Information processing method, information processing device, and information processing program

Abstract

This information processing device: acquires a manufacturing plan; calculates, for each of a plurality of manufacturing tasks constituting the acquired manufacturing plan, an index value indicating the desirability of each of the plurality of manufacturing tasks on the basis of a grace period from the completion of a product to the delivery date of the product in each of the plurality of manufacturing tasks; creates an image in which a relationship between the calculated index value for each of the plurality of manufacturing tasks and the number of products to be produced in each of the plurality of manufacturing tasks is graphed; and outputs the created image.

Description

Information processing method, information processing device, and information processing program

[0001] This disclosure relates to a technology that assists users in evaluating manufacturing plans in response to industrial demand response, which responds to requests to reduce electricity demand, for companies with industrial facilities.

[0002] Traditionally, during times of power shortages, power providers such as electric power companies may request that their customers, the consumers, temporarily reduce their electricity usage. A demand response (DR) business model has emerged, targeting general households and buildings, which aims to ensure a stable supply of electricity by formulating a system where power providers pay incentives to consumers who reduce their electricity usage (for example, through temporary energy conservation).

[0003] For example, the technology in Patent Document 1 discloses a demand response decision means that identifies continuously operating equipment that can always respond to the reduction of power consumption by partially stopping or suppressing capacity, and time-movable equipment that can respond to the reduction of power consumption by changing the operating time, and calculates the amount of reduction that can be achieved for each of the continuously operating equipment and the time-movable equipment. Also, for example, the technology in Patent Document 1 discloses a demand response decision means that identifies time-movable equipment and process-reconfiguration-required equipment that can respond to the reduction of power consumption by rearranging processes, based on the usage plan of equipment installed in an industrial facility, and periodically updates a demand response response capacity list that summarizes the amount of reduction that can be achieved for each of the continuously operating equipment, time-movable equipment and process-reconfiguration-required equipment.

[0004] However, the conventional technology described above made it difficult for users to evaluate manufacturing plans simply and intuitively, potentially increasing the burden on users in evaluating manufacturing plans, and thus requiring further improvement. In addition, the conventional technology increased the number of processing steps required by the computer to evaluate manufacturing plans, potentially increasing the burden on the computer.

[0005] Patent No. 6096735

[0006] This disclosure was made to solve the above-mentioned problems and aims to provide a technology that allows users to evaluate manufacturing plans simply and intuitively, thereby reducing the burden of evaluation work on users.

[0007] An information processing method according to one aspect of the present disclosure is an information processing method performed by a computer, which includes: acquiring a manufacturing plan; calculating an index value indicating the desirability of each of the multiple manufacturing tasks based on the grace period from the completion of the product to the delivery date of the product in each of the multiple manufacturing tasks constituting the acquired manufacturing plan; creating an image that graphs the relationship between the calculated index value for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks; and outputting the created image.

[0008] According to this disclosure, users can evaluate manufacturing plans simply and intuitively, reducing the burden on users for evaluating manufacturing plans. In addition, the number of computer processing steps required to evaluate manufacturing plans can be reduced, thereby reducing the burden on the computer.

[0009] This figure shows an example of the configuration of the information processing system according to this embodiment. This figure shows an example of a manufacturing plan in this embodiment. This figure shows an example of manufacturing performance data stored in the manufacturing performance data storage unit in this embodiment. This is a flowchart for explaining information processing by the information processing device in the embodiment of this disclosure. This figure shows an example of the calculation result of the number of products produced in a manufacturing task and the preference index value of the manufacturing task in this embodiment. This figure shows an example of an image displayed on a display device in this embodiment. This figure shows an example of a method for determining parameters used in a formula for calculating the preference index value.

[0010] (Knowledge forming the basis of this disclosure) The conventional technologies described above do not take into account the cost of user verification work before the automatically generated demand response-based manufacturing plan is reflected in the actual manufacturing line. Specifically, when the manufacturing plan is adapted to the demand response, the amount of available power decreases, which may cause the manufacturing plan to be pushed back and increase the risk of delivery delays. Users need to properly understand these risks and the effects of the demand response in order to evaluate the manufacturing plan.

[0011] However, with conventional technology, it was difficult to allow users to evaluate manufacturing plans simply and intuitively, potentially increasing the burden on users for evaluating manufacturing plans. Furthermore, with conventional technology, the number of processing steps required for evaluating manufacturing plans increased, potentially increasing the computer's workload.

[0012] To address the above challenges, the following technologies are disclosed.

[0013] (1) An information processing method according to one aspect of the present disclosure is an information processing method performed by a computer, which includes: acquiring a manufacturing plan; calculating an index value indicating the desirability of each of the plurality of manufacturing tasks based on the grace period from the completion of the product to the delivery date of the product in each of the plurality of manufacturing tasks that constitute the acquired manufacturing plan; creating an image that graphs the relationship between the calculated index value for each of the plurality of manufacturing tasks and the number of products produced in each of the plurality of manufacturing tasks; and outputting the created image.

[0014] This configuration presents the user with a graph showing the relationship between indicator values ​​indicating the desirability of each of the multiple manufacturing tasks that make up the manufacturing plan, and the number of products produced for each of the multiple manufacturing tasks. This allows the user to evaluate the manufacturing plan simply and intuitively, reducing the burden on the user in evaluating the manufacturing plan. Furthermore, it reduces the number of computer processing steps required to evaluate the manufacturing plan, thereby reducing the burden on the computer.

[0015] (2) In the information processing method described in (1) above, the calculation of the indicator value may be based on, in addition to the grace period, on at least one of the importance of the product, the inventory cost indicating the cost required to store the completed product, the risk value relating to delays in the completion of the product in subsequent manufacturing tasks, and the penalty value relating to violations of constraints that must be observed in the manufacturing plan, and the calculation of the indicator value for each of the multiple manufacturing tasks may be performed for each of the multiple manufacturing tasks.

[0016] This configuration allows for more accurate calculation of indicator values ​​showing the desirability of multiple manufacturing tasks.

[0017] (3) The information processing method described in (2) above may further include: accepting user input of rankings for a plurality of different manufacturing plan samples; and calculating at least one of a first parameter, a second parameter, and a third parameter that indicate the weights to be assigned to the grace period, the inventory cost, and the penalty value used in the formula, based on the relative magnitudes of the formulas for calculating the index values ​​for each of the plurality of manufacturing plan samples according to their rankings.

[0018] With this configuration, the first, second, and third parameters used in the calculation formula for the indicator value are determined by the user ranking multiple manufacturing plan samples, thus allowing the user's subjective judgment criteria to be reflected in the indicator value.

[0019] (4) The information processing method described in (2) above may further include: accepting input from the user of difference values ​​obtained by comparing a plurality of different manufacturing plan samples; and calculating at least one of a first parameter, a second parameter, and a third parameter that indicates the weight to be assigned to the grace period, the inventory cost, and the penalty value used in the formula, based on the formula for calculating the index value for each of the plurality of manufacturing plan samples and the difference value.

[0020] With this configuration, the first, second, and third parameters used in the formula for calculating the indicator value are determined by the user inputting the difference values ​​between multiple manufacturing plan samples. This allows the user's subjective judgment criteria to be reflected more accurately in the indicator value.

[0021] (5) In the information processing method described in any one of (1) to (4) above, the manufacturing plan is a manufacturing plan that corresponds to a demand response, and the method further includes obtaining a manufacturing plan that does not correspond to a demand response, and calculating the amount of reduction in electricity charges in the manufacturing plan that corresponds to a demand response compared to the electricity charges in the manufacturing plan that does not correspond to a demand response as a demand response effect, and the output of the image may also include outputting the calculated demand response effect together with the image.

[0022] With this configuration, the user is presented with the reduction in electricity costs in a manufacturing plan that incorporates demand response compared to a manufacturing plan that does not incorporate demand response, as the effect of demand response. This allows the user to appropriately evaluate the manufacturing plan by considering the effects of demand response.

