JP7650249B2 - Maintenance Support System - Google Patents

Description

The present invention relates to a maintenance support system that supports machine maintenance work.

For machines that operate continuously for long periods of time, such as construction machinery or wind turbines, it is important to improve the operating rate so that the customer (the machine owner, user, agent, or sales office) can maximize their profits. Therefore, it is desirable to be able to carry out appropriate and prompt part replacement or repair before a breakdown occurs, or to be able to quickly repair the machine and get it back up and running again if a breakdown does occur. To achieve this, it is desirable to have the parts necessary for machine maintenance deployed as quickly as possible without running out of stock.

For example, Patent Document 1 proposes a system for calculating the failure probability of parts in semiconductor manufacturing equipment and managing the inventory of maintenance parts according to the failure probability.

On the other hand, multiple similar machines such as construction machinery and wind turbines are often operated simultaneously in remote locations, and warehouses that stock parts are often set up in each region. If the time spent transporting parts increases, it will take longer to repair and restart the machine, which can have a negative impact on the operation rate. For this reason, a system has been proposed in which parts that are expected to be needed are pre-arranged and delivered near the machine. For example, Patent Document 2 discloses a system that determines the need to pre-arrange maintenance parts by calculating the probability that the equipment will be restored by replacing maintenance parts based on the equipment's fault diagnosis information, and the expected value of the return cost when the parts are not used.

JP 2003-257808 A JP 2013-105221 A

As mentioned above, when an abnormality is detected in a machine, it is desirable to respond to that abnormality as early as possible, and it is important to prevent parts from running out of stock in advance. In addition, when a machine is operated overseas or in a remote location, such as construction machinery, it takes a long time to transport the parts from the country where they are manufactured or stored to the local parts warehouse, so it is important to shorten the total parts procurement time, including the time required to transport the parts.

The technology disclosed in Patent Document 1 calculates the failure probability of parts and manages the inventory of maintenance parts according to the failure probability, but does not take into consideration the transportation of parts. If parts are delivered to the site from the point when it is determined that parts need to be stocked, there is a concern that machine downtime will increase due to longer delivery times.

In order to shorten the total parts procurement time, including the time it takes to deliver the parts to the site, it is desirable to deliver the parts to the site in advance. However, from the perspective of the parts provider (seller), delivering the parts to the site before an order is received carries the risk that the parts will not be used, and is therefore not an easy thing to do. In order to pre-deploy parts while taking such risks into account, a mechanism is needed to determine the need for pre-deployment of maintenance parts by comparing the probability that the equipment will be restored by replacing a maintenance part based on the equipment's failure diagnosis information with the expected value of the return cost when the part is not used, as in Patent Document 2, for example.

However, although Patent Document 2 calculates the expected return cost when a part is not used, it does not take into account other costs or factors that affect the costs, making it difficult to apply in practice. Other factors that affect the costs include the cost of storing the parts and fluctuations in the probability of receiving an order for the parts over time. Therefore, a comprehensive evaluation is required from the perspectives of both the parts supplier and the customer.

The present invention was made in consideration of these problems, and its purpose is to provide a maintenance support system that realizes optimal parts procurement and management from the perspectives of both the supplier and the customer of the parts used in the machine, and that makes it possible to minimize machine downtime due to breakdowns.

In order to achieve the above object, the present invention provides a maintenance support system for supporting maintenance work on a plurality of machines operating in the same region, comprising a control device having a calculation function, and a communication device for communicating between the plurality of machines, the control device, and a communication terminal of a customer who owns the plurality of machines, and the control device predicts failures of the plurality of machines based on operation data of the plurality of machines, selects countermeasure parts necessary to repair the predicted failures of the plurality of machines, calculates a predicted order quantity which is a predicted value of the number of orders for the countermeasure parts in the same region, obtains the inventory quantity of the countermeasure parts at a sales management base corresponding to the same region, and, if the inventory quantity falls below the predicted order quantity, selects the ... and, if the inventory quantity falls below the predicted order quantity, selects the countermeasure parts necessary to repair the predicted failures of the plurality of machines, calculates a predicted order quantity which is a predicted value of the number of orders for the countermeasure parts in the same region, and, if the inventory quantity falls below the predicted order quantity, The cost expectation values on the customer side are compared between a case where the parts are pre-deployed at the sales management base and a case where the parts are not pre-deployed, and the cost expectation values of the parts supplier of the countermeasure parts are compared between a case where the countermeasure parts are pre-deployed at the sales management base and a case where the countermeasure parts are not pre-deployed. Based on the comparison result of the customer's cost expectation values and the comparison result of the parts supplier's cost expectation values, it is determined whether or not the countermeasure parts should be pre-deployed. If it is determined that the countermeasure parts should be pre-deployed, a request to the parts warehouse for the countermeasure parts to deploy the countermeasure parts at the sales management base is transmitted, and information on the predicted failures in the multiple machines and information on the countermeasure parts are transmitted to the customer's communication terminal.

