WO2022038832A1 - Système d'assistance aux affaires et procédé d'assistance aux affaires - Google Patents

Système d'assistance aux affaires et procédé d'assistance aux affaires Download PDF

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Publication number
WO2022038832A1
WO2022038832A1 PCT/JP2021/015986 JP2021015986W WO2022038832A1 WO 2022038832 A1 WO2022038832 A1 WO 2022038832A1 JP 2021015986 W JP2021015986 W JP 2021015986W WO 2022038832 A1 WO2022038832 A1 WO 2022038832A1
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prediction
past
support system
prediction error
business support
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PCT/JP2021/015986
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English (en)
Japanese (ja)
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晃治 陰山
智裕 山本
秀之 田所
茂寿 崎村
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株式会社日立製作所
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention relates to a business support system and a business support method that support a manager who formulates a business plan based on predicted values of machine learning.
  • Prediction systems that obtain predicted values using statistical prediction models such as neural networks, deep learning, and multiple regression analysis, which are machine learning, are being put to practical use in various fields.
  • These prediction models give training data in which the values of input items and output items are aligned in advance at the training stage, and adjust the values of the parameters in the prediction model to appropriate values so that the calculated values of the prediction model match as much as possible. .. After that, in the prediction stage, the value of the input item is given to this prediction model, and the prediction value is calculated.
  • the prediction model is identified so as to match the training data given to the learning stage as much as possible, but the prediction value is not always accurate. This becomes remarkable when the quality and quantity of training data are not sufficient. Therefore, it is difficult for the manager who formulates the business plan with reference to the predicted value to judge how much the predicted value can be trusted. In addition, even if it is possible to roughly estimate the prediction error of how much the predicted value deviates, it may not be possible to immediately formulate a business plan such as how to allocate resources accordingly.
  • Patent Document 1 As a background technology in this technical field, for example, there is a technology such as Patent Document 1.
  • the sewage inflow amount prediction device of Patent Document 1 includes a storage unit for storing predicted values and prediction errors, and a prediction step width determining unit as components. With this configuration, the time length from the current time to the future time when the inflow of sewage is to be predicted can be changed arbitrarily.
  • Patent Document 3 indicates that in a conventional prediction method using a regression model, a neural net, or the like, an error bar, a standard deviation, or the like is used to evaluate an error in a future predicted value. (Fig. 13 etc.)
  • Patent Document 1 does not describe the function of showing the user how much the prediction error was. No statistics on the prediction error are given, so the user does not know how likely the prediction is to be off.
  • Patent Document 2 is a frequency distribution of predicted values and not an error frequency distribution, the user does not know how likely the predicted values are to deviate.
  • an object of the present invention is to determine how much the manager who formulates a business plan can trust the predicted value in a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning.
  • the purpose is to provide a business support system and a business support method that can make an accurate judgment and can quickly formulate a business plan.
  • the present invention is a business support system that supports the formulation of a business plan, in which the past predicted value and the past actual value, or the past predicted value and the past actual value are calculated.
  • a storage unit that stores prediction errors, a statistical analysis unit that statistically processes past prediction errors and obtains prediction error statistical processing results, and a prediction error statistical processing result obtained by the statistical analysis unit are added to or subtracted from future prediction values. It is characterized by including a prediction error reflection prediction value calculation unit for obtaining a prediction error reflection prediction value, and a display unit for displaying the prediction error reflection prediction value obtained by the prediction error reflection prediction value calculation unit.
  • the present invention is a business support method for supporting the formulation of a business plan, in which (a) a step of calculating a past prediction error from a past predicted value and a past actual value, and (b) the above (a). Prediction error by statistically processing the past prediction error calculated in step and obtaining the prediction error statistical processing result, and (c) adding or subtracting the prediction error statistical processing result obtained in step (b) to the future prediction value. It is characterized by having a step of obtaining a reflected predicted value, and (d) a step of displaying the predicted error reflected predicted value obtained in the step (c) above on the display unit.
  • a business support system and a business support method that support the formulation of a business plan based on the predicted value of machine learning, it is possible to accurately determine how much the manager who formulates the business plan can trust the predicted value. It is possible to realize a business support system and a business support method that can make a judgment and can quickly formulate a business plan.
  • Example 1 of this invention It is a functional block diagram of the business support system which concerns on Example 1 of this invention.
