Machine learning where it measurably beats the baseline

Forecast enhancement that learns

Statistical forecasting works well for stable items and poorly for the long tail of erratic ones. Sofco's ML models learn from historical sales patterns and from user adjustments, selecting and tuning the best approach per SKU so the forecast improves where statistical methods struggle.

Crucially, the system tracks whether ML actually beats the statistical baseline, so adoption is based on evidence rather than belief. Where ML helps, it is used; where it does not, you stay with what works. The human stays in the loop on the decisions that matter.

Demand sensing

Traditional forecasts update on a planning cycle, but demand moves daily. Sofco's demand sensing picks up short-cycle signals, shipment, order and channel data, to adjust the near-term view before traditional methods would notice the change.

That short-horizon accuracy is what makes the supply response sharper, because the next few weeks are planned against the latest signal rather than a forecast produced weeks ago. Demand sensing closes the gap between plan and reality.

Anomaly and exception intelligence

Planners are buried in exceptions, most of which do not matter. Sofco uses ML to detect genuine anomalies and prioritise exceptions by impact, so attention goes to the few changes that actually affect service, cost or inventory rather than a wall of noise.

That prioritisation is what makes exception management sustainable as volume grows. A planner working on the right ten exceptions delivers more value than one drowning in a hundred.

Continuous learning and governance

Models that do not adapt go stale as the business changes. Sofco's models learn continuously as new data arrives, so recommendations stay relevant, with accuracy tracked over time so you can see whether the system is getting better or needs attention.

Governance keeps ML accountable: the models, their inputs and their performance are visible and explainable, so you can defend the forecast to finance and leadership. AI is a managed capability, not a black box.

AI you can trust and defend

AI in a business-critical planning process has to be explainable, not just accurate. Sofco surfaces the drivers behind each ML recommendation, so a planner can see why the system forecast what it did, test it against their own judgement and defend the number to the rest of the business.

That explainability is what makes ML adoptable. A black-box forecast that cannot be questioned will be overridden on principle, which removes the benefit; a forecast whose reasoning is visible earns trust and gets used, which is how ML actually reaches the bottom line.

It also keeps the human in the right place. Sofco's ML augments the planner's judgement on the high-volume, difficult decisions and leaves the strategic calls to the people who own them, so the technology extends the team rather than replacing the accountability.

ML that improves the things that matter

ML is only worth the investment if it improves the decisions the business cares about, forecast accuracy on the lines that drive revenue, exception handling on the issues that threaten service. Sofco focuses ML where it measurably moves those outcomes, not across the board for its own sake.

That focus is what keeps ML credible in a planning function. A model that demonstrably lifts accuracy on the top revenue SKUs earns its place; one that yields a marginal average gain across a long tail looks clever and adds little.

It also keeps the team's attention on the value. When ML targets the decisions that matter, planners engage with it, finance funds it, and the technology becomes a genuine part of how the business plans rather than a showcase no one uses.