Logistics planning and delivery-risk simulation for an online store

For an online retail company, we built a logistics planning model that assessed in advance whether an order could pass every fulfillment stage on time: from website checkout and stock check to warehouse picking, shipment, route, and delivery to a customer or partner.

Overview

An order execution model from website to delivery

The system showed more than current order status: it let teams simulate operational scenarios in advance and understand their effect on timing, load, and service quality.

When the warehouse was overloaded, the model showed which orders would miss cut-off time, where queues would appear, how many orders would move to the next day, and which resources were needed to preserve SLA.

cut-off time SLA Risk orders Warehouse load

How the business used the model

The model served as a decision-support tool: the business could test whether the process would withstand order growth, promotions, stock shortages, transport delays, or route changes.

The system shows risky orders, the reason for risk, and the process bottleneck.
Teams see possible actions: move orders, change priorities, add a shift, redistribute load, or adjust a route.
Planners can detect orders that need manual intervention before SLA is missed.

Scenarios simulated in the model

The model connected demand, warehouse operations, stock availability, transport, and customer commitments in one scenario loop.

Order growth after a promotion or new product launch.
Warehouse overload and queue formation during picking.
Picking delays before cut-off time and shipment rollover to the next day.
Partial stockouts and their effect on picking, shipment, and SLA.
Transport delay, route change, or missed delivery window.
Priority processing of urgent or partner orders and its effect on other orders.
Combined stress scenario: order peak, stock shortage, warehouse overload, and delivery delay at the same time.

Result

Result for operational management

The team sees delivery-failure risk in advance and understands which resource or action can preserve SLA.

Decisions about shifts, priorities, routes, and warehouse load are tested before the issue becomes a customer incident.

The model helps assess process resilience during peaks and combined stress situations.

Ready to test a similar scenario?

Describe the process, constraints, and decision you need to validate. We will show how it can be simulated before implementation.

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