JP2005182558A - Cost prediction/evaluation method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To evaluate/improve a design proporsal without using much time and many man-hours for quickly offering a proporsal complying with a client request and change in it and minimizing a TCO in proposal of a building solution such as a building plan, an equipment plan, an energy-saving plan, a renewal plan, and a conversion plan. <P>SOLUTION: In equipment designing, when a construction cost, a maintenance cost, and an energy (running) cost are predicted by using data or an analysis result in a data center, correlations such as trade-off are evaluated simultaneously for predicting/evaluating a total cost in the time of planning. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to prediction / evaluation of a total cost consisting of equipment cost, construction cost / maintenance cost / energy cost, etc. in an architectural plan, equipment plan, energy saving plan, renewal plan, conversion plan, and the like.

  Conventionally, (1) equipment design, (2) construction design, and (3) maintenance design have been performed individually, and performance / function, service level, cost, etc. have been set and calculated for each design work. Cost prediction / evaluation and decision based on it could not be made.

JP 2003-141178 A

  An object of the present invention is to quantitatively obtain a construction cost, a maintenance cost, and the like during an assumed use period for an arbitrary design plan using product information and performance information, and to optimize a design based on the result.

  At the time of facility design, the construction cost, maintenance cost, and energy (running) cost are predicted using the data / analysis results of the data center, so that mutual relationships such as trade-offs can be evaluated at the same time. Cost estimation and evaluation.

  Evaluate and improve design plans by calculating the total cost (TCO) consisting of construction costs, maintenance costs, energy costs, etc. for design plans based on design targets that include the period of use of buildings and equipment (Life) It can be executed without taking time and man-hours. As a result, when building solutions are proposed, such as building plans, equipment plans, energy saving plans, renewal plans, conversion plans, etc., we will promptly provide proposals that can respond to customer requests and changes and minimize TCO.

  FIG. 1 shows the overall configuration of a system embodying the present invention. The GUI (operation terminal) displays the prediction / evaluation result of the total cost (TCO), that is, the construction cost, maintenance cost, energy cost, etc. for the design plan, while performing the design work or inputting the design result. To do. The total cost prediction function plays the role of calculating the total cost for the input design plan. The TCO (LCC) simulator is an implementation of the total cost prediction function. The TCO (LCC) simulator consists of the functions of capital investment cost calculation, construction cost simulator, maintenance cost simulator, and energy cost simulator. ) The simulator outputs the total value of the output results of these four functions as a result of the total cost prediction. The capital investment cost calculation function adds up the purchase costs of equipment, building materials, parts, etc. in the design plan. Each function of the construction cost simulator, maintenance cost simulator, and energy cost simulator assigns similar information to each part of the design plan from information that combines past design information and cost results stored in the database in the data center. Estimate and output construction costs, maintenance costs, and energy costs based on the information.

  In the data center, information on building specifications, equipment specifications, construction results, operation results, and maintenance results is collected from the data collection target buildings and stored in a database. The building specification information includes building usage, building structure / scale, equipment configuration, and the like. The equipment specification information includes purchase price, part configuration, inspection / maintenance standard / cost of each part, part life, part replacement man-hour / cost, etc. The construction record information includes a process plan, a process record, a construction method, a man-hour / cost, etc. at the time of building a building including equipment. The operation result information includes operation time, number of operations, power consumption, etc. for each facility. The maintenance result information is composed of inspection / maintenance history, predictive warning history, preventive replacement history, failure history, and the like. The component life in the facility specification information stores the results obtained by the component life analysis system using the facility configuration, the component configuration, the preventive replacement history, and the failure history, and is updated periodically.

FIG. 2 is an example of a GUI (in the case of an elevator) for inputting design plan specifications, which is commonly used in the four functions of capital investment cost calculation, construction cost simulator, maintenance cost simulator, and energy cost simulator. All or some of the items shown in the GUI are input as specifications and used as calculation conditions for each cost. For items that are not input from the GUI, default values are used or “unconditional” is handled.
FIG. 3 shows a procedure for calculating the capital investment cost. It is determined whether there is a similar case on the condition of the design plan input from the GUI shown in FIG. The similarity is determined by calculating the distance between the design plan and the specifications of the past design examples, and the closest example within the distance d ₁ is adopted. Distance reads the standard estimate if there is no fixed value d ₁ within the case. The equipment investment cost of the design plan is estimated by correcting the distance from the design plan with respect to the purchase cost of equipment, building materials, parts, etc. in the similar case or standard estimate adopted.

