JPWO2018193512A1 - Improvement measure recommendation device, improvement measure recommendation method, and improvement measure recommendation program - Google Patents

Abstract

The improvement measure recommendation device stores work record information and correspondence information in which measures and measure viewpoints are associated with each other in a storage device. The work performance information is a factor of variation that indicates the worker attribute, the work time attribute, the product attribute, and the location attribute in the work performance data that indicates the worker, the product, the work time zone, and the work location in the warehouse for each order. Is associated information. The processor calculates a basic unit including at least one of the work frequency, the work amount, and the unit time for each of the elemental works constituting the warehouse work, based on the work performance information, and for each of the different categories, Based on the calculation result of the same variation factor calculated in step 1, a specific combination that causes a work loss is extracted from the set of combinations of element work, basic unit, and variation factor, and based on the specific combination, a specific combination is extracted. The measure viewpoint is specified, and the correspondence information is referred to determine the measure corresponding to the specific measure viewpoint.

Description

  The present invention relates to an improvement measure recommendation device, an improvement measure recommendation method, and an improvement measure recommendation program.

  At distribution warehouses, distribution centers, and other product supply centers (hereinafter, centers), products manufactured by the manufacturer on the supply side are received, temporarily stored in a predetermined place in the distribution warehouse, and shipped to the shipping destination on the demand side. There is a daily business of collecting (also called picking) the merchandise from a predetermined place in a distribution warehouse according to an order and delivering the merchandise.

  Before the delivery, the center may perform distribution processing such as assembling and decoration of the collected items, or packaging into a shape suitable for delivery such as cardboard. In the distribution warehouse business, it is desirable that more products can be received, stored, and shipped at a lower cost in this arrival → storage → collecting (picking) → distribution processing → packaging → delivery cycle.

  The costs mentioned here include, for example, costs related to space required for storage of goods such as warehouse management costs and land charges, and costs related to labor costs required for goods receipt and shipping operations. In order to improve this cost-effectiveness, daily improvement of various works is necessary.

  In the physical distribution warehouse business, picking work is one of the works that labor costs occupy. The picking work is a work of selecting and collecting each product from a predetermined place in a warehouse based on a work instruction.

  Usually, shelves of a person's height are lined up in a stock room in a warehouse, and a worker picks a required product from the shelves. The shelves are made up of a plurality of shelves, and the shelves are divided into compartments, called small frontages, according to the products to be placed. That is, a plurality of products are placed on one shelf. The worker moves to each shelf, finds a shelf and a frontage on which a desired product is placed, and picks the product. The work order describes which product, how many pieces, and from which place to collect, for which shipping destination, and the picking worker performs the work by looking at this work order.

  Picking carts are often used for collection. The picking cart has a box for sorting the collected products and a screen for displaying work instructions. The worker pushes the cart according to the work instruction displayed on the screen, moves to the target shelf, and repeatedly collects and sorts the target products.

  As one of the means for site improvement, efforts have been made to analyze work performance data, consider improvement measures based on the analysis results, and implement the measures. In such a data analysis-based method, how to derive useful measures from the analysis results is a major point for site improvement.

  Conventionally, in the method of deriving a measure by data analysis, the person in charge of improvement considers the problem to be improved and the hypothesis of the measure for solving the problem in advance, and the person in charge of analysis needs to prove the hypothesis. Data were collected and analyzed. However, in this case, the viewpoint of analysis was limited to the extent of the problem awareness of the person in charge of improvement, and there was a possibility that a truly useful measure could not be derived. In addition, it took a long time to carry out a trial-and-error cycle for hypothesis planning → analysis → confirmation of analysis results, and the efficiency of improvement was not achieved.

  As one of the means for solving this problem, Japanese Patent Laid-Open No. 2004-242242 discloses that an explanatory variable is automatically generated by executing a pre-defined arithmetic operator with respect to data input to an analysis system, and the explanatory variable is correlated with the objective variable. Disclosed is a method of automatically extracting and visualizing an explanatory variable related to an objective variable by comprehensively calculating the relationship.

  Further, in Patent Document 2, based on past improvement cases, business tasks, items for which data should be extracted, and a method of calculating an evaluation value when a measure is implemented are stored as a log, and at the time of new analysis, business tasks are stored. A method of extracting logs having the same improvement effect and a high improvement effect and presenting them to a data analyst will be disclosed.

JP, 2016-31624, A JP, 2014-81750, A

  However, Patent Document 1 only points out the problem of the analysis target, and the improvement person decides the policy derivation. Therefore, the experience and intuition of the person in charge of improvement are necessary for deriving the measure, and the improvement becomes personal. Further, in Patent Document 2, it is still impossible for the field improvement person to consider the measure from the new analysis result because the measure that is not in the past case cannot be extracted. As described above, Patent Documents 1 and 2 have a problem that the measures to be taken are unclear.

  In order to clarify the problem to be solved by the present invention, the definition of “measure” in the present application will be supplemented. The “measure” in the present application refers to a specific action for improvement. On the other hand, the measures described in Patent Document 1 and the like are positioning of problems and focus points in the field. For example, when information such as “the work time of shelf AAA is longer than others” is obtained as the analysis result, this merely points out the problematic point and presents the focus of the measure, and the “measure "Is not applicable.

  In addition, there are many patterns of measures that can be considered from the point of view of such vague problems, and it is difficult to narrow down truly effective measures. The "measures" in the present application are, for the above-mentioned analysis result, "to clarify the display of the front door tag in order to make it easier to find the product on the shelf AAA" or "to shorten the moving distance to the shelf AAA," Change the location of the shelf AAA ”.

  Summarizing the above, the present invention aims to clarify the measures.

  In the improvement measure recommendation device, the improvement measure recommendation method, and the improvement measure recommendation program according to one aspect of the invention disclosed in the present application, a processor that can access a storage device executes the improvement measure recommendation program. The improvement measure recommendation device stores work result information in the storage device and supports measures showing measures for improving work loss, and measures point of view showing the focus of the measures on problems caused by the work loss. The attached correspondence information is stored. The work record information is the work attribute data that classifies the worker in the work record data of the worker, the product, the work time zone, and the work place in the warehouse for each order, and the work that classifies the work time zone. The information is associated with a variation factor of the work indicating at least one of a time zone attribute, a product attribute that classifies the product, and a place attribute that classifies the work place. The processor, based on the work performance information, for each of the divisions of the variation factor, for each element work that constitutes a warehouse work, a basic unit including at least one of the work frequency, the work amount, and the unit time of the work. Based on the calculation process of calculating the calculation factor and the calculation result of the same variation factor calculated in different categories by the calculation process, a work loss occurs from the set of the combination of the elemental work, the basic unit, and the variation factor. The element work, the basic unit, and an extraction process of extracting a specific combination of the variation factors, based on the specific combination extracted by the extraction process, a specific process of identifying a specific measure viewpoint, A decision process of referring to the correspondence information and deciding a measure corresponding to the specific measure viewpoint specified by the specifying process; To run.

  According to the representative embodiment of this invention, it is possible to clarify the measures. Problems, configurations and effects other than those described above will be clarified by the following description of the embodiments.

FIG. 1 is an explanatory diagram showing an example of improvement measure recommendation. FIG. 2 is a block diagram showing a hardware configuration example of the improvement measure recommendation device. FIG. 3 is an explanatory diagram showing an example of the stored contents of the work record table. FIG. 4 is an explanatory diagram showing an example of stored contents of the variation factor master. FIG. 5 is an explanatory diagram showing an example of stored contents of the operation analysis table. FIG. 6 is an explanatory diagram showing an example of stored contents of the measure viewpoint-measure table. FIG. 7 is a block diagram showing a functional configuration example of the improvement measure recommendation device. FIG. 8 is an explanatory diagram showing a first example of generation of a work record table with variation factors. FIG. 9 is an explanatory diagram showing a second example of generating a work result table with variation factors. FIG. 10 is an explanatory diagram showing a third generation example of the work result table with variation factors. FIG. 11 is an explanatory diagram showing a fourth example of generation of the work record table with variation factors. FIG. 12 is an explanatory diagram showing an example of stored contents of the work record table with variation factors. FIG. 13 is an explanatory diagram showing the concept of calculating the work process time. FIG. 14 is an explanatory diagram showing a first generation example of the work process time table. FIG. 15 is an explanatory diagram showing a second generation example of the work process time table. FIG. 16 is a flowchart illustrating an example of a procedure of calculating the work process time by the calculation unit. FIG. 17 is an explanatory diagram showing the concept of extracting work loss factors. FIG. 18 is an explanatory diagram illustrating an example of generating the work loss degree table by the extraction unit. FIG. 19 is an explanatory diagram illustrating an example of the work loss factor table generated by the extraction unit. FIG. 20 is a flowchart illustrating an example of a procedure of a work loss factor table generation process performed by the extraction unit. FIG. 21 is a flowchart showing an example of a procedure for identifying an improvement measure viewpoint. FIG. 22 is a flowchart showing a detailed processing procedure example of the education content determination processing (step S2122) of the relevant worker shown in FIG. FIG. 23 is a flowchart showing a detailed processing procedure example of the education content determination processing (step S2123) of the applicable work environment shown in FIG. FIG. 24 is a flowchart showing a detailed processing procedure example of the workability improvement determination processing (step S2302) of the corresponding work place shown in FIG. FIG. 25 is a flowchart showing a detailed processing procedure example of the handling improvement improvement determination processing (step S2303) of the corresponding object shown in FIG. FIG. 26 is a flowchart showing a detailed processing procedure example of the work schedule improvement determination processing (step S2304) in the relevant time zone shown in FIG. FIG. 27 is a flowchart showing a detailed processing procedure example of the improvement measure viewpoint specifying processing (step S2103) relating to the work instruction shown in FIG. FIG. 28 is an explanatory diagram showing an example of optimizing order allocation.