  • This is an example of a display unit that displays a histogram of the prediction error together with the predicted value. This is an example of displaying the frequency (frequency) of the prediction error in one day as a histogram. This is a display example showing the frequency distribution.
  • Example 2 of this invention This is an example of a display unit in which a figure (square) that can be freely moved up and down is superimposed and displayed.
  • This is an example of a display unit that displays an automatically calculated future business plan.
  • This is an example of a display unit that displays data that is affected by the automatically calculated future business plan.
  • the business to be supported by the business support system of the present invention specifically includes freight delivery business, plant and equipment operation business, etc., and the future business plan is determined with reference to the predicted values. If it is a business, it is not limited to these.
  • FIG. 1 is a diagram showing the overall configuration of the business support system of this embodiment.
  • the past prediction error 12 stored in the storage unit 10 is given to the statistical analysis unit 14.
  • the learning time storage unit 32 which stores the date and time data when learning is performed by a method such as machine learning in the past, gives the learning time 34 of the prediction model to the specific period setting unit 28.
  • the learning time 34 of the prediction model is displayed in the specific period setting unit 28, and a screen is displayed on which the administrator can input the specific period setting input value 30 with reference to it.
  • the input specific period setting input value 30 is given to the statistical analysis unit 14.
  • the statistical analysis unit 14 takes out the data of the time corresponding to the specific period setting input value 30 from the past prediction error 12 and statistically analyzes it.
  • the prediction error statistical processing result 16 obtained as a result is given to the prediction error reflection prediction value calculation unit 18.
  • the prediction error reflection prediction value calculation unit 18 is also given a future prediction value 20 predicted by the prediction unit 26.
  • the prediction unit 26 outputs a future prediction value from the reference time.
  • the prediction error reflection prediction value calculation unit 18 obtains the prediction error reflection prediction value 22 by adding or subtracting the prediction error statistical processing result 16 to the future prediction value 20, and gives it to the display unit 24.
  • the display unit 24 displays the prediction error reflection prediction value 22 on the screen. For example, display a graph in which one axis is time (days) and the other axis is a future prediction error reflection predicted value 22. As a result, the manager can know the predicted value including the statistical value of the past prediction error, and can formulate the business plan more accurately.
  • a future predicted value 20 is also given to the display unit 24, and a future predicted value 20 before addition / subtraction by the prediction error reflection predicted value calculation unit 18 is also displayed.
  • the manager can know how much the prediction error was, and can accurately judge how reliable the predicted value is.
  • the past prediction error 12 is given to the statistical analysis unit 14 from the storage unit 10 and used as it is, but the storage unit 10 may store the past predicted value and the past actual value, in which case.
  • the past prediction error 12 may be calculated based on the past predicted value and the past actual value as preprocessing, and the past prediction error 12 may be statistically analyzed.
  • the actual value 45 stored in the actual value storage unit 43 at the past time point is also given to the display unit 24.
  • the predicted value 36 stored in the predicted value storage unit 42 at the past time point is also given to the display unit 24.
  • the display unit 24 displays the actual value 45 at the past time point and the predicted value 36 at the past time point. This makes it possible to know with what accuracy the future predicted value 20 was correct in the past.
  • the predicted value 36 at the past time point may exist more than once depending on how far ahead is predicted even at the same past time point. For example, if there is a forecasting unit 26 that predicts the future forecast value 20 for three periods of 10 minutes, 20 minutes, and 30 minutes as the time length, at the stage of 12:00 yesterday, 12:10, 12:20, Three future forecasts of 20 at 12:30 have been obtained. At 12:10, 10 minutes later, three future forecast values of 20 at 12:20, 12:30, and 12:40 were obtained.
  • the target data time length setting unit 48 sets a fixed time length 50.
  • the time length 50 set by the target data time length setting unit 48 is given to the display unit 24. For example, if the time length 50 is set to 10 minutes, the display unit 24 displays the future predicted value 20 predicted 10 minutes later at 12:20 at 12:30 yesterday, and 20 at 12:10. Even if there is a future predicted value 20 that predicts minutes later or a future predicted value 20 that predicts 30 minutes later at 12:00, it is not displayed on the screen.
  • the prediction error statistical processing result 40 at the past time point stored in the prediction error statistical processing result storage unit 38 is given to the past time point prediction error reflection prediction value calculation unit 44.
