Methods for estimating and monitoring realistic consumption of spare parts for industrial equipment

Abstract

The present invention relates to methods capable of realistically estimating the consumption of spare parts by target equipment (brand and model) and monitoring the actual consumption by this brand and model within a time frame defined by the user. The invention includes a primary estimation function that provides a prediction of parts to be consumed, together with their associated data, within a time frame relative to the start of operation of the equipment, based on the consumption history and processing, which results in probable average quantities or probable maximum quantities to be consumed by type / identification; and an auxiliary consumption monitoring function, offering a view / profile of the consumption of parts by a given piece of equipment within a time frame that may be relative or absolute to the start of operation. This function, among other information, allows filtering of the consumption behaviour of one or more pieces of equipment and comparison of consumption profiles across groups of equipment, either within the same industrial plant or across different plants. Together with the type / identification of each part consumed, the following information is provided in the results section of both the estimation and monitoring functions: manufacturer, lead time for each consumed part, manufacturer's part numbers, material numbers and, optionally, prices historically charged.

Description

[0001] METHODS FOR ESTIMATING AND MONITORING REALISTIC SPARE PARTS CONSUMPTION OF INDUSTRIAL EQUIPMENT Field of the invention

[0001] The present invention is within the technical field of oil and gas, more specifically, it has application throughout the oil, natural gas and energy industry, more specifically still, it relates to a computer-implemented method for realistically estimating the consumption of spare parts by commissionable equipment within a user-defined time window for the areas of refining, E&P, G&E, maintenance planning, supply, engineering, operational reliability and logistics. Background of the invention

[0002] It is now known that the inventory management process, especially the inventory of spare parts in the maintenance area of ​​the oil and gas industry, has become a major challenge as it causes several economic and environmental problems for the entire ecosystem of the oil sector.

[0003] Specifically, the lack of adequate information for defining spare parts throughout the oil, natural gas, and energy industrial plant triggers some problems such as spare parts purchased for new equipment that are not actually used within the specific operating time, price increases, delays in the delivery of spare parts due to individual purchases, heavily demanded procurement teams in situations of emergency or non-requirement purchases, high inventory with high disposal value, impacts on the environment that generate fines and air pollution, in addition to the operational discontinuity of equipment due to the lack of spare parts.Currently, the widespread practices for defining and selecting spare parts are: 1- definition according to the user's own experience, which are normally different between them, 2- the definition is already ready from the manufacturer, especially for the operation of the first two years of operation and, sometimes, 3- they are selected taking into account the failure rates and availability calculated via references obtained from the oil and gas industry, such as OREDA (offshore and onshore relyability data), which due to issues of temperature, humidity, among other factors originating from the host countries of the participating companies, these rates, in many cases, when they exist, are not very representative.A critical evaluation of all these methods shows significant potential for improvement, taking as a reference the significant numbers of production losses due to lack of spare parts in all major business units and the value of materials disposed of (purchased but not used) per year in the company. In general, it is known that production loss due to lack of spare parts only occurs when the main equipment fails, followed by the backup equipment. This reinforces the evidence that, to an even greater extent, the necessary part is not available when the equipment needs it.

[0004] Consequently, the oil and gas industry's supply chain logistics require assertiveness based on the speed and efficiency of the available tools and the team involved, to avoid costly bottlenecks for the operation and, consequently, environmental problems.

[0005] In view of this, and in order to solve the technical problems described above, the present invention aims to analyze the consumption profile of commissionable equipment, with an analytical view of the actual consumption in a given operational window of the equipment, making it possible to: identify which specific parts are likely to be effectively used in this operational window of the equipment's useful life, based on the historical basis of the operation of that equipment and consumption of parts in the company's own industrial plant, bringing more knowledge and control of the consumption profile of the equipment, allowing to know more quickly and clearly the need for spare parts (quantity / identification of parts), according to the probable period of consumption from the start of operation of the main commissionable equipment,equipment with TAG (identification code according to the company's identification standard) and certain locations (unit inserted within an industrial plant, or in the industrial plant, or in the well, or because it is underwater application equipment, or inserted in a vessel, and the equipment(s) can be found at any address), and also that the same equipment can be applied to different environments with different operational parameters.

[0006] It's clear that in practice, for example, the parts consumption list estimated by equipment manufacturers for the equipment's useful life differs from the actual usage needs of that equipment, either by considering an excessive number of parts, by not considering parts that will be used, or even by including parts that will not be used, thus generating delays and unnecessary expenses for the company. It's also important to emphasize that the current spare parts control in the oil and gas industry is based on so-called strategic and high-value spare parts in capital spares. This partially addresses the need, but it's insufficient when considering production losses caused by the lack of low-value spare parts, high-value disposal of industrial materials, and other data related to more common spare parts with a high impact on losses.