  In the following examples, when it is necessary for convenience, they are divided into a plurality of sections or embodiments, but unless otherwise specified, they are not unrelated to each other, and one is not There are some or all of the modifications, details, supplementary explanations, and the like. In addition, in the following examples, unless otherwise mentioned, such as the number of elements (including the number, numerical value, amount, range, etc.) of the elements, unless explicitly stated or in principle limited to a specific number, etc. However, the number is not limited to the specific number, and may be greater than or less than the specific number.

  Further, in the following embodiments, it is needless to say that the constituent elements (including element steps and the like) are not necessarily essential, unless otherwise specified or in principle considered to be essential. Yes. Similarly, in the following examples, when referring to the shapes of components and the like, the positional relationship, etc., the shapes, etc. are substantially the same, unless otherwise specified or in principle it is considered that otherwise. And the like, etc. are included. This also applies to the above numerical values and ranges.

<Example of recommendation for improvement measures>
FIG. 1 is an explanatory diagram showing an example of improvement measure recommendation. The improvement measure recommendation device 100 analyzes the work record data 101 in the distribution warehouse, but instead of the analysis result 102 such as “the work time of the shelf AAA is longer than others”, “to make it easier to find the product of the shelf AAA”. , Clarifying the display of the frontage bill, and "changing the location of the shelf AAA in order to shorten the movement distance to the shelf AAA", the work improvement measure 103 is output. The work result data 101 is work result data indicating a worker, a product, a work time zone, and a work place in a warehouse for each order. Further, in the present embodiment, “moving” is, for example, an action in which the worker walks or runs together with a cart for storing the product taken out from the shelf by the worker.

<Example of hardware configuration of improvement measure recommendation device 100>
FIG. 2 is a block diagram showing a hardware configuration example of the improvement measure recommendation device 100. The improvement measure recommending apparatus 100 includes a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF) 205. The processor 201, the storage device 202, the input device 203, the output device 204, and the communication IF 205 are connected by the bus 206. The processor 201 controls the improvement measure recommendation device 100. The storage device 202 serves as a work area of the processor 201. The storage device 202 is a non-temporary or temporary recording medium that stores various programs and data. Examples of the storage device 202 include a ROM (Read Only Memory), a RAM (Random Access Memory), a HDD (Hard Disk Drive), and a flash memory. The input device 203 inputs data. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, and a scanner. The output device 204 outputs data. Examples of the output device 204 include a display and a printer. The communication IF 205 connects to the network 210 and transmits / receives data.

  Further, the communication IF 205 accesses the DB 220 via the network 210 according to an instruction from the processor 201. The DB 220 may be provided in the storage device 202. Further, in FIG. 2, the improvement measure recommendation device 100 is configured by one computer, but the improvement measure recommendation device 100 may be configured by a plurality of computers.

<Various tables>
A group of tables accessible by the improvement measure recommendation device 100 will be described with reference to FIGS. 3 to 6. These table groups are stored in the storage device 202 in the improvement measure recommendation apparatus 100 or in the DB 220 outside the improvement measure recommendation apparatus 100 and are accessible by the processor 201. In the following description of each table, the value of the AA field bbb (AA is a field name, bbb is a code) may be referred to as AAbbb. For example, the value of the order number field 301 is described as the order number 301.

  FIG. 3 is an explanatory diagram showing an example of the stored contents of the work record table. The work record table 300 is a table that stores work record data 101. The work record table 300 includes an order number (denoted as “order No.” in FIG. 3) field 301, a work time field 302, a worker ID field 303, a shelf ID field 304, and a product ID field 305. A shipping destination field 306, an area field 307, a product number field 308, a moving distance field 309, and a working time field 310 are included.

  A combination of the values of the fields 301 to 310 in the same row is an entry that constitutes one work performance data 101. The work performance data 101 is acquired, for example, from the Warehouse Management System (WMS).

  The order number field 301 is a storage area for storing an order number. The order number 301 is an identification number that uniquely identifies an order.

  The work time field 302 is a storage area for storing the work time. The work time 302 is the start time of the order work specified by the order number 301.

  The worker ID field 303 is a storage area for storing a worker ID. The worker ID 303 is identification information that uniquely specifies the worker who performs the order work specified by the order number 301.

  The shelf ID field 304 is a storage area for storing a shelf ID. The shelf ID 304 is identification information that uniquely identifies the shelf that stores the product picked in the ordering work identified by the order number 301.

  The product ID field 305 is a storage area for storing a product ID. The product ID 305 is identification information that uniquely specifies the product ordered in the ordering work specified by the order number 301.

  The shipping destination field 306 is a storage area for storing the shipping destination. The shipping destination 306 is a destination of the product to be shipped by the ordering work specified by the order number 301.

  The area field 307 is a storage area that stores an area. The area 307 is an area in which the product with the product ID 305 is stored. That is, it corresponds to the position of the shelf specified by the shelf ID 304.

  The product number field 308 is a storage area for storing the number of products. The number of products 308 is the number of products ordered in the ordering work specified by the order number 301.

  The moving distance field 309 is a storage area for storing the moving distance. The moving distance 309 is the distance to move to the shipping destination 306.

  The work time field 310 is a storage area for storing work time. The work time 310 is the time required for the order work specified by the order number 301. From the work time 302 and the work time 310, the work time zone in the order 301 is specified.

  In this way, in the work record table 300, when (“work time 302” and “work time 310”), a certain worker (“worker ID 303”) determines which shelf (“shelf ID 304” tp “area 307”) From this, a value indicating what kind of product (“product ID 305”) has been picked up is recorded in the respective fields 301 to 305 and 307.

  In addition, as the incidental information of the person, the time, the place, and the thing, for example, “shipping destination 306” (incidental information of the place) may be recorded together. Further, as work productivity-related information, "work time 310" and "moving distance 309" required for each line and "number of products 308" picked are recorded. The recording timing (entry issue granularity) is the timing of sorting a certain product (“product ID 305”) into a certain order (“order number 301”).

  FIG. 4 is an explanatory diagram showing an example of stored contents of the variation factor master. The variation factor master 400 is a master table that defines variation factors. The variation factor master 400 is a master table created by the user of the improvement measure recommendation device 100. The variation factor master 400 has a 4W field 401, a variation factor field 402, an attribute field 403, and a condition field 404. A combination of the values of the fields 401 to 404 in the same row is an entry that constitutes one piece of variation factor information.

  The 4W field 401 is a storage area for storing 4W. 4W401 is information indicating when (where: working hours), who (who: worker), what (what: product), and where (where: work place), and classifies variation factor information. It becomes an element to do. When, what, and where are collectively called the work environment.

  The variation factor field 402 is a storage area for storing the variation factor. The variation factor 402 is information that defines a specific cause according to the type of the 4W 401 among the causes of variation related to the element work. For example, the causes of variation are four types of attributes (working time zone attribute, worker attribute, product attribute, “working time zone”, “worker experience”, “product weight”, and “area (work place)”. Location attribute). If the 4W 401 is “when”, the variation factor 402 is the “working time zone”.

  The attribute field 403 is a storage area for storing attributes. The attribute 403 is information indicating a section defined from one or more feature groups included in the variation factor 402. For example, when the variation factor 402 is “working time zone”, the attribute 403 is defined as a category from a plurality of features such as “AM”, “PM1”, “PM2”, ....

  The condition field 404 is a storage area for storing conditions. The condition 404 is constraint information that satisfies the variation factor 402 and the attribute 403, or specifies that the variation factor 402 and the attribute 403 are satisfied. For example, when the variation factor 402 is “working time zone” and the attribute 403 is “AM”, the condition 404 is “working time zone ≦ 12: 00”. Further, since the difference cannot be understood only by “PM1” and “PM2” indicating the attribute 403, the condition 404 is used to distinguish and define. For example, the condition 404 of “PM1” is “12:00 <working time zone ≦ 15: 00”, and the condition 404 of “PM2” is “15:00 <working time zone”.

