91TV

Turning a better plan into better performance.
By Don Diego Padilla II and Kalpesh Patel

Route optimization creates lasting value only when advanced route planning is connected to governed data, executable routes, field technology, performance management, and continuous learning. For waste and recycling organizations, the opportunity is not simply to draw shorter routes—it is also to build a safer, more productive, and more profitable operating system.

What Optimization Really Means
For waste and recycling executives, efficient collection is central to a profitable, reliable operation. Labor, fuel, maintenance, equipment, and disposal costs all converge on the route. Even modest gains in productivity can create meaningful savings or capacity. Yet, route optimization is still commonly confused with dispatching.

Dispatching is the act of assigning stops or tickets to drivers. Route optimization is the analytical process of determining the most efficient and cost-effective grouping, sequence, and path for the work while respecting operational constraints. A dispatch screen, tablet, or map will help execute a plan, but it does not by itself optimize that plan. True optimization uses mathematically based algorithms to evaluate far more combinations than a person could reasonably compare. More advanced systems may also use artificial intelligence (AI) to improve assumptions and detect patterns in actual performance.

Software alone, however, is not the finish line. Sustainable gains come when optimization is treated as a continuous operating discipline: define the objective, govern the data, design executable routes, move the plan into daily operations, measure field performance, and refine it over time.

Start with the Business Objective
Before changing routes, an organization should define what it wants to improve. The shortest route is not always the best route. A hauler may need to reduce overtime, balance workloads across the week, improve service reliability, create capacity for growth, reduce disposal trips, improve route profitability, or delay the purchase of another truck. Municipal operations may place greater weight on resident expectations, service-day consistency, safety restrictions, or equitable workloads. In every case, safety should remain a non-negotiable design requirement rather than a tradeoff for greater route density or lower cost.

Clear objectives help the optimization team select the right constraints and evaluate tradeoffs. Reducing mileage may appear successful until one route routinely finishes late or requires impractical disposal trips. Likewise, minimizing the number of trucks may overload the remaining routes and increase overtime, maintenance risk, or service failures. Good optimization balances financial goals with operational reality.

Baseline measurements are equally important. Document current route miles, paid driver hours, stops or units, lifts, disposal trips, overtime, missed services, finish-time variation, vehicle use, route cost per unit, and relevant safety indicators such as backing events, harsh maneuvers, or preventable incidents. These measures create a credible comparison between the existing operation and the proposed design. The real test is not the projected savings on a planning screen; it is whether field performance shows safer execution and stronger route profitability after the routes reach the street.

Build on Accurate, Governed Data
A routing solution is only as good as the information supplied to its algorithm. At a minimum, the model needs accurate customer locations, service types and frequencies, container characteristics, access restrictions, time windows, disposal facilities and accepted materials, vehicle specifications and capacities, break rules, and service times. Incomplete or outdated information causes even a sophisticated engine to solve the wrong problem precisely.

Service-time assumptions deserve particular attention. Two customers next to each other may require very different amounts of time because of container placement, enclosure access, traffic, backing requirements, contamination procedures, or site rules. Averaging every stop into a generic service time can make routes look balanced when the workload is not balanced at all.

Geographic data also requires validation. A misplaced stop, incorrect entrance, unpublished road, seasonal restriction, or illegal turn can undermine an otherwise strong plan. Dispatchers, supervisors, and experienced drivers often hold knowledge that is absent from the customer record. Bringing that knowledge into data preparation makes the model more representative of the real operation.

Data cleanup is not a one-time project. Customers are added and removed, service levels change, development alters collection density, and disposal conditions evolve. These changes cause routes to drift, quietly accumulating deadhead, imbalance, overtime, and risk. Establishing a single source of truth—and assigning ownership for its quality—is therefore one of the most important optimization practices. Optimization and AI capabilities can assist by flagging incomplete records, unusual values, or patterns that merit review, but operational owners must still validate and govern the data.

Design Routes That Can Be Executed
The model should reflect the constraints that shape actual collection: vehicle type and capacity, disposal cycles and break points, service side, turn restrictions, road access, shift length, breaks, customer time windows, and differences among residential, commercial, roll-off, and recycling services. It should also recognize that every minute is not interchangeable. Fifteen minutes saved early in a route may prevent a disposal-facility delay or help a driver complete service before afternoon congestion.

