Global Supply Chain Complexity in Consumer Goods: Webinar Takeaways With Wild Republic

Global supply chains in consumer goods are under pressure from tariffs, channel fragmentation, unstable demand, and longer lead times. We recently sat down with Adam Ford, Former Global Senior Supply Chain Manager at Wild Republic, and Sheetal Yadav, COO at Anamind, to discuss how these shifts change planning operations inside modern CPG companies. 

The conversation explored why traditional planning models fail under modern supply chain pressure and how Wild Republic adapted. Read on for practical insights from teams dealing with these problems in real operations.

A $5.5 trillion market getting harder to serve

The global CPG market hit $5.47 trillion in 2024 and is projected to reach $7.8 trillion by 2033. North America still holds the largest share, but the growth is coming from APAC: shorter trend cycles, faster-moving consumers, and demand patterns that historical data alone won't predict.

E-commerce is a minority of volume today but the fastest-growing channel, and it runs on fulfillment speed and availability expectations that most supply chains weren't designed for. The companies absorbing this growth without proportional stockouts, markdowns, or working capital blowouts are doing it using better planning infrastructure.

The end of stable supply chain planning

Supply chain teams still operate on an implicit assumption: disruption is the exception, stability is the default. That assumption has been wrong for six years running. Port shutdowns, pandemic logistics failures, raw material crunches, and broad tariff swings have arrived in sequence.

"It seems like over the last six to ten years, it's just been one thing after another: a continual run of once-in-a-lifetime events. It just shows you how important it is to have a rigid structure, but a flexible response."


Tariffs revealed a specific blind spot. Companies that thought they were dual-sourced discovered their two suppliers were in the same country, meaning a single country-of-origin tariff eliminated both options at once.  Geographic spread on paper, zero risk reduction in practice.

Four supply chain pressures reshaping CPG planning

CPG supply chain failures rarely come from one isolated issue. Seasonality, channel fragmentation, e-commerce velocity, and product launches now compound operational pressure across forecasting, replenishment, and inventory planning.

Seasonality breaks forecast timing

Consumer goods demand is event-driven, but production decisions happen long before demand signals fully form. Companies managing this well move earlier on weaker signals and position safety stock before volatility hits.

"Understanding seasonality, getting out ahead of your lead times, having a good plan within your planning horizon and beyond it. That's what lets you make a more rapid response when something changes."

Channel fragmentation creates forecasting gaps

Grocery, mass retail, D2C, e-commerce, and big-box channels operate on different replenishment rhythms and service expectations. One aggregate forecast rarely serves all of them well.

E-commerce changes demand visibility

Amazon and Walmart.com often react faster than traditional retail channels. Companies feeding that velocity data into broader planning decisions gain earlier visibility into demand shifts.

Product launches carry higher supply risk

Stockouts during initial launch windows can damage retailer confidence before products establish stable sales velocity. Lost shelf space is often harder to recover than lost sales.

How Wild Republic reduced inventory while improving service levels

Our strategic partner Anamind, helped Wild Republic implement Streamline to build a structured SIOP process with a shared monthly planning cadence across sales, supply chain, and finance. The system went live in under one month. Operational results followed within the first year.

Inventory levels dropped 30% within 12 months while service levels improved by more than 5%. That combination only works when planning teams can separate necessary inventory from excess inventory with higher precision. Dynamic safety stock, adjusted automatically based on lead times and demand variability, made those decisions operational instead of assumption-based.

"We got our forecast over 90% accuracy, understanding what our consumers needed, when they needed it. That got a good flow of inventory coming in, matched the seasonality, and prevented reships on partial orders and back orders."

The team also improved lost opportunity identification. Traditional planning systems often miss demand signals hidden behind stockouts because shipment history alone does not reflect unmet demand. Better visibility into what customers wanted but could not buy improved future planning decisions and inventory positioning.

When planning needs to change mid-cycle

After Wild Republic reduced its inventory to a more defensible level, tariffs changed the equation. The priority shifted toward selectively increasing inventory on top-performing SKUs to protect availability and reduce sourcing exposure.

That adjustment only works when teams trust the forecast and can react quickly without rebuilding the entire plan from scratch.

"Using the dynamic safety stocking to ensure our top items had the appropriate amount of safety stock that increased our forecasting accuracy and got us a great buy plan out of the system."

Why forecasting quality shapes every other supply chain decision

Dual sourcing, nearshoring, supplier partnerships, and inventory positioning all depend on forecast quality. Supplier coordination improves when vendors can plan against stable demand signals instead of reacting to constant forecast changes.

"Enhanced forecasting is the baseline, the foundation of how you set yourself up for success. Once you fix that forecast, you'll see the benefits rolling downhill from there."

Forecasting accuracy also defines how effectively companies position safety stock, allocate inventory across channels, and respond to demand shifts before they become operational problems. Teams with stronger planning infrastructure are improving decision quality with every planning cycle. Teams still operating on fragmented spreadsheets and delayed signals are compounding instability instead.