As a leading technology provider for some of the largest retail brands globally, we have witnessed how inventory can be the Achilles Heel for businesses across multiple industries. From millions of dollars drained in lost sales owing to inaccurate demand planning to inefficient promotions degrading the profitability, and high holding costs chipping the bottom lines as a double-edged sword – inefficient inventory planning comes with multiple drawbacks.

Among multiple other causes, the lack of proper tracking and optimizing as per the tracking metrics is one of the most challenging aspects of inventory planning. How to track inventory, what are critical inventory KPIs, what do they mean, and how to optimize inventory as per these metrics?

Below, we explore all these answers and discuss the top ten inventory KPIs every retailer must track.

The inventory turnover ratio is obtained by dividing the total cost of goods sold by the average inventory. The inventory turnover ratio indicates how often inventory is sold and replaced and shows the overall business performance. A high inventory turnover ratio or stock turnover ratio indicates that the inventory is efficient.

Tracking the inventory turnover ratio helps retailers understand what they can sell, and how efficient sales are for the stock. Based on this ratio, they can plan the order frequency and stock replenishment.

Average inventory is another important inventory KPI that is used to determine the inventory turnover ratio and indicates the amount of inventory available at a time. Average inventory is calculated periodically based on the unique business requirements.

Average inventory formula:

Average Inventory = (Beginning inventory – Ending inventory) / 2

Ending inventory is yet another metric that is used to calculate average inventory, and is depends on all the purchases done around the year.

Ending Inventory = Beginning Inventory + Purchases – COGS

Stockouts are a direct indicator of inventory health and show how often customer demand can’t be met owing to the lack of the right products. Apart from degrading customer experience, stockouts can cause direct harm to the revenue as 30% of customers switch stores after a single stockout event. Further, stockouts are directly responsible for a 10% sales loss.

Another key metric related to stockouts is the stockout rate, which is the percentage of products not available when they are required for sale. Stock rate is calculated by dividing the out-of-stock items by the total items available in the inventory.

The sell-through rate or sell-through ratio refers to the percentage of stock that is sold. It is one of those inventory KPIs that requires periodic measurement and tracking and a high sell-through ratio indicates efficient inventory and good sales.

Sell-Through Ratio = Amount to Sold Items / Amount of Received Items

It is also called liquidity ratio as it indicates the amount of inventory that has been sold. As stated above, a higher sell-through ratio is good, however, figures close to 100% might also indicate that retailers are running out of inventory. Hence, it is also one of the most critical inventory KPIs that every retailer should closely monitor.

As a standard practice, the sell-through rates are calculated on a monthly basis and are used to decide the replenishment planning schedule.

Holding costs, also known as stock holding costs or inventory holding costs, can directly impact a retail business’s revenue and sustainability. They refer to the cost of storing unsold inventory that has been stored for specific periods or has become obsolete.

The duration of holding, nature of items in the inventory, shelf life of the items, depreciation, opportunity costs, and reason/intent behind storing the inventory are the determining factors of the overall cost a retailer must bear towards inventory holding.

Inventory Holding Cost = (Total Inventory Costs / Total Inventory Value) ×100

Having high inventory holding costs directly indicates poor inventory and business health and indicates situations like reduced profit margins, cash flow constraints, increased risk of obsolescence, operational inefficiencies, missed opportunities, higher storage & maintenance costs, etc.

DSI indicates how long it will take a retailer to sell the entire inventory during a specific period. It is yet another must-track inventory KPI for retailers that helps them understand and assess their sales performance and inventory management efficiency.

In simpler words, DSI means how many days the retailer takes to convert the entire inventory into sales, and it is calculated using the following formula:

DSI = (Average Inventory / Cost of Goods Sold COGS) × 365

Days sales of inventory directly indicate inventory management efficiency and a low DSI indicates efficient turnover and effective sales strategies, while a high DSI may suggest overstocking or weak sales. DSI also offers insight into cash flow, sales performance, etc.

Ideally, DSI should lie between 30 to 60 days and values above 60 days might indicate declining customer demand, or poor demand anticipation.

How well the demand predictions match the actual recorded sales and how well the inventory sells as per the planned projections is another critical inventory KPI that every retailer should track. Demand forecasting accuracy is directly proportional to sales, revenue, customer satisfaction, and inventory efficiency, and paves the way for strategic business growth.

The demand forecast accuracy is calculated by many methods, such as Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE).

DFA = (1− (|Actual Demand−Forecasted Demand∣ / Actual Demand)) × 100

A high demand forecast accuracy indicates optimal inventory, efficient replenishment planning, supply chain efficiency, and informed decision-making. Having a consistent high-demand forecast accuracy also reduces inventory costs by 10 to 20%.

It is yet another inventory KPI that every retailer must track and measure consistently. Inventory accuracy indicates how accurate the recorded inventory is and how well it aligns with the physical inventory in the warehouse.

Inventory Accuracy = (Actual Inventory / Recorded Inventory) × 100

High inventory accuracy indicates better operational efficiency and inventory replenishment planning. Maintaining high inventory accuracy helps reduce costs, and improve business planning, and robust decision-making.

When it comes to inventory KPIs, it is very easy to overlook customer-related KPIs that can hinder the understanding of customer expectations and experience. To ensure granular accuracy and an in-depth understanding of demand patterns, it is important to measure and track customer-related KPIs, such as service level.

Service level means the percentage of customers that didn’t experience any stockout during an inventory replenishment cycle. So, it is a direct indicator of a retailer’s ability to fulfill customer demand in the most optimal manner.

