A travel center can feel busy for many different reasons.
Guest traffic may be higher than expected. One department may be understaffed. Food service demand may spike during a short period. Cleaning tasks may fall behind after a large group arrives. A local event may change the normal pattern for several hours.
These situations are easy to notice while they are happening.
The harder question is whether they represent a temporary issue or part of a larger trend.
PFJ Today can help Pilot teams reach operational reporting and dashboards that give location leaders a broader view of what is happening across the business. Infor has documented Pilot Flying J’s use of PFJ Today to distribute reporting, dashboards, and self-service analytics to a large internal audience.
The purpose of a dashboard is not to replace local judgment.
It is to give managers and teams more context before they make decisions.
A Dashboard Turns Activity Into a Pattern
Daily work produces a large amount of operational information.
A single travel center may generate data connected to:
- Guest activity
- Food service demand
- Product movement
- Staffing coverage
- Location performance
- Service timing
- Facility priorities
- Seasonal patterns
- Department results
- Operational exceptions
Looking at each event separately can make the work feel random.
A dashboard groups information so leaders can compare periods, departments, and locations more consistently.
That can help answer questions such as:
- Is today unusually busy?
- Has this trend continued for several weeks?
- Which department is changing most?
- Does the result match what the team observed?
- Is the issue local or appearing across several locations?
- Does the current schedule match the workload?
Data gives the team another way to understand the operation.
One Number Rarely Tells the Whole Story
A metric may look positive or negative without enough context.
For example, a higher activity level could mean:
- Stronger guest demand
- A holiday weekend
- A nearby event
- A temporary closure at another location
- Unusual weather
- A promotion
- A reporting difference
A lower result could reflect:
- Road construction
- Seasonal change
- Reduced operating hours
- Equipment problems
- A short-term staffing issue
- A comparison against an unusually strong previous period
The number identifies a difference.
It does not always explain the cause.
Start With the Time Period
Before interpreting a dashboard, check the reporting period.
The data may represent:
- Current shift
- Previous day
- Week to date
- Previous week
- Month to date
- Same period last year
- Rolling average
- Custom date range
A result that looks concerning for one shift may appear normal across the full week.
Likewise, a weekly total may hide a recurring problem during one particular daypart.
The selected period changes the meaning of the number.
Compare Similar Periods
Comparisons work best when the periods are reasonably similar.
A normal weekday should not always be compared directly with a holiday weekend.
A winter period may not behave like midsummer travel.
Before drawing a conclusion, consider:
- Day of the week
- Holiday activity
- Season
- Weather
- Local events
- Road conditions
- Operating hours
- Temporary disruptions
A technically correct comparison can still be operationally misleading.
Location Context Matters
Pilot operates travel centers across many different markets.
A location near a major interstate interchange may follow a different pattern from a smaller regional site. Some locations may experience strong freight traffic, tourism, seasonal travel, or nearby industrial activity.
This means company averages should not automatically become the standard for every location.
Local leaders may need to interpret dashboards using knowledge of:
- Community events
- Construction
- Tourism
- Nearby competitors
- Commercial traffic
- Weather
- Location layout
- Available services
- Regular guest patterns
The dashboard provides evidence.
Local knowledge explains the environment around it.
Department-Level Results Can Reveal Hidden Problems
A total location result may look stable while one department is changing significantly.
For example:
- Guest traffic remains steady, but food service demand increases.
- Overall activity rises, but cleaning capacity falls behind.
- One service area performs well while another struggles.
- A busy period shifts from morning to evening.
Department-level reporting can help managers see where the workload is actually changing.
This matters because staffing and operational decisions are often role-specific.
A location may not need more people everywhere.
It may need stronger coverage in one area during one time window.
Use Dashboards to Prepare, Not Only React
Operational reporting becomes more valuable when it helps teams prepare before a problem appears.
A manager may notice:
- A recurring weekend increase
- A department that becomes busy earlier than expected
- A seasonal pattern beginning
- Several similar weeks of activity
- A repeated gap during shift handoff
- A location trend that affects stocking or cleaning
These patterns can influence future planning.
Possible responses may include:
- Adjusting coverage
- Preparing more product
- Changing task timing
- Reviewing training
- Revising shift priorities
- Discussing the trend with regional leadership
The goal is to act before the team is already overwhelmed.
Data Should Be Paired With Team Observation
The people working the shift often notice details that a dashboard cannot capture.
They may know that:
- One piece of equipment caused delays
- A large group arrived at once
- A delivery blocked a normal work area
- A temporary issue increased cleaning time
- A guest service problem affected one period
- A new employee needed additional support
Managers should compare the dashboard with what the team experienced.
A useful conversation might ask:
- Does this result match what you saw?
- What happened during the busiest period?
- Which task became difficult?
- Was the issue caused by demand or process?
