System, method and program for detecting productivity abnormality

Abstract

To provide a productivity abnormality detection system, a productivity abnormality detection method, and a productivity abnormality detection program that calculate the productivity of people or an organization, and detect an object of which the productivity increases or decreases abnormally.SOLUTION: A productivity abnormality detection system includes abnormality detection means that uses, as parameters, determination items and determination units of productivity, determination criteria indicating criteria of productivity, a tabulation period, and a fiscal year in comparison to the previous year, and that refers to an employee information master table, a work daily report table, a contract information table, a sales table, and a cost price table, in order to calculate determination criteria of the determination items, for each determination unit. Further, the abnormality detection means calculates a fluctuation coefficient of productivity from the fiscal year in comparison to the previous year regarding the calculated determination criteria of the determination items, in order to detect and output a determination unit for which the calculated fluctuation coefficent of productivity exceeds an upper limit or a lower limit of a prescribed determination method.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a productivity anomaly detection system, a productivity anomaly detection method, and a productivity anomaly detection program. [Background technology]

[0002] For example, the aging of the population has progressed in recent years, and as the DX report warns, the training and securing of new workers in the medium to long term has become an important issue. Human resource information and management are closely related, mainly in the construction industry, and real-time advanced analysis is required. For example, Patent Document 1 is an example of a system for managing employee performance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2022-27251 Summary of the Invention [Problem to be solved by the invention]

[0004] However, Patent Document 1 does not disclose anything about calculating the productivity of people and organizations and detecting targets whose productivity is abnormally increasing or decreasing.

[0005] In view of the above, an object of the present invention is to provide a productivity anomaly detection system, a productivity anomaly detection method, and a productivity anomaly detection program that are capable of calculating the productivity of people and organizations and detecting targets whose productivity is abnormally increasing or decreasing. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the object, the present invention provides a productivity anomaly detection system including a control unit, the control unit including an employee information master table in which an employee, an affiliated business establishment, an affiliated department, an age, a date of joining the company, a year, and a job title are registered in association with each other, a daily work report table in which an employee, a work date, a work category, a case, a work detail, and a work time are registered in association with each other, a contract information table in which a project, a sales representative, an engineer, a contract amount, a delivery date, a project type, a contract start date, and a contract end date are registered in association with each other, a sales table in which a project, a sales date, a sales amount, and a cost amount are registered in association with each other, and a productivity anomaly detection system including a control unit that controls ... and a cost table in which productivity judgment items, judgment units, judgment criteria indicating productivity standards, aggregation period, and year-to-year comparison year are used as parameters, and the employee information master table, the daily work report table, the contract information table, the sales table, and the cost table are configured to be accessible, and the employee information master table, the daily work report table, the contract information table, the sales table, and the cost table are referred to to calculate the judgment criteria of the judgment items for each judgment unit, calculate the productivity fluctuation rate from the year-to-year comparison for the calculated judgment criteria of the judgment items, and detect and output a judgment unit for which the calculated productivity fluctuation rate exceeds an upper or lower limit of a predetermined judgment method as a productivity abnormality.

[0007] Furthermore, according to one aspect of the present invention, the control unit is configured to be able to access a definition master table for abnormality determination in which a definition ID, a reference table specifying the table to be referenced, judgment criteria, judgment items, judgment units, and judgment methods are registered in association with one another, and the abnormality detection means may obtain the judgment criteria, judgment items, judgment units, and judgment methods as the parameters from the definition master table for abnormality determination, and refer to the table specified in the reference table of the definition master table for abnormality determination.

[0008] According to one aspect of the present invention, the control unit is configured to manage relationship information that links data that is separated for management purposes, and is configured to be able to access a human resources information relation table in which a base table, a joined table, a key item of the joined table, a combined aggregation unit, and an aggregation item are registered in association with each other, and the anomaly detection means may use the human resources information relation table when calculating the judgment criteria for the judgment items for each judgment unit.

[0009] According to one aspect of the present invention, the project may include a construction project, the judgment items may include sales revenue, value added amount, or value added rate, the judgment criteria may include productivity per person or productivity per hour, and the judgment unit may include an employee, a business establishment, a department, a construction project, or a type of work.

[0010] According to one aspect of the present invention, the control unit includes an analysis means for analyzing the productivity anomaly detected by the anomaly detection means, and the analysis means displays on the analysis screen a graph of the productivity per employee or productivity per hour of sales, added value, or added value rate for each employee for the target year, a graph of the annual trends in the productivity per employee or productivity per hour of sales, added value, or added value rate for each employee, and a graph of the annual trends in the sales, added value, or added value rate for each type of construction work in which an employee in which a productivity anomaly was detected is involved for the target year. The system is characterized by outputting graphs of productivity or productivity per hour, graphs of annual trends in per capita productivity or productivity per hour of sales revenue, value added, or value added rate for each type of construction work involving an employee with a productivity abnormality, or graphs of lists including the name of construction contract, sales representative, technical representative, type of work, contract amount, cost amount, value added amount, gross profit amount, working hours by construction work, annual working hours, annual overtime hours, number of days worked on holidays per year, number of days of unplanned leave per year, number of working days per year, and number of calendar working days per year for each type of construction work involving an employee with a productivity abnormality for the target year.

[0011] In order to solve the above-mentioned problems and achieve the object, the present invention provides a productivity anomaly detection method executed by an information processing device having a control unit, the control unit configuring an employee information master table in which an employee, an affiliated business establishment, an affiliated department, an age, a joining date, a year, and a job title are registered in association with each other, a daily work report table in which an employee, a work date, a work category, a case, a work detail, and a work time are registered in association with each other, a contract information table in which a project, a sales representative, a technical representative, a contract amount, a delivery date, a project type, a contract start date, and a contract end date are registered in association with each other, a sales table in which a project, a sales date, a sales amount, and a cost amount are registered in association with each other, and a productivity anomaly detection method for detecting anomalies in a productivity and an anomaly detection step executed by the control unit, which refers to the employee information master table, the contract information table, the daily work report table, the sales table, and the cost table, calculates the judgment criteria of the judgment items for each judgment unit using parameters including productivity judgment items, judgment units, judgment criteria indicating productivity standards, an aggregation period, and a year compared to the previous year, calculates a productivity fluctuation rate from the year compared to the calculated judgment criteria of the judgment items, and detects and outputs a judgment unit for which the calculated productivity fluctuation rate exceeds an upper limit or a lower limit of a predetermined judgment method as a productivity anomaly.

[0012] In order to solve the above-mentioned problems and achieve the object, the present invention provides a productivity anomaly detection program to be executed by an information processing device having a control unit, the control unit storing an employee information master table in which an employee, an affiliated business establishment, an affiliated department, an age, a joining date, a year, and a job title are registered in association with each other; a daily work report table in which an employee, a work date, a work category, a case, a work detail, and a work time are registered in association with each other; a contract information table in which a project, a sales representative, a technical representative, a contract amount, a delivery date, a project type, a contract start date, and a contract end date are registered in association with each other; a sales table in which a project, a sales date, a sales amount, and a cost amount are registered in association with each other; and a sales table in which a project, an accounting date, a cost item, and a cost amount are registered in association with each other. and a cost table obtained by accessing the productivity judgment item, the judgment unit, the judgment criterion indicating the productivity standard, the aggregation period, and the year compared to the previous year as parameters, the control unit refers to the employee information master table, the daily work report table, the contract information table, the sales table, and the cost table, calculates the judgment criterion of the judgment item for each judgment unit, calculates the productivity fluctuation rate from the year compared to the calculated judgment criterion of the judgment item, and detects and outputs a judgment unit for which the calculated productivity fluctuation rate exceeds an upper limit or lower limit of a predetermined judgment method as a productivity abnormality. Effect of the Invention

[0013] The present invention has an effect of making it possible to calculate the productivity of a person or organization and to detect a target whose productivity is abnormally increasing or decreasing. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram showing a hardware configuration of a productivity abnormality detection system according to an embodiment. [Diagram 2] FIG. 2 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 3]FIG. 3 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 4] FIG. 4 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 5] FIG. 5 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 6] FIG. 6 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 7] FIG. 7 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 8] FIG. 8 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 9] FIG. 9 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 10] FIG. 10 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 11] FIG. 11 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 12] FIG. 12 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 13] FIG. 13 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 14A] FIG. 14A is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 14B] FIG. 14B is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 15]FIG. 15 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 16] FIG. 16 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 17] FIG. 17 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 18] FIG. 18 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 19] FIG. 19 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 20] FIG. 20 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 21] FIG. 21 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 22] FIG. 22 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 23] FIG. 23 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 24] FIG. 24 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 25] FIG. 25 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 26] FIG. 26 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 27] FIG. 27 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 28]FIG. 28 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 29] FIG. 29 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 30] FIG. 30 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 31] FIG. 31 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 32] FIG. 32 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 33] FIG. 33 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 34] FIG. 34 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 35] FIG. 35 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 36] FIG. 36 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 37] FIG. 37 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 38] FIG. 38 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 39] FIG. 39 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 40] FIG. 40 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 41]FIG. 41 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 42] FIG. 42 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 43] FIG. 43 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 44] FIG. 44 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 45] FIG. 45 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Diagram 46] FIG. 46 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 47] FIG. 47 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 48] FIG. 48 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 49] FIG. 49 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 50] FIG. 50 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 51] FIG. 51 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 52] FIG. 52 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. [Figure 53] FIG. 53 is a diagram for explaining a specific example of the process of the control unit of the productivity abnormality detection system in the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A productivity abnormality detection system according to an embodiment of the present invention will be described below in detail with reference to the drawings.

