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Improve Your Asset Maintenance

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Optimize Asset Maintenance in Infor EAM for Reliability

Our platform helps you uncover hidden bottlenecks that slow down your maintenance workflows. You can identify delays in scheduling and procurement that often lead to increased costs and asset downtime. By visualizing the actual flow of work, you can streamline operations and improve overall reliability.

Download our pre-configured data template and address common challenges to reach your efficiency goals. Follow our six-step improvement plan and consult the Data Template Guide to transform your operations.

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The Business Impact of Optimizing Asset Maintenance

Managing physical assets effectively is the backbone of any industrial, manufacturing, or facility management operation. When you utilize Infor EAM, you are sitting on a goldmine of data regarding your equipment health, technician performance, and operational costs. However, without a clear view of how work orders actually flow through your organization, you risk high operational costs, unnecessary labor expenditures, and the ever-present threat of unexpected equipment failure. Optimizing asset maintenance is not just about fixing things faster, it is about shifting from a reactive mindset to a proactive, reliable strategy that treats maintenance as a value driver rather than a cost center. By focusing on the flow of work, you can ensure that your most critical assets receive the attention they need before a minor issue turns into a catastrophic breakdown.

How Process Mining Visualizes the Maintenance Lifecycle

Process mining takes the timestamps, status changes, and event logs already present in Infor EAM and transforms them into a living map of your maintenance operations. Instead of looking at static spreadsheets or simple counts of how many work orders were closed last month, you can see the actual path each work order took from inception to closure. This technology allows you to visualize the loops where work is sent back for re-inspection, the long pauses between material requisition and work commencement, and the deviations from your standard operating procedures. This transparency allows maintenance managers to see the as-is process, which is often significantly different from the documented procedures. By surfacing these hidden realities, you can make decisions based on objective data rather than anecdotal evidence.

Identifying Bottlenecks in the Maintenance Flow

One of the most common areas for improvement in Infor EAM is the transition between planning and execution. A work order might sit in a waiting for parts status for several days, but traditional reporting might not show you which specific suppliers or internal logistics steps are causing the delay. Process mining helps you identify these specific bottlenecks with precision. You might discover that the primary delay is not the technical execution itself, but the administrative burden of technical inspections or the complexity of the financial settlement process. By uncovering these friction points, you can reallocate resources, streamline approval workflows, or renegotiate supplier contracts to reduce the overall cycle time of your maintenance activities. Reducing these delays directly translates to increased asset uptime and more efficient use of your skilled labor force.

Driving Reliability and Compliance through Data

The ultimate goal of optimizing asset maintenance is to increase asset availability while ensuring full compliance with safety and industry regulations. By reducing the cycle time of a maintenance work order, you directly decrease the time an asset is offline, which has a direct impact on your bottom line. Furthermore, process mining provides an automated audit trail that is invaluable for compliance. You can prove that every technical inspection, quality control test, and safety sign-off was performed in the correct sequence and within the required timeframe. This level of detail helps mitigate risk, prepares your organization for audits, and ensures that your maintenance records in Infor EAM are a true reflection of the work performed in the field.

Building a Sustainable Maintenance Strategy

Transitioning to a data-driven approach allows your maintenance team to move away from firefighting and toward a strategy of continuous improvement. By using the insights gained from analyzing your Infor EAM data, you can refine your resource scheduling, optimize your spare parts inventory, and move closer to predictive maintenance models. The journey toward excellence starts with a deep understanding of your current state. Once you see the patterns and inefficiencies in your work order lifecycle, you can begin making the incremental changes that lead to significant long-term gains in reliability, safety, and cost management. Embracing process mining for asset maintenance ensures that your organization remains competitive and that your infrastructure is always ready to meet the demands of the business.

Asset Maintenance work order management equipment reliability preventive maintenance resource planning maintenance engineering plant operations

Common Problems & Challenges

Identify which challenges are impacting you

Piles of open work orders in Infor EAM lead to equipment degradation and increased safety risks. When maintenance teams cannot keep up with the volume of requests, critical assets often fail, leading to costly emergency repairs and operational downtime.

