Support for the maintenance of equipment ledgers and solutions for equipment maintenance management.
Digitize equipment information, failure history, reports, etc., and 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, except for some companies, 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. We want to organize the ledger of data that serves as the fundamental requirement for the implementation of a maintenance system. 2. We want to utilize the failure information accumulated on-site to establish inspection cycles that minimize costs. 3. We want to consider maintenance methods tailored to the situation, as the usage conditions and environments differ even for the same equipment. 4. We want to create a risk matrix from accident and failure information. 5. We want to graph the relationship between maintenance items, reliability, and costs to serve 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. Implementation of various analyses according to objectives. 4. Addition of management items based on the analysis results.
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basic information
1) Data Digitization We convert your data (such as equipment information, failure history, and reports in various formats like paper, Excel, PDF, Access, etc.) into electronic data and manage it centrally. Initially, we will manage it in an Excel or Access format for easier handling later. 2) Organization and Classification When converting multiple pieces of information into electronic data, inconsistencies may arise, such as duplicate management items or different wording for the same meaning. Among these inconsistencies, the following items are particularly important: - Failure/Accident Information - Emergency Measures - Permanent Measures - 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 select methods according to the purpose and conduct the analysis. Typical methods include: - 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 one 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 towards digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to enhance operational efficiency. In line with this trend, our company provides a solution aimed at improving efficiency in research and development environments, known as the Laboratory Information Management System (LIMS), which includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.