FIG. 4 shows a construction cost simulation procedure. It is determined whether there is a similar case on the condition of the design plan input from the GUI shown in FIG. The similarity is determined by calculating the distance between the design plan and the specifications of the past design examples, and the closest example within the distance d ₂ is adopted. Distance reads the standard construction pattern if there is no fixed value d ₂ within the case. The construction cost of the design plan is estimated by correcting the distance from the design plan with respect to the construction cost in the adopted similar case or standard construction pattern.

FIG. 5 shows the procedure of energy cost simulation. It is determined whether there is a similar case on the condition of the design plan input from the GUI shown in FIG. The similarity is determined by calculating the distance between the design plan and the specification of the past design case, and the closest case within the distance d ₃ is adopted. Distance reads the standard energy consumption pattern if there is no fixed value d ₃ within the case. The energy cost of the design plan is estimated by correcting the distance from the design plan with respect to the energy cost in the adopted similar case or the standard energy consumption pattern.

FIG. 6 shows a maintenance cost simulation procedure. It is determined whether there is a similar case on the condition of the design plan input from the GUI shown in FIG. The similarity is determined by calculating the distance between the design plan and the specifications of the past design examples, and the closest example within the distance d ₄ is adopted. Distance reads the standard conservation pattern if there is no fixed value d ₄ within the case. The maintenance cost of the design plan is estimated by correcting the distance from the design plan for the maintenance cost in the similar case or the standard maintenance pattern adopted.

FIG. 7 is a conceptual diagram of a simulation target when a standard maintenance pattern and its (maintenance) cost are derived. Each of the customers C _i (i = 1,..., NC) has NM _i maintenance target devices M ⁱ _j (j = 1,..., NM _i ). Further, it is assumed that each maintenance target device M ⁱ _j has NP ⁱ _j maintenance target parts _i P ^j _k (k = 1,..., NP ⁱ _j ). Here, the component life _i τ ^j _k of the maintenance target component _i P ^j _k is a random variable according to the probability density function _i f ^j _k ( _i τ ^j _k ). The probability density function of the component life may be a multivariable function.

  FIG. 8 is a conceptual diagram of maintenance work assumed in this model. In this model, “preventive replacement” in which parts are replaced after a certain period of time after installation (hereinafter referred to as preventive replacement interval), and when the parts reach the end of their life before the scheduled preventive replacement, Consider “post-replacement” to replace parts. Costs and man-hours (emergency delivery, faulty parts-specific man-hours, etc.) are incurred during an emergency response, so “subsequent replacement” generally requires more costs and man-hours than “preventive replacement”. In actual maintenance work, there are other inspection / maintenance work, legal inspection work, etc., but such a model can be handled with the same idea as this model.

FIG. 9 shows a flowchart of the maintenance work simulator 24. In this embodiment, since the Monte Carlo method is used, as a basic flow, a component life is generated using a pseudo random number sequence, and the number of maintenance work _i NWP ^j _k and _i NWB ^j _k is counted. Here, _i NWP ^j _k and _i NWB ^j _k are the number of times the preventive replacement work and the post-replacement work have been performed on the part _i P ^j _k , respectively. When the maintenance work simulator 24 is executed, in step S2, customer data (usage frequency, owned equipment, operation start time, etc.), equipment data (component parts, work man-hours, etc.), parts data (life distribution, unit price, etc.), Various data such as maintenance data (preventive replacement interval, work unit price, etc.) are acquired from the database system 10. Subsequent maintenance work counter in step 3

Is initialized with 0. here,

Is the number of sample functions for preventive replacement _i NWP ^j _k times and post-replacement _i NWB ^j _k times for the part _i P ^j _k . In subsequent step S4, it is determined whether the number of sample functions specified by the user has been generated. If [Yes], the process proceeds to step S5, and if [No], the process proceeds to step S13. In step S5, the simulation time t is initialized to zero. Here, it is assumed that the simulation time t is discretized so as to take an integer value by using a certain time step. In the subsequent step S6, the lifetime _i τ ^j _k of each component _i P ^j _k is determined using a pseudo-random number according to the probability density function _i f ^j _k ( _i τ ^j _k ), and the determined lifetime is further determined by the time step. To discretize. In the subsequent step S7, it is determined whether or not a replacement operation occurs at the current simulation time t (the lifetime _i τ ^j _k has been reached, or a time corresponding to the preventive replacement interval has elapsed since component mounting). If the determination result is [Yes], the process proceeds to step S8, and if [No], the process proceeds to step S10. In step S8, the type of replacement work (preventive replacement or post-replacement) is determined for the part _i P ^j _k determined that the replacement work occurs in step S7, and the counter ( _i NWP ^j _k or _i NWB ^j _k ) is increased by 1, and the process proceeds to step S9. In the subsequent step S9, a new component life _i τ ^j _k is set for the component _i P ^j _k determined to be replaced in step S7, and the process proceeds to step S7. In step S10, the simulation time t is increased by 1, and the process proceeds to step S11. In the subsequent step S11, it is determined whether or not the simulation time t has exceeded the simulation period set by the user. If [Yes], the process proceeds to step S12. If [No], the process proceeds to step S7. In the subsequent step S12, depending on the number of operations _i NWP ^j _k and _i NWB ^j _k for each part.