Workload balancing and asset use are major opportunities. Many operations have a peak-day problem: one day consistently requires overtime while another ends early. Routes may have evolved around customer additions rather than weekly capacity. Optimization can test service-day changes, territory adjustments, route combinations, and disposal strategies to create denser, more predictable work. Properly “packing” routes can reduce the number of vehicles required without creating unmanageable shifts or unsafe time pressure. Fewer routed vehicles can lower fuel, maintenance, and capital costs, while better load use can reduce unnecessary disposal travel. Together, these improvements strengthen route profitability by reducing the cost required to serve each customer or unit.

The people closest to the work should review proposed routes before deployment. Drivers can identify difficult approaches, unsafe maneuvers, excessive backing, gate schedules, school traffic, and construction that may not appear in the data. Dispatchers can assess whether the plan leaves enough flexibility for daily exceptions. Supervisors can evaluate yard departure routines and disposal patterns. Their input does not weaken optimization; it makes the design safer and more executable. Removing hazardous movements and unrealistic sequences can also protect profitability by reducing incident exposure, vehicle damage, service delays, and unplanned operating costs.

Test Before a Full Rollout
A phased implementation reduces risk and creates an opportunity to improve assumptions. Begin with a representative group of routes, compare planned and actual performance, and investigate meaningful differences. If service takes longer than expected in a neighborhood, a disposal trip falls at a congested time, or navigation suggests an unsuitable maneuver, correct the underlying data before expanding the rollout.

Drivers should receive clear communication about why routes are changing and how success will be measured. Without context, optimization can feel like an attempt to demand more work or monitor individuals. The goal should be stated accurately: remove unnecessary travel and inconsistency, create achievable workloads, improve service, and give drivers better information. Training should cover both the new route and the technology used to execute it.

Avoid changing too many variables at once. A pilot should have a defined period, documented expectations, and a structured process for feedback. Managers can then distinguish temporary learning-curve effects from problems in the design. Once routes stabilize, compare field results with the baseline before making a broader rollout decision.

Bring the Route into the Cab
The onboard computer is where planning meets execution. It gives the driver access to the assigned route, customer sequence, navigation, and service information. A route management and onboard computing solution connects drivers and vehicles with back-office operations through dispatching, real-time tracking, and turn-by-turn navigation.

Drivers receive the route in a consistent digital format rather than relying on paper instructions or informal knowledge. That is especially valuable when a regular driver is absent, a new employee is learning the territory, or work must be reassigned during the day. The workflow should support the driver rather than add complexity. Timely customer notes, access instructions, exceptions, and route changes reduce calls to dispatch and uncertainty in the field.

Real-time visibility also improves exception management. When a route falls behind, supervisors can investigate early instead of discovering the problem at the end of the shift. They can determine whether traffic, a vehicle issue, an unusually long service, a disposal delay, or an unrealistic assumption is responsible, then make a targeted decision while preserving the optimized plan where possible.

Close the Loop with Performance Data and AI
Integration creates a benefit beyond navigation and dispatch: a feedback loop. Optimization begins with assumptions about travel, service, capacity, and constraints. The onboard system records what actually happened. Comparing planned and actual results helps managers determine whether routes are performing as designed and why results differ.

Repeated delays at a group of stops may reveal inaccurate service times, access problems, or a sequence that conflicts with local traffic. A recurring disposal delay may indicate that a planned trip should occur earlier or use another facility. Consistent early finishes may reveal capacity, while chronic overtime may signal overload or an operational issue. These patterns are difficult to see when field activity is captured on paper or stored separately from the route plan.

Managers should focus on trends and exceptions, not simply collect more data. Useful measures include planned versus actual paid hours and miles, units per hour, route completion, service duration, disposal time, missed or blocked services, overtime, vehicle use, and daily workload variation. AI can help assess data completeness and identify relationships hidden in standard reports. In the integrated environment, those insights can improve travel, service, and disposal assumptions so future routes become more realistic. Human review remains essential: recommendations must be checked for safety, service commitments, and practical execution.

An “always learning” system can also improve driver acceptance. When field feedback results in visible corrections—rather than disappearing into a report—drivers are more likely to see the plan as achievable and the technology as a support tool.

Establish a Regular Optimization Rhythm
Optimization should not end after implementation. The review rhythm should match the pace of change in the business. Dispatchers may monitor daily exceptions, supervisors may review performance weekly, and management may assess route balance monthly or quarterly. Significant growth, acquisitions, service-day changes, disposal changes, or new contracts may trigger broader re-optimization.
Not every issue requires rebuilding the entire route network. Often, the best response is a targeted adjustment to the routes that have drifted most. Integrated data helps teams prioritize that effort and prevents small inefficiencies from compounding for years.

