Using Data To Make The Case For More Resources For Maintenance Tasks

Optimizing Maintenance Processes with Data Analysis

Effective maintenance of critical equipment and infrastructure is vital for operations management. However, maintenance teams often face constraints in budget, headcount, and availability of spare parts. By leveraging data analysis of maintenance workflows, managers can identify inefficiencies and make the case for securing additional resources.

The Maintenance Dilemma: Balancing Proactive and Reactive Work

Maintenance organizations struggle to balance preventative maintenance and unplanned corrective maintenance. Preventative work is scheduled proactively to detect and mitigate degradation before failure. However, reactive break-fix work often takes priority. This vicious cycle means root causes go unaddressed, leading to more emergency repairs down the road.

Managing Limited Budgets and Headcount

Maintenance teams are under constant pressure. With limited budget and headcount, they are unable to work proactively. Teams get locked into a reactive cycle of fixing imminent failures at the expense of more strategic preventative maintenance. This approach is frustrating for technicians and leads to higher costs from equipment downtime.

Quantifying Maintenance Needs

Tracking Maintenance Activities Over Time

To effectively size maintenance resourcing, management needs quantifiable metrics. Work order systems should track key details like request descriptions, labor hours logged, parts used, and equipment identifiers. Maintaining this data over time provides insights through trend analysis.

Identifying Problem Areas

Data analysis will spotlight pain points – particular machines that show sharply rising maintenance needs. Consider an aging centrifugal pump that requires steadily more attention. Its failure has cascading impacts on production throughput. By tracking labor hour trends by equipment, priority areas emerge.

Using Data to Make the Case

Visualizing Workloads to Demonstrate Resource Constraints

Charts summing labor hours by month over years provide clear visualization. If maintenance workloads rise while budget and headcount remain static, limitations become apparent. Presented the right way, such data-driven visualizations can sway opinions.

The Cost Avoidance of Preventative Maintenance

The benefits of preventative maintenance include avoiding failures that risk safety, environmental incidents, or outages. Where possible, estimate costs of potential failures and the probability reduction from preventative maintenance. This business case helps justify relatively small PM expenses that can prevent major issues.

Critical Systems Analysis to Prioritize Resourcing

Pareto analysis will quickly identify the vital 20% of equipment that contributes the most maintainance burden and downtime impact. For example, the failure of two 60-year-old cooling towers may require 200 labor hours monthly. Replacing them could dramatically reduce maintenance workload.

Data-Driven Maintenance Planning

Forecasting Workload and Budget Needs

Historical maintenance data feeds predictive models forecasting workload, costs, staffing needs, and inventory requirements. A time series analysis may reveal 7% annual increases in preventative maintenace hours, while corrective work grows 11% yearly. Understanding these trajectories better sizes future budget and staffing requests.

Optimizing Schedules to Balance Resources

Higher quality maintenance planning considers real-world constraints like crew sizes, equipment availability windows, and seasonality. For example, codifying which specialist roles are required for tasks allows optimization of schedules. This balances workloads across teams for smoother capacity allocation.

Recommending Targeted Investments

The best justification for added investment is a quantifiable return on investment (ROI) estimate. Simulation and risk analysis can build business case models for upgrading equipment, adding headcount, or increasing inventory. Leadership is more likely to fund requests linked to strategic goals with data-backed ROI projections.

Gaining Leadership Buy-In

Linking to Organizational Priorities

Educate leadership on maintenance considerations around:

• Reliability – Preventing unplanned downtime
• Safety – Mitigating risks of critical component degradation
• Compliance – Meeting regulatory equipment inspection requirements
• Efficiency – Reducing reactive maintenance through prevention

Building Trust in Data

Leaders scrutinize funding requests by questioning underlying data. Ensure integrity by:

• Linking to established systems like CMMS
• Performing sense checks relative to metrics like equipment age
• Collaborating with operators to confirm on-the-ground realities match analytics

Implementation Roadmap for Improved Maintenance Processes

Phased Data Collection Ramp-Up

Roll out additional data tracking in three phases:

1. Manual documentation of critical spare part inventory and costs
2. Tagging of work orders with equipment identifiers
3. Logging task-level labor hours at point of work

Allow time for technicians to adjust to new processes before expanding efforts.

Early Wins to Demonstrate Quick Value

Prioritize analysis that yields quick, tangible benefits – like spotting inexpensive parts prone to frequent failure. This builds confidence in data-driven maintenance. Celebrate and communicate successes, providing incentive for deeper buy-in.

Expanding Analysis Over Time

Let early wins drive participation in tracking and providing data. Leverage interest to progressively collect richer information, like failure causes and equipment operating contexts. Over time, build capacity for more advanced modeling methods to optimize maintenance investments.