CN110909966B - Efficiency evaluation method and efficiency evaluation system - Google Patents
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Abstract
The embodiment of the invention provides an efficiency evaluation method and an efficiency evaluation system, wherein the efficiency evaluation method comprises the following steps: obtaining a plurality of storage parameters corresponding to the evaluation objects; dividing model types of the evaluation models corresponding to the evaluation objects according to the post content; establishing a plurality of evaluation models corresponding to the evaluation objects according to the warehousing parameters and the model types; and respectively calculating the working efficiency corresponding to the plurality of evaluation models according to the plurality of storage parameters, and forming the total working efficiency of the evaluation object after superposition. By dividing model types according to post content, generating a plurality of evaluation models corresponding to one evaluation object according to the model types, respectively calculating the total work efficiency of the evaluation objects by overlapping the work efficiency, the evaluation results can reflect the work efficiency of a plurality of different operations in the same work post at the same time, and relatively fair evaluation results are obtained.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an efficiency evaluation method and an efficiency evaluation system.
Background
In the warehouse production process, warehouse management personnel need to evaluate the workload of each employee, and the equitable, reasonable and effective evaluation scheme can mobilize the enthusiasm of the employee to a great extent.
For staff checking goods post in the warehouse, the warehouse at present mainly measures the working efficiency according to the number of goods checked and accepted in the effective working time, however, due to different types, types and operation modes of different order types of the warehouse, the working evaluation of staff is affected to a certain extent, and the working efficiency of staff can not be reasonably evaluated. For example, the adopted and sold commodity can be inspected by adopting different sampling measures according to the difference of the number of the commodity, for example, when the number of the commodity is less than 50, the commodity needs to be inspected completely, when the number of the commodity is between 50 and 500, 50+ (T-50) 0.15 (T is the number of the commodity in the warehouse), the sampling rate is 10% when the number of the commodity is between 500 and 1000, the sampling rate is 5% when the number of the commodity is between 1000 and 5000, and the sampling rate is 1% when the number of the commodity is greater than 5000. When the customer returns goods to warehouse, spare parts warehouse or the goods to be sold are the mobile phone, luxury goods and other goods with serial number management, the number of the goods is checked one by one. If the person's personal effects are reflected by the number of items checked by the person within a certain effective time, for example, only 100 items are checked for 1000 common items, and all 1000 items managed by the serial number are checked, although both items are checked for 1000 items, the latter takes significantly more time than the former, and the former is far higher than the latter from the personal effects, which is obviously not appropriate. Therefore, only the working efficiency of the staff is evaluated according to the mode, only a certain factor is considered to evaluate the workload, which is detrimental and fair, and the loyalty of the staff is reduced, the resource is wasted to a certain extent, and the whole production efficiency is finally reduced.
Therefore, the inventor believes that the above-mentioned method for evaluating the work efficiency of warehouse personnel has a great limitation, and that the calculation of the work load by using the number of checked pieces in the effective time has a problem that the actual work condition of warehouse personnel cannot be evaluated fairly and reasonably.
Disclosure of Invention
In view of this, the embodiment of the invention provides an efficiency evaluation method and an evaluation system, which adopt a plurality of storage parameters and modify configuration parameters of an evaluation model during calculation, so as to avoid the phenomenon that an evaluation result is unfair due to the fact that a single evaluation factor is adopted to evaluate the workload of staff in the prior art, and improve the work enthusiasm of the staff.
According to a first aspect of the present invention, there is provided an efficiency evaluation method comprising: obtaining a plurality of storage parameters corresponding to the evaluation objects; dividing model types of the evaluation models corresponding to the evaluation objects according to the post content; establishing a plurality of evaluation models corresponding to the evaluation objects according to the warehousing parameters and the model types; and respectively calculating the working efficiency corresponding to the plurality of evaluation models according to the plurality of storage parameters, and forming the total working efficiency of the evaluation object after superposition.
Preferably, classifying the model types of the evaluation models corresponding to the evaluation objects according to the post content includes: classifying different warehouses according to the types of stored commodities; dividing each designated warehouse into warehouse types according to commodity types; dividing the types of the preliminary models according to the warehousing operation mode of the commodity; and determining a model type of the evaluation model from the preliminary model type and the warehouse type.
Preferably, the preliminary model types comprise a sales and in-distribution warehouse-in model, a customer warehouse-out model and a spare part warehouse-in model; the warehouse types comprise a small-piece A warehouse and a small-piece B warehouse.
