INSIGHT | DIGITAL SUPPLY CHAIN & OPERATIONS
The business value of artificial intelligence becomes visible only when improvements hold up in day-to-day operations.
SAP describes new AI capabilities in Extended Warehouse Management, while DHL Supply Chain is bringing digital AI agents together with physical automation. For COOs and supply chain leaders, the more practical question is: What measurably improves service, productivity or working capital?
A working algorithm does not mean a working process
Many performance problems predate the introduction of new technology. Unreliable master data, outdated replenishment parameters and unclear decision rights create a gap between planning and execution. AI may detect deviations, support forecasts and suggest alternatives. It cannot establish a missing management routine on its own.
Consider inventory management: even a more accurate demand forecast will not release cash if minimum order quantities, lead times, safety stocks and production lot sizes remain uncoordinated. A company can report a successful forecast pilot while inventory continues to rise.
Three management questions before scaling
1. Which operational problem are we solving?
Start with a credible baseline. Material shortages, unstable delivery dates, excess inventory, unplanned downtime and manual planning effort call for different interventions. Without a clearly defined use case, the effect of an AI application is difficult to evaluate.
2. How will we prove the economic benefit?
Model accuracy is not a line item in the profit and loss statement. Relevant outcomes may include OTIF, throughput, days of inventory, capital employed and actual planning effort. Baseline, target, accountable owner and measurement period must be defined before the pilot. The calculation should also capture unintended costs, such as additional manual review.
3. Who owns the operational decision?
An AI recommendation still needs an accountable decision-maker. Who resolves conflicting priorities? When may a planning proposal be executed automatically? Who intervenes when actual performance deviates from the plan? Without clear governance, a technically sound solution can become another parallel process.
From pilot project to routine operation
A practical approach starts with a short process and data diagnosis. A limited use case is then tested under real operating conditions. Assessment covers both the quality of the recommendation and the effort required to execute, control and escalate it.
Scaling makes sense only when the improvement remains measurable across successive planning and execution cycles. Established S&OP / IBP and S&OE routines provide a useful framework: the plan is reconciled with capacity and financial consequences, while daily execution reviews whether the expected impact is actually materializing.
The management perspective: understand the constraints first
Before deploying AI in supply chain or operations, management should understand where performance is genuinely constrained. Are planning decisions binding? Do replenishment settings match the product portfolio? Is the production bottleneck known? And are there KPIs that translate technical progress into financial outcomes?
InterimCOO offers four structured, free management assessments to establish that baseline: SCM Maturity, S&OP / IBP, Inventory & Working Capital and Production & Operations. They help identify the dominant gaps and prioritize areas for deeper operational analysis.