How we streamlined enterprise logistics for A&M with TigerGraph

Alvarez & Marsal (A&M) is a global enterprise-level consulting company specializing in turnaround management, performance improvement, and advisory services. Founded in 1983, the firm provides expertise in restructuring, operational excellence, and strategic consulting across various industries. A&M is known for working with clients during critical transitions, helping them address complex challenges and achieve sustainable growth.

The client turned to Genuisee due to our proven expertise in working with niche technologies like TigerGraph and our ability to deliver custom data-intensive systems under tight technical constraints.

Services

Industry

Logistics

Location

United States

Business context

Alvarez & Marsal approached Genuisee to support a logistics optimization initiative for one of their end clients. The client, a large logistics service provider, faced operational inefficiencies in dispatching, load scheduling, and route optimization. These inefficiencies led to inflated costs, underutilized resources, and delayed deliveries.

The mission was to craft a customized logistics management solution that could streamline planning, automate load assignments, and enable smarter data-driven decisions using real-time data insights.

Key challenges:

An inefficient centralized system for dispatchers to manage real-time logistics
Inconsistent route planning due to reliance on manual coordination
A legacy backend system with an unoptimized codebase
A niche database technology (TigerGraph) with limited global expertise

Solutions we implemented

Our approach was specifically tailored to the client’s existing TigerGraph-based architecture, where we needed to support custom graph queries and complex logistical relationships.

Following a Kanban methodology, our team delivered an end-to-end system refactoring that included the following services and steps:

We built and fine-tuned a suite of asynchronous, multithreaded ETL data pipelines based on output quality. This helped us automate logistics data processing and significantly reduce data handling time.
We elaborated upon Ogma.js-based solution to develop a driver dispatch map, allowing users to track trips in real time and visualize route data in a friendly and intuitive way.
We developed an advanced solution to optimize load assignments and delivery routes, reducing manual coordination and improving resource efficiency. This included building interactive load scheduling and assignment visualization pages that allowed dispatchers to quickly assess available capacity, match loads with drivers, and view routes in real time. We aimed to minimize vehicle usage and driving time without compromising delivery accuracy.
To ensure a holistic and centralized logistics control, our team elaborated dispatcher dashboards, which facilitated the management of the drivers’ schedules. Moreover, these dashboards impacted the aligned communication between teams.
To enhance system performance and reduce execution time for data-heavy operations, we implemented a combination of asynchronous task execution, multithreading, and multiprocessing.

Project tech stack

Python

Angular

Genuisee team

Development team

Product team

Project manager

Project manager

QA team

QA specialists

Features

KPI monitoring and analytics panels
- total mileage per driver over customizable periods
- load completion rates
- downtime and idle time analysis
- route optimization performance and delivery precision
Dispatcher dashboards
- route updates
- Ogma.js-based map
- active and pending delivery statuses
- real-time location tracking
- mileage per driver
- upcoming delivery windows
- driver’s working hours, vacations, etc
File management page
- historical data analysis
- bulk data uploads
- data updates
- trip records & load requests

Are you looking for full cycle product development?

You found what you were looking for. Just fill out the contact us form.

Results

Genuisee’s tailored solution delivered measurable improvements that directly addressed key operational pain points. By enhancing data processing, logistics coordination, and dispatcher oversight, the system brought lasting business value to the client.

50% reduction in ETL processing time (from 7 min to ~3 min).
Estimated 10–20% decrease in daily fleet usage based on improved route logic.
Enhanced visibility and control for dispatchers, leading to faster decision-making and better resource allocation.