Ryan, head of supply chain at a leading beauty retailer, thought he’d nailed the skincare order—all products were in high demand last summer, and he even adjusted the order for demand fluctuations in the last few months. However, even after two weeks, the products still lie on all major stores’ shelves. Overnight, the consumer buzz has shifted to another brand, driven by a viral trend and season-induced demand spikes.

Now, Ryan is in a fix! What should he do – push forward with the original plan, risking overstock and markdown, or does he pivot, recalibrating the inventory strategy to seize the moment?

Imagine being Ryan. Certainly, not a good place to be, right?

More often than not, retailers in extremely volatile retail segments, like health, beauty, and wellness end up in shambles owing to inaccurate inventory planning. Further, as per McKinsey’s global consumer survey in 2023, more than 50% of consumers use three or more brands for fragrance, hair, and skincare, and one-third use five or even more for cosmetics. This means inaccurate inventory planning can lead to critical fiascos, and yet, the inventory accuracy rate in beauty retail stays in the range of 30% to 50%, leading to markdowns, overstocks, expiry of products, obsolescence, and other such losses.

What if Ryan’s replenishment engine could sense these shifts as they happen and dynamically adjust the orders automatically—no last‑minute fixes, no manual inventory cranking, just precision stock levels that follow market movements. He would be registering record sales while slashing through stockouts and overstock.

Let’s explore how strategic inventory optimization helps retailers not only react to market changes but also anticipate them, leverage intelligent algorithms for demand forecasting, and craft dynamically adjusting replenishment strategies to stay ahead of the curve.

Having a strong omnichannel presence is crucial for retailers to position themselves in an extremely competitive health, beauty, and wellness ecosystem. However, having the right inventory in the right place is a challenge. It requires exhaustive data analysis across all the entities – customers, market, channel, geography, sales/purchase drives, emerging trends, and more. Gathering all this unstructured and unliked data and processing it to identify hidden trends and predictive insights can neither be done manually nor by traditional demand forecasting frameworks.

AI-powered inventory optimization solutions that are tuned for health and beauty retail nuances can easily process unstructured data from multiple resources to identify hidden patterns and offer predictive as well as prescriptive analytics for what-if scenarios and forward-looking trends. Retailers can generate highly accurate inventory plans as much as 90% of the time to ensure high availability without overstocking or cannibalization. They can create granular forecasts for different stores, channels, and locations to ensure the right products are always available at the right time for better customer experience and boosting brand loyalty.

Replenishment is a critical part of inventory optimization as it involves restocking inventory based on demand data. Traditionally, retailers have created replenishment plans with static values for standard deviations. However, there are many other deviations, such as seasonality, new assortments, pricing and promotions, and weather

Stockouts are not only bad for customers but they hurt the retailers too. Stats reveal that every year retailers lose an estimated USD 350 billion to stockouts in the US and Canada alone. Also, as much as 70% to 90% of stockouts are caused by poor shelf replenishment practices. Investing in AI-powered solutions for auto-replenishment that leverage Machine Learning algorithms for modeling and demand sensing across all channels can change the entire scenario. Retailers can set custom criteria and constraints for demand planning to create dynamic replenishment planning strategies that help them avoid and manage business risks, like the one faced by Ryan, in the introduction.

Overstocking in health, beauty, and wellness retail can kill revenue faster than ever because of intensely competitive price points, higher unit costs, and volatility induced by microtrends and influencers. A perfectly working assortment/bundle of a beauty range can become irrelevant overnight with a single influencer making a single reel/post about a single “could-be harmful” ingredient.

Hence, retailers no longer have the luxury of stocking the top-grossing product range as they can quickly go obsolete leading to markdowns or even worse, dropped sales. Managing these fluctuations alongside the supply chain planning for sourcing and supplier collaboration is impossible in a manual setting.

The situation, however, changes with intelligent inventory optimization. Getting highly granular forecasts and replenishment planning insights for timelines as small as a day or two or a week frees up the retailers from overstocking or obsolescence blues. They can get alerts for any long-term and short-term demand changes and adjust their inventories accordingly.

Unpredictable lead times, in-transit delays, and supply chain constraints like minimum order quantities can easily derail both supply chain efficiency and inventory planning. Further, longer lead times can translate into excess inventory, stockouts, and wastage of resources, which ultimately amount to a loss of sales.

Algorithmic replenishment automates the critical processes of inventory management, like receiving and ordering the inventory as per the demand patterns and market fluctuations. This saves countless manhours and makes the overall supply chain planning efficient. The retailers are ordering items as per the current needs and upcoming market disruptions, which means they can plan better shipment loads and foster mutually fulfilling supplier collaboration.

Advanced capabilities like predictive and prescriptive analytics empower retailers to take appropriate action at the right time to minimize losses owing to supply chain constraints. They can also automate mundane processes to make more time for strategic inventory planning. Further, as the retailers have a granular view of their channel/store/location-specific stock requirements, they can easily overcome the existing supply chain bottlenecks and create more efficient consignment flows as per the immediate and long-term needs.

