Inventory management method for electronic commerceTechnical Field
The invention relates to the field of electronic commerce, in particular to an inventory management method of electronic commerce.
Background
In an e-commerce environment, a supply chain becomes more complex and scattered, more suppliers and distribution channels are involved, the difficulty of inventory management is increased, traditional inventory management usually depends on a single data source such as historical sales data, however, such data often has hysteresis, market changes cannot be reflected timely, traditional prediction methods based on experience rules such as a moving average method, an exponential smoothing method and the like are simple and feasible, but only historical sales trends can be considered, influences of factors such as market dynamics, consumer behavior changes and the like are ignored, market demand fluctuation is large, accurate prediction is difficult, and especially during a promotion activity, the demand amount may suddenly increase, which requires more accurate demand prediction capability of an e-commerce industry.
Lack of real-time data updates and information sharing can result in poor inventory visibility, affecting accuracy and timeliness of inventory decisions, excessive inventory can result in increased capital occupation and storage costs, and too little inventory can result in reduced inventory risk and customer satisfaction, traditional restocking mechanisms tend to check inventory levels periodically and then manually initiate purchase orders, which can be time consuming and labor intensive, and can not respond in time when demand suddenly increases, resulting in inventory shortages, the return flow often involves multiple steps including merchandise verification, restocking, refund processing, etc., which would greatly increase operating costs and time consumption if there were no systematic way to manage returns.
Disclosure of Invention
The invention aims to provide an inventory management method for electronic commerce.
The invention aims to realize dynamic adjustment and efficient replenishment of the stock by comprehensively utilizing a big data analysis and prediction model, accurately predicting market demands, reasonably setting safety stock level, avoiding stock backlog or backorder phenomenon, simultaneously reducing the articles in stock and properly processing the returned goods, thereby improving stock turnover rate and order accuracy, reducing stock cost, avoiding backorder or overage conditions, ensuring smooth operation of a supply chain, optimizing management of a returned goods flow, improving customer satisfaction and reducing operation cost.
An inventory management method of electronic commerce adopts the following technical scheme:
S1, collecting an internal historical sales record, a user browsing record, a shopping cart abandoning rate, a customer service consultation record, product discussion heat on social media and price change of competitors, and establishing a prediction model to generate a demand prediction result in a future period of time;
S2, setting a safety stock level by combining the historical sales data and the demand forecast result in the S1, adjusting the safety stock quantity in each quarter, setting a trigger condition according to the demand forecast result, automatically sending a purchase request to a supplier when the actual stock is lower than a preset safety stock level, negotiating with the supplier to determine a quick response mechanism, completing replenishment in a short time, forecasting the peak demand, and pre-increasing the stock before major sales promotion;
S3, synchronizing data of all sales channels in real time according to the historical sales data in S1, connecting an E-commerce platform through an API interface, updating an inventory state every time an order is generated and canceled, tracking the goods moving condition by adopting the Internet of things equipment, and updating the inventory quantity;
s4, analyzing historical return data according to the historical sales data in S1, finding out return reasons, constructing a return prediction model, making a check standard of return goods, and judging whether the return goods can be directly resaled, discounted for sale and scrapped;
s5, periodically analyzing the historical sales data in the step S1, marking out continuous commodities without sales records for a plurality of weeks, setting a checking period for the commodities with short shelf life, and donating the temporary but edible commodities with the charity in a collaboration way;
And S6, setting KPIs (performance indicator) by adopting the historical sales data and the prediction model in the S1, including inventory turnover rate and order accuracy, monitoring the performance of the KPIs, and feeding back the inventory management results and problems to related departments of purchasing, selling and warehousing through a data feedback adjustment strategy to form closed-loop management.
