WO2022146376A2 - Real-time spare part prediction system - Google Patents

Real-time spare part prediction system Download PDF

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Publication number
WO2022146376A2
WO2022146376A2 PCT/TR2021/051536 TR2021051536W WO2022146376A2 WO 2022146376 A2 WO2022146376 A2 WO 2022146376A2 TR 2021051536 W TR2021051536 W TR 2021051536W WO 2022146376 A2 WO2022146376 A2 WO 2022146376A2
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Prior art keywords
data
server
spare part
failure
product
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PCT/TR2021/051536
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French (fr)
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WO2022146376A3 (en
Inventor
Eren ESGIN
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M.B.I.S Bilgisayar Otomasyon Danismanlik Ve Egitim Hizmetleri Sanayi Ticaret Anonim Sirketi
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Publication of WO2022146376A2 publication Critical patent/WO2022146376A2/en
Publication of WO2022146376A3 publication Critical patent/WO2022146376A3/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Definitions

  • the present invention relates to a system for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services.
  • each customer call made to a customer call center in a current status process of an after-sales customer support service is triggered upon a new failure log is created in a failure tracking system and/or CRM (customer relationship management) system.
  • details of failure occurrence for example, failure details from most general to most specific
  • customer details for example, customer profile and location
  • product details for example, product code, material type, material group and product hierarchy
  • the related failure log is assigned to a nearby technical service with regard to the customer’s location.
  • the technical service pays a feasibility visit to control the failure cause and the defective component.
  • each customer visit is managed as a unique maintenance line item and the spare part consumption or maintenance workmanship for the related customer visit is set off to this line item.
  • the said invention provides a spare part demand prediction method: firstly a graph structure is built; wherein any node of the graph structure is used for representing a spare part or a machine; the relationship between spare parts, the relationship between spare parts and machines or the relationship between machines and spare parts are created by the data gathered from at least one new spare part and at least one historical spare part; based on the designed graph structure and the historical damage data of one historical spare part of any new spare part in the at least one new spare part, it is enabled to determine predicted damage data of any new spare part in the at least one new spare part and then to determine the demand quantity of any new spare part based on the predicted damage data of any new spare part.
  • the invention further provides a spare part demand prediction device and electronic equipment.
  • An objective of the present invention is to realize a system which enables to predict the optimum spare part in accordance with a new failure log and a customer profile reaching a call center and then to transmit it to a technical service.
  • Figure l is a schematic view of the inventive system.
  • the inventive system (1) for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services comprises: at least one failure tracking server (2) which is configured to manage attributes about the failure logs created and to track customer visits; at least one data warehouse (3) which is configured to store the data gathered periodically by means of data transfer procedures; at least one enterprise resource planning server (4) which is configured to enable use of resources such as workforce, machinery and material required for production of goods and services; at least one data preparation server (5) which is configured to acquire data about products, problems and customers from data sources; and at least one modelling server (6) which is configured to obtain the related data from the data preparation server (5) and to make inferences related to the main underlying reasons for the spare part consumption.
  • the failure tracking server (2) included in the inventive system (1) is configured to manage main attributes about the failure logs, that are created at the operational level, at the header level and to track customer visits, that are paid for active or inactive failure calls, within the framework of maintenance and repair history.
  • the failure tracking server (2) is configured to set off the spare part consumed in a customer visit or maintenance and repair workmanship to a related maintenance confirmation item in terms of quantity and time.
  • the failure tracking server (2) is configured to carry out management of master data apart from operational data.
  • the failure tracking server (2) is configured to manage data such as attributes of a customer who is provided with after-sales service, attributes of a spare part that is consumed in a customer visit and information of technical service who paid the customer visit or performed the first assembly, within the framework of a B2C (business-to-customer e-commerce) business model.
  • the data warehouse (3) included in the inventive system (1) is configured to store data such as product code, material type, material group, product hierarchy about a product.
  • the data warehouse (3) is configured to extract and store the data created at the operational level in enterprise resource planning systems (ETL-extract, transform and load), periodically by means of designated data transfer procedures.
  • the data warehouse (3) is configured to periodically acquire the data about the operation occurring on the failure tracking server (2) and the enterprise resource planning server (4) and the temporal master data and to store these data within itself.
  • the enterprise resource planning server (4) included in the inventive system (1) is configured to provide arrangement of business processes and data flow at the operational level.
  • the enterprise resource planning server (4) is configured to operate by means of integrated management algorithms enabling efficient use of resources such as workforce, machinery and material required for production of goods and services in enterprises, and to comprise attributes of end products.
  • the data preparation server (5) included in the inventive system (1) is configured to extract the first raw data set from various data sources, in accordance with a designated SQL query procedure.
