If you’re a leader in heavy industry or manufacturing, you’re no stranger to the pressure. Aging assets, rising costs, and labor constraints are daily realities. Production targets are non-negotiable. Through all this, you may feel stuck – frustrated by firefighting, trying to make the right call without clear insight into what’s really driving poor performance.

One of the most common challenges in maintenance and reliability is knowing where to focus. Everyone has ideas, but without data and structure, decisions are often driven by gut feel or internal politics. This results in temporary fixes, misaligned priorities, and wasted time.

What’s needed is a methodical approach to uncovering improvement opportunities – one that replaces guesswork with clarity and turns scattered problems into actionable next steps.  Identifying specific opportunities requires a detailed look at available data.  Looking into specifics is similar to using a microscope.  It’s invaluable when diving deep into an issue and its underlying causes. 

Step 1: Lay the Groundwork

Before beginning a detailed analysis, the first step is to do initial preparations and ensure the groundwork is in place.

Frame the Problem and Look Broadly at the Issue

The first step is making sure you’re solving the right problem.  Spend time developing a problem statement that captures the key information and constraints.  Then, develop a decision tree to explore potential causes. This helps break down a complex issue into manageable layers – such as reliability losses from specific assets, work identification issues, or gaps in planning and scheduling. Use a mix of critical and creative thinking to branch out from the problem statement into key drivers. Prioritize and develop hypotheses for branches that are most likely to have a measurable impact.

Analyzing using an initial telescope view helps tremendously at this stage. Don’t look just at the maintenance department.  Look across your plant, the company, and industry. This broader lens can reveal whether the problem is truly M&R related or a symptom of something else, such as operations discipline, engineering standards, or production planning.

Develop a Data Plan for Detailed Analysis

Once you have a working hypothesis about what’s driving the issue, develop a plan to test it.

That starts by identifying the variables you want to measure. What inputs might be causing the issue? What outputs reflect success? What variables will you hold constant? How will you measure improvement? For example:

• If you suspect poor work identification is driving unplanned downtime, measure the percentage of work requests with clear problem descriptions and how early the work requests are being entered.  
• If you think maintenance crews are not productive or there is a high percentage of rework, measure the quality of the work orders by looking at the details in the job plans.

Without a clear plan to analyze the data, there’s a risk you and your teams will be overwhelmed by the information and suffer from analysis paralysis.  The goal isn’t to measure every variable, it’s to focus only on the 3–5 indicators that could confirm or disprove your hypothesis and guide your next steps.

Step 2: Gather Data and Conduct Tests

With your data plan in place, it’s time to execute. This phase is where many teams lose momentum, but a bit of discipline goes a long way.

Clean and Validate the Data

Work with the frontline team to understand data sources, how they’re used, and what gaps exist. This builds credibility and gets early buy-in. Don’t assume the system is telling the full story—validate what you find with the people who live it day-to-day.  A word of caution: no data set is perfect.  It’s not uncommon for reluctant stakeholders to use poor data quality as a reason to avoid future implementation.  Discussing issues early and bringing others into the solution process can help remove hurdles.  

Some sources of data are common across all industries and locations:

• Work order information for upcoming and past jobs are located in the Computerized Maintenance Management System (CMMS).  Maintenance backlogs are often in the CMMS; however, some sites may be using Excel to manage their actual backlog if the CMMS is cluttered.  CMMS capabilities for easily accessing and analyzing data vary widely.  The amount of training and experience site users have with the CMMS also play a big role in data availability.  Systems can range from SAP and Maximo to MP2 and EasyMaint. 
• Financial data related to past work order costs, including time and materials, are often in the CMMS.  Some sites utilize a separate program to manage cost accounting.  Systems like JD Edwards handle the financial accounting side while work orders are planned, scheduled, and executed in the CMMS.  Data availability and ease of analysis varies based on the company process to link the two systems and how disciplined the site is in carrying out the processes on a consistent basis.  
• Accurate and detailed production data can be a challenge, especially for short stops or speed losses.  Systems like Red Zone are common in the food/beverage industry.  Data quality can vary based on the shift inputting the data and how diligently site leadership follows up with quality issues.  Engaging production frontline supervisors will uncover data quality issues that will need to be addressed.  Uncovering the need to improve production data tracking is a common finding at this point in the overall improvement process.  