Preferably, for the warehouse which is not subjected to warehouse type classification, the model types are 3, including a sales and in-warehouse acceptance model, a customer-warehouse entry acceptance model and a spare part warehouse entry acceptance model; the warehouse for classifying the warehouse types is 6, and comprises a small A warehouse picking and selling and internal matching warehouse checking and accepting model, a small A warehouse customer returning and warehouse entering checking and accepting model, a small A warehouse spare part warehouse warehousing and accepting model, a middle and small B warehouse picking and selling and internal matching warehouse checking and accepting model, a middle and small B warehouse customer returning and accepting model and a middle and small B warehouse spare part warehouse entering checking and accepting model.
Preferably, the efficiency evaluation method further includes: setting a plurality of links according to the operation flow of the working position; and setting storage parameters according to the links and the model types respectively.
Preferably, the efficiency evaluation method further includes: and preprocessing data, and removing abnormal parameters in the storage parameters.
Preferably, the anomaly parameters include: data with continuous operation time less than a preset value, data with continuous operation time less than a preset value and repeated data in a warehouse.
Preferably, the step of establishing the evaluation model comprises: generating a training set according to the plurality of storage parameters; extracting an evaluation index and analyzing the corresponding relation between the evaluation index and a plurality of storage parameters; selecting a model type according to the corresponding relation, and fitting the evaluation index according to the model type; calculating according to a plurality of storage parameters, and inputting a model result; and determining an evaluation model corresponding to each of the plurality of evaluation objects.
Preferably, the model type is a nonlinear model.
Preferably, the evaluation index includes an acceptance time.
Preferably, the warehouse parameters include: SKU number, number of accepted pieces after conversion, total weight of commodity, and total volume of commodity.
According to a second aspect of the present invention, there is provided an efficiency evaluation system comprising: the data acquisition unit is used for acquiring a plurality of storage parameters corresponding to the evaluation object; the model dividing unit is used for dividing model types of the evaluation models corresponding to the evaluation objects according to the post content; a model establishing unit, configured to establish a plurality of evaluation models corresponding to the evaluation objects according to the storage parameters and the model types; and the efficiency calculation unit is used for calculating the working efficiency corresponding to the plurality of evaluation models according to the plurality of storage parameters respectively, and forming the total working efficiency of the evaluation object after superposition.
Preferably, the efficiency evaluation system further comprises: the data preparation unit is used for setting a plurality of links according to the operation flow of the working post; the parameter setting unit is used for setting storage parameters according to the links and the model types respectively; and the data processing unit is used for removing abnormal parameters in the storage parameters.
Preferably, the warehouse parameters include: SKU number, number of accepted pieces after conversion, total weight of commodity, and total volume of commodity.
According to a third aspect of the present invention there is provided a computer readable storage medium storing computer instructions which when executed implement a method of efficiency assessment as described above.
According to a fourth aspect of the present invention, there is provided an efficiency evaluation apparatus comprising: a memory for storing computer instructions; a processor coupled to the memory, the processor configured to perform an efficiency assessment method as described above based on computer instructions stored by the memory.
Embodiments of the present invention have the following advantages or benefits: by dividing model types according to post content, generating a plurality of evaluation models corresponding to one evaluation object according to the model types, respectively calculating the total work efficiency of the work efficiency superposition as the evaluation object, the evaluation result can reflect the work efficiency of a plurality of different operations in the same work post at the same time, and thus, a relatively fair evaluation result is obtained.
Another preferred embodiment of the present invention has the following advantages or benefits: storage parameters are set according to a plurality of links, so that the accuracy of the evaluation model is improved; and model types are divided according to the warehouse-in operation mode of the commodity, so that the setting of an evaluation object can be closer to the actual operation, and the evaluation result is more accurate.
The preferred embodiments of the present invention have the following advantages or benefits: and data preprocessing is carried out, data useful for establishing a model is reserved, and abnormal parameters in a plurality of storage parameters are removed, so that the evaluation model has universality and strong applicability.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments thereof with reference to the following drawings in which:
Fig. 1 shows a flowchart of an efficiency evaluation method in the first embodiment;
Fig. 2 shows a flowchart of a plurality of steps performed before the step S101 shown in fig. 1 in the second embodiment;
Fig. 3 shows a specific flowchart of step S102 shown in fig. 1 in a third embodiment;
Fig. 4 shows a specific flowchart of step S103 shown in fig. 1 in the fourth embodiment;
fig. 5 shows a structural diagram of an efficiency evaluation system in a fifth embodiment;
fig. 6 is a block diagram showing an efficiency evaluation system summarized in the sixth embodiment;
fig. 7 shows a structural diagram of an efficiency evaluation device in the seventh embodiment.