  • the data preparation server (5) is configured to gather data from a data source of failure log keeping data such as failure log id, date and time of event, complaint or symptom codes, failure status, the related customer number and the number of defective product at the operational level of failure log.
  • the data preparation server (5) is configured to gather data from the data source of maintenance item keeping the consumption of spare parts consumed in a customer visit and the confirmation information of maintenance-repair workmanship.
  • the data preparation server (5) is configured to receive data from a product data source that keeps features being managed in the enterprise resource planning server (4) such as material code, material type, material group, product hierarchy, brand and product costing group.
  • the data preparation server (5) is configured to receive data from a data source of product details that keeps information such as date of production, start of warranty date.
  • the data preparation server (5) is configured to receive data from a customer data source that keeps basic information about a customer having a defective product such as customer profile, customer location information with district/province/region details.
  • the data preparation server (5) is configured to associate each observation with a unique failure call and to characterize it at a higher level of abstraction.
  • the data preparation server (5) is configured to gather consumption of spare part in each consumption item related to the same failure event, to transfer values of spare part in the related maintenance items, and to singularize repeating values by combining these values in a single derived target class.
  • the data preparation server (5) is configured to extract only combinations of positive-associated spare part by using a predetermined filter algorithm.
  • the data preparation server (5) is configured to convert target class values of combined spare parts into more refined forms according to rules of association being revealed as a result of the second step.
  • the data preparation server (5) is configured to determine relatively important rules according to a predetermined minimum confidence parameter.
  • the data preparation server (5) is configured to sort filtered association rules in a descending order according to criterion values of lift and confidence.
  • the data preparation server (5) is configured to control whether the target class value of a combined spare part in a created data set involves both antecendent and consequent values mentioned in the association rules.
  • the data preparation server (5) is configured to extract the resulting value from the target class value of a combined spare part and to process this new value obtained to a new target class, in case of involving.
  • the data preparation server (5) is configured to use a combined target class value directly, in the event that the target class value of a combined spare part in the set thereof does not involve both antecendent and consequent values mentioned in the association rules.
  • the data preparation server (5) is configured to ensure that a refined transaction set obtained is converted into an item set according to complaint codes, product hierarchies and attribute values of a spare part product group.
  • the data preparation server (5) is configured to extract usage rules of spare parts at different levels of complaint code and product hierarchy according to criterion values of lift, confidence and support for the created item sets.
  • the data preparation server (5) is configured to determine the rules that are relatively weak among usage rules of spare parts, in accordance with a predetermined algorithm.
  • the modelling server (6) included in the inventive system (1) is configured to classify characteristics of historical failure logs (particularly, complaint details), characteristics of defective product, information about the customer having the defective product and the most important (and dominant) factors and patterns that explain the main underlying fact (or phenomenon) for the spare part consumption for the spare part consumption according to the accountable technical service performing the assembly, within the framework of specific concrete rules or closed forms.
  • the modelling server (6) is configured to determine attributes to be used in a spare part prediction operation and effect (weight) of these attributes on prediction.
  • the modelling server (6) is configured to scan a neighbourhood for a training record of k that is closest to an unknown sample by means of a kNN (K- nearest neighbour) classifier and to identify the proximity as a distance function in the form of Euclidean distance, in the event that an unknown record is given in a spare part prediction step.
  • the modelling server (6) is configured to digitize values of each attribute before calculation of distance.
  • the modelling server (6) is configured to normalize categorical attributes by a digitization weight value assigned to each significant attribute by adapting the weight, interdimensional similarity obtained in the operation of attribute selection.
  • the modelling server (6) is configured to measure the similarity between a new failure event and neighbour objects in the last dataset, by means of a predetermined algorithm.
  • the modelling server (6) is configured to abstract the number per a value of spare part target class, the values of total and average similarity for the nearest-neighbour objects.
  • the modelling server (6) is configured to determine the spare part values in the result list of prediction according to the number of repetition (frequency).
  • the modelling server (6) is configured to determine the spare parts included in the result list of spare part prediction according to an average similarity value.
  • the modelling server (6) is configured to conclude the result list of spare part prediction by means of a predetermined k-limit algorithm.
  • the inventive system (1) information about a product are stored in the data warehouse (3) at first.
  • the enterprise resource planning server (4) manages use of resources such as workforce, machinery and material required for production of goods and services.
  • the information received from the failure tracking server (2) and the data warehouse (3) are made ready to be processed by the data preparation server (5) and the modelling server (6) analyses the related data by using various predetermined algorithms and creates a prediction list of spare parts.