[0023] (6) In the information processing method described in any one of (1) to (4) above, the manufacturing plan is a manufacturing plan corresponding to a demand response, and further includes obtaining a manufacturing plan that does not correspond to the demand response, and calculating the reduction in CO2 emissions in the manufacturing plan corresponding to the demand response as a demand response effect compared to the CO2 emissions in the manufacturing plan that does not correspond to the demand response, and the output of the image may also include outputting the calculated demand response effect together with the image.

[0024] With this configuration, the reduction in CO2 emissions in a manufacturing plan that incorporates demand response compared to a manufacturing plan that does not incorporate demand response is presented to the user as the effect of demand response. This allows the user to appropriately evaluate the manufacturing plan by considering the effects of demand response.

[0025] (7) In the information processing method described in any one of (1) to (4) above, the manufacturing plan is a manufacturing plan corresponding to a demand response, and further includes obtaining a manufacturing plan that does not correspond to the demand response, and calculating the reduction in power consumption in the manufacturing plan corresponding to the demand response as a demand response effect relative to the power consumption in the manufacturing plan that does not correspond to the demand response, and the output of the image may also include outputting the calculated demand response effect together with the image.

[0026] With this configuration, the reduction in power consumption in a production plan that incorporates demand response compared to a production plan that does not incorporate demand response is presented to the user as the effect of demand response. This allows the user to appropriately evaluate the production plan by considering the effects of demand response.

[0027] (8) The information processing method described in any one of (5) to (7) above further includes calculating the demand response efficiency by dividing the demand response effect by the total number of the products produced in at least one manufacturing task in which the index value is lower than a threshold, and the output of the image may also include outputting the calculated demand response efficiency together with the image.

[0028] With this configuration, the demand response effect is divided by the total number of products produced in at least one manufacturing task where the index value is lower than the threshold, and this value is presented to the user as the demand response efficiency. As a result, the user can appropriately evaluate the manufacturing plan by considering whether or not the demand response efficiency of the manufacturing plan has improved.

[0029] (9) The information processing method described in any one of (1) to (8) above may further include generating the manufacturing plan such that there are no manufacturing tasks in which the index value is lower than a threshold.

[0030] This configuration allows for the creation of a more optimal manufacturing plan by adding the constraint that no manufacturing tasks have an index value lower than a threshold to the optimization algorithm used to generate the manufacturing plan.

[0031] (10) The information processing method described in (9) above may further include accepting the user's selection of at least one manufacturing task from among the plurality of manufacturing tasks that requires improvement, and setting the largest index value among at least one index value corresponding to the selected at least one manufacturing task as the threshold.

[0032] With this configuration, the largest of at least one metric values ​​corresponding to at least one manufacturing task selected by the user is set as the threshold, thus allowing the user's ideas to be fed back into the generation of the manufacturing plan.

[0033] (11) In the information processing method described in any one of (1) to (10) above, the image may include a stacked bar graph in which the horizontal axis represents the index value and the vertical axis represents the number, and the number of the plurality of manufacturing tasks is accumulated according to the index value.

[0034] With this configuration, the image includes a stacked bar graph where the horizontal axis represents the indicator value and the vertical axis represents the quantity, stacking the quantities of multiple manufacturing tasks according to the indicator value. This allows for the visualization of the favorability of the manufacturing plan, enabling users to intuitively evaluate the manufacturing plan.

[0035] In addition, the present disclosure can be realized not only as an information processing method for executing the characteristic processing as described above, but also as an information processing apparatus including a characteristic configuration corresponding to the characteristic processing executed by the information processing method. Further, it can be realized as a computer program that causes a computer to execute the characteristic processing included in such an information processing method. Therefore, in the following other aspects, the same effects as those of the above information processing method can be achieved.

[0036] (12) An information processing apparatus according to another aspect of the present disclosure is an information processing apparatus including a processor. The processor acquires a production plan, and based on the lead time from the completion of the product to the delivery date of the product in each of the plurality of production tasks constituting the acquired production plan, calculates an index value indicating the preference of each of the plurality of production tasks for each of the plurality of production tasks, creates an image in which the relationship between the calculated index value for each of the plurality of production tasks and the number of products produced in each of the plurality of production tasks is graphed, and outputs the created image.

[0037] (13) An information processing program according to another aspect of the present disclosure causes a computer to function so as to acquire a production plan, calculate an index value indicating the preference of each of the plurality of production tasks for each of the plurality of production tasks based on the lead time from the completion of the product to the delivery date of the product in each of the plurality of production tasks constituting the acquired production plan, create an image in which the relationship between the calculated index value for each of the plurality of production tasks and the number of products produced in each of the plurality of production tasks is graphed, and output the created image.

[0038] (14) A non-temporary computer-readable recording medium according to another aspect of the present disclosure records the information processing program described in (13) above.

[0039] The embodiments of the present disclosure will be described below with reference to the accompanying drawings. Note that the embodiments described below are all specific examples of the present disclosure. The numerical values, shapes, components, steps, order of steps, etc. shown in the following embodiments are merely examples and are not intended to limit the present disclosure. In addition, among the components in the following embodiments, the components not described in the independent claims indicating the most general concept are described as optional components. Also, in all the embodiments, the respective contents can be combined with each other.

[0040] (Embodiment) FIG. 1 is a diagram showing an example of the configuration of an information processing system 1 according to the present embodiment.

[0041] The information processing system 1 includes an information processing apparatus 10 and a display apparatus 20.

[0042] The display apparatus 20 is, for example, a liquid crystal display apparatus and displays information presented to the user. The display apparatus 20 is communicably connected to the information processing apparatus 10 by wire or wirelessly. Note that the display apparatus 20 may be communicably connected to the information processing apparatus 10 via a network. The network is a local area network or a wide area network.

[0043] In the present embodiment, the user is, for example, a production manager in a factory.

[0044] The information processing apparatus 10 includes a production plan generation unit 101, a production plan acquisition unit 102, a metric value calculation unit 103, a DR (demand response) efficiency calculation unit 104, an image creation unit 105, an output unit 106, an inventory management cost information storage unit 201, and a production result data storage unit 202.

[0045] The information processing apparatus 10 includes at least a computer system including, for example, a control program, a processing circuit such as a processor or a logic circuit that executes the control program, and a recording device such as an internal memory or an accessible external memory that stores the control program. Note that the information processing apparatus 10 may be realized, for example, by hardware implementation by a processing circuit, or by execution of a software program held in a memory by a processing circuit, or distributed from an external server, or by a combination of these hardware implementation and software implementation.

[0046] Further, the information processing apparatus 10 may be a server or an edge computer. When the information processing apparatus 10 is a server, the display device 20 may be a terminal such as a personal computer, a smartphone, or a tablet computer. Further, the configuration of the information processing apparatus 10 may be implemented in the display device 20. Further, the configuration of the information processing apparatus 10 may be distributed and arranged in the display device 20 and the server.

[0047] The production plan generation unit 101 generates a production plan corresponding to the demand response. Further, the production plan generation unit 101 generates a production plan that does not correspond to the demand response. The production plan generation unit 101 automatically generates a production plan. The production plan generation unit 101 generates a production plan in which the value of the objective function is minimized by an optimization process.

[0048] For example, the objective function Obj is represented by the following formula (1).

[0049] Obj = p ＤＲ * Term ＤＲ + p ｔｉｍｅ * Term ｔｉｍｅ + p ｐｏｗｅｒ * Term ｐｏｗｅｒ + Σp ｃｏｎｓｔ * N ｃｏｎｓｔ ... (1)

[0050] In the above formula (1), Term ＤＲ represents the excess power consumption during the demand response implementation period, Term ｔｉｍｅ represents the total production time, Term ｐｏｗｅｒ represents the total power consumption, Nｃｏｎｓｔ This represents the number of constraint violations. Also, p ＤＲ , p ｔｉｍｅ , p ｐｏｗｅｒ , and p ｃｏｎｓｔ These represent the weighting parameters for power consumption excess, total manufacturing time, total power consumption, and the number of constraint violations, respectively.