Detailed Description
The present invention is described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth in detail. The present invention will be fully understood by those skilled in the art without the details described herein. Well-known methods, procedures, and flows have not been described in detail so as not to obscure the nature of the invention. The figures are not necessarily drawn to scale.
Fig. 1 shows a flowchart of an efficiency evaluation method in the first embodiment, and specific steps S101 to S103.
In step S101, a plurality of warehouse parameters corresponding to the evaluation object are obtained.
In step S102, model types of the evaluation models corresponding to the evaluation objects are classified according to the post content.
In step S103, a plurality of evaluation models corresponding to the evaluation objects are created according to the warehouse parameters and the model types.
In step S104, the working efficiencies corresponding to the plurality of evaluation models are calculated according to the plurality of storage parameters, and the total working efficiency of the evaluation object is formed after superposition.
As described in the background art, the calculation workload using the statistical method of the number of checked-in pieces in the effective time cannot reasonably evaluate the actual work efficiency of the warehouse checked-in staff, and thus in this embodiment, an evaluation model is adopted for the evaluation object to evaluate. The evaluation object is, for example, a warehouse post inspection personnel, and the warehouse post inspection personnel with different operation modes aiming at different commodity warehouse-in can correspond to different warehouse parameters affecting the effectiveness, so that a plurality of different evaluation models can be set for one evaluation object, and the total working efficiency is calculated according to the results counted by the plurality of evaluation models. Firstly, obtaining a plurality of storage parameters corresponding to an evaluation object in operation; then dividing model types corresponding to the evaluation objects according to different operation modes aiming at different commodity warehouse-in when the inspection and acceptance personnel work, wherein one model type corresponds to one operation mode; determining an evaluation index to represent the working efficiency, selecting a proper model type, substituting the data of the corresponding storage parameters into the evaluation model, and finally sorting out a plurality of different evaluation models suitable for different operation modes of the evaluation object according to the storage parameters and the model type to obtain the working efficiency of the evaluation object corresponding to the different operation modes; then, the work efficiency is calculated respectively, and the total work efficiency of the evaluation object is calculated in a superposition manner.
The same working post operation mode is different, for example, warehouse commodity warehouse entry inspection post, each worker inspects different warehouse entry commodities each time, the SKU quantity, brands, object shapes, actual inspection quantity, special attributes and the like contained in each collection list are different, the difficulty level is different, and the time and energy consumption is different. The number of the checked and accepted commodities of different warehouse-in types is calculated in different manners, for example, the number of the checked and accepted commodities in warehouse-in is not the whole number, and whether sampling and checking are carried out is determined according to whether the serial number commodity and the number of the commodity per se are obtained; and the commodities in the customer and spare part warehouse can be checked and accepted one by one. All the above factors will cause the final evaluation difference of the working efficiency, so for the evaluation objects of different operation modes, part or all of the factors which can affect the working efficiency are selected as the storage parameters of the evaluation model. And then dividing model types, respectively establishing different assessment models corresponding to different model types, and finally integrating the influence of a plurality of storage parameters in the assessment models, so that the working efficiency of actual acceptance of each acceptance guard can be calculated, and a relatively fair assessment result is obtained.
In the embodiment of the invention, the model types are divided according to the post content, a plurality of evaluation models corresponding to one evaluation object are generated according to the model types, and the work efficiency is calculated and overlapped to the total work efficiency of the evaluation object, so that the evaluation result can reflect the work efficiency of a plurality of different operations in the same work post at the same time, and a relatively fair evaluation result is obtained.
Fig. 2 shows a flowchart of a plurality of steps performed before the step S101 shown in fig. 1 in the second embodiment, specifically including the following steps.
In step S111, a plurality of links are set according to the operation flow of the work station.
In step S112, warehouse parameters are set according to the links and model types, respectively.
In step S113, the data is preprocessed to remove abnormal parameters in the plurality of warehouse parameters.
The same or different working posts of different warehouses can involve a plurality of links, taking the inspection post as an example for explanation, and different kinds of warehouses such as super business, 3C, department stores, fresh warehouses and the like, and the basic operation links of staff inspecting the inspection post are as follows:
1. The inspector scans the pre-inspection bill number through the handheld PDA, and the system can automatically display the SKU and the number of pieces required to be inspected in the pre-inspection bill number;
2. The inspector determines whether the total inspection or the spot inspection is performed or what the spot inspection is performed according to whether the SKU is the serial number management commodity and the number;
3. after counting and confirming the commodity and the number thereof, the container number is scanned, and the binding relation of the commodity SKU, the number thereof and the container number is completed.