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Abstract

The present invention relates to a system (1) for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services.

Description

REAL-TIME SPARE PART PREDICTION SYSTEM
Technical Field
The present invention relates to a system for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services.
Background of the Invention
Today, each customer call made to a customer call center in a current status process of an after-sales customer support service is triggered upon a new failure log is created in a failure tracking system and/or CRM (customer relationship management) system. During this call, details of failure occurrence (for example, failure details from most general to most specific) are learned from customers. Then, customer details (for example, customer profile and location) are enriched and product details (for example, product code, material type, material group and product hierarchy) are removed from the previous product assembly history in a CRM system. Thereafter, the related failure log is assigned to a nearby technical service with regard to the customer’s location. Lastly, the technical service pays a feasibility visit to control the failure cause and the defective component. For the related failure incident, each customer visit is managed as a unique maintenance line item and the spare part consumption or maintenance workmanship for the related customer visit is set off to this line item.
Considering the studies included in the state of the art, it is understood that there is need for a system which enables to predict the class of categorical spare part to be used by means of classification-type algorithms. The Chinese patent document no. CN110909997A, an application in the state of the art, discloses a spare part demand prediction method and a spare part demand prediction system. The said invention provides a spare part demand prediction method: firstly a graph structure is built; wherein any node of the graph structure is used for representing a spare part or a machine; the relationship between spare parts, the relationship between spare parts and machines or the relationship between machines and spare parts are created by the data gathered from at least one new spare part and at least one historical spare part; based on the designed graph structure and the historical damage data of one historical spare part of any new spare part in the at least one new spare part, it is enabled to determine predicted damage data of any new spare part in the at least one new spare part and then to determine the demand quantity of any new spare part based on the predicted damage data of any new spare part. The invention further provides a spare part demand prediction device and electronic equipment.
Summary of the Invention
An objective of the present invention is to realize a system which enables to predict the optimum spare part in accordance with a new failure log and a customer profile reaching a call center and then to transmit it to a technical service.
Detailed Description of the Invention
“A Real-Time Spare Part Prediction System” realized to fulfil the objective of the present invention is shown in the figure attached, in which:
Figure l is a schematic view of the inventive system.
The components illustrated in the figure are individually numbered, where the numbers refer to the following: 1. System
2. Failure tracking server
3. Data warehouse
4. Enterprise resource planning server
5. Data preparation server
6. Modelling server
The inventive system (1) for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services comprises: at least one failure tracking server (2) which is configured to manage attributes about the failure logs created and to track customer visits; at least one data warehouse (3) which is configured to store the data gathered periodically by means of data transfer procedures; at least one enterprise resource planning server (4) which is configured to enable use of resources such as workforce, machinery and material required for production of goods and services; at least one data preparation server (5) which is configured to acquire data about products, problems and customers from data sources; and at least one modelling server (6) which is configured to obtain the related data from the data preparation server (5) and to make inferences related to the main underlying reasons for the spare part consumption.
The failure tracking server (2) included in the inventive system (1) is configured to manage main attributes about the failure logs, that are created at the operational level, at the header level and to track customer visits, that are paid for active or inactive failure calls, within the framework of maintenance and repair history. The failure tracking server (2) is configured to set off the spare part consumed in a customer visit or maintenance and repair workmanship to a related maintenance confirmation item in terms of quantity and time. The failure tracking server (2) is configured to carry out management of master data apart from operational data. The failure tracking server (2) is configured to manage data such as attributes of a customer who is provided with after-sales service, attributes of a spare part that is consumed in a customer visit and information of technical service who paid the customer visit or performed the first assembly, within the framework of a B2C (business-to-customer e-commerce) business model.
The data warehouse (3) included in the inventive system (1) is configured to store data such as product code, material type, material group, product hierarchy about a product. The data warehouse (3) is configured to extract and store the data created at the operational level in enterprise resource planning systems (ETL-extract, transform and load), periodically by means of designated data transfer procedures. The data warehouse (3) is configured to periodically acquire the data about the operation occurring on the failure tracking server (2) and the enterprise resource planning server (4) and the temporal master data and to store these data within itself.
