What is Maintenance Data?
Maintenance data can be referred to as all the information, insights, and data that help businesses keep assets and equipment up and working. Data captured by the maintenance department, including asset metrics, inventory levels, work orders, key performance indicators, and asset reports, help managers draft maintenance policies, procedures, checklists for maintenance technicians, and asset maintenance schedules.
Maintenance data provide a comprehensive picture of all the maintenance-related business operations of the company, enabling maintenance managers to act on data-driven insights rather than relying on gut feelings or educated guesses. With access to robust maintenance data insights, businesses ensure the accuracy of operations and enable businesses to make more informed decisions.
Types of Maintenance Data
Primarily, maintenance data can be classified into three broad categories – Sensor Data, Operational Data, and Management data.
Sensor Data
The data generated by sensors installed in assets that monitor asset activities is known as sensor data. Backed by Internet-of-Things (IoT) applications, these sensors generate data, enabling systems to connect people with processes to unlock new opportunities and improve the efficiency of assets
Most companies use predictive sensors to identify and detect potential issues with assets. Data maintenance analytics raise red flags when a part of the machine needs replacement and predict the time duration of different asset parts for which they are expected to be available for use. By leveraging sensor data for planning and undertaking maintenance operations, organizations are boosting productivity, enhancing customer engagement, and getting ahead of daily issues.
Operational Data
A maintenance team needs operational data and insights, including work orders, procedures, and inventory reporting, for seamless and uninterrupted asset maintenance. Operational data involves planning and recording crucial maintenance information that is generally generated by Computerized Maintenance Management System (CMMS) or a robust Enterprise Asset Management (EAM) program.
The objective of operational data is to streamline the activities of maintenance technicians by effectively allocating resources and organizing the responsibilities of the maintenance team.
Management Data
The historical data and results obtained from operational data are referred to as management data. It consists of asset history, maintenance reports, KPIs, budgeting information, and analytics, which are used by the organization’s management to manage the efficiency of their practices, evaluate measures, and make informed maintenance decisions.
It is important to keep this data safe, update data regularly, and maintain a consistent format that is easily accessible and navigable by all technicians and employees. Also, it is crucial to ensure that the data collected from sensors and operations is accurate and free from interventions or errors.
Collection and Analysis of Maintenance Data
The value of the decisions depends on the data quality. Incorrect, outdated, and bad data can lead to poor decision-making and higher maintenance costs. Thus, it is paramount for maintenance technicians to access quality data for enhanced asset maintenance.
Methods for Collecting Maintenance Data
Broadly, there are two methods of collecting maintenance data – manually and automatically.
Manual procedures of collecting maintenance data involve employees observing the performance of assets to identify potential errors and determine areas of improvement. Since manual procedures are based on human judgments, they are comparatively less accurate and efficient.
Robust automated tools such as CMMS automate the entire process of collecting maintenance data, streamlining data communication, data cleansing, data monitoring, and data processing. Data sets generated by automated processes are more reliable and offer higher returns as the human factor is reduced.
Tools for Analyzing Maintenance Data
After collecting good data, maintenance employees must crunch the raw data into actionable insights with robust tools like CMMS. The insights that are driven by the data analytical tool enable businesses to schedule maintenance requests and streamline the entire maintenance program.
Maintenance data analytical tools enable businesses to know which assets are more cost-effective, which assets require more costs, and the urgency of repairs needed for critical assets.
Role of Key Performance Indicators (KPIs)
Maintenance Key Performance Indicators (KPIs) measure and analyze the performance of maintenance operations. KPI insights track maintenance goals, helping managers understand the effectiveness of maintenance activities.
KPIs indicate the progress made in the maintenance programs, setting benchmarks and highlighting larger objectives of the organization.
For example, an organization strives to cut costs by 20%, and managers decide to reduce maintenance and repair costs. KPIs will help managers set targets and monitor progress against the set targets by attaching numbers and data to repairs and maintenance components.
Role of CMMS in the Collection and Analysis of Maintenance Data
New-age tools like CMMS systems have a central database where all the data operations and company data are securely stored. The centralized database facilitates seamless data entry operations, automating maintenance operations of organizations, including generating & assigning work orders and reviewing maintenance requests.
CMMS systems are easily navigable, facilitating smooth scalability of maintenance activities. Users can easily update work orders in real-time and can include missing data in the platform, as the data fields and pull-down menus are already present in the application.
Other benefits of using CMMS tools and applications include seamless asset visibility, workflow visibility, automation of maintenance operations and processes, up-to-date data recording, compliance management, preventive maintenance of assets and equipment, consistency in data quality & data cleaning, and streamlined compliance management.
Why is the Data Maintenance Process Crucial For Your Business?
The importance of reliable maintenance data and an effective data maintenance process is paramount for all companies as it helps managers make crucial business decisions about business assets, inventory, and overall business operations.
Following are some of the major reasons why companies must leverage an effective data maintenance process.
Equipment Performance Monitoring
Quality maintenance data provides actionable insights and information to maintenance managers and technicians, helping organizations reduce unscheduled maintenance of assets & equipment, increase ROI, improve the safety of the facility and employees, and ensure environmental compliance.
The maintenance process ensures that all assets and equipment are in optimal condition by monitoring equipment performance and detecting early signals that may negatively affect the profitability of the business in the near future.
Based on these insights, businesses can adjust their operating parameters and realign maintenance schedules, avoiding unplanned downtime in the long run.
Inventory Management
Inventory management is a crucial aspect of a business that can elevate overall asset maintenance management. Effective inventory management can also lower operational costs and avoid unnecessary halts in business operations.
By accessing quality and correct data from maintenance processes, technicians can keep track of parts and supplies located in different locations at different facilities. This help technicians understand if a supplier is understocked or if a department requires large minimum order quantities, facilitating quick availability of crucial supplies and boosting the efficiency of business processes.
Maintenance Scheduling
In order to improve efficiency, speed up workflow, cut costs, and prevent operational emergencies in a facility, managers schedule maintenance work orders for undertaking repairs and upgradation of assets and equipment.
Maintenance data such as past failure history, maintenance history, and historical work orders help managers accurately predict the condition of assets, enabling them to make informed judgments based on technical, communication, and organizational insights. It also enables managers to assign and track work hours taken to complete the task.
Cost Savings Through Data-Driven Maintenance
Maintenance decisions based on gut feeling or hit-and-trial methods can be tedious, ineffective, and costly. Thus, it is important to leverage data-driven maintenance to save time, effort, and resources.
Data maintenance helps managers understand and follow workplace practices, providing them with insights into which assets and equipment require attention. Rather than focusing on the assets of the entire facility, businesses can undertake pin-pointed maintenance operations with quality maintenance data.
Data-driven maintenance facilitates efficient route planning, quick resolution of issues, and accurate expenditure forecasts on inventory, which leads to increased profitability and cost savings.
Unlock the Potential of Maintenance Data With The NonStop Suite
Data maintenance is an ongoing process that includes performing regular checks to recognize and correct data that does not align with standard working procedures. Organizations undertake data maintenance and data cleansing operations for different purposes, including improving maintenance operations, monitoring data management processes, and making sure that all business assets are up and running. However, for effective results, it is important to note that maintenance data is properly recorded and used in a systematic way.
NonStop Suite (NSS) offers a unique approach to facilitating top-notch asset care and production operations. Businesses looking to make the most out of their maintenance data must leverage NSS tools and applications for effective management of manufacturing facilities throughout their life cycle. NSS helps businesses increase the quality of decisions, improve efficiency by accessing maintenance data in time, and improves the quality of maintenance activities.