[0051] The manufacturing plan that responds to demand response includes the excess power consumption during the demand response period. ＤＲ A manufacturing plan that does not address demand response is generated by minimizing the value of the objective function Obj which includes the excess power consumption Term during the demand response period. ＤＲ It is generated by minimizing the value of the objective function Obj, which does not contain [the specified value].

[0052] The manufacturing plan generation unit 101 may create multiple manufacturing plans corresponding to the demand response by changing the values ​​of several parameters of the objective function. Alternatively, the manufacturing plan generation unit 101 may create multiple manufacturing plans corresponding to the demand response by changing the values ​​of parameters that affect the demand response.

[0053] The manufacturing plan acquisition unit 102 acquires a manufacturing plan. The manufacturing plan acquisition unit 102 acquires a manufacturing plan corresponding to the demand response generated by the manufacturing plan generation unit 101. The manufacturing plan generation unit 101 may generate a manufacturing plan based on a plan generation start instruction entered by the user. The manufacturing plan generation unit 101 may store the generated manufacturing plan in a memory (not shown). The manufacturing plan acquisition unit 102 may acquire the manufacturing plan from the memory.

[0054] Furthermore, the manufacturing plan acquisition unit 102 acquires manufacturing plans that do not correspond to the demand response generated by the manufacturing plan generation unit 101.

[0055] Furthermore, in this embodiment, the manufacturing plan generation unit 101 generates the manufacturing plan, but this disclosure is not particularly limited thereto. An input device (not shown) may accept input of a manufacturing plan by the user. That is, the user may create the manufacturing plan. The manufacturing plan acquisition unit 102 may acquire the manufacturing plan created by the user.

[0056] Figure 2 shows an example of a manufacturing plan in this embodiment.

[0057] As shown in Figure 2, each row of the production plan stores information about a production task. A production task is a set of processes for manufacturing products of the same part number that are produced consecutively. For example, consider the production of two types of products, part number A and part number B. If 100 units of product A and 100 units of product B are produced, and all products of the same part number are produced consecutively, the production plan consists of two production tasks. On the other hand, if instead of 100 units of product A being produced consecutively, 50 units of product A are produced first, then 100 units of product B are produced, and finally 50 units of product A are produced, the production of the first 50 units of product A and the production of the second 50 units of product A are each generated as separate production tasks.

[0058] Each manufacturing task includes attribute information such as manufacturing ID, quantity, manufacturing start time, manufacturing completion time, delivery date, and manufacturing ID of the next process. In addition to the above attribute information, information such as part number, possible manufacturing start time, and delivery destination may also be added.

[0059] The manufacturing ID is identification information used to identify a manufacturing task. The quantity indicates the number of products to be produced in the manufacturing task. The manufacturing start time indicates the date and time when the manufacturing of the product begins in the manufacturing task. The manufacturing completion time indicates the date and time when the manufacturing of the product is completed in the manufacturing task.

[0060] The due date for each manufacturing task represents the deadline for completing the manufacturing process of that task. Ideally, the manufacturing completion time should be earlier than the due date, but depending on the manufacturing plan, the manufacturing completion time may exceed the due date.

[0061] The manufacturing ID of a subsequent process indicates another manufacturing task that will be performed after the completion of a manufacturing task. For example, in the example shown in Figure 2, after the manufacturing task with manufacturing ID "0" is completed, the manufacturing task with manufacturing ID "12" is performed.

[0062] The inventory management cost information storage unit 201 pre-stores inventory management cost information that shows the storage cost per unit time for one product for each of multiple manufacturing tasks. The unit time is, for example, one hour.

[0063] The manufacturing performance data storage unit 202 stores manufacturing performance data that shows past manufacturing performance.

[0064] Figure 3 shows an example of manufacturing performance data stored in the manufacturing performance data storage unit 202 in this embodiment.

[0065] The manufacturing performance data storage unit 202 stores manufacturing performance data for each manufacturing task. The manufacturing performance data includes the number of products produced in the manufacturing task, the planned start time of manufacturing for the manufacturing task, the planned completion time of manufacturing for the manufacturing task, the actual start time of manufacturing for the manufacturing task, and the actual completion time of manufacturing for the manufacturing task. The planned start time and planned completion time are the start time and completion time included in the manufacturing plan that was generated in advance.

[0066] The index value calculation unit 103 calculates an index value for each of the multiple manufacturing tasks that make up the manufacturing plan acquired by the manufacturing plan acquisition unit 102, based on the grace period from product completion to product delivery date for each of the multiple manufacturing tasks that make up the manufacturing plan.

[0067] The indicator value calculation unit 103 calculates an indicator value for each of the multiple manufacturing tasks based on, in addition to the grace period, the importance of the product, inventory costs indicating the costs required to store the finished product, a risk value related to delays in the completion of the product in subsequent manufacturing tasks, and a penalty value related to violations of constraints that must be complied with in the manufacturing plan.

[0068] The quality of a manufacturing plan can be determined by various evaluation criteria. For example, many factories are required to complete the manufacturing of products by the specified deadline and deliver the completed products to the customer. Considering this, a manufacturing plan in which manufacturing tasks are completed by the deadline will receive a high evaluation, while a manufacturing plan in which manufacturing tasks are not completed by the deadline will receive a low evaluation.

[0069] Furthermore, in factory production sites, production lines are often temporarily suspended due to equipment failures or other problems. Such incidents cause delays in production from the original production plan, resulting in situations where manufacturing tasks that were scheduled to be completed by the deadline cannot actually be completed. One possible countermeasure in production planning is to include a buffer period between the completion time of a manufacturing task and the deadline, thereby providing a buffer. By including a buffer period, the production plan becomes more robust against unforeseen delays, resulting in a more favorable production plan for the user.

[0070] Furthermore, in addition to the delay risk of the manufacturing task at hand, if there are subsequent manufacturing tasks, the manufacturing plan must also take these subsequent tasks into consideration. For example, if it is known from past manufacturing experience that subsequent manufacturing tasks frequently experience delays, it is desirable to include a buffer in the manufacturing plan. Past manufacturing performance data is used to consider the delay risk of subsequent manufacturing tasks.

[0071] On the other hand, if an excessive buffer is provided between the completion time of manufacturing and the delivery date to prepare for unforeseen delays in manufacturing tasks, the finished product or intermediate goods must be stored as inventory during that buffer period, leading to increased inventory costs. Indicators representing the feasibility of a manufacturing plan should take such inventory costs into consideration. Inventory management cost information is used to account for inventory costs.

[0072] By taking the above factors into consideration, the user can obtain indicators that are useful for evaluating the manufacturing plan, especially considering the risk of manufacturing delays. The indicator value calculation unit 103 calculates indicator values ​​useful for evaluating each manufacturing task from the acquired manufacturing plan.

[0073] The index value calculation unit 103 calculates the favorability index value for each manufacturing task in the manufacturing plan based on the following formula (2).

[0074] Indicator value = α * Grace period * Product importance * Risk of delay in subsequent processes - β * Inventory cost - γ * Penalty value for constraint violation ... (2)

[0075] As mentioned above, the indicator for the feasibility of a manufacturing plan needs to be an indicator that comprehensively takes into account an evaluation of the manufacturing plan from various perspectives. Considerations that should be included in the indicator include robustness against production delay risks, economic costs, and whether the constraints necessary for actually executing the manufacturing plan on the production line are met.

[0076] A variable that directly expresses robustness against the risk of delays in production activities is the grace period between the completion time of a manufacturing task and the delivery date. Therefore, the grace period between the completion time of a manufacturing task and the delivery date is incorporated as a variable in equation (2).