The enterprise resource planning server (4) included in the inventive system (1) is configured to provide arrangement of business processes and data flow at the operational level. The enterprise resource planning server (4) is configured to operate by means of integrated management algorithms enabling efficient use of resources such as workforce, machinery and material required for production of goods and services in enterprises, and to comprise attributes of end products.
The data preparation server (5) included in the inventive system (1) is configured to extract the first raw data set from various data sources, in accordance with a designated SQL query procedure. The data preparation server (5) is configured to gather data from a data source of failure log keeping data such as failure log id, date and time of event, complaint or symptom codes, failure status, the related customer number and the number of defective product at the operational level of failure log. The data preparation server (5) is configured to gather data from the data source of maintenance item keeping the consumption of spare parts consumed in a customer visit and the confirmation information of maintenance-repair workmanship. The data preparation server (5) is configured to receive data from a product data source that keeps features being managed in the enterprise resource planning server (4) such as material code, material type, material group, product hierarchy, brand and product costing group. The data preparation server (5) is configured to receive data from a data source of product details that keeps information such as date of production, start of warranty date. The data preparation server (5) is configured to receive data from a customer data source that keeps basic information about a customer having a defective product such as customer profile, customer location information with district/province/region details. The data preparation server (5) is configured to associate each observation with a unique failure call and to characterize it at a higher level of abstraction. The data preparation server (5) is configured to gather consumption of spare part in each consumption item related to the same failure event, to transfer values of spare part in the related maintenance items, and to singularize repeating values by combining these values in a single derived target class. The data preparation server (5) is configured to extract only combinations of positive-associated spare part by using a predetermined filter algorithm. The data preparation server (5) is configured to convert target class values of combined spare parts into more refined forms according to rules of association being revealed as a result of the second step. The data preparation server (5) is configured to determine relatively important rules according to a predetermined minimum confidence parameter. The data preparation server (5) is configured to sort filtered association rules in a descending order according to criterion values of lift and confidence. The data preparation server (5) is configured to control whether the target class value of a combined spare part in a created data set involves both antecendent and consequent values mentioned in the association rules. The data preparation server (5) is configured to extract the resulting value from the target class value of a combined spare part and to process this new value obtained to a new target class, in case of involving. The data preparation server (5) is configured to use a combined target class value directly, in the event that the target class value of a combined spare part in the set thereof does not involve both antecendent and consequent values mentioned in the association rules. The data preparation server (5) is configured to ensure that a refined transaction set obtained is converted into an item set according to complaint codes, product hierarchies and attribute values of a spare part product group. The data preparation server (5) is configured to extract usage rules of spare parts at different levels of complaint code and product hierarchy according to criterion values of lift, confidence and support for the created item sets. The data preparation server (5) is configured to determine the rules that are relatively weak among usage rules of spare parts, in accordance with a predetermined algorithm.
The modelling server (6) included in the inventive system (1) is configured to classify characteristics of historical failure logs (particularly, complaint details), characteristics of defective product, information about the customer having the defective product and the most important (and dominant) factors and patterns that explain the main underlying fact (or phenomenon) for the spare part consumption for the spare part consumption according to the accountable technical service performing the assembly, within the framework of specific concrete rules or closed forms. The modelling server (6) is configured to determine attributes to be used in a spare part prediction operation and effect (weight) of these attributes on prediction. The modelling server (6) is configured to scan a neighbourhood for a training record of k that is closest to an unknown sample by means of a kNN (K- nearest neighbour) classifier and to identify the proximity as a distance function in the form of Euclidean distance, in the event that an unknown record is given in a spare part prediction step. The modelling server (6) is configured to digitize values of each attribute before calculation of distance. The modelling server (6) is configured to normalize categorical attributes by a digitization weight value assigned to each significant attribute by adapting the weight, interdimensional similarity obtained in the operation of attribute selection. The modelling server (6) is configured to measure the similarity between a new failure event and neighbour objects in the last dataset, by means of a predetermined algorithm. The modelling server (6) is configured to abstract the number per a value of spare part target class, the values of total and average similarity for the nearest-neighbour objects. The modelling server (6) is configured to determine the spare part values in the result list of prediction according to the number of repetition (frequency). The modelling server (6) is configured to determine the spare parts included in the result list of spare part prediction according to an average similarity value. The modelling server (6) is configured to conclude the result list of spare part prediction by means of a predetermined k-limit algorithm.
