Refinery Shipment Planning: Complete Guide

       Refinery shipment planning is the process of coordinating production scheduling, storage, and logistics across petroleum refining operations to ensure petroleum products are delivered according to production targets and contractual commitments. It synchronizes refinery operations, tank farms, and transportation resources so that shipment plans can be executed safely and efficiently. Shipment planning begins long before a product leaves the refinery. Every shipment depends on upstream production, available tank capacity, and transportation infrastructure. For this reason, effective shipment planning requires the integration of production planning, production scheduling, inventory management, refinery scheduling, and dynamic simulation.

       Shipment planning and shipment execution are closely related but fundamentally different. Shipment planning defines what should be delivered and when. Shipment targets are typically generated by production (optimization) planning models based on customer demand, contractual obligations, and business objectives.
       Shipment execution determines how those targets can be achieved under real operating conditions while supporting refinery operations, production scheduling, and logistics optimization. It must account for production rates, equipment capacities, storage availability, loading facilities, pipeline constraints, and transportation schedules. In refinery digital twins, shipment execution requires a dedicated simulation object capable of transforming production targets into controllable material flow transfers.

       Although shipment plans are generated by optimization models, they represent only business targets. Before implementation, every plan must be validated against production capacity, storage availability, loading infrastructure, and transportation constraints. Dynamic simulation performs this validation by reproducing refinery operations over time.
       The most common application of shipment planning is the dispatch of finished products such as gasoline, diesel, jet fuel, and LPG through pipelines, truck loading racks, rail terminals, or marine facilities. The same principles also apply to planned internal transfers. For example, stabilized condensate may be delivered to the crude blending system according to a monthly production plan. Although the material never leaves the refinery, the transfer is still planned, scheduled, and executed in the same way as an external shipment.
       For example, an optimization model may recommend shipping 100,000 tonnes of diesel during the month because it maximizes refinery profit. However, this result is often obtained under simplified and approximate assumptions, where many operational details are intentionally omitted to keep the optimization problem computationally tractable. A digital twin then verifies whether the refinery can actually produce, store, and dispatch that volume within its real operational constraints while optimizing refinery operations and the downstream supply chain.

       Refineries typically execute shipment plans using one of two planning horizons: monthly or daily. Both approaches aim to meet shipment commitments but differ in the level of operational flexibility. The choice depends on refinery operating practices, planning systems, and customer requirements. Some refineries receive a detailed daily shipment schedule, while others work toward monthly shipment targets that are continuously reviewed and updated.

       Monthly planning defines the total quantity of each product to be shipped during the reporting period rather than fixed daily shipment volumes. For example: diesel: 120,000 tonnes, gasoline: 85,000 tonnes, jet fuel: 42,000 tonnes. Then determines how these volumes are distributed throughout the month while accounting for production rates, tank inventories, equipment availability, and transportation constraints. This approach provides the flexibility needed in continuous refinery operations, where production and logistics rarely follow a perfectly constant schedule. It also simplifies production scheduling because refinery process units can operate continuously while shipment schedules remain flexible.
       Daily planning specifies the required shipment volume for each day.
Date - Diesel Shipment
July 1 - 4,200 t
July 2 - 3,900 t
July 2 - 4,500 t
...
       It is commonly used when shipment dates are fixed by customer commitments or transportation schedules, such as pipeline nominations, marine terminal operations, rail logistics, or truck dispatching. Compared with monthly planning, daily planning offers greater scheduling precision but less operational flexibility.

       A monthly shipment plan is rarely executed without modification. During the planning period, actual performance is continuously compared with planned targets. When significant deviations occur, planners generate an updated shipment plan based on current production, inventory levels, equipment availability, customer demand, and logistics constraints. This continuous replanning is a fundamental part of modern refinery scheduling systems. The revised plan replaces the remaining portion of the original schedule, allowing shipment commitments to remain realistic as operating conditions change. Unfinished monthly shipment volumes are not automatically carried forward. Instead, any shortfall is addressed during the next planning cycle through re-optimization rather than by rolling over the previous plan. A refinery digital twin supports both approaches by validating that shipment plans remain executable under actual production, storage, and logistics constraints.