Is increased by 1, and the process proceeds to step S4. In step S13, the maintenance work counter is divided by the number of sample functions, and the probability function of the number of maintenance work _i NWP ^j _k and _i NWB ^j _k .

Is calculated.
In the subsequent step S14, evaluation indexes such as maintenance costs, probability distribution of maintenance man-hours, expected values, and the like are calculated from the number of maintenance operations as necessary.

  An example of the evaluation index calculation formula is shown below.

(Number of parts demand) = (Number of preventive replacements + Number of subsequent replacements)
× (Parts replacement unit)
(Parts cost) = (Parts demand) x (Parts unit price)
(Effects man-hours) = (Number of preventive replacements) x (Preventive replacement man-hours)
+ (Post-replacement times) x (Post-replacement man-hours)
+ (Number of parts inspection work) x (part inspection work man-hours)
(Working cost) = (Working time) x (Worker cost)
In the subsequent step S15, the result calculated in step S14 is output to the simulation result file 4 and the screen as necessary, and the process ends.

  FIG. 10 is an output image example of the maintenance business simulator. The maintenance business simulator not only stores the numerical data of the simulation result in the simulation result file 4, but also performs output as shown in FIGS. 10 (a) to 10 (d) as necessary. In FIG. 10 (a), the horizontal axis represents parameters related to maintenance (replacement cycle, inspection cycle, number of warehouse bases, number of personnel, etc.), and the vertical axis represents maintenance cost, number of failures, etc. Used for pre-examination at the time of planning. FIG. 10B confirms time-series changes such as maintenance costs and the number of occurrences of failures. In FIG. 10C, parameters on the customer (travel time, number of floors, etc.) are taken on the horizontal axis, and maintenance costs, the number of occurrences of failures, etc. are taken on the vertical axis, which are used for maintenance contract estimation. FIG. 10D shows the probability distribution of the maintenance cost and the number of parts replacement, and is used when the examination is performed in consideration of not only the expected value but also the variance.

  FIG. 11 is an explanatory diagram of the condition setting status and the total cost prediction result, and FIG. 12 is a diagram illustrating an output image example (in the case of an elevator) of the condition setting status and the total cost prediction result. In “Product specification”, “Installation specification”, and “Other conditions”, calculation / simulation conditions set by the GUI for inputting the design plan specification shown in FIG. 2 are displayed. The "Comprehensive cost evaluation results" section includes the equipment investment cost calculation, construction cost simulator, maintenance cost simulator, energy cost simulator, and the equipment cost, construction cost, maintenance cost, and energy cost. The total cost is displayed.

  This is not only an output screen showing the condition setting status and the total cost prediction result, but also fulfills a GUI function for optimizing the design plan while referring to the evaluation result. There is a relationship that affects each other between the condition parameters constituting the “product specification”, “installation specification”, and “other conditions” or the costs that are the evaluation results. For this reason, by making it possible to modify the setting conditions on the GUI having the configuration shown in FIG. 11, the trade-off between items, particularly costs, is adjusted so that the design plan can be optimized.