By analyzing each link of commodity warehousing checking operation, the number calculation modes of checking and accepting of different warehousing types can be known to be different. The number of the adopted and matched commodities is not all the number, and whether sampling is carried out or not is determined according to whether the serial number commodity and the number of the commodity per se are adopted; and the commodities in the customer and spare part warehouse can be checked and accepted one by one.
And then, through analyzing each link of the acceptance operation and combining different operation modes, counting the storage parameters related to the evaluation work efficiency in each link, and setting the final storage parameters corresponding to different models respectively.
The various parameters associated with acceptance are, for example, warehouse information, employee information, pre-check order number information, acceptance information, SKU base information, and the like. The warehouse information includes warehouse names (discriminating a/B bins), category names, and the like. Wherein the class name includes, for example, raw fresh, food, etc. The employee information includes ERP account numbers of the employees. The pre-examination list number information comprises the contained SKU ID and the number of pieces. The acceptance information includes the SKU actually accepted, the number of actually accepted pieces, and the container number. The SKU basic information includes the category of the commodity SKU, length, width, height, weight, whether the commodity SKU is fragile, whether dangerous goods are dangerous goods, whether serial number management is performed, and the like. The plurality of warehouse parameters adopted in the present embodiment, such as order SKU, number of actual checks, total weight of commodity, are selected from the above parameters.
In order to make the resulting assessment model more efficient, the method further comprises: screening the data of the warehouse parameters. In some cases, due to reasons such as system or manual operation or non-standardization, the collected original data is abnormal to some extent, including repeated data, missing data, invalid data, dirty data, etc., and if the abnormal data is not removed, the evaluation effect of the evaluation model is affected to some extent. Therefore, when the model is built, the abnormal data are required to be cleaned, the data which are useful for building the model are reserved, and the abnormal parameters in the storage parameters are removed.
In one embodiment, the anomaly parameters include, for example, data for which the warehouse is continuously operated for less than a predetermined value, data for which the continuous operation time is less than a predetermined value, and duplicate data. Specifically, since the acceptance service in the early stage of the new warehouse is unstable, the data cannot represent a general rule and does not have analysis conditions, a warehouse which is operated normally for a period of time needs to be selected, a threshold value T is required to be set, a unit month is required to represent continuous operation time of the warehouse, modeling data is required to be the latest T-3 month, generally T=6, the warehouse is required to be operated continuously for 6 months, modeling is performed by data of the latest 3 months, and data of which continuous operation time is less than 6 months need to be removed. In addition, as the operation proficiency of staff directly influences the working efficiency, temporary workers exist in a warehouse at ordinary times, storage parameters corresponding to the temporary workers are considered as abnormal parameters, and data of the temporary workers are deleted firstly; the proficiency of the officials cannot meet the requirements in the first N hours of work, so that the officials in the warehouse can select data with the effective checking time exceeding 100 hours and the continuous working time being less than 100 hours. Moreover, due to the fact that the ERP account is shared in a period of time possibly caused by warehouse management and other reasons, one ERP account is selected to only correspond to the data of one pre-check list number in the same time, and more than one ERP account is selected, so that the information corresponding to the two pre-check list numbers needs to be deleted. Similarly, for the pre-check number, in actual operation, multiple people can perform simultaneous checking and accepting operation on the commodities in the same pre-check number, and at this time, the checking and accepting time of the pre-check number can be affected by multiple uncontrollable factors, and the pre-check number needs to be deleted; for dirty data caused by other system reasons, for example, the same pre-check list number and the same SKU have the same check time record for a plurality of times, or the check time, SKU number, number of pieces, empty personnel and other data need to be deleted.
Fig. 3 shows a specific flowchart of step S102 shown in fig. 1 in the third embodiment, specifically including the following steps.
In step S1021, different warehouses are classified according to the type of stored commodity.
The characteristics of the commodities stored in different kinds of warehouses are different, and the warehouses are classified according to the characteristics, namely the models are subdivided in granularity of the kinds of warehouses, and the kinds of warehouses comprise super commercial products, 3C, department products, small household appliances, fresh products and the like.
In step S1022, each warehouse specified is classified into warehouse types according to commodity types.
Some kinds of warehouses (such as super commercial warehouses and 3C warehouses) can be divided into small warehouses (A warehouses) and medium and small warehouses (B warehouses) according to different types of warehouses, and the types of the commodity can influence the checking and accepting efficiency, so that the types of the warehouses need to be divided into the small A warehouses and the medium and small B warehouses by distinguishing the types of the warehouses for the model.
In step S1023, the classification of the preliminary model types is performed according to the warehouse-in operation mode of the commodity.