In the inventive system (1), information about a product are stored in the data warehouse (3) at first. The enterprise resource planning server (4) manages use of resources such as workforce, machinery and material required for production of goods and services. The information received from the failure tracking server (2) and the data warehouse (3) are made ready to be processed by the data preparation server (5) and the modelling server (6) analyses the related data by using various predetermined algorithms and creates a prediction list of spare parts.
With the intention, it is enabled to resolve a failure transmitted to a company, by means of a first feasibility visit; to reduce the average number of visits per failure; to reduce expenses of sales operation and consumption of unnecessary spare parts; and to improve the quality level of after-sales services.
Within these basic concepts; it is possible to develop various embodiments of the inventive system (1); the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS A system (1) for gathering data about a product in spare parts services, determining product attributes by using at least one mathematical model, and estimating which spare part will be used with reference to a failure call, in after sales services; comprising: at least one failure tracking server (2) which is configured to manage attributes about the failure logs created and to track customer visits; at least one data warehouse (3) which is configured to store the data gathered periodically by means of data transfer procedures; characterized by: at least one enterprise resource planning server (4) which is configured to enable use of resources such as workforce, machinery and material required for production of goods and services; at least one data preparation server (5) which is configured to acquire data about products, problems and customers from data sources; and at least one modelling server (6) which is configured to obtain the related data from the data preparation server (5) and to make inferences related to the main underlying reasons for the spare part consumption. A system (1) according to Claim 1; characterized by the failure tracking server (2) which is configured to manage main attributes about the failure logs, that are created at the operational level, at the header level and to track customer visits, that are paid for active or inactive failure calls, within the framework of maintenance and repair history. A system (1) according to Claim 1 or 2; characterized by the failure tracking server (2) which is configured to set off the spare part consumed in a customer visit or maintenance and repair workmanship to a related maintenance confirmation item in terms of quantity and time.
8 A system (1) according to any of the preceding claims; characterized by the failure tracking server (2) which is configured to carry out management of master data apart from operational data. A system (1) according to any of the preceding claims; characterized by the failure tracking server (2) which is configured to manage data such as attributes of a customer who is provided with after-sales service, attributes of a spare part that is consumed in a customer visit and information of technical service who paid the customer visit or performed the first assembly, within the framework of a B2C business model. A system (1) according to any of the preceding claims; characterized by the data warehouse (3) which is configured to store data such as product code, material type, material group, product hierarchy about a product. A system (1) according to any of the preceding claims; characterized by the data warehouse (3) which is configured to extract and store the data created at the operational level in enterprise resource planning systems, periodically by means of designated data transfer procedures. A system (1) according to any of the preceding claims; characterized by the data warehouse (3) which is configured to periodically acquire the data about the operation occurring on the failure tracking server (2) and the enterprise resource planning server (4) and the temporal master data and to store these data within itself. A system (1) according to any of the preceding claims; characterized by the enterprise resource planning server (4) which is configured to provide arrangement of business processes and data flow at the operational level.
9
10. A system (1) according to any of the preceding claims; characterized by the enterprise resource planning server (4) which is configured to operate by means of integrated management algorithms enabling efficient use of resources such as workforce, machinery and material required for production of goods and services in enterprises, and to comprise attributes of end products.
11. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to extract the first raw data set from various data sources, in accordance with a designated SQL query procedure.
12. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to gather data from a data source of failure log keeping data such as failure log id, date and time of event, complaint or symptom codes, failure status, the related customer number and the number of defective product at the operational level of failure log.
13. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to gather data from the data source of maintenance item keeping the consumption of spare parts consumed in a customer visit and the confirmation information of maintenance-repair workmanship.
14. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to receive data from a product data source that keeps features being managed in the enterprise resource planning server (4) such as material code, material type, material group, product hierarchy, brand and product costing group.