Further, the step of establishing a prediction model in S1 generates a demand prediction result in a future period of time, including:
s11, extracting time sequence features from the historical sales records, and extracting the historical sales volume of a period of time tHorizontal initial componentTrend initial componentSeasonal initial componentThe horizontal initial component is the first value of the time sequence, the trend initial component is the difference between the first two values of the time sequence, and the seasonal initial component is the average value of months within a period of time t;
Extracting the number of daily accesses of a userUser residence time per dayShopping cart abandoning proportion of users in time tNumber of counseling times in time tConsultation keywords in time tFront evaluation ratio in time tNegative evaluation proportion in time tNumber of discussions in time tCompetitor price change over time t;
S12, updating horizontal componentTrend componentSeasonal component,,,WhereinThe smoothing factors of the horizontal component, the trend component and the seasonal component take values between 0 and 1,For the length of the cycle to be the same,Is thatA seasonal component of the time in which the time is to be used,Is thatA horizontal component and a trend component in time;
S13, constructing a feature matrix,Setting target variableThe model is trained asWhereinIs a gradient elevator, and the prediction model is thatWhereinFor modular operation, h is the predicted future time period,As seasonal componentThe value in the period of h,The characteristic matrix is in the t+h time period;
And S14, after a prediction model is established, relevant data are collected in real time according to the step S11, corresponding processing is carried out in the step S12, and the processed data are input into the prediction model to obtain a corresponding demand prediction result.
Further, setting a safety stock level in S2, adjusting the safety stock amount in quarters, and setting a trigger condition according to a demand prediction result, including:
S21, calculating standard deviation of sales by adopting historical sales data and demand prediction results, calculating safety stock by combining a service level with a Z value corresponding to the service level, wherein the service level is probability of meeting customer demands in a certain time, namely the stock meets the demands in the probability time, the Z value is obtained from a standard normal distribution table Z table corresponding to the probability, and the safety stock is obtained from the standard normal distribution table Z table corresponding to the probabilityWhereinAs the standard deviation of the daily requirement,Is an early period, for the time required from ordering to receiving goods from the warehouse, for the safe stock quantity of each quarter, calculating the standard deviation of the requirements of the quarter;
S22, automatically sending a purchase request to a provider when the actual stock level is lower than the result obtained by multiplying the average demand by the lead time;
S23, predicting upcoming peak demands according to historical sales data and a demand prediction model, gradually increasing inventory a few weeks before major sales promotion, and carrying out purchasing by average weekly quantification and suppliers according to the inventory amount which is needed to be supplemented.
Further, in the step S4, a return goods prediction model is constructed, and a test standard of the return goods is formulated, so as to judge whether the return goods can be directly resold, discounted, sold and scrapped, including:
s41, collecting returned commodity typesTime of returnPrice of goods returnedTransaction time informationCollecting customer level informationCollecting weather conditions at returnWhether or not it is on holidays;
Converting commodity categories into n virtual variables using one-hot encodingConverting customer class into m virtual variables by using single hot coding;
Extracting temperature T, humidity H and rainfall R, and marking whether the mark is holidays, wherein 0 represents non-positive, and 1 represents positive;
s42, adopting a logistic regression model as model prediction,WhereinWhereinA vector representing the composition of all of the input features,Is the intercept term of the term,AndThe coefficients of the commodity category and the customer level respectively,The coefficient of the price of the commodity,Is a coefficient of the time of the transaction,The coefficients of temperature, humidity and rainfall,Is a factor of holidays;
S43, when a return request is made, automatically calling a return prediction model, calculating the return probability, and setting a threshold value of the return probability according to the service requirementWhen the return probability is smaller thanJudging that the commodity is put on the shelf again for sale, and when the return probability is positionedAndIf the goods are discounted and sold, the probability of returning goods is larger thanJudging whether the commodity needs to be scrapped;
And S44, when a return order is generated, temporarily deducting the corresponding stock, restoring the stock state of the commodity which can be directly resaled, transferring the commodity to a discounted sales area for the discounted commodity, and removing the commodity from the stock for the scrapped commodity.
Further, the step S6 of monitoring performance of KPIs, feeding back results and problems of inventory management to related departments of purchasing, selling and warehousing through a data feedback adjustment strategy to form closed-loop management, including:
S61, calculating inventory turnover rate, calculating by dividing annual sales by average inventory cost, calculating order accuracy, and calculating by dividing the number of correct shipments by the total shipment number;
And S62, the purchasing department predicts and adjusts the purchasing plan according to the inventory turnover rate and the demand, the sales department optimizes the sales flow according to the order accuracy, and the storage department optimizes the inventory management and order processing flow according to the inventory turnover rate and the order accuracy.