Technicians often wait days for essential spare parts because the requisition process is disconnected from the scheduling phase. This delay increases the mean time to repair and keeps assets out of production longer than necessary.

Relying on emergency repairs rather than preventive maintenance leads to unpredictable costs and unstable operations. This reactive cycle prevents maintenance departments from planning resource usage effectively, causing high overtime expenses and asset unreliability.

The time taken from receiving a request to completing the initial inspection is often a significant bottleneck. Slow assessments delay the entire planning and estimating phase, pushing back the actual repair and increasing the risk of secondary equipment damage.

Technical work is frequently completed long before the work order is officially closed in the system. This administrative lag distorts performance metrics, delays financial settlement, and makes it difficult to assess real-time resource availability.

Misalignment between available technician hours and planned maintenance tasks leads to underutilized labor or excessive overtime. Without a clear view of how resources are scheduled versus how they actually execute work, maintenance productivity remains stagnant.

When assets require repeat maintenance shortly after a repair, it indicates poor execution quality or incomplete inspections. Rework drains resources, increases total maintenance costs, and erodes trust in the reliability of the asset portfolio.

Failing to meet service level targets for high-priority equipment can have catastrophic impacts on production and safety. When maintenance priorities are not managed strictly, critical repairs are often delayed by low-priority administrative tasks.

Wide variances between estimated and actual costs or labor hours make it impossible to budget maintenance activities accurately. Significant deviations suggest that the planning process is based on flawed assumptions or outdated technical data.

Maintenance work orders often stall while waiting for financial or management approvals, particularly for high-cost repairs. These delays keep critical assets offline longer than necessary and can lead to a cascade of scheduling conflicts.

Without granular data on how much time technicians spend on execution versus administrative tasks, it is difficult to improve workforce productivity. Managers often lack the visibility to see if teams are spending too much time on material handling or record updates.

Maintenance records that lack technical detail or final sign-offs create compliance risks and hinder future troubleshooting. When engineers do not have a complete history of past repairs, they are forced to reinvent solutions, increasing repair times.

Typical Goals

Define what success looks like

Clearing the maintenance backlog ensures that equipment receives necessary attention before minor issues escalate into catastrophic failures. Reducing the volume of open work orders improves operational safety and prevents the accumulation of deferred maintenance costs that can strain future budgets. This focus on throughput helps stabilize the facility and protects long term asset health.

ProcessMind analyzes the aging of work orders within Infor EAM to identify where tickets stall. By visualizing the flow from request to execution, managers can reallocate resources to specific bottleneck stages and successfully reduce backlog levels by up to 30 percent, ensuring a more responsive maintenance operation.

Shifting from reactive to proactive maintenance is essential for maximizing asset uptime and extending equipment life cycles. A higher ratio of planned activities reduces the chaos of emergency repairs and allows for more predictable resource planning and lower overall spend. This shift directly correlates with improved operational stability and reduced emergency costs.

Our platform tracks the balance between corrective and preventive work orders in real time. By identifying assets that frequently trigger emergency repairs, you can adjust maintenance schedules in Infor EAM to favor preventive actions, targeting a 20 percent reduction in unplanned downtime through better planned maintenance coverage.

Technical execution often stalls when spare parts are unavailable, leading to idle labor and increased equipment downtime. Shortening the time between material requisition and part delivery ensures that technicians can complete work orders immediately upon arrival at the site. This alignment between inventory and maintenance is a key driver of workforce productivity.

ProcessMind maps the relationship between material requests and work order progression to pinpoint procurement delays. By streamlining the handoff between maintenance and inventory teams, organizations can reduce wait times for parts by 25 percent or more, ensuring materials are ready when technicians are scheduled.

Efficiently deploying skilled labor is critical for maintaining high throughput in a plant or facility. Optimizing utilization involves reducing travel time, administrative overhead, and idle periods, ensuring that technicians spend more time performing actual wrench work. Better utilization leads to higher job completion rates without increasing headcount.