[0016] [1. Overview] The present invention will be outlined in the following order: (1-1. Background), (1-2. Measures and Effects for Solving the Problems), and (1-3. Scope of Applicability of the Present Invention).

[0017] (1-1. Background) In recent years, management that maximizes the value of human resources has been attracting attention. As stated in the DX Report, from the perspective of "people," one of the three fundamental elements of management, there is an urgent need to take measures to address the shortage of human resources and technology.

[0018] For example, in the construction industry, efforts to improve human productivity, with the keyword "human capital," are gaining momentum, and attention is being paid to systems that can address this issue. The construction industry is facing an aging workforce, and the training and securing of new workers for the medium to long term is being highlighted as an important issue.

[0019] Efforts to develop and utilize young talent, prevent the loss of talent due to retirement, and improve the efficiency of construction work and productivity are urgent issues that must be addressed in a realistic manner. Issues related to training and improving productivity require a multifaceted analysis of human resource information. There are situations where this is necessary even for industry personnel who do not have system know-how.

[0020] In this invention, productivity is calculated and visualized from the perspective of sales and profits generated by people. By visualizing people's productivity, it is possible to analyze the suitability and attitude of the work involved, and provide information that can serve as hints for human resource development and productivity improvement from the perspective of considering personnel placement and switching to experienced work. In addition, this invention envisions a scenario in which it can be used throughout the industry, regardless of the presence or level of know-how, by utilizing a function that allows even personnel without system know-how to convey information in an easy-to-understand manner.

[0021] (1-2. Measures and Effects for Issues) Explain the basic understanding, measures and effects for issues (1) and (2).

[0022] (Prerequisite recognition) · Human resource information, in general, is divided into multiple management formats such as "daily reports, attendance management, experience work" from the viewpoint of security such as allocation of responsibilities between organizations and personnel and protection of personal information in the data managed by companies. Generally, systems and BI tools that analyze data need to define the data to be analyzed, build logic to link the data, and link the data to a table dedicated to storing the data to be analyzed.

[0023] (Challenge (1)) There are many different perspectives to analyze human resources information, and it is difficult to prioritize which information to check first when analyzing it.

[0024] Generally, the following perspectives are defined as productivity indicators: 1. Physical labor productivity: [Formula] Production volume / number of workers or production / (number of workers x working hours) 2. Value-added labor productivity: [calculation formula] Value-added / number of workers or value added / (number of workers x working hours) 3. Productivity per person: [Formula] Production volume or value added / Number of workers 4. Productivity per hour: [Calculation formula] Production volume or value added / (number of workers x working hours)

[0025] In this scenario, "production volume" is determined as the sales amount of the construction project. In this scenario, "value added rate" focuses on the value added rate in addition to the sales amount and value added amount. As the amount scale varies greatly depending on the construction project, we also focus on the productivity of each construction project, so we assume that the information will be visualized at a rate that is not affected by scale.

[0026] 1. Physical labor productivity, 2. Value-added labor productivity are classifications of indices focusing on whether the numerator is production volume or value-added amount (multiple denominators are possible). 3. Productivity per person, 4. Productivity per hour are classifications of indices focusing on whether the denominator is per person or per hour (multiple numerators are possible).

[0027] In addition to these, there are other indicators that should be viewed in accordance with business operations. Ideally, indicators should be used in the order of priority that suits the company, but the reality is that it is quite difficult to find information on "indicators that suit your company," and most companies are unable to do so. Also, since it is not realistic to check everything, prioritization is difficult. Depending on the analysis system and BI tool used, there may be indicators that can be checked and those that cannot.

[0028] As a measure to address this issue, the present invention uses the above-mentioned general productivity indicators and provides a function that allows you to check the information of each indicator and switch between indicators. This makes it possible to analyze indicators that suit your company and prioritize indicators that require attention.

[0029] (Challenge (2)) Since the units of data differ depending on the management purpose, there is a challenge in that it is difficult to simply match data.

[0030] Specifically, the units and granularity of data managed vary depending on the data, such as personal information, daily reports, attendance, and productivity. In some cases, processing such as checking key information or comparing aggregates is required, making simple data matching difficult.

[0031] As a solution to this problem, the present invention stores key information for each data and relationship information between the keys, which enables analysis without the need to consider the units and granularity of the data.

[0032] (1-3. Scope of Applicability of the Invention) In this specification, an example of a construction project will be described as an example of a project, but the project is not limited to construction projects. The present invention is applicable not only to the construction industry, but also to companies and industries that manage projects, such as construction and equipment construction, IT software, content, and real estate.

[0033] [2. Configuration] Fig. 1 is a block diagram showing an example of the configuration of a productivity abnormality detection system 100 according to the present embodiment. In Fig. 1, the productivity abnormality detection system 100 includes a control unit 102, a communication interface unit 104, a storage unit 106, and an input / output interface unit 108. Each unit included in the productivity abnormality detection system 100 is connected to each other so as to be able to communicate with each other via an arbitrary communication path.

[0034] The communication interface unit 104 communicatively connects the productivity abnormality detection system 100 to a network 300 via a communication device such as a router and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 has a function of communicating data with other devices via the communication line. Here, the network 300 has a function of communicatively connecting the productivity abnormality detection system 100 and the server 200, etc., and is, for example, the Internet or a LAN (Local Area Network), etc.

[0035] An input device 112 and an output device 114 are connected to the input / output interface unit 108. The output device 114 may be a monitor (including a home television), a speaker, or a printer. The input device 112 may be a keyboard, a mouse, a microphone, or a monitor that cooperates with a mouse to realize a pointing device function. In the following, the output device 114 may be referred to as a monitor 114.

[0036] Various databases, tables, files, etc. are stored in the storage unit 106. Computer programs for cooperating with an OS (Operating System) to give instructions to a CPU (Central Processing Unit) to perform various processes are recorded in the storage unit 106. For example, memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, optical disks, etc. can be used as the storage unit 106.

[0037] The memory unit 106 includes an abnormality determination definition master table 106a, an abnormality determination result table 106b, an abnormality determination result message table 106c, an employee information master table 106d, an attendance management table 106e, a daily work report table 106f, a construction contract information table 106g, a construction sales table 106h, a construction cost table 106i, a human resources information relation table 106j, and the like.

[0038] The abnormality determination definition master table 106a, the abnormality determination result table 106b, and the abnormality determination result message table 106c are tables for executing abnormality detection.

[0039] As shown in FIG. 4, the abnormality judgment definition master table 106a is a table that manages predefined information required for performing abnormality judgment, and is a table in which definition IDs, reference tables, judgment criteria, judgment items, judgment values, judgment units, judgment methods, etc. are registered in association with each other. The "definition ID" is a key item. The "judgment criteria," "judgment items," and "judgment units" are set according to the conditions to be detected. The "judgment criteria" can be selected from productivity per hour and productivity per person, which corresponds to the denominator in productivity calculation. Productivity per hour is expressed in units of (number of workers x working hours), and productivity per person is expressed in units of (number of workers). The "judgment items" correspond to the numerator in productivity calculation, and can be selected from added value, sales, and added value rate. The "judgment units" can be selected from employees and organizations.

[0040] In the example shown in the same figure, the first line has the definition ID "AB01," the reference tables "employee information master table, daily work report table, construction sales table, construction cost table," the judgment criterion "productivity per hour," the judgment item "value added," the judgment value "increase rate from the previous year," the judgment unit "employee," and the judgment method "quartile range detection, upper threshold: "third quartile + 1.5 x IQR," lower threshold: "first quartile - 1.5 x IQR," values ​​exceeding this are judged to be abnormal."