The method has the beneficial effects that the future market demand is predicted more accurately through the demand prediction model established by integrating various data sources, the stock backlog or stock shortage phenomenon caused by prediction errors is reduced, the stock cost is effectively controlled through setting the reasonable and dynamically adjusted safety stock level and automatically triggering the purchase request according to the demand prediction, and meanwhile, the sufficient stock supply in the demand peak period is ensured;
A quick response mechanism is negotiated with a supplier to finish replenishment in a short time, so that the overall reaction speed and efficiency of a supply chain are improved, multichannel sales data are synchronized in real time, the stock state is updated, the speed and accuracy of order processing are improved, and the problem caused by stock information lag is reduced;
The reason of the return is better understood by constructing a return prediction model, and a direct resale, discounted sales or scrapping treatment decision is made according to the reason, so that the influence of the return on enterprises is minimized, and measures such as donation of temporary foods are timely taken for long-time unsold commodities;
By monitoring KPI performance and feeding back inventory management results to related parties, information sharing and cooperation among departments such as purchasing, selling, warehousing and the like are promoted, an effective closed-loop management mechanism is formed, and the overall service level is improved due to the reduction of backorder and delayed delivery, so that the shopping experience and satisfaction of customers are improved.
Drawings
FIG. 1 is a flow chart of a method of inventory management for electronic commerce.
Detailed Description
The present invention will be further described more fully hereinafter, but the scope of the invention is not limited thereto.
An inventory management method of electronic commerce adopts the following technical scheme:
S1, collecting an internal historical sales record, a user browsing record, a shopping cart abandoning rate, a customer service consultation record, product discussion heat on social media and price change of competitors, and establishing a prediction model to generate a demand prediction result in a future period of time;
S2, setting a safety stock level by combining the historical sales data and the demand forecast result in the S1, adjusting the safety stock quantity in each quarter, setting a trigger condition according to the demand forecast result, automatically sending a purchase request to a supplier when the actual stock is lower than a preset safety stock level, negotiating with the supplier to determine a quick response mechanism, completing replenishment in a short time, forecasting the peak demand, and pre-increasing the stock before major sales promotion;
S3, synchronizing data of all sales channels in real time according to the historical sales data in S1, connecting an E-commerce platform through an API interface, updating an inventory state every time an order is generated and canceled, tracking the goods moving condition by adopting the Internet of things equipment, and updating the inventory quantity;
s4, analyzing historical return data according to the historical sales data in S1, finding out return reasons, constructing a return prediction model, making a check standard of return goods, and judging whether the return goods can be directly resaled, discounted for sale and scrapped;
s5, periodically analyzing the historical sales data in the step S1, marking out continuous commodities without sales records for a plurality of weeks, setting a checking period for the commodities with short shelf life, and donating the temporary but edible commodities with the charity in a collaboration way;
And S6, setting KPIs (performance indicator) by adopting the historical sales data and the prediction model in the S1, including inventory turnover rate and order accuracy, monitoring the performance of the KPIs, and feeding back the inventory management results and problems to related departments of purchasing, selling and warehousing through a data feedback adjustment strategy to form closed-loop management.
Referring to FIG. 1, a flowchart of an inventory management method for electronic commerce is shown.
Further, the step of establishing a prediction model in S1 generates a demand prediction result in a future period of time, including:
s11, extracting time sequence features from the historical sales records, and extracting the historical sales volume of a period of time tHorizontal initial componentTrend initial componentSeasonal initial componentThe horizontal initial component is the first value of the time sequence, the trend initial component is the difference between the first two values of the time sequence, and the seasonal initial component is the average value of months within a period of time t;
Extracting the number of daily accesses of a userUser residence time per dayShopping cart abandoning proportion of users in time tNumber of counseling times in time tConsultation keywords in time tFront evaluation ratio in time tNegative evaluation proportion in time tNumber of discussions in time tCompetitor price change over time t;
S12, updating horizontal componentTrend componentSeasonal component,,,WhereinThe smoothing factors of the horizontal component, the trend component and the seasonal component take values between 0 and 1,For the length of the cycle to be the same,Is thatA seasonal component of the time in which the time is to be used,Is thatA horizontal component and a trend component in time;
S13, constructing a feature matrix,Setting target variableThe model is trained asWhereinIs a gradient elevator, and the prediction model is thatWhereinFor modular operation, h is the predicted future time period,As seasonal componentThe value in the period of h,The characteristic matrix is in the t+h time period;
And S14, after a prediction model is established, relevant data are collected in real time according to the step S11, corresponding processing is carried out in the step S12, and the processed data are input into the prediction model to obtain a corresponding demand prediction result.
