Agriculture & chemicals · Ocean freight logistics
Ocean Freight Capacity Forecasting: A Proof of Concept
Can planning data support a dependable ocean freight tender? PROVADIT tested the approach with real data, documented nine gap areas and assessed three architecture options for an international company’s agricultural and chemical business.
Explore the case- Industry
- Agriculture & chemicals · Ocean freight logistics
- Our role
- Proof of concept, validation, gap analysis, requirements and architecture design
- Focus & technology
- SAP Business Warehouse · Data Lakehouse · Capacity Planning
- Scope
- Proof of concept with real planning and shipment data
Client anonymized.
The starting point
Can we trust the capacity forecast we put in front of shipping carriers?
After the pandemic disrupted global supply chains, an international company launched a resilience program for its agricultural and chemical business. Regions manually forecast the pallets, trucks and containers needed for the following year, frequently using historical shipments. Global Procurement then merged and reconciled the regional files for the annual carrier tender. The process took time and carried regional differences in data quality into the global forecast.
If forecast demand is too low, carriers can release capacity early and the group must buy the shortfall on the more expensive spot market. If it is too high, the group commits to unused capacity. Procurement needed a consistent, auditable basis for the numbers, supported by forward-looking planning data.
Our contribution
PROVADIT’s contribution
PROVADIT connected logistics planning, procurement and IT to test an existing SAP Business Warehouse transportation model with actual planning data and historical shipment records. Comparing the output with the most recent carrier tender file made both the potential and the limits measurable. The findings became a full user requirement specification and a conditional architecture recommendation.
Validate with real data
Run a focused proof of concept with real data, reusing an SAP BW model already used elsewhere in the organization. Compare calculated volumes with procurement tender volumes and document where the approach produces credible results and where the data needs improvement.
Trace gaps to their causes
Identify nine distinct gap categories, document their root causes and define the required actions. These ranged from missing master data and route mappings to planning patterns that did not reflect the actual shipping lane. The analysis made corrections at the source possible instead of relying on adjustments in the report.
Specify the forecast and calculation rules
Author the full requirements for sources, refresh frequency, a forward-looking 12-month forecast, historical validation and a master-data quality dashboard. Define base-unit to pallet, truck and 20-foot (TEU) or 40-foot (FEU) container conversions, dynamic stacking factors, European and industrial pallet dimensions, and weight limits. Include historical fallback logic, transport-mode gap filling and inbound/outbound flow identification.
Make architecture choices comparable
Assess extending SAP BW, rebuilding in the target data lakehouse and transferring the existing model. Compare effort, risk and the future route of planning data. Recommend considering a short pause until the planning-platform migration timeline is confirmed, so the next investment follows a clear architectural direction.
Inside the work
Nine gap areas. Clear next steps.
The validation showed credible results on lanes with complete source data and route mappings. Other comparisons revealed material deviations, missing coverage and conversion failures. PROVADIT documented the causes and the corrections needed to improve reliability.
Master-data completeness
Missing or inconsistent origin and destination regions, ports and conversion factors caused gaps for some materials and routes. Document the affected records and the corrections required at the source.
Mode of transport
Planning could assign road transport where sea freight was intended, distorting container volumes. Define identification and gap-filling logic for incorrect or missing assignments.
Location, region and port mapping
Sites needed to map to the port clusters used in procurement’s tender file. Missing or incorrect links left volumes absent or in the wrong cluster. Identify and resolve those mapping gaps.
Country-specific capacity norms
Pallet capacities per truck and container vary by country. Specify country-tailored fill rates and a defined fallback when the relevant local parameters are unavailable.
Triangulation cases
Some planning destinations did not reflect the actual shipping pattern. Dedicated identification and handling logic was specified so that the forecast could represent these flows consistently.
Pallet-conversion accuracy
Some conversion factors did not reconcile with physical pallet dimensions. Design a validation report to flag those materials and direct attention to the underlying master data.
Late product and country assignments
Late assignments could make logistics volumes visible only after the planning period had closed. Document the timing gap and the handling required for a complete forecast.
Non-integrated sites
Sites not fully connected to the planning platform left gaps in forecast coverage. Identify the affected sites and their volume impact so the integration work can be assessed.
Inbound logistics scope
Upstream procurement flows matter to the full ocean freight picture but were outside the initial PoC. Flag their inclusion for further assessment and distinguish inbound from outbound flows in the requirements.
Three paths, tied to the future data flow.
The recommendation depended on how planning data would reach the reporting environment. A short pause pending confirmation of the planning-platform migration timeline was therefore part of the decision framework.
Extend the existing SAP BW model
Lowest near-term effort. Recommended if planning data continues to be accessed through the existing interface. This path builds on the foundation already tested in the PoC.
Rebuild in the target data lakehouse
Aligned with the long-term architecture, with higher effort and a longer timeline. Recommended if planning data will be delivered directly to the lakehouse.
Transfer the existing model to the lakehouse
Assessed but not recommended in the scenario examined. This option involved the highest effort relative to its incremental benefit.
Deliverables
The work delivered. The decisions it supports.
The PoC provided a quantified basis for deciding whether and how to invest. It showed where the approach was credible, where it failed and which corrections were needed. Requirements and architecture options turned those findings into a clear set of next steps for the client’s leadership and delivery teams.
- A quantified PoC validation against real planning, shipment and procurement tender data.
- Nine documented gap categories, each linked to its root cause and required action.
- A full user requirement specification, including conversion and fallback rules, a 12-month forecast and data-quality reporting.
- Three assessed architecture options with conditional recommendations and an explicit migration dependency.
Engagement context
The mandate covered proof of concept, validation, gap analysis, requirements, solution and architecture design. Scheduled updates, forecasting reports and the data-quality dashboard belong to the specified target solution. Inbound logistics was outside the initial PoC scope and flagged for further assessment. The documented outcome is a validated decision basis and a design for the next steps.
What this illustrates
A useful PoC gives an investment decision a factual basis. Reuse what works, validate it against the business reality and identify exactly what needs to change. Clear requirements and documented calculation rules then give the client’s team a foundation for implementation and future ownership.
A related engagement
Inventory Optimization and Integrated Planning
Inventory steering and transport capacity planning use planning information for different decisions. Another case explores PROVADIT’s work on inventory rules and the planning-tool landscape.
Read case study: Inventory Optimization and Integrated PlanningRelated expertise
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