According to the present invention configured as described above, the necessity for pre-deployment of countermeasure parts is determined based on a comparison of the customer's expected cost values when countermeasure parts are pre-deployed at a sales management base with those when they are not pre-deployed, and a comparison of the parts supplier's expected cost values when countermeasure parts are pre-deployed at a sales management base with those when they are not pre-deployed, thereby making it possible to realize optimal parts procurement and management from the perspectives of both the parts supplier and the customer. In addition, by performing parts replacement work according to predicted failure information, machine failures can be prevented in advance, making it possible to minimize machine downtime.

The maintenance support system of the present invention makes it possible to realize optimal parts procurement and management from the perspectives of both the parts supplier and the customer used in the machine, and to minimize machine downtime due to breakdowns.

1 is a configuration diagram of a maintenance support system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram of a control device according to the first embodiment of the present invention. FIG. 2 is a diagram showing a data structure of a storage device according to the first embodiment of the present invention. FIG. 2 is a diagram showing a data structure of a machine information database in the first embodiment of the present invention. FIG. 2 is a diagram showing a data structure of a failure/repair history database in the first embodiment of the present invention. FIG. 2 is a diagram showing a data structure of a parts sales history database in the first embodiment of the present invention. FIG. 2 is a diagram showing a data structure of an inventory management database in the first embodiment of the present invention. FIG. 13 is a diagram showing an output result of a parts order probability calculation unit in the first embodiment of the present invention. FIG. 13 shows a comparison of machine downtime due to part replacement in a case where a countermeasure part is ordered in response to a failure prediction (a case where countermeasure parts are pre-deployed) and a case where countermeasure parts are ordered after a failure occurs (a case where countermeasure parts are not pre-deployed). FIG. 13 is a diagram showing a calculation algorithm of a customer's expected profit and loss in the first embodiment of the present invention. FIG. 11 is a diagram showing an algorithm for calculating an expected profit and loss on the part supplier side in the first embodiment of the present invention. FIG. 11 is a diagram showing an example of a calculation result of an expected profit and loss on the part supplier side in the first embodiment of the present invention. FIG. 11 is a diagram showing a determination flow of a part pre-deployment necessity determining unit in the first embodiment of the present invention. FIG. 2 is a diagram showing an example of a delivery instruction sheet according to the first embodiment of the present invention. FIG. 11 is a configuration diagram of a maintenance support system according to a second embodiment of the present invention.

The maintenance support system according to an embodiment of the present invention will be described below with reference to the drawings. Note that in each drawing, the same reference numerals are used for equivalent components, and duplicate descriptions will be omitted as appropriate.

Figure 1 shows the configuration of a maintenance support system in a first embodiment of the present invention. The maintenance support system 1 is a system that supports maintenance work for multiple machines 10 operating in the same area, and includes a server 100 and a storage device 200. The server 100 has a control device 100a and a communication device 100b. Note that in Figure 1, a hydraulic excavator, which is a type of construction machine, is shown as an example of the machine 10, but this system can also be applied to machines other than construction machines as long as it is related to parts delivery.

The control device 100a is composed of one or more computers equipped with a power supply, a CPU (Central Processing Unit), memory, input/output devices, etc. The control device 100a is connected to a communication network 20 composed of a LAN (Local Area Network), the Internet, etc., via a communication device 100b.

The storage device 200 is composed of, for example, one or more storage devices (such as hard disks). The storage device 200 may be installed in the server 100, or may be connected to the server 100 via the communication network 20. The storage device 200 stores data related to the processing of the control device 100a in electronic file format or database format such as a relational database. In this embodiment, it is assumed that the data is stored in database format.