Further, setting a safety stock level in S2, adjusting the safety stock amount in quarters, and setting a trigger condition according to a demand prediction result, including:
S21, calculating standard deviation of sales by adopting historical sales data and demand prediction results, calculating safety stock by combining a service level with a Z value corresponding to the service level, wherein the service level is probability of meeting customer demands in a certain time, namely the stock meets the demands in the probability time, the Z value is obtained from a standard normal distribution table Z table corresponding to the probability, and the safety stock is obtained from the standard normal distribution table Z table corresponding to the probabilityWhereinAs the standard deviation of the daily requirement,Is an early period, for the time required from ordering to receiving goods from the warehouse, for the safe stock quantity of each quarter, calculating the standard deviation of the requirements of the quarter;
S22, automatically sending a purchase request to a provider when the actual stock level is lower than the result obtained by multiplying the average demand by the lead time;
S23, predicting upcoming peak demands according to historical sales data and a demand prediction model, gradually increasing inventory a few weeks before major sales promotion, and carrying out purchasing by average weekly quantification and suppliers according to the inventory amount which is needed to be supplemented.
Further, in the step S4, a return goods prediction model is constructed, and a test standard of the return goods is formulated, so as to judge whether the return goods can be directly resold, discounted, sold and scrapped, including:
s41, collecting returned commodity typesTime of returnPrice of goods returnedTransaction time informationCollecting customer level informationCollecting weather conditions at returnWhether or not it is on holidays;
Converting commodity categories into n virtual variables using one-hot encodingConverting customer class into m virtual variables by using single hot coding;
Extracting temperature T, humidity H and rainfall R, and marking whether the mark is holidays, wherein 0 represents non-positive, and 1 represents positive;
s42, adopting a logistic regression model as model prediction,WhereinWhereinA vector representing the composition of all of the input features,Is the intercept term of the term,AndThe coefficients of the commodity category and the customer level respectively,The coefficient of the price of the commodity,Is a coefficient of the time of the transaction,The coefficients of temperature, humidity and rainfall,Is a factor of holidays;
S43, when a return request is made, automatically calling a return prediction model, calculating the return probability, and setting a threshold value of the return probability according to the service requirementWhen the return probability is smaller thanJudging that the commodity is put on the shelf again for sale, and when the return probability is positionedAndIf the goods are discounted and sold, the probability of returning goods is larger thanJudging whether the commodity needs to be scrapped;
And S44, when a return order is generated, temporarily deducting the corresponding stock, restoring the stock state of the commodity which can be directly resaled, transferring the commodity to a discounted sales area for the discounted commodity, and removing the commodity from the stock for the scrapped commodity.
Further, the step S6 of monitoring performance of KPIs, feeding back results and problems of inventory management to related departments of purchasing, selling and warehousing through a data feedback adjustment strategy to form closed-loop management, including:
S61, calculating inventory turnover rate, calculating by dividing annual sales by average inventory cost, calculating order accuracy, and calculating by dividing the number of correct shipments by the total shipment number;
And S62, the purchasing department predicts and adjusts the purchasing plan according to the inventory turnover rate and the demand, the sales department optimizes the sales flow according to the order accuracy, and the storage department optimizes the inventory management and order processing flow according to the inventory turnover rate and the order accuracy.
The invention provides an electronic commerce inventory management method, which integrates links such as multisource data analysis and prediction, intelligent replenishment, dynamic inventory tracking, return management and the like, utilizes information such as historical sales records, user behaviors, market dynamics and the like to construct a demand prediction model, sets a safety inventory level based on a prediction result, implements an automatic purchasing request and quick response replenishment mechanism, realizes real-time inventory synchronization and monitoring inventory change through an API (application program interface) and an Internet of things technology, guides commodity processing decisions aiming at the return development prediction model, periodically inspects the diapause and reasonably disposes products close to the quality guarantee period, establishes a KPI (key performance) including inventory turnover rate and order accuracy, and realizes closed-loop control of inventory management.