[0077] Furthermore, the degree to which the delivery date of a finished product must be strictly adhered to varies depending on the product. For example, if there is a certain amount of spare stock of a certain product, even if the manufacturing of that product is slightly delayed, it may be possible to avoid violating the delivery deadline of an already ordered product. In this case, the importance of the product is low. Also, for example, if it is possible to adjust the delivery date with the customer according to the manufacturing status, the importance of the product is low. Also, if there is leeway in the original delivery date setting, the importance of the product is low. For such products, even if the time leeway in the manufacturing plan is short, it is not considered that the desirability of the manufacturing plan will be low. For this reason, the importance of the product is incorporated as a variable in equation (2). As shown in equation (2), the importance of the product is multiplied by the leeway period. The importance of the product is predetermined for each manufacturing task and is stored in memory not shown. The importance of the product may also be set by the user. The importance of the product is expressed in three levels: "high," "medium," and "low," and a value is set for each of "high," "medium," and "low."

[0078] Furthermore, if the delay risk of a subsequent manufacturing task is significant, the delay risk of the subsequent manufacturing task is added to the indicator of the favorability of the manufacturing plan. The delay risk of a subsequent manufacturing task can be calculated, for example, from the manufacturing performance of past manufacturing tasks. If the execution time of the target manufacturing task in the past is often longer than the planned time, it can be determined that the manufacturing task has a high delay risk. Execution time is the time from the actual start time to the actual completion time, and planned time is the time from the planned scheduled start time to the planned scheduled completion time. The indicator value calculation unit 103 uses the average difference between the planned time and the execution time as the delay risk value of the subsequent manufacturing task. Equation (2) incorporates the delay risk value of the subsequent process as a variable. In this case, the longer the execution time is compared to the planned time, the larger the delay risk value becomes, and the longer the grace period needs to be. Therefore, in equation (2), the delay risk value is multiplied by the grace period.

[0079] The indicator value calculation unit 103 identifies the manufacturing ID of the next process in a manufacturing task based on the manufacturing plan and acquires manufacturing performance data corresponding to the identified manufacturing ID of the next process. Based on the acquired manufacturing performance data corresponding to the manufacturing ID of the next process, the indicator value calculation unit 103 calculates the average difference between the planned time and the execution time as the delay risk value for the subsequent manufacturing task.

[0080] Furthermore, inventory costs are incorporated as a variable in equation (2). The indicator value calculation unit 103 calculates the grace period from the manufacturing completion time to the delivery date based on the manufacturing plan. The indicator value calculation unit 103 also obtains the number of products to be produced in the manufacturing task from the manufacturing plan. The indicator value calculation unit 103 also obtains the storage cost per unit time for one product from the inventory management cost information storage unit 201. The indicator value calculation unit 103 calculates inventory costs by multiplying the grace period from the manufacturing completion time to the delivery date, the number of products to be produced in the manufacturing task, and the storage cost per unit time for one product.

[0081] Furthermore, equation (2) incorporates a constraint violation penalty value as a variable. Typically, there are multiple constraints that must be observed within a manufacturing plan. For example, one constraint is to complete the manufacturing of the product by the delivery date. Another constraint is to maintain the order of the manufacturing tasks if a single product is completed through multiple manufacturing tasks. These constraints are often essential for carrying out actual production activities, and failure to satisfy them may prevent production activities from being carried out. However, the manufacturing plan generated by the manufacturing plan generation unit 101 does not always satisfy the constraints, and it is necessary to reflect such constraint violations in the manufacturing plan favorability index. The index value calculation unit 103 subtracts a predetermined value from the manufacturing plan favorability index value if there is one constraint violation. The index value calculation unit 103 may also use the number of constraint violations as the constraint violation penalty value.

[0082] Furthermore, if even one manufacturing plan fails to meet the constraints, the user is not required to evaluate the manufacturing plan. If a manufacturing plan fails to meet the constraints, the information processing device 10 may not perform the manufacturing plan visualization process and instead notify the user that a constraint violation has been detected, prompting them to regenerate the manufacturing plan.

[0083] Furthermore, when calculating the indicator value for the favorability of the manufacturing plan, the extent to which the grace period, inventory costs, and constraint violation penalty value each influence the evaluation of the manufacturing plan is considered to vary depending on the situation. Therefore, it is desirable that the grace period, inventory costs, and constraint violation penalty value be controllable. For example, in equation (2), the first parameter α, the second parameter β, and the third parameter γ are set as coefficients. The first parameter α indicates the weight to be assigned to the grace period, the second parameter β indicates the weight to be assigned to inventory costs, and the third parameter γ indicates the weight to be assigned to the constraint violation penalty value. If the grace period is emphasized and the length of the time leeway in the manufacturing plan is prioritized, the value of the first parameter α in equation (2) should be set to increase. Also, if the increase in inventory costs due to the increase in inventory is emphasized, the value of the second parameter β in equation (2) should be set to increase. Also, if the constraint violation penalty value is emphasized, the value of the third parameter γ in equation (2) should be set to increase.

[0084] The specific method for calculating the favorability index value will be explained using equation (2). If there is a 72-hour grace period between the completion time of manufacturing and the delivery date for a manufacturing task in the acquired manufacturing plan, "72" is substituted into the grace period in equation (2). Also, if the importance of a product is expressed in three stages: "low (=1)", "medium (=2)", and "high (=3)", and the importance of the product for the manufacturing task in question is "medium", then "2" is substituted into the product importance in equation (2). Also, if the average difference between the planned time and the execution time of a subsequent manufacturing task is 3 hours, then "3" is substituted into the delay risk value of the subsequent process in equation (2). Also, if the inventory cost is 20 million yen, then "2000" is substituted into the inventory cost in equation (2). Also, if there is one constraint violation, then "1" is substituted into the constraint violation penalty value in equation (2). Furthermore, the first parameter α is set to "100", the second parameter β is set to "0.2", and the third parameter γ is set to "100". In this case, the index value for the desirability of the manufacturing task is calculated as 100 * 72 * 2 * 3 - 0.2 * 2000 - 100 * 1 = 42700.

[0085] In this embodiment, equation (2) is given as an example of a method for calculating the index value of the desirability of the manufacturing tasks in the manufacturing plan, but the index value may be calculated by other methods. For example, if the manufacturing plan includes manufacturing processes that involve many employees, such as the assembly process in a factory, the manufacturing plan may affect the content or working hours of the employees. For example, if the manufacturing plan causes frequent overtime and employees finish work later than the regular time, the physical and mental burden on the employees will increase, and the overtime pay paid by the factory operator will also increase. If such employee working hours are also taken into account in the evaluation of the manufacturing plan, a variable indicating the total overtime hours of employees may be added to the index value calculation formula. Specifically, the index value calculation unit 103 may calculate the index value of the desirability of each manufacturing task in the manufacturing plan based on the following equation (3).

[0086] Indicator value = α * Grace period * Product importance * Risk of delay in subsequent processes - β * Inventory costs - γ * Penalty value for constraint violation - δ * Total overtime hours ... (3)

[0087] Total overtime hours represent the total overtime hours of employees in manufacturing tasks, and the fourth parameter δ represents the coefficient (weight) multiplied by the total overtime hours.

[0088] Furthermore, the index value may be calculated using a different calculation method than that shown in equation (2), using the variables and parameters shown in equation (2). For example, in equation (2), inventory costs and constraint violation penalty values ​​are subtracted from the value obtained by multiplying the grace period, product importance, and downstream delay risk value. However, if the range of the favorability index value is limited to 0 or greater due to the specifications of the graph that will be ultimately visualized, the value obtained by multiplying the grace period, product importance, and downstream delay risk value may be divided by the value obtained by adding inventory costs and constraint violation penalty values. Specifically, the index value calculation unit 103 may calculate the favorability index value for each manufacturing task in the manufacturing plan based on the following equation (4). As a result, the favorability index value will only take positive values.