  • the predicted value calculation unit 44 that reflects the past time point prediction error is also given the predicted value 36 at the past time point from the predicted value storage unit 42.
  • the past time point prediction error reflection prediction value calculation unit 44 obtains the past time point prediction error reflection prediction value 46 by adding or subtracting the past time point prediction error statistical processing result 40 to the past time point prediction value 36, and the display unit 24. Give to.
  • the display unit 24 displays the past time point prediction error reflection prediction value 46 on the screen. This makes it possible to know how the past time point prediction error reflection prediction value 46 at the past time point has changed.
  • the prediction error statistical processing result 40 at the past time point given to the past time point prediction error reflection prediction value calculation unit 44 is the actual value 45 and the predicted value storage at the past time point stored in the actual value storage unit 43. It may be the result of statistically processing the predicted value 36 stored in the part 42 at the past time point.
  • FIG. 2 is an example of the display screen of the display unit 24.
  • the future predicted value 20 is displayed by a dotted dotted line from the 0th day on the horizontal axis (number of days) to the 4th day.
  • Prediction error reflection The predicted value 22 is displayed as a histogram rotated 90 degrees to the left at the 1st, 2nd, 3rd, and 4th days on the horizontal axis (number of days).
  • the horizontal axis (number of days) is a negative value
  • the transition from the past to the present is shown as a broken line at the points from -4 days to 0 days
  • the solid line is the actual value 45 at the past time
  • the dotted line is. It is a predicted value 36 at a past time point.
  • the histogram displayed in Fig. 2 is obtained by the following procedure.
  • the specific period setting input value 30 is input by the specific period setting unit 28.
  • the period of the data to be analyzed is set.
  • the learning time 34 of the prediction model is displayed in the specific period setting unit 28.
  • the learning time 34 of the previous prediction model is 3 months ago
  • the learning time 34 of the previous prediction model is 1 year and 3 months ago
  • the learning time 34 of the previous prediction model is 2 years and 6 months ago. Is displayed.
  • the administrator refers to this information and inputs the data to be the target of the histogram from when to when in the specific period setting unit 28. For example, if the period is set from the past 2 months to 1 month ago, the period of 1 month after the learning time 34 of the previous prediction model can be set as the analysis target.
  • the statistical analysis unit 14 performs statistical analysis on the past prediction error 12 of the period of the specific period setting input value 30 set in (1). For example, for the histogram displayed at the position where the horizontal axis (number of days) of FIG. 2 is "1 day", the past prediction error 12 that predicts the future by only one day from the present as the time length is used. Table 1 shows an example of frequency distribution data with a past prediction error of 12.
  • FIG. 3 The results of displaying the data in Table 1 as a histogram are shown in FIG. Looking at FIG. 3, it can be seen that the minimum value of the data range of the prediction error of the number of cargoes is -50 [pieces / day] and the maximum value is -10 [pieces / day]. Since the prediction error is about -30 [pieces / day] on average and the frequency around it is high, the future prediction value 20 may deviate from the future prediction value 20 by about -30 [pieces / day] for only one day. Can be inferred to be high. Since the predicted value shown by the dotted line on the horizontal axis (number of days) in Fig.
  • the histogram in FIG. 3 was rotated 90 degrees to the left, and the value of the data section of the prediction error was placed at the position where the future prediction value 20 was added. It is a horizontal histogram inside.
  • the administrator can accumulate the learning data. It becomes possible to confirm how much the learning has progressed and the accuracy has improved.
  • the actual value 45 at the past time is shown by the solid line, and the dotted line is shown. It is a predicted value 36 at a past time point.
  • the predicted value 36 at the past time point is, for example, a value for which the future is predicted for only one day as the time length at each time point. This figure shows that the actual value 45 at the past time point from -4 days to 0 day was smaller than the predicted value 36 at the past time point.
  • the horizontal axis (days) in Fig. 2 is a negative value-4 days to 0 days, the horizontal axis (days) is the same as 1 to 4 days.
  • the past time prediction error reflection prediction value 46 may be superimposed and displayed on the horizontal histogram. This makes it possible to know how the past time point prediction error reflection prediction value 46 has changed with respect to the past time point prediction value 36.
  • the maximum value, the minimum value, the average value, the mode value, the median value, and the standard deviation are used as long as the past prediction error 12 is targeted. It may be the method you ask for. All of them are added to or subtracted from the future predicted value 20 and displayed in the figure of FIG. If it is the maximum value and the minimum value, it is displayed as an error bar. If it is the average value, mode value, or median value, it is displayed as one point or one short line.