[0041] The anomaly judgment result table 106b is a table for managing the results of anomaly judgment, as shown in Fig. 5, in which definition IDs, judgment results, etc. are registered in association with each other. The "definition ID" is a key item. In the example shown in the figure, the first line shows the definition ID "AB01" and the judgment result "Employee A, productivity per hour, sharp decrease."

[0042] The abnormality determination result message table 106c is a table for managing information in which the abnormality determination result has been processed in language so that it can be easily understood by humans, and is a table in which definition IDs, determination result messages, etc. are registered in association with each other, as shown in FIG. 6. The "definition ID" is a key item. In the example shown in the figure, the first line contains the definition ID "AB01" and the determination result message "Employee A's productivity per hour is decreasing rapidly." In this way, the determination result message has been processed in language so that the determination result in the abnormality determination result table 106b can be easily understood by humans.

[0043] The employee information master table 106d, the attendance management table 106e, the daily work report table 106f, the construction contract information table 106g, the construction sales table 106h, and the construction cost table 106i are tables for managing data to be detected and analyzed, and are data linked by batch processing from actual business data of a business system (not shown) for the purpose of anomaly detection and analysis, with column data necessary for anomaly detection and analysis being extracted and linked.

[0044] The employee information master table 106d is a table of a human resources database that manages personal information such as the name and affiliation of employees, and as shown in Fig. 7, is a table in which employee, affiliated business establishment, affiliated department, age, date of joining the company, year, job title, etc. are registered in association with each other. "Employee" is the key. In the example shown in the figure, the first row is "Employee A", affiliated business establishment "Tokyo", affiliated department "Sales Dept. 1", ...

[0045] The attendance management table 106e is a table of a labor-related DB that manages attendance information such as arrival and departure times and holidays for each employee. As shown in FIG. 8, it is a table that associates and registers employees, attendance days, days of the week, arrival times, departure times, overtime hours, and attendance categories. "Employee" is the key. In the example shown in the figure, the first row is employee "Employee A", attendance date "2021 / 5 / 13", day of the week "Friday", arrival time "09:00", departure time "19:00", overtime hours "1:00", attendance category "normal", etc.

[0046] The daily work report table 106f is a construction DB table that manages the work hours related to work such as construction work for each employee, and is a table that associates and registers employees, work dates, work categories, cases, work details, and work hours, as shown in FIG. 9. "Employee" is the key. In the example shown in the figure, the first row is employee "Employee A", work date "2021 / 5 / 13", work category "construction", case "construction A", work details "on-site work", and work hours "10 hours".

[0047] The construction contract information table 106g is a table of a construction DB that manages basic information related to construction such as the contract name, construction period, and type of work. As shown in FIG. 10, it is a table in which the sales representative, technical representative, contract amount, construction period, type of work, contract start date, and contract end date are associated and registered. "Construction" is the key. In the example shown in the figure, the first row is "Construction A", the sales representative "Personnel A", the technical representative "Technologist A", the contract amount "20 million", the construction period "1 year", the type of work "Construction", the contract start date "2021 / 6 / 1", and the contract end date "2022 / 5 / 31".

[0048] The construction sales table 106h is a table in a construction DB that manages information related to sales, such as the sales amount and sales timing of construction, and is a table that associates and registers construction, sales date, sales amount, and cost amount, as shown in FIG. 11. "Construction" is the key. In the example shown in the figure, the first row is construction "Construction A", sales date "2021 / 5 / 31", sales amount "20 million", and cost amount "6 million".

[0049] The construction cost table 106i is a table of a construction DB that manages information related to sales such as construction cost items (material costs, subcontract costs, labor costs, expenses, etc.) and cost amounts, and is a table that associates and registers construction, recording date, cost items, and cost amounts, as shown in FIG. 12. "Construction" is the key. In the example shown in the figure, the first row is construction "Construction A", recording date "2021 / 6 / 30", cost item "subcontract cost", and cost amount "500,000".

[0050] The personnel information relation table 106j is a table for managing the association of business data. The personnel information relation table 106j is a table for managing relation information that associates data separated according to management purposes, and is a table in which an index, a base table, a base table key, a join destination table, a join destination table key, a join aggregation unit, and an aggregation item are registered in association with each other, as shown in FIG. 13. In the example shown in the figure, the first row is an index "0", a base table "employee information master table", a base table key "employee", a join destination table "time management table", a join destination table key "employee", a join aggregation unit "employee", and an aggregation item "overtime hours, number of holiday work days, number of unplanned leave days, number of working days, number of calendar working days". Number of holiday work days: Attendance category = Counts the days when working on a holiday. Number of unplanned leave days: Attendance category = Counts the days when leaving on an unplanned leave.

[0051] The control unit 102 is a CPU or the like that performs overall control of the productivity abnormality detection system 100. The control unit 102 has an internal memory for storing control programs such as an OS, programs that define various processing procedures, and required data, and executes various information processing based on these stored programs.

[0052] The control unit 102 is configured to be able to access the abnormality determination definition master table 106a, the abnormality determination result table 106b, the abnormality determination result message table 106c, the employee information master table 106d, the attendance management table 106e, the daily work report table 106f, the construction contract information table 106g, the construction sales table 106h, the construction cost table 106i, the human resources information relation table 106j, and the like, which are stored in the storage unit 106. Note that the abnormality determination definition master table 106a, the abnormality determination result table 106b, the abnormality determination result message table 106c, the employee information master table 106d, the attendance management table 106e, the daily work report table 106f, the construction contract information table 106g, the construction sales table 106h, the construction cost table 106i, the human resources information relation table 106j, and the like, may be provided in another location (for example, the server 200) as long as the control unit 102 is able to access them.

[0053] The control unit 102 conceptually includes a master maintenance unit 102a, an abnormality detection unit 102b, and an analysis unit 102c.

[0054] The master maintenance unit 102a performs settings such as inputting, adding, changing, and updating data in the abnormality determination definition master table 106a and the human resources information relation table 106j in response to operations by an administrator on a master maintenance screen (not shown) displayed on the monitor 114, for example.

[0055] The abnormality detection unit 102b is for detecting abnormalities in the productivity of employees and departments, and refers to the employee information master table 106d, the daily work report table 106f, the construction sales table 106h, and the construction cost table 106i, using the productivity judgment item, judgment unit, judgment criteria indicating the productivity standard, the aggregation period, and the year compared to the previous year as parameters, calculates the judgment criteria of the judgment item for each judgment unit, calculates the productivity fluctuation rate from the year compared to the calculated judgment criteria of the judgment item, and detects a judgment unit for which the calculated productivity fluctuation rate exceeds the upper or lower limit of a predetermined judgment method as an abnormality in productivity, and displays and outputs it on the detection screen.

[0056] When performing anomaly detection, the anomaly detection unit 102b obtains the judgment criteria, judgment items, judgment units, and judgment methods as parameters from the anomaly judgment definition master table 106a, and refers to a table specified in the reference table of the anomaly judgment definition master table 106a.

[0057] The abnormality detection unit 102b uses the human resource information relation table 106j when calculating the judgment criteria for the judgment items for each judgment unit.

[0058] The project may include a construction project, the evaluation items may include sales revenue, value added amount, or value added rate, the evaluation criteria may include productivity per person or productivity per hour, and the evaluation unit may include an employee, a business establishment, a department, a construction project, or a type of work.

[0059] The analysis unit 102c controls the display of an analysis screen and is for analyzing anomalies in productivity detected by the anomaly detection unit 102b, and displays on the analysis screen graphs of productivity per hour or per person for each employee for the target year, graphs of annual trends in productivity per hour or per person for each employee for the target year, sales revenue, value added amount, or value added rate for each type of work in which an employee in whom a productivity anomaly was detected is involved, and It can output charts and graphs of productivity for each employee, charts and graphs showing annual trends in sales, value added, or value added rate per hour or per person for each type of construction work involving employees with productivity abnormalities, or charts and graphs listing the name of the construction contract, sales person, technical person, type of work, contract amount, cost amount, value added amount, gross profit amount, working hours by construction work, annual working hours, annual overtime hours, number of days worked on holidays per year, number of days of unplanned leave per year, number of working days per year, and number of calendar working days per year for each type of construction work involving employees with productivity abnormalities for the target year.