[0089] Indicator value = α * Grace period * Product importance * Risk of delay in subsequent processes / (β * Inventory cost + γ * Penalty value for constraint violation) ... (4)

[0090] Furthermore, in this embodiment, the indicator value calculation unit 103 calculates an indicator value for each of the multiple manufacturing tasks based on the grace period, the importance of the product, the risk value of delays in subsequent processes, inventory costs, and the penalty value for violating constraints, but this disclosure is not particularly limited thereto. The indicator value calculation unit 103 may calculate an indicator value for each of the multiple manufacturing tasks based on the grace period alone. Alternatively, the indicator value calculation unit 103 may calculate an indicator value for each of the multiple manufacturing tasks based on the grace period and at least one of the following: the importance of the product, the risk value of delays in subsequent processes, inventory costs, and the penalty value for violating constraints.

[0091] The DR efficiency calculation unit 104 calculates the amount of electricity cost reduction in a production plan that supports demand response compared to an electricity cost reduction in a production plan that does not support demand response, as the demand response effect. The DR efficiency calculation unit 104 calculates the electricity cost in a production plan that does not support demand response, as well as the electricity cost in a production plan that supports demand response. Then, the DR efficiency calculation unit 104 calculates the amount of electricity cost reduction by subtracting the electricity cost in a production plan that supports demand response from the electricity cost in a production plan that does not support demand response.

[0092] Furthermore, the DR efficiency calculation unit 104 calculates the reduction in CO2 emissions in a production plan that responds to demand response as a demand response effect, compared to the CO2 emissions in a production plan that does not respond to demand response. The DR efficiency calculation unit 104 calculates the CO2 emissions in a production plan that does not respond to demand response, as well as the CO2 emissions in a production plan that responds to demand response. Then, the DR efficiency calculation unit 104 calculates the reduction in CO2 emissions by subtracting the CO2 emissions in a production plan that responds to demand response from the CO2 emissions in a production plan that does not respond to demand response.

[0093] Furthermore, the DR efficiency calculation unit 104 calculates the reduction in power consumption in a production plan that supports demand response as the demand response effect, compared to the power consumption in a production plan that does not support demand response. The DR efficiency calculation unit 104 calculates the power consumption in a production plan that does not support demand response, as well as the power consumption in a production plan that supports demand response. Then, the DR efficiency calculation unit 104 calculates the reduction in power consumption by subtracting the power consumption in a production plan that supports demand response from the power consumption in a production plan that does not support demand response.

[0094] The DR efficiency calculation unit 104 calculates at least one of the following as the demand response effect: the amount of reduction in electricity charges, the amount of reduction in CO2 emissions, and the amount of reduction in electricity consumption.

[0095] The DR efficiency calculation unit 104 may also calculate the demand response effect as the increase in the amount of electricity cost reduction in the manufacturing plan corresponding to the demand response obtained this time, relative to the amount of electricity cost reduction in the manufacturing plan corresponding to the demand response obtained last time.

[0096] Furthermore, the DR efficiency calculation unit 104 may calculate the increase in the reduction of CO2 emissions in the production plan corresponding to the demand response obtained this time as the demand response effect, relative to the reduction of CO2 emissions in the production plan corresponding to the demand response obtained last time.

[0097] Furthermore, the DR efficiency calculation unit 104 may calculate the increase in the reduction of power consumption in the manufacturing plan corresponding to the demand response obtained this time as the demand response effect, relative to the reduction in power consumption in the manufacturing plan corresponding to the demand response obtained last time.

[0098] Furthermore, the DR efficiency calculation unit 104 may calculate the achievement rate of the amount of power consumption in the manufacturing plan corresponding to the demand response against the power consumption limit presented by the demand response as the demand response effect. Specifically, the DR efficiency calculation unit 104 may calculate the achievement rate by dividing the difference between the amount of power consumption when the manufacturing equipment is running at full capacity and the amount of power consumption when production activities are carried out according to the manufacturing plan corresponding to the DR by the difference between the amount of power consumption when the manufacturing equipment is running at full capacity and the upper limit of power consumption during the DR implementation period requested by the aggregator. In an actual calculation example, if the amount of power consumption when the manufacturing equipment is running at full capacity during the DR implementation period is 1000 kWh, the upper limit of power consumption during the DR implementation period requested by the aggregator is 600 kWh, and the amount of power consumption when production activities are carried out according to the manufacturing plan corresponding to the DR is 700 kWh, the DR achievement rate will be 75% ((1000 - 700) / (1000 - 600) = 0.75).

[0099] The DR efficiency calculation unit 104 calculates the demand response efficiency by dividing the calculated demand response effect by the total number of products produced in at least one manufacturing task whose index value is lower than a threshold. The threshold may be predetermined or determined by the user.

[0100] The DR efficiency calculation unit 104 may also calculate the demand response efficiency by dividing the calculated demand response effect by the sum of the values ​​obtained by dividing the number of products produced in each of the multiple manufacturing tasks by the index value for each of the multiple manufacturing tasks.

[0101] Alternatively, the DR efficiency calculation unit 104 may calculate the demand response efficiency by dividing the calculated demand response effect by the amount of decrease in the sum of the index values ​​of each of the multiple manufacturing tasks in the demand response-dependent manufacturing plan relative to the sum of the index values ​​of each of the multiple manufacturing tasks in the manufacturing plan that does not correspond to demand response.

[0102] Alternatively, the DR efficiency calculation unit 104 may calculate the demand response efficiency by dividing the calculated demand response effect by the decrease in the sum of the index values ​​of each of the multiple manufacturing tasks in the manufacturing plan corresponding to the demand response acquired this time, relative to the sum of the index values ​​of each of the multiple manufacturing tasks in the manufacturing plan corresponding to the demand response acquired last time.

[0103] Furthermore, the DR efficiency calculation unit 104 may acquire the index values ​​for each of the multiple manufacturing tasks in a manufacturing plan that does not support demand response, and the index values ​​for each of the multiple manufacturing tasks in a manufacturing plan that supports demand response. The DR efficiency calculation unit 104 may then calculate the demand response efficiency by dividing the calculated demand response effect by the product of the decrease in the number of manufacturing tasks whose index values ​​have decreased and the number of products produced in those manufacturing tasks.

[0104] The image creation unit 105 creates an image that graphs the relationship between the index values ​​for each of the multiple manufacturing tasks calculated by the index value calculation unit 103 and the number of products produced in each of the multiple manufacturing tasks. The image is a stacked bar graph in which the horizontal axis represents the index value and the vertical axis represents the number, with the numbers of multiple manufacturing tasks stacked up according to the index value.

[0105] The output unit 106 outputs the image created by the image creation unit 105 to the display device 20. The output unit 106 also outputs the demand response efficiency calculated by the DR efficiency calculation unit 104 to the display device 20 along with the image.

[0106] The display device 20 displays an image that graphs the relationship between the indicator values ​​for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks. The display device 20 also displays the demand response efficiency along with the image.

[0107] The output unit 106 may output the demand response effect calculated by the DR efficiency calculation unit 104 along with an image. The display device 20 may display the demand response effect along with an image. The output unit 106 may also output the demand response effect and demand response efficiency calculated by the DR efficiency calculation unit 104 along with an image. The display device 20 may display the demand response effect and demand response efficiency along with an image.

[0108] Next, the information processing by the information processing device 10 in the embodiment of this disclosure will be described.

[0109] Figure 4 is a flowchart illustrating the information processing performed by the information processing device 10 in the embodiment of this disclosure.

[0110] First, in step S1, the manufacturing plan acquisition unit 102 acquires a manufacturing plan corresponding to the demand response generated by the manufacturing plan generation unit 101.

[0111] Next, in step S2, the index value calculation unit 103 calculates an index value for each of the multiple manufacturing tasks that make up the manufacturing plan acquired by the manufacturing plan acquisition unit 102, based on the grace period from product completion to product delivery date for each of the multiple manufacturing tasks that make up the manufacturing plan.