       While planning specifies shipment targets, execution coordinates production units, tank farms, loading facilities, and transportation systems to ensure those targets can be met safely and efficiently. Shipment execution spans the entire production and logistics chain:

Production → Tank Farm → Loading Infrastructure → Shipment

       It represents one of the key workflows in petroleum refining operations. Each stage depends on the previous one. Production interruptions, insufficient inventory, or limited loading capacity can immediately affect shipment performance. For this reason, shipment execution is not a single logistics activity but the coordinated operation of the entire refinery. Shipment execution begins long before the first truck is loaded or the first pipeline starts pumping.

       A refinery digital twin connects production planning with physical operations. Optimization determines the desired shipment targets, while dynamic simulation validates that they can be executed under actual production, storage, and logistics constraints.
In simple terms:

Planning defines the destination. Execution determines the route.

       Without effective shipment execution, even the best shipment plan remains only a target rather than an achievable operating strategy.

       A Shipment Node does not create shipment plans or optimize production schedules. Instead, it executes predefined shipment targets and continuously tracks their completion during simulation. In a refinery digital twin, the Shipment Node represents the operational boundary where shipment plans become measurable material transfers. It serves as the operational execution layer within the refinery digital twin and supports engineering decision-making through continuous shipment monitoring.
       Shipment plans are typically generated by production planning, optimization models, ERP, or other business systems. The Shipment Node therefore acts as the execution interface between planning systems and the physical refinery. The digital twin assumes these targets already exist and focuses on executing them under real operating conditions.
The Shipment Node is responsible for:

• controlling product transfer;
• measuring transferred volume;
• comparing actual shipments with planned targets;
• determining when a shipment plan has been completed;
• collecting operational statistics.

       Shipment execution may optionally allow plan overfulfillment. When enabled, product transfer can continue after the planned quantity has been reached. This is useful for disposal or relief streams, where excess production must continue flowing. When disabled, the Shipment Node stops the transfer as soon as the shipment target is achieved.

       Each Shipment Node manages the shipment plan for a single product stream. Different destinations are typically modeled as separate shipment plans, even if they handle the same physical product, allowing each stream to be monitored independently.

       Throughout execution, the Shipment Node continuously records:

• planned shipment volume;
• transferred volume;
• remaining shipment volume;
• shipment status;
• operational statistics.

       This information provides the feedback required for production planning, inventory management, and operational analysis, ensuring that shipment commitments can be monitored throughout the planning period.

       To support different refinery planning practices, the Shipment Node provides three shipment execution algorithms. Monthly execution treats the shipment target as a cumulative volume for the entire month and transfers product whenever operating conditions permit until the monthly target is reached. Daily execution follows predefined daily shipment targets, where each day's plan is executed independently according to the production schedule. Evenly Distributed Monthly execution also starts from a monthly target but automatically spreads shipments uniformly across the reporting period, maintaining a nearly constant shipment rate throughout the month while adapting to operational constraints.
       The concepts presented in this section describe the general principles of shipment execution. The complete implementation of the Shipment Node, including its configuration parameters, execution algorithms, API, and practical modeling examples, is available in the dedicated Shipment Node solution page.

       A refinery cannot execute a shipment plan unless sufficient product is available when shipments begin. Tank farms therefore serve not only as storage facilities but also as dynamic inventory buffers that synchronize continuous production with scheduled product deliveries. While production units operate continuously, shipments follow customer demand, transportation schedules, and contractual commitments. Tank farms bridge this gap by accumulating inventory in advance and supplying products when shipments are required. Effective tank farm management is therefore essential for reliable shipment execution.

       A fundamental principle of refinery shipment planning is that future shipment demand determines today's inventory requirements. This represents one of the key principles of inventory optimization in refinery logistics.
For example, if the refinery must ship 120,000 tonnes of diesel during the month, the tank farm immediately calculates (A-B-C-D principe):

• the required inventory;
• the additional product that must be received;
• whether upstream production can satisfy future demand.

