Optimization method, optimization system, and program

Abstract

To propose an optimal work plan while considering the current resources.SOLUTION: An optimization method includes setting a target important achievement degree index which is a target value of an important achievement degree index indicating an important element in a process until achievement of a plan target, setting a changeable range of a parameter having influence on achievement of the plan target, setting an evaluation expression for calculating an index to be used for optimizing the parameter, and calculating the parameter to make an evaluation value which is a value of the evaluation expression calculated using the parameter set within the changeable range, to be equal to or greater than a threshold which is a value of the evaluation expression calculated from the target important achievement degree index.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an optimization method, an optimization system, and a program. [Background technology]

[0002] Patent Document 1 discloses a supply chain optimization system that optimizes a series of supply chains consisting of multiple business areas with multiple bases from material procurement of products through production to sales. The supply chain optimization system uses a supply chain model and performs simulations corresponding to changes in sales plans based on simulation data. The supply chain optimization system also calculates the inventory quantity of products and materials that minimizes the total cost of the inventory value of the products and materials and the logistics costs of the products and materials. The supply chain optimization system also selects a logistics route based on the increase in product inventory. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2010 / 010788 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, there has been a growing need to optimize multiple linked business activities in the field of logistics, such as delivery systems linked to warehouses where goods are stored, in the manufacturing field such as factories, and in the retail field.

[0005] In Patent Document 1, a simulation is performed using a supply chain model so as to minimize the total cost of inventory value of products and materials and logistics costs. However, there is a problem that the results obtained from the simulation may not be realized with the current resources (e.g., facilities, number of employees, or facility operation rate, etc.).

[0006] The present disclosure has been devised in view of the above-mentioned conventional situation, and aims to propose an optimal business plan taking into account current resources. [Means for solving the problem]

[0007] The present disclosure provides an optimization method that sets a target importance achievement index, which is a target value of an importance achievement index that represents an important factor in the process of achieving a planned goal, sets a change range for a parameter that affects the achievement of the planned goal, sets an evaluation formula for calculating an index used to optimize the parameter, and calculates a parameter such that an evaluation value, which is the value of the evaluation formula calculated using the parameter set within the change range, is equal to or greater than a threshold value, which is the value of the evaluation formula calculated from the target importance achievement index.

[0008] The present disclosure also provides an optimization system that includes a warehouse distribution system in which a warehouse system that manages goods and a system that distributes the goods are linked, and a computing device that optimizes parameters that affect achievement of a planned goal of the warehouse distribution system, wherein the warehouse distribution system transmits current values ​​of the parameters to the computing device, and the computing device sets a target importance achievement index that is a target value of an importance achievement index that represents an important factor in the process of achieving the planned goal, sets a change range of the parameters from the current value, sets an evaluation formula for calculating an index used to optimize the parameters, and calculates parameters such that an evaluation value, which is the value of the evaluation formula calculated using the parameters set within the change range, is equal to or greater than a threshold value, which is the value of the evaluation formula calculated from the target importance achievement index.

[0009] The present disclosure also provides a program that causes a computer to execute the following processes: setting a target importance achievement index, which is a target value of an importance achievement index that represents an important factor in the process of achieving a planned goal; setting a change range for a parameter that affects the achievement of the planned goal; setting an evaluation formula for calculating an index used to optimize the parameter; and calculating a parameter such that an evaluation value, which is the value of the evaluation formula calculated using the parameter set within the change range, is equal to or greater than a threshold value, which is the value of the evaluation formula calculated from the target importance achievement index.

[0010] These comprehensive or specific aspects may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium, or may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. Effect of the Invention

[0011] According to the present disclosure, it is possible to propose an optimal business plan taking into account current resources. [Brief description of the drawings]

[0012] [Figure 1] System configuration diagram of the optimization system [Diagram 2] FIG. 13 is a diagram showing an example of a user intention table; [Diagram 3] Flowchart showing the overall process for optimizing warehouse delivery plans [Figure 4] A flowchart showing an example of a KPI target setting process. [Diagram 5] A flowchart showing an example of a process for setting constraint conditions for input parameters. [Figure 6] Flowchart showing an example of a process of a warehouse delivery linked SIM [Figure 7] A diagram showing an example of a conversion table for standardizing UPH KPIs [Figure 8] An example of a conversion table for standardizing TPH KPIs [Figure 9] FIG. 13 is a diagram showing an example of a reflection rule of a user intention table. [Figure 10] A diagram showing an example of the simulation results displayed on a display device. [Figure 11] FIG. 13 is a diagram showing an example of a conversion table for calculating a value Z. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, with reference to the drawings as appropriate, an embodiment specifically disclosing an optimization method, an optimization system, and a program according to the present disclosure will be described in detail. However, more detailed explanation than necessary may be omitted. For example, detailed explanation of already well-known matters and duplicate explanation of substantially the same configuration may be omitted. This is to avoid the following explanation becoming unnecessarily redundant and to facilitate understanding by those skilled in the art. Note that the attached drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims.

[0014] In recent years, the field of logistics has become more active. Before a product reaches a customer, there are multiple business areas in the warehouse, such as the number of workers, the proficiency of the workers, shelf allocation, product placement, and material handling, and multiple business areas related to delivery, such as the number of bases, the connection status between bases, the number of trucks, and modal shift. A series of business activities are carried out by linking the business activities in each business area. In this way, multiple business activities are intricately intertwined in the field of logistics, and optimizing the chain of a series of business activities is a very important issue in order to efficiently deliver products and maintain high-quality service. Here, a series of business activities until the product stored in the warehouse reaches a customer is called a warehouse delivery plan. Hereinafter, various parameters in the warehouse, such as the number of workers, the proficiency of the workers, shelf allocation, product placement, and material handling, and various parameters in delivery, such as the number of bases, the connection status between bases, the number of trucks, and modal shift, are called input parameters. Input parameters are parameters that affect the achievement of the goal of the warehouse delivery plan. In addition, a system in which such a warehouse system and a delivery system are linked is called a warehouse-delivery system.

