WS

Services built for operational clarity

We work alongside your operations team to assess challenges, design workflows, and implement systems that turn data into decisive action. No generic templates—every engagement is customized to your network complexity and goals.

Logistics Control Towers

A unified operational command center for multi-site, multi-carrier networks. Real-time visibility into shipments, facility performance, and labor availability—with exception routing and prioritization built in.

The Challenge

When you operate across dozens of facilities and work with multiple carriers, you're managing fragmented data from incompatible systems. Critical problems surface only after impact.

Our Approach

We integrate your TMS, WMS, and labor systems into a single operational view. We normalize conflicting data, correlate events, and surface what matters. The control tower becomes the single source of truth for operations leadership.

Typical Deliverables

  • • Network-wide shipment and facility dashboard
  • • Real-time exception queue with context and impact scoring
  • • KPI tracking and trend analysis
  • • Alert routing and escalation workflow

Expected Benefit

18% reduction in preventable service exceptions through earlier visibility; 8+ hours of ops leadership time reclaimed weekly.

Implementation Timeline

Discover (2-3 weeks)

Interview ops leaders, audit current data sources, identify integration points and pain points.

Model (3-4 weeks)

Build data pipelines, design KPI framework, prototype control tower interface with your team.

Launch (1-2 weeks)

Go live with pilot team, validate data accuracy, train operations staff, document workflows.

Improve (ongoing)

Monitor adoption, refine workflows, expand to additional facilities or add new data sources.

Operational Analytics & KPI Design

Define the metrics that actually matter. We work with your team to establish decision-ready KPIs that drive behavior, not just report it.

The Challenge

Most logistics operations are drowning in metrics but starving for clarity. KPIs are often misaligned, conflicting, or measuring the wrong thing—creating the wrong incentives.

Our Approach

We map your operational levers, understand the causal relationships in your network, and design a lean set of forward-looking KPIs that guide daily decision-making. We focus on outcome KPIs and the operational drivers that influence them.

Typical Deliverables

  • • KPI framework aligned to business goals
  • • Calculation methodology and data definitions
  • • Dashboard templates and drill-down workflows
  • • Benchmark and target setting

Expected Benefit

Faster decision-making with aligned metrics; 12% improvement in on-time delivery through better visibility into root causes.

Implementation Timeline

Discover (2-3 weeks)

Interview stakeholders, audit current metrics, identify misalignments and data gaps.

Model (2-3 weeks)

Design KPI framework, define calculations, validate with finance and operations.

Launch (1 week)

Implement dashboards, socialize KPIs, establish governance and reporting cadence.

Improve (ongoing)

Monthly reviews, refine targets, add operational detail as teams become data-fluent.

Transportation and Route Performance Intelligence

Understand why routes perform the way they do. See variance, forecast demand, and optimize dispatch decisions with data-driven confidence.

The Challenge

Route performance varies unpredictably. You know on-time performance overall, but not why individual routes miss, or which decisions drive the variance.

Our Approach

We correlate historical route data with conditions—load profile, carrier, weather, traffic, facility performance, special handling. You'll see which factors actually predict variance and where your optimization efforts will pay off.

Typical Deliverables

  • • Route performance dashboard with variance drivers
  • • Predictive analytics for on-time probability
  • • Carrier performance and spend intelligence
  • • Route optimization recommendations

Expected Benefit

3.2x faster identification of problem routes; 9% improvement in on-time delivery through better dispatch decisions.

Implementation Timeline

Discover (2-3 weeks)

Audit TMS data, identify variance sources, understand current dispatch and routing rules.

Model (4-5 weeks)

Build performance correlations, develop predictive models, validate with planners.

Launch (1-2 weeks)

Deploy dashboards, implement recommendations, establish feedback loop.

Improve (ongoing)

Refine models quarterly, expand to additional route segments or carriers.

Warehouse Flow and Labor Visibility

See where bottlenecks happen and whether you're staffed for the work. Real-time labor efficiency and forecast-driven scheduling.

The Challenge

You schedule labor on forecasts that miss. You don't see which activities are eating time or whether you're understaffed until you're in the middle of a shift.

Our Approach

We ingest your WMS, time-clock, and inbound data to show labor productivity in real time. We build models that predict staffing needs based on actual volume trends, not static forecasts. You can optimize schedules and staffing years ahead.

Typical Deliverables

  • • Shift-by-shift labor efficiency analytics
  • • Bottleneck identification and root-cause analysis
  • • Staffing forecast and optimization
  • • Activity-level productivity benchmarks

Expected Benefit

11 hours of planner time reclaimed weekly through automated scheduling; 14% reduction in overtime through better staffing fit.

Implementation Timeline

Discover (2-3 weeks)

Audit WMS and labor data, understand current staffing and scheduling practices.

Model (3-4 weeks)

Build productivity models, forecast algorithms, and optimization framework.

Launch (1-2 weeks)

Implement Shift Lens, deploy forecasts, train scheduler and labor management team.

Improve (ongoing)

Refine models monthly, expand to additional facilities, optimize based on outcomes.

Exception Management Workflow Design

Turn exceptions from surprises into structured, routable problems. Design workflows that get the right person the right context in seconds.

The Challenge

Problems surface through phone calls, emails, and Slack. There's no clear routing, no context, no auditability. Teams waste time hunting for root cause.

Our Approach

We design exception workflows that detect problems early, contextualize them with data, score them by impact, and route them to the right owner with history and recommended actions. Built into Waymark's Exception Desk.

Typical Deliverables

  • • Exception detection rules and thresholds
  • • Routing logic and team ownership
  • • Context-rich exception templates
  • • SLA and escalation playbooks

Expected Benefit

3.4x faster exception resolution; 40% reduction in repeat problems through better root-cause data and accountability.

Implementation Timeline

Discover (2 weeks)

Interview ops teams about current exception handling, identify top problem types.

Model (2-3 weeks)

Design detection rules, routing logic, and SLAs with stakeholder input.

Launch (1 week)

Implement in Exception Desk, train teams, document runbooks.

Improve (ongoing)

Review metrics monthly, refine thresholds and routing, add new exception types.

Data Integration and Reporting Modernization

Unify fragmented systems and replace static reports with decision-ready analytics. Move from reactive reporting to proactive intelligence.

The Challenge

You have data in TMS, WMS, ERP, and a dozen other systems. Everyone runs custom reports. Data is stale and conflicting by the time anyone acts on it.

Our Approach

We build a unified data platform that normalizes and correlates your sources in real time. We replace static reports with dynamic dashboards tied to operational workflows. Your team gets decision-ready context on demand.

Typical Deliverables

  • • Data integration and ETL architecture
  • • Unified operational data model
  • • Real-time operational dashboards
  • • Self-service analytics platform

Expected Benefit

6+ hours of analyst time reclaimed weekly; 40% faster decision-making through always-current data.

Implementation Timeline

Discover (2-3 weeks)

Inventory all data sources, audit current reports, identify highest-value integration targets.

Model (4-6 weeks)

Build data pipelines, establish data model, develop initial dashboards.

Launch (2 weeks)

Go live with core analytics, train analysts and ops teams, retire legacy reports.

Improve (ongoing)

Extend to additional systems, add self-service capabilities, optimize performance.

Ready to streamline your operations?

Let's start with a focused discovery conversation to understand your challenges and design a path forward.

Contact Our Team