Support for the maintenance of equipment ledgers and solutions for equipment maintenance management.
Digitize information such as equipment data, failure history, and reports to organize the equipment ledger. Establish the basis for calculating LCC and operating rates, re-evaluate maintenance methods, and create system requirements.
Many companies have proposed maintenance methods utilizing large-scale data and AI (artificial intelligence/machine learning), and there are numerous reports on their effectiveness. However, with a few exceptions, the management of maintenance on-site is predominantly done using various formats such as paper, Excel, Access, and PDF, and the information managed differs by department. Our company provides services to address the following objectives through the organization of equipment ledgers: 1. To establish a ledger of data that serves as the foundational requirements for the implementation of a maintenance system. 2. To utilize accumulated failure information on-site to formulate inspection cycles that minimize costs. 3. To consider maintenance methods tailored to the conditions, as usage and environments differ even for the same equipment. 4. To create a risk matrix from accident and failure information. 5. To graph the relationship between maintenance items, reliability, and costs, and use it as a guideline for planning. The steps for organizing the equipment ledger are as follows: 1. Digitization of various information. 2. Organization and classification of the digitized information. 3. Conducting various analyses according to objectives. 4. Adding management items based on the analysis results.
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basic information
1) Data digitization of various information We convert your existing data (in various formats such as paper, Excel, PDF, Access, etc.), including equipment information, failure history, and reports, into electronic data for centralized management. Initially, we will manage this data in an easily manageable format, either Excel or Access. 2) Organization and classification When converting multiple pieces of information into electronic data, inconsistencies may arise, such as duplicate management items or different wording that conveys the same meaning. Among these inconsistencies, the following items are particularly important: - Failure/accident information - Emergency measures - Permanent countermeasures - Impact when a failure/accident occurs Here, we will eliminate duplicate information and classify and code the above items to organize the data necessary for analysis. 3) Analysis We will select methods appropriate to the objectives and conduct the analysis. The representative methods are as follows: - Weibull analysis - Bayesian statistics - MCMC method - Text mining - Machine learning - Reliability assessment 4) Addition of management items Based on the results of the analysis and the objectives, we will add any necessary or missing items.
Price range
P5
Delivery Time
※3 months～
Applications/Examples of results
We have a track record in the following industries: - Petrochemical plants - Nuclear power generation - Electric wires - Aerospace - Railways - Assembly manufacturing
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Company information
Our company develops and sells a "Maintenance Management System" for managing and operating various plants, factories, and other facilities and assets. Currently, this system is undergoing significant evolution into a system that incorporates IoT technologies, such as sensor information and input from tablet devices, as well as AI technologies like machine learning, featuring functions for failure prediction and automatic scheduling. Additionally, as part of the recent trend of digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to improve operational efficiency. In line with this trend, our company provides a solution aimed at enhancing efficiency in research and development environments, which is the Laboratory Information Management System (LIMS). This software includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.