Governance matters as much as frequency. Someone should own route data and analytics, approve changes, document assumptions, and confirm that updates reach the field. Without clear responsibility, informal changes accumulate and the official plan stops reflecting actual operations. A connected optimization, route management, and onboard environment makes governance easier by maintaining a consistent flow of information.

Measure Value in the Operation
The business case for optimization extends beyond mileage. Reduced drive time can lower labor and fuel costs, reduce risk exposure, create capacity, stabilize overtime, improve vehicle availability, and support more reliable service. Safer route design can reduce backing, difficult turns, rushed work, and exposure to preventable incidents. Denser, balanced routes can reduce the number of routed vehicles and simplify performance management. Better information can shorten driver training and reduce dependence on tribal knowledge. Faster exception response can prevent missed services and customer calls. Together, these operational gains improve route profitability—not merely by cutting miles, but by controlling the full cost and risk of completing the work.

The most meaningful metrics reflect the objective established at the beginning. If the goal is capacity, measure customers or units served per route without sacrificing safety or service. If the goal is consistency, track finish-time variation, overtime, and missed collections. If the goal is safety, monitor backing exposure, harsh events, route hazards, incidents, and driver-reported concerns. If the goal is route profitability, measure revenue or service value against paid hours, miles, fuel, disposal trips, maintenance, and fleet requirements at the route level. Productivity and profitability claims should be validated against the organization’s own baseline rather than treated as guaranteed results.

From Static Plan to Continuous Improvement
Route optimization software is powerful, but it cannot produce sustainable results in isolation. The design must be operationally realistic, transferred accurately, executed with the right information, and measured against field performance. Each layer strengthens the next.

When route optimization is integrated an onboard computer solution, the organization creates a connected cycle: plan, dispatch, execute, measure, and improve. Dispatchers gain visibility, drivers receive clearer guidance, and managers gain real-world intelligence for future decisions. The route is no longer a static map reviewed every few years; it becomes a living operational asset.

That is the central best practice: optimize the entire process, not only the route. 91TV and recycling organizations that connect analytical design, disciplined data management, asset use, field execution, and continuous learning are better positioned to convert hidden inefficiency into measurable capacity, consistent service, and stronger financial performance. | WA

Connect Planning to Daily Operations

Even the best route design loses value when it must be transferred manually into daily operations. Printed route books, spreadsheets, handwritten notes, and disconnected systems create outdated information and transcription errors. They also make it difficult for dispatchers to know whether a driver received a change or whether the route is progressing as expected.

The integration of ZignEx route optimization with the Univerus 91TV & Recycling route management platform converts the optimized plan into an operational assignment. Dispatchers send routes to the appropriate vehicle, view progress, manage exceptions, and respond when conditions change. This connection matters because collection is dynamic: vehicles break down, customers request extra service, roads close, disposal facilities experience delays, and weather disrupts schedules. ZignEx helps create the best plan based on known conditions; Univerus route management and FleetLink help protect and execute that plan when reality intervenes.

A waste industry fleet management veteran, Don Diego Padilla II is Vice President of Operations at Univerus 91TV & Recycling, where he spearheads business and customer development activities. Univerus is a leading provider of software solutions in North America providing integrated fleet safety management technologies to help cities work smarter and stay safer. Previously, Don Diego was a Regional Sales Director for Allied 91TV (Republic Services), a leading provider of solid waste collection, transfer, recycling, and disposal services in the U.S. His industry white paper on fleet safety garnered a Network Products Guide Award in the “Best White Paper” category. Don Diego has been published in numerous industry magazines and is a frequent speaker at industry forums and regional municipal waste management events. He can be reached at (866) 241-4009 or e-mail [email protected].

Kalpesh Patel is Chief Business Development Officer for ZignEx, where he spearheads business development, strategic partnerships, and client success activities. ZignEx is a leading provider of logistics solutions for the waste industry providing integrated logistics platform for strategic planning, route optimization, and analytics across the Commercial, Residential, Roll-off/Industrial, Container/Cart Delivery, and Port-a-let lines of businesses in the industry. He can be reached at [email protected].

Univerus and ZignEx look forward to bringing this robust integration of their complementary solutions to the waste and recycling industry—helping operators turn sophisticated route planning into safer, more efficient, and more consistently executed service. For more information, visit or .