The commodity warehouse entry comprises four warehouse entry modes including acquisition and sales, internal allocation, customer warehouse entry and spare part warehouse entry, wherein the customer warehouse entry refers to the commodity which is returned to the warehouse after a user purchases the commodity and cancels an order when the user does not leave the warehouse, the spare part warehouse commodity refers to the commodity which is required to be overhauled or maintained in the spare part warehouse for some reasons and is continuously transferred into the big warehouse after being reevaluated to be qualified), the acquisition and sales and the internal allocation warehouse entry are similar in operation, so the primary division is carried out according to the operation, the acquisition and sales and the internal allocation warehouse entry are divided into one type of models, the customer warehouse entry and the spare part warehouse entry are respectively divided into one type of models, namely the primary model types divided according to the warehouse entry operation modes of the commodity comprise: the system comprises a sales and internal allocation warehouse-in model, a customer warehouse-out model and a spare part warehouse-in model.
In step S1024, the model type of the evaluation model is determined from the preliminary model type and the warehouse type.
And finally dividing model types. For example, for a class warehouse that does not require division into a small a warehouse and a small B warehouse, the model classes are three, including: the method comprises the steps of acquiring a sales and internal matching warehouse entry acceptance model, a customer check-in warehouse entry acceptance model and a spare part warehouse entry acceptance model; for the class warehouse that needs to divide into widget A storehouse and well widget B storehouse, there are 6 kinds to the model category that corresponds, include: the system comprises a small part A bin picking and selling and internal matching warehouse-in acceptance model, a small part A bin passenger returning to warehouse-in acceptance model, a small part A bin spare part warehouse-in acceptance model, a middle and small part B bin picking and selling and internal matching warehouse-in acceptance model, a middle and small part B bin passenger returning to warehouse-in acceptance model and a middle and small part B bin spare part warehouse-in acceptance model.
After determining the plurality of warehouse parameters and the plurality of model types of the embodiment, an evaluation model is established. For example, sample training may be performed using artificial intelligence algorithms including a variety of artificial neural networks, deep neural networks, decision trees, SVMs, and the like, to obtain an assessment model. In an alternative embodiment, the plurality of relationships corresponding to the evaluation object are obtained through sample training of an artificial intelligence algorithm, see in particular FIG. 4.
Fig. 4 shows a specific flowchart of step S103 shown in fig. 1 in the fourth embodiment. The method specifically comprises the following steps.
In step S1031, a training set is generated based on the plurality of warehouse parameters. As previously mentioned, the various parameters associated with the warehouse are, for example, warehouse information, employee information, pre-check order number information, acceptance information, SKU base information, and the like. The storage parameters are selected to be integrated into a training set, for example, for meeting the post, the operation object is a collection list, and the storage parameters of the training set need to comprise SKU number, total weight of commodity, total volume and acceptance time. In the process of acceptance operation, because the system only records the acceptance time of each SKU in each pre-inspection bill number, two adjacent pre-inspection bill numbers are adopted, and the time difference between the first acceptance record time of the last bill number and the last acceptance record time of the last bill number comprises other time such as communication between an inspector and a provider and does not belong to the effective time of acceptance, so that the information contained in the first acceptance task of the pre-inspection bill number is deleted. The final statistical data granularity is the pre-check list number, and the SKU number, total weight, total volume and acceptance time of other acceptance tasks after the first acceptance task is removed. Wherein the total number is the converted total number determined according to the spot check rule, and the acceptance time=the last acceptance record time of the pre-check list-the second acceptance record time of the pre-check list.
In step S1032, the evaluation index is extracted and the correspondence between the evaluation index and the plurality of storage parameters is analyzed. And extracting the acceptance time as an evaluation index, carrying out single factor analysis in a training set, respectively analyzing the relation between a plurality of storage parameters and the acceptance time, and selecting the characteristic with the most obvious trend as an important influence factor for model input. Because of the great difference of acceptance time in different warehouse-in types, the three parts of purchasing and internal distribution warehouse-in, customer warehouse-in and spare part warehouse-in are discussed. Observing the factors that affect acceptance time includes, as described above: SKU number, total number (serial number management total check, non-serial number management requires calculation of the proportion of spot check), total weight, total volume of the commodity (when there is a different acceptance time for the same SKU number or total number, the median representation of time is taken, weight and volume are converted into classification fields, and the median value is taken for different time values for each same classification). The purchasing and internal distribution warehouse entry are selected as an example illustration, the relation between each influencing factor and the acceptance time is made into a scatter diagram, and the relation between the SKU number, the number of pieces, the total weight of the commodity and the total volume and the acceptance time is respectively analyzed according to the scatter diagram. As can be seen from the graph, the selected factors have obvious correlation with the acceptance time, the SKU number, the number of pieces after conversion and the acceptance time show linear relations, and the weight and volume segments and the acceptance time show exponential function relations. Similar analysis is also performed for the customer and spare part library. Meanwhile, the correlation between the factors is analyzed, and as a result, most of the factors have obvious correlation with time, but the embodiment is not limited thereto.