- What would have helped?
Data and frontline observation are strongest when used together.
Avoid Using Dashboards Only to Blame
A dashboard should support problem-solving.
It should not become a tool for publicly embarrassing one employee or team.
A weak response to a poor result might be:
This department failed again.
A stronger response asks:
- What changed?
- Was coverage appropriate?
- Were instructions clear?
- Was equipment working?
- Did the team have enough training?
- Was the workload unusually high?
- Is the metric being interpreted correctly?
Accountability still matters.
But accountability should be based on the full situation rather than one isolated figure.
A Metric Should Have a Clear Owner
Every important dashboard measure should connect to someone who understands what it represents.
That does not mean one person controls the result completely.
It means someone should be able to explain:
- How the metric is calculated
- What period it covers
- Which department it reflects
- What can influence it
- What action may be appropriate
- When the result requires escalation
Without ownership, dashboards can become collections of numbers that everyone sees but nobody uses.
Check Whether the Data Is Complete
Operational data may occasionally be delayed, partial, or affected by a system issue.
Before reacting strongly, ask:
- Has the full period finished?
- Is the dashboard refreshed?
- Are all locations reporting?
- Was there a known system interruption?
- Is the metric still provisional?
- Has a correction been announced?
A dashboard viewed too early may not represent the final result.
Leaders should understand whether the data is complete enough to support a decision.
Distinguish Trend From Exception
An exception is a single unusual result.
A trend is a repeated direction across time.
One difficult shift may not require a major process change.
Several similar shifts may indicate something more significant.
Useful questions include:
- Has this happened before?
- Is the pattern becoming stronger?
- Does it occur on the same day or time?
- Is one department repeatedly affected?
- Did the same issue appear at similar locations?
- Is the current response working?
Dashboards help separate isolated events from recurring conditions.
Percentages Need a Base Number
A large percentage change may come from a small starting point.
For example, an increase from one event to two events is a 100 percent increase, but the total volume remains small.
Before reacting to a percentage, check:
- Original value
- Current value
- Actual difference
- Time period
- Operational significance
Percentages can attract attention while hiding scale.
Both the rate and the underlying number matter.
Averages Can Hide Peaks
An average smooths activity across a period.
That can make the overall result easier to read, but it may hide the moments when the team struggled most.
A daily average may look manageable even though one two-hour period was extremely busy.
Managers may need to review:
- Hourly patterns
- Daypart performance
- Shift-level results
- Weekend versus weekday activity
- Peak periods
Operational planning often depends more on the peak than on the average.
Targets Should Be Understood, Not Memorized
A dashboard may compare current results with a target.
Employees and managers should understand what that target represents.
Questions may include:
- Is it based on company expectation?
- Is it location-specific?
- Was it adjusted for season?
- Does it reflect the current operating environment?
- Is it intended as a goal or a warning threshold?
- Which actions can realistically influence it?
A target without context can create pressure without direction.
A useful target helps the team understand where attention is needed.
Red, Yellow, and Green Need Interpretation
Dashboards often use visual indicators.
A red status may signal that a measure is outside a selected range. Green may indicate the target was met.
These colors are useful for scanning.
They are not complete explanations.
A red result may require investigation rather than immediate blame.
A green result may still hide a developing issue.
The color tells the user where to look.
The underlying details explain why.
Leaders Should Explain Why a Metric Matters
Team members are more likely to respond to operational information when they understand the connection to daily work.
Instead of saying:
We need to improve this number.
A manager can explain:
This result shows that the evening team is handling more guest activity after 7:00 p.m., but our current coverage still reflects the older pattern.
The second explanation connects the dashboard to a real planning decision.
Metrics become more meaningful when employees can see how their work influences the outcome.
Not Every Metric Belongs in Every Conversation
Senior leaders may review many measures at once.
A frontline team may need only the information connected to the current shift or role.
Sharing too many numbers can distract from the main priority.
Managers should select the information that helps employees understand:
- What changed
- Why it matters
- What action is needed
- How success will be recognized
Operational transparency is useful.
Information overload is not.
Use Data to Ask Better Questions
The best dashboard review may begin with questions rather than conclusions.
Examples include:
- Why does this time period differ from the previous week?
- Does the schedule match the new traffic pattern?
- Is the change concentrated in one department?
- Did the team face an unusual operational issue?
- Is the result temporary or recurring?
- What should we test next?
- Which support is missing?
Questions create room for investigation.
Premature conclusions can push teams toward the wrong solution.
Operational Data Can Support Scheduling
Dashboard trends can help managers prepare future coverage.
If demand repeatedly increases during a particular daypart, the schedule may need to reflect it.
That could mean:
- Earlier food service coverage
- Stronger evening staffing
- More overlap during handoff
- Additional cleaning support
- Different break timing
- More experienced leadership during peaks
The schedule should not change because of one unusual day.
