Client background
The client is a logistics and transport group operating across three countries in the Middle East and North Africa region. Their core business is managed transport services — long-haul freight, last-mile delivery, and dedicated fleet contracts for enterprise clients in the FMCG, construction, and healthcare sectors. By the time they engaged Squash Apps, the group was managing a fleet of over 200 vehicles across three countries, employing more than 400 drivers, and handling roughly 3,000 individual delivery orders per day across their network.
Despite this scale, the group was operating almost entirely on manual processes. Dispatch was coordinated by phone. Route assignments were made by supervisors with years of pattern knowledge but no systematic optimisation. Fuel consumption was tracked in paper logs, vehicle maintenance was scheduled based on supervisor intuition rather than data, and driver performance was assessed through a combination of customer complaints and supervisor observation. The only technology in use was a basic GPS tracker on each vehicle — but the data was siloed in the GPS vendor's proprietary portal and not connected to any of the operational workflows.
The founders had grown the business aggressively and recognised that the manual processes that had worked at 50 vehicles were becoming a serious liability at 200. A significant enterprise contract the group was pursuing required them to provide real-time tracking data to the client's supply chain management system — something the existing setup simply couldn't do.
The challenge
The fundamental problem was that the group's operational data was invisible. Dispatchers knew what orders needed to go out today, and supervisors had a rough mental model of where vehicles were, but nobody had a live view of the entire fleet. This meant that when a vehicle broke down, when a driver was running late, or when an urgent priority order came in at 2pm, the response depended entirely on a supervisor's availability and knowledge — creating both bottlenecks and single points of failure.
Route optimisation was entirely absent. Drivers were assigned routes based on familiarity and supervisor intuition, which meant fuel costs were higher than they needed to be, driving hours were not being managed efficiently, and there was significant variance in delivery times for similar routes depending on which driver was assigned. The group estimated they were losing 15–20% of their fuel budget to sub-optimal routing.
Vehicle maintenance was reactive rather than preventive. Without systematic tracking of mileage and service history, vehicles were breaking down in the field at a rate that was disrupting deliveries and generating significant repair costs. The group had experienced three major breakdowns in a single month that had resulted in contract penalty clauses being triggered.
The enterprise contract they were pursuing — which represented a meaningful share of projected revenue for the following year — specifically required real-time GPS integration with the client's SAP system, driver performance reporting, and proof of delivery workflows with digital signatures. None of this was possible with the current infrastructure.
How we engaged
Squash Apps began with a two-week discovery sprint. A senior business analyst and solution architect from our team spent the first week on-site with the operations team in Dubai, shadowing dispatchers, interviewing drivers, and mapping every operational workflow in detail. This was essential: ERP projects that are designed from requirements documents often fail because they misunderstand the real workflow. The on-site discovery identified several nuances that wouldn't have been captured remotely — including the fact that drivers communicated primarily in Arabic and Hindi, and that the dispatch workflow had significant complexity around multi-drop routes that the initial brief had understated.
The project was scoped into three phases: a 12-week MVP covering dispatch, real-time tracking, and proof of delivery; a second phase covering route optimisation and predictive maintenance; and a third phase covering the SAP integration for the enterprise contract. The client committed to all three phases from the outset, but with milestone-based payment gates at the end of each.
The engineering pod consisted of two senior Node.js backend engineers, one React web engineer, one React Native mobile engineer (for the driver app), one QA engineer, and a part-time DevOps engineer for AWS infrastructure. The PM maintained a daily log visible to both teams and ran structured daily standups at 10am Gulf Standard Time.
What we built
The MVP delivered in 12 weeks comprised three interconnected products. The dispatch dashboard — a React web application — gave dispatchers a live map view of every vehicle in the fleet, colour-coded by status (available, assigned, in-transit, delayed), with the ability to assign orders to vehicles, see real-time ETAs, and communicate with drivers through an in-app messaging system. Dispatchers immediately had the visibility they had previously lacked.
The driver app — a React Native application supporting both Android and iOS — gave drivers their daily order list, turn-by-turn navigation integrated with Google Maps, the ability to capture digital proof of delivery (photo, signature, and timestamp), and a breakdown reporting flow that would automatically alert dispatch and a maintenance coordinator. The app was localised in English and Arabic. A significant effort went into making the app reliable on low-bandwidth connections, given that drivers in remote areas were frequently on 2G or edge networks.
The admin portal gave operations managers access to the data layer: vehicle utilisation reports, driver performance scorecards, maintenance history, and fuel consumption analytics. This was the first time the group had ever had access to this data in structured, queryable form.
The route optimisation engine, delivered in phase two, used the OR-Tools library from Google to calculate optimised multi-drop routes given vehicle capacity, driver hours limits, customer delivery windows, and historical traffic patterns. When integrated into the dispatch workflow, it reduced average route distances by 18% compared to manually-assigned routes.
The SAP integration, completed in phase three, used SAP's standard IDocs interface to push proof-of-delivery confirmations and order status updates in real time into the enterprise client's supply chain system. This was the deliverable that unlocked the enterprise contract.
Technical approach
The backend is a Node.js REST API running on AWS Fargate, backed by PostgreSQL on RDS for operational data and a TimescaleDB instance for the GPS telemetry time-series data. TimescaleDB was chosen over standard PostgreSQL for the telemetry data because of its hypertable architecture, which gave us orders-of-magnitude better query performance on time-windowed queries (e.g., "show me the path of vehicle X between 8am and 2pm today").
Real-time vehicle positions are ingested via a WebSocket connection from the GPS hardware vendor's streaming API, normalised, and stored in TimescaleDB. The dispatch dashboard receives position updates via Server-Sent Events from the backend, updating vehicle positions on the map at 30-second intervals. This architecture was chosen over WebSockets for the dashboard because it's simpler to implement correctly behind a load balancer and reduces server connection overhead.
The driver app uses React Native with Expo for the camera and signature capture modules. Offline resilience was a first-class requirement: the app stores pending proof-of-delivery submissions locally and syncs them when connectivity is restored, using a job queue backed by AsyncStorage. This addressed a real operational need — deliveries to warehouse facilities in industrial zones frequently had poor cellular coverage.
Results
The group went live with the MVP across all three countries simultaneously, 12 weeks after project kick-off. Within three months of go-live, dispatcher productivity had improved significantly: the average time to assign and dispatch an order fell from 8 minutes to under 2 minutes, and the number of orders a single dispatcher could manage increased substantially. Supervisors were no longer needed to act as information conduits between drivers and dispatch.
Fuel costs fell by 22% in the six months following the route optimisation rollout — better than the 15–20% the group had estimated, because the optimisation also surfaced a pattern of vehicles idling excessively at certain stops, which had been completely invisible before.
The enterprise contract was signed three months after the SAP integration went live. It represented the group's largest single contract and provided strong justification for the technology investment from a purely financial standpoint.
Perhaps most telling: the group renewed and expanded the engagement, adding two more engineers to the pod for a fourth phase covering driver earnings and incentive management, and a predictive maintenance module based on vehicle telemetry analysis.
This project demonstrates the end-to-end capability that purpose-built fleet management ERP software development delivers for logistics operators. From custom dispatch dashboards and driver mobile apps to route optimisation algorithms and enterprise ERP integration, the platform transformed a manual, phone-and-paper operation into a data-driven logistics business capable of winning enterprise contracts that require real-time digital integration. For fleet operators across the Middle East, South Asia, and beyond looking to digitise operations and demonstrate supply chain transparency to clients, this engagement is a working blueprint.