Build an Implementation Plan

Have a clear implementation plan that defines key responsibilities for each aspect of the data plan.  For each task, identify the person responsible, due dates, and required outcomes.  For major milestones, it may be beneficial to develop a full description showing who is Responsible, Accountable, Consulted, and Informed (RACI). 

Step 3: List Verified Opportunities 

The point of conducting tests is to develop insights.  The next step is to turn those insights into a prioritized list of opportunities.  To make the list, they must relate to the problem statement and have a measurable impact.  

Verify direct connection to the original problem statement

In the course of detailed data analysis, other issues will be uncovered.  Inefficiency, waste, and points of confusion will be surfaced as you dig into problems with stakeholder groups.  An idea that doesn’t trace back through the decision tree to the original problem statement should be set aside.  Focus on issues that relate directly to the problem at hand.

Real World Case Study from My Maintenance and Reliability Career

In my travels and working with leaders in multiple industries, I’ve seen data handled in lots of different ways.  At one heavy industry site in the US, I worked with an engineer who clearly loved data.  He was very proud of the Power BI charts that showed work orders broken down by plant site, type, and cost.  Pages and pages of reports could be generated at the click of a button.  It wasn’t until 6 months later that it came to light every site was using a different definition for work order classification.  It’s no wonder actionable insight was missing from the data.  

In most locations data isn’t created equal.  At two different food manufacturing companies, I’ve seen maintenance data go uncollected while up-to-the-minute production data was available.  In one maintenance shop, the tv displaying the production data was mounted above the maintenance KPI board that had year-to-date metrics that were three and a half years old.  They had great visibility of their production losses and no opportunity to analyze or uncover key drives in work processes.

Data isn’t valuable just because it exists.  It’s valuable when it’s consistent, trusted, and tied to decisions. Without alignment on definitions or attention to what’s being measured, even the best dashboards can become distractions. If you want real insight, start by making sure your data is speaking – and you’re listening.

Quantify the Size of the Impact

Estimate the size of the opportunity.  Data in terms of reduced downtime or quality losses are relatively straightforward to convert to dollars.  Other opportunities like improved productivity and labor cost saved should be handled consistently to provide a clear comparison between all opportunities.  Cost avoidance and risk reduction are also potential benefits and can be difficult to quantify.  Engaging with appropriate stakeholders and validating the data will help in developing a clear value.  Annualize the value from every opportunity so you can compare options side by side.

For example:

• A 1% increase in production throughput would result in a $900,000 value to the bottom line, with a reduction in repetitive failures leading to a capture of 50%, resulting in an annual benefit of $450,000. 
• Eliminating unnecessary PM tasks that consume 500 hours/year could save $50,000, using a labor rate of $100/hour.
• Improving planning effectiveness on reactive work could reduce overtime by 20%, saving $60,000 annually.

By translating ideas into value, you empower informed prioritization rather than emotional debate.

The Outcome: A Shortlist of Opportunities and a Unified Strategy

At the end of this process, you’ll have a prioritized list of validated improvement opportunities – each linked to a root cause, backed by data, and translated into business impact.

From here, it’s straightforward to build a business case. Present the top opportunities, their value, and the resources needed to close the gap. The process removes guesswork, cuts through politics, and gives everyone – from the maintenance planner to the VP of Ops – a clear path forward.

In reliability, the hardest part isn’t usually solving the problem. It’s knowing which problem to solve.  A structured approach helps you shift from reactive tactics to proactive decisions. You can’t improve everything all at once – but with the right insight, you can improve what matters most right now.