[0015] In warehouse and distribution systems, key performance indicators are used when considering the optimization of warehouse distribution plans. Key performance indicators are indicators that represent important factors in the process of achieving the final goal of a warehouse distribution plan. Hereinafter, key performance indicators are referred to as KPIs. Examples of KPIs include cost per hour, cost per unit, number of units per hour, total throughput per hour, utilization rate, and level of service. Below, we will explain the warehouse distribution optimization method, which is a method for optimizing warehouse distribution plans using KPIs.

[0016] First, a system configuration diagram of the optimization system 1 will be described with reference to Fig. 1. Fig. 1 is a system configuration diagram of the optimization system 1.

[0017] The optimization system 1 includes a computing device 10 and a warehouse and distribution system 20 .

[0018] The computing device 10 is a device that performs calculations related to optimization of a warehouse delivery plan in the warehouse / delivery system 20. The computing device 10 is, for example, a personal computer. The computing device 10 includes a communication I / F 11, a memory 12, an input device 13, a display device 14, and a processor 15.

[0019] The communication I / F 11 is an interface circuit that performs wireless or wired communication between the computing device 10 and the warehouse / distribution system 20. Here, I / F stands for interface. The communication between the computing device 10 and the warehouse / distribution system 20 may be via a network. The communication method by the communication I / F 11 is, for example, a Wide Area Network, a Local Area Network, Long Term Evolution, mobile communication such as 4G or 5G, power line communication, short-range wireless communication (e.g., Bluetooth (registered trademark) communication), or communication for mobile phones.

[0020] The memory 12 is configured using, for example, a Random Access Memory (hereinafter referred to as "RAM") and a Read Only Memory (hereinafter referred to as "ROM"), and temporarily stores programs necessary for the operation of the computing device 10, an interactive AI model, and further data generated during operation. The RAM is, for example, a work memory used during the operation of the computing device 10. The ROM stores and holds, for example, a program for controlling the computing device 10 in advance.

[0021] The input device 13 is a device that accepts input from a user such as a manager of the warehouse / distribution system. The input device 13 is, for example, a mouse, a keyboard, a touch panel, or the like, or a combination of these.

[0022] The display device 14 displays the results of calculations performed by the calculation device 10. The display device 14 is, for example, a display. The input device 13 and the display device 14 may be integrated together. In this case, the input device 13 is a touch panel display or the like.

[0023] The processor 15 is, for example, a Central Processing Unit, a Digital Signal Processor, a Graphical Processing Unit, or a Field Programmable Gate Array. The processor 15 functions as a controller that manages the overall operation of the calculation device 10. The processor 15 performs control processing for managing the operation of each part of the calculation device 10, input / output processing of data between each part of the calculation device 10, calculation processing of data, and storage processing of data. The processor 15 operates according to a program stored in the memory 12. The processor 15 uses the memory 12 during operation, and temporarily stores data generated or acquired by the processor 15 in the memory 12. The processor 15 realizes the functions of the KPI processing unit 16, the condition setting unit 17, the optimization processing unit 18, and the result reflection unit 19 by using the programs and data stored in the memory 12.

[0024] The KPI processing unit 16 acquires various parameters from the warehouse / distribution system 20 and calculates the current KPI value of the warehouse / distribution system 20. The KPI processing unit 16 also calculates a target KPI (hereinafter referred to as a "target KPI") based on information input by the user to the input device 13. The target KPI can be read as a target importance achievement indicator.

[0025] The condition setting unit 17 sets the range of change for the current input parameters of the warehouse / distribution system 20. The condition setting unit 17 also sets the input parameters to be fixed. In response to these, the condition setting unit 17 sets a "user intention table" of table data including information on the priority of each input parameter. An example of the user intention table will be described with reference to FIG. 2.

[0026] The optimization processing unit 18 calculates the values ​​of the input parameters that realize the target KPI.

[0027] The result reflecting unit 19 outputs the results of the input parameters calculated by the optimization processing unit 18 to the warehouse / distribution system 20 and reflects them.

[0028] The warehouse / delivery system 20 is a system that links a warehouse system and a delivery system. Here, the warehouse system is a system that uses a known technology to collectively manage the number of workers, the skill level of the workers, shelf allocation, product placement, material handling, etc. in a warehouse where goods such as merchandise are stored. Also, the delivery system is a system that uses a known technology to arrange and plan vehicles required for delivering goods.

[0029] Here, KPIs will be described. There are multiple KPIs, and multiple KPIs need to be considered when simulating the optimization of the warehouse delivery plan. When multiple KPIs are considered, there may be a KPI that the user wishes to optimize. For example, there are various possible cases, such as a case where the user wishes to maximize delivery efficiency with the current number of workers without increasing the number of workers, a case where the user wishes to maximize the number of deliveries by increasing the number of workers, or a case where the user wishes to maximize delivery efficiency by increasing the number of machines in the warehouse. The optimization system 1 according to this embodiment can set in advance the KPI that the user wishes to optimize.

[0030] There may also be cases where a user wishes to optimize multiple KPIs. In this case, the optimization system 1 sets the weighting ratio for each of the multiple KPIs specified by the user based on the user's input. In other words, the optimization system 1 sets weights for each of the multiple KPIs. When multiple KPIs are emphasized, the optimization system 1 evaluates the results of input parameters obtained by simulating using values ​​obtained by normalizing and taking the weighted average of each KPI.