  FIG. 5 is an explanatory diagram showing an example of stored contents of the operation analysis table. The operation analysis table 500 is a table for storing an operation analysis result in which the total work time of the picking work is divided into the work time 310 for each element work. The operation analysis result is obtained by manual operation analysis. The operation analysis is a method used in the field of production engineering, and is a method of observing and tabulating how much time is required for which work out of the total work time.

  The operation analysis table 500 has an element work field 501, a total work time field 502, and a work number field 503. The combination of the values of the fields 501 to 503 in the same row is the entry that constitutes the operation analysis result in the element work.

  The element work field 501 is a storage area for storing the element work that constitutes the warehouse work. The element work 501 is one of the work groups constituting the operation analysis target. In this example, for example, a warehouse work group to be an operation analysis target is configured by three element works 501 of “sorting”, “product search”, and “movement”. “Assortment” is a work action in which an operator sorts products to be sorted. The “product search” is a work action in which the worker searches for products to be sorted. “Move” is a work action in which the worker moves to the storage location (shelf) of the product to be sorted.

  The total work time field 502 is a storage area for storing the total work time. The total work time 502 is the total work time of the element work 501.

  The work count field 503 is a storage area for storing the work count. The work count 503 is the number of times the element work 501 was performed. As for the total work time 502, the total value is stored in the total field 504.

  A plurality of operation analysis tables 500 are provided for each measurement target worker and each measurement time period. In FIG. 5, as an example, the operation analysis table 500A is created for the measurement time zone of the worker whose measurement target is the worker ID 303 = WK01 = 10: 00 to 12:00. Further, the operation analysis table 500B is created for the measurement time zone = 13: 00 to 15:00 of the worker having the worker ID 303 = WK03 to be measured.

  FIG. 6 is an explanatory diagram showing an example of stored contents of the measure viewpoint-measure table. Measure viewpoint-measure table 600 is a table that associates measure viewpoints and measures. The measure point-measure table 600 includes an element work field 501, an intensity unit field 601, a variation factor field 402, a measure point field 602, and a measure field 603. The combination of the values of the fields 501, 402, 601 to 603 in the same row is the entry that constitutes the measure viewpoint and the information relating the measure in the element work 501.

  The basic unit field 601 is a storage area for storing the basic unit. The basic unit 601 is the quantity required for the element work 501, and for example, the unit time, the work amount, and the work frequency of the element work 501.

  The measure viewpoint field 602 is a storage area for storing the measure viewpoint. The measure viewpoint 602 is the focus of the measure regarding the problem due to work loss, and is specified by the element work 501, the basic unit 601, and the variation factor 402. For example, a measure viewpoint 602 regarding a problem such as “the work time of the shelf AAA is longer than other work hours” is “improvement of easiness of finding destination”.

  The measure field 603 is a storage area for storing measures. The measure 603 is a measure for improving work loss, and is specified by the element work 501, the basic unit 601, and the variation factor 402. For example, the measure 603 for the measure viewpoint 602 of “improvement of searchability of destination” is “clarify the display of the front door tag to make it easy to find the product on the shelf AAA” or “moving to the shelf AAA”. Change the location of the shelf AAA in order to shorten the distance ”.

  In FIG. 6, the element work field 501, the basic unit field 601, and the variation factor field 402 are optional. Specifically, for example, when the processor 201 is caused to execute the processes shown in the flowcharts of FIGS. 21 to 27 described later, in the measure viewpoint-measure table 600, the element work field 501, the basic unit field 601, and the variation factor The field 402 is unnecessary. On the other hand, in the case of causing the processor to perform coincidence determination with the element work 501, the basic unit 601, and the variation factor 402 of the work loss factor table, which will be described later, the element work field 501, the basic unit field 601, and the variation factor field 402. Is necessary.

<Example of functional configuration of improvement measure recommendation device 100>
FIG. 7 is a block diagram showing a functional configuration example of the improvement measure recommendation device 100. The improvement measure recommendation device 100 includes a generation unit 701, a calculation unit 702, an extraction unit 703, a specification unit 704, and a determination unit 705. Specifically, the generation unit 701 to the determination unit 705 are functions realized by causing the processor 201 to execute the program stored in the storage device 202 illustrated in FIG. 2, for example.

  The generation unit 701 generates a variation factor-added work record table 711 by adding the variation factor master 400 to the work record table 300. An example of generating the work record table 711 with factor of variation will be described later with reference to FIGS.

  The calculation unit 702 calculates the work process time using the operation analysis table 500 and the work record table 711 with a variation factor generated by the generation unit 701. The work process time is stored in the work process time table 712. An example of calculating the work process time will be described later with reference to FIGS.

  The extraction unit 703 extracts a work loss factor using the work process time table 712 and generates a work loss factor table 713. The work loss factor is a factor of work loss. Work loss is an action or condition that reduces work efficiency. An example of extracting the work loss factor will be described later with reference to FIGS.

  The identifying unit 704 identifies the improvement measure viewpoint based on the work loss factor extracted by the extracting unit 703, and outputs the measure viewpoint list 714. A specific example of the improvement measure viewpoint will be described later with reference to FIGS. 21 to 27.

  The determining unit 705 uses the measure viewpoint-measure table 600 to determine the measure 603 based on the improvement measure viewpoint identified by the identifying unit 704, and outputs the measure list 715. An example of determining the measure 603 will be described later with reference to FIG.

<Example of generating work result table 711 with variation factor>
FIG. 8 is an explanatory diagram showing a first generation example of the work record table 711 with variation factor. Generation example 1 is an example in which a [when] work time zone field 801 corresponding to "when" of 4W401 in the variation factor master 400 is added to each entry of the work record table 300. The [when] work time zone field 801 is a storage area for storing an attribute 403 relating to the work time zone.

  When the [when] work time zone field 801 is added, the generation unit 701 uses an entry in which the 4W401 of the variation factor master 400 is “when”. Specifically, for example, the generation unit 701 obtains a work time zone for each entry in the work record table 300. The start time of the work time zone is the work time 302, and the end time of the work time zone is the time obtained by adding the work time 310 to the work time 302. For each entry in the work record table 300, the generation unit 701 identifies the entry corresponding to the calculated work time zone from the entry in which the 4W 401 of the variation factor master 400 is “when”, and sets the attribute 403 of the identified entry to [when]. ] Add to the work time zone field 801.

  For example, in the case of the entry in the first row of the work record table 300, the order number 301 is “0001”, the work time 302 is “10:01”, and the work time 310 is “60 (seconds)”. Therefore, the working time period is “10:01 to 10:02”. The calculated work time zone “10:01 to 10:02” corresponds to the entry in which the attribute 403 of the variation factor master 400 is “AM” and the condition 404 is “work time zone ≦ 12: 00”. The generation unit 701 adds “AM” of the attribute 403 as a value to the [when] work time zone field 801 of the entry in the first row of the work record table 300.

  FIG. 9 is an explanatory diagram showing a second generation example of the work record table 711 with variation factor. Generation example 2 is an example in which the [who] worker experience field 901 corresponding to "who" of 4W401 in the variation factor master 400 is added to each entry of the work record table 300. [Who] worker experience field 901 is a storage area for storing an attribute 403 relating to the worker experience. The worker experience is information regarding the years of experience of the worker.

  When the [who] worker experience field 901 is added, the generation unit 701 uses an entry in which the 4W 401 of the variation factor master 400 is “who”. Specifically, for example, the generation unit 701 acquires the worker ID 303 from the work record table 300, and specifies, from the variation factor master 400, an entry having a condition 404 including the acquired worker ID 303. Then, the generation unit 701 adds the identified attribute 403 of the variation factor master 400 to the [who] worker experience field 901.

  For example, in the case of the entry in the first row of the work record table 300, the order number 301 is “0001” and the worker ID 303 is “WK01”. The worker ID 303 “WK01” corresponds to the entry having the attribute 403 of the variation factor master 400 of “> 5years” and the condition 404 of “worker ID 303 in [WK01, WK04]”. “> 5years” of attribute 403 is added as a value to the [who] worker experience field 901 of the first row of the work record table 300.

  FIG. 10 is an explanatory diagram showing a third generation example of the work record table 711 with variation factor. Generation Example 3 is an example in which a [what] product weight field 1001 corresponding to “what” of 4W 401 in the variation factor master 400 is added to each entry of the work result table 300. [What] product weight field 1001 is a storage area for storing an attribute 403 relating to product weight.

