Alvarez & Marsal · Response to Waste Management's RFP

Our proposal for Driver Safety Playbook Transformation

A&M's response to Waste Management's RFP. From static rules to real-time decision support — a Collections pilot built as an enterprise-scalable model. Waste Management has built the discipline; here is how A&M will turn it into a continuous learning system for driver safety, and why we are the right partner to deliver it.

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Prepared for WM EHS&S Six phases · four decision gates Collections pilot first
Why Alvarez & Marsal

Introduction from Andy Page, Managing Director

A short introduction to how A&M would approach Waste Management's Driver Safety Playbook Transformation, and why the Collections pilot is the right place to start.

Introduction from Andy Page, Managing Director — video to follow
A&M
Andy Page · Managing Director, Alvarez & Marsal — engagement lead and single point of accountability.
Executive summary

From a static rulebook to a continuous learning system

Waste Management's Operations and Safety Rules Book for Post Collection Operations created consistency across a large, diverse Collections workforce. A static, paper-based reference cannot support the thousands of in-the-moment decisions drivers make each day, or capture and apply what is learned from how work is actually performed.

Significant investment in safety technology and standardized rules has delivered consistency — but not yet the step-change in safety performance it was meant to unlock.

The gap this transformation closes

The challenge

Five conditions limit what the current Rules Book can do:

  • Hard-copy rules book with no in-field or digital access.
  • Heavy reliance on memorization and post-incident review. Learning arrives after the event.
  • Rules, safe practices and training not mapped to each driver's daily activities.
  • DriveCam, Smart Truck, HERE Maps and other onboard systems give feedback independently, applied differently by each District Manager.
  • Five-year, high-level safety performance is trending down across TRIR, Lost Days and Hours between Incidents.

Our approach

Build a field-integrated driver safety system, SafeIQ, that combines updated safety rules with standardized work and makes applying them dynamic, contextual and interactive. Safety learning happens at the start of every shift based on that day's route. Execution and variance against the standard are captured for coaching and for updating the standard.

Why it is different: WM captures learning from variation before behavior becomes an event. Drivers are participants in improving the system (HOP: with the frontline, not to it). Technology enables the system; it is not the transformation.

Today
  • Paper-based Rulebook
  • Generic up-front training
  • Post-incident discipline
  • Non-integrated technologies
  • Inconsistent coaching
Future state
  • Digital rules repository with interactive content
  • Daily training and reinforcement based on that day's activities and conditions
  • Daily and weekly coaching that reduces risk exposure
  • Technologies integrated and mapped to WM rules
  • Consistent coaching model, moving each operator up a defined Safety Learning Curve
A worn, dog-eared paper safety rulebook on a desk — the static, hard-copy reference the transformation moves beyond.
Today: a static, paper-based Rules Book — consistent, but unable to support thousands of in-the-moment decisions each shift.
A collage illustrating the eight drivers of driver safety performance, spanning behavior, conditions, technology and coaching.
The safety picture is multi-factor: behavior, conditions, technology and coaching all shape outcomes — and must be connected.
7priority risks addressed, each with a defined safe behavior and leading indicator.
7required KPIs, grouped under adoption, behavior change and risk reduction.
1accountability model: A&M is the single point of accountability across all phases.
A refuse collection driver in hi-vis and hard hat reviews a shift briefing on a rugged tablet beside a collection truck in a depot yard at the start of a shift.
Pre-trip: the driver reviews a personalized shift briefing on the in-cab tablet before starting the route.

A&M's proposed role

Measurable change in operator understanding of safety thinking delivers fewer repeat at-risk behaviors, more behavior-specific coaching, faster incident-to-update cycles and targeted reductions in WM's highest-risk incident categories. The Collections pilot becomes the enterprise template.

Approach

The driver does not search SafeIQ — SafeIQ surfaces what matters for the work at hand

The right standard for this work, right now. We make standardized work dynamic, contextual and interactive. One loop runs across the shift: context, standardized work, execution, variation, feedback and coaching, learning, and an improved standard. There is one WM way of doing things, and everyone is responsible for improving it.

How every shift becomes a source of learning

  1. Planning & known informationLocation, weather, traffic, vehicle, task, site hazards, operator history.
  2. Standardized work by route & clientThe safest known way, designed to be the most efficient and repeatable.
  3. Execution & data captureDriver performs the work with guidance and microlearning at the point of risk.
  4. Variation applied as requiredIf conditions do not allow work per standard, variation is applied through WM safety thinking.
  5. Variation feedback captureDriver records in real time why the job was faster, slower or riskier, and makes suggestions.
  6. Review variation & learnIncidents, near misses, RCAs, observations and driver input enter one process.
  7. Coach & reinforceRecognize positive behaviors and frontline contributions; provide specific skill and conditional training.
  8. Improved standardUpdates redeployed to the field through governed content management.

Seven integrated capabilities of SafeIQ

Training curriculum

Leadership training to shift the mindset from compliance-driven to performance-driven.

Interactive content library

Updated rules mapped to questions, microlearnings and interactive exercises, scaled easy, medium and hard.

Learning curve & levels

Defined levels and an operator maturity profile from beginner to advanced.

