By Phillip Sewell, CEO, Predyktable
Warehouse operations have spent the last two decades getting better at execution.
Across the sector, businesses have invested heavily in warehouse management systems, workforce planning tools, automation, robotics and process improvement. These technologies have helped operators move goods faster, schedule labour more effectively and create more efficient warehouse environments.
But the operating environment has changed.
Demand is now more volatile, driven by ecommerce, promotions, tighter delivery windows and shorter planning cycles. Labour has become more constrained, more expensive and harder to scale. Automation is increasing, but it does not always make operations easier to flex. In some cases, it makes flexibility more complex, because the operation has to work around fixed flow, fixed capacity and new downstream constraints.
The result is a warehouse environment that is becoming harder to predict, harder to flex and harder to control.
This is not simply an execution problem. It is not even just a forecasting problem.
It is a decision problem.
Many warehouse operations are now highly capable at executing work once a plan is agreed. The bigger challenge is whether the right decision was made before execution began.
The gap between planning and execution
Most warehouse systems were not designed to make operational trade-offs. They were designed to execute, schedule, manage or record.
- Warehouse Management Systems manage tasks.
- Workforce systems schedule labour.
- Automation executes physical flow.
- ERP systems record outcomes.
All of these systems are essential. But they do not usually answer the question that determines much of the cost and service outcome before the week has even started:
What is the right labour commitment for the expected workload, service requirement and level of risk?
In most warehouses, labour remains the main lever for absorbing uncertainty. It is how operations respond to demand changes, absence, productivity variation, bottlenecks and service pressure. It is also one of the largest controllable operational costs.
That creates a difficult balancing act.
Under-commit labour and the operation risks missed service, backlog, overtime and reactive agency spend. Over-commit labour and unnecessary cost is locked in before the work has even arrived. In either case, the financial and operational impact can be significant.
This is where the gap sits. The decision that drives cost, service and resilience is often still being made outside the core system landscape, in spreadsheets, meetings and local judgement calls.
Those decisions may be made by highly experienced teams. But they are often made under pressure, without a consistent way to compare options or understand the trade-offs before commitment.
Once the decision is made, every downstream system executes against it.
Forecasting is important, but it is not the full answer
Better forecasting is often seen as the answer to warehouse volatility. It is certainly part of the answer. More accurate demand signals can help operators prepare earlier and reduce surprise.
But forecasting alone does not determine the right action.
A forecast may show the expected workload, but it does not automatically decide how much labour should be committed, where that labour should be placed, what level of risk the business should carry, or whether a lower-cost option is worth the service exposure.
The same is true of automation.
Automation can increase throughput and reduce manual handling, but it does not remove the need for operational decision-making. In fact, as warehouses become more automated, the quality of the decision behind execution becomes more important, not less.
AI can predict what might happen. Automation can execute work more efficiently.
But prediction does not decide. Execution does not correct a weak decision. It amplifies it.
The next phase of warehouse improvement will therefore depend not only on better forecasting or faster execution, but on better decisions before execution begins.
What Decision Intelligence means in practice
Decision Intelligence means using data, AI and operational context to help teams evaluate choices before resources are committed.
In warehouse operations, that means understanding the trade-offs between cost, service and risk before the plan is locked. It helps teams move from asking, “what does the plan say?” to asking, “what is the best decision available, and what are the consequences of that decision?”
Consider a warehouse team planning for a promotional peak next week.
The forecast suggests higher volume, but there is uncertainty around order mix, absence, productivity and automation capacity. The team can commit more labour to protect service, but that increases cost. It can commit less labour to protect margin, but that increases the risk of backlog, overtime or agency reliance later in the week.
Neither option is automatically right or wrong.
The point is that the trade-off needs to be visible before the decision is made.
One option may carry lower labour cost but higher service risk. Another may require a higher labour commitment but provide stronger SLA protection. A third may offer a more balanced position between cost and resilience.
The value is not simply in choosing the cheapest or safest option. The value is in making the trade-off explicit.
What workload is expected? What labour is required? What level of risk is acceptable? What will the decision cost? What service exposure does it create? What assumptions does it depend on?
That fundamentally changes how planning decisions are made.
It is not just what the operation is doing. It is why the operation is doing it.
Supporting judgement, not replacing it
Warehouse operations have always relied on experienced managers. In many businesses, local knowledge remains one of the most valuable assets in the operation.
Experienced teams understand the realities of a site: which shifts perform well, where constraints usually appear, how reliable certain assumptions are and where risk tends to build.
That knowledge should not be replaced.
But the environment around those teams has become more complex. Planning cycles are shorter. Demand changes faster. Labour is harder to secure. Automation creates new dependencies. Service expectations remain high. Cost pressure is constant.