In one embodiment, the evaluation model includes one or more evaluation indexes, and an evaluation result of the working efficiency is obtained according to the plurality of evaluation indexes. For example, the plurality of evaluation indexes respectively obtained include average time consumption of the first SKU acceptance, average time consumption of the second SKU acceptance, average time consumption of the third SKU acceptance, … …, and the average time consumption is accumulated to obtain total acceptance time consumption as a final evaluation index, and an evaluation result of the working efficiency is calculated. For another example, the average time consumption is weighted and accumulated to obtain the total acceptance time consumption as a final evaluation index, and the evaluation result of the work efficiency is calculated. Wherein the first, second and third are used only to identify different SKUs and do not represent differences in priority or importance. For example, only one evaluation index, for example, the acceptance time, the acceptance time=the last acceptance record time of the pre-test order-the second acceptance record time of the pre-test order is used as the evaluation index of the calculation work efficiency.
In step S1033, a model type is selected according to the correspondence, and the evaluation index is fitted according to the model type.
One model is selected from a plurality of models such as a linear model, a nonlinear model, a tree regression model (random forest, GBDR) and the like, and various influencing factors have a linear relation and a nonlinear relation, but the final model is a multi-factor model and is not a single-factor model, so that the nonlinear model is selected to be used as a model type at this time. Alternatively, the nonlinear model combines gradient descent method, gaussian Newton method by the Levenberg (LM) algorithm. The nonlinear model is selected to fit the acceptance time, and various storage parameters (such as SKU number, actual acceptance number, total commodity weight and total volume) which are taken in as influencing factors are combined.
In step S1034, calculation is performed according to the plurality of warehouse parameters, and a model result is input. Various storage parameter combinations as influencing factors are brought into the fitting model, and as not all factors are meaningful to be substituted into the model, storage parameters which are meaningless to the model are deleted, and then the output of the final model, which is larger in R side of the model and smallest in MAPE in the test set, is selected.
The calculation and screening are performed once for each model, respectively, resulting in a plurality of assessment models corresponding to a plurality of different model classes.
In step S1035, a plurality of evaluation models corresponding to the evaluation objects are determined. For example, in the acceptance post, after calculation, three different evaluation models are obtained for the evaluation object corresponding to the three model types, as follows.
The commodity is derived from purchasing and internally distributed flow model formulas: predtime = a x tqty b + c x sky + d
The commodity is derived from an acceptance model formula of a customer return process: predtime = a x sky + b
The commodity is derived from an acceptance model formula of a spare part library: predtime = a x sky + b
Here the evaluation type calculated according to the warehouse type not divided. Wherein tqty, SKU represent the actual number of checks and SKU number respectively, a, B, c and d are configuration parameters respectively, and when the small part a bin and the middle and small part B bin need to be divided, 6 models can be obtained by modifying the configuration parameters of the models.
Work efficiency of the check-in staff is calculated according to the model, for example, purchasing and internal allocation work is performed, only 1 SKU is in a warehouse entry list, and only 1 commodity is in the SKU, so that the predicted check-in time is 11.00 seconds.
Because one inspection post personnel can carry out inspection operation of different warehoused commodities within one day, inspect and receive the customer and return the commodity for a period of time, inspect and receive the sales and the internal commodity for a period of time, the operation is different, the corresponding evaluation models are different, and the calculation of the working efficiency is different, the working efficiency can be calculated according to the different evaluation models corresponding to each period of time respectively, and the total working efficiency of one inspection post personnel is formed after superposition.
And during calculation, the configuration parameters are adjusted to obtain the respective time consumption. The method can enable calculation to be simple and convenient, saves labor, and is particularly advantageous for tasks needing to process large data volume.
It should be noted that although the above-described embodiments are not intended to limit the present invention. The efficiency evaluation method provided by the embodiment of the invention can also be practiced because of other parameters or other evaluation objects are divided.
Fig. 5 shows a structural diagram of an efficiency evaluation system in the fifth embodiment.
The efficiency evaluation system 500 includes a data acquisition unit 501, a model division unit 502, a model establishment unit 503, and an efficiency calculation unit 504.