  When the [what] product weight field 1001 is added, the generation unit 701 uses an entry whose 4W401 of the variation factor master 400 is “what”. Specifically, for example, the generation unit 701 acquires the product ID 305 from the work record table 300, and specifies, from the variation factor master 400, an entry having a condition 404 including the acquired product ID 305. Then, the generation unit 701 adds the identified attribute 403 of the variation factor master 400 to the [what] product weight field 1001.

  For example, in the case of the entry in the first row of the work record table 300, the order number 301 is “0001” and the product ID 305 is “SH03”. The product ID 305 “SH03” corresponds to the entry having the attribute 403 of the variation factor master 400 of “1.5 kg” and the condition 404 of “product ID 305 in [SH02, SH03, SH05]”. "1.5 kg" of the attribute 403 is added as a value to the [what] product weight field 1001 of the first row entry of the work record table 300.

  FIG. 11 is an explanatory diagram showing a fourth generation example of the work record table 711 with variation factor. Generation Example 4 is an example in which a [where] area field 1101 corresponding to “where” of 4W 401 in the variation factor master 400 is added to each entry of the work result table 300. The [where] area field 1101 is a storage area that stores an attribute 403 relating to the area 307.

  When the [where] area field 1101 is added, the generation unit 701 uses an entry in which the 4W 401 of the variation factor master 400 is “where”. Specifically, for example, the generation unit 701 acquires the area 307 from the work record table 300, and specifies, from the variation factor master 400, an entry having a condition 404 that includes the acquired area 307. Then, the generation unit 701 adds the identified attribute 403 of the variation factor master 400 to the [where] area field 1101.

  For example, in the case of the entry in the first row of the work record table 300, the order number 301 is “0001” and the area 307 is “A”. Since the area 307 “A” corresponds to the entry of which the attribute 403 of the variation factor master 400 is “A” and the condition 404 is “Area 307 = A]”, the generation unit 701 sets 1 in the work result table 300. “A” of the attribute 403 is added as a value to the [where] area field 1101 of the entry in the line.

  FIG. 12 is an explanatory diagram showing an example of stored contents of the work record table 711 with variation factor. The work record table 711 with variation factor is generated by the generation examples 1 to 4 illustrated in FIGS. 8 to 11.

<Example of calculating work process time>
FIG. 13 is an explanatory diagram showing the concept of calculating the work process time. The purpose of the work process time calculation is to understand what kind of breakdown the total work time 502 spent for a certain work is. Specifically, for example, (A) total work time 502 is decomposed at each stage. When (A) is decomposed by the number of rows, (B) work time 310 per row is specified. A row is an entry corresponding to the variation factor 402 in the variation-based work record table 711. When (B) is decomposed by element work 501, (C) work time per row 310 is specified. When (C) is decomposed into basic units 601, (D1) sorting time, (D2) product search time, and (D3) moving time are specified.

  That is, it is possible to clarify which breakdown portion is the target of improvement. For example, in (D3), if the moving distance 309 is longer than others, the measure 603 for reducing the moving distance 309 is necessary, and conversely, if the unit time of movement is long, the measure 603 for increasing the moving speed is necessary. On the contrary, without such decomposition, the problem would be a vague problem such as "shortening of the work time 310".

  FIG. 14 is an explanatory diagram showing a first generation example of the work process time table 712. In the first generation example of the work process time table 712, the calculation unit 702 uses the operation analysis table 500A. The operation analysis table 500A shows the operation analysis results under the operation analysis conditions in which the worker ID 303 to be measured is "WK01" and the measurement time zone is "10:00 to 12:00".

  FIG. 15 is an explanatory diagram showing a second generation example of the work process time table 712. In the second generation example of the work process time table 712, the calculation unit 702 uses the operation analysis table 500B. The operation analysis table 500B shows the operation analysis results under the operation analysis conditions in which the measurement target worker ID 303 is “WK03” and the measurement time zone is “13:00 to 15:00”.

  In the first and second generation examples of the work process time table 712, the calculation unit 702 identifies an entry corresponding to the operation analysis condition from the work result table with variation factor 711. In this case, in FIGS. 14 and 15, it is assumed that the entry surrounded by the thick line frame is specified in the work record table 711 with variation factor. Each of the identified entries indicates a sorting operation. Therefore, the number of identified entries matches the number of work 503 in which the element work 501 is “sorting”.

  The work process time table 712 includes an element work field 501, a total work time field 502, a time per row field 1401, a work frequency field 1402, a time per time field 1403, a work amount field 1404, and a unit time field 1405. And have. The element work field 501 and the total work time field 502 are duplicated from the operation analysis table 500.

  The time per line field 1401 is a storage area that stores the time per line. The time per line 1401 indicates the total work time 502 for each element work 501 as the number of entries in the work record table 711 with variation factors corresponding to the operation analysis conditions (in the above example, the number of entries is 1000 in the generation example 1, In Example 2, it is the time divided by the number of entries (550). That is, the time per row 1401 is an average work time per one element work 501 under the operation analysis conditions.

  The work frequency field 1402 is a storage area for storing the work frequency. The work frequency 1402 is the number of times that the work count 503 is divided by the number of entries in the work record table 711 with variation factor corresponding to the above-described operation analysis conditions for each element work 501. That is, the work frequency 1402 is the average number of works per work under the operation analysis conditions.

  Since the number of entries in the work record table 711 with variation factors that corresponds to the operation analysis conditions matches the number of work times 503 in which the element work 501 is a classification, the work frequency 1402 in which the element work 501 is a classification is “1. 00 ”.

  The per time field 1403 is a storage area for storing the per time. The time per operation 1403 is a time obtained by dividing the total work time 502 for each element work 501 by the number of work times 503. That is, the per-work time 1403 is an average work time per work under each operation analysis condition for each element work 501.

  The work amount field 1404 is a storage area for storing the work amount. The work amount 1404 is an average value for the target of the element work 501. For example, when the elemental work 501 is sorting and product search, the work amount 1404 is the variation that corresponds to the above-mentioned operation analysis condition, and the total value of the number of products 308 in the work record table 711 with variation factor that corresponds to the operation analysis condition. It is a value divided by the number of entries in the work record with factor table 711. That is, when the element work 501 is sorting and product search, the work amount 1404 is the average number of products per work under the operation analysis conditions.

  Further, when the elemental work 501 is movement, the work amount 1404 is the work with variation factor corresponding to the above-described operation analysis condition, which is the total value of the movement distance 309 in the work result table 711 with variation factor corresponding to the operation analysis condition. It is a value divided by the number of entries in the result table 711. That is, the work amount 1404 when the element work 501 is a movement is an average movement distance per work under the operation analysis conditions.

  The unit time field 1405 is a storage area for storing unit time. The unit time 1405 is a value obtained by dividing the time 1403 per operation by the work amount 1404. That is, the unit time 1405 is the work time 310 per one work amount (one product when the element work 501 is sorting and product search, one moving product when moving, and a moving distance of 1 [m]) in one element work 501. Is.

  FIG. 16 is a flowchart showing an example of a procedure of calculating the work process time by the calculation unit 702. The operation process conditions described above (for example, the measurement target worker ID 303 is “WK01” and the measurement time period is “10:00 to 12:00”) are the calculation processes of the work process time illustrated in the flowchart of FIG. It is executed every time.

  First, the calculation unit 702 reads the operation analysis table 500 and the work record table 711 with variation factor, and extracts the target data of the operation analysis from the work record table 711 with variation factor (step S1601). The work analysis target data is work with variation factors that satisfy the above-described work analysis conditions (for example, the measurement target worker ID 303 is “WK01” and the measurement time zone is “10:00 to 12:00”). It is an entry of the result table 711.

  The calculation unit 702 uses the target data extracted in step S1601, and uses time per row 1401 (step S1602), work frequency 1402 (step S1603), time per time 1403 (step S1604), work amount 1404 (step S1605), And the unit time 1405 (step S1606) is calculated.

  Then, the calculation unit 702 outputs the calculation results of steps S1602 to S1606 to the work process time table 712 (step S1607). As a result, the work process time table 712 is generated as shown in FIGS. 14 and 15.

<Example of extracting work loss factors>
FIG. 17 is an explanatory diagram showing the concept of extracting work loss factors. Here, the (D3) movement time shown in FIG. 13 will be described as an example. The purpose of the work loss factor extraction is to clarify which combination of [element work 501, basic unit 601, variation factor 402] is a loss factor, that is, an improvement target. In the example of FIG. 13, the values of the element work 501 = “moving” and the basic unit 601 = “unit time 1405” are large depending on the (D3) moving time, and the measure 603 for increasing the moving speed is narrowed down.

  In the example of FIG. 17, the target of improvement is clarified by comparing the (D3) movement time with a plurality of variation factors 402. The reason for evaluating the variation is that the following patterns are considered to hold.