[0031] However, when a user sets the importance of KPIs, it may be difficult and time-consuming for the user to decide how high the importance of each KPI should be set. The KPI processing unit 16 may support the user in setting the importance of KPIs by using an interactive AI technology such as ChatGPT. For example, the KPI processing unit 16 uses an interactive AI technology in which a template of questions to be asked to the user is set in advance. The KPI processing unit 16 may set the importance of KPIs by using an AI model stored in advance in the memory 12 or a storage (not shown), or may set the importance of KPIs by using an AI model of an external device (not shown) via a network.

[0032] An example of the flow of setting the importance of KPIs using dialogue-based AI technology is explained below. Note that the questions and answers explained below are merely examples and are not limited to the above.

[0033] First, the KPI processing unit 16 uses interactive AI technology to display the following question on the display device 14: "What KPIs would you like to focus on in your evaluation to optimize the simulation results? The following KPIs are possible: 1. Cost efficiency per hour; 2. Cost efficiency per unit; 3. Number of units produced per hour; 4. Total production volume per hour; 5. Overall efficiency of the equipment; 6. Violation of service level agreements."

[0034] The KPI processing unit 16 receives the user's response "I would like to focus on the amount of production per hour and the overall efficiency of the equipment" from the input device 13.

[0035] The KPI processing unit 16 uses conversational AI technology to display the following question on the display device 14: "I understand. We focus on the number of units produced per hour and the overall efficiency of the equipment. In addition to these KPIs, are you also interested in cost efficiency or service levels?"

[0036] The KPI processing unit 16 receives from the input device 13 the reply from the user, "I'm also concerned about violations of the service level agreement."

[0037] The KPI processing unit 16 uses an interactive AI technique to display the question "Taking into consideration violations of the service level agreement. Of these three KPIs, which is the most important?" on the display device 14.

[0038] The KPI processing unit 16 receives from the input device 13 the reply from the user that "The most important KPI is the overall efficiency of the equipment."

[0039] The KPI processing unit 16 uses conversational AI technology to display on the display device 14 the question, "Understood. We will evaluate the results of the simulation taking into consideration the overall efficiency of the equipment as the most important KPI, followed by the number of units produced per hour and violations of the service level agreement. The priorities are as follows: 1. Overall efficiency of the equipment, 2. Number of units produced per hour, 3. Violations of the service level agreement. Please let us know if there are any other KPIs you would like to check."

[0040] The KPI processing unit 16 receives from the input device 13 the reply from the user, "There is nothing else I would like to confirm."

[0041] The KPI processing unit 16 uses conversational AI technology to display on the display device 14 the result, "Understood. Now, based on the content of the dialogue so far, we will automatically assign a priority percentage to each KPI. The priority percentages for the KPIs have been set as follows: 1. Overall equipment efficiency - 60% 2. Production volume per hour - 30% 3. Violation of service level agreement - 10%. We will evaluate the simulation results and optimize them using these percentages. Please let us know if you have any comments on these percentages. Please let us know if there are any other points you would like to check or adjustments that need to be made."

[0042] As described above, the KPI processing unit 16 can automatically ask questions using an interactive AI technique to extract information about the importance of KPIs from the user. This enables the KPI processing unit 16 to support the user in easily setting the importance of KPIs.

[0043] Furthermore, the computing device 10 can also determine the setting range and priority of the input parameters based on the input from the user. For example, if the user prefers to increase the working hours of existing workers rather than adding new workers, the computing device 10 sets the priority of the number of workers to a low priority and the priority of the working hours of the workers to a high priority. By setting the priorities of the input parameters in advance in this manner, the computing device 10 can reflect the user's intentions in the simulation. The condition setting unit 17 of the computing device 10 creates a user intention table in which the priorities of the input parameters are set.

[0044] An example of the user intention table will now be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the user intention table.

[0045] The user intention table 100 shown in FIG. 2 is table information set by the condition setting unit 17 based on information input by the user to the input device 13. The user intention table 100 includes information on the "number of receiving inspection workers", "number of shelving workers", "number of picking workers", "total number of workers", and "working time per person" as input parameters that are variable values ​​(hereinafter referred to as "variable input parameters"). The variable input parameters are input parameter items that the user sets as variable values. The variable input parameter items are not limited to the above-mentioned items, and can be added or deleted at the user's discretion.

[0046] The "current value" shown in the user intention table 100 indicates the current value of the warehouse / distribution system 20. For example, the current number of receiving inspection workers is two.

[0047] The "lower limit setting value" is the lower limit value of the input parameter that the user can set at will. The "upper limit setting value" is the upper limit value of the input parameter that the user can set at will. For example, the "number of receiving inspection workers" is set by the user to a range of 1 to 4 people.

[0048] "Priority" is the priority level of an input parameter that can be arbitrarily set by the user. The optimization system 1 calculates the importance of a KPI based on the "priority" information. "Priority" is expressed, for example, in a three-level evaluation. Here, 1 indicates a value that is low priority and is not desired to be changed much. 2 indicates a value that is medium priority and may be changed. 3 indicates a value that is high priority and is desired to be changed actively. Note that priority is not limited to a three-level evaluation, and may be set in a ranking format from highest priority, or may be expressed as a percentage as a proportion of importance.

[0049] In this way, the condition setting unit 17 can arbitrarily set the setting range and priority of the input parameters based on the user's input information. This allows the calculation device 10 to optimize the warehouse delivery plan by reflecting the user's intention.

[0050] Next, an example of the overall process related to the optimization of the warehouse delivery plan will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the overall process related to the optimization of the warehouse delivery plan.

[0051] The KPI processing unit 16 sets a target value for the KPI as "KPI target setting" (step St100). The detailed process of KPI target setting will be described with reference to FIG.