Using process mining, you can visualize the actual time spent in each phase of the maintenance lifecycle. By identifying activities that consume excessive labor without adding value, managers can restructure shifts and assignments within Infor EAM to increase productive labor hours by up to 15 percent across the workforce.

Meeting Service Level Agreements for high criticality assets is vital for operational continuity and safety. High compliance rates ensure that the most important equipment is serviced within established timeframes, protecting production targets and meeting strict regulatory requirements. This focus ensures that the highest risks are always managed first.

ProcessMind provides automated monitoring of SLA milestones for every maintenance work order. By flagging orders that are at risk of breaching targets, the system enables proactive interventions by supervisors, helping teams achieve over 95 percent compliance for their most critical equipment categories.

Prompt administrative closure of work orders is necessary for accurate financial reporting and up to date asset history. Delays in closing orders can obscure the true state of the maintenance budget and prevent the capture of important technical data needed for reliability engineering. Fast closure improves the overall data integrity of the maintenance system.

Our solution identifies bottlenecks in the sign off and financial settlement stages within Infor EAM. By eliminating redundant approval steps and reducing manual data entry requirements, organizations can reduce the time from technical work completion to final administrative closure by 40 percent.

Rapid inspections are the foundation of a responsive maintenance program. Reducing the time it takes to assess a reported fault allows for faster planning and procurement, ensuring that repairs begin as soon as possible after a problem is detected. This speed is critical for preventing minor faults from becoming major failures.

Process mining tracks the duration between a maintenance request and the completion of the technical inspection. By uncovering patterns where inspections are delayed due to lack of information or scheduling conflicts, you can cut inspection lead times by significant margins and improve overall response times.

Doing the job right the first time is essential for resource efficiency and asset reliability. High rework rates indicate issues with technician training, part quality, or the clarity of technical instructions, all of which lead to increased costs and additional equipment downtime. Reducing rework directly improves the reliability of the entire asset base.

ProcessMind detects rework loops by identifying assets that require repeat maintenance shortly after a work order closure. This insight allows managers to address root causes in the technical execution phase, aiming for a 50 percent reduction in repeat failures and improving the quality of maintenance interventions.

Accurate cost estimation is crucial for budget integrity and long term financial planning. When actual costs frequently exceed estimates, it creates funding gaps and suggests inefficiencies in the planning and procurement stages of maintenance. Better alignment ensures that maintenance remains within its annual budgetary constraints.

By comparing planned versus actual costs across thousands of work orders, our platform highlights specific asset categories or maintenance types with high variance. This data enables more precise estimating models in Infor EAM, reducing budget deviations by up to 20 percent through data driven planning.

Manual or complex approval chains often delay critical maintenance work unnecessarily. Speeding up these workflows ensures that resources can be mobilized quickly, particularly for high priority or safety related tasks that cannot wait for administrative sign off. Efficient approvals lead to faster time to repair and improved plant safety.

ProcessMind visualizes every step of the approval process, identifying specific actors or departments where tickets linger. Streamlining these digital workflows can reduce the total time spent in the approval stage by 35 percent, accelerating the start of technical work and reducing idle time for planning teams.

Thorough documentation of maintenance activities is required for regulatory audits and effective asset lifecycle management. Missing technical logs or incomplete data fields can lead to compliance penalties and a loss of historical insights needed for root cause analysis. Consistent documentation supports better long term engineering decisions.

Our analytics engine checks for the presence and quality of required documentation at the point of work order closure. By identifying technicians or departments with high rates of incomplete logs, managers can implement targeted training and process guardrails to ensure 100 percent data compliance within Infor EAM.

Minimizing the mean time to repair is a primary goal for any maintenance organization focused on asset availability. Faster repairs mean less downtime for production lines and a more agile response to unexpected equipment failures. This improvement directly increases the overall equipment effectiveness across the organization.

ProcessMind breaks down the end to end repair cycle into discrete stages, from initial commencement to technical sign off. By identifying the specific activities that take the longest during execution, you can implement targeted process improvements that lower the average repair time by 15 to 20 percent.