[0060] [3. Specific Examples] With reference to Figures 2 to 53, specific examples of processing by the control unit 102 of the productivity anomaly detection system 100 in this embodiment will be described in the order of [3-1. Analysis screen], [3-2. (Common processing) Productivity calculation], [3-3. Processing flow], [3-4. Usage image and effects of the present invention], and [3-5. Example of analysis pattern].

[0061] [3-1.Analysis screen] 2 and 3 are diagrams for explaining an analysis screen of the productivity abnormality detection system of the present embodiment. The analysis screen of the productivity abnormality detection system of the present embodiment will be described with reference to Figs. 2 and 3.

[0062] As shown in FIG. 2, the analysis screen includes an area for displaying extraction conditions, an area for displaying an anomaly detection message, and an area for displaying each of charts (1) to (5).

[0063] Chart (1) visualizes productivity by element. The current productivity situation can be understood by comparing it with other elements. There are four selectable elements: "Establishment, Department, Employee, and Construction." Elements and productivity units can be switched using the extraction conditions "Aggregation (productivity standard)" and "Aggregation (analysis unit)."

[0064] Chart (2) visualizes the information in Chart (1) in a chronological order by year. Compare the trends and situations with other elements, and pay attention to trends such as sudden increases and decreases or gradual increases and decreases. The extraction conditions "Aggregation (productivity standard)" and "Aggregation (unit of analysis)" allow you to switch between elements and productivity units.

[0065] Figure (3) visualizes productivity by type of work. The units of interest are used when analyzing the status of construction projects managed by construction companies. The productivity units can be switched by selecting "Aggregation (productivity standard)" under the extraction conditions.

[0066] Chart (4) visualizes the information in Chart (3) in a chronological order by year. Compare the trends and situations with other elements, and pay attention to trends such as sudden increases and decreases or gradual increases and decreases. The productivity unit can be changed by selecting "Aggregation (productivity standard)" under the extraction conditions.

[0067] Figure (5) visualizes the construction and labor status within a single fiscal year. By comparing the actual construction work carried out within a fiscal year with the annual labor performance, it is possible to analyze the causal relationship between productivity and labor conditions. By selecting the fiscal year in Figures (2) and (4), it is possible to switch the year to be visualized. Items beginning with "Annual~" output the annual totals for employees in whom an abnormality was detected (in the example of Figure 2, it is the annual performance of Employee A in whom an abnormality was detected).

[0068] As shown in Figure 3, the extraction conditions are used to specify extraction conditions such as fiscal year, number of previous years for comparison, fiscal year and month, aggregation (productivity standard) (select from sales amount, value added amount, value added rate), aggregation (analysis unit), business establishment, department, employee, construction, etc. for charts (1) to (5). The aggregation (productivity standard) unit can be selected from sales amount, value added amount, and value added rate. The aggregation (analysis unit) can be selected from business establishment, department, employee, and construction.

[0069] [3-2. (Common processing) Productivity calculation] 14A and 14B are diagrams for explaining the (common process) productivity calculation. The (common process) productivity calculation will be explained with reference to Fig. 14A and Fig. 14B.

[0070] In this scenario, productivity calculations are performed each time information visualization processing is performed for anomaly detection and analysis. Calculation logic is switched according to the values ​​set in the anomaly judgment definition master table. Calculation processing is performed according to the parameters shown in the figure.

[0071] The above table lists the specified parameters, when an anomaly is detected, when the analysis screen is initially launched, and when the analysis screen extraction conditions are changed. The specified parameters "Judgment item," "Judgment unit," and "Judgment criteria" when an anomaly is detected shall be the judgment item, judgment unit, and judgment criteria in the anomaly judgment definition master table. The specified parameter "Collection period" shall be the fiscal year to which the previous month belongs when processing is executed, and the specified parameter "Year compared to previous year" shall be the year prior to the output year. Details when the analysis screen is initially launched are described below in "3-3. Processing flow." When the analysis screen extraction conditions are changed, follow the specifications in "3-3. Processing flow" and "3-5. Example analysis patterns" below.

[0072] The calculation process uses the following pattern shown in the table below. The vertical items indicate the calculation procedure, and the horizontal items indicate the calculation method, with the calculation procedure branching according to the parameter "determination item." The aggregation unit is all aggregation, and the target period = aggregation period.

[0073] First, for the index item 3-1, the judgment item "Sales amount" is calculated from the construction sales amount. The sales amount is calculated by aggregating the sales amount from the construction sales table 1. For example, the total sales amount for construction A and B is 35 million yen.

[0074] For the judgment item "added value", the added value is calculated by subtracting material costs, subcontracting costs, and labor costs from construction sales. Sales amounts are calculated by summing up the sales amounts from the 1. Construction Sales Table. To calculate the cost amount, extract the cost items of materials, subcontracting, and labor from the construction cost table in 2., and tally up the cost amount. To calculate the added value, subtract the cost amount in 2. from the sales amount in 3.1. For example, the total added value amount for constructions A and B is sales amount "35 million yen" - cost "15 million yen" = "20 million yen."

[0075] The "added value rate" of the judgment item is calculated in the same way as the "added value amount" of the judgment item, and the "added value rate" is calculated by "added value amount" / "sales amount." For example, the added value rate of the total of constructions A and B is added value amount "20 million yen" / sales amount "35 million yen" = 57%.

[0076] 3-2. Regarding the number of workers, count the number of employees who worked on the construction. Specifically, extract the employees who worked on the construction from the [1] daily work report table, and calculate the number of people by counting [2][1]. For example, 20 people.

[0077] 3-3. Calculate the average working hours per person for employees who worked on construction-related tasks. Specifically, extract the time spent working on construction-related tasks from the [1] daily work report table, calculate the hours by aggregating [2][1], and calculate the working hours (average per person) by dividing the time aggregated in [3][2] by [3-2 number of workers]. For example, 800 hours / 20 people = 40 hours.

[0078] 3-4. For productivity according to the evaluation criteria, calculate as follows according to the evaluation criteria. For "productivity per person," calculate using [3-1 Index item] / [3-2 Number of workers]. For "productivity per hour," calculate using [3-1 Index item] / ([3-2 Number of workers] x [3-3 Working hours (average per person]). For example, for "productivity per person" of added value, calculate using 20 million yen / (20 people x 40 hours) = 25,000 yen.

[0079] 3-5. Regarding the task time ratio (%) per judgment unit, calculate the ratio using the task time for each judgment unit. [1] Calculate the judgment unit average. If the judgment unit is an employee, calculate the average across employees. If it is an organization, calculate the average across organizations. [2] Calculate the target judgment unit. Calculate the time for the judgment unit selected during anomaly detection or analysis, and if the judgment unit is by organization, calculate the average time for employees belonging to the organization.

[0080] It is calculated as follows: Ratio of work time per evaluation unit = [2] / [1] (%). For example, if the productivity per hour of added value is the evaluation unit = employee, it will be 40 hours / 60 hours = 150%.

[0081] For example, if the productivity per hour of added value is judged as a department, and the average working hours of employees in department A is 50 hours, then it is 40 hours / 50 hours = 150%.

[0082] Although the calculation uses a different unit than 3-3 (by employee or by department), the resulting value is the same, so 3-3 will be used.

[0083] 3-6. Productivity according to the evaluation criteria for each evaluation unit is calculated as [3-4 Productivity according to evaluation criteria] / [3-5 Work time ratio for each evaluation unit]. For example, if the productivity per hour of added value is the evaluation unit = employee, it is 25,000 yen / 150% = 17,000 yen.

[0084] 3-7. Calculate the productivity change rate from the previous year only when an anomaly is detected. [1] Current year = [3-6 Productivity according to the judgment criteria for each judgment unit], [2] Previous year = Target period is the year compared to the previous year, calculate [3-1] to [3-6], and calculate the productivity change rate from the previous year = ([1] - [2]) / [2]. For example, in the case of productivity per hour of added value, judgment unit = employee, for example, previous year: 25,400 yen, current year: 17,000 yen, productivity change rate: -33%.

[0085] In the above table, for the target year for anomaly judgment, data that occurred within the fiscal year to which the previous month belongs is obtained. For example, the previous month is 2023 / 03 = fiscal year 2022 is the target.

[0086] The time axis unit for the detection analysis in this scenario is specified as a fiscal year range, since it is common to check productivity on a fiscal year basis. The idea is to compare productivity from year to year and check for increases or decreases in productivity.

[0087] In this example, the analysis focuses on working hours as a premise. Since the confirmation is based on the hypothesis that working hours can be used as an axis of analysis, it can be recognized that the following perspectives can be used as a premise. Productivity changes depending on whether or not the working hours are long. If the working hours are long, productivity is low, and if the working hours are short, productivity is high.

