9-Step Checklist for World-Class Enterprise Asset Management

Posted by Georgios Galanakis on May 1, 2020

Why Enterprise Asset Management?

An EAM environment is a comprehensive and integrated solution for asset-intensive companies that rely on high-quality, accurate, and comprehensive data. Companies want to learn initiatives that optimize available capacity, increase output, and lower costs. It is a common understanding that well-managed plant maintenance increases production capacity and throughput. Working hand-in-hand, operations, and production can optimize production capabilities resulting in higher revenue at greater margins. Revenue is created through sales orders, which then drive production demand. The production demand is met through effective equipment utilization.

EAM is often confused with Computerized Maintenance Management Systems (CMMS), which are departmentally focused, maintenance software packages designed to simplify maintenance management and are often typically less expensive than EAM systems. EAM is a comprehensive and integrated solution, with much broader benefits to manufacturing companies.

Financial Opportunities & Benefits

The increase in revenue, margin improvement, and lower costs are undoubted business targets. With assets operating effectively, well-run projects come in on time and on budget, so revenue objectives and return on investment (ROI) can be met. Efficient equipment increases product yield. Moreover, reduced downtime and improved quality lower the cost of goods sold and improve return on assets. Improved maintenance planning results in lower repair costs, longer equipment life, and well-managed Material-Repair-Overhaul (MRO) supply chain mean purchasing-related costs decrease, and inventory levels are right-sized.

Data Foundation

The critical steps in data collection and verification are:

  • Assess data integrity
  • Collect values for missing attributes
  • Enrich existing data
  • Classify & consolidate
  • Normalize database

Having existing data available provides a point of reference for comparing registered and identified assets. To complete the desired deliverable that is on-going data integrity, a governance process and policy is needed to ensure sustainability.

Holistic Approach to EAM would be a data strategy to identify, assess and consequently depict the processes in place (AS-IS) to determine the processes sought (TO-BE) as well as the roadmap to get there.

It is imperative to know what needs to be done to designate, prescribe, and collect the tools necessary to achieve the desired outcome.

Critical Success Factor 1: Strategic Consulting

Critical Success Factor 2: Cleansing, creating, classifying and consolidating data to populate the system

Critical Success Factor 3: Data Governance

Apart from the existing data in systems of record regarding assets, asset data may come from a variety of other sources, including:

  • Documents (Manufacturer Drawings, P&IDs, etc.)
  • Supervisory Control and Data Acquisition (SCADA) devices
  • Brass Tags on equipment 
  • Radio Frequency Identification (RFID) tags
  • Geo-location data 
  • Barcode signs
  • QR stamps
  • Visual Recognition by Engineers/Technicians

9-Step Checklist for Getting the Data Right

It is important to populate the new business information system with accurate, reliable, and validated data via automatic (API, HTML, Json, Excel, Flat file) or manual entry. The generation of data into the system needs to be carefully planned and validated to ensure that only accurate data is loaded. The 9 critical steps and activities for leveraging a data-driven EAM approach are detailed below:

1. Agree on the conversion approach

Will the data be transferred automatically or manually? Where is the source of the data? How many records are involved? What are the data dependencies, etc.? These critical questions must be investigated and answered before final decisions are made.

2. Define the conversion rules

How will the data be matched in the new business system compared to the old system? What are the data comparison rules between systems? What will happen to missing data fields, etc.?

3. Check data accuracy of source databases

How accurate is the data in the old system or manual records? Will there need to be a data cleansing exercise beforehand? Will only current or current and historical data be transferred, etc.?

4. Develop conversion programs 

For those data areas being transferred automatically; there is a need to develop actual data transfer programs. This will probably include data extract programs from the old system, data reformatting programs, and data input programs to the new system. The best and safest method for loading data on to the new business system needs to be considered.

5. Prepare a live database environment 

The generation of the live database and the setting up of the basic system parameters needs to be considered. It is also essential that access to this database is strictly controlled while data conversion work is continuing.

It is essential that all data is loaded into the system is fully validated prior to allowing users to commence. Loading of data onto the new business system can be split into 2 distinct areas - static and dynamic data. The data may be transferred manually or automatically. This will normally depend on the number of records being transferred, the ease of matching field values, the timing of the data load, etc.

Static data includes master engineering data, master material data, charts of accounts, customers, suppliers, control files and generalized codes, etc. It may be possible to load this type of data several days before the go-live date. However, if this occurs, it is essential that temporary procedures are put into place to ensure that new or amended data is updated on both systems in the interim.

Dynamic data includes stock balances, open sales, purchase and work orders, WIP balances, etc. This data must be loaded as the last step, and there should be no attempt to parallel run with the old system.

The loading of data manually is more common with static data. This often allows a data clean-up exercise to occur and provides good end-user training.

Validation of this data is sometimes more difficult, but it should be possible to check on the number of records loaded. With automatic loading, it is essential that the sequence of data loads is correct to reduce the risk of transactions being rejected.

After each automatic data load, it is essential that a full data validation exercise is undertaken. This validation will include the number of records and, quite often, the need to validate financial totals. When validation has been completed, a backup should be taken prior to starting on the next data load.

6. Convert static data

How and when will the static data programs be loaded? It is often possible to load static data several days before going live, but there will be a need for parallel running or the creation of additional static data update programs to keep the systems in line prior to going live.

7. Convert dynamic data

Similar considerations need to be made for dynamic data, except that this data load should generally be executed last. It is therefore essential that timings of data loads are known, and that extensive testing is undertaken. When the actual data loads are actioned, there will be little or no time to fix last-minute problems.

8. Check data accuracy

The accuracy of the data conversion and transfer programs needs to be constantly checked to ensure accuracy. Any minor change to a program causes a full retest to be undertaken. It is sometimes possible to start developing data transfer programs earlier in the project. If this is done, it is essential that these programs are constantly analyzed to ensure that later decisions taken on system users do not change the rules.

9. Validate Data Entry

Planning for the accurate transfer of all required static and dynamic data is critical. The objective is to ensure that all data entering the new business system, prior to going live, is fully validated to ensure it is clean and accurate.

Master Data Governance in Enterprise Asset Management 

Data strategy deployment utilizes SAP Master Data Governance Enterprise Asset Management & SAP Asset Information Workbench solution extensions by Utopia (SAP MDG-EAM & AIW). Deployment achieves the following goals:

  • Capital Project Management: Properly manage capital projects, so they are delivered on-time and on-budget enabling new equipment to be moved into production as soon as possible.
  • Plant Maintenance: Maximize manufacturing equipment utilization and minimize repair costs 
  • MRO Inventory: Ensure critical spare parts are on hand, so equipment downtime is minimized while reducing inventory costs
  • MRO Purchasing: Quickly and easily source-critical spare parts from approved suppliers in a timely manner and at the lowest possible cost 

Contact us today for a 15-minute discussion with one of our EAM Subject-Matter Experts

Topics: EAM, big data, Data Governance, Data Quality

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