Each machine 10 is equipped with sensors that measure pressure, temperature, etc., and transmits sensor data and operation information such as mileage to the communication device 100b via communication means such as satellite communication. The operation information received by the communication device 100b is recorded in the storage device 200 by the control device 100a. The operation and maintenance of each machine 10 is performed based on the operation plan and maintenance plan stored in the storage device 200. The machine owner 30 of the machine 10 owns a communication terminal 31. The communication terminal 31 is an electronic device with communication and display functions, such as a smartphone, tablet terminal, or personal computer. The machine owner 30 receives information regarding the operation status, failure detection status, maintenance plan, etc. of the machine 10 he owns via the communication terminal 31, and transmits orders for parts used in the machine 10 to the local depot 50.

The local depot 50 is a sales management base in the same region, and functions as a supplier that supplies parts necessary for the maintenance and repair of the machine 10. Parts are replenished at the local depot 50 from the parts warehouse 40. The parts warehouse 40 is a parts management base that comprehensively stocks parts and supplies parts to each sales management base. The local depot 50 manages parts using an inventory management database 205 described below, and transports parts in response to orders from the machine owner 30. The machine owner 30 is the customer of the parts, and the parts warehouse 40 and the local depot 50 are the parts suppliers.

Figure 2 shows a functional block diagram of the control device 100a. In Figure 2, the control device 100a has a parts order probability calculation unit 101, a parts inventory confirmation unit 102, a first economic efficiency calculation unit 103, a second economic efficiency calculation unit 104, a parts pre-deployment necessity determination unit 105, a parts pre-deployment arrangement unit 106, and a prediction result transmission unit 107. The control device 100a realizes the functions of each unit by executing a program stored on a hard disk or the like.

Figure 3 shows the data configuration of the storage device 200. The storage device 200 stores a machine information database 201, a machine operation database 202, a failure and repair history database 203, a parts sales history database 204, an inventory management database 205, and an operation plan database 206. In each figure, the databases are abbreviated as "DB."

Figure 4 shows the data structure of the machine information database 201. The machine information database 201 records information on all machines 10 operating in the same area. In the example shown in Figure 4, the database 201 records model information, operation plans, maintenance plans, average excavation volume per unit time, and owner information. The model information includes basic information such as the model, model name, type, serial number, and manufacturing date of the machine. The operation plan includes information on the operation schedule, such as the operating time of the machine at the target site and the operation mode indicating the work content. The maintenance plan includes the maintenance schedule for the machine and a list of parts to be used during maintenance.

Figure 5 shows the data structure of the failure/repair history database 203. The failure/repair history database 203 records information on all failure cases that have occurred in each machine 10. In the example shown in Figure 5, the date on which the failure occurred (failure date), the failure mode, a list of parts to be replaced in the repair (replacement parts list), the time required for the repair (required repair time), and the cost (repair cost) are recorded. The failure mode includes information that indicates the state of the machine failure, such as battery deterioration or tooth damage.

Figure 6 shows the data structure of the parts sales history database 204. The parts sales history database 204 records information on all parts sold to each machine owner. In the example shown in Figure 6, the date of the part sale, the part ID, the part name, the cost of the part, the selling price of the part, the weight of the part, the local depot ID (depot ID), the customer ID of the buyer, and the transportation time and transportation cost from the parts warehouse 40 to the local depot 50 are recorded.

Figure 7 shows the data structure of the inventory management database 205. Information about the parts warehouse 40 and all local depots 50 is recorded in the inventory management database 205. In the example shown in Figure 7, the depot ID, depot name, depot address, depot contact information, part storage cost unit price, and an inventory table of all parts stored in the depot are recorded. The part inventory table records the part ID, part name, and inventory quantity of each part. The part storage cost unit price is the storage cost per unit volume per day.

Next, each function of the control device 100a shown in FIG. 2 will be described.
The parts order probability calculation unit 101 calculates in advance the probability of receiving an order for a part from the machine owner 30 at regular time intervals (for example, one day). The order probability p _o of part k is calculated by Equation 1.