Context-aware daily delivery

Content personalized per shift by route, truck, location, weather, traffic and operator maturity.

Performance data & coaching

Proactive data (SafeIQ) and reactive data (DriveCam) together guide coaching.

Standardized work by site

Geofencing and customer details defined; variation to standard captured.

Continuous improvement engine

Operator feedback captured, content improved, progress linked to recognition.

One WM way

A single operating standard everyone is responsible for improving — with the frontline, not to it.

Guidance in the flow of a driver's day

Pre-trip

Personalized safety briefing; truck and route-specific checklist.

On route

Contextual hazard alerts; point-of-work decision support.

Backing

Backing standard and sequence; site-specific risk reminders.

Disposal

Facility-specific guidance; changing-condition alerts.

Post-trip

Capture exceptions and observations; close-out and maintenance inputs.

Incident

Immediate response protocol; guided documentation and escalation.

Coaching & learning

Targeted coaching; lessons fed back into the Playbook to improve the next shift.

Built on WM's existing technology and data

WM's truck technology stack already generates operational, behavioral and contextual data. SafeIQ integrates it rather than replacing it.

Technology provides the data. Integration creates intelligence. Coaching changes behavior. Continuous learning improves performance.

A collection truck surrounded by eight technology callout tiles — DriveCam video, onboard computer, telematics and ADAS, HERE Maps, hopper and service cameras, fleet apps, safety systems and the learning management system.
WM's existing in-truck technology stack — the operational, behavioral and contextual signals SafeIQ integrates rather than replaces.
An in-cab tablet showing the SafeIQ interface with a route-specific safety quiz and point-of-risk microlearning prompt.
The in-cab SafeIQ interface: a fast, visual, low-text experience that surfaces the right standard for the work at hand — no interaction while moving.

Conceptual driver interface

Three moments from a routine workday, validated with Collections drivers in Phase 1. Designed for the cab: fast, visual, minimal text, no interaction while moving.

  • Approach & pre-trip. Route and geofence data surface stop details and conditions; driver confirms readiness.
  • Point-of-risk at the backing zone. A short backing refresher on geofence entry; driver confirms ready to proceed.
  • Capture & learn at disposal. Driver selects stop status and an optional note; usage and issues feed coaching and improvement.
A map view showing a geofenced disposal site with a microlearning backing refresher prompt triggered on geofence entry.
Geofenced, point-of-risk microlearning: a short backing refresher is triggered on entry to the disposal site.

From Collections pilot to enterprise

Built once, reused for every line of business: content architecture, offline-first multi-device platform, governance charter, KPI framework and measurement chain, adoption playbook and supervisor coaching routines.

Added for each new line of business: line-specific risks and use cases, local content owners, a line governance committee, site and device onboarding, and baselines and targets for that line's KPIs.

Measurement

Seven KPIs, one chain from use to outcome

Every interaction creates a measurable signal. The chain connects what the driver sees, what the driver does, how the supervisor responds, whether behavior changes and whether risk declines. WM's seven required KPIs are grouped under adoption, behavior change and risk reduction, each backed by simple behaviors we can measure from day one.

A continuous-learning-system collage centered on an analytics dashboard, showing how shift data, coaching and standards updates form one closed improvement loop.
The continuous learning system: dashboards turn every shift's signals into coaching decisions and governed updates to the standard. Structure and sample data only; baselines and thresholds are set with WM in Phase 1.
WM's seven KPIs and the starter set of leading behaviors. Definitions, thresholds and data sources are set with WM in Phase 1.
WM KPIStarter leading behaviors we measure
Driver adoption and active usePre-trip briefing completed; driver logs issue or idea at a stop
Behavior change in high-risk activitiesBacking to the standard method; securing and rollaway checks done; safe driving checks (observed)
Supervisor coaching integrationAt-risk observations coached on time; coaching follow-up completed
Context-aware learning effectivenessPrompts acted on, not dismissed; microlearning done at the point of risk
Operational behavior and leading indicatorsPre-trip steps done, including clearance check; post-trip steps completed
Risk reduction and outcome alignmentHazards and near misses reported; hazards closed on time
Responsiveness and continuous improvementInput or RCA to updated standard; driver suggestions submitted
A supervisor and a refuse truck driver in hi-vis stand together beside a collection truck in a depot yard, looking at a tablet mid-conversation in a coaching moment.
Supervisor and driver coaching at the yard — behavior-specific, grounded in the day's measured signals.

Dashboards trigger decisions, not just report numbers. Structure and sample data only; baselines and thresholds are set with WM in Phase 1.

The path forward

Six phases, four WM decision gates

A&M provides single-point accountability across six phases, coordinating technology partners and WM stakeholders. WM provides SMEs, an internal project manager and the Driver Training Team as integration partner. Select any phase or gate to see what it delivers and what WM decides.

Six phases

Four WM decision gates

Team

Frontline operations experience, behavioral science and HOP fluency

A&M brings technology enablement without being technology-led, and a design built for enterprise scale. Named team, grades and time commitment by phase are confirmed before contracting.

AP

Andy Page

Managing Director, A&M · Engagement lead

Single point of accountability across all six phases, including partner-delivered work.