  (Pattern) If a worker of a certain level works in a certain work environment (place, product, time zone, etc.) according to a work instruction carefully scrutinized to a certain level, the work time 310, the work amount 1404 and the work frequency 1402 uniformly fall within ideal values, but when some condition 404 deviates from the “certain level”, the work time 310, the work amount 1404, and the work frequency 1402 of the corresponding condition 404 are large. And, as a result, variations occur.

  For example, when there is a variation in the worker experience of the worker as in the example 1701 of FIG. 17, the worker is the cause of the large work time 310, so the corresponding work for increasing the moving speed is performed. Measures 603 for the education of persons are necessary. On the other hand, when there is a variation in location, as in Example 1702, the cause of the long working time 310 is the location, so a measure 603 of easiness of moving the location to increase the moving speed is provided. It can be narrowed down as needed. In this way, by clarifying which combination of [element work 501, basic unit 601, variation factor 402] has a problem, a measure 603 having a clear action can be derived.

  FIG. 18 is an explanatory diagram showing an example of generation of the work loss degree table 1800 by the extraction unit 703. The extraction unit 703 creates the work loss degree table 1800 using the work process time table 712. The work loss degree table 1800 is a table that defines the work loss degree for the element work 501 (may be simply referred to as “loss degree”).

  The work loss degree table 1800 has an element work field 501, a basic unit field 601, a variation factor field 402, and a work loss degree field 1801 (in FIG. 18, simply referred to as “loss degree”). The work loss degree field 1801 is a storage area for storing the work loss degree. The work loss degree 1801 is a value indicating the degree of work loss, and the work loss is an action or state in which work efficiency is reduced. Specifically, for example, the work loss degree 1801 is specified by the element work 501 and the basic unit 601 (work frequency 1402, work amount 1404, or unit time 1405) in the work process time table 712 obtained from a certain variation factor 402. It is a value that indicates the variation in the actual value that is set, and is, for example, the variance or the standard deviation.

  If there is no problematic attribute 403 in the variation factor 402 of interest, the variation is small because all of the attributes 403 fall within a fixed reference value. On the contrary, if there is a problematic attribute 403, the value of the problematic attribute 403 becomes large, and the variation becomes large. The work loss degree 1801 is a value focused on this point.

  An example of calculating the work loss degree 1801 will be specifically described. Here, six types of work process time tables 712A to 712F are used. The work process time tables 712A and 712B are as shown in FIGS. 14 and 15. The work process time table 712C is a work process time table 712 obtained from an entry corresponding to “> 5years” in the [who] worker experience 901 that is the fluctuation factor 402 in the work result table with fluctuation factor 711. The work process time tables 712D to 712F are obtained from entries corresponding to “A”, “B”, and “C” in the [where] area 1101 that is the variation factor 402 in the work result table 711 with variation factor. It is a work process time table 712.

  The work loss degree 1801 indicates the variation of the values specified by the same element work 501 and the same basic unit 601 in the work process time table 712 having the same variation factor 402. For example, when the variation factor 402 is [who] operator experience 901, the work process time tables 712A to 712C are applied. Here, when the elemental work 501 is “product search” and the basic unit 601 is “unit time 1405”, the extraction unit 703 causes the extraction unit 703 to select the actual value of the cell indicated by the thick frame of the work process time tables 712A to 712C (2. 0 [sec / piece], 1.8 [sec / piece], 5.0 [sec / piece]) are specified. Then, the extraction unit 703 calculates a value (for example, variance or standard deviation) indicating the variation in the actual value of the specified cell. The calculated value becomes “4.5” which is the work loss degree 1801 of the element work 501 “product search”, the basic unit 601 “unit time 1405”, and the variation factor 402 “worker experience” (work loss degree table 1800 thick line box entry).

  Similarly, when the variation factor 402 is the [where] area 1101, the work process time tables 712D to 712F are applied. Here, when the element work 501 is “move” and the basic unit 601 is “work frequency 1402”, the extraction unit 703 determines that the actual value of the cell (0. 52 [times / line], 0.28 [times / line], 0.31 [times / line]). Then, the extraction unit 703 calculates a value (for example, variance or standard deviation) indicating the variation in the actual value of the specified cell. The calculated value becomes “3.9” which is the work loss degree 1801 of the element work 501 “movement”, the basic unit 601 “work frequency 1402”, and the variation factor 402 “area 307” (of the work loss degree table 1800). Bold dotted line entry).

  As the work loss degree 1801 becomes higher, there is a higher possibility that there is a problem in the combination of the element work 501, the basic unit 601, and the variation factor 402 that make up the entry.

  FIG. 19 is an explanatory diagram showing an example of generation of the work loss factor table 713 by the extraction unit 703. The work loss factor table 713 is a table that defines work loss factors. The work loss factor is an entry including the problematic work loss degree 1801 among the entries of the work loss degree table 1800 defined by the combination of the element work 501, the basic unit 601, the variation factor 402, and the work loss degree 1801. is there. The problematic work loss degree 1801 is a work loss degree 1801 of the work loss degree 1801 or more, which is equal to or higher than a preset threshold value, or the highest N (N is an integer of 1 or more) in relative descending order. Is.

  The work loss factor table 713 is a table in which a loss attribute field 1901 is added to the work loss degree table 1800. The loss attribute field 1901 is a storage area for storing the loss attribute 1901. The loss attribute 1901 is the value of the variation factor field 402 having the worst value among the respective values from which the work loss degree 1801 specified by the work loss factor is calculated.

  For example, when the entry in the thick line frame of the work loss degree table 1800 is the work loss factor, the actual value (2.0 [sec / piece]) of the cell indicated by the thick line frame of the work process time tables 712A to 712C in FIG. From 1.8 [sec / piece] and 5.0 [sec / piece]), the worst value is 5.0 [sec / piece]. Then, the extraction unit 703 identifies the value “<1year” of the [who] operator experience 901 that is the variation factor 402 from the work process time table 712C having the identified actual value (5.0 [sec / piece]). Then, it is registered in the loss attribute field 1901.

  Similarly, when the entry in the bold dotted line frame of the work loss degree table 1800 is the work loss factor, the actual value of the cell (0.52 [times / time / Line], 0.28 [times / line], 0.31 [times / line]), and the worst value is 0.52 [times / line]. Then, the extraction unit 703 identifies the value “A” in the [where] area 1101 that is the variation factor 402 from the work process time table 712D that has the identified actual value (0.52 [times / line]), It is registered in the loss attribute field 1901.

  FIG. 20 is a flowchart showing an example of the processing procedure of generating the work loss factor table 713 by the extraction unit 703. The extraction unit 703 selects one combination of the element work 501, the basic unit 601, and the variation factor 402 one by one, and repeatedly executes steps S2002 and S2003 for all combinations (step S2001). As described with reference to FIG. 18, the extraction unit 703 refers to the work process time table 712 for each combination of the element work 501, the basic unit 601, and the variation factor 402 selected in step S2001, and refers to each group of the variation factor 402. The work loss degree 1801 is calculated for the actual value of (step S2002). The extraction unit 703 stores the calculated work loss degree 1801 in the entry of the combination of the element work 501, the basic unit 601, and the variation factor 402 selected in step S2001 (step S2003).

  When the calculation (step S2002) and storage (step S2003) of the work loss degree 1801 are completed for all the combinations, the extraction unit 703 extracts an entry having a high work loss degree 1801 from the work loss degree table 1800 (step S2004), Stored in the work loss factor table 713. Then, the extraction unit 703 registers the loss attribute 1901 for each entry of the work loss factor table 713 as described with reference to FIG. 19 (step S2006). As a result, the work loss factor table 713 is created.

<Specific example of viewpoint of improvement measures>
Next, a specific example of the improvement measure viewpoint by the specifying unit 704 will be described with reference to FIGS. 21 to 27. The identifying unit 704 identifies, for each combination of the element work 501, the basic unit 601, and the variation factor 402, an improvement measure viewpoint from the combination. The flowcharts of FIGS. 21 to 27 show an example of a processing procedure in which the identifying unit 704 identifies the improvement measure viewpoint for a certain combination.

  FIG. 21 is a flowchart showing an example of a procedure for identifying an improvement measure viewpoint. The identifying unit 704 determines whether the basic unit 601 is the unit time 1405 for the combination of the element work 501, the basic unit 601, and the variation factor 402 of the entry selected from the loss attribute 1901 table (hereinafter, selected combination). It is determined whether the quantity is 1404 or the work frequency 1402 (step S2101). When the basic unit 601 is the unit time 1405 (step S2101: the unit time 1405), the process proceeds to the improvement measure viewpoint specifying process (step S2102) regarding the work efficiency, and when the basic unit 601 is the work amount 1404 or the work frequency 1402 ( Step S2101: Work amount 1404 or work frequency 1402), and shift is made to improvement measure viewpoint specifying processing (step S2103) regarding work instructions (FIG. 27).