Here, the meaning of each parameter is as follows:

i: The failure mode number in which part k is used.

p _fi : Failure probability of failure mode i.

p _bi : The probability that part k will be purchased if a failure occurs in failure mode i.

The failure probability can be calculated using known methods such as Weibull analysis or the MTBF (Mean Time Between Failures) method. It can also be calculated by learning the machine's operation information data and failure/repair history data using machine learning methods. For example, operation data on the day of the failure and the day before it can be labeled as abnormal data, and operation data without failures during a certain period (for example, one month before and after) can be labeled as normal data, and a prediction model can be created using a known machine learning model such as random forest, and the predicted probability can be used as the failure probability.

The probability that a part will be purchased after a breakdown has occurred can be calculated from the information in the breakdown/repair history database 203 and the parts sales history database 204. For example, all dates of breakdowns are extracted, parts sales history within one week of those dates is searched, and the hit rate for the target part is set as the order probability for the part. Note that the search period for parts sales history is not limited to one week and can be set arbitrarily.

Figure 8 shows an example of the output result of the parts order probability calculation unit 101. In the example shown in Figure 8, information on parts with an order probability greater than zero (part number, part name, required quantity, corresponding failure mode, order probability) is described in a matrix format. Hereinafter, these parts will be referred to as "countermeasure parts." In addition, the parts order probability calculation unit 101 calculates a predicted value for the number of orders for countermeasure parts in the same region (predicted order number) based on the past sales history of the countermeasure parts and the maintenance plans for multiple machines 10.

The parts inventory confirmation unit 102 obtains the number of countermeasure parts in stock at the local depot 50 from the inventory management database 205. If the number of countermeasure parts in stock is less than the predicted order number, the unit 102 determines whether or not the countermeasure parts need to be pre-deployed based on the calculation results of the first economic efficiency calculation unit 103 and the second economic efficiency calculation unit 104.

The first economic efficiency calculation unit 103 calculates the expected value of the total cost reduction amount (expected profit and loss) for the customer 30 when countermeasure parts are pre-deployed. Pre-deployment here refers to the act of stocking countermeasure parts at the local depot 50 before a failure occurs. Pre-deployment of countermeasure parts makes it possible to reduce the downtime of the machine 10 due to part replacement. The following explanation uses part k as an example, but it goes without saying that a similar method can be used to calculate when a part other than part k or multiple parts are the target.

Figure 9 shows the downtime of the machine 10 due to part replacement, comparing the time when a countermeasure part is ordered in response to a failure prediction (when the countermeasure part is pre-deployed) with the time when the countermeasure part is ordered after the failure occurs (when the countermeasure part is not pre-deployed). The meaning of each parameter in the figure is as follows.

Tt: Time required to transport parts from local depot 50 to the machine location.

Tr: Time required for part replacement or repair. This can be estimated from the average time required for part replacement or repair when the same part has previously failed, as recorded in the failure/repair history database 203.

Tw: Parts transportation time from parts warehouse 40 to local depot 50.

When part k is pre-deployed and replaced/repaired, the downtime (non-operating time) of the machine is the time Tr required for part replacement or repair, and the operating loss C _D due to the downtime is calculated by Equation 2.

Here, Pr is the total construction project profit, A is the total construction excavation volume, and can be obtained from the operation plan database 206. _WK is the average excavation volume per unit time of the machine, and can be obtained from the machine information database 201.

When part k is pre-deployed and replaced/repaired, the total cost C _Total1 of the customer is calculated by Equation 3.

Here, C _P is the selling price of part k, and is obtained from the part sales history database 204. C _R is the replacement/repair cost of part k, and is obtained from the failure/repair history database 203.

Next, the expected loss is calculated when part k is not pre-deployed and the part is purchased after the machine breaks down to replace/repair it. In this case, as shown in Fig. 9, the machine downtime is (Tw + Tt + Tr), and the machine operation loss _CND and the customer's total cost _CTotal2 are calculated by Equation 4.

10 shows an algorithm for calculating the expected profit/loss (C _EC ) on the customer 30 side. The expected profit/loss (C _EC ) on the customer 30 side is a value obtained by subtracting the expected cost value (C _F ) in the case where parts are pre-deployed from the expected cost value (C _NF ) in the case where parts are not pre-deployed, and is calculated by Equation 5.