[0088] However, it may be difficult to say that whether the working hours are more or less than the average between the judgment units necessarily affects productivity. For example, in the case of a simple reduction in assignments, it is possible that people are working hard to create value even with the reduced amount of time they can work, so this example is merely an explanation from one analytical perspective.

[0089] Regarding linking of business data, data is linked using the "Human Resources Information Relation Table" which is the relation information between the original data shown in Figure 15.

[0090] [3-3. Processing flow] The basic process flow of the productivity abnormality detection system 100 will be described with reference to Figs. 16 to 37. Fig. 16 is a diagram showing the process flow. In Fig. 16, the master maintenance unit 102a (1) executes an abnormality judgment setting master maintenance process (step S1). The abnormality detection unit 102b (2) executes an abnormality detection process (step S2). The analysis unit 102c (3) executes an analysis screen initial display and (4) an analysis screen output process of switching analysis (recalculation, calculation, graphic redrawing) (step S3).

[0091] The details of each process will be explained along this processing flow. Since the processes in (3) and (4) are similar, they will be collectively explained as the "analysis screen output process."

[0092] (S1: Abnormality judgment setting master maintenance process) A specific example of the abnormality judgment setting master maintenance process will be described with reference to Fig. 17. The master maintenance unit 102a registers data in the abnormality judgment definition master table in response to an administrator's operation on a master maintenance screen (not shown) displayed on the monitor 114. In the abnormality judgment setting master maintenance process, initial settings are made in the abnormality judgment definition master table before abnormality detection is used. In addition, after data analysis, settings are also made when detecting again by adding or changing information on the necessary judgment criteria, judgment items, and judgment units.

[0093] (S2: Anomaly detection process) A specific example of the abnormality detection process will be described with reference to FIGS.

[0094] 1. Refer to the master table for abnormality judgment definition 1. Refer to the anomaly determination definition master table will be described with reference to Fig. 18. Specify the definition ID to be executed when executing the anomaly detection execution process, and obtain the data to be used this time from the anomaly determination definition master table using the specified definition ID as a key. Obtain data for each definition ID set in the anomaly determination setting master table.

[0095] In the example shown in Figure 18, using the definition ID "AB01" as a key, the following are retrieved from the anomaly judgment definition master table: reference tables "employee information master table, daily work report table, construction sales table, construction cost table", judgment criterion "productivity per hour", judgment item "value added", judgment value "increase rate from previous year", judgment unit "employee", judgment method "quartile range detection, upper threshold: "third quartile + 1.5 x IQR", lower threshold: "first quartile - 1.5 x IQR", values ​​exceeding this are judged to be abnormal".

[0096] 2. Acquire detection data With reference to Fig. 19, 2. Acquisition of detection data will be described. Detection data is acquired based on data acquired from the abnormality determination definition master table. In the example of Fig. 18, the employee information master table, daily work report table, construction sales table, and construction cost table are acquired. The data acquisition parameters are, for example, "5 business days before the closing date of each month."

[0097] 3.Calculation / Analysis The calculation and analysis will be described with reference to Fig. 20. Based on the information in the anomaly judgment definition master table acquired in 1, the productivity is calculated using the detection data acquired in 2. For the productivity calculation process, the calculation table in "(Common Processing) Productivity Calculation" above is used.

[0098] In the example shown in Figure 20, the productivity per hour for each employee is calculated according to the above "(common process) Productivity calculation" using the parameters of the judgment criterion "productivity per hour", judgment item "value added", judgment value "increase from previous year", judgment unit "employee", aggregation period "FY2022", and year-on-year comparison year "FY2021" obtained from the anomaly judgment definition master table. In this example, the productivity (value added) per hour for employee "A" is 17,000 yen, and the rate of change from the previous year is -33%.

[0099] The figure also shows the calculation results of the sales amount and the added value rate obtained for each judgment item (sales amount, added value rate). The figures are quoted from "(Common Processing) Productivity Calculation."

[0100] 4. Abnormality determination 21 and 22, the step 4. Abnormality judgment will be described. Based on the judgment method of the abnormality judgment definition master table, abnormality judgment is executed using the data calculated in 3, and abnormal data is detected.

[0101] In this example, in Figure 21, detection is performed using the productivity (value added) per hour for each employee. The method of determination in the anomaly determination definition master table is "Interquartile range detection, upper threshold: "Third quartile + 1.5 x IQR", lower threshold: "First quartile - 1.5 x IQR", values ​​in excess are determined to be abnormal." An image of a box plot is shown using "Interquartile range detection, upper threshold: "Third quartile + 1.5 x IQR", lower threshold: "First quartile - 1.5 x IQR", values ​​in excess are determined to be abnormal."

[0102] The box plot image shows the upper and lower limits (lower limit = -20%), with exceeding the upper limit = increased productivity, and exceeding the lower limit = decreased productivity. In the example shown in the figure, employee "A" is -33%, which exceeds the lower limit and is therefore judged to be abnormal. Note that there are also patterns that exceed the upper limit.

[0103] If abnormal data is detected, it is written to the abnormality judgment result table. In the example shown in the figure, the definition ID "AB01", judgment result "Employee A, productivity per hour, sharp decrease", change rate from the previous year "-33%", detection year "FY2022", etc. are written to the abnormality judgment result table.

[0104] Other determination methods will be described with reference to Fig. 22. In this scenario, (1) the above-mentioned interquartile range detection and (2) outlier detection can be selected as the determination method.

[0105] This section explains the merits of selecting "Interquartile range detection, upper threshold: "Third quartile + 1.5 × IQR", lower threshold: "First quartile - 1.5 × IQR", values ​​exceeding this limit are judged to be abnormal" as the method of judgment in the anomaly judgment definition master table, and selecting "Outlier detection, "3-month moving average" as the reference value, a range of 0.3 times the reference value as the threshold, values ​​exceeding this limit are judged to be abnormal."

[0106] In the case of interquartile range detection, the median of the data is calculated, and the degree of variation from the median is confirmed and anomalies are judged. The advantage of using it is that it can detect anomalies by comparing data across the board. Both time series data and cross-sectional data focus on the element of numerical values, and detection is performed by applying it to the box plot shown above.

[0107] When detecting outliers, a reference value is calculated, and values ​​that deviate from the reference value by a certain amount or more are deemed abnormal. Values ​​that exceed a "range of 0.3 times" the "three-month moving average" are defined as abnormal. The advantage of using this method is that it can detect abnormalities by focusing on changes in time-series data. It is possible to capture sudden increases or decreases in values ​​over time, and capture and detect information regarding the timing of the sudden change.

[0108] Choose which to use depending on whether you want to focus on the "simple magnitude of the value" or "sudden changes". If you want to focus on the "simple magnitude of the value", select interquartile range detection, and if you want to focus on "sudden changes", select outlier detection.

[0109] 5. Judgment result update 5. Updating of Judgment Results will be described with reference to Figure 23. The result of the abnormality judgment is updated in the abnormality judgment result table. Furthermore, message data is generated in the abnormality judgment result message table, which verbalizes the judgment results in the abnormality judgment result table so that they are easily understandable to humans, and is updated in the abnormality judgment result message table. Execution ID = an identification key that is automatically set for each execution when anomaly detection is executed. For example, 1st abnormality detection execution...execution ID = EX01, 2nd abnormality detection execution...execution ID = EX02.

[0110] (S3: Analysis screen output processing) A specific example of the analysis screen output process will be described with reference to Figures 24 to 37. Here, (3) and (4) of the process flow image will be described together.

[0111] 1. Obtain judgment results 24 and 25, 1. Obtaining the judgment result will be described. This is only performed in (3) of the processing flow image. In Fig. 24, the definition ID is used as a parameter (key) to obtain data from the abnormality judgment definition master table, the abnormality judgment result table, and the abnormality judgment result message table. In the example shown in the figure, the definition ID "AB01" is used as a parameter.

[0112] As shown in Figure 25, the data from the acquired anomaly judgment result message table is displayed on a screen (message list screen). In this example, the message displayed on the screen is "Employee A's hourly productivity has dropped sharply. Rate of change from last year: -33%." Clicking on the message on the screen will take you to the analysis screen (see Figure 26).

[0113] 2. Obtaining reference information The acquisition of reference information will be explained with reference to Figures 26 to 28. This is carried out in both (3) and (4) of the processing flow image. As shown in Figure 26, the clicked message is redrawn on the analysis screen. In this scenario, it is assumed that a total of five figures will be used during analysis, and a printing area for five figures will be reserved.

