The hidden cost of exceptions in oil operations

Oil operations tend to be far more digitized than they appear. There are systems for orders, inventory, product allocation, documentation and invoicing. Even so, a large part of the daily work still depends on calls, messages, spreadsheets and the knowledge of teams that have spent years solving problems.

The reason is fairly simple: systems work best when everything happens as planned. The real operation, however, is full of exceptions.

A load can be delayed at a terminal, a customer can change its receiving window, a volume difference can appear, or documentation needed to close a delivery can be missing. Taken one by one, these are normal situations. The problem appears when a company handles dozens or hundreds of movements a day and each deviation needs manual attention.

The plan changes as soon as the day begins

In theory, the flow is clear: an order comes in, product is allocated, loading is scheduled at the terminal, the product is delivered and invoiced.

In practice, a delay during loading can change everything that follows. If loading finishes an hour late, the delivery may miss the customer’s receiving window. That can affect another delivery scheduled with the same product and force part of the day to be reorganized.

The system can show that a delay exists. What it generally cannot do on its own is determine how the operation should be reorganized.

That is where the manual work begins.

The operations team reviews pending deliveries, checks constraints, talks to the terminal, notifies the customer and decides what to change. It is not necessarily a complex decision, but it requires context that is usually spread across several sources.

Much of the knowledge never reaches the system

Experienced teams know details that rarely appear in a database.

They know which customers need advance notice, which terminals tend to cause delays at certain hours, which facility has access restrictions and which delivery can really be rescheduled without creating a commercial problem.

That knowledge keeps the operation running, but it also creates a significant dependency.

When a key person is unavailable, the orders still exist and the systems keep working. What temporarily disappears is part of the judgment needed to interpret what to do when something deviates.

That is why learning the software can take weeks, while really understanding an operation can take years.

Every exception starts a small investigation

When a delivery fails, the problem does not end with identifying that something went wrong.

The team has to understand what happened, confirm the product status, talk to the terminal, decide whether to reschedule the delivery, notify the customer and update the relevant systems.

If it also affects the rest of the day, other allocations have to be reviewed.

The same happens with volume differences, missing documents, price changes, access restrictions or problems at terminals.

Most of the cost lies in the work needed to rebuild the situation before a decision can be made.

And much of that process still happens by phone.

A call can be the fastest way to confirm a fact. The problem appears when that conversation holds information that then has to be carried by hand to other areas and systems.

The team solves the issue and moves on with the day. Hours later, another area needs to understand why a delivery was changed and the investigation starts over.

The delivery can finish before the paperwork does

Once the product is delivered, what was planned still has to be reconciled with what actually happened.

Volumes, documentation, costs, prices and any change made during execution have to be validated. A small difference that seemed settled during the day can resurface later, when the finance team tries to process an invoice.

By then, time has passed. The information is spread across systems, documents and conversations, and the context has to be rebuilt once again.

This kind of work is especially hard to see because it does not necessarily show up as a major incident. It is a series of small interruptions spread across the whole day.

Together, though, they consume a considerable number of hours.

More information does not always mean less work

Today a company can know the status of an order, which tank supplies it, what documentation is attached to it and what its scheduled time was.

That does not necessarily mean it can resolve an exception faster.

If understanding a situation requires opening three systems, reading a message, finding a document and finally calling someone, the information exists, but a layer that connects it all is still missing.

This is one of the main challenges of automation in oil operations.

Generating more data or adding another dashboard contributes little. The value lies in reducing the work between detecting a problem and resolving it.

Automating exception management

Normal processes are relatively easy to automate because they are predictable. Exceptions are harder because they require context.

But a large part of the work around them can be automated.

If a delivery is delayed, for example, a system can gather information about the order, identify which other operations may be affected, contact the owner to confirm the situation and record the answer before involving the operations team.

Human intervention is still necessary when there is a commercial decision, a significant risk or a situation that genuinely requires judgment.

What should shrink is all the work that comes before.

nudu works in that space: it investigates each exception, resolves the routine tasks around it and escalates only the cases that need a human decision. The sector detail is on the oil and gas page.

Oil operations will always have surprises. The goal is for each one to be recorded with its context, so that none of them forces an investigation to start from scratch.