  In the improvement measure viewpoint specifying process regarding work efficiency (step S2102), the specifying unit 704 determines whether the variation factor 402 is who, where, where or what for the selected combination in which the basic unit 601 is the unit time 1405. Is determined (step S2121). When the variation factor 402 is who (step S2121: who), the identifying unit 704 executes the education content determination process (step S2122) of the corresponding worker (FIG. 22). On the other hand, when the variation factor 402 is when, where or what (step S2121: where, where or what), the identifying unit 704 executes the improvement content determination process (step S2123) of the work environment (FIG. 23).

  After steps S2103, S2122, and S2123, the identifying unit 704 adds the loss attribute 1901 of the selected combination to the character string data indicating the improvement measure viewpoint obtained from steps S2103, S2122, and S2123 (step S2104). As a result, the specifying unit 704 ends the improvement measure viewpoint specifying process for the selected combination.

  FIG. 22 is a flowchart showing a detailed processing procedure example of the education content determination processing (step S2122) of the relevant worker shown in FIG. The identifying unit 704 determines whether the element work 501 corresponds to sorting, product search, or movement for a selected combination in which the basic unit 601 is the unit time 1405 and the variation factor 402 is who. Step S2201). When the elemental work 501 corresponds to sorting (step S2201: sorting), the identifying unit 704 determines the education content of the corresponding worker to be “training method / procedure education” (step S2202), and The education content determination process (step S2122) is ended.

  When the element work 501 corresponds to product search (step S2201: product search), the identifying unit 704 determines the education content of the worker to be “product search method / procedure education” (step S2203). Then, the education content determination process (step S2122) of the worker is finished.

  If the elemental work 501 corresponds to movement (step S2201: movement), the identifying unit 704 determines that the education content of the relevant worker is “education of moving method / procedure” (step S2204), and the corresponding work is performed. The education content determination process (step S2122) of the person is completed.

  FIG. 23 is a flowchart showing a detailed processing procedure example of the improvement content determination processing (step S2123) of the relevant work environment shown in FIG. The identifying unit 704 determines whether the variation factor 402 corresponds to where, where or what in the selected combination in which the basic unit 601 is the unit time 1405 and the variation factor 402 is when, where or what. (Step S2301). When the variation factor 402 is where (step S2301: where), the identifying unit 704 determines the work content improvement determination process (step S2302) of the work place as the work content improvement of the work place. After executing (FIG. 24), the improvement content determination process (step S2123) of the relevant work environment is ended.

  Further, when the variation factor 402 is what (step S2301: what), the identifying unit 704 determines the improvement of the work environment to be the ease of handling the relevant object and determines the handling of the relevant object (step S2303). ) Is executed (FIG. 25), and the improvement content determination process (step S2123) of the relevant work environment is ended.

  If the variation factor 402 is when (step S2301: when), the identifying unit 704 sets the work schedule improvement determination process for the corresponding time zone as the work schedule improvement for the corresponding time zone based on the improvement content of the corresponding work environment (step S2304). ) Is executed (FIG. 26), and the improvement content determination process (step S2123) of the relevant work environment is ended.

  FIG. 24 is a flowchart showing a detailed processing procedure example of the workability improvement determination processing (step S2302) of the corresponding work place shown in FIG. The identifying unit 704 determines whether the element work 501 corresponds to sorting, product search, or movement for a selected combination in which the basic unit 601 is the unit time 1405 and the variation factor 402 is where ( Step S2401). When the element work 501 corresponds to sorting (step S2401: sorting), the identifying unit 704 determines the workability improvement content of the corresponding work place to be “improvement in product pick up at the corresponding place” (step S2402), The workability improvement determination process (step S2302) of the corresponding work place is ended.

  When the element work 501 corresponds to product search (step S2401: product search), the identifying unit 704 determines the workability improvement content of the work place to be “improvement of product searchability of the corresponding place” ( Step S2403), and the workability improvement determination process (step S2302) of the corresponding work place is ended.

  When the element work 501 corresponds to movement (step S2401: movement), the identifying unit 704 determines the workability improvement content of the work place to be “improvement in searchability of corresponding movement destination” (step S2404). ), The workability improvement determination process (step S2302) of the corresponding work place is ended.

  FIG. 25 is a flowchart showing a detailed processing procedure example of the handling improvement improvement determination processing (step S2303) of the corresponding object shown in FIG. The identifying unit 704 determines whether the element work 501 corresponds to sorting, product search, or movement for a selected combination in which the basic unit 601 is the unit time 1405 and the variation factor 402 is what ( Step S2501). When the elemental work 501 corresponds to sorting (step S2501: sorting), the identifying unit 704 determines that the handling improvement content of the corresponding item is “improvement of handling ease of corresponding product” (step S2502), and The handling improvement improvement determination process (step S2303) is ended.

  Further, when the element work 501 corresponds to product search (step S2501: product search), the identifying unit 704 determines that the handling improvement content of the corresponding product is “improvement in searchability of corresponding product” (step S2501). (S2503), the handling easiness improvement determination process (step S2303) of the corresponding object is terminated.

  Further, when the element work 501 corresponds to movement (step S2501: movement), the identifying unit 704 determines that there is no measure (improvement of shipping tendency) as the handling improvement improvement content of the corresponding object (step S2504). ), The handling improvement improvement determination processing (step S2303) of the corresponding object is terminated.

  FIG. 26 is a flowchart showing a detailed processing procedure example of the work schedule improvement determination processing (step S2304) in the relevant time zone shown in FIG. The identifying unit 704 determines whether the element work 501 corresponds to sorting, product search, or movement for a selected combination in which the basic unit 601 is the unit time 1405 and the variation factor 402 is when ( Step S2601). When the element work 501 corresponds to sorting (step S2601: sorting), the identifying unit 704 determines the work schedule improvement content in the relevant time zone to be “obstacle removal during sorting” (step S2602), and The work schedule improvement determination process for the belt (step S2304) ends.

  Further, when the element work 501 corresponds to the product search (step S2601: product search), the identifying unit 704 determines the work schedule improvement content in the corresponding time zone as “obstacle removal at the time of search” (step S2603). ), And the work schedule improvement determination process (step S2304) in the relevant time period is ended.

  If the elemental work 501 corresponds to movement (step S2601: movement), the identifying unit 704 determines that the work schedule improvement content in the relevant time zone is “improvement of work fatigue” (step S2604), and the corresponding time is reduced. The work schedule improvement determination process for the belt (step S2304) ends.

  Further, regardless of whether the elemental work 501 corresponds to any of sorting, product search, and movement, none of sorting, product search, and movement may be selected by a user operation (step S2605: non-selection). ). In this case, the identifying unit 704 determines the work schedule improvement content in the relevant time period as “improvement in work fatigue” (step S2605), and ends the work schedule improvement determination process in the relevant time period (step S2304).

  FIG. 27 is a flowchart showing a detailed processing procedure example of the improvement measure viewpoint specifying processing (step S2103) relating to the work instruction shown in FIG. The identifying unit 704 determines whether the variation factor 402 corresponds to who, where, where or what for the selected combination in which the basic unit 601 is the work amount 1404 or the work frequency 1402 (step S2701). When the variation factor 402 is who (step S2701: who), the identifying unit 704 determines the improvement measure viewpoint regarding the work instruction to be “adequate worker allocation” (step S2702), and the improvement measure regarding the work instruction. The viewpoint specifying process (step S2103) ends.

  If the variation factor 402 is where (step S2701: where), the identifying unit 704 determines whether the element work 501 is sorting or product search or movement (step S2703). When the element work 501 is sorting or product search (step S2703: sorting or product search), the identifying unit 704 determines the improvement measure viewpoint regarding the work instruction to be “no measure” (influence of shipping tendency) (step S2703). (S2704), the improvement measure viewpoint specifying process regarding the work instruction (step S2103) is ended.

  On the other hand, when the elemental work 501 is a move (step S2703: move), the identifying unit 704 determines that the improvement measure viewpoint regarding the work instruction is “adequate product placement” (step S2705), and improves the work instruction. The policy viewpoint specifying process (step S2103) ends.

  If the variation factor 402 is what (step S2701: what), the identifying unit 704 determines whether the element work 501 is sorting, product search, or movement (step S2706). When the element work 501 is sorting or product search (step S2706: sorting or product search), the identifying unit 704 determines the improvement measure viewpoint regarding the work instruction to be “no measure” (influence of shipping tendency) (step S2706). (S2707), the improvement measure viewpoint specifying process related to the work instruction (step S2103) is ended.

  On the other hand, when the element work 501 is a move (step S2706: move), the identifying unit 704 determines that the improvement measure viewpoint regarding the work instruction is “adequate order allocation” (step S2708), and improves the work instruction. The policy viewpoint specifying process (step S2103) ends.

  In addition, "optimization of order allocation" is an improvement in which the movement distance 309 of one work is shortened by exchanging the order of one work and the order of the other work in two picking works. ..