P1

Strategy & Content lead

Phases 1–2

Field research with drivers and supervisors; Driver Safety Principles; KPI framework.

P3

Platform lead

Phase 3 & partner coordination

Platform path, integration with WM tablets and existing signals, offline testing, KPI data feed.

P5

Change & Governance lead

Phases 5–6

Adoption strategy, coaching workflow, governance charter and handover to WM.

P4

Training integration lead

Phase 4 · with WM's Driver Training Team

Training alignment mapping, impact register, reinforcement recommendations.

BS

Behavioral science & HOP

Cross-phase

Performance coaching design, motivational drivers for game-based learning, HOP integration into coaching and governance.

Technology partners

Vexillum

Digital standardized work for heavy industry. The WorkD app automates and integrates safety into pre-start routines and inspections. Candidate platform for real-time, field-based, geofenced guidance.

MaxMine

Fleet data and AI algorithms for driver behavior. Candidate source of existing behavior analytics and coaching logic.

The platform path (Vexillum, MaxMine or a custom build) is confirmed in Phase 1 against WM's tablets, existing signals and ownership requirements. Prime and subcontractor structure to be confirmed.

Why A&M

Frontline operations pedigree

Heavy-industry and industrial-services experience where standardized work must succeed at the point of work.

Technology-enabled, not technology-led

Field workflow and contextual guidance, using the right platform without letting it define the operating model.

Behavior change & HOP fluency

Behavioral science, performance coaching and HOP so the Playbook changes decisions, not only content delivery.

Designed for enterprise scale

Governance, content architecture and platform decisions set so the Collections pilot becomes a repeatable WM model.

WM owns the operational future: its data, safety standards, technology decisions and long-term governance. A&M is technology-agnostic and does not sell software, SaaS or hosting. We transfer knowledge and build internal ownership. Continued A&M advisory support is optional.

Relevant experience

84%reduction in ramp overspeed events, with a clear change within about four shifts

Data-driven coaching reduced high-risk driving behavior

A large open-pit mine had a high rate of haul-truck overspeed events on downhill ramps. A&M installed vehicle data loggers across 500+ vehicles, defined behaviors and thresholds, gave operators real-time and post-shift feedback, and equipped supervisors with daily alerts, event replay and coaching tools.

Why it matters to WM: objective vehicle data and timely feedback changed operator behavior at scale, with supervisors coaching where it mattered.

98%of task confirmations recorded automatically

Digital standardized work transformed field execution

A large mining operation had a mature work management system but inconsistent field execution on paper. A&M structured work into step-by-step elements, deployed a technician app with task-specific instructions, images, tools and PPE, integrated it with SAP, and trained crews and supervisors.

Why it matters to WM: structured work, built-in safety checks and step-level confirmation changed frontline behavior in a live operating environment.

53%more crane lifts per day, sustained; 30,000 frontline users

Frontline digital adoption at scale (LinkNBuild, now part of A&M)

A listed construction group was constrained by crane logistics and paper-based crew control. IoT crane loggers tracked lifts and SOP compliance; a simple operator app needed no training and nothing typed during lifts; daily and weekly coaching used automatic data, not self-reporting.

Why it matters to WM: one-week site implementation, frontline adoption without distraction from the work, and coaching on measured behavior.

WM's side of the team

Timeline

A value-gated delivery sequence

Phase durations, pilot cohort and pilot length are set with WM during contracting, so the delivery sequence below is relative, not to scale. Phase 4 runs alongside Phases 1 to 3; the governance committee stands up before Gate 4 and operates after.

Illustrative sequence only. Durations are set during contracting. Gate 1 follows Phase 1; Gate 2 follows Phase 2; Gate 3 is pilot go-live; Gate 4 is the scale decision at the end of the pilot window.

Phase 1 → Gate 1

Field research, KPI definitions and baselines, platform path. WM approves the design framework, KPI baselines and platform path.

Phase 2 → Gate 2

Design and prototype, tested in the field with drivers. WM approves content architecture and prototype as the build specification. Phase 4 training alignment runs alongside Phases 1 to 3.

Phase 3 → Gate 3

Build with technology partners; beta test in the field. Offline tests passed on WM tablets, training aligned, supervisors ready to coach. Phase 5 adoption plan prepared.

Collections pilot → Gate 4

Usage, behavior, coaching and outcomes tracked against the baseline; first proof point is disposal-site backing guidance. Governance committee stood up (Phase 6). WM's governance committee decides to scale, adjust or extend.

The ask

Turn a static rulebook into a continuous learning system — with three decisions to start.

1

Align the path — confirm the Collections-first scope and the six-phase, four-gate delivery model with WM Supply Chain.

2

Mandate the team and governance — appoint the executive sponsor and Corporate Safety Director(s), and commit SMEs, the project manager and the Driver Training Team.

3

Set the baseline at Gate 1 — agree KPI definitions, baselines and the platform path so every later decision uses the same measurement chain.

Next steps

Ready to mobilize Phase 1 on contract award

Begin with the Collections pilot, prove the measurement chain, and build the enterprise template once — reused for every line of business.