The data acquisition unit 501 is configured to acquire a plurality of warehouse parameters corresponding to an evaluation object; the model dividing unit 502 is used for dividing model types of the evaluation models corresponding to the evaluation objects according to the post content; the model building unit 503 is configured to build a plurality of evaluation models corresponding to the evaluation objects according to the warehouse parameters and the model types; the efficiency calculating unit 504 is configured to calculate the working efficiencies corresponding to the plurality of evaluation models according to the plurality of storage parameters, and form the total working efficiency of the evaluation object after superposition.
Fig. 6 shows a structural diagram of the efficiency evaluation system summarized in the sixth embodiment.
The efficiency evaluation system 600 includes a data preparation unit 601, a parameter setting unit 602, and a data processing unit 603 in addition to 501-504 described above.
The data preparation unit 601 is configured to set a plurality of links according to an operation flow of a working post; the parameter setting unit 602 is configured to set storage parameters according to a plurality of links and model types, respectively.
In one embodiment, the efficiency evaluation system 600 further includes a data processing unit 603, where the data processing unit 603 is configured to remove abnormal parameters in the plurality of warehouse parameters, so as to ensure accuracy of evaluation.
In this embodiment, the data acquisition unit 601 performs a large number of data acquisitions for different evaluation objects, sets a plurality of links according to the operation flow of the working post, and the parameter setting unit 603 processes and stores the collected data as storage parameters. After an evaluation model is established, the working efficiency is evaluated, and the data is acquired and preprocessed in advance, so that the efficiency evaluation method is more scientific and reliable; and different operation modes of the evaluation object are evaluated by adopting different parameter evaluation models, so that the efficiency evaluation is finer and has fairness and rationality; the storage parameters corresponding to each object to be evaluated are brought into the model to evaluate the working efficiency, so that the evaluation process is efficient and concise, the display is transparent, and the enthusiasm of staff can be well mobilized.
It should be understood that the systems and methods of embodiments of the present invention are corresponding and, therefore, are performed in a relatively abbreviated manner in the description of the system. Fig. 7 shows a structural diagram of an efficiency evaluation device in the seventh embodiment. The apparatus shown in fig. 7 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention in any way.
Referring to fig. 7, the efficiency evaluation apparatus 700 includes a processor 701, a memory 702, and an input-output device 703 connected by a bus. The memory 702 includes Read Only Memory (ROM) and Random Access Memory (RAM), and the memory 702 stores various computer instructions and data required to perform system functions, and the processor 701 reads the various computer instructions from the memory 702 to perform various appropriate actions and processes. The input-output device includes an input section of a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section including a hard disk or the like; and a communication section including a network interface card such as a LAN card, a modem, and the like. The memory 702 also stores the following computer instructions to perform the operations specified by the efficiency assessment method of an embodiment of the present invention: obtaining a plurality of storage parameters corresponding to the evaluation objects; dividing model types of an evaluation model corresponding to the evaluation object according to the post content; establishing a plurality of evaluation models corresponding to the evaluation objects according to the storage parameters and the model types; and respectively calculating the working efficiency corresponding to the plurality of evaluation models according to the plurality of storage parameters, and forming the total working efficiency of the evaluation object after superposition.
Accordingly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed, perform operations specified by the above-described efficiency evaluation method.
The flowcharts, block diagrams in the figures illustrate the possible architectural framework, functions, and operations of the systems, methods, apparatus of the embodiments of the present invention, and the blocks in the flowcharts and block diagrams may represent a module, a program segment, or a code segment, which is an executable instruction for implementing the specified logical function(s). It should also be noted that the executable instructions that implement the specified logic functions may be recombined to produce new modules and program segments. The blocks of the drawings and the order of the blocks are thus merely to better illustrate the processes and steps of the embodiments and should not be taken as limiting the invention itself.
The various modules or units of the system may be implemented in hardware, firmware, or software. The software includes, for example, code programs formed using various programming languages such as JAVA, C/C++/C#, SQL, and the like. Although steps and sequences of steps of embodiments of the present invention are presented in terms of methods and apparatus, executable instructions for implementing the specified logical function(s) of the steps may be rearranged to produce new steps. The order of the steps should not be limited to only the order of the steps in the method and method illustration, but may be modified at any time as required by the function. For example, some of the steps may be performed in parallel or in reverse order.