  • the standard deviation the value obtained by subtracting the constant multiple of the standard deviation from the mean value and the value obtained by adding the constant multiple of the standard deviation to the mean value are displayed as error bars.
  • the histograms, error bars, single points, or single short lines may not be displayed separately, but may be displayed in duplicate at the same time.
  • a histogram, a color, a shade of color, a size of a width, a length of a length, a line graph, a curve, an envelope, etc. are displayed. May be.
  • the prediction unit 26 in FIG. 1 includes at least one of neural network, deep learning, and multiple regression calculation, but is not limited to these as long as it is a calculation method for machine learning using training data.
  • the business support system of this embodiment has a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point), or a past predicted value.
  • the storage unit storage unit 10, predicted value storage unit 42, actual value storage unit 43
  • the storage unit stores the past prediction error 12 calculated from the past actual values and the past prediction error 12 are statistically processed, and the prediction error is predicted.
  • Prediction error reflection predicted value calculation unit that obtains the prediction error reflection prediction value 22 by adding / subtracting the prediction error statistical processing result 16 obtained by the statistical analysis unit 14 and the statistical analysis unit 14 that obtains the statistical processing result 16 to the future prediction value 20.
  • 18 and a display unit 24 for displaying the prediction error reflection prediction value 22 obtained by the prediction error reflection prediction value calculation unit 18.
  • a past prediction error 12 is obtained from a past predicted value (predicted value 36 at a past time point) and a past actual value (actual value 45 at a past time point).
  • the manager who formulates the business plan can easily guess how much the future forecast value will be after grasping the prediction error of the forecasting department based on the past performance, and a more appropriate business plan. It can be used for the formulation of.
  • the business plan can be formulated in a short time.
  • FIG. 5 is a diagram showing the overall configuration of the business support system of this embodiment.
  • the graphic position information 52, the business plan automatic calculation unit 54, the business plan 56, and the affected data 58 are added to the components shown in the first embodiment (FIG. 1). ing.
  • Other configurations are the same as those in the first embodiment (FIG. 1). Of these, the figure position information 52 will be described with reference to FIG.
  • Fig. 6 squares are displayed on the horizontal axis (number of days) at the 1st, 2nd, 3rd, and 4th days.
  • This figure shows the number of cargoes estimated by the administrator, and can be moved up and down on the display unit 24 on each day by operating the mouse, keyboard, touch panel, and the like.
  • the shape of the figure is not limited to a square, and may be a different shape.
  • the value of the vertical axis (number of cargoes) of the moved figure becomes the figure position information 52.
  • the figure position information 52 is given from the display unit 24 to the business plan automatic calculation unit 54.
  • the business plan automatic calculation unit 54 calculates the business plan 56 based on the graphic position information 52 and gives it to the display unit 24. Further, the data 58 affected by the business plan 56 is also given to the display unit 24 and displayed.
  • FIG. 7 shows an example of the business plan 56 displayed on the display unit 24.
  • the business plan 56 to be drafted is the number of trucks in operation in this example.
  • FIG. 7 shows how many trucks are required to deliver the cargo to the number of cargoes shown in FIG.
  • the horizontal axis (days) is -4 days to 0 days, and the past actual values including today are displayed, and the horizontal axis (days) is 1 to 4 days in the future business plan 56. Equivalent to.
  • FIGS. 6 and 7 are displayed simultaneously on the same screen, and the display of the business plan 56 of FIG. 7 may change in real time according to the operation of moving the figure (square) in FIG. 6 up and down. desirable.
  • the manager who formulates the business plan estimates the number of cargoes while checking the future predicted value 20 of the number of cargoes and the predicted error reflection predicted value 22 obtained by adding / subtracting the frequency distribution of the past predicted error to the predicted value in FIG. , It is easy to interactively grasp how many trucks will be operated if the estimated number changes.
  • the number of trucks in operation is small, and if the number of trucks in operation can be reduced by one by slightly reducing the estimated number, it is possible to easily formulate a plan under the condition that the estimated number is reduced. On the contrary, if the number of trucks operated increases by one by slightly increasing the estimated number, it is possible to formulate a plan under the condition that the estimated number is increased in consideration of the risk.