[0007] The computer-implemented methodology of this invention was based on practical studies of the routine reality of spare parts management in the oil and gas industry. Thus, it was possible to identify that many of the tools used for spare parts control and management are limited by inaccurate information from manufacturers, limited and even divergent opinions among company experts, and central inventory control software that relies on the general history of parts output over the last 12 months or so, without focusing on the equipment receiving the part.With the present invention, it is possible to create a usage history based on the actual parts requirements of a piece of equipment within each year of its useful life, including parts not defined in the manufacturer's list and the quantity of parts actually used, which in the vast majority, according to all cases analyzed, are lower than those defined in the manufacturer's manual or official list. The analytical methodology of the present invention can thus, for example, define, within the equipment's actual consumption profile, which parts are consumed within the defined time window and which part is consumed most frequently in a given period of the equipment's useful life. This is accomplished through a logical step that also generates an estimate of the cost of spare parts that will be consumed during the third and fourth years of a piece of equipment's useful life, for example.

[0008] Specifically, the consumption monitoring function of the monitoring method of the present invention seeks to provide complementary support to the estimating function of the estimating method of the present invention, in achieving the singular objective of bringing more knowledge about the consumption profile of parts of commissionable equipment.Consequently, with this knowledge accessible, fast, and consistent for all stakeholders, such as procurement, maintenance, operations, logistics, inventory, engineering, planning, among others, the resulting benefits are: the provision of realistic lists of spare parts required for the target equipment within the user-defined timeframe; avoiding or minimizing the lack of spare parts necessary for equipment operation, equipment downtime, and production shutdowns, in addition to avoiding the emergency purchase of spare parts; avoiding the purchase of unnecessary spare parts; optimizing the supply of spare parts by reducing CAPEX and OPEX; increasing transparency and scientific technical basis in the spare parts selection process; reducing indirect costs due to rework, team shifts, untimely procurement processes, frequent replanning, and lack of predictability. Prior art.

[0009] In the state of the art, there are different methodologies implemented by computer programs to manage the stock of spare parts. However, even with the use of these tools available on the market to assist in the management of parts for maintenance of commissionable equipment, it is not possible to carry out assertive management that solves the aforementioned problem.

[0010] Patent document CN115713169A, for example, describes a method and apparatus for predicting the quantity of spare parts, specifically, the method comprises: inputting data at a current time of each spare part of a product to be predicted into a pre-trained feature extraction model to obtain a first feature of each spare part determined by the feature extraction model, the first feature is used to characterize the current number of individual spare parts and the relationship between individual spare parts and other spare parts, as well as changes in the quantity of spare parts at different times for a product to be predicted;spare parts quantity data for the specified time period after the current time determined by the forecasting model to determine the spare parts quantity data for each spare part in a future period based on the first feature of each spare part determined by the feature extraction model from the historical data of each spare part. ;

[0011] However, the present invention differs from document CN115713169A, because although the present invention also uses as an analysis / prediction parameter an operational window of the equipment for the specified period of time, document CN115713169A is based on a feature extraction model from the historical data of each spare part, unlike the present invention which analyzes within a given time window the consumption history of the equipment through the real consumption profile, which details the parts used, brand, model, including the parts that were not defined in the manufacturer's manual, but were used in that specific period of time of the useful life of the commissionable equipment, defined in the analysis.

[0012] Document CN113487187A describes a method for configuring time-varying spare parts inventory based on level maintenance, which comprises the following steps: constructing a level maintenance and spare parts supply system, wherein the level maintenance comprises a base level and a base level, the spare part supply comprises a field replaceable unit LRU and a workshop replaceable unit SRU, the maintenance time and repair quantity of different faulty units are independent of each other, and the spare parts demand rate obeys the Poisson distribution; establishing a time-varying demand rate model based on a level maintenance and spare parts supply system, wherein the time-varying demand rate model is established based on the availability and passivation effect of time-varying equipment;Establish a spare parts supply channel model and correct the numerical probability distribution of each level of supply channels; construct benefit indices of spare parts guarantee rate and equipment availability based on a time-varying demand rate model and the numerical probability distribution of each level of supply channels; combine the spare parts guarantee rate and equipment availability benefit index with the benefit index to construct a cost-effectiveness model; and establish an optimization model and perform optimization configuration on spare parts based on the cost-effectiveness model.