[0114] The extraction conditions required for analysis are placed in Figures 27 and 28. Items are embedded as fixed items (items required for the analysis of this scenario are fixedly defined in the system).

[0115] In process flow (3), the extraction conditions "Aggregation (productivity standard)" and "Aggregation (unit of analysis)" are initially set to the judgment item and judgment unit of the anomaly judgment definition master obtained in 1. In process flow (4), the item selected by the user is set as a parameter. The extraction condition "Fiscal year" is initially set to the detection year in the anomaly judgment result table. The extraction condition "Number of previous years for comparison" is initially set to a fixed value of "5." This is used as the initial value because in most cases actual results are generally compared and analyzed over a range of up to the past five years. The extraction condition "Establishment / department / employee / construction" is initially set to empty in process flow (3). In process flow (4), the value entered by the user is set.

[0116] In addition, the acquisition parameters required for analysis (fiscal year / month From To, fiscal year, comparison past year, business establishment / department / employee / construction) are obtained. When extracting employee / organization / construction data from the employee information table, construction contract table, daily work report table, construction sales table, and construction cost table, the business establishment / department / employee links are used. The business establishment / department / employee items in the extraction conditions are placed as items to be extracted from the employee affiliation in the personnel information. Separate organizational information also exists that links to sales and technical staff for construction work, but since the analysis axis in this scenario is solely from the perspective of productivity by employee affiliation, this will not be considered as a subject for controlling extraction.

[0117] In the construction contract information table, the "sales representative" is the employee who made the construction contract and is the manager, and the "engineer" is the employee in charge of managing the technical staff required to actually carry out the construction. The employees on the analysis axis of this scenario are employees who worked on the construction work, but are different from the above-mentioned manager.

[0118] Also, obtain the productivity calculation parameters (aggregation (productivity standard), aggregation (analysis unit), productivity standard, output year, number of previous years for comparison). The productivity calculation parameters are used when aggregating and calculating the acquired data required for analysis for use in charts and graphs. The parameter values ​​change slightly for each chart to be output, so this is just one example (for specific details, see process 3 below).

[0119] 3. Creating data for drawing diagrams The creation of data for drawing charts will be explained with reference to Figures 29 to 37. The data acquired in 3-1.2 is calculated into a form that can be drawn on five charts. For charts (1) to (4), the productivity is calculated according to the process of "(Common Process) Productivity Calculation". For each chart, the data for chart output is created using the parameters for aggregation and calculation obtained in process 2. Figure 29 shows a table of settings when the analysis screen extraction conditions for the specified parameters are changed. For "Judgment item", the extraction conditions are set to "Aggregation (productivity standard)" (at initial startup, the year in which the abnormality was detected in the anomaly judgment result linked to the message selected from the message list). For "Judgment unit", charts (1) and (2): the extraction conditions are set to "Aggregation (analysis unit)" (at initial startup, the same as the definition table), and charts (3) and (4): the type of work. The "Decision Criteria" shall be the "Productivity Standard" extraction condition (same as the definition table when initially started), and the "Collection Period" shall be the "Output Year" extraction condition (same as the definition table when initially started) in Figure (1)(3), and each year from the "Output Year" extraction condition up to the "Number of Years for Comparison" in Figure (2)(4).

[0120] When calculations are performed according to [3-2. (Common Processing) Productivity Calculation], the data for Figures (1) to (4) will be as shown in the figures.

[0121] Regarding Figure (5), the output data is created by combining the other personnel information tables obtained in 2 with the personnel information relation table obtained in 2.

[0122] Bind the data for chart output calculated in 3-2.3-1 to a graph. Figure 30 is a diagram showing an example of visualization of productivity per hour for each employee in chart (1). The figure shows the calculation results of productivity data (added value) per hour for each employee, and shows an image of the chart for this calculation result. The horizontal axis is employees, and the vertical axis is added value per hour. Based on the information linked to the anomaly judgment result message acquired in acquiring the judgment result in 1, employees for whom an anomaly has been detected are highlighted by coloring them red, etc.

[0123] The figure also shows an image of how the aggregate (productivity standard) would be expressed in terms of sales revenue and value-added rate.

[0124] Figure 31 is a diagram showing an example of visualization of productivity per hour for each employee by year in Chart (2). The figure shows the calculation results for productivity data (value added) per hour for each employee by year, and shows an image of the chart for these calculation results. The horizontal axis is the fiscal year, the vertical axis is the value added per hour, and each employee is plotted on a line graph.

[0125] The figure also shows an image of how the aggregate (productivity standard) would be expressed in terms of sales revenue and value-added rate.

[0126] Figure 32 is a diagram showing an example of visualization of the productivity per hour for each type of work involved in the construction work of employee A in Figure (3). The figure shows the calculation results of the productivity data (value added) per hour for each type of work of employee A, and shows a diagrammatic image of these calculation results. The horizontal axis is the type of work, and the vertical axis is the value added per hour.

[0127] The figure also shows an image of how the aggregate (productivity standard) would be expressed in terms of sales revenue and value-added rate.

[0128] Figure 33 is a diagram showing an example of visualization of the productivity per hour for each type of work involved in the construction work by employee A by year in Figure (4). The figure shows the calculation results for the productivity data (value added) per hour for each type of work by employee A by year, and shows an image of the chart for these calculation results. The horizontal axis is the fiscal year, the vertical axis is the value added per hour, and a line graph for each type of work is plotted.

[0129] The figure also shows an image of how the aggregate (productivity standard) would be expressed in terms of sales revenue and value-added rate.

[0130] Figure 34 is a diagram showing an example of visualization of a list of construction projects carried out within the fiscal year in which employee A was involved in Figure (5). Using the data acquired in the reference information acquisition step 2, a list of data is created that links information by construction project.

[0131] The top part of Figure 34 shows data on the list of performance by construction project involving employee A in fiscal year 2022. The middle part shows an image of this data in a graph. The list of performance by construction project involving employee A includes the following items: construction contract name, sales representative, technical representative, type of work, contract amount, cost amount, added value amount, gross profit amount, working hours by construction project, annual working hours, annual overtime hours, number of days worked on holidays per year, number of days of unplanned leave per year, number of days worked per year, and number of calendar days worked per year.

[0132] The lower section also shows a pattern where an organization is specified in the aggregation (analysis unit). Here, an example is shown when Sales Department 1 is specified. When outputting by organization, the average of the employees belonging to the organization is calculated and output. Furthermore, the average age and average years of service are additionally output as supplementary information for the employees.

[0133] 4. Diagram Drawing The chart drawing will be explained with reference to Figures 35 to 37. In Figure 35, the data of charts (1) to (5) acquired in 3 is output to the analysis screen. The X-axis of charts (2) and (4) outputs the fiscal year, and when the fiscal year is selected by clicking the X-axis, only the information related to the fiscal year can be output to chart (5).

[0134] Figure 36 is a diagram showing an image of when to select a year. For example, when you select a year by clicking the X-axis of chart (2)(4), the accounting year and month and accounting year of the acquisition parameters are changed, and data on the list of performance by construction work for the changed year is acquired and output to chart (5). Figure 37 is a diagram showing an image of the analysis screen when the initial display is completed.

[0135] [3-4. Image and effect of the use of this invention] The usage image and effect of the present invention will be described with reference to Fig. 38. Fig. 38 shows a business image table. An image of actual business use, i.e., how a user uses the system, will be described.

[0136] (Prerequisite recognition) In productivity analysis, the main axis to confirm is the current state of productivity increase / decrease and understanding of areas for improvement. By understanding the current production volume and the power to generate value / profit and the problems, it becomes possible to consider and implement measures in terms of how to move to improve in order to generate future sales / value / profit. In order to confirm the current state of productivity increase / decrease, it is possible to do so by visualizing productivity using the productivity index mentioned in the "Overview" explanation. In order to confirm understanding of areas for improvement, it is necessary to visualize and make available the information that is affecting productivity increase / decrease. It is desirable that this information is detectable, visualized, and analyzable. In this scenario, it is assumed that information is detected and visualized so that it can be confirmed.

[0137] (Business use image) It is also possible to make a judgment by combining other data outside the system. The table in Figure 38 shows an image of business use from the perspective of 1. What can be done on the system, 2. Detection and analysis elements (employees, organizations (business locations, departments)), 3. Points to check on the system, and 4. Information that people need to consider and judge based on the information confirmed in 3 (causes of anomalies, detailed situations).

[0138] [3-5. Analysis pattern example] An example of an analysis pattern will be described with reference to Fig. 39 to Fig. 52. Here, an image of how information is visualized as the analysis for business use proceeds will be described.