  FIG. 28 is an explanatory diagram showing an example of optimizing order allocation. (A) shows a state before the optimization, and (B) shows a state after the optimization. In (A), the worker WK01 performs the work of orders 0011 to 0013, and the worker WK02 performs the work of orders 0014 to 0016. Each of the orders 0011 to 0016 includes products SH11 to SH22 to be taken out (the number is omitted for simplification of description). Here, the worker WK01's order 0013 includes the products SH16 of the orders 0015 and 0016 handled by the worker WK02, and the worker WK02's order 0014 includes the orders 0011 and 0012 handled by the worker WK01. The products SH11 and SH14 are included.

  In this case, the order allocation is optimized by exchanging the order 0013 and the order 0014 as in (B). Specifically, the movement distance 309 of the worker WK01 becomes shorter when the worker WK01 processes the order 0014. That is, the worker WK01 can pick the commodities SH11 of the orders 0011, 0012, and 0014 from the shelves in which the commodities SH11 are stored, and from the shelf in which the commodities SH14 are stored, the commodities of the orders 0012 and 0014. The SH14 can be picked. Therefore, the worker WK01 does not need to move to the shelf where the products SH16 to SH18 of the order 0013 are stored.

  Similarly, when the worker WK02 processes the order 0013, the movement distance 309 of the worker WK02 becomes shorter. That is, the worker WK02 can pick the products SH16 of the orders 0013, 0015, and 0016 from the shelf in which the products SH16 are stored. Therefore, the worker WK01 does not need to move to the shelf where the products SH11 and SH14 of the order 0014 are stored.

  Returning to FIG. 27, when the variation factor 402 is when (step S2701: when), the identifying unit 704 determines whether the element work 501 is sorting or product search or movement (step S2709). .. When the element work 501 is sorting or product search (step S2709: sorting or product search), the identifying unit 704 determines the improvement measure viewpoint regarding the work instruction to be “efficiency of ordering unit” (step S2710), The improvement measure viewpoint specifying process (step S2103) relating to the work instruction is ended. The ordering unit is a unit indicating an ordering amount, and is, for example, a dozen.

  On the other hand, when the element work 501 is a move (step S2706: move), the identifying unit 704 determines the improvement measure viewpoint regarding the work instruction to be “no measure” (influence of shipping tendency) (step S2711), The improvement measure viewpoint specifying process (step S2103) related to the instruction ends.

<Determination example of measure 603>
An example of determining the measure 603 by the determining unit 705 will be described with reference to FIG. In FIG. 6, the measure viewpoint list 714 is data indicating a list of improvement measure viewpoints to which the loss attribute 1901 is added by the identifying unit 704. The determination unit 705 uses the measure viewpoint-measure table 600 to determine the measure 603 based on the improvement measure viewpoint identified by the identifying unit 704. Specifically, for example, the determining unit 705 identifies a policy viewpoint that matches each improvement policy viewpoint in the policy viewpoint list 714 from the policy viewpoints in the policy viewpoint-measure table 600, and associates with the specified policy viewpoint (same). The measure 603 (which is an entry) is specified from the measure viewpoint of the measure viewpoint-measure table 600. Then, the determining unit 705 incorporates the loss attribute 1901 given to the improvement measure viewpoint into the specified measure viewpoint. Specifically, for example, the determining unit 705 inserts the loss attribute 1901 into the character string “XX” of the measure 603.

  For example, in the case of viewpoint 1 in the policy viewpoint list 714, "education of product search method / procedure" is specified as the policy viewpoint, and as the corresponding measure 603, "product search method of XX (mark of search, procedure of line of sight). Confirm etc) '' is specified. The determining unit 705 defines the loss attribute 1901 “worker experience = less than one year” of viewpoint 1 as the measure 603 “check the product search method of XX (mark of search, procedure etc of eye line)” of “○”. "○" is inserted as a specific measure 603 to generate "confirm product search method for" worker experience = less than one year "(search mark, eye line procedure etc)". As a result, the determination unit 705 outputs the measure list 715 including the specific measure 603 to the output device 204.

  As described above, the improvement measure recommendation device 100 according to the present exemplary embodiment includes, in the storage device, a work result table 711 with a variation factor as work result information, a measure 603 indicating a measure for improving work loss, and a measure viewpoint indicating a measure point. A measure viewpoint-measure table 600 is stored as correspondence information in which 602 is associated with each other. The work result information classifies the worker attribute and work time period for classifying the worker into the work result data for each order, which product was taken out from which place in the warehouse by which worker in which work time period. This is information associated with a work variation factor 402 indicating at least one of a work time zone attribute, a product attribute for classifying a product, and a place attribute for classifying a place in a warehouse.

  Based on the work record information, the processor 201 sets at least one of the work frequency, the work amount, and the unit time for each element work 501 constituting the warehouse work for each classification (attribute 403) of the variation factor 402. The calculation process of calculating the basic unit 601 including the calculation is executed by the calculation unit 702.

  The processor 201, based on the calculation result (work process time table 712 (712A to 712C, 712D to 712F)) of the same variation factor calculated in different categories by the calculation process, the element work 501, the basic unit 601, and the variation. The extraction unit 703 executes an extraction process of extracting a specific combination of the element work 501, the basic unit 601, and the variation factor 402 that cause a work loss from the set of combinations of the factors 402.

  The processor 201 executes, as the specifying unit 704, a specifying process for specifying the specific measure viewpoint 602 based on the specific combination extracted by the extracting process.

  The processor 201 refers to the correspondence information and executes, as the determination unit 705, a determination process of determining the measure 603 corresponding to the specific measure viewpoint specified by the specifying process.

  As a result, the improvement measure recommendation device 100 can clarify the measure 603, and can present the measure 603 whose action for improvement is clear, instead of pointing out the problem. Therefore, the decision process of the measure 603 can be simplified and the speed of on-site improvement can be improved. Further, the on-site improvement does not belong to an individual person, and even a person in charge of improvement who has little experience in on-site improvement can implement the on-site improvement. Further, it is possible to present a new measure 603 in addition to referring to past measure cases.

  The correspondence information described above is information that associates a measure 603 indicating a measure for improvement with respect to an undetermined improvement target (“XX” in the measure 603) with a measure viewpoint 602 indicating a measure viewpoint. In the extraction process, the processor 201 associates a specific combination (loss attribute 1901), which causes a work loss, among different classifications (attribute 403) with a specific combination. In the determination processing, the processor 201 sets the specific category (loss attribute 1901) associated with the specific combination for the measure 603 corresponding to the specific measure viewpoint 602 as an undecided improvement target, so that the improvement target is determined. The defined measure 603 is output.

  Thereby, the improvement target can be clearly specified, and the measure 603 having a clear improvement action can be presented.

  In the extraction process, the processor 201 calculates the work loss degree 1801 indicating the degree of work loss in the combination based on the calculation result, and extracts the specific combination based on the work loss degree 1801.

  As a result, the improvement measure recommendation device 100 can objectively determine which combination is likely to cause a work loss, and improve the accuracy of determining the measure 603.

  Further, in the specific processing, when the basic unit 601 forming the specific combination is the work amount or the work frequency of the element work 501, the processor 201 determines the measure viewpoint 602 corresponding to the specific combination to improve the work instruction. Yes (step S2103). Thereby, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the improvement of the work instruction and realize the corresponding measure 603.

  Further, in the specific processing, when the basic unit 601 forming the specific combination is the unit time of the element work 501, the processor 201 determines the measure viewpoint 602 corresponding to the specific combination to improve the work efficiency (step). S2102). As a result, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the improvement of the work efficiency and realize the corresponding measure 603.

  Further, in the identification processing, the processor 201 identifies the content related to the improvement of the work instruction (step S2103) based on the variation factor 402 that constitutes the particular combination (steps S2701 to S2711). As a result, the improvement policy recommending apparatus 100 can specifically optimize the policy 603 related to the work instruction and improve efficiency.

  Further, in the specific processing, when the variation factor 402 forming the specific combination is the worker attribute, the processor 201 determines to improve the work efficiency (step S2102) as education of the corresponding worker (step S2122). As a result, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the education of the corresponding worker and realize the corresponding measure 603.

  Further, in the specific processing, when the variation factor 402 forming the specific combination is the work time zone attribute, the product attribute, or the location attribute (work environment attribute), the processor 201 improves the work efficiency (step S2102). , And decides to improve the work environment (step S2123). As a result, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the improvement of the work environment and realize the corresponding measure 603.

  Further, in the specific processing, when the variation factor 402 that constitutes a specific combination is a worker attribute, the processor 201 educates the relevant worker who is the measure viewpoint 602 based on the element work 501 that constitutes the specific combination. The content related to (step S2122) is specified (steps S2202 to S2204). As a result, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the contents related to the education of the corresponding worker and realize the corresponding measure 603.