[0013] Additionally, document CN113487187A discloses a method comprising the steps of constructing a level maintenance and supplying spare parts system, in which the level maintenance comprises a base level, the supply of spare parts comprises a field replaceable unit LRU and a workshop replaceable unit SRU, the maintenance and repair time, quantity of different failure units are independent of each other, and the spare parts demand rate obeys the Poisson distribution, when detecting that each guarantee point of the level maintenance and spare parts supply system is out of stock, following continuous supply.

[0014] However, the present invention differs from document CN113487187A in that it focuses on the granularity of commissionable equipment, primarily on the actual consumption per piece of equipment. This is based on an equipment operating window. The window is analyzed based on the start of the equipment's operation. This could be, for example, from the start of operation to the second year, or from the second year to the fifth year—any window for all equipment of the same make and model in operation at the company's industrial plant. Thus, it is possible to identify which specific parts a piece of equipment of the same make and model used in a given operating window, avoiding the purchase of parts that will not be used. This means that necessary parts are not missed. In general, emergency or unenforceable purchases are avoided, as well as environmental damage and the reduced use of human resources in purchasing spare parts.

[0015] Document CN115689456A describes a spare parts system and method based on automated maintenance tracking, comprising: a maintenance tracking module communicatively coupled to a server module for recording spare parts replacement and maintenance information of equipment, and uploading spare parts replacement and maintenance information to the server module;a spare parts analysis module connected to the server module for generating weighting factors corresponding to different spare parts based on information corresponding to different spare parts in the spare parts information base; a spare parts library output module, a spare parts library output module is connected to a spare parts analysis module, for generating and outputting a detailed spare parts library table, including the theoretical inventory quantity corresponding to each spare part according to the weighting factors corresponding to different spare parts;

[0016] Specifically, the CN115689456A document can accurately give the type and number of spare parts that the spare parts library needs to store, can reduce the input costs of spare parts, and reduce downtime losses due to spare parts problems.

[0017] Thus, there are technological differences between the present invention and document CN115689456A, mainly regarding the parameters used in the functions of the present invention, such as a time window based on a history of actual consumption, in addition to allowing filtering the consumption behavior of one or more equipment, making consumption comparisons between groups of equipment, availability time of each of the consumed parts and survey of unit and total prices. Brief description of the invention

[0018] The present invention relates to computer-implemented methods for realistically estimating and monitoring spare parts consumption by target equipment within a user-defined time window.

[0019] Specifically, the present invention relates to a computer-implemented method for estimating realistic spare parts consumption for industrial equipment, comprising: (1) identifying and acquiring the main data, in which: (1a) a user must enter a TAG and then confirm a brand and model, or enter a manufacturer and then choose the brand and model; (1b) the user must enter the time window of interest, months and years after the start of operation of a piece of equipment; (1c) the user must decide whether or not to group said brand and model of equipment as a set, in which this brand and model are the main ones, in which: if yes, (1.1 c) all equipment with a TAG within the main TAG in the SAP tree are selected and become part of the set equipment; if no, (1.2 c) it is not complemented in the initial characterization;(1d) search all the company's industrial plants via database (datalake), identifying these pieces of equipment of this brand and model that have the requested operating time window; (1e) survey all spare parts that were consumed for each piece of equipment of the same brand and model (TAGs / locations) defined by the user in the established time window; (2) process understanding the user deciding between the probabilistic realistic view or the conservative realistic view: (2a) if the user decides for the probabilistic realistic view, the processing will highlight to be presented in the estimated output list the average quantity (µ) with or without the value of +- 1 standard deviation σ of each type of part consumed; or (2b) if the conservative realistic view is decided, the largest quantity of identical parts consumed by one of the pieces of equipment of the same brand and model identified within the group of equipment analyzed is highlighted to be presented in the estimated output list;(3) correlate the parameters of interest and issue an estimated realistic list, comprising: (3a) correlating a respective piece of data for each of the parts and quantities defined in the processing step (2); (3b) optionally, historically check the individual price charged and calculate the total price of the parts estimated for consumption of the selected equipment based on said data from step (3a);and (3c) issue an estimated list of parts to be consumed by the equipment within the time window established by the user. In step (3b), preferably, said unit price is calculated based on the average price of the item in purchase orders over the last 5 years. In step (3a), said correlated data includes a manufacturer name, a period for making the part available between the purchase order and the part's availability to the requesting area, a manufacturer number, a material number, and optionally, a unit price and a reference currency. In step (3b), the calculation of the total price of the parts estimated for consumption by the selected equipment is performed, preferably, using data that includes unit prices and quantities. ;

