Optimising container allocation as demand shifts and carrier capacity changes

A large retailer moves tens of thousands of shipping containers every year. Getting that volume onto boats, on time and at the right cost, is one of the foundations of its business. The retailer is made up of several subsidiaries, each with its own orders, and all of them ship under carrier contracts negotiated once a year. There are limitations that they need to adhere to. For example, each contract fixes a price and a volume, then divides that volume into weekly limits, some tied to a single port, others to a region.

On paper, fitting demand into those limits looks like a simple allocation exercise. In practice it is one of the hardest planning problems in the business, because nothing stays still. Volume that was meant to flow through one port surges somewhere else and runs into its weekly limit. A carrier goes back on its commitment at short notice, leaving the planners scrambling to manage the shortfall.

Before, planners managed all of this by hand. Each month they gathered the orders due to ship, grouped them by route, added a rough safety margin, and shifted volume around until everything fit within each carrier's limits. A single plan could take hours, sometimes the better part of a week. It was slow, it was prone to error, and it was rarely done once. The plan had to be rebuilt from scratch every time conditions changed. And when things changed too late to plan around, teams paid premium rates for whatever space they could find.

The Spatialedge solution started from the single problem beneath all of this: if conditions never stop changing, the plan has to be something a planner can quickly rebuild with the new conditions, and not require hours or days of manual work.

With the Spatialedge solution, a planner now logs in and sees their shipping volume laid out across the months ahead, with clear indications on whether it will fit within their carrier contracts. The tool fits demand across ports and carriers within every contractual limit, then shows exactly where the business is exposed. When conditions change, for example if a carrier goes back on its commitment, the planner then marks that carrier unavailable, reruns, and gets a fresh plan that covers the lost capacity. What used to mean hours of rework is now a short wait for an answer.

One element we had to take into account was consistency. Each time conditions change, the most efficient plan might look completely different from the last, and carriers cannot absorb that kind of swing week to week without losing trust. So the tool keeps the plan steady. When it rebuilds, it stays as close as possible to the previous version, changing only what the new conditions require. The planner gets a plan that is both current and stable, with the option to ask for a clean break when that is the better choice.

The tool also helps planners lower cost directly. It can recommend shifting orders earlier or later to smooth out demand, or combining two suppliers' shipments into a single container so space is used more fully and fewer containers are needed. For each recommendation, it estimates the saving, so the planner can weigh the value before deciding.

The planner has gone from rebuilding fragile plans by hand to adjusting a plan that stays current as conditions change. The constant guesswork, the rough margins and the last-minute scrambles have given way to decisions made with confidence. The judgement stays with the planner, while the heavy lifting is handled by the tool.