Operations leaders in heavy industry are often frustrated by missed production goals and runaway maintenance spending.  Transitioning teams from emotionally driven gut-feel problem-solving to a more data-driven approach is often recommended.  While this shift is critical for improving maintenance and reliability outcomes the questions arise, “How do we know where to begin? and Where should we focus our analysis?”  Implementing data-driven approaches requires a structured methodology.  Two essential steps are developing clear problem statements and decision trees.  Once these foundational elements are established, the next crucial step is prioritizing options and forming hypotheses before collecting data.

Prioritizing Options for Analysis

The goal of prioritization is to determine the best starting point for detailed analysis.  This begins by revisiting the final level of your decision tree.  For most problems associated with maintenance and reliability, this is generally between levels 4 to 7, and at that stage, you should have around 18 to 20 potential options. To prioritize these options, rank them based on their potential impact on the problem statement.  Consider the following criteria:

• Effectiveness – Which option has the greatest potential to resolve the issue?
• Speed of Implementation – Which option can deliver results in the shortest time?
• Feasibility – Which option can be realistically implemented with available resources?  Does the option lead to solutions that defy previously identified constraints?

Assign each option a numerical ranking from highest to lowest priority.  The sum of the prioritized options’ impacts should be sufficient to address the original problem statement.  If an option is unlikely to contribute meaningfully to solving the issue, it does not need to be investigated further.

See Article: The $250K Hypothesis

Developing Hypotheses for Prioritized Options

Once your options are prioritized, the next step is to formulate hypotheses for each high-priority option.  A hypothesis is a structured statement that describes how the associated option will best address the problem and the corresponding cause-and-effect consequences.  The standard formula for a hypothesis is:

Due to X, Y, which leads to Z.

For example, if improper lubrication is suspected to be causing equipment failures, a hypothesis might be:

Due to a lack of lubrication routes (X), mechanics conduct lubrication activities based on tribal knowledge (Y), which leads to inadequate/improper lubrication and eventual rotating equipment failure (Z).

Each hypothesis should remain focused on a single prioritized option.  Avoid merging multiple options into a single hypothesis, as this breaks the MECE principal and will cause overlap in the analysis and wasted time.

Evaluating Hypotheses

To ensure the quality and effectiveness of your hypotheses, ask the following questions:

1. Is the hypothesis true?  –  Based on the experience of the team and historical data, does the cause-and-effect relationship hold?
2. If solved, will it have a sufficient impact?  –  Will resolving this issue make a meaningful difference in addressing the original problem?  Of all the hypotheses we could have formed about the option, have we selected the most powerful lever to drive resolution of the problem?
3. Is it worth the effort?  – Given the required resources and potential benefits, is this hypothesis worth testing?

If a hypothesis fails to meet these criteria, it may indicate a misprioritized option or an incorrect assumption.  This step acts as a final check to ensure analytical efforts are focused on the most impactful areas.

The Purpose of Hypotheses in Problem-Solving

Hypotheses serve three critical functions in maintenance and reliability analysis:

1. They define what to test.  Instead of collecting data randomly, hypotheses guide the investigation, ensuring efforts are directed at meaningful variables.
2. They capture our understanding of how to resolve an issue.  A well-structured hypothesis provides a clear pathway for testing and verification.  They clearly define our current understanding, making testing easier and allowing the team to challenge assumptions. 
3. They establish an expected cause-and-effect relationship.  By explicitly stating the assumed relationships, hypotheses create a structured framework for validation and refinement.

Examples of Hypotheses for Maintenance and Reliability Problem Solving

Below are several examples of how to develop hypotheses for prioritized options that are commonly encountered in maintenance and reliability problem solving.  Hypotheses are written in conjunction with the numbered and prioritized options.

Transitioning from instinct-based decision-making to a structured, data-driven approach requires discipline and a systematic method.  Prioritizing options and developing hypotheses before data collection ensures that efforts are focused on the most impactful solutions.  By using this approach, operations leaders can drive meaningful improvements in maintenance and reliability performance, reducing downtime, lowering costs, and improving overall efficiency.

What if your next cost-saving idea started with a better question? We help teams turn gut feelings into testable hypotheses — and guesswork into results.