[0020] Furthermore, the present invention also relates to a computer-implemented method for monitoring the realistic consumption of spare parts for industrial equipment, which comprises: (1) identifying and acquiring the main data, in which: (1a) a user must enter a TAG and then confirm a brand and model, or enter a manufacturer and then choose the brand and model; (1b) the user must enter the time window of interest in absolute or relative mode, after the start of operation of an equipment; (1c) the user must decide whether or not to group said equipment brand and model as a set, in which this brand and model are main, in which: if yes, (1.1 c) all equipment with TAG within the main TAG in the SAP tree are selected and become part of the set equipment, if not, (1.2 c) is not complemented in the initial characterization; (1d) search all databases of the company's industrial plants via database tools (datalake), identifying these equipment of this brand and model that have the requested operating time window; (1e) carry out a survey of all spare parts that were consumed for each equipment of the same brand and model (TAGs / locations) defined by the user in the established time window; (2) define filters, choosing a graphical representation, preferably represented in curves, and / or in the form of lists, containing search criteria for at least the locations and / or TAGs of interest; in which: (2a) the user defines the type of information they want to see in the output to represent the consumption data, such as: (2.1a) consumption in quantity and accumulated over time; or (2.2a) consumption probability density; (2b) the user defines the location(s); (2c) present the search history through a list of all TAGs of the brand and model surveyed; (2d) the user confirms the default option with all TAGs selected or one or some of them; (3) correlate the parameters of interest and issue an estimated realistic list, comprising: (3a) correlating a respective data for each of the parts and quantities defined in the process step (2); (3b) historically check, optionally, the individual price charged and calculate the total price per type of part consumed by the selected equipment(s) from said data from step (3a); and (3c) issue the list of parts actually consumed by the equipment within the time window established by the user.In step (2b), the user-defined location(s) preferably comprise: the entire industrial plant, and / or platforms (UEPs) and / or thermoelectric plants (UTEs), and / or refineries, or any industrial unit, or their location in their respective units in the industrial plant, or subsea equipment. In step (3a), preferably, said unit price is optionally presented as an average of historically practiced prices for the item in purchase orders over the last 5 years, or alternatively, another period of interest. In step (3a), said correlated data comprises a manufacturer name, a period for making the part available between the purchase order and the part's availability to the requesting area, a manufacturer number, a material number, a unit price, and a currency.In step (3b) the composition of the calculation to form the total price of the estimated parts to be consumed by the selected equipment is carried out, preferably, using data that includes unit prices and quantities.

[0021] Furthermore, in the preferred embodiment of the invention, the granularity of the commissionable equipment is a significant factor in the analytical function performed in the estimating function of the estimating method, in which historical information on the demand for parts used, such as quantity, description, dates of respective consumption and their relationships with each identified TAG of the defined equipment and equipment maintenance orders, are evaluated. Brief description of the figures

[0022] In order to complement the present description and obtain a better understanding of the characteristics of the present invention, and in accordance with a preferred embodiment thereof, a set of figures is attached, where in an exemplified, although not limitative, manner its preferred embodiment is represented.

[0023] Figure 1 shows how the communication of the elements involved in the computer-implemented methods for estimating and monitoring occurs, in accordance with a preferred embodiment of the present invention.

[0024] Figure 2 shows a flowchart of the macro step (1) of identifying and acquiring the main data of the spare parts estimating function, of the method for realistically estimating spare parts, in accordance with a preferred embodiment of the present invention.

[0025] Figure 3 shows a flowchart of the macro step (2) for processing the spare parts estimator function of the method for realistically estimating spare parts, in accordance with a preferred embodiment of the present invention.

[0026] Figure 4 shows a flowchart of the macro step (3) of correlating parameters of interest and issuing the estimated realistic list of the spare parts estimator function of the method for realistically estimating spare parts, in accordance with a preferred embodiment of the present invention.

[0027] Figure 5 shows a flowchart of the macro step (1) of identifying and acquiring the main data of the spare parts monitoring function, of the monitoring method, according to a preferred embodiment of the present invention.

[0028] Figure 6 shows a flowchart of the macro step (2) for defining the filters, the choices of curves, locations and TAGs of interest, the spare parts monitoring function, and the monitoring method, in accordance with a preferred embodiment of the present invention.