Here, _pf is the failure probability of the machine, and is calculated by the parts order probability calculation unit 101.

The second economic efficiency calculation unit 104 calculates the expected value of the total cost reduction amount (expected profit and loss) on the part supplier's 40, 50 side when countermeasure parts are pre-deployed. Fig. 11 shows an algorithm for calculating the expected profit and loss (S _EC ) on the part supplier's side. The expected profit and loss (S _EC ) on the part supplier's 40, 50 side is a value obtained by subtracting the expected cost value (S _F ) when parts are pre-deployed from the expected cost value (S _NF ) when parts are not pre-deployed, and is calculated by Equation 6.

The expected cost (S _F ) when parts are pre-allocated is calculated by Equation 7.

Here, the meaning of each parameter is as follows:

S _Total1 : The total cost when the parts are purchased after the parts are pre-deployed, and is calculated by Equation 10 described later.

S _Total2 : The total cost in the case where the part is not purchased after the part is pre-deployed, and is calculated by the formula 11 described later.

p _s1 : the probability that the part will be purchased after the part is pre-deployed, and is estimated from the customer's part purchase history recorded in the parts sales history database 204 .

The probability p _s1 that a part will be purchased after the part is pre-arranged is calculated by the following equation (8).

Here, d is the date on which the part is purchased. The probability (p _s1 ) that the part is purchased after the part is pre-allocated is a function of d and is therefore denoted as p _s1 (d). p _o is the order probability of part k calculated by the part order probability calculation unit 101. _{p op} (d) is the probability that part k is purchased for a machine other than the target machine, and is defined, for example, by equation 9.

Here, D0 is the date when part k arrives at the local depot 50, and p _L is the probability that the part is purchased per day. p _L can be calculated from the parts sales history database 204.

The total cost (S _Total1 (d)) in the case where the parts are purchased after the parts are pre-arranged is calculated by Equation 10.

Here, the meaning of each parameter is as follows:

S _P : Profit from selling the part, which is the selling price of the part minus the cost price of the part, and can be calculated from the parts sales history database 204.

S _T : The total of various expenses incurred in advance deployment of parts, including parts transportation charges, customs duties, administrative fees, etc. It can be calculated from publicly known data of the transport company based on the addresses of the parts warehouse 40 and the local depot 50 and the means of transportation of the parts obtained from the inventory management database 205. For example, when using the EMS (Express Mail Service) service, the publicly known data includes a fee table posted on the Japan Post website (https://www.post.japanpost.jp/int/charge/list/ems_all.html) according to weight and region.

S _S (d): Warehouse storage cost incurred until the part is sold, which can be calculated by multiplying the storage cost unit price in the inventory management database 205 by the number of days of storage (=d-D0).

The total cost (S _Total2 ) in the case where the parts are not purchased after the parts are pre-arranged is calculated by the following equation (11).

Here, the meaning of each parameter is as follows:

S _T : The total of various costs incurred in advance deployment of parts, as explained in equation (10).

S _{N S} (d): The warehouse storage cost incurred until the part is sold, which can be calculated from the storage cost unit price in the inventory management database 205 in the same way as S _S in equation (10).

_SND : Depreciation cost incurred when a previously prepared part is not purchased. When a previously prepared part is not purchased, the part must be discarded or returned to the parts warehouse 40, and the cost incurred at that time is recorded.

The expected cost (S _NF ) when parts are not pre-deployed is calculated by Equation 12.

Here, the meaning of each parameter is as follows:

_SE : The amount charged above normal procurement to procure parts urgently.

p _s2 (d): Probability that the part will be purchased on the next arrival date even if the part is not in stock. Since _{p s2} (d) decreases as the date d passes, it is defined by, for example, formula 13.

Here, q is the probability that customers will wait for a part to be restocked even if the part is out of stock.

12 shows an example of the calculation result of the expected profit/loss ( _SEC ) on the part supplier 40, 50 side. The expected profit/loss ( _SEC ) tends to decrease as the date d passes due to an increase in warehouse storage costs or a change in the probability of purchasing the part after the part arrives at the local depot 50. The date when the expected profit/loss ( _SEC ) becomes zero (the disposal reference date) serves as a guide for the timing to dispose of the part. After this date, the expected profit/loss ( _SEC ) becomes negative, and there is a high possibility that the part supplier 40, 50 will incur a loss.