10 A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to receive data from a data source of product details that keeps information such as date of production, start of warranty date. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to receive data from a customer data source that keeps basic information about a customer having a defective product such as customer profile, customer location information with district/province/region details. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to associate each observation with a unique failure call and to characterize it at a higher level of abstraction. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to gather consumption of spare part in each consumption item related to the same failure event, to transfer values of spare part in the related maintenance items, and to singularize repeating values by combining these values in a single derived target class. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to extract only combinations of positive-associated spare part by using a predetermined filter algorithm. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to convert target class values of combined spare parts into more refined forms according to rules of association being revealed as a result of the second step.
11 A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to determine relatively important rules according to a predetermined minimum confidence parameter. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to sort filtered association rules in a descending order according to criterion values of lift and confidence. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to control whether the target class value of a combined spare part in a created data set involves both antecendent and consequent values mentioned in the association rules. A system (1) according to Claim 23; characterized by the data preparation server (5) which is configured to extract the resulting value from the target class value of a combined spare part and to process this new value obtained to a new target class, in case of involving. A system (1) according to Claim 23; characterized by the data preparation server (5) which is configured to is configured to use a combined target class value directly, in the event that the target class value of a combined spare part in the set thereof does not involve both antecendent and consequent values mentioned in the association rules. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to ensure that a refined transaction set obtained is converted into an item set according to complaint codes, product hierarchies and attribute values of a spare part product group.
12 A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to extract usage rules of spare parts at different levels of complaint code and product hierarchy according to criterion values of lift, confidence and support for the created item sets. A system (1) according to any of the preceding claims; characterized by the data preparation server (5) which is configured to determine the rules that are relatively weak among usage rules of spare parts, in accordance with a predetermined algorithm. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to classify characteristics of historical failure logs, characteristics of defective product, information about the customer having the defective product and the most important factors and patterns that explain the main underlying fact for the spare part consumption for the spare part consumption according to the accountable technical service performing the assembly, within the framework of specific concrete rules or closed forms. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to determine attributes to be used in a spare part prediction operation and effect of these attributes on prediction. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to scan a neighbourhood for a training record of k that is closest to an unknown sample by means of a kNN (K-nearest neighbour) classifier and to identify the proximity as a distance function in the form of Euclidean distance, in the event that an unknown record is given in a spare part prediction step.
13
32. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to digitize values of each attribute before calculation of distance.
33. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to normalize categorical attributes by a digitization weight value assigned to each significant attribute by adapting the weight, interdimensional similarity obtained in the operation of attribute selection.
34. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to measure the similarity between a new failure event and neighbour objects in the last dataset, by means of a predetermined algorithm.
35. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to abstract the number per a value of spare part target class, the values of total and average similarity for the nearest-neighbour objects.
36. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to determine the spare part values in the result list of prediction according to the number of repetition.
37. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to determine the spare parts included in the result list of spare part prediction according to an average similarity value.
38. A system (1) according to any of the preceding claims; characterized by the modelling server (6) which is configured to conclude the result list of spare part prediction by means of a predetermined k-limit algorithm.
14
PCT/TR2021/051536 2020-12-29 2021-12-27 Real-time spare part prediction system WO2022146376A2 (en)

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TR2020/22270A TR202022270A2 (en) 2020-12-29 2020-12-29 REAL TIME SPARE PARTS PREDICTION SYSTEM

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994734A (en) * 2023-03-14 2023-04-21 百福工业缝纫机（张家港）有限公司 Production equipment maintenance part inventory management method and system

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9971991B2 (en) * 2015-04-28 2018-05-15 Accenture Global Services Limited Automated, new spare parts forecasting and demand planning system
CN110428170A (en) * 2019-08-01 2019-11-08 优必爱信息技术（北京）有限公司 A kind of automobile parts demand dynamic prediction method, system and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994734A (en) * 2023-03-14 2023-04-21 百福工业缝纫机（张家港）有限公司 Production equipment maintenance part inventory management method and system
CN115994734B (en) * 2023-03-14 2024-01-30 百福工业缝纫机（张家港）有限公司 Production equipment maintenance part inventory management method and system

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