[0029] Figure 7 shows a flowchart of the macro step (3) of correlating parameters of interest and issuing the estimated realistic list of the method for monitoring, in accordance with a preferred embodiment of the present invention.

[0030] Figure 8 shows the probability density curve for 1 standard deviation, according to a preferred embodiment of the present invention.

[0031] Figure 9 shows the listing or report format in which the information is made available in step (3c) of the estimation method, according to a preferred embodiment of the present invention. Detailed description of the invention

[0032] In general, the main steps of this invention are as follows: identification of the target equipment and time window, which should be relative to the start of the equipment's operation, and how many months and / or years after the start of the target equipment's operation; with the make and model of the equipment identified through more common information such as manufacturer, make / model, or tag, make / model, and whether the equipment of interest is a set or individual. If the interest is a motor pump set, for example, you should select the "merge" function, which represents the set of equipment that operates together, as is the case with the motor pump set, to perform a specific function. In the motor pump set, for example, there are other pieces of equipment such as a tank, motor, and instruments that operate together to perform the pumping function.After identifying the equipment, a scan is performed across the company's entire industrial plant, including offshore platforms, thermoelectric units, and refineries, for equipment that fits this make and model and the operating windows of interest. This scan is performed on the database (datalake), which contains data on maintenance orders, parts, dates, and actions performed on the equipment of interest, as well as other relevant information related to the company's processes, such as purchasing, maintenance, and others. These data are recorded in SAP and updated in the datalake.Additionally, the start date of operation of each piece of equipment at its respective location (TAG) for the group of equipment of the same make and model is also recorded in this data lake. Using the user-defined time window, a survey of all spare parts consumed for each piece of equipment of the same make and model is performed within the established time window. As previously mentioned, this survey is performed in the periodically updated database, called the data lake, based on process data recorded in SAP. This database gathers all necessary consumption information and its relationship to the equipment in question, such as the consumption date, the equipment targeted for consumption, and the quantity and type of part consumed. At this point, the identification and acquisition of the main data is complete.The processing stage begins, and then the user can choose between more conservative and less conservative processing. The most conservative is based on a calculation that takes the largest number of parts consumed in a given time window defined by the user (probability of 99.7% as shown in the attached flowchart). The least conservative is based on a calculation that takes the average number of parts consumed in this same time window defined by the user (probability of 68.0% as shown in the attached flowchart). For example, using a normal curve to calculate the standard deviation, which can be presented in the output as shown in the attached flowchart.Once the processing type is defined, the quantity consumed of each part for each piece of equipment / location within its time window is processed according to the defined formula (conservative realistic, median realistic, or probabilistic). Thus, the description of the parts is defined, and their quantities are estimated based on the quantity actually consumed of each part by the equipment brand and model in its various locations within the company, within the time window of interest specified by the user. At this point, the processing stage is completed. The correlation stage with parameters of interest begins, and the estimated realistic list is issued for the target equipment brand and model within the time window defined by the user.For each part and quantity defined in the processing step, the respective manufacturer name, the lead time (or period between the purchase order and the part's availability to the requesting area), manufacturer number, material number, and optionally a unit price and a reference currency are correlated. The unit price is calculated based on the item's average price over the last five years. After correlating these parameters, the total price of the estimated parts for consumption by the equipment in question within the defined time window is calculated. This information is then made available in a list or report format, as shown in Figure 9.

[0033] The data lake gathers updated information on parts consumed by equipment and their consumption timeframes through various routine operations in the company's various work processes, such as maintenance orders that specify the equipment targeted for maintenance and the planned action to be performed on it, including the parts to be replaced. These orders and all their information are stored in the data lake along with other records of all the other operations that occur via the SAP system. The information is copied from SAP and forms a set of selected information with periodic updates. This set of periodic information forms the data lake used in the realistic spare parts estimator method.

[0034] In addition to the estimator function of the estimator method, which provides a future view, a consumption monitoring function is also available, providing a view of equipment consumption in the past, according to a user-defined period. This consumption monitoring function allows the user to specify, in addition to the target equipment (brand and model) and time window of interest, options for viewing actual consumption, such as a cumulative curve that shows the cumulative consumption amounts over the life of the selected brand and model. Multiple equipment of the same brand and model can be selected, where the user can select one, some, or all to be displayed on the curve desired by the user.In addition to these consumption views of different equipment of the same brand and model, comparative views of consumption between groups of equipment of the same brand and model by location (offshore platforms, thermal units, business units, etc.) and consumption views by different types of curves such as accumulated curve and density and probability curve.

