Vinayak
Bhadani

Most demand planning in the GCC is still "last year plus a percentage" — which sounds like a method but is actually just institutional inertia dressed up as a forecast. What good actually looks like is a statistical baseline built on rolling sell-out data, layered with structured commercial intelligence — promotions, new listings, range deletions — and then reconciled against a financial plan before anyone commits to a purchase order. The GCC compounds this because you're dealing with Ramadan surges that can spike category demand by 40%, import lead times of 45–90 days from Asia, and a retail landscape where a single key account can represent 30% of your volume. The margin for error is far smaller here than in markets with domestic production and short replenishment cycles. The planners who thrive in this environment are the ones who treat forecasting as a decision-making discipline — not a spreadsheet exercise.

S&OP fails for one reason almost every time: it becomes a reporting meeting instead of a decision-making forum. Teams show up with slides, numbers get reviewed, nobody makes a call, and everyone leaves to run their own plans anyway. The process I designed at ANDS was built around the opposite principle — every meeting in the 4-week cycle has a defined output, a decision owner, and a constraint that must be resolved before the next stage. Demand review produces an unconstrained plan. Supply review produces a constrained one. Pre-S&OP reconciles the gap. Executive S&OP makes the trade-offs. When the process has that architecture — and when Finance is genuinely integrated, not just invited — it changes from a coordination exercise into the operating system of the business. The transformation took 18 months. The first three months were the hardest because you're asking commercial teams to give up their private forecasts and commit to a shared number. That requires trust, not just process design.

The CFO conversation is the one most supply chain professionals avoid — and that avoidance is exactly what keeps the function underfunded. The financial case is straightforward once you build it properly. A 5% improvement in forecast accuracy at a mid-size FMCG distributor in Dubai reduces emergency air freight by roughly AED 800K–1.2M annually. A 10% reduction in inventory days outstanding releases AED 3–5M in working capital depending on turnover. Eliminating one major stockout event in a high-velocity category saves the margin on hundreds of thousands of lost units. I model all of this explicitly — not as estimates but as ranges with assumptions you can stress-test. When you walk into a CFO's office with a scenario model that ties operational metrics to P&L and balance sheet impact, you stop being a cost centre and start being a strategic lever. That shift in perception changes what resources you get, which changes what outcomes you can deliver.

The UAE's geographic position is both its greatest supply chain advantage and its most underappreciated risk. You have world-class port infrastructure, a logistics hub connecting Asia, Africa, and Europe — and you are also within 33km of the Strait of Hormuz, adjacent to the Red Sea corridor that was disrupted by Houthi attacks in 2023–24 sending freight rates up 323%, and dependent on Black Sea wheat that was directly impacted by the Ukraine conflict. Resilience in this context is not about having a backup plan in a drawer. It is about three things: redundancy built into sourcing before the crisis hits — not after, visibility tools that give you signal of disruption before your shipment is already at risk, and pre-negotiated contingency contracts with alternative carriers and suppliers that can be activated in days, not weeks. The supply chains that held up through the Red Sea crisis were the ones that had already invested in multi-route flexibility. The ones that collapsed were the ones that had optimised entirely for cost.

AI meaningfully improves demand planning in three places: detecting non-linear seasonal patterns that statistical smoothing misses, processing external signals — weather, search trends, commodity prices — at a speed no analyst team can match, and identifying demand anomalies before they propagate into the supply plan. Where the hype outruns reality is in the assumption that better algorithms eliminate the need for human judgment. They don't. AI produces a better baseline — it does not replace the commercial intelligence that comes from a sales director knowing a key account is about to double their order, or a planner knowing that a competitor just went out of stock. The models I've built use Holt's Double Exponential Smoothing as the statistical engine, with a structured overlay process for commercial inputs. That combination — rigorous baseline plus structured human judgment — consistently outperforms either pure algorithmic or pure intuitive approaches. The future is not AI replacing planners. It is planners who understand AI outperforming those who don't.

Execution is necessary but it is not differentiating. Most competent supply chain professionals can run a planning cycle, manage a 3PL, and hit their OTIF targets in a stable environment. Transformation requires a different capability: the ability to diagnose why a system is underperforming, design a better one, bring cross-functional stakeholders into alignment around it, and then sustain that performance when conditions change. The work I did at ANDS was transformation — not because the numbers moved, but because the underlying system changed. We went from a planning function that reacted to orders to one that shaped them. From a team that reported on OTIF to one that was accountable for it. From a process where demand and supply were separate conversations to one where they were reconciled into a single plan every month. That kind of change does not happen because of a new tool or a new hire. It happens because someone decides to hold the system to a higher standard and has the technical and interpersonal capability to redesign it from the inside.