2021-04-09 北京沃东天骏信息技术有限公司 Inventory network optimization method and device based on single commodity flow

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101303588A (en) * 2008-06-04 2008-11-12 东北大学 Method and system for automatically making steel-smelting continuous casting furnace sub batch plan
US20180046971A1 (en) * 2016-08-11 2018-02-15 International Business Machines Corporation Determining a non-optimized inventory system
CN108108994A (en) * 2017-11-10 2018-06-01 浙江中控软件技术有限公司 For the plan optimization method of chemical enterprise supply chain
CN108280538A (en) * 2018-01-05 2018-07-13 广西师范学院 Based on distributed logistics inventory's optimization method under cloud computing environment
CN109543881A (en) * 2018-10-23 2019-03-29 上海大学 Equipment inventory management decision optimization method, system, electronic equipment and storage medium
CN110322203A (en) * 2019-07-05 2019-10-11 江苏云脑数据科技有限公司 Retail business inventory optimization analysis method
CN112633541A (en) * 2019-09-24 2021-04-09 北京沃东天骏信息技术有限公司 Inventory network optimization method and device based on single commodity flow

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
郭修甫;翁克瑞;杨磊;: "煤矿物资动态库存控制策略研究", 煤炭工程, no. 10, pages 124 *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113996233A (en) * 2021-10-21 2022-02-01 盛和资源(连云港)新材料科技有限公司 Product batching system based on artificial intelligence algorithm
CN113823395A (en) * 2021-11-25 2021-12-21 天津医药集团众健康达医疗器械有限公司 Medical consumable stock method, system, terminal equipment and storage medium
CN113823395B (en) * 2021-11-25 2022-02-18 天津医药集团众健康达医疗器械有限公司 Medical consumable stock method, system, terminal equipment and storage medium
CN115660491A (en) * 2022-11-02 2023-01-31 中国石油天然气股份有限公司 Method for optimizing and evaluating continuous production inventory containing inferior heavy crude oil
CN115660491B (en) * 2022-11-02 2023-05-26 中石油云南石化有限公司 Continuous production inventory optimization evaluation method for inferior heavy crude oil
CN115965140A (en) * 2022-12-27 2023-04-14 北京航天智造科技发展有限公司 Inventory optimal planning method, system, equipment and storage medium
CN115994779A (en) * 2023-03-17 2023-04-21 联一信息技术(北京)有限公司 ERP management system and method
CN116452243A (en) * 2023-05-11 2023-07-18 长沙麦可思信息科技有限公司 Enterprise order prediction method, system and medium based on big data
CN116452243B (en) * 2023-05-11 2023-08-29 长沙麦可思信息科技有限公司 Enterprise order prediction method, system and medium based on big data

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