[0035] In a preferred embodiment, the method of the present invention does not automatically condition the analysis of equipment consumption on its removal from inventory, especially since most of the parts that leave inventory are not intended for use with a single piece of equipment. These parts can be sent to various destinations, such as the platform's warehouse or the thermal unit, to a center that serves multiple platforms, or to a workshop. Alternatively, the part required by the equipment may come from various sources, such as directly from the manufacturer, the platform's warehouse, the workshop, etc. Therefore, the removal of parts from inventory and the control of this removal are not very representative for the purpose of the present invention—the realistic spare parts estimator method. In short, this method seeks to understand the consumption profile or need of the target equipment during the period of interest, based primarily on the estimator function and supplemented by the monitoring function.

[0036] Furthermore, the computer-implemented methods of the present invention consist of the main consumption estimator function, and the other serves as marginal support with the consumption monitoring function. The estimator function has three macro steps, which are detailed in substeps below. The first macro step is (1) identifying and acquiring the main data, which consists of the following steps detailed below as shown in Figure 2. In substep (1a), the user enters the TAG and then confirms the brand and model, or the user enters the Manufacturer and then chooses the brand and model; then in (1b), the user enters the time window of interest, months and years after the start of equipment operation; in (1c), the user decides whether or not to group that equipment brand and model as a set in which this brand and model is the main one. If so, all equipment with a TAG within the main TAG in the SAP tree are selected and become part of the equipment set.If not, it is not complemented in the initial characterization. In step (1d), a search is made in all the databases of the company's industrial plants via the database (datalake), already detailed previously, to identify the equipment of this make and model that has the requested operating time window; in step (1e), a survey of all spare parts that were consumed for each equipment of the same make and model (all TAGs / locations) defined by the user in the established time window. At this point, the macro step (1) Identification and acquisition of main data is concluded and the processing step (2) begins, as follows: a substep (2a) in which the user decides between the probabilistic realistic view or the conservative realistic view. In substep (2.1a) if you decide for the probabilistic realistic view, the processing will highlight to be displayed in the estimated output list with the average quantity (µ) and the value of +- 1 standard deviation σ of each type of part consumed, variable X, by the equipment or equipment set within the defined time window. .

[0037] Ex.: Quantity consumed of a specific part, Xi, in the defined time window, for each of the n pieces of equipment of the same brand and model surveyed according to the scan performed. X1 = 3, X2 = 3, X3 = 5, X4 = 2, X5 = 0, X6 = 3 µ = (X1 + X2 + X3 + X4 + X5 + X6) / 6 = 2.67

[0038] Where you will round the whole part up and zero the decimal part. (µ) = 3.0

[0039] The standard deviation σ is calculated considering the mean without rounding, using the following formula: σ = 1.63 summation symbol. Indicates that all terms must be added, from the first position (i = 1) to position n Xi: Quantity consumed of a specific part at position i in the data set µ: arithmetic mean of the data n: quantity of data

[0040] The estimated probability for this processing option is 68% according to the probability density curve shown in figure 8, for 1 standard deviation.

[0041] In Figure 8, we have: normal distribution (probability density function) probability density function:

[0042] A result in the estimated list for this part would be 3.0 and +_ 1.63. In substep (2b) if the user decides on the conservative realistic view, the largest quantity of identical parts consumed by one of the equipment of the same brand and model identified within the group of equipment surveyed is highlighted to be presented in the estimated output list.

[0043] Example: quantity consumed of a specific part, Xi, in the defined time window, for each of the n pieces of equipment of the same brand and model surveyed according to the scan performed. X1 = 3, X2 = 3, X3 = 5, X4 = 2, X5 = 0, X6 = 3

[0044] A result in the estimated list for this part would be 5.0. The probability for this option is 99.7%, according to the probability density curve in figure 8. At this point, the macro step of (2) processing is completed and the macro step of (3) correlation with parameters of interest and issuance of the estimated realistic list is initiated, which comprises the substep step (3a) in which for each of the parts and quantities defined in the processing step, the respective manufacturer's name, the period for making the part available between the purchase order and the part's availability to the requesting area, manufacturer number, material number, unit price and currency are correlated.The unit price is calculated according to the average price of the item in purchase orders over the last 5 years; then, in step (3b) in which, with the unit prices and quantities in hand, the individual price charged is historically checked and the total price of the parts estimated for consumption of the selected equipment is added up; and in step (3c) the estimated list of parts to be consumed by the equipment within the time window established by the user is presented as shown in the attached figure in the detailed flowchart.