[0112] Next, in step S3, the indicator value calculation unit 103 calculates the number of products corresponding to each of the calculated indicator values ​​for each manufacturing task based on the manufacturing plan. The number of products is information that makes it easier for the user to understand what volume each manufacturing task with a preference indicator value represents of the entire manufacturing plan.

[0113] Figure 5 shows an example of the calculation results of the number of products produced in a manufacturing task and the preference index value of the manufacturing task in this embodiment. In Figure 5, the manufacturing ID is identification information for identifying the manufacturing task.

[0114] The index value calculation unit 103 uses the quantity attribute within the manufacturing plan to calculate the number of products produced in a manufacturing task. The index value calculation unit 103 calculates the sum of the quantities corresponding to manufacturing tasks with the same preference index value. For example, in Figure 5, there are two manufacturing tasks (manufacturing ID "2" and manufacturing ID "7") with an index value of 15, and the quantities of products are 370 and 85 respectively. Therefore, the quantity corresponding to the manufacturing tasks with an index value of 15 is 455.

[0115] Furthermore, the index value calculation unit 103 may divide the desirability index value into multiple classes and calculate the number for each class. For example, in Figure 5, when the class width is set to 5, the number of manufacturing tasks with a desirability index value class of 5 to 10 is 510 for manufacturing ID "5", the number of manufacturing tasks with a desirability index value class of 11 to 15 is the sum of manufacturing IDs "2", "3", and "7" totaling 555, the number of manufacturing tasks with a desirability index value class of 16 to 20 is 400 for manufacturing ID "1", and the number of manufacturing tasks with a desirability index value class of 21 to 25 is the sum of manufacturing IDs "0", "4", "6", and "8" totaling 700.

[0116] Returning to Figure 4, in step S4, the DR efficiency calculation unit 104 calculates the demand response effect, which indicates how effective the acquired manufacturing plan is from a demand response perspective. The demand response effect is obtained by calculating how well the demand response requests from the power aggregator are met, and how much power consumption, electricity costs, and CO2 emissions have been reduced compared to a manufacturing plan that does not address demand response.

[0117] The DR efficiency calculation unit 104 may calculate the amount of reduction in electricity costs in a production plan that corresponds to demand response as the demand response effect, relative to electricity costs in a production plan that does not correspond to demand response. The DR efficiency calculation unit 104 may also calculate the amount of reduction in CO2 emissions in a production plan that corresponds to demand response as the demand response effect, relative to CO2 emissions in a production plan that does not correspond to demand response. The DR efficiency calculation unit 104 may also calculate the amount of reduction in power consumption in a production plan that corresponds to demand response as the demand response effect, relative to power consumption in a production plan that does not correspond to demand response.

[0118] Next, in step S5, the DR efficiency calculation unit 104 calculates the demand response efficiency by dividing the calculated demand response effect by the total number of products produced in at least one manufacturing task whose index value is lower than the threshold.

[0119] Next, in step S6, the image creation unit 105 creates an image that graphs the relationship between the index values ​​for each of the multiple manufacturing tasks calculated by the index value calculation unit 103 and the number of products produced in each of the multiple manufacturing tasks. The image creation unit 105 creates an image that displays the calculated preference index values ​​for each of the multiple manufacturing tasks and the information regarding the number of products produced in each of the multiple manufacturing tasks in a format that is easy for humans to visually understand.

[0120] Next, in step S7, the output unit 106 outputs the image created by the image creation unit 105 and the demand response efficiency calculated by the DR efficiency calculation unit 104 to the display device 20. The display device 20 displays an image that graphs the relationship between the index value for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks, as well as the demand response efficiency.

[0121] In this way, the user is presented with an image that graphs the relationship between indicator values ​​showing the desirability of each of the multiple manufacturing tasks that make up the manufacturing plan and the number of products produced for each of the multiple manufacturing tasks. This allows the user to evaluate the manufacturing plan simply and intuitively, reducing the burden on the user in evaluating the manufacturing plan. Furthermore, it reduces the number of computer processing steps required to evaluate the manufacturing plan, thereby reducing the burden on the computer.

[0122] In addition, in Figure 4 above, the process of calculating the demand response effect in step S4 does not depend on the index value of the manufacturing task's desirability and the calculation result of the number of products produced in the manufacturing task, and therefore may be performed before the processes in steps S2 and S3.

[0123] Figure 6 shows an example of an image displayed on the display device 20 in this embodiment.

[0124] Visualizing the manufacturing plan requires presenting information that is useful for users to determine whether there are any problems with the manufacturing plan and how desirable it is. Therefore, an index value indicating the desirability of each manufacturing task in the manufacturing plan is calculated, and this index value is used to visually present the desirability of the manufacturing plan.

[0125] As an example of actual visualization, Figure 6 shows a stacked bar graph where the horizontal axis represents the preference index value for each manufacturing task, and the vertical axis represents the number of products corresponding to the manufacturing task corresponding to each index value. Manufacturing tasks A through H are identified by color or pattern.

[0126] This presentation method allows users to visually understand how favorable each manufacturing task is to them. For example, if the distribution of favorability index values ​​for multiple manufacturing tasks is skewed towards higher values ​​overall, it means that a manufacturing plan including multiple manufacturing tasks is favorable. Also, if there are manufacturing tasks whose favorability index values ​​are below a threshold, the user can instruct the manufacturing plan to be regenerated. Furthermore, as shown in Figure 6, the relationship between each bar and the manufacturing task is illustrated, allowing the user to identify manufacturing tasks with low favorability index values ​​and modify the manufacturing plan to improve those identified tasks.

[0127] In this embodiment, an image created based on a single manufacturing plan corresponding to demand response is displayed on the display device 20. However, the disclosure is not limited thereto, and an image created based on a previously generated manufacturing plan and an image created based on a newly generated manufacturing plan may also be displayed on the display device 20. The previously generated manufacturing plan may be a manufacturing plan that does not correspond to demand response, or a manufacturing plan that does correspond to demand response. The newly generated manufacturing plan is a manufacturing plan that corresponds to demand response. In this case, the user can compare an index value indicating the desirability of the previously generated manufacturing plan with an index value indicating the desirability of the newly generated manufacturing plan. As a result, the user can understand how much the newly generated manufacturing plan has improved or worsened compared to the previously generated manufacturing plan.

[0128] Furthermore, the image creation unit 105 may highlight, in the image created based on the newly generated manufacturing plan, the bars in the bar graph corresponding to manufacturing tasks whose indicator values ​​are higher than those of the previously generated manufacturing plan, and the bars in the bar graph corresponding to manufacturing tasks whose indicator values ​​are lower than those of the previously generated manufacturing plan. For example, the bars in the bar graph corresponding to manufacturing tasks whose indicator values ​​are higher than those of the previously generated manufacturing plan may be displayed in blue, and the bars in the bar graph corresponding to manufacturing tasks whose indicator values ​​are lower than those of the previously generated manufacturing plan may be displayed in red.

[0129] Furthermore, the display device 20 may display both an image created based on a single manufacturing plan generated previously and multiple images created based on multiple newly generated manufacturing plans.

[0130] The parameters in the formula used to calculate the favorability index of a manufacturing plan should be set to reflect the current factory conditions or the user's perspective on the manufacturing plan. However, while the favorability index is calculated using a formula, it is difficult for users without expertise in the calculation algorithm to appropriately set the parameters within this formula. Therefore, a mechanism is provided to easily set the parameters within the formula.

[0131] Figure 7 shows an example of a method for determining the parameters used in the formula for calculating the preference index value.

[0132] In determining the parameters, to reflect the user's subjective judgment criteria in the formula, the user is presented with several simple, distinct manufacturing plan samples that can be easily judged as good or bad, and the user is allowed to input their ranking of these manufacturing plan samples. Then, the parameter values ​​are calculated so that the relative preferences of each manufacturing plan sample match the ranking provided by the user, and these calculated parameter values ​​are reflected in the formula.