[0139] Example 1. Analysis pattern for switching aggregation units by employee / organization The tabulation unit switching analysis pattern for Example 1: Employees and Organizations will be explained with reference to Figures 39 to 45. In Figure 39, the content related to the process in the bold frame (4) in the processing flow (switching the output content and analyzing from a different perspective) will be explained. By modifying the information in the anomaly judgment definition master, it is also possible to switch from the initial display based on processes (1) to (3).

[0140] To switch the pattern from the initial display flow of process flows (1) to (3), change the "Judgment unit" in the anomaly judgment definition master table. To redraw process flow (4), the user selects "Aggregation (analysis unit)" in the extraction conditions on the analysis screen.

[0141] In Figure 40, when the user selects "department" in the aggregation (analysis unit) of the extraction conditions on the analysis screen, new data is created for Figures (1) to (5), and data is created for Figure (1) (productivity data per hour by department), Figure (2) (productivity data per hour by year and department), Figure (3) (productivity data per hour by work type), Figure (4) (productivity data per hour by year and work type), and Figure (5) (list of performance by work type).

[0142] In Figure (5), the output data is created by combining the other personnel information tables obtained in 2 based on the personnel information relation table obtained in 2. If the "aggregation (analysis unit)" is an organization, the average data is also aggregated and calculated.

[0143] For each chart, compare the information output for "aggregation (unit of analysis)" by employee and by organization. Figure 41(A) shows an example of chart (1) output by department, and Figure 41(B) shows an example of chart (1) output by employee. Comparing the two, this shows that productivity is low on an employee basis, but there is little difference in productivity on a department basis.

[0144] Figure 42(A) shows an example of Chart (2) output by department, and Figure 42(B) shows an example of Chart (2) output by employee. Comparing the two, it is clear that productivity has declined only for Employee A alone, suggesting that there is a problem with the individual.

[0145] Figure 43(A) shows an example of Chart (3) output by a department, and Figure 43(B) shows an example of Chart (3) output by an employee. Comparing the two, productivity is clearly low in architectural construction work, so we will focus on this type of work.

[0146] Figure 44(A) shows an example of Figure (4) output by a department, and Figure 44(B) shows an example of Figure (4) output by an employee. Comparing the two, individual productivity is decreasing year by year, and an investigation is required to see if there are any problems within the construction work involved.

[0147] Figure 45 shows an example of outputting Chart (5) by department. Since the employees in the department are relatively old, it is possible that Employee A or the whole department is experiencing factors such as a lack of motivation due to years of work (investigation required).

[0148] Example 2. Analysis pattern for switching aggregation units based on sales amount, value added amount, and value added rate With reference to Figures 46 to 52, Example 2: Analysis pattern of switching aggregation units using sales amount, value added amount, and value added rate will be explained. In Figure 46, the content related to the process of (4) in the bold frame of the processing flow (switching the output content and analyzing from a different perspective) will be explained. By modifying the information in the anomaly judgment definition master, it is also possible to switch from the initial display by processes (1) to (3).

[0149] To switch the pattern from the initial display flow of process flows (1) to (3), change the "Determination Item" in the anomaly determination definition master table. To redraw process flow (4), the user selects "Aggregation (productivity standard)" in the extraction conditions on the analysis screen.

[0150] In Figure 47, when the user selects "Sales amount" in the aggregation (productivity standard) of the extraction conditions on the analysis screen, new data is created for Figures (1) to (5), and data is created for Figure (1) (productivity data per hour (sales) by employee), Figure (2) (productivity data per hour (sales) by year and employee), Figure (3) (productivity data per hour (sales) by work type), Figure (4) (productivity data per hour (sales) by year and work type), and Figure (5) (list of performance by work type).

[0151] In Figure (5), the output data is created by combining the other personnel information tables obtained in 2 based on the personnel information relation table obtained in 2. If the "aggregation (analysis unit)" is an organization, the average data is also aggregated and calculated.

[0152] For each chart, compare the information output for "Total (productivity standard)" in terms of sales amount and value added amount. Figure 48(A) shows an example of chart (1) output in terms of sales amount, and Figure 48(B) shows an example of chart (1) output in terms of value added amount. Comparing the two, there is no difference in the sales amount, but employee A's value added amount is low. There may be some construction work being carried out that is incurring too much cost.

[0153] Figure 49(A) shows an example of Chart (2) output in sales amount, and Figure 49(B) shows an example of Chart (2) output in value added amount. Comparing the two, the value added amount has recently decreased, which may be due to factors such as changes in the construction work in charge or organizational changes.

[0154] Figure 50(A) shows an example of Chart (3) output in sales amount, and Figure 50(B) shows an example of Chart (3) output in value added amount. Comparing the two, we focus on this type of construction work, which clearly has a low value added amount.

[0155] Figure 51(A) shows an example of Chart (4) output in sales amount, and Figure 51(B) shows an example of Chart (4) output in value added amount. Comparing the two, the value added amount for architectural works is decreasing year by year, while the sales amount is unchanged, so it is necessary to investigate whether there are any factors such as the status of the construction work involved or a change in the person in charge.

[0156] Figure 52 shows an example of outputting Chart (5) in value added amount. Figure 53 shows an image of the characteristics that emerge when you switch the output year and check. In Figure 53, switch to fiscal year 2021 and compare 2022 and 2021.

[0157] situation -Reduced portion of construction work · The construction work involved is mainly sales projects by person in charge A, and the added value and gross profit are low compared to other similar construction works. Therefore, there is a possibility that a different perspective will be noticed, such as whether there is a problem with the construction management of person in charge A. -Has experience in construction work for the past 5 years and has been able to contribute to the trend in added value (has know-how and experience). - In the past, we have been in charge of large-scale construction projects and have been able to contribute solid value and profits.

[0158] Analysis Perspective Should we increase the number of people in charge of construction-related work? From the perspective of capabilities → If this is the case even though there are construction work projects assigned, it is worth reconsidering. - Would it be better to adjust the assignment to larger projects? From the perspective of past experience → If there is a large-scale construction project to be assigned, it is worth considering him / her as a candidate for a member who requires coordination. -Are you filling in the gaps for smaller assignments?: From the perspective of relatively small assignments → If there is a lack of experienced personnel when assigning construction members, this is valuable information to use.

[0159] As described above, according to this embodiment, the system is configured to be able to access the employee information master table in which employees, affiliated business establishments, affiliated departments, ages, employment dates, years, job titles, etc. are registered in association with each other; the daily work report table in which employees, work dates, work categories, cases, work details, and work times are registered in association with each other; the contract information table in which projects, sales representatives, engineers, contract amounts, delivery dates, project types, contract start dates, and contract end dates are registered in association with each other; the sales table in which projects, sales dates, sales amounts, and cost amounts are registered in association with each other; and the cost table in which projects, accounting dates, cost items, and cost amounts are registered in association with each other. The employee information master table, the daily work report table, the contract information table, the sales table, and the cost table are used as parameters for the judgment item, judgment unit, judgment criteria indicating productivity standards, the aggregation period, and the year compared to the previous year, and the judgment criteria for the judgment item are calculated for each judgment unit, the productivity fluctuation rate from the year compared to the calculated judgment item judgment criteria is calculated, and anomaly detection means is provided which detects and outputs a judgment unit for which the calculated productivity fluctuation rate exceeds the lower limit of a prescribed judgment method as a productivity abnormality, thereby making it possible to calculate the productivity of people and organizations and detect targets for which productivity is abnormally increasing or decreasing.

[0160] [4. Contribution to the United Nations-led Sustainable Development Goals (SDGs)] This embodiment can contribute to improving business efficiency and promoting appropriate management decisions by companies, thereby contributing to goals 8 and 9 of the SDGs.

[0161] Furthermore, this embodiment can contribute to reducing waste and promoting paperless and electronic systems, thereby contributing to the achievement of SDGs Goals 12, 13, and 15.

[0162] Furthermore, this embodiment can contribute to strengthening control and governance, making it possible to contribute to Goal 16 of the SDGs.

[0163] 5. Other embodiments The present invention may be embodied in various different embodiments other than those described above within the scope of the technical concept set forth in the claims.

[0164] For example, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods.

[0165] In addition, the processing procedures, control procedures, specific names, information including parameters such as registered data and search conditions for each process, screen examples, and database configurations shown in this specification and drawings may be changed as desired unless otherwise specified.

[0166] Furthermore, with regard to the productivity anomaly detection system 100, each of the components shown in the figures is a functional concept, and does not necessarily have to be physically configured as shown in the figures.

