Ryan is a retail supply chain executive pacing up and down in his office. The shipment of skincare products he meticulously ordered 15 days ago is finally arriving at their distribution center today. However, today’s sales data and market trends report speak a different reality. While he had anticipated a surge in demand for the skincare essentials, the landscape has shifted. The once-coveted skincare essentials are now amidst a wave of new trends—the market for hair care is spiking, driven by seasonal disruptions and evolving consumer preferences.
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?
Now, imagine being Ryan. Certainly, not a good place to be; right?
More often than not, retailers in extremely volatile 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.
Let’s explore how strategic inventory optimization helps retailers not only react to market changes but anticipate them, leverage intelligent algorithms for demand forecasting, and craft dynamically adjusting replenishment strategies to stay ahead of the curve.
5 Ways Algorithmic Replenishment Empowers Retailers via Strategic Inventory Optimization
1 – Granular Visibility and Planning
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.
2 – Dynamic Replenishment Strategies
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
Stokouts 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 strategies that help them avoid and manage business risks, like the one faced by Ryan, in the introduction.
3 – Reducing Overstock and Obsolescence
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 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.
4 – Supply Chain Efficiency
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.
5 – Just-in-Time Inventory Management
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”.