The relationship between items that influence each other is shown by taking an elevator as an example.
(1) Increasing the number of installed units will increase the equipment cost, construction man-hours and costs, and lower the operating rate.
(2) Conversely, if the number of installed units is reduced, the equipment cost, construction man-hours and costs will decrease, and the operating rate will increase.
(3) When the operation rate is increased, it is necessary to increase the allowable failure rate or increase the maintenance man-hour and cost in order to maintain the allowable failure rate. In addition, energy costs increase. (When the number is constant)
(4) If the operating rate is lowered, maintenance man-hours and costs can be reduced. Also, energy costs are reduced. (When the number is constant)
(5) Increasing the allowable failure rate can reduce maintenance man-hours and costs.
(6) If the allowable failure rate is lowered, maintenance man-hours and costs need to be increased.
(7) If the renewal cycle is extended, the equipment cost and construction man-hours / costs will decrease, but the maintenance man-hours / costs and energy costs will increase (if the number is constant).
(8) If the renewal cycle is shortened, the equipment cost and construction man-hours / costs will increase, but the maintenance man-hours / costs and energy costs will decrease (if the number is constant).

  In a situation where business forms such as CM (Construction Management) and PFI (Private Finance Initiative) are spreading, it is necessary to create a design plan that satisfies customer requirements and suppresses total costs in a short period of time. Cost prediction / evaluation methods that can adjust off and optimize design proposals are indispensable.

1 is a diagram illustrating an overall configuration of a system embodying the present invention. It is a figure which shows the example (in the case of an elevator) of GUI which inputs the specification of a design plan used in common in four functions, capital investment cost calculation, construction cost simulator, maintenance cost simulator, and energy cost simulator. It is a figure which shows the procedure of capital investment cost calculation. It is a figure which shows the procedure of construction cost simulation. It is a figure which shows the procedure of an energy cost simulation. It is a figure which shows the procedure of a maintenance cost simulation. It is a conceptual diagram of the simulation object when deriving a standard maintenance pattern and its (maintenance) cost. It is a conceptual diagram of the maintenance work assumed in this model. Flow chart of maintenance work simulator 24 It is a figure which shows the example of an output image of a maintenance business simulator. It is explanatory drawing of a condition setting condition and a total cost prediction result. It is a figure which shows the example of an output image (in the case of an elevator) of a condition setting situation and a total cost prediction result.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Total cost prediction 110 TCO (LCC) simulator 111 Capital investment cost calculation 112 Construction cost simulator 113 Maintenance cost simulator 114 Energy cost simulator 200 Data center 210 Actual data collection / analysis 211 Parts life analysis system 212 Operation result collection system 213 Maintenance result collection System 300 Database 301 Building specification DB
302 Equipment specification DB
303 Construction results DB
304 Operation results DB
305 Maintenance results DB

Claims (8)

  While checking the total cost (TCO) consisting of equipment cost, construction cost, maintenance cost, and energy cost when there is a mutual influence between the condition parameters that constitute the design plan or the costs that are the evaluation results A cost prediction / evaluation method that can adjust the trade-offs between items and optimize design proposals by enabling modification of setting conditions.   Adjusting trade-offs between items and optimizing design proposals by enabling the setting conditions to be corrected while checking the total cost (TCO) consisting of equipment costs, construction costs, maintenance costs, and energy costs Cost prediction / evaluation method.   Cost prediction / evaluation method that calculates total cost (TCO) consisting of equipment cost, construction cost, maintenance cost, and energy cost for a design plan that assumes the design target value including the period of use of buildings and equipment (Life).   At the time of planning such as building plans, equipment plans, energy-saving plans, renewal plans, conversion plans, etc., the equipment costs, construction costs, maintenance costs, etc. for the design plan based on the design target values including the life of buildings and equipment (Life) Cost prediction / evaluation method that calculates total cost (TCO) consisting of energy costs.   At the time of planning such as building plans, equipment plans, energy-saving plans, renewal plans, conversion plans, etc., the equipment costs, construction costs, maintenance costs, etc. for the design plan based on the design target values including the life of buildings and equipment (Life) A cost prediction / evaluation method that makes it possible to evaluate and improve a design plan without spending time and effort by calculating the total cost (TCO) consisting of energy costs.   Total cost consisting of equipment cost, construction cost, maintenance cost, and energy cost for a design plan that consists of a design system, a data center, and a total cost prediction function, and that assumes a design target value including the life of a building or equipment. Cost estimation / evaluation method to calculate (TCO).   By matching design information with past design information and cost results, the equipment cost, construction cost, maintenance cost, etc. for the design plan based on the design target value including the life of the building and equipment (Life) Cost prediction / evaluation method that calculates total cost (TCO) consisting of energy costs. It is composed of a design system, data center, and total cost prediction function. By combining design information with past design information and cost results, the design target value including the life of buildings and facilities is assumed. A cost prediction / evaluation method that calculates the total cost (TCO) consisting of equipment cost, construction cost, maintenance cost, and energy cost for a design plan.

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