With more and more influencers, brands, and researchers focusing on the freshness of ingredients and formulations, and people finding reviews citing results in 15 days, one month, etc., shelf life management has become a major factor in just-in-time (JIT) inventory planning. While the life of standard beauty and wellness products like creams and lotions is still more, when it comes to well-defined popular categories like actives, serums, and chemical agents, retailers need highly reliable JIT indicators for strategic inventory planning.

Processing such huge data sets with different variables, like SKU type, size, shelf life, location-based demand, and consumption trends, without advanced analytics and intelligent replenishment solutions is a lost cause. Algorithmic replenishment takes the guesswork out of the entire planning process and gives extremely accurate insights for JIT inventory management while adhering to granular constraints such as SKU size, location, type, and more.

Positioning a retail brand in competitive categories can become challenging without any reliable, scalable, and intelligent tech infrastructure powered by advanced technology. Strategic inventory optimization is crucial for rising above the competition while selling similar products across different locations and channels. They empower retailers with actionable insights and predictive as well as prescriptive analytics, ensuring high shelf availability, reduced stockouts or overstocks and cannibalization, and preventing them from becoming “the Ryan”.

Earlier categorized under the standard umbrella of cosmetics, the sector has now branched into health, beauty, personal care, skin care, wellness, and more. What makes it a definite opportunity for retailers is the fact that the industry is about to hit the USD $580 billion mark by 2027. However, the opportunity doesn’t come sans challenges.

As many as 42% of consumers across the major global economies enjoy trying new brands and are increasingly shopping across price points. The emergence of online webpages for skincare guidance and buying imported products, live streams, influencer-led brand hopping, and a dynamic shift in consumers’ purchasing habits, have put retailers under pressure. Taming the spikes and lows induced by seasonality, forecasting demands in a highly volatile landscape, and planning for stockouts and markdowns are becoming increasingly challenging. Enter the diminishing boundaries between two buckets, such as beauty and wellness, where the combined sector is accounting for as much as USD 2 trillion globally for brands, retailers, and investors, and the problem snowballs.

Rising above such disruptions requires a careful and strategic supply chain and retail planning, and hinges on granular analysis. Let’s find out how granular forecasting can be a game-changer for retailers in highly competitive health, beauty, and wellness segments.

In the bustling world of health, beauty, and wellness retail, staying ahead of the curve is not just a strategy – it’s a necessity. With customers demanding more personalized experiences and seamless transactions, retailers are faced with a myriad of challenges. From managing promotions to balancing complex product assortments, the road to success can seem daunting. And it doesn’t help that previously successful merchandising and supply chain planning strategies are now glaringly failing.

In this blog, we’ll delve into the obstacles faced by health, beauty, and wellness retailers when it comes to retail planning and explore the transformative role AI plays in overcoming them.

One of the biggest challenges for beauty retailers is predicting and managing demand fluctuations. Demand for beauty products can vary depending on factors such as seasons, holidays, promotions, fashion trends, customer preferences, and even social media.

Especially during seasonal shifts and holidays, sales can fluctuate significantly, requiring retailers to adapt their orders to accommodate heightened demand. Given the prevalence of imported goods, anticipating spikes in demand poses a unique challenge. Failure to order popular products in advance from overseas suppliers could result in missed sales opportunities, while excessive caution may lead to surplus inventory. Hence, precision in forecasting becomes imperative.

Social media is another culprit. A single post by a popular influencer can instantly convert inventory of your beauty product to deadstock. Retailers therefore need to be agile to respond to such demand changes to achieve profitability.

According to a research conducted by Shareablee, Health and Beauty is the most watched video category on Youtube and draws the 2nd highest amount of social media action only after Fashion and Apparel.

With the complexity of demand patterns for beauty products, it becomes challenging to forecast demand accurately and avoid overstocking or understocking. Overstocking can lead to high inventory holding costs, markdowns, and waste, while understocking can result in lost sales, stockouts, and customer dissatisfaction.

To cope with demand fluctuations, beauty retailers need to use data-driven AI/ML based forecasting methods that predict accurate demand at channel category and store nuances.

So it is not a surprise that McKinsey notes that using ML and AI in inventory demand prediction and supply chain management can reduce errors by up to 50% and reduce lost sales and product unavailability situations by 65%. This can lower warehousing costs by up to 10% and administration costs by up to 40%.

Learn how machine learning improves retail demand forecasting

As a beauty retailer, maintaining seamless communication with suppliers is paramount to avoid delays and frustration. The cosmetics and personal care industry moves at lightning speed, demanding swift responses for thriving businesses. With innovation at its core, there’s little margin for error.

Introducing new products presents its own set of challenges. From supplier collaboration onboarding to approvals, updating information, placing orders, processing payments, and sharing vital demand signals, every step requires meticulous attention.

Unfortunately, traditional collaboration methods often rely on manual processes, prone to errors and missed opportunities. It’s no surprise that nearly 80% of retail product launches end in failure without effective supplier processes in place.