The part pre-deployment necessity determination unit 105 determines whether or not countermeasure parts are to be deployed in advance. Fig. 13 shows a determination flow of the part pre-deployment necessity determination unit 105. When the expected profit/loss (C _EC ) of the customer 30 side calculated by the first economic efficiency calculation unit 103 is zero or less, or when the expected profit/loss (S _EC ) of the part suppliers 40, 50 side calculated by the second economic efficiency calculation unit 104 is zero or less, the part is not pre-deployed. When both the expected profit/loss (C _EC ) of the customer 30 side and the expected profit/loss (S _EC ) of the part suppliers 40, 50 side are greater than zero, the part is pre-deployed. As a result, the part is pre-deployed only when both the part suppliers 40, 50 and the customer 30 are profitable.

When the parts pre-deployment necessity determination unit 105 determines that a part needs to be pre-deployed, the parts pre-deployment arrangement unit 106 arranges for the delivery of that part. Specifically, it automatically creates a delivery instruction for the part and transmits it to a delivery person at the parts warehouse 40 via the communication network 20. The delivery person carries out the shipping procedure for the target part in accordance with the delivery instruction. Figure 14 shows an example of a delivery instruction. The delivery instruction includes information such as the part ID, part name, quantity, delivery date, delivery destination, delivery method, and accounting item of the part to be pre-deployed.

The prediction result transmission unit 107 transmits the failure probability and failure mode of the machine 10 calculated by the part order probability calculation unit 101, and the expected profit and loss (C _EC ) calculated by the first economic efficiency calculation unit 103 to the communication terminal 31 of the machine owner 30. The machine owner 30 checks the received failure probability and failure mode, and replaces parts of the machine 10 as necessary.

(summary)
In this embodiment, a maintenance support system 1 for supporting maintenance work of a plurality of machines 10 operating in the same region includes a control device 100a having a calculation function, and a communication device 100b for communicating between the plurality of machines 10, the control device 100a, and a communication terminal 31 of a customer who owns the plurality of machines 10. The control device 100a predicts failures of the plurality of machines 10 based on operation data of the plurality of machines 10, selects countermeasure parts necessary for repairing the predicted failures of the plurality of machines 10, calculates a predicted order quantity which is a predicted value of the number of orders for the countermeasure parts in the same region, acquires the inventory quantity of the countermeasure parts in a sales management base 50 corresponding to the same region, and, if the inventory quantity falls below the predicted order quantity, compares the cost expectation values (C F , C NF ) on the customer 30 side between a case where the countermeasure parts are pre-deployed at the sales management base 50 and a case where the countermeasure parts are not pre-deployed, and calculates the cost expectation values ( _SF , S _NF ) on the part suppliers 40 _, 50 side of the countermeasure parts _between a case where the countermeasure parts are pre-deployed at the sales management base 50 and a case where the countermeasure parts are not pre-deployed. ) and determines whether or not to pre-deploy the countermeasure parts based on the comparison result (C _EC ) of the cost expectation values on the customer 30 side and the comparison result ( _SEC ) of the cost expectation values on the parts suppliers 40, 50 side. If it is determined that the countermeasure parts are required to be pre-deployed, it instructs the parts warehouse 40 for the countermeasure parts to deploy the countermeasure parts at the sales management base 50, and transmits information on the predicted failures in a plurality of machines 10 and information on the countermeasure parts to the communication terminal 31 of the customer 30.

According to this embodiment configured as described above, the necessity of pre-deployment of countermeasure parts is determined based on a comparison result (C _EC ) of the expected costs (C _F , C _NF ) on the customer 30 side when countermeasure parts are pre-deployed at the sales management base 50 and when they are not pre-deployed, and a comparison result ( _SEC ) of the expected costs (S _F , S _NF ) on the part suppliers 40, 50 sides when countermeasure parts are pre-deployed at the sales management base 50 and when they are not pre-deployed, thereby making it possible to realize optimal parts procurement and management from the viewpoints of both the part suppliers 40, 50 and the customer 30. Furthermore, by performing part replacement work in accordance with information on predicted failures, it is possible to prevent failures of the machine 10 in advance, thereby minimizing the downtime of the machine 10.