Repeated evidence provides a stronger reason.
Data Can Support Training Decisions
A recurring operational problem may reveal a training need.
For example:
- A procedure takes too long
- One area shows inconsistent results
- New employees struggle with the same task
- A standard is repeatedly missed
- Shift handoffs create confusion
The response may not be more staffing.
It may be clearer instruction, more supervised practice, or better role definition.
Dashboards identify where to investigate.
They do not determine the solution automatically.
Data Can Support Local Experiments
Managers may test a small operational change and watch the result over time.
Examples may include:
- Moving a task to a different time
- Adjusting shift overlap
- Changing preparation timing
- Assigning clearer ownership
- Adding a short checklist
- Reviewing one process during shift meetings
A useful experiment should have:
- A specific change
- A reason for trying it
- A defined period
- A relevant measure
- Team feedback
- A decision to continue, adjust, or stop
This keeps improvement grounded in evidence rather than constant random changes.
Avoid Changing Too Many Things at Once
If staffing, task timing, training, and procedures all change simultaneously, it becomes difficult to understand what affected the result.
Small, focused adjustments are easier to evaluate.
Managers should consider:
- What is the main problem?
- Which change is most likely to help?
- How long should it be tested?
- What else must remain stable?
- What feedback should be collected?
Dashboards become more useful when changes can be connected to outcomes.
Share Improvements With the Team
Operational reporting should not focus only on problems.
When a team improves, employees should understand what changed and why it worked.
A manager may say:
Evening readiness improved over the last three weeks after we moved stocking earlier and added a clearer handoff.
This reinforces useful behavior.
It also shows employees that operational data is used to recognize progress, not only identify failure.
Common Dashboard Mistakes
Reacting to One Isolated Number
A single result may not represent a trend.
Ignoring the Date Range
The user compares different periods without realizing it.
Comparing Unlike Locations
Local operating patterns may differ significantly.
Looking Only at Percent Change
The underlying volume may be small.
Using Averages Without Reviewing Peaks
The busiest period remains hidden.
Assuming Red Means Employee Failure
Equipment, coverage, demand, or process may be involved.
Sharing Too Many Metrics
The team loses sight of the actual priority.
Changing Several Processes at Once
The effect of each change becomes impossible to evaluate.
Ignoring Frontline Explanation
The data is interpreted without understanding what happened during the shift.
A Dashboard Review Checklist
Before making a decision from PFJ Today reporting, ask:
✅ What period does the data cover?
✅ Is the information fully refreshed?
✅ What is the comparison period?
✅ Are the periods operationally similar?
✅ Does the result reflect the full location or one department?
✅ Is this a one-time exception or a repeating trend?
✅ What actual volume sits behind the percentage?
✅ Does the team’s experience match the dashboard?
✅ Were there unusual local conditions?
✅ Which action can realistically influence the result?
✅ How will we know whether the response worked?
This prevents the dashboard from becoming a source of rushed conclusions.
A Team Discussion Checklist
When sharing operational information, managers can explain:
✅ What changed
✅ Which period is being reviewed
✅ Why the result matters
✅ What local factors may be involved
✅ What the team observed
✅ Which action will be tested
✅ Who owns the next step
✅ When the result will be reviewed again
The discussion should end with a clearer plan, not just a number on a screen.
Common PFJ Today Dashboard Questions
Does every team member see the same operational information?
Not necessarily. Visibility may depend on role, responsibility, location, or leadership level.
Does a bad result mean the team performed badly?
Not automatically. Demand, equipment, staffing, local events, and incomplete data may all affect the result.
Should one unusual day change the schedule?
Usually not by itself. Repeated patterns provide stronger evidence.
Why does the dashboard differ from what the shift felt like?
The selected period, metric definition, or department may not match the specific experience being remembered.
Can dashboards replace manager observation?
No. Reporting should be combined with local knowledge and team feedback.
Why are some results shown as percentages?
Percentages help compare change, but the original and current values should also be reviewed.
What should happen after a trend is identified?
The manager should investigate the cause, select a focused response, and review whether the change improves the result.
Why PFJ Today Operational Dashboards Matter
Pilot Flying J operates across a large network of travel centers, which makes consistent operational information valuable.
A local manager sees one location in detail.
A regional or company leader sees broader patterns.
PFJ Today dashboards can help connect those perspectives by making reporting available through a shared internal environment.
The data can show where activity is changing, which departments need attention, and whether a one-time issue is becoming a trend.
Its value depends on interpretation.
A number without context can mislead.
A target without explanation can create pressure.
A dashboard used only for blame can damage trust.
The strongest approach combines operational reporting with local knowledge, employee observation, and focused follow-up.
PFJ Today data should not make decisions by itself.
It should help Pilot teams ask better questions before they make them.