Since most supply chains are not built to absorb sharp seasonal demand spikes in real time, companies rely on pre-season forecasts, which are often inaccurate.

Many products see seasonal sales spikes, like ACs in summer, woollens in winter, umbrellas during monsoons. Events, festivals, and marketing activities also cause steep buying spikes. This cyclical shift in consumer demand pattern is called seasonality, and companies struggle to mobilise capacity, manage inventory, and ensure products reach customers exactly when they are needed.

WHY IS MANAGING SEASONALITY A STRUGGLE?

Most supply chains are not built to absorb sharp seasonal demand spikes in real time. During summers or festive periods, categories like ACs and refrigerators can see demand jump over 20%, far beyond what production, suppliers, and logistics networks can support in real-time. This forces reliance on pre-season forecasts, which are often inaccurate, so companies either overproduce or underprepare.

In-season volatility amplifies this risk. Warmer-than-expected winters can dampen demand for moisturisers, or woollens by 5%-10%, leaving excess inventory in stores, which after occupying shelf space for extended periods, will be cleared through aggressive discounting, directly eroding margins.

On the other hand, prolonged heatwaves may sharply increase demand for cooling products, forcing companies into hurried capacity ramp-ups, inflating labour and operating costs. Nevertheless, long supply lead times may still limit mid-season adjustments, leading to stockouts, missed sales, and dissatisfied customers.

ACCURATE FORECAST – AN OXYMORON

Since forecasting errors drive these issues, the instinctive response is to improve forecast accuracy. Yet, despite heavy investment in advanced tools, forecasts remain inherently imperfect.

Instead, brands must build a more resilient supply framework that limits prediction risk by leveraging some of the inherent characteristics of forecasts:

Aggregated estimates across markets are more reliable than store-level ones, as overall variations cancel out.

Demand for fast-moving items with relatively consistent sales patterns is easier to predict than slow-moving SKUs.

A shorter forecast horizon has a lower probability of error than longer forecast horizons.

KEY SOLUTION ELEMENTS

Build Agility: To shorten forecast horizons, replenish faster. This allows brands to hold less inventory across the network while offering a wider SKU range, improving availability without additional investment.

·        Most inventory must be maintained at aggregate nodes, such as central warehouses, where demand variability is lower, decoupling long supply lead times from market responsiveness and significantly reduce replenishment time.

·        Clear inventory limits must be defined for each SKU across locations. Replenishment must be triggered by daily consumption and must be halted when thresholds are breached. These limits should be adjusted dynamically using real-time data to surface demand shifts early, so that overstocking, or understocking is prevented.

·        Production must prioritise SKUs at highest stockout risk.

BUILDING UP FOR THE SEASON

Despite supply chain agility, initial inventory build-up for seasonal demand remains prone to errors. So, proactive planning measures must be implemented to minimise the impact of forecast uncertainty. These measures are required for the following decisions:

·        How Much to Make: POS-level forecasting often creates stock imbalances, due to demand fluctuations across SKUs, geographies, and timelines. Hence, companies should use aggregated demand forecasts, hold inventory held at aggregated nodes, and replenish based on actual consumption, to optimise allocation, reduce variability, and stabilise the supply chain.

·        When to Start Making: Forecasted production should be scheduled as close to the season as possible. This shortens the forecast horizon, improves accuracy, and avoids blocking working capital too early, while maintaining a necessary buffer time. For example, if a plant can produce 1,000 ACs per month, with 700 for regular consumption and 300 for spare capacity, and seasonal demand peaks at 1,600 units, the extra 600 units can be built gradually over the two months prior, using spare capacity. This gradual ramp-up prevents raw material (RM) shortages and production stress.

·        Which SKUs to Make: Segmenting SKUs into runners (fast movers representing the top 80% of sales), repeaters, and laggards (slow movers representing the lowest 5% sales) helps minimise risk. Since runners continue to sell well even post-season, they can be safely built in advance using spare capacity, freeing up in-season capacity to respond to repeaters and laggards based on real-time demand signals. 

Ramp Down:As the season tapers off, inventory limits that were temporarily raised for peak demand must be restored. Ramp-down should begin slightly ahead of the season’s end through controlled reductions to avoid service issues or the accumulation of excess stock.

Automated Decision Making: Automated planning ensures disciplined and consistent build-up, replenishment, and ramp-down, enabling rapid response to demand shifts, allowing retailers to adjust assortments smoothly, without resorting to constant operational firefighting.

CONCLUSION

Poor seasonal inventory management can cause revenue losses of 5%-30% from stockouts during peak demand, and margin erosion of 10%-40% from excess stock liquidation. A pull-based, dynamically managed supply chain aligns with real demand, improving availability, reducing working capital and boosting profitability through structures build-up and ramp-down.