In addition, the control device 100a in this embodiment calculates the predicted order quantity based on the past sales history of the countermeasure parts and the maintenance plans for the multiple machines 10. This makes it possible to accurately predict the order quantity of the countermeasure parts.

Moreover, the control device 100a in this embodiment calculates the expected cost value (C NF ) on the customer 30 side in a case where the countermeasure parts are not pre-deployed at the sales management base 50, based on the failure probability (p _f ) of the multiple machines 10. This makes it possible to accurately calculate the expected cost value (C _NF ) on the customer 30 side in a case where the countermeasure parts are not pre-deployed at the sales management _base 50.

Furthermore, the control device 100a in this embodiment calculates the expected cost value (S _NF ) of the part suppliers 40, 50 in the cases where the countermeasure parts are pre-deployed at the sales management base 50 and where they are not pre-deployed, based on the purchase probabilities (p _s1 , p _s2 ) of the countermeasure parts. This makes it possible to accurately calculate the expected cost values (S _F , S _NF ) of the part suppliers 40, 50 in the cases where the countermeasure parts are pre-deployed at the sales management base 50 and where they are not pre-deployed.

Furthermore, the control device 100a in this embodiment determines that pre-deployment of countermeasure parts is necessary when the expected cost value (C _F ) of the customer 30 in the case where the countermeasure parts are pre-deployed at the sales management base 50 is smaller than the expected cost value (C _NF ) of the customer 30 in the case where the countermeasure parts are not pre-deployed at the sales management base 50, and the expected cost value (S _F ) of the parts suppliers 40, 50 in the case where the countermeasure parts are pre-deployed at the sales management base 50 is smaller than the expected cost value (S _NF ) of the parts suppliers 40, 50 in the case where the countermeasure parts are not pre-deployed at the sales management base 50. This makes it possible to pre-deploy countermeasure parts only when there is a benefit to both the parts suppliers 40, 50 and the customer 30.

In addition, in this embodiment, when the control device 100a determines that the countermeasure parts need to be pre-deployed, it creates a delivery instruction for the countermeasure parts and transmits it to the delivery person in the parts warehouse 40. This makes it possible to quickly pre-deploy the countermeasure parts at the sales management base 50.

The following describes the maintenance support system according to the second embodiment of the present invention, focusing on the differences from the first embodiment (shown in Figure 1).

Figure 15 shows the configuration of the maintenance support system in this embodiment. In the first embodiment (shown in Figure 1), a scenario is assumed in which parts are supplied directly from the local depot 50 to the customer 30, whereas in this embodiment, a scenario is assumed in which parts are supplied via the agency 60. For example, if there are multiple local depots in the same region, it is preferable from the viewpoint of transportation efficiency to have one agency manage them collectively. Therefore, in this embodiment, the agency 60 performs services such as sales, maintenance, and contracts of machines and parts for the customer 30 on behalf of the parts suppliers 40 and 50, thereby improving transportation efficiency. In addition, by having the agency directly check the condition of the machines and parts on site, it becomes possible to provide the customer with appropriate advice regarding part replacement. Note that in Figure 15, the number of agencies 60 in charge of the same region is one, but this system can also be applied to cases in which multiple agencies are in charge of the same region.

The prediction result transmission unit 107 transmits the machine failure probability and failure mode calculated by the parts order probability calculation unit 101, and the expected profit/loss (C _EC ) of the customer 30 calculated by the first economic efficiency calculation unit 103 to the machine owner 30 via the agency 60. The prediction result transmission unit 107 also transmits the expected profit/loss ( _SEC ) of the parts suppliers 40, 50 calculated by the second economic efficiency calculation unit 104 to the agency 60. The agency 60 carries out sales promotion activities for the parts based on the expected profit/loss ( _SEC ) of the parts suppliers 40, 50, and sells the parts to the customer 30. Such activities of the agency 60 affect the sales performance of the parts, and are reflected in the parts sales history database 204.

(summary)
The control device 100a in this embodiment transmits information on predicted failures in multiple machines 10, information on the countermeasure parts, and a comparison result ( _SEC ) of the cost expectations ( _SF , _SNF ) of the part suppliers 40, 50 to the agency 60 in charge of the same region. This enables the agency 60 to sell parts to the customer 30 at a timing that does not cause losses to the part suppliers 40, 50.