Moreover, the costs associated with managing supplier information and the order-to-payment cycle are significant. Many retailers still rely on labor-intensive methods, with an estimated cost of nearly $1 million for health and beauty retailers just to maintain these records.

Furthermore, effective coordination with retail vendor collaboration is critical for successful trade promotions. Failure to do so can result in significant financial losses, with retailers risking over $1.5 million in missed opportunities and operational costs. To mitigate these risks and maximize profitability, streamlined supplier processes and enhanced communication channels are essential.

Check out your potential savings with true supplier collaboration

In the fast-paced realm of health, beauty, and wellness retail, challenges like fluctuating demand and complex supply chains are ever-present. Yet, AI-powered solutions offer a transformative path forward. With AI driving demand forecasting, store replenishment, and supplier collaboration, retailers can not only meet but surpass customer expectations, all while boosting profitability. In this ever-changing landscape, AI stands as the cornerstone of success.

Ah, the joyous season is almost upon us, and retailers are donning their festive hats to prepare for the promotional whirlwind – scrambling to finalize the assortment and promotions for the season.

Yet, amidst the tinsel and cheer, there’s a not-so-merry disruptor lurking—In-store product cannibalization.

What exactly is this grinch-like phenomenon, how does it threaten the holiday merchandising feast, and how can AI save the day? Let’s unwrap the mystery.

Picture this: one product munching on the demand of another, akin to a festive feast gone awry. This, my friends, is the essence of in-store cannibalization. It’s like musical chairs, but instead of chairs, products are vying for the attention of eager shoppers.

But why does this happen? Blame it on the combination of rampant discounts and the waning loyalty towards brands, especially for everyday products.

The grocery aisle becomes a battleground, and every product hopes to be the shining star of the season. Steep discounts and promotions become the magic wand, but sometimes, the spell goes awry, leading to products devouring each other’s demand. It’s a retail jungle out there!

While we’re busy calculating the demand lift due to promotions, the silent shift in demand often goes unnoticed. Well, my fellow retailers, it’s not just a minor hiccup—it’s a disruptive force.

In the study by Van Heerde et al., it’s a tale of two stores: one offering a tantalizing price promotion, and the other, well, just doing its regular thing. Imagine the suspense!

For the peanut butter enthusiasts (yes, that’s a thing), the findings are like music notes in a symphony. There are cross-brand effects (43% shift in secondary demand), cross-period effects (24% shift due to primary demand borrowed from other time periods), and category expansion (33% shift due to market expansion and cross-store effects).

What does this mean for Retailers?

It means shelves groaning under the weight of aging stock, margins doing a disappearing act, and, in some cases, products reaching their expiration date in bulk. Talk about post-holiday blues lingering like an unwelcome guest into the next year.

It’s time to take off the rose-tinted glasses and acknowledge the ripple effect of in-store cannibalization.

Fear not, weary retailers, for there’s a solution to tame the cannibal Grinch! The key lies in a symphony of demand forecasting solutions and replenishment frameworks that not only capture the demand “LIFTS” but also dance to the tune of demand “SHIFTS” caused by promotions.

Picture this: a symphony of algorithms orchestrating a flawless forecasting and replenishment planning solutions framework that not only predicts the future but does so with a keen awareness of cannibalization effects.

AI, with its prowess in data analysis and pattern recognition, transforms the art of prediction into a science. By sifting through mountains of historical sales data, weather patterns, social trends, and even the subtle nuances of consumer behavior, AI paints a vivid picture of what lies ahead. But what sets it apart in the realm of cannibalization is its ability to dissect the intricate relationships between products.

Traditional forecasting models often struggle to capture these nuanced interactions. AI, however, navigates this labyrinth effortlessly. Through advanced machine learning techniques, it identifies not only the direct impact of one product on another but also the ripple effects that resonate through the entire inventory.

Moreover, its ability to optimize order schedule based on supply chain constraints such as lead time, minimum order quantity, order frequency etc. as well as category factors such as expiration dates, and shelf life at a granular level clearly sets it apart from the traditional approach.

Achieve lean retail inventory optimization solution, reduce out-of-stocks and food wastage by algorithmically optimizing your replenishment with automatic store replenishment.

What makes this framework future-proof is the adaptability of AI. It doesn’t just learn from historical data; it evolves with the changing landscape of consumer preferences and market dynamics.

Moreover, AI is rooted in its ability to handle complexity. Traditional methods might falter when faced with a myriad of variables, but AI thrives in complexity. The more data it has, the better it becomes at untangling the web of relationships within a product portfolio.

Imagine a world where your store replenishment processes are aligned not just for individual products but for the entire product class. It’s a harmonious dance where each product complements the other, ensuring that post-holiday blues are replaced with a festive glow of success.

So, as you gear up for the seasonal planning extravaganza, remember the AI carol that sings the tune of demand shifts and lifts. Let it be your guiding star through the retail holiday galaxy, ensuring that jingle bells are heard, not the knells of cannibalization.

Happy seasonal planning, and may your shelves be merry and bright!