[0133] The information processing system 1 may further include an input device, and the information processing device 10 may further include a parameter calculation unit.

[0134] The input device is, for example, a keyboard, mouse, or touch panel, and accepts information input from the user. The input device is connected to the information processing device 10 via wired or wireless means so as to be able to communicate with each other. Alternatively, the input device may be connected to the information processing device 10 via a network so as to be able to communicate with each other. The network is a local area network or a wide area network.

[0135] The display device 20 displays multiple different manufacturing plan samples. The input device accepts user input of rankings for multiple different manufacturing plan samples. The user ranks the displayed multiple manufacturing plan samples. In the example shown in Figure 7, the first, second, third, and fourth manufacturing plan samples are displayed, with the second manufacturing plan sample selected as first place, the fourth manufacturing plan sample as second place, the first manufacturing plan sample as third place, and the third manufacturing plan sample as fourth place.

[0136] The parameter calculation unit calculates a first parameter, a second parameter, and a third parameter, which indicate the weights to be assigned to the grace period, inventory cost, and penalty value used in the formulas, based on the relative order of the formulas used to calculate the indicator values ​​for each of the multiple manufacturing plan samples.

[0137] The specific derivation methods for the first parameter α, the second parameter β, and the third parameter γ in equation (2) above will be explained. A user is presented with multiple manufacturing plan samples and ranks them in order of preference. At this time, the preference index values ​​for the top three manufacturing plan samples are α*a1-β*b1-γ*c1, α*a2-β*b2-γ*c2, and α*a3-β*b3-γ*c3, respectively, and the relative sizes of the top three manufacturing plan samples can be expressed, for example, as α*a1-β*b1-γ*c1 > α*a2-β*b2-γ*c2 and α*a2-β*b2-γ*c2 > α*a3-β*b3-γ*c3. Here, a1, a2, and a3 represent the values ​​of "grace period * product importance * risk of delay in subsequent processes" calculated for each of the three manufacturing plan samples, b1, b2, and b3 represent the values ​​of "inventory costs" calculated for each of the three manufacturing plan samples, and c1, c2, and c3 represent the values ​​of "constraint violation penalty values" calculated for each of the three manufacturing plan samples. The parameter calculation unit defines a constraint satisfaction problem with two inequalities as constraints, and by solving the constraint satisfaction problem, it can derive the first parameter α, the second parameter β, and the third parameter γ that satisfy the magnitude relationship of the constraints.

[0138] To consider specific numerical values, if the favorability index value for the first-ranked manufacturing plan sample is 51α-15β-20γ, the favorability index value for the second-ranked manufacturing plan sample is 36α-35β-39γ, and the favorability index value for the third-ranked manufacturing plan sample is 30α-20β-41γ, then solving the constraint satisfaction problem yields one solution: α = 1.5, β = 0.5, and γ = 1.0. These parameters can satisfy the magnitude relationship specified by the user. In this way, the parameters in the formula for calculating the favorability index value of a manufacturing plan, which reflects the user's judgment criteria, can be determined.

[0139] In the parameter determination method described above, the user only provides a ranking evaluation for multiple manufacturing plan samples. Therefore, the parameters estimated from the ranking results only reflect the relative magnitudes of the preference index values. In contrast, in other parameter determination methods, the user not only ranks multiple manufacturing plan samples but also quantitatively evaluates how favorable each of the samples is to the others. By determining parameters based on the evaluation results, it becomes possible to estimate parameters that can calculate preference index values ​​that are more closely aligned with the user's preferences.

[0140] The display device 20 displays several different manufacturing plan samples. The input device accepts user input of the difference value obtained by comparing the several different manufacturing plan samples. The user inputs a numerical value indicating how much more favorable one of the displayed manufacturing plan samples is compared to the other manufacturing plan samples.

[0141] The parameter calculation unit calculates a first parameter, a second parameter, and a third parameter, which indicate the weights to be assigned to the grace period, inventory cost, and penalty value used in the formula, based on the formula for calculating the indicator value for each of the multiple manufacturing plan samples and the input difference value.

[0142] This parameter determination method will be explained using equation (2) as an example. As above, the preference index values ​​for the three manufacturing plan samples are α*a1 - β*b1 - γ*c1, α*a2 - β*b2 - γ*c2, and α*a3 - β*b3 - γ*c3. The input device accepts not only the relative preference of the three manufacturing plan samples, but also the difference values ​​for each of the three manufacturing plan samples. For example, the difference values ​​d1 and d2 of the preference of two manufacturing plan samples are expressed by the equations (α*a1 - β*b1 - γ*c1) - (α*a2 - β*b2 - γ*c2) = d1 and (α*a2 - β*b2 - γ*c2) - (α*a3 - β*b3 - γ*c3) = d2. In this case as well, as when only the relative preference is input, the parameter calculation unit calculates the first parameter α, the second parameter β, and the third parameter γ that satisfy the two equations. This allows us to derive parameters that align with the user's intentions.

[0143] To give a concrete example, if the first index value of the most favorable manufacturing plan sample is 51α-15β-20γ, the second index value of the second most favorable manufacturing plan sample is 36α-35β-39γ, and the third index value of the third most favorable manufacturing plan sample is 30α-20β-41γ, and the difference d1 between the first and second index values ​​is 59.5, and the difference d2 between the second and third index values ​​is 2.0, then one solution derived is α=2.0, β=1.0, and γ=0.5, and these parameters can satisfy the differences between the manufacturing plan samples specified by the user.

[0144] Furthermore, for manufacturing plan samples that the user deems clearly unacceptable, an evaluation result of "unacceptable" may be entered without inputting a ranking or numerical evaluation. This allows the indicator value calculation unit 103 to avoid calculating indicator values ​​for each manufacturing task of unacceptable manufacturing plans, thereby preventing the user from being presented with indicator values ​​for each manufacturing task of unacceptable manufacturing plans.

[0145] As explained above, visualizing the desirability indicators for each of the manufacturing tasks in the manufacturing plan enables intuitive and easy evaluation of the manufacturing plan. However, it is also necessary for users to be presented with information on the extent to which the manufacturing plan can achieve demand response.

[0146] Demand response is a mechanism that responds to a certain power reduction request issued by a power aggregator during peak power consumption periods. By calculating and displaying the extent to which the input manufacturing plan achieved this requested power reduction, it becomes possible to evaluate the manufacturing plan from a demand response perspective. The DR efficiency calculation unit 104 may calculate the ratio between the planned power reduction amount based on the manufacturing plan corresponding to the demand response and the target power reduction amount requested by the power aggregator. For example, the DR efficiency calculation unit 104 may calculate the ratio of the planned power reduction amount to the target power reduction amount. The output unit 106 may output the calculation result to the display device 20.

[0147] Furthermore, the DR efficiency calculation unit 104 may numerically compare the extent to which a manufacturing plan that does not support demand response and a manufacturing plan that supports demand response meet the demand response's power reduction request, and may present the comparison results or improvement amount to the user.

[0148] Furthermore, while this embodiment presents the demand response effect for an entire manufacturing plan, information on how each manufacturing task affects the demand response effect may also be presented. For example, if the amount of power reduction does not reach the target amount due to manufacturing tasks being executed during the time period in which demand response is being implemented, the manufacturing tasks being executed during that time period may be presented to the user.

[0149] The image creation unit 105 may highlight the bars on the bar graph corresponding to manufacturing tasks that exceed the demand response threshold during the demand response implementation period. Furthermore, if the amount of power reduction during the demand response implementation period does not reach the target power reduction amount requested by the demand response, the image creation unit 105 may highlight the bars on the bar graph corresponding to manufacturing tasks performed during the demand response implementation period.

[0150] In this way, not only is the demand response effect presented to the user, but it is also shown which manufacturing tasks are contributing to the reduction in the demand response effect. This allows the user to understand which manufacturing tasks in the manufacturing plan need to be improved.

