One of India's largest home essentials retailers with a nationwide network of fulfillment centers and delivery points
Large-format retail and omnichannel e-commerce with high-volume last-mile logistics
Value-led home essentials delivered on tight schedules; customer experience anchored in reliable, time-bound service
Replace manual trip planning with intelligent automation to improve route efficiency, maximize capacity, and scale reliably through seasonal spikes while protecting brand promise and margins
Dispatchers clustered shipments and assigned vehicles by hand across multiple FCs, delaying departures, inflating overtime, and leaving no resilience for rapid replans or exceptions
Without algorithmic guidance, drivers doubled back, hit congestion, and revisited zones missing time windows, raising fuel spend, and depressing daily throughput
Lack of automated load balancing sent trucks out partially filled. Extra runs, empty miles, and uneven utilization spiked cost per order
Sales and festive surges overwhelmed manual coordination, breaking handoffs between FCs and last-mile teams, triggering bottlenecks, failed scans, and missed cutoffs
Siloed lists and offline tools masked ETA risk and capacity headroom, preventing timely re-sequencing, load splits, or shift adds
Operational fragility stalled entry into new zones; scaling required disproportionate headcount and risk, undermining SLAs and growth timelines
Groups shipments by proximity, time windows, and fulfillment mode to form dense, logical trips that minimize zigzags and prep cleaner inputs for routing
Continuously re-sequences stops with live traffic, cutoff times, and hub constraints to cut miles per drop and missed windows
Unifies home delivery and pickup point flows in a single plan, removing manual splits and preserving SLAs
Maximizes vehicle fill to consistently achieve 25–30 deliveries per trip, reducing runs and cost per order
Streams orders and statuses for near real-time grouping and assignment, enabling rapid replans during peaks
Builds clustering, routing, and load-balancing logic tailored to geography, windows, and capacity; rapid iteration delivers dense trips and consistent 25–30 drops per route
Stateless APIs for create/replan/assign keep dispatch responsive; horizontal scaling and isolation improve resilience at peak load
Bi-directional data flows for orders, capacity, status, and POD enable seamless handoffs across fulfillment and last mile
Real-time events for orders, locations, and exceptions power live re-sequencing, retries, and back-pressure handling during spikes
Automated clustering and routing accelerated dispatch readiness and stabilized departure cutoffs across zones
Load balancing improved vehicle utilization, reduced empty miles, and lowered cost per order
Dynamic re-sequencing against traffic and service windows shortened ETAs and reduced reattempts
Better packing density and fewer extra runs decreased fuel spend and operational overhead
Event-driven replans maintained throughput during sales spikes and festive surges without schedule chaos
Real-time dashboards surfaced exceptions and capacity headroom, enabling faster interventions and continuous improvement
Live capacity, ETA, and exception views enabled faster interventions and steadier cutoffs
Higher vehicle fill and fewer empty miles lowered runs, fuel, and overtime
Real-time dashboards replaced manual planning, accelerating replans and zone balancing
Dynamic re-sequencing cut missed windows and shortened ETAs
Standardized workflows and audit-ready logs aligned teams and reduced variance
API-first design added zones and modes without proportional headcount
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You’re one step away from building great software. This case study will help you learn more about how Simform helps successful companies extend their tech teams.