What used to be manageable through experience alone now requires a more structured way to evaluate choices.
A typical planning decision may sound simple: how many people should be committed for next week’s workload?
In practice, that decision has to account for volume, productivity, absence, skills, shift structure, automation capacity, inbound flow, outbound deadlines, service priorities and cost.
Getting that call wrong has consequences.
A single decision, made days or weeks in advance, can swing cost and service outcomes significantly. And once that decision is committed, the costs are largely sunk and very difficult to unwind.
Decision Intelligence does not remove operational judgement. It strengthens it.
It gives experienced teams a clearer way to evaluate choices, compare scenarios, explain decisions and act before cost and service risk are locked into the operation.
From planning to learning
Many warehouses measure outcomes well. They know what happened. They can see productivity, cost, service, absence, overtime and throughput after the event.
The harder question is why it happened.
Was the original labour commitment wrong? Did productivity assumptions fail? Did demand shift? Did automation create a downstream bottleneck? Was the risk visible before the decision was made? Were better options available?
In many operations, that decision context is lost. The outcome is recorded, but the reasoning behind the original decision is not.
Decision Intelligence changes that by recording not only the outcome, but the context behind the decision.
Over time, that creates a learning loop.
Teams can see where assumptions consistently break down, which shifts underperform, where plans drift and which decisions create avoidable cost or risk. AI becomes operationally useful because it is not just generating a forecast; it is helping the operation learn from previous decisions and improve future recommendations.
That is where this becomes more than planning.
It becomes a way to improve decision quality over time.
The network opportunity
For multi-site operators, the opportunity is even greater.
Individual warehouses often develop their own ways of planning, managing risk and committing labour. Some of that variation is necessary because sites are different. But some of it reflects inconsistent decision-making, different assumptions or limited visibility across the network.
When decisions become visible and comparable across sites, patterns start to emerge.
What works well in one site can inform decisions elsewhere. Persistent constraints can be identified. Planning assumptions can be tested. Leaders can see where cost is being locked in, where service risk is being carried and where the operation is consistently relying on reactive labour decisions.
This matters because warehouse performance is rarely just a site-level issue.
For retailers, 3PLs, ecommerce operators and distribution networks, decisions made in one part of the network can affect cost, capacity, service and flow elsewhere.
The real value comes from understanding operational behaviour across the network, not just within an individual site.
That creates the foundation for stronger operational control, where decisions are consistent, performance is understood and outcomes are no longer left to chance.
Labour is the starting point, not the whole story
Labour is often the logical place to start because it sits at the intersection of cost, flexibility and service. It is one of the biggest controllable costs in the warehouse and one of the main levers used to respond to change.
But the wider opportunity is not just labour optimisation. It is operational optimisation.
Warehouse decisions do not exist in isolation. Improving one area can create pressure somewhere else. Increasing automation in one process may improve efficiency locally, but it can also create downstream bottlenecks that reduce overall throughput. Reducing labour cost in one function may increase service exposure elsewhere.
The system only performs as well as its constraints.
That means operators need to understand how labour, workload, automation, service requirements and site constraints interact. The goal is not simply to optimise one metric. It is to understand the overall performance impact of each decision.
This is where Decision Intelligence becomes strategically important. It gives operators a way to evaluate decisions in context, rather than treating labour, automation and service as separate planning problems.
Questions warehouse leaders should be asking
For warehouse and logistics leaders, the shift from execution to decision quality starts with a different set of questions:
- Are labour decisions visible before they are committed?
- Are cost, service and risk trade-offs understood before execution begins?
- Are teams planning from one answer, or comparing options?
- Is the business learning from previous planning decisions, or simply repeating them?
- Can decision quality be compared across sites?
- Are execution systems working from the best available decision, or simply executing the decision they were given?
These questions matter because warehouse performance is no longer determined only by what happens during execution. It is increasingly shaped by the quality of the decisions made before execution begins.
The next frontier of warehouse performance
Warehouse performance is no longer defined by execution alone.
Execution still matters. Forecasting still matters. Automation still matters. But as operations become more complex, more automated and more constrained, the organisations that improve will be those that make better decisions earlier, understand trade-offs more clearly and learn from outcomes more consistently.
The next frontier of warehouse performance will be defined by the quality of the decisions that sit before execution.
In a more volatile, automated and labour-constrained environment, the operators that improve will be those that can see trade-offs earlier, commit resources more intelligently and learn from every decision they make.
Ultimately, execution can only ever be as good as the decision behind it.
That is why Decision Intelligence is becoming the next frontier of warehouse performance.
See Phillip’s full interview here.