[0151] Furthermore, a manufacturing plan that accommodates demand response may result in temporary shutdowns of the production line as part of the response operations, ultimately leading to a decrease in productivity. If the product manufacturing completion time is pushed back due to responding to demand response, the favorability index of the manufacturing plan will worsen. When a user considers changing from the original manufacturing plan to a new one, comparing the favorability index of the manufacturing plan and the effect of demand response, which are in a trade-off relationship, is expected to be a significant burden on the user. Therefore, the DR efficiency calculation unit 104 may calculate a demand response efficiency that integrates these two indicators.

[0152] When changing from the original manufacturing plan to a new one in order to enhance the effectiveness of demand response, the favorability index of the new manufacturing plan may decrease. However, this decrease in the favorability index can be interpreted as a contribution to improving the effectiveness of demand response. Therefore, if the decrease in the favorability index is small and the demand response effect is large, the index obtained by dividing the demand response effect by the decrease in the favorability index will increase. This index is calculated as demand response efficiency. By presenting demand response efficiency to the user along with the favorability index of the new manufacturing plan and the demand response effect, multiple manufacturing plans can be compared and evaluated more easily.

[0153] Furthermore, in addition to simply presenting the demand response efficiency value to the user, the manufacturing plan may be filtered based on the demand response efficiency, and manufacturing plans with a demand response efficiency below a threshold may not be presented to the user.

[0154] As described above, by visualizing the preference index values ​​for each manufacturing task in the manufacturing plan, users can not only evaluate the entire manufacturing plan but also easily identify which manufacturing tasks within the plan have problems. Therefore, the user's selection of problematic manufacturing tasks may be accepted, and the specified manufacturing tasks may be fed back into the manufacturing plan generation unit 101.

[0155] The highest preference index value among the multiple manufacturing tasks selected as problematic is set as the threshold acceptable to the user as a manufacturing plan, and the manufacturing plan generation unit 101 generates a manufacturing plan such that there are no manufacturing tasks with a preference index value lower than the threshold.

[0156] In generating manufacturing plans using optimization processing, it is possible to incorporate user feedback into the manufacturing plan generation by adding constraints to ensure that the indicator values ​​of all manufacturing tasks exceed a threshold, or by adding a term to the objective function that imposes a penalty if any manufacturing tasks have indicator values ​​below a threshold.

[0157] In other words, the manufacturing plan generation unit 101 may generate a manufacturing plan such that there are no manufacturing tasks with an index value lower than a threshold. The input device may accept the user's selection of at least one manufacturing task that needs improvement from among a plurality of manufacturing tasks. The manufacturing plan generation unit 101 may set the largest index value among at least one index value corresponding to the selected at least one manufacturing task as the threshold.

[0158] Furthermore, some or all of the functions of the apparatus according to the embodiments of this disclosure may be realized by a processor such as a CPU executing a program.

[0159] Furthermore, all figures used above are illustrative examples provided to illustrate this disclosure, and this disclosure is not limited to these illustrative figures.

[0160] Furthermore, the order in which the steps shown in the flowchart above are performed is illustrative for the purpose of specifically illustrating this disclosure, and other orders are acceptable as long as similar effects are achieved. Also, some of the steps above may be performed simultaneously (in parallel) with other steps.

[0161] The technology disclosed herein allows users to evaluate manufacturing plans simply and intuitively, reducing the burden of evaluation work on users. Therefore, it is useful as a technology to assist users in evaluating manufacturing plans in response to demand.

Claims

1. An information processing method performed by a computer, comprising: acquiring a manufacturing plan; calculating an index value indicating the desirability of each of the multiple manufacturing tasks based on the grace period from the completion of the product to the delivery date of the product in each of the multiple manufacturing tasks constituting the acquired manufacturing plan; creating an image that graphs the relationship between the calculated index value for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks; and outputting the created image.

2. The information processing method according to claim 1, wherein the calculation of the indicator value is based on, in addition to the grace period, at least one of the importance of the product, inventory costs indicating the costs required to store the completed product, a risk value relating to delays in the completion of the product in subsequent manufacturing tasks, and a penalty value relating to violations of constraints that must be observed in the manufacturing plan, and the calculation of the indicator value for each of the plurality of manufacturing tasks is performed for each of the plurality of manufacturing tasks.

3. The information processing method according to claim 2, further comprising: accepting user input of rankings for a plurality of different manufacturing plan samples; and calculating at least one of a first parameter, a second parameter, and a third parameter indicating the weights to be assigned to the grace period, the inventory cost, and the penalty value used in the formula for calculating the index value for each of the plurality of manufacturing plan samples, based on the magnitude relationship corresponding to the rankings.

4. The information processing method according to claim 2, further comprising: accepting user input of difference values ​​obtained by comparing a plurality of different manufacturing plan samples; and calculating at least one of a first parameter, a second parameter, and a third parameter indicating the weight to be assigned to the grace period, the inventory cost, and the penalty value used in the formula, based on the formula for calculating the index value for each of the plurality of manufacturing plan samples and the difference value.

5. The information processing method according to claim 1, wherein the manufacturing plan is a manufacturing plan that corresponds to demand response, and further includes obtaining a manufacturing plan that does not correspond to demand response, and calculating the amount of reduction in electricity charges in the manufacturing plan that corresponds to demand response compared to the electricity charges in the manufacturing plan that does not correspond to demand response as a demand response effect, and the output of the image includes outputting the calculated demand response effect together with the image.

6. The information processing method according to claim 1, wherein the manufacturing plan is a manufacturing plan corresponding to demand response, and further includes: obtaining a manufacturing plan that does not correspond to demand response, and calculating the reduction in CO2 emissions in the manufacturing plan corresponding to demand response as a demand response effect compared to the CO2 emissions in the manufacturing plan that does not correspond to demand response, and the output of the image includes outputting the calculated demand response effect together with the image.

7. The information processing method according to claim 1, wherein the manufacturing plan is a manufacturing plan that corresponds to a demand response, and further includes: obtaining a manufacturing plan that does not correspond to a demand response, and calculating the reduction in power consumption in the manufacturing plan that corresponds to a demand response as a demand response effect compared to the power consumption in the manufacturing plan that does not correspond to a demand response, and the output of the image includes outputting the calculated demand response effect together with the image.

8. The information processing method according to any one of claims 5 to 7, further comprising calculating the demand response efficiency by dividing the demand response effect by the total number of the products produced in at least one manufacturing task in which the index value is lower than a threshold, wherein the output of the image includes outputting the calculated demand response efficiency together with the image.

9. The information processing method according to claim 1, further comprising generating the manufacturing plan such that no manufacturing tasks have an index value lower than a threshold.

10. The information processing method according to claim 9, further comprising: accepting a user selection of at least one manufacturing task from among the plurality of manufacturing tasks that requires improvement; and setting the largest index value among at least one index value corresponding to the selected at least one manufacturing task as the threshold value.

11. The information processing method according to claim 1, wherein the image includes a stacked bar graph in which the horizontal axis represents the index value and the vertical axis represents the number, and the number of the plurality of manufacturing tasks is accumulated according to the index value.

12. An information processing device comprising a processor, wherein the processor acquires a manufacturing plan, calculates an index value indicating the desirability of each of the multiple manufacturing tasks based on the grace period from the completion of the product to the delivery date of the product in each of the multiple manufacturing tasks constituting the acquired manufacturing plan, creates an image that graphs the relationship between the calculated index value for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks, and outputs the created image.

13. An information processing program that obtains a manufacturing plan, calculates an index value indicating the desirability of each of the multiple manufacturing tasks based on the grace period from the completion of the product to the delivery date of the product in each of the multiple manufacturing tasks that make up the obtained manufacturing plan, creates an image that graphs the relationship between the calculated index value for each of the multiple manufacturing tasks and the number of products produced in each of the multiple manufacturing tasks, and outputs the created image.

Images