[0052] The condition setting unit 17 sets the constraint conditions of the input parameters as "input parameter constraint condition setting" (step St101). The detailed process of setting the constraint conditions of the input parameters will be described with reference to FIG.

[0053] The optimization processing unit 18 optimizes the warehouse delivery plan using a simulation technique as a "warehouse delivery linked SIM" using the target KPI and the constraint conditions of the input parameters set in the processing of step St100 (step St102). Here, SIM means simulation. The detailed processing of the warehouse delivery linked SIM will be described in FIG. 6.

[0054] The optimization processing unit 18 outputs the simulation result obtained in the process of step St102 to the display device 14 (step St103).

[0055] The user checks the simulation results displayed on the display device 14, and inputs an instruction to the input device 13 as to whether to confirm the warehouse delivery plan if the results are satisfactory, or to change the constraint conditions of the target KPI and input parameters and cause the calculation device 10 to perform the optimization process again. The optimization processing unit 18 determines whether to perform the optimization process again based on the input information from the input device 13 (step St104).

[0056] When the optimization processing unit 18 determines to re-execute the optimization process (step St104, YES), the process of the processor 15 returns to the process of step St100, and the KPI processing unit 16 executes the process of step St100 again.

[0057] When the optimization processing unit 18 determines not to re-perform the optimization process (step St104, NO), it displays on the display device 14 a message asking whether or not to reflect the optimized warehouse / delivery plan in the warehouse / delivery system 20.

[0058] Based on the content displayed on the display device 14, the user inputs into the input device 13 an instruction as to whether or not to reflect the optimized warehouse / distribution plan in the warehouse / distribution system 20.

[0059] The result reflecting unit 19 determines whether or not to reflect the optimized warehouse delivery plan in the warehouse / delivery system 20 based on the instruction regarding whether or not to reflect the warehouse delivery plan received from the input device 13 (step St105).

[0060] When the result reflecting unit 19 determines not to reflect the optimized warehouse-distribution plan in the warehouse-distribution system 20 (step St105, NO), the process ends.

[0061] When the result reflection unit 19 determines that the optimized warehouse-distribution plan is to be reflected in the warehouse-distribution system 20 (step St105, YES), it reflects the result of the optimized warehouse-distribution plan in the warehouse-distribution system 20 (step St106).

[0062] As described above, the calculation device 10 can optimize the warehouse delivery plan based on the target KPI and the constraint conditions of the input parameters set by the user, and reflect the result in the warehouse / delivery system 20. As a result, the calculation device 10 can calculate a feasible and optimal warehouse delivery plan in consideration of the current resources of the warehouse / delivery system 20. Furthermore, the calculation device 10 can calculate an optimal warehouse delivery plan in line with the user's intention by considering the KPI and input parameters that the user considers important. As a result, the calculation device 10 can calculate a warehouse delivery plan that flexibly meets the intention of the user, such as not wanting to increase personnel due to cost considerations or not being able to invest in equipment due to expenses considerations, and propose an optimal warehouse delivery plan to the user.

[0063] Next, an example of the KPI target setting process will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the KPI target setting process.

[0064] The KPI processing unit 16 acquires information on current input parameters from the warehouse / distribution system 20, and calculates the current KPIs (step St200).

[0065] The KPI processing unit 16 displays the result of the KPI calculated in the process of step St200 on the display device 14 (step St201).

[0066] The KPI processing unit 16 causes the display device 14 to display a screen for prompting input of information on the type and value of the target KPI. The KPI processing unit 16 may display on the display device 14 a screen that uses an interactive AI technique as the screen for prompting input of information on the type and value of the target KPI.

[0067] The KPI processing unit 16 acquires information on the type and value of the target KPI input by the user to the input device 13 based on the screen displayed on the display device 14 (step St202).

[0068] Next, an example of a process for setting constraint conditions for input parameters will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of a process for setting constraint conditions for input parameters.

[0069] The condition setting unit 17 acquires the current value X of the input parameter from the warehouse / distribution system 20 (step St300).

[0070] The condition setting unit 17 displays a screen on the display device 14 to prompt the user to input information on the change range of the input parameter to be set as a variable input parameter. Based on the screen displayed on the display device 14, the user inputs information on the change range of the input parameter to be set as a variable input parameter to the input device 13. The condition setting unit 17 acquires information on the change range of the input parameter from the input device 13 (step St301).

[0071] The condition setting unit 17 sets the change range of the input parameters based on the information acquired in the process of step St301 (step St302).

[0072] The condition setting unit 17 sets the unspecified input parameters that have not been specified as variable input parameters to the current value X as fixed values ​​(step St303).

[0073] The condition setting unit 17 displays a screen on the display device 14 to prompt the user to input the priority of the variable input parameter. The user inputs the priority of the variable input parameter based on the screen displayed on the display device 14. The condition setting unit 17 acquires information on the priority of the variable input parameter from the input device 13, and sets the priority of the variable input parameter as a "user intention table" (step St304).

[0074] Next, an example of the process of the warehouse delivery-linked SIM will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of the process of the warehouse delivery-linked SIM.

[0075] The optimization processing unit 18 creates an evaluation formula (Step St400). Formula (1) is an example of the evaluation formula when multiple target KPIs exist.

[0076]

number

[0077] Here, Nor() is a standardization function, and after standardization, the value becomes 0 to 1. i is the weighting value of the KPI and takes values ​​from 0 to 1. i is the value of the KPI, which is a target KPI or a KPI value calculated from variable input parameters. i is an integer equal to or greater than 1 and represents the number of target KPIs. Note that the evaluation formula created by the optimization processing unit 18 is not limited to formula (1), but is merely an example. The evaluation formula is a formula for calculating an index used to optimize input parameters.