[0167] For example, the processing functions of the productivity abnormality detection system 100, particularly the processing functions performed by the control unit, may be realized in whole or in part by a CPU and a program interpreted and executed by the CPU, or may be realized as hardware using wired logic. The program is recorded in a non-transient computer-readable recording medium including programmed instructions for causing the information processing device to execute the processes described in this embodiment, and is mechanically read by the productivity abnormality detection system 100 as necessary. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in a storage unit such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into a RAM, and cooperates with the CPU to form the control unit.

[0168] In addition, this computer program may be stored in an application program server connected to the productivity abnormality detection system 100 via any network, and it is also possible to download all or a part of it as needed.

[0169] In addition, the program for executing the process described in this embodiment may be stored in a non-transient computer-readable recording medium, or may be configured as a program product. Here, the "recording medium" includes any "portable physical medium" such as a memory card, a Universal Serial Bus (USB) memory, a Secure Digital (SD) card, a flexible disk, a magneto-optical disk, a ROM, an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable and Programmable Read Only Memory (EEPROM (registered trademark)), a Compact Disk Read Only Memory (CD-ROM), a Magneto-Optical disk (MO), a Digital Versatile Disk (DVD), and a Blu-ray (registered trademark) Disc.

[0170] Moreover, a "program" is a data processing method written in any language or description method, and may be in any form, such as source code or binary code. Note that a "program" is not necessarily limited to a single configuration, but also includes a distributed configuration as multiple modules or libraries, and a program that works with a separate program, such as an OS, to achieve its function. Note that the specific configuration and reading procedure for reading a recording medium in each device shown in the embodiments, as well as the installation procedure after reading, may use well-known configurations and procedures.

[0171] The various databases etc. stored in the memory unit are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and store various programs, tables, databases, and web page files etc. used for various processes and providing websites.

[0172] The productivity abnormality detection system 100 may be configured as an information processing device such as a known personal computer or a workstation, or may be configured as the information processing device to which any peripheral device is connected. The productivity abnormality detection system 100 may be realized by installing software (including programs or data, etc.) that causes the device to realize the processing described in this embodiment.

[0173] Furthermore, the specific form of distribution and integration of the devices is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed and integrated in any unit according to various additions or functional loads. In other words, the above-mentioned embodiments can be implemented in any combination, or the embodiments can be implemented selectively. [Explanation of symbols]

[0174] 100 Productivity Anomaly Detection System 102 Control section 102a Master Maintenance Department 102b Anomaly detection unit 102c Analysis Department 104 Communication interface section 106 Storage section 106a Abnormality judgment definition master table 106b Abnormality judgment result table 106c Abnormality judgment result message table 106d Employee information master table 106e Attendance management table 106f Daily work report table 106g Construction contract information table 106h Construction Sales Table 106i Construction cost table 106j Human Resources Information Relation Table 108 Input / Output Interface Section 112 Input Devices 114 Output Device 300 Network

Claims

1. A productivity anomaly detection system including a control unit, The control unit is An employee information master table that associates and registers employees, their affiliated offices, departments, ages, joining dates, years, and job titles, A daily work report table that associates and registers employees, work dates, work categories, cases, work details, and work hours. A contract information table that associates and registers projects, sales representatives, technical representatives, contract amounts, delivery dates, project types, contract start dates, and contract end dates. A sales table that associates projects, sales dates, sales amounts, and cost amounts, and A cost table that associates projects, accounting dates, cost items, and cost amounts, and is configured to be accessible to Using the productivity judgment item, judgment unit, judgment criterion indicating a productivity standard, aggregation period, and year compared to the previous year as parameters, the employee information master table, the daily work report table, the contract information table, the sales table, and the cost table are referenced to calculate the judgment criterion of the judgment item for each judgment unit, calculate the productivity fluctuation rate from the year compared to the calculated judgment criterion of the judgment item, and compare the calculated productivity fluctuation rate with the upper limit or lower limit of a predetermined judgment method. A productivity anomaly detection system comprising an anomaly detection means for detecting and outputting a judgment unit exceeding the above as a productivity anomaly.

2. The control unit is The system is configured to be able to access a master table of anomaly determination definitions in which a definition ID, a reference table for specifying a table to be referenced, a determination criterion, a determination item, a determination unit, and a determination method are associated and registered, The productivity anomaly detection system according to claim 1, characterized in that the anomaly detection means obtains the judgment criteria, judgment items, judgment units, and judgment methods as the parameters from the anomaly judgment definition master table, and refers to a table specified in a reference table of the anomaly judgment definition master table.

3. The control unit is This is for managing relation information that links data that is separated for different management purposes, and is configured to be able to access a human resources information relation table that associates and registers a base table, a key item of the base table, a join destination table, a key item of the join destination table, a unit of aggregation at the time of joining, and an aggregation item; 2. The productivity abnormality detection system according to claim 1, wherein the abnormality detection means uses the human resources information relation table when calculating the judgment criteria for the judgment items for each judgment unit.

4. The project includes construction work; The judgment items include sales amount, added value amount, or added value rate, The criteria include productivity per person or productivity per hour, 4. The productivity anomaly detection system according to claim 1, wherein the judgment unit includes an employee, a business establishment, a department, a construction project, or a type of work.

5. The control unit is an analysis means for analyzing the productivity abnormality detected by the abnormality detection means, The analysis means displays on the analysis screen: For the target year, a graph or chart showing the sales, value added, or value added rate per hour or per person for each employee; Charts showing the annual trends in sales, value added, or value added rate per hour or per person for each employee, For the target fiscal year, a chart showing the sales, value added, or value added rate per hour or per person for each type of construction work involving employees with productivity abnormalities detected; A chart showing the annual trends in sales, value added, or value added rate per hour or per person for each type of work involving employees with detected productivity anomalies, or The productivity anomaly detection system as described in claim 4, characterized in that it outputs a chart that lists, for each project involving an employee in whom a productivity anomaly has been detected for a target year, the name of the construction contract, the sales representative, the technical representative, the type of work, the contract amount, the cost amount, the added value amount, the gross profit amount, the working hours per project, the annual working hours, the annual overtime hours, the number of days worked on holidays per year, the number of days of unplanned leave per year, the number of days worked per year, and the number of calendar days worked per year.

6. A productivity abnormality detection method executed by an information processing device having a control unit, The control unit is An employee information master table that associates and registers employees, their affiliated offices, departments, ages, joining dates, years, and job titles, A daily work report table that associates and registers employees, work dates, work categories, cases, work details, and work hours. A contract information table that associates and registers projects, sales representatives, technical representatives, contract amounts, delivery dates, project types, contract start dates, and contract end dates. A sales table that associates projects, sales dates, sales amounts, and cost amounts, and A cost table that associates projects, accounting dates, cost items, and cost amounts, and is configured to be accessible to Executed in the control unit: a productivity anomaly detection step of calculating the judgment criteria of the judgment items for each judgment unit by referring to the employee information master table, the contract information table, the daily work report table, the sales table, and the cost table using productivity judgment items, judgment units, judgment criteria indicating productivity standards, an aggregation period, and a year for comparison with the previous year as parameters, calculating a productivity fluctuation rate from the comparison year for the calculated judgment criteria of the judgment items, and detecting and outputting a judgment unit for which the calculated productivity fluctuation rate exceeds an upper or lower limit value of a predetermined judgment method as a productivity anomaly.

7. A productivity anomaly detection program to be executed by an information processing device having a control unit, The control unit is An employee information master table that associates and registers employees, their affiliated offices, departments, ages, joining dates, years, and job titles, A daily work report table that associates and registers employees, work dates, work categories, cases, work details, and work hours. A contract information table that associates and registers projects, sales representatives, technical representatives, contract amounts, delivery dates, project types, contract start dates, and contract end dates. A sales table that associates projects, sales dates, sales amounts, and cost amounts, and A cost table that associates projects, accounting dates, cost items, and cost amounts, and is configured to be accessible to In the control unit, A productivity anomaly detection program for executing an anomaly detection step which uses productivity judgment items, judgment units, judgment criteria indicating productivity standards, aggregation period, and year compared to the previous year as parameters, refers to the employee information master table, the daily work report table, the contract information table, the sales table, and the cost table to calculate the judgment criteria of the judgment items for each judgment unit, calculates the productivity fluctuation rate from the comparison year for the calculated judgment criteria of the judgment items, and detects and outputs as a productivity anomaly any judgment unit for which the calculated productivity fluctuation rate exceeds an upper or lower limit value of a predetermined judgment method.

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