Systems and methods according to the present invention may be deployed on a single or multiple servers. For example, different modules may be deployed on different servers, respectively, to form a dedicated server. Or the same functional units, modules, or systems may be distributed across multiple servers to relieve load pressure. The server includes, but is not limited to, a plurality of PCs, PC servers, blades, supercomputers, etc. connected on the same local area network and through the Internet.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (15)
1. A method of efficiency assessment, comprising:
Obtaining a plurality of storage parameters corresponding to the evaluation objects;
Dividing model types of the evaluation models corresponding to the evaluation objects according to the post content;
Establishing a plurality of evaluation models corresponding to the evaluation objects according to the warehousing parameters and the model types; and
Working efficiency corresponding to a plurality of evaluation models is calculated according to the plurality of storage parameters, the total working efficiency of the evaluation object is formed after superposition,
The step of classifying model types of the evaluation model corresponding to the evaluation object according to the post content comprises the following steps:
Classifying different warehouses according to the types of stored commodities;
dividing each designated warehouse into warehouse types according to commodity types;
Dividing the types of the preliminary models according to the warehousing operation mode of the commodity; and
And determining the model type of the evaluation model according to the preliminary model type and the warehouse type.
2. The method for evaluating efficiency according to claim 1, wherein,
The preliminary model types comprise a sales and internal matching warehouse-in model, a customer warehouse-out model and a spare part warehouse-in model;
The warehouse types comprise a small-piece A warehouse and a small-piece B warehouse.
3. The method for evaluating efficiency according to claim 2, wherein,
For the warehouse which is not subjected to warehouse type classification, the model types are 3, and the model types comprise a sales and internal warehouse entry acceptance model, a customer warehouse entry acceptance model and a spare part warehouse entry acceptance model;
The warehouse for classifying the warehouse types is 6, and comprises a small A warehouse picking and selling and internal matching warehouse checking and accepting model, a small A warehouse customer returning and warehouse entering checking and accepting model, a small A warehouse spare part warehouse warehousing and accepting model, a middle and small B warehouse picking and selling and internal matching warehouse checking and accepting model, a middle and small B warehouse customer returning and accepting model and a middle and small B warehouse spare part warehouse entering checking and accepting model.
4. The efficiency evaluation method according to claim 1, characterized by further comprising:
Setting a plurality of links according to the operation flow of the working position; and
And setting storage parameters according to the links and the model types respectively.
5. The efficiency evaluation method according to claim 1, characterized by further comprising: and preprocessing data, and removing abnormal parameters in the storage parameters.
6. The efficiency evaluation method according to claim 5, wherein the abnormality parameters include: data with continuous operation time less than a preset value, data with continuous operation time less than a preset value and repeated data in a warehouse.
7. The efficiency evaluation method according to claim 1, wherein the step of establishing an evaluation model includes:
Generating a training set according to the plurality of storage parameters;
extracting an evaluation index and analyzing the corresponding relation between the evaluation index and a plurality of storage parameters;
Selecting a model type according to the corresponding relation, and fitting the evaluation index according to the model type;
Calculating according to a plurality of storage parameters, and inputting a model result; and
And determining an evaluation model corresponding to each of the plurality of evaluation objects.
8. The method of claim 7, wherein the model type is a nonlinear model.
9. The efficiency evaluation method of claim 7 wherein the evaluation index comprises an acceptance time.
10. The efficiency assessment method of claim 1, wherein the warehouse parameters include: SKU number, number of accepted pieces after conversion, total weight of commodity, and total volume of commodity.
11. An efficiency evaluation system, comprising:
The data acquisition unit is used for acquiring a plurality of storage parameters corresponding to the evaluation object;
The model dividing unit is used for dividing model types of the evaluation models corresponding to the evaluation objects according to the post content;
a model establishing unit, configured to establish a plurality of evaluation models corresponding to the evaluation objects according to the storage parameters and the model types; and
An efficiency calculation unit for calculating the working efficiency corresponding to the plurality of evaluation models according to the plurality of storage parameters, and forming the total working efficiency of the evaluation object after superposition,
The model dividing unit is also used for classifying different warehouses according to the types of stored commodities; dividing each designated warehouse into warehouse types according to commodity types; dividing the types of the preliminary models according to the warehousing operation mode of the commodity; and determining a model type of the evaluation model from the preliminary model type and the warehouse type.
12. The efficiency evaluation system of claim 11 further comprising:
The data preparation unit is used for setting a plurality of links according to the operation flow of the working post;
the parameter setting unit is used for setting storage parameters according to the links and the model types respectively; and
And the data processing unit is used for removing abnormal parameters in the storage parameters.
13. The efficiency evaluation system of claim 11 wherein the warehouse parameters include: SKU number, number of accepted pieces after conversion, total weight of commodity, and total volume of commodity.
14. A computer readable storage medium storing computer instructions which, when executed, implement the efficiency assessment method of any one of claims 1 to 10.
15. An efficiency evaluation device, characterized by comprising:
a memory for storing computer instructions;
A processor coupled to the memory, the processor configured to perform implementing the efficiency assessment method of any one of claims 1 to 10 based on computer instructions stored by the memory.
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