[0078] The optimization processing unit 18 acquires constraint conditions for the input parameters from the condition setting unit 17 (step St401).

[0079] The optimization processing unit 18 sets the non-variable input parameter to the current value X of the warehouse / distribution system 20 (step St402). Here, the current value X is a fixed value.

[0080] The optimization processor 18 sets the variable input parameters to values ​​within the designated change ranges based on the information acquired in the process of step St401 (step St403).

[0081] The optimization processing unit 18 executes a simulation based on the values ​​set in the processes of steps St402 and St403, and calculates the value Z of the evaluation formula created in the process of step St400 (step St404). For example, the optimization processing unit 18 arbitrarily sets the value of each variable input parameter within the change range set in the process of step St403. The optimization processing unit 18 calculates the value of the KPI based on the values ​​of the set variable input parameters, and calculates Nor(KPIt i ) is calculated. i A specific method for calculating Nor(KPIt) will be described in detail later. i ) each with a weight value a i The value Z of the evaluation formula is calculated by multiplying these two together.

[0082] The optimization processing unit 18 refers to the user intention table and corrects the evaluation formula (step St405). That is, the optimization processing unit 18 corrects the result of the evaluation formula taking into consideration the priority of the variable input parameters set by the user. For example, the optimization processing unit 18 lowers the evaluation if the value of an input parameter that the user has set to a low priority and does not want to change has changed. A specific method of correcting the evaluation formula will be described in detail in a specific example described later.

[0083] The optimization processing unit 18 calculates a judgment threshold value based on the target KPI. Specifically, the optimization processing unit 18 calculates the value of the evaluation formula of formula (1) using the value of the target KPI, and sets the result as the judgment threshold value Z_th. The method of calculating the judgment threshold value Z_th is not limited to the method using formula (1), and a predetermined value may be used, or another calculation formula may be used. The optimization processing unit 18 judges whether or not the value Z_re of the evaluation formula corrected in the process of step St405 has achieved the judgment threshold value Z_th (step St406). When calculating and simulating the judgment threshold value using formula (1), the value of the evaluation formula is better as it is larger. In other words, the optimization processing unit 18 judges whether or not the value Z_re of the evaluation formula corrected is equal to or larger than the judgment threshold value Z_th.

[0084] When the optimization processor 18 determines that the result of the process in step St405 does not achieve the predetermined determination threshold Z_th of the evaluation formula (step St406, NO), the optimization processor 18 proceeds to the process in step St408.

[0085] When the optimization processing unit 18 determines that the result of the processing in step St405 achieves the predetermined judgment threshold Z_th of the evaluation formula (step St406, YES), it stores the value Y of the variable input parameter and the value Z_re of the evaluation formula in the memory 12 (step St407).

[0086] The optimization processing unit 18 judges whether the time required for the simulation or the number of simulations has reached a predetermined upper limit (step St408).

[0087] When the optimization processing unit 18 determines that the time or number of times related to the simulation has not reached the predetermined upper limit (step St408, NO), the optimization processing unit 18 returns to the process of step St403. That is, the optimization processing unit 18 repeatedly executes the simulation until the time or number of times related to the simulation reaches the predetermined upper limit. The optimization processing unit 18 repeats the simulation so that the value of the evaluation formula becomes large, and calculates the maximum value of the evaluation formula.

[0088] When the optimization processing unit 18 determines that the time or number of times related to the simulation has reached a predetermined upper limit (step St408, YES), it displays the variable input parameters that have achieved the evaluation formula and the value of the evaluation formula on the display device 14 (step St409).

[0089] As described above, the optimization processing unit 18 can optimize the values ​​of the input parameters by repeatedly executing a simulation using the evaluation formula. Furthermore, since the optimization processing unit 18 optimizes the input parameters including the target KPI and the user intention table set by the user, the optimization processing unit 18 can optimize the input parameters in accordance with the intention of the user. As a result, the optimization processing unit 18 can improve the convenience of the user and support the improvement of business efficiency and productivity in accordance with the user's management plan. Furthermore, since the optimization processing unit 18 optimizes the input parameters based on the constraint conditions of the input parameters, the optimization processing unit 18 can calculate the input parameters within the range that can be realized by the warehouse / distribution system 20.

[0090] Here, a specific example of the optimization process for the warehouse delivery plan will be described with reference to Fig. 7 to Fig. 9. Note that the specific example described below is merely an example and is not limiting.

[0091] For example, the user selects Unit Per Hour (hereinafter referred to as "UPH") and Total Throughput pcs per Hour (hereinafter referred to as "TPH") as types of target KPIs for a warehouse system.

[0092] UPH is the value obtained by dividing the passing production volume at each process, i.e., incoming inspection, put-away, picking for warehousing, etc., by the working hours at each process. UPH is calculated for each process, but it is desirable for these UPH values ​​to be uniform. This is because if the UPH differs between processes, for example, if the UPH of the previous process is large but the UPH of the next process is small, the previous process has a higher processing capacity and the waiting inventory between the previous process and the next process will swell. The bottleneck between the previous process and the next process will cause problems such as processing not proceeding smoothly and the need for space to store the waiting inventory.

[0093] TPH is calculated by dividing the number of shipments in the entire warehouse by the total working hours. TPH is an index of the performance of the entire warehouse, and the higher the value, the higher the processing capacity of the warehouse system.

[0094] Here, it is assumed that the KPI processing unit 16 sets the target value of UPH based on the user's input as "keep the difference in UPH between processes within 1%" as the KPI target setting. Also, it is assumed that the KPI processing unit 16 sets the priority of TPH to be three times more important than the priority of UPH based on the user's input as the KPI target setting.