  • Figure 8 shows an example of displaying the data 58 that is affected by the automatically calculated future business plan.
  • data 58 affected by future business plans changes in the number of cargoes in stock in the warehouse are displayed in a line graph. Similar to FIG. 7, the horizontal axis (days) is -4 days to 0 days, and the past actual values including today of the inventories in the warehouse are displayed.
  • the data 58 on the horizontal axis (number of days) from 1 to 4 days is affected by the future business plan 56.
  • There are various methods for calculating the affected data 58 but a simple method is the result of subtracting the value obtained by multiplying the number of trucks in FIG. 7 by the transport capacity per truck from the number of cargoes in FIG. It can be calculated as the value obtained by adding the cargo in stock in the warehouse up to the time before that.
  • FIG. 8 is also displayed at the same time on the same screen as FIG. 6, and the display of the data 58 affected by FIG. 8 changes in real time according to the operation of moving the figure (square) of FIG. 6 up and down. Is desirable.
  • the trend graph of the affected data 58 for each item should be displayed on the same screen at the same time.
  • the warehouse inventory cargo is displayed, but the manager who formulates the business plan can see the change in the number of warehouse inventory cargo while moving the figure (square) in FIG. 6 up and down.
  • it is possible to formulate a plan for the number of trucks in operation so that the number of cargo in stock in the warehouse will be at an appropriate level.
  • the data affected by the automatically calculated business plan is also displayed. Therefore, in addition to the effect of the first embodiment, the displayed business plan and the affected data are displayed. You can adjust the estimated value while checking.
  • the present invention is not limited to the above-described embodiment, but includes various modifications.
  • the above embodiments have been described in detail to aid in understanding of the present invention and are not necessarily limited to those comprising all of the described configurations.
  • it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

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Abstract

L'invention concerne un système d'assistance aux affaires et un procédé d'assistance aux affaires qui assistent à la formulation d'un plan d'affaires sur la base de valeurs prédites par l'apprentissage automatique, et permettent à un gestionnaire qui formule un plan d'affaires de déterminer avec précision le degré de fiabilité des valeurs prédites, et de rédiger rapidement le plan d'affaires. Le système d'assistance aux affaires qui assiste à la formulation d'un plan d'affaires est caractérisé en ce qu'il comprend : une unité de stockage qui stocke des valeurs prédites passées et des valeurs réelles passées, ou des erreurs de prédiction passées calculées à partir de valeurs prédites passées et de valeurs réelles passées ; une unité d'analyse statistique qui effectue un traitement statistique sur les erreurs de prédiction passées, et obtient un résultat du traitement statistique sur les erreurs de prédiction ; une unité de calcul de valeur prédite reflétant l'erreur de prédiction qui calcule une valeur prédite reflétant l'erreur de prédiction en ajoutant à valeur prédite future, ou en soustrayant de celle-ci, le résultat du traitement statistique sur les erreurs de prédiction obtenues dans l'unité d'analyse statistique ; et une unité d'affichage qui affiche la valeur prédite reflétant l'erreur de prédiction calculée dans l'unité de calcul de valeur prédite reflétant l'erreur de prédiction.
PCT/JP2021/015986 2020-08-19 2021-04-20 Système d'assistance aux affaires et procédé d'assistance aux affaires WO2022038832A1 (fr)

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JP5886407B1 (ja) * 2014-12-05 2016-03-16 中国電力株式会社 予測装置
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JP5190311B2 (ja) 2008-07-09 2013-04-24 株式会社東芝 取引対象物に関する将来需要の予測シナリオ作成方法およびその作成装置
JP2014054048A (ja) 2012-09-06 2014-03-20 Toshiba Corp 電力予測方法、装置、及びプログラム
JP2016211877A (ja) 2015-04-30 2016-12-15 三菱電機株式会社 下水流入量予測装置

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JP2004336890A (ja) * 2003-05-08 2004-11-25 Hitachi Ltd 電力売買支援システム
JP5886407B1 (ja) * 2014-12-05 2016-03-16 中国電力株式会社 予測装置
JP2016192864A (ja) * 2015-03-31 2016-11-10 日本電気株式会社 予測分布推定システム、予測分布推定方法、および予測分布推定プログラム
JP2020102133A (ja) * 2018-12-25 2020-07-02 株式会社日立製作所 データ処理装置及びデータ処理方法

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