[0045] In addition, the detailed steps of the consumption monitoring function, viewing the past, follow, as per the steps presented in figures 5, 6 and 7.

[0046] For macro step (1) identifying and acquiring the main data, steps (1a) through (1e) are almost completely identical to the steps in the estimator function, with the only difference being the additional option for the user to enter the time window in absolute mode, in addition to the mode relative to the start of the operation, as occurs in the estimator function. Therefore, for ease of use, if the user has already filled in the input data and defined the equipment and the time window, in the estimator function, the data appears pre-filled, allowing editing of these data fields in the consumption monitoring function.

[0047] The differences begin in macro step (2) as shown below: (2) defining the filters, choosing a graphical representation, preferably represented in curves, and / or in the form of lists, containing search criteria for at least the locations and / or TAGs of interest, as detailed below. In step (2a) the user defines the type of information they want to see in the output to represent the consumption data, such as: (2.1a) consumption in quantity and accumulated over time or (2.2a) consumption probability density. In step (2b) the user defines the location(s), such as: the entire industrial plant, and / or platforms (UEPs) and / or thermoelectric plants (UTEs) and / or refineries, or any industrial unit, or their location in their respective units in the industrial plant, or subsea equipment. Then, in step (2c) after the previous step, the search history is presented through a list of all TAGs of the brand and model surveyed.In step (2d) the user confirms the default option with all TAGs selected or one or some of them. At this point the macro step (2) definition of filters is completed, choosing a graphical representation, preferably represented in curves, and / or in the form of lists, containing search criteria for at least the locations and / or TAGs of interest and starting the macro step (3) of correlating parameters of interest and issuing the estimated realistic list, similar to step (3) of the estimator method.

[0048] In other words, the methods of the present invention have two main functions: the first is to provide a realistic estimate of spare parts consumption per target piece of equipment within a user-defined time window relative to the start of operation; the second is to present the actual consumption monitoring of a given piece of equipment (brand and model) over a given absolute or relative period defined by the user. As presented, the estimating function consists of presenting a consumption forecast within a time window based on consumption history, and the monitoring function is a view of the consumption of a given piece of equipment within a time window. In this window, among other information, it is possible to filter the consumption behavior of one or more pieces of equipment, compare consumption between groups of equipment, the availability time of each of the consumed parts, and obtain unit and total prices. Implementation Example / Tests / Results

[0049] In a preferred embodiment of the present invention, a significant difference was revealed during a test between the results of the present invention's realistic spare parts estimator method and the parts list indicated by the manufacturer for the consumption of four pumps during the first two years of operation. The main conclusions were that none of the parts indicated by the manufacturer were actually consumed in three years of operation of the four pumps. The total value of the parts indicated by the manufacturer exceeds one million reais. If the company had purchased the indicated parts, it would have spent more than one million, two hundred and seventy thousand reais in CAPEX alone, in addition to running the risk of production downtime, increased purchasing costs, emergency processes, and other problems already mentioned. Additionally, in another test conducted similar to the one mentioned above, the list resulting from the invention was compared with the manufacturer's list for two cranes, and the differences were again very significant.Among the main conclusions, it is worth highlighting that from the list indicated by the manufacturer for the first 2 years of operation, only one part was actually used in the equipment, the total value of the manufacturer's list exceeds one million reais and the realistic list totals R$ 144,747.62.

[0050] Additionally, tests were carried out with the developed method, in which problems were indicated and solutions provided as follows.

[0051] For the test carried out comparing the manufacturer's list of 2 cranes for the 2-year operating window since the start of operation, with the realistic list estimated for the same 2 cranes for 3 years of operation, the same conclusions were reached as for the test in the realistic estimate made for the 4 pumps, except that there was only one correspondence, one item, between the list issued by the manufacturer and the list issued by the realistic estimator, that is, there was only a correspondence for one crane arm.

[0052] Therefore, it was concluded that as the real need for equipment is known in advance, at any given time, the previously mentioned spare parts problems can be avoided.