Although the embodiments of the present invention have been described above in detail, the present invention is not limited to the above-mentioned embodiments and includes various modified examples. For example, the above-mentioned embodiments have been described in detail to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to those having all of the configurations described. It is also possible to add part of the configuration of one embodiment to the configuration of another embodiment, and it is also possible to delete part of the configuration of one embodiment or replace it with part of another embodiment.

1...Maintenance support system, 10...Machine, 20...Communication network, 30...Machine owner (customer), 31...Communication terminal, 40...Parts warehouse (parts supplier), 50...Local depot (parts supplier, sales management base), 60...Agent, 100...Server, 100a...Control device, 100b...Communication device, 101...Parts order probability calculation unit, 102...Parts inventory confirmation unit, 103...First economic efficiency calculation unit, 104...Second economic efficiency calculation unit, 105...Parts pre-arrangement necessity determination unit, 106...Parts pre-arrangement arrangement unit, 107...Prediction result transmission unit, 200...Storage device, 201...Machine information database, 202...Machine operation database, 203...Failure and repair history database, 204...Parts sales history database, 205...Inventory management database, 210...Operation plan database.

Claims (7)

In a maintenance support system for supporting maintenance work on multiple machines operating in the same area,
A control device having a calculation function;
a communication device for communicating between the plurality of machines, the control device, and a communication terminal of a customer who owns the plurality of machines;
The control device includes:
predicting failures of the plurality of machines based on operation data of the plurality of machines;
selecting countermeasure parts necessary to repair the predicted failures in said plurality of machines;
Calculating a forecast order quantity, which is a forecast value of the number of orders for the countermeasure parts in the same region;
Acquire the inventory quantity of the countermeasure parts at the sales management base corresponding to the same region;
When the inventory quantity falls below the forecasted order quantity, comparing the customer's expected cost values between a case where the countermeasure parts are pre-deployed at the sales management base and a case where the countermeasure parts are not pre-deployed, and comparing the part supplier's expected cost values between a case where the countermeasure parts are pre-deployed at the sales management base and a case where the countermeasure parts are not pre-deployed,
determining whether or not to pre-deploy the countermeasure parts at the sales management base based on a comparison result between the customer-side cost expectation value and the part supplier-side cost expectation value;
When it is determined that the countermeasure component needs to be pre-deployed at the sales management base, a request is sent to a parts warehouse of the countermeasure component to deploy the countermeasure component at the sales management base;
A maintenance support system characterized in that information on predicted failures in said plurality of machines and information on said countermeasure parts are transmitted to a communication terminal of said customer. 2. The maintenance support system according to claim 1,
The control device calculates the predicted order quantity based on a past sales history of the countermeasure part and a maintenance plan for the plurality of machines. 2. The maintenance support system according to claim 1,
The control device calculates an expected cost value on the customer side in a case where the countermeasure parts are not pre-deployed at the sales management base, based on a failure probability of the plurality of machines. 2. The maintenance support system according to claim 1,
the control device calculates an expected cost value of the parts supplier side in a case where the countermeasure part is pre-deployed at the sales management base and a case where the countermeasure part is not pre-deployed, based on a purchase probability of the countermeasure part. 2. The maintenance support system according to claim 1,
the control device determines that pre-deployment of the countermeasure parts is necessary when the customer's expected cost value in a case where the countermeasure parts are pre-deployed at the sales management base is smaller than the customer's expected cost value in a case where the countermeasure parts are not pre-deployed at the sales management base, and when the parts supplier's expected cost value in a case where the countermeasure parts are pre-deployed at the sales management base is smaller than the parts supplier's expected cost value in a case where the countermeasure parts are not pre-deployed at the sales management base. 2. The maintenance support system according to claim 1,
when it is determined that the countermeasure parts need to be pre-deployed at the sales management base, the control device creates a delivery instruction for the countermeasure parts and transmits it to a delivery person at the parts warehouse. 2. The maintenance support system according to claim 1,
The control device transmits information on predicted failures in the plurality of machines, information on the countermeasure parts, and a comparison result of the cost expectation value of the parts supplier to an agency in charge of the same region.

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