       Instead of reacting to inventory shortages, the tank farm begins building the required stock from the start of the planning period.

       In a refinery digital twin, shipment planning and inventory management form a single control process.
Shipment Plan → Required Inventory → Required Tank Farm Receipts → Required Production
       Shipment demand creates inventory requirements, which in turn drive upstream production. This ensures that products are available when shipment commitments must be fulfilled. In other words, shipment plans define inventory targets, and inventory targets determine production requirements. By preparing inventory before shipments occur, they reduce the risk of shipment delays, stabilize production, and keep production, storage, and logistics synchronized throughout the planning horizon.

       Shipment planning defines what should be shipped. The physical shipment infrastructure including pipeline scheduling, truck loading operations, rail logistics, and marine logistics determines how products leave the refinery.Petroleum logistics infrastructure determines how quickly shipment plans can be executed.
       Depending on the refinery configuration, finished products may be dispatched through:

• pipeline systems;
• rail loading facilities;
• truck loading racks;
• marine terminals.

       Each transportation method has different capacities, operating schedules, and logistical constraints. These factors influence how quickly shipment plans can be executed, even when sufficient product is available in the tank farm. A refinery digital twin models these physical constraints to verify that shipment plans remain achievable under real operating conditions.

       Production plans are typically created using optimization models that assume ideal operating conditions. In practice, however, refinery operations are constrained by equipment capacities, storage limits, transportation availability, and continuously changing process conditions. As a result, an optimal shipment plan is not always an executable one.
       A refinery digital twin bridges this gap by simulating the complete production and logistics system over time. As a refinery decision support system, it evaluates production scheduling, logistics planning, and inventory optimization simultaneously. Instead of evaluating shipment volumes in isolation, it verifies whether products can be produced, stored, and delivered according to the planned schedule.

       Dynamic simulation helps answer critical operational questions:

• Can the shipment plan be completed on time?
• Will tank farm inventories remain within operating limits?
• Which process units become production bottlenecks?
• How will equipment outages affect product deliveries?
• What changes are required to maintain contractual shipments?

By validating shipment plans before they are implemented, a digital twin reduces operational risk and gives planners confidence that production targets can be achieved under real refinery conditions.

       In addition to executing shipment plans, the Shipment Node continuously collects operational statistics that can be used for real-time monitoring, performance analysis, and historical reporting. These statistics also support operational dashboards, KPI reporting, performance analytics, and historical trend analysis.
       The built-in statistics subsystem provides information such as:

• incoming product flow rates;
• planned and actual shipment volumes;
• shipment plan completion percentage;
• remaining volume to be shipped;
• shipment execution status;
• selected shipment algorithm;
• historical shipment records for KPI reporting and long-term performance analysis.

These statistics allow engineers to compare planned and actual shipments, evaluate shipment performance, generate operational reports, and analyze historical execution trends across different planning periods.

       Shipment planning is required wherever refinery products are transferred according to production targets or contractual commitments. Typical applications include:

• Finished product dispatch to customers through pipelines, truck loading racks, rail terminals, or marine terminals.
• Intermediate product transfers between tank farms and processing units.
• Blending operations, where components must be delivered according to production schedules.
• Feedstock distribution, ensuring crude oil or intermediate streams are supplied to the correct processing units.
• Export terminals, where shipments must comply with vessel schedules and loading constraints.

       Although these applications differ operationally, they share the same objective: ensuring that the right quantity of product reaches the right destination at the right time while respecting refinery operating constraints. A refinery digital twin provides a common framework for validating all of these shipment scenarios before they are executed in the real plant.

       In the Petroleum Refining Library (PRL), shipment execution is implemented through the Shipment Node simulation object. Each Shipment Node manages the shipment plan for a single product stream by executing predefined monthly or daily shipment targets and continuously tracking their completion. The node supports multiple execution algorithms, including uniform shipment distribution, maximum-throughput execution, daily scheduling, working-hour restrictions, and optional plan overfulfillment. During simulation, it automatically records planned and actual shipment volumes, remaining quantities, execution status, and operational KPIs. Because the Shipment Node represents a logical shipment control point rather than a physical loading facility, the same component can model finished product dispatch, internal transfers, blending operations, and any other planned material movement within the refinery.

