Inside P&G’s inventory playbook: how Procter & Gamble optimizes forecasting for supply chain precision

Forecasting isn’t what it used to be. Today, supply chains face real-time demand swings, retail penalties for stockouts and rising expectations for speed and accuracy.

And yet, Procter & Gamble, managing one of the largest consumer goods portfolios in the world, continues to deliver with remarkable precision.

How? 

By turning forecasting from a static function into an adaptive, data-powered rhythm across systems and teams. 

This blog unpacks the architecture behind P&G’s demand sensing and inventory strategy and what supply chain leaders can borrow from it.

The challenge: how to match inventory to real demand at global scale

Legacy forecasting models rely on slow-moving, historical data. But in today’s market:

• Consumer preferences shift weekly
• Promotions can trigger demand spikes in hours
• External shocks (weather, political events, social trends) require instant re-planning

For a company like P&G that manages thousands of SKUs across 180+ countries, forecasting must be both fast and accurate. Anything less means excess inventory, missed revenue or eroded customer trust.

How P&G gets it right: real-time sensing + cross-system orchestration

One of the biggest gaps in most demand forecasting discussions is the role of data latency, how fast demand signals are collected, shared and actioned across functions.

P&G addresses this with a multi-layered demand sensing model that includes:

1. Retail data in near real-time 

P&G pulls shelf-level POS data directly from retail partners to monitor actual consumption, not just shipments. This allows demand signals to update hourly, not just weekly.

2. AI forecasting with external context

Their models ingest more than internal sales. P&G’s forecasting engines adapt to variables like:

• Weather and natural disasters
• Marketing and promo calendars
• Social media trends and competitor activity

This turns static forecasts into dynamic, self-correcting systems.

3. Integrated inventory planning across teams

Supply chain, sales and manufacturing teams work from a unified visibility layer. This allows rapid reallocation of inventory when regional spikes occur, within hours and not days.

It’s more than just "better data". It’s better co-ordination.

The result: agility without overstocking

The outcome for P&G’s system isn’t just predictive accuracy, it’s real-time adaptability that leads to:

• Reducing excess inventory across distribution centers
• Improving working capital by matching supply to actual demand
• Increasing forecast accuracy, especially during promotional and seasonal spikes

According to recent earnings calls, P&G’s investment in predictive analytics has contributed to a 2 – 4% improvement in forecast accuracy year over year, even during macro-economic uncertainty.

What supply chain leaders can learn from Proctor & Gamble

Many companies invest in AI, but still miss the mark because:

• Their retail data is delayed
• Forecasting lives in a silo
• Execution systems can’t act fast enough

Here’s what P&G gets right and how others can apply it:

1. Data is only useful if it’s in real-time:

Daily buying habits require daily signals. Weekly cycles are already outdated. Invest in retail data integration that reduces latency. If you’re forecasting weekly but consumers are buying daily, you’re already behind.

2. Lean into connected systems, not just smart ones

It’s not enough to have AI. Your OMS, WMS, and TMS need to integrate forecasting outputs directly into fulfillment workflows. Fragmented tech stacks = fragmented forecasts.

3. Forecasting is not planning:

Forecasting is not planning: The best forecast is useless without the infrastructure to act. Even with perfect predictions, without a flexible OMS to act on those signals, your response will lag. P&G’s orchestration model proves that modular execution systems, from micro-fulfillment to dynamic sourcing are the bridge between sensing and action.

Modular execution systems, like micro-fulfillment, automated sourcing and dynamic routing close the loop between prediction and response. 

Demand forecasting is no longer a back-office function

P&G’s success isn’t just about better algorithms. It’s about aligning sensing, planning and execution into a single operating rhythm.

If you’re forecasting without visibility or sensing demand without acting on it, the gap is already costing you. As supply chains face increasing disruption, it won’t be the biggest players that win. It will be the ones that move fastest.

image copyright: https://internetretailing.net/company-spotlight-procter-gamble/