  Further, in the specific processing, when the variation factor configuring the specific combination is the location attribute, the processor 201 determines that the improvement of the relevant work environment (step S2123) is the workability improvement of the relevant work location (step S2302). .. As a result, the improvement measure recommendation device 100 can narrow down the measure viewpoint 602 to the workability improvement of the corresponding work place and realize the corresponding measure 603.

  Further, in the specific processing, when the variation factor that constitutes the specific combination is the product attribute, the processor 201 determines that the improvement of the corresponding work environment (step S2123) is the improvement of the easiness of handling the corresponding object (step S2303). ). As a result, the improvement policy recommending apparatus 100 can narrow down the measure viewpoint 602 to the improvement of the easiness of handling the corresponding object and realize the corresponding measure 603.

  Further, in the specific processing, when the variation factor that constitutes the specific combination is the work time zone attribute, the processor 201 determines that the improvement of the work environment (step S2123) is the improvement of the work schedule of the corresponding time zone (step S2123). Step S2304). As a result, the improvement policy recommending apparatus 100 can narrow down the measure viewpoint 602 to the improvement of the work schedule in the corresponding time zone and realize the corresponding measure 603.

  The present invention is not limited to the above-described embodiments, but includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all the configurations described. Further, part of the configuration of one embodiment may be replaced with the configuration of another embodiment. Further, the configuration of another embodiment may be added to the configuration of one embodiment. Moreover, you may add, delete, or replace another structure with respect to a part of structure of each Example.

  Further, the above-described respective configurations, functions, processing units, processing means, etc. may be realized by hardware, for example, by designing a part or all of them with an integrated circuit, and a processor realizes each function. It may be realized by software by interpreting and executing the program.

  Information such as programs, tables, and files for realizing each function is recorded in a memory, a hard disk, a storage device such as SSD (Solid State Drive), or an IC (Integrated Circuit) card, an SD card, a DVD (Digital Versatile Disc). It can be stored on a medium.

  Further, the control lines and the information lines are shown to be necessary for explanation, and not all the control lines and the information lines necessary for mounting are shown. In reality, it can be considered that almost all configurations are connected to each other.

Claims (14)

An improvement measure recommendation device comprising: a processor that executes a program; and a storage device that stores the program,
The storage device stores work record information, and stores correspondence information in which a measure indicating a measure for improving work loss and a measure viewpoint indicating a focus of the measure on a problem caused by the work loss are associated with each other. And
The work record information is the work attribute data for classifying the worker, the work for classifying the work time period, into the work result data indicating the worker, the product, the work time period, and the work place in the warehouse for each order. It is information in which a work variation factor is associated with at least one attribute of a time zone attribute, a product attribute that classifies the product, and a location attribute that classifies the work place,
The processor is
Calculation for calculating each basic factor including at least one of the frequency of work, the amount of work, and the unit time for each elemental work that constitutes a warehouse work for each of the categories of the variation factor based on the work performance information Processing and
Based on the calculation result of the same variation factor calculated in different categories by the calculation process, the element work, the basic unit, and the set of combinations of the variation factors, the element work causing the work loss, the basic unit , And extraction processing for extracting a specific combination of the variation factors,
Based on the specific combination extracted by the extraction process, a specific process for specifying a specific measure viewpoint,
With reference to the correspondence information, a determination process for determining a measure corresponding to the specific measure viewpoint specified by the specifying process,
An improvement measure recommendation device characterized by executing. The improvement measure recommendation device according to claim 1,
The correspondence information is information that associates a measure indicating an improvement policy for an undetermined improvement target with the measure viewpoint,
In the extraction processing, the processor associates the specific combination with a specific section that causes the work loss among the different sections,
In the determination process, the processor defines the improvement target by setting the specific category associated with the specific combination with respect to the measure corresponding to the specific measure viewpoint as the undetermined improvement target. An improvement measure recommending device, which outputs specified measures. The improvement measure recommendation device according to claim 1,
In the extraction processing, the processor calculates a work loss degree indicating a degree of the work loss in the combination based on the calculation result, and extracts the specific combination based on the work loss degree. Improvement measure recommendation device. The improvement measure recommendation device according to claim 1,
In the specific processing, when the basic unit forming the specific combination is the work amount or the work frequency of the element work, the processor determines a measure point of view corresponding to the specific combination to improve a work instruction. An improvement measure recommendation device characterized by: The improvement measure recommendation device according to claim 1,
In the specific processing, when the basic unit forming the specific combination is a unit time of the element work, the processor determines a measure point of view corresponding to the specific combination to improve work efficiency. A characteristic improvement measure recommendation device. The improvement measure recommendation device according to claim 4,
In the identification processing, the processor identifies the content related to the improvement of the work instruction based on the variation factor that constitutes the specific combination, and an improvement measure recommendation device. The improvement measure recommendation device according to claim 5,
In the specific processing, the processor determines that the improvement of the work efficiency is the education of the corresponding worker when the variation factor constituting the specific combination is the worker attribute. Recommendation device. The improvement measure recommendation device according to claim 5,
In the specific processing, the processor, if the variation factor that constitutes the specific combination is the work time zone attribute, the product attribute, or the location attribute, improves the work efficiency of the corresponding work environment. An improvement measure recommendation device characterized by making a decision to improve. The improvement measure recommendation device according to claim 7,
In the identifying process, the processor identifies the content related to the education of the corresponding worker based on the elemental work configuring the specific combination, the improvement measure recommending apparatus. The improvement measure recommendation device according to claim 8, wherein
In the specific processing, the processor determines that the improvement of the corresponding work environment is the workability improvement of the corresponding work place when the variation factor configuring the particular combination is the place attribute. Improvement measure recommendation device. The improvement measure recommendation device according to claim 8, wherein
In the specific processing, the processor determines that the improvement of the corresponding work environment is an improvement of the manageability of the corresponding object when the variation factor constituting the specific combination is the product attribute. Improvement measure recommendation device. The improvement measure recommendation device according to claim 8, wherein
In the specific processing, when the variation factor that constitutes the specific combination is the work time zone attribute, the processor determines that the improvement of the corresponding work environment is an improvement of the work schedule of the corresponding time zone. A characteristic improvement measure recommendation device. A method for recommending improvement measures by an improvement measure recommendation device having a processor for executing a program and a storage device for storing the program,
The storage device stores work record information, and stores correspondence information in which a measure indicating a measure for improving work loss and a measure viewpoint indicating a focus of the measure on a problem caused by the work loss are associated with each other. And
The work record information is the work attribute data for classifying the worker, the work for classifying the work time period, into the work result data indicating the worker, the product, the work time period, and the work place in the warehouse for each order. It is information in which a work variation factor is associated with at least one attribute of a time zone attribute, a product attribute that classifies the product, and a location attribute that classifies the work place,
The improvement measure recommendation method is
The processor is
Calculation for calculating each basic factor including at least one of the frequency of work, the amount of work, and the unit time for each elemental work that constitutes a warehouse work for each of the categories of the variation factor based on the work performance information Processing and
Based on the calculation result of the same variation factor calculated in different categories by the calculation process, the element work, the basic unit, and the set of combinations of the variation factors, the element work causing the work loss, the basic unit , And extraction processing for extracting a specific combination of the variation factors,
Based on the specific combination extracted by the extraction process, a specific process for specifying a specific measure viewpoint,
With reference to the correspondence information, a determination process for determining a measure corresponding to the specific measure viewpoint specified by the specifying process,
A method for recommending improvement measures, characterized by executing. A program for recommending improvement measures to be executed by a processor that can access a storage device,
The storage device stores work record information, and stores correspondence information in which a measure indicating a measure for improving work loss and a measure viewpoint indicating a focus of the measure on a problem caused by the work loss are associated with each other. And
The work record information is the work attribute data for classifying the worker, the work for classifying the work time period, into the work result data indicating the worker, the product, the work time period, and the work place in the warehouse for each order. It is information in which a work variation factor is associated with at least one attribute of a time zone attribute, a product attribute that classifies the product, and a location attribute that classifies the work place,
In the processor,
Calculation for calculating each basic factor including at least one of the frequency of work, the amount of work, and the unit time for each elemental work that constitutes a warehouse work for each of the categories of the variation factor based on the work performance information Processing and
Based on the calculation result of the same variation factor calculated in different categories by the calculation process, the element work, the basic unit, and the set of combinations of the variation factors, the element work causing the work loss, the basic unit , And extraction processing for extracting a specific combination of the variation factors,
Based on the specific combination extracted by the extraction process, a specific process for specifying a specific measure viewpoint,
With reference to the correspondence information, a determination process for determining a measure corresponding to the specific measure viewpoint specified by the specifying process,
A program for recommending improvement measures, which is characterized by executing.

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