Claims

CLAIMS 1. Computer-implemented method for estimating realistic spare parts consumption for industrial equipment, comprising: (1) identifying and acquiring master data, wherein: (1a) a user must enter a TAG and then confirm a make and model, or enter a manufacturer and then choose the make and model; (1b) the user must enter the time window of interest, months and years after the start of operation of a piece of equipment; (1c) the user must decide whether or not to group said equipment make and model as a set, in which this make and model are main, wherein: if yes, (1.1 c) all equipment with a TAG within the main TAG in the SAP tree are selected and become part of the set equipment, if not, (1.2 c) is not complemented in the initial characterization; (1d) scan all the company's industrial plants via database (datalake), identifying equipment of this brand and model that have the requested operating time window; (1e) carry out a survey of all spare parts that were consumed for each equipment of the same brand and model (TAGs / locations) defined by the user in the established time window;. (2) processing comprising the user deciding between the probabilistic realistic view or the conservative realistic view: (2a) if the user decides on the probabilistic realistic view, the processing will highlight to be presented in the estimated output list the average quantity (µ) with or without the value of +- 1 standard deviation σ of each type of part consumed; or (2b) if the user decides on the conservative realistic view, the largest quantity of identical parts consumed by one of the equipment of the same brand and model identified within the group of equipment analyzed is highlighted to be presented in the estimated output list; (3) correlate the parameters of interest and issue an estimated realistic list, comprising: (3a) correlating a respective piece of data for each of the parts and quantities defined in the processing step (2);(3b) optionally, historically check the individual price charged and calculate the total price of the estimated parts for consumption of the selected equipment from said data from step (3a); and (3c) output an estimated list of parts to be consumed by the equipment within the time window established by the user.

2. Method, according to claim 1, characterized by the fact that in step (3b),; preferably, said unit price is calculated based on the average price of the item in purchase orders over the last 5 years.

3. Method according to claim 1, characterized in that in step (3a) said correlated data comprises a manufacturer's name, a period for making the part available between the purchase order and the part's availability to the requesting area, a manufacturer's number, a material number, and optionally, a unit price and a reference currency.

4. Method according to claim 1, characterized in that in step (3b) the calculation of the total price of the parts estimated for consumption of the selected equipment is carried out, preferably, using data comprising unit prices and quantities. 5.Computer-implemented method for monitoring the realistic consumption of spare parts for industrial equipment, characterized by the fact that it comprises: (1) identifying and acquiring the main data, in which: (1a) a user must enter a TAG and then confirm a brand and model, or enter a manufacturer and then choose the brand and model; (1b) the user must enter the time window of interest in absolute or relative mode, after the start of operation of a piece of equipment; (1c) the user must decide whether or not to group said brand and model of equipment as a set, in which this brand and model are the main ones, in which: if yes, (1.1 c) all equipment with a TAG within the main TAG in the SAP tree are selected and become part of the joint equipment; if no, (1.2 c) it is not complemented in the initial characterization; (1d) search all databases of the company's industrial plants via database tools (datalake), identifying equipment of this brand and model that have the requested operating time window; (1e) carry out a survey of all spare parts that were consumed for each equipment of the same brand and model (TAGs / locations) defined by the user in the established time window; (2) define filters, choosing a graphical representation, preferably represented in curves, and / or in the form of lists, containing search criteria for at least the locations and / or TAGs of interest; where: (2a) the user defines the type of information they want to see in the output to represent the consumption data, such as: (2.1a) consumption in quantity and accumulated over time; or (2.2a) consumption probability density; (2b) the user defines the location(s); (2c) present the search history through a list of all the TAGs of the brand and model surveyed;. (2d) the user confirms the default option with all the selected TAGs or one or some of them; (3) correlate the parameters of interest and issue an estimated realistic list, comprising: (3a) correlating a respective data for each of the parts and quantities defined in the process step (2); (3b) optionally, historically check the individual price charged and calculate the total price of the estimated parts for consumption of the selected equipment from said data in step (3a); and (3c) issue an estimated list of parts to be consumed by the equipment within the time window established by the user. 6.Method, according to claim 5, characterized by the fact that in step (2b) the user-defined location(s), preferably, comprise: the entire industrial plant, and / or platforms (UEPs) and / or thermoelectric plants (UTEs), and / or refineries, or any industrial unit, or their location in their respective units in the industrial plant, or subsea equipment.

7. Method, according to claim 5, characterized by the fact that in step (3a) preferably, said unit price is, optionally, presented as an average of historically practiced prices of the item in purchase orders of the last 5 years, or alternatively, another period of interest.

8. Method, according to claim 5, characterized by the fact that in step (3a) said correlated data comprises a manufacturer name, a period for availability of the part between the purchase order and the availability of the part to the requesting area, a manufacturer number, a material number, a unit price and a currency.

9. Method, according to claim 5, characterized by the fact that in step (3b) the composition of the calculation for forming the total price of the estimated parts to be consumed by the selected equipment is carried out, preferably, by data that comprise unit prices and quantities.

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