Maintenance organizations are always under pressure.
Pressure to perform better, pressure to reduce unplanned events, pressure to reduce budgets and pressure to reduce spare parts inventories.
Many are resorting to purchasing reliability analytic systems thinking that alone would increase the capability to make data-driven decisions only to find out that there is little or no useful data to analyze.
The next managerial response is typically to make the use of the computerized maintenance management system (CMMS system) mandatory.
This, however, also fails when management finds out that the maintenance technicians (and sometimes planners) charge effort and resources to generic work orders, which provides no more useful data than what was previously available.
What to do? What is missing? Have you thought about system usability?
Getting down to basics, the usability of any tool or system is predicated on its ability to make a job easier, less time consuming, produce better results or all three.
Can you apply that claim to your CMMS? Let's look at the practical matter…
Your maintenance technicians are typically individuals who thrive on solving problems. When your CMMS was implemented, was the usability of the system for planners or technicians a consideration?
Likely not. There are some axiomatic relationships that must be presented here.
First, without consistent, correct user application, the CMMS will not produce any useful historical data.
Second, without careful consideration of the users and their job focus, there will not be consistent or correct use.
Let's dive a little deeper into this. Some characteristics that make a CMMS useful to a maintenance technician or planner include:
- All equipment can be easily found within the system
- Spare parts are found and related to the equipment
- Work tasks and schedules are personalized
- Jobs include information such as tools, safety procedures, spare parts and instructions
- Parts are available when needed for each job and inventory is accurate
- Failure, causal and repair code application is easy to understand
- Wrench time entry is a simple, intuitive process
Think about it. If the information your smart mobile device provided was inaccurate or incomplete, would you use it?
Would you shop at an online site that claimed product was in inventory, but when you placed your order it returned an out-of-stock message?
Of course not. Why would you expect your maintenance teams to work with a system that they don't trust?
Experience with several large companies using systems from SAP to JDE, Maximo, Infor, etc. has indicated a common theme. The master data integrity and spare parts data is of inconsistent completeness and generally of poor quality.
Often times the equipment exists, but the relationship to systems or spare parts is omitted. As often as we see this, it is surprising that there is limited discussion around the topic.
The capability of a CMMS to provide useful information is rooted within the framework and structure of the master data foundation.
The key to correcting the system usability problem for CMMS' is fairly prescriptive but requires commitment, rigor, governance, ownership, cross-functional collaboration, and most of all executive sponsorship.
The path to success is broken into four categories: define, collect, create and deploy:
- Asset data standards and structure
- Asset classification / attributes
- Asset failure / cause / remedy codes
- The Installed Base Asset Data
- Asset master data in CMMS
- Failure / cause / remedy codes
- Links between assets and locations
- Item masters for spare parts
- Links between spare parts and assets
- A detailed process map for identified processes
- A change management plan for the field
- Process changes to support objectives
This is not a small task; however, implementing the corrections will pay huge dividends.
After the data and process issues are corrected, the value to the maintenance technicians and planners will consequently increase, use of the system will improve and the resulting historical data will provide the capability to analyze and make effective data driven decisions related to equipment availability.
Learn how to turn a large amount of maintenance data within your assets into useful operational intelligence.