Reviewing Results

After an optimization completes, carefully review the results before implementing any changes. This guide helps you understand what the optimizer recommends and make informed decisions.

Results Overview

When you view optimization results, you'll see several sections:

Summary Metrics

At the top, you'll see high-level statistics:

• Total postal codes affected - How many postal codes would change team assignments
• Total team caseloads affected - How many teams would see changes to their territory
• Projected improvement - Expected improvement in balance or geographic efficiency

These give you a quick sense of the scope and potential benefit.

Before/After Comparison

Visual comparison showing:

• Current state - How assignments look today
• Proposed state - How assignments would look after applying changes
• Difference - What specifically would change

Understanding the Metrics

Travel Metrics

Total Travel Time Sum of estimated travel time across all employees.

• Look for reductions of 15-40% in a typical optimization
• Larger reductions suggest significant current inefficiency
• Minimal reductions may mean you're already well-optimized

Average Travel Per Employee Mean travel time per worker.

• More meaningful than total for comparing across organizations
• Watch for high outliers that might need attention

Travel Distribution How evenly travel burden is distributed.

• High variance means some employees drive much more than others
• Optimization typically reduces this variance

Workload Metrics

Caseload Balance How evenly clients are distributed.

• Measured as variance or standard deviation
• Lower numbers = more balanced distribution
• Zero variance would mean everyone has identical caseloads

Capacity Utilization Percentage of each employee's capacity being used.

• Aim for 70-90% to allow for growth and flexibility
• 100% utilization leaves no buffer for new clients

Max/Min Caseload Highest and lowest assignments on the team.

• Large gaps indicate imbalance
• Optimization narrows this range

Using the Comparison Map

The map is your most powerful tool for understanding results.

Map Views

Current Assignments Shows how postal codes are currently assigned to team caseloads:

• Each team caseload represented by a different color
• Geographic areas colored by their assigned team
• Boundaries show current territory coverage

Proposed Assignments Shows the recommended assignments:

• Same color scheme for easy comparison
• Notice how boundaries shift and territories cluster differently
• Geographic patterns become more apparent

Changes Only Highlights just the reassignments:

• Shows which postal codes would change teams
• Helpful for focusing on what's different
• Reduces visual noise from unchanged assignments

What to Look For

Geographic Clustering Well-optimized assignments show tight geographic clusters.

• Teams serving contiguous geographic areas
• Minimal overlap between territories
• Logical geographic boundaries

Outliers Postal codes far from the rest of their assigned team's territory.

• May indicate data issues
• Could be necessary for balance
• Investigate before accepting

Boundary Crossings Teams with postal codes crossing region boundaries.

• May be necessary for balance
• Could create coordination issues
• Review if you have strict territory requirements

Evaluating the Recommendations

Questions to Ask

Do the numbers make sense?

• Is the improvement realistic?
• Are affected counts reasonable?
• Does the data match what you know?

Are the geographic clusters logical?

• Do proposed territories make sense for your area?
• Are there natural barriers (rivers, highways) being ignored?
• Would employees accept these territories?

What about special cases?

• Long-term relationships that shouldn't change
• Clients with specific requirements
• Employees with limitations

Is the change manageable?

• Can you realistically implement this many changes?
• Would a smaller set of changes be better?
• Is the timing right for disruption?

Red Flags

Watch for these warning signs:

Too many changes If 80%+ of assignments would change, the recommendations might be impractical. Consider:

• Running again with higher continuity weight
• Implementing in phases
• Questioning whether the data is accurate

Counter-intuitive assignments If some recommendations seem obviously wrong:

• Check the underlying data
• Look for missing location information
• Verify employee and client details

Excessive distance in proposed assignments If proposed distances are higher than current:

• Settings may be misconfigured
• Data may have issues
• Constraints may be too tight

Making Your Decision

After reviewing, you have several options:

Accept All

Apply all recommended changes.

• Appropriate when results look good
• Maximizes potential improvement
• Requires confidence in the recommendations

Accept Partial

Select specific changes to apply.

• Useful when some recommendations are better than others
• Allows gradual implementation
• Gives more control over the outcome

Reject and Re-run

Discard results and try again.

• Appropriate when results don't look right
• Adjust settings before running again
• Check data if issues persist

Save for Later

Keep results for future reference without applying.

• Useful for planning discussions
• Allows time for stakeholder input
• Results remain accessible in history

Involving Stakeholders

Before implementing major changes:

Team Leads

• Review proposed assignments for their teams
• Identify potential concerns
• Gather buy-in before changes

Affected Employees

• Communicate upcoming changes
• Address concerns about new assignments
• Explain the reasoning and benefits

Management

• Present summary of recommendations
• Highlight expected improvements
• Get approval for significant changes

Next Steps

Ready to implement? See the Applying Changes guide for:

• How to apply full or partial changes
• Managing the transition
• Communicating with your team
• Exporting results for reference