[0095] Next, the condition setting unit 17 sets the variable input parameters and the priority of each of the variable input parameters as a constraint condition setting for the input parameters based on the information input by the user to the input device 13. Here, it is assumed that the variable input parameters and the priority of each of the variable input parameters are set according to the user intention table 100 shown in FIG.

[0096] In addition, the condition setting unit 17 sets the "stocking plan," "shipping out plan (picking list)," "shelf arrangement," "worker proficiency," "standard work time (standard work time set according to proficiency)," and "number of pieces of equipment required for each work" as non-variable input parameters (i.e., fixed input parameters) as constraint conditions for the input parameters.

[0097] The optimization processor 18 sets the formula (1) as the evaluation formula. The optimization processor 18 sets the weighting values ​​of the evaluation formula to "a_UPH=0.25 (25%)" and "a_TPH=0.75 (75%)" based on the priority set in the KPI target setting.

[0098] The optimization processing unit 18 standardizes (normalizes) the value of each KPI to a value between 0 and 1. The standardization method is arbitrary, but the optimization processing unit 18 performs standardization, for example, using the conversion tables shown in Figs. 7 and 8.

[0099] 7 is a diagram showing a conversion table for standardizing the KPI of UPH. For example, if the original value of the KPI of UPH is within 0.1%, the standardized value of the KPI will be 1. Also, if the original value of the KPI of UPH is within 0.5%, the standardized value of the KPI will be 0.95.

[0100] Fig. 8 is a diagram showing a conversion table for standardizing the KPI of TPH. For example, if the original value of the KPI of TPH is 80 or more, the standardized value of the KPI will be 1. Also, if the original value of the KPI of TPH is 70 or more, the standardized value of the KPI will be 0.9. In this way, the conversion tables shown in Figs. 7 and 8 link the original KPI value and the standardized value in a one-to-one correspondence.

[0101] The optimization processing unit 18 sets the KPI to 0.9 because the target KPI for UPH (KPI_UPH) is "inter-process error within 1%." The optimization processing unit 18 sets the KPI to 0.7 because the target KPI for TPH (KPI_TPH) is "TPH is 50 pcs per hour or more." Note that the optimization processing unit 18 may standardize the KPIs using a formula instead of a conversion table.

[0102] The optimization processing unit 18 calculates the evaluation formula (1) using the standardized KPI. The formula (1) is Z=a_UPH×Nor(KPI_UPH)+a_TPH×Nor(KPI_TPH)=0.25×Nor(KPI_UPH)+0.75×Nor(KPI_TPH). Here, Nor() is a function that converts the KPI value into 0 to 1, and corresponds to the conversion process in the conversion tables shown in Figs. 7 and 8.

[0103] The optimization processor 18 calculates the judgment threshold Z_th of the evaluation formula using the target KPIs KPI_UPH and KPI_TPH. The judgment threshold Z_th is calculated as follows: Z_th=0.25×0.9+0.75×0.7=0.75.

[0104] Next, the optimization processor 18 sets the variable input parameters to arbitrary values ​​within the set change range and executes the simulation. Specifically, the optimization processor 18 sets the variable input parameters to arbitrary values ​​within the set change range and calculates the value of the evaluation formula.

[0105] For example, when the values ​​of the variable input parameters a to e in FIG. 2 are a=4, b=2, c=3, d=9, and e=7, the optimization processor 18 calculates the value of the KPI (UPH_KPI_X) of UPH to be 2.3%, and the value of the KPI (TPH_KPI_X) of TPH to be 62 pieces per hour. Here, the value of the KPI of UPH is the value of KPI(1), and the value of the KPI of TPH is the value of KPI(2). When the optimization processor 18 standardizes the value of the KPI of UPH using the conversion table of FIG. 7, Nor(UPH_KPI_X) becomes 0.7. When the optimization processor 18 standardizes the value of the KPI of TPH using the conversion table of FIG. 8, Nor(TPH_KPI_X) becomes 0.8. As a result, the optimization processor 18 calculates the value Z of the evaluation formula to be Z=0.25×0.7+0.75×0.8=0.775.

[0106] Z=0.775, which is a value larger than the judgment threshold Z_th=0.75 and satisfies the target KPI. Here, in the user intention table 100 of FIG. 2, the value of d is set to a priority of 1 (do not want to change the value too much). However, according to the user intention table 100 of FIG. 2, the current value of d is 8, and the optimization processing unit 18 changes the value to d=9 even though the priority of d is 1. Here, the optimization processing unit 18 applies the rule shown in FIG. 9. FIG. 9 is a diagram showing an example of a reflection rule of the user intention table 100. For example, when two or more parameters with priority 1 are changed in the user intention table 100, the rule to be applied is a rule to reduce the value of the evaluation formula by 10%. This time, since it corresponds to the case of "changing one parameter with priority 1", the optimization processing unit 18 performs a correction to reduce the value of the evaluation formula by 5% according to the table of FIG. 9. The corrected value Z_re of the evaluation formula is Z_re = 0.0775 × 0.95 = 0.73625, which is smaller than Z_th = 0.75. As a result, the optimization processor 18 determines that the value Z_re of the evaluation formula does not satisfy the determination threshold Z_th, and executes the simulation again.

[0107] Next, for example, when a=1, b=2, c=5, d=8, and e=7, the optimization processor 18 calculates that the KPI (UPH_KPI_X) value for UPH is 0.4%, and the KPI (TPH_KPI_X) value for TPH is 48 pieces per hour. When the optimization processor 18 standardizes the KPI value for UPH using the conversion table of FIG. 7, Nor(UPH_KPI_X) becomes 0.95. When the optimization processor 18 standardizes the KPI value for TPH using the conversion table of FIG. 8, Nor(TPH_KPI_X) becomes 0.65. As a result, the optimization processor 18 calculates the value Z of the evaluation formula as Z=0.25×0.95+0.75×0.65=0.725.