       Refinery shipment planning is much more than scheduling product deliveries. It connects production, storage, and logistics into a single operational workflow, ensuring that shipment commitments can be fulfilled safely and efficiently. While optimization systems determine shipment targets, they cannot guarantee that those targets are achievable under real operating conditions. Equipment capacities, tank inventories, transportation constraints, and unexpected operational events all influence shipment execution.
       A refinery digital twin closes this gap by validating shipment plans before they are implemented. By simulating the entire production and logistics chain, it helps engineers identify bottlenecks, improve production scheduling, optimize refinery operations, strengthen the refinery supply chain, and support better operational decisions.
       Whether the objective is finished product dispatch, internal product transfers, or production planning, a refinery digital twin ensures that every shipment plan is both operationally feasible and executable under real refinery conditions.

1. What is refinery shipment planning?
Refinery shipment planning is the process of coordinating production, storage, and logistics to ensure petroleum products are delivered according to production targets and contractual commitments. It synchronizes production units, tank farms, loading infrastructure, and transportation resources into a single executable operating strategy.

2. What is the difference between shipment planning and shipment execution?
Shipment planning defines what products should be delivered and when. Shipment execution determines how those deliveries are performed under actual refinery operating conditions while considering production capacity, inventory, storage availability, and transportation constraints.

3. What is a Shipment Node?
A Shipment Node is a simulation component that executes predefined shipment plans and continuously tracks their progress during simulation. It controls material transfer, measures shipped volume, compares actual performance with planned targets, and records operational statistics for each shipment stream.

4. Can a Shipment Node execute both monthly and daily shipment plans?
Yes. A Shipment Node supports both monthly and daily planning horizons. Monthly plans execute cumulative shipment targets over the reporting period, while daily plans execute independent shipment targets for each calendar day.

5. Does a Shipment Node create shipment plans?
No. Shipment plans are normally generated by production planning systems, optimization models, ERP systems, or external planning software. The Shipment Node only executes predefined plans inside the refinery digital twin.

6. Can shipment plans be overfulfilled?
Yes. Depending on the selected execution mode, a Shipment Node can optionally continue transferring product after the planned shipment quantity has been reached. This is useful for disposal streams or operational scenarios where uninterrupted flow is required.

7. How many products can one Shipment Node manage?
One Shipment Node manages the shipment plan for a single product stream. Multiple products or multiple shipment destinations are typically modeled using separate Shipment Nodes.

8. How do tank farms support shipment planning?
Tank farms accumulate inventory before shipments begin and supply products when shipment demand occurs. They synchronize continuous refinery production with variable transportation schedules and customer delivery requirements.

9. Why is dynamic simulation important for shipment planning?
Optimization can determine the optimal shipment targets, but only dynamic simulation can verify that those targets remain executable under real operating conditions. A refinery digital twin evaluates production constraints, storage capacity, loading facilities, transportation availability, and operational disturbances before the shipment plan is implemented.

10. What shipment execution algorithms are supported?
Depending on the refinery configuration, Shipment Nodes can execute shipment plans using different strategies, including:

• uniform shipment distribution;
• maximum throughput execution;
• daily shipment scheduling;
• working-hour restrictions;
• configurable operational safety margins.

11. What operational statistics does a Shipment Node collect?
During simulation, a Shipment Node continuously records planned shipment volume, transferred volume, remaining shipment quantity, shipment completion status, and other execution statistics. These KPIs allow engineers to evaluate shipment performance and compare actual execution against the production plan.

12. Can Shipment Nodes be used for internal refinery transfers?
Yes. Shipment Nodes are not limited to finished product dispatch. They can also execute and monitor planned transfers between process units, tank farms, blending systems, intermediate storage facilities, and other refinery operations whenever material movement follows a predefined production or logistics plan.