The 6-Step Improvement Path for Asset Maintenance

1

Download the Template
2

Export Your Data
3

Upload Your Dataset
4

Analyze Your Process
5

Implement Improvements
6

Monitor Your Process
1

Download the Template

What to do

Access the standardized Excel template designed for Infor EAM maintenance work orders and asset records.

View Data Template Guide Download Template
Why it matters

Ensures your maintenance data aligns with process mining requirements, saving time on data mapping and preparation.

Expected outcome

A ready-to-use template for your maintenance data.

YOUR MAINTENANCE INSIGHTS

Visualize Your Entire Asset Maintenance Lifecycle

Our platform maps your actual maintenance workflows to reveal exactly how work orders move through Infor EAM. You will see every bottleneck in parts ordering and labor scheduling with absolute clarity.
  • Map your end to end work order process
  • Identify bottlenecks in parts procurement
  • Find inefficiencies in technician scheduling
  • Compare performance across maintenance sites
Discover your actual process flow
Discover your actual process flow
Identify bottlenecks and delays
Identify bottlenecks and delays
Analyze process variants
Analyze process variants
Design your optimized process
Design your optimized process

PROVEN OUTCOMES

Optimizing Asset Maintenance Performance

Organizations using process mining on Maintenance Work Orders within Infor EAM consistently uncover hidden inefficiencies, allowing them to reduce cycle times and improve equipment reliability. These metrics represent the typical operational gains achieved through data-driven process optimization.

0 %
Reduced Repair Cycle Time

Reduction in mean time to repair

Streamlining the path from maintenance request to final closure minimizes equipment downtime and improves overall operational availability.
+ 0 %
Proactive Maintenance Shift

Increase in preventive work ratio

Identifying bottlenecks in PM scheduling helps organizations move away from costly reactive repairs toward a more stable planned maintenance model.
0 %
Labor Utilization Gain

Increase in value added wrench time

Reducing administrative overhead and waiting periods ensures that technicians spend more time on technical repairs rather than documentation.
0 %
Critical Asset Compliance

Improvement in SLA achievement

Targeted process improvements ensure that high criticality assets receive priority attention to meet strict service level agreements and safety targets.
0 days
Faster Part Procurement

Reduction in material lead times

Eliminating delays between material requisition and work commencement prevents technicians from waiting on necessary spare parts and supplies.
0 %
Lower Maintenance Rework

Reduction in repeat repair cycles

Analyzing rework loops helps identify root causes such as technical skill gaps, leading to more consistent right first time execution.

Results vary based on process complexity and data quality. These figures represent typical improvements observed across various maintenance implementations.

FAQs

Frequently asked questions

Process mining applies specialized algorithms to the transaction logs within Infor EAM to visualize the end to end flow of your maintenance work orders. It reveals the actual path of every request from creation to closure, identifying where delays or deviations occur in your real world operations.

Data is typically extracted from the core work order and activity tables, such as EVT and ACT, using standard database connectors or API calls. You will need to export the event logs that track status changes, timestamps, and resource assignments to build the process model.

By pinpointing the specific stages where work orders stall, such as waiting for parts or pending manager approval, you can address root causes of delays. Visualizing these bottlenecks allows you to implement targeted changes to your approval workflows or material requisition cycles to keep tasks moving.

Once the initial data extraction is configured, the first process maps can usually be generated within two to four weeks. This timeline depends on the complexity of your Infor EAM configuration and the availability of historical work order data.

The minimum requirements are a unique work order identifier, a timestamp for each status change, and an activity description. Adding dimensions like equipment type, department, or technician ID allows for much deeper filtering and more granular analysis of team performance.

Process mining uses the work order type field to categorize and compare different maintenance strategies automatically. You can analyze why preventive tasks might be delayed compared to emergency repairs and identify opportunities to better balance your scheduling.

The analysis reveals the gaps between work order assignment and actual execution, highlighting periods of inactivity or administrative overhead. This visibility helps managers reschedule labor more effectively and ensures technicians spend more time on actual maintenance tasks.

You do not need to change your current processes to begin, but the analysis often reveals where data entry is inconsistent. Improving how your team logs status updates and completions will significantly enhance the accuracy and value of future process mining insights.

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