[0108] Z=0.725, which is a value smaller than the judgment threshold Z_th=0.75 and does not satisfy the target KPI. Here, in the user intention table 100 of FIG. 2, the optimization processing unit 18 changes only the values ​​of a and c, which have a priority of 3 (may be changed actively). With reference to FIG. 9, this corresponds to the case of "change of priority 3 only", so the optimization processing unit 18 performs a correction to increase the value of the evaluation formula by 5% according to the table of FIG. 9. Z_re=0.725×1.05=0.76125, which is a value equal to or larger than Z_th=0.75. As a result, the optimization processing unit 18 judges that the value Z_re of the evaluation formula satisfies the threshold Z_th. As described above, each KPI value is calculated by executing a simulation with each variable input parameter.

[0109] The optimization processing unit 18 continues to calculate the value of the evaluation formula and performs optimization until the time required for the simulation or the number of simulations reaches a predetermined upper limit. When the time required for the simulation or the number of simulations reaches a predetermined upper limit, the optimization processing unit 18 displays on the display device 14 the variable input parameters and the value of the evaluation formula that have achieved the evaluation formula.

[0110] FIG. 10 is a diagram showing an example of a screen showing the simulation results displayed on the display device 14. As shown in FIG.

[0111] Screen MN includes item P1 and item P2.

[0112] Item P1 displays information such as the target KPI setting value, the judgment threshold Z_th, the number of times the simulation was run (number of SIM runs), the number of times the evaluation formula achieved the judgment threshold Z_th (number of evaluation formula achievements), and the time the simulation was run (evaluation execution time).

[0113] Item P2 displays the numerical values ​​for "Value of the evaluation formula," "Value of KPI(1)," "Standardized value of KPI(1)," "Value of KPI(2)," "Standardized value of KPI(2)," "Number of receiving inspection companies for variable input parameter a," "Number of shelving workers for variable input parameter b," "Number of picking workers for variable input parameter c," "Total number of workers for variable input parameter d," and "Working time per person for variable input parameter e," in order of best evaluation formula value (in this case, largest evaluation formula value).

[0114] The user can refer to the screen MN displayed on the display device 14 and select the input parameters to be reflected in the warehouse / distribution system 20. For example, when the user selects input parameters a=1, b=1, c=6, d=8, and e=10 with the first rank and inputs them into the input device 13, the result reflection unit 19 reflects the information of the input parameters received from the input device 13 in the warehouse / distribution system 20.

[0115] FIG. 11 is a diagram illustrating an example of a conversion table for calculating the value Z.

[0116] In the above, the value Z of the evaluation formula is calculated using the formula (1), but the value Z of the evaluation formula may be calculated using a conversion table for calculating the value Z as shown in Fig. 11. In other words, such a conversion table is also included in the evaluation formula.

[0117] Although a delivery system linked to a warehouse has been given as an example of the logistics field, the present invention is not limited to a case in which both a warehouse system (hereinafter referred to as a "warehouse system") and a delivery system are provided, and the present invention may target only one of the systems. The optimization system 1 may optimize input parameters not only in the logistics field but also in the manufacturing field, such as a factory system. Examples of KPIs in the manufacturing field include production efficiency, productivity per person, waste loss rate, delivery deadline compliance rate, delivery delay rate, inventory turnover rate, product defect rate, lead time, labor cost rate, total production volume, and customer satisfaction. The optimization system 1 may optimize input parameters in the retail and distribution fields. Examples of KPIs in the retail and distribution fields include sales, profit margins, average customer spending, selling expenses, general and administrative expenses, inventory turnover rate, sales composition ratio, customer satisfaction, store-specific sales growth rate, customer acquisition costs, and net profit margins on sales.

[0118] Summary of the Disclosure The above description of the embodiments discloses the following techniques.

[0119] <Technology 1> The optimization method of this embodiment sets a target importance achievement index, which is a target value of an importance achievement index that represents an important factor in the process of achieving a planned goal, sets a change range for a parameter that affects the achievement of the planned goal, sets an evaluation formula for calculating an index used to optimize the parameter, and calculates a parameter such that the evaluation value, which is the value of the evaluation formula calculated using the parameters set within the change range, is greater than or equal to a threshold value, which is the value of the evaluation formula calculated from the target importance achievement index.

[0120] As a result, the optimization method according to the present embodiment can optimize parameters including the goal importance achievement index and the parameter change range designated in advance by the user, thereby supporting the proposal of an optimal business plan within a feasible range, taking into account the current resources.

[0121] <Technology 2> In the optimization method described in Technique 1, the evaluation formula is a sum of multiple weighted important achievement indicators.

[0122] As a result, the optimization method according to the present embodiment can optimize parameters including the goal importance achievement index and the parameter change range designated in advance by the user, thereby supporting the proposal of an optimal business plan within a feasible range, taking into account the current resources.

[0123] <Technology 3> In the optimization method according to Technique 1 or 2, the calculation of the parameters for which the evaluation value is equal to or greater than a threshold is repeated by changing the parameters until the calculation time or the number of calculations reaches or exceeds an upper limit.

[0124] As a result, the optimization method according to the present embodiment can calculate better parameters by repeatedly performing simulations.

[0125] <Technology 4> In the optimization method according to either technique 1 or technique 3, a parameter that maximizes the evaluation value is calculated.

[0126] As a result, the optimization method according to the present embodiment can optimize the parameters by repeatedly calculating the parameters until the evaluation value is maximized.

[0127] <Technology 5> In the optimization method according to any one of Techniques 1 to 4, a priority of a change from a current value of the parameter is set for each parameter, an evaluation value is corrected using the priority, and the parameter that maximizes the corrected evaluation value is calculated.

[0128] As a result, the optimization method according to the present embodiment can calculate parameters in line with the user's intentions by correcting the evaluation values ​​and optimizing them while taking into account the priority of the parameters set by the user. As a result, the optimization method can improve the user's convenience and support the improvement of business efficiency and productivity in line with the user's management plan.

[0129] <Technology 6> In the optimization method according to any one of Techniques 1 to 5, a parameter whose evaluation value is equal to or greater than a threshold is reflected in a system that is the subject of optimization of a planning goal.

[0130] As a result, the optimization method according to the present embodiment can reflect the optimized parameters in the system to be optimized, thereby improving the business efficiency and productivity of the target system.

[0131] <Technology 7> In the optimization method according to any one of Techniques 1 to 6, a screen including the evaluation value and the calculated parameter results is output to a display device.

[0132] In this way, the optimization method according to the present embodiment allows the calculated parameter values ​​to be displayed on a display device for the user to confirm, and thus allows the user to select which of the calculated parameter results should be reflected in the target system.

[0133] <Technology 8> In the optimization method according to any one of Techniques 1 to 7, a user instruction as to whether or not to recalculate the parameters for which the evaluation value is equal to or greater than the threshold is obtained from an input device, based on a screen displayed on a display device.

[0134] As a result, the optimization method according to the present embodiment can execute the simulation again when the calculated parameters do not meet the user's wishes, thereby improving the convenience for the user.

[0135] <Technology 9> In the optimization method according to any one of Techniques 1 to 8, a parameter change range and priority are set based on a user's instruction received from an input device.

[0136] As a result, the optimization method according to the present embodiment can calculate parameters that meet the user's wishes. In other words, the optimization method can optimize parameters by finely setting various requests from the user, such as parameters that the user actively wants to change, parameters that the user does not want to increase, and parameters that are set to fixed values. As a result, the optimization method can improve user convenience.

[0137] Although the embodiments have been described above with reference to the accompanying drawings, the present disclosure is not limited to such examples. It is clear that a person skilled in the art can conceive of various modifications, corrections, substitutions, additions, deletions, and equivalents within the scope of the claims, and it is understood that these also belong to the technical scope of the present disclosure. In addition, the components in the above-mentioned embodiments may be arbitrarily combined within the scope of the invention. [Industrial Applicability]

[0138] The technology disclosed herein is useful as an optimization method, optimization system, and program for proposing an optimal business plan taking into account current resources. [Explanation of symbols]

[0139] 1 Optimization System 10 Computing equipment 11 Communication I / F 12. Memory 13 Input Devices 14 Display Devices 15 Processors 16 KPI Processing Section 17 Condition setting section 18 Optimization Processing Section 19 Result reflection section 20 Warehouse and distribution system 100 User Intention Table

Claims

1. Set target key achievement indicators, which are the target values ​​of key achievement indicators that represent important factors in the process of achieving the planned goal; Setting ranges of parameter changes that affect achievement of said planning goals; Setting an evaluation formula for calculating an index used to optimize the parameter; Calculating parameters such that an evaluation value, which is a value of the evaluation formula calculated using the parameters set within the change range, is equal to or greater than a threshold value, which is a value of the evaluation formula calculated from the goal importance achievement index. Optimization methods.

2. The evaluation formula is a sum of weighted multiple important achievement indicators.

2. The optimization method of claim 1.

3. repeating the calculation of the parameter for which the evaluation value is equal to or greater than the threshold value by changing the parameter until the calculation time or the number of calculations reaches or exceeds an upper limit value; 3. The optimization method according to claim 2.

4. Calculating the parameter that maximizes the evaluation value; 4. The optimization method according to claim 3.

5. setting a priority of a change from a current value of the parameter for each of the parameters; correcting the evaluation value using the priority; Calculating the parameters that maximize the corrected evaluation value; 4. The optimization method according to claim 3.

6. The parameter for which the evaluation value is equal to or greater than the threshold is reflected in a system for which the planning goal is to be optimized.

6. The optimization method according to any one of claims 1 to 5.

7. outputting a screen including the evaluation value and the calculated parameter results to a display device; 6. The optimization method according to any one of claims 1 to 5.

8. acquiring, from an input device, a user's instruction as to whether or not to perform calculation again of the parameter for which the evaluation value is equal to or greater than the threshold value, based on the screen displayed on the display device; 8. The optimization method according to claim 7.

9. setting the change range and the priority of the parameter based on a user instruction received from an input device; 6. The optimization method according to claim 5.

10. The system includes a warehouse distribution system in which a warehouse system for managing goods and a system for distributing the goods are linked, and a calculation device for optimizing parameters that affect the achievement of a planning goal of the warehouse distribution system, the warehouse distribution system transmitting current values ​​of the parameters to the computing device; The computing device comprises: Setting a target key achievement indicator, which is a target value of a key achievement indicator representing an important factor in the process of achieving the planned goal; setting a range for changing the parameter from the current value; Setting an evaluation formula for calculating an index used to optimize the parameter; Calculating parameters such that an evaluation value, which is a value of the evaluation formula calculated using the parameters set within the change range, is equal to or greater than a threshold value, which is a value of the evaluation formula calculated from the goal importance achievement index. Optimization system.

11. A process of setting a target important achievement index, which is a target value of an important achievement index representing an important factor in the process of achieving the planned goal; setting ranges of change for parameters that affect the achievement of the planning goal; A process of setting an evaluation formula for calculating an index used to optimize the parameters; A process of calculating a parameter such that an evaluation value, which is a value of the evaluation formula calculated using the parameter set within the change range, is equal to or greater than a threshold value, which is a value of the evaluation formula calculated from the goal importance achievement index. program.

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