A high service level means the retailers are able to deliver across customer expectations and the number of customers leaving without making a purchase owing to the lack of stock or absence of the right stock is low. Service level is one of those metrics that can significantly alter the sales value, revenue, and customer satisfaction with a slight increase.

As the name suggests, the gross margin by product refers to the profit percentage earned for a single product sold. This metric calculates the profit generated by the sale of each product against its cost and offers granular visibility over the efficiency of inventory at the product level.

Hence, it is a direct indicator of overall profitability and helps retailers make better purchasing decisions and plan optimal inventory.

Gross Margin = ((Sales Revenue − COGS) / Sales Revenue) × 100

Understanding gross margins by product allows the retailers to identify the highest-performing products, and sales performance of different items and make informed decisions for product assortment, pricing strategy, inventory replenishment, and more.

While measuring and tracking inventory KPIs is one of the most reliable and data-driven approaches to inventory optimization and replenishment planning solutions, it is crucial to do so consistently and periodically for a uniform and reliable strategy. The next step in the process of building a data-driven and demand or profit-centric inventory is to leverage the insights at scale and take decisions at a product-location level for different stores, categories, etc.

According to Statista, global inventory distortions cost retailers as much as USD 580 million. Also, the value of grocery retail items going out of stock exceeds USD 500 million. Add increasing fulfillment costs for merchants, longer lead times, demand for better assortment across multiple categories, and breakneck competition to the mix and the retailers end up with a Pandora’s Box in their hands.

While technology-driven inventory planning and management systems are replacing manual and traditional planning frameworks, the overall inventory costs are rising and stock optimization still remains a critical challenge for profitability in the retail sector. This is mainly due to the highly nuanced nature of the various segments such as microtrends and huge seasonality in the health, beauty, and wellness segment, rich assortment and variety constraints in the convenience sector, and demand-based planning in grocery.

However, the nuances are not limited to these factors only. The overlapping of multiple sales influencers, sales and promotional events, holidays, seasons, social media trends, weather, and the deep impact of macro and micro-geographic-level trends/activities make demand anticipation hard and inventory optimisation even harder.

How can retailers optimize inventory with granular precision at the SKU or store level, and what makes optimization of retail inventory so important? What are the various benefits of retail inventory optimization and how to optimize inventory for different retail segments? Finally, is inventory planning different from inventory optimization?

Below, we explore the answers to all these questions at length and discover the benefits of optimizing inventory in an industry-wise manner.

Optimising inventory means ensuring that the right stock is present in the right quantities at the right location and time, such that the wastage, spoilage, inventory holding costs, and stock issues are minimized without affecting the shelf availability. It also involves planning stock for promotional and sales events while considering the in-store cannibalization and inter-SKU demand shifts and lifts to keep the revenue intact.

Inventory cannot be optimized at such a granular level either manually or with traditional inventory management solutions. Optimized inventory is achieved with the help of intelligent inventory optimization software solutions that consider historical demand patterns, custom sales influencing parameters, and demand planning capabilities.

Inventory optimization empowers retailers to adapt to demand patterns and supply chain dynamics at an ultra-granular level effortlessly and quickly. They can anticipate the demand in real-time and get automatic updates for just-in-time inventory (JIT inventory), days of stock, out-of-stock, and other configurable inventory events.

Inventory stock optimization allows retailers to quickly adjust the stock replenishment or order plans based on automatically generated forecasts while incorporating the custom rules and settings for specific stores, zones, regions, and geographies. The retailers no longer need to manually adjust for increased demand in specific SKUs or categories and they can easily visualize the inter-category/SKU and intra-category/SKU interplay at a highly granular level.

Another excellent advantage of inventory optimisation is the ability to adapt to the supply side disruptions in an agile and robust manner. AI and ML-driven inventory optimization solutions come with built-in self-learning capabilities to model variations in lead times, safety stock, fill rates, pending orders, and more to ensure optimal stock across the entire value chain.

Finally, the most critical benefit of stock optimization is the ability to outsmart the sales and profit-crushing factors stemming from incomplete or faulty data analytics (for the org-level data as well as competitor and market data).

While the emergence of cloud-based and continuously-developed software solutions is constantly blurring the lines between capability-based software platforms, understanding the distinction between operational systems is critical for choosing the right platform, especially in retail.

While inventory planning and inventory optimization are related, they are conceptually distinct. Inventory planning focuses on ensuring that there is sufficient inventory to meet customer demand, keeping stockouts and overstocks at bay, and also involves stock management capabilities, which have been a major offering in ITSM software solutions for many years now.

On the other hand, the core focus of inventory optimization is to maximize inventory replenishment efficiency while reducing costs and wastage and increasing shelf availability. Inventory optimization solutions offer many advanced capabilities to users that allow for custom adjustments in inventory planning, demand forecasting, and supply-side disruption management.

In easier words, it can be considered inventory management optimization and is the next step that retailers should take for better stock efficiency, lower carrying or holding costs, and reduced risks of out-of-stock and overstock events. The retailers can effectively cut the wastage and manage in-store cannibalization to achieve higher profit margins.

ADA Global offers a highly advanced Machine Learning-driven inventory optimization software solution – Order Right, with a built-in demand forecasting engine running on AI and advanced data analytics.

Thus, retailers from all segments of the retail industry can plan the inventory, and demand, and take care of supply-side disruptions, all from a single dashboard interface. Order Right comes with automatic outlier detection and ensemble algorithms, self-defines the best-fit criteria, and allows the users to configure them. It offers custom algorithm selection and allows retailers to add or drop demand predictors and set their priorities for extremely precise forecasting.

Order Right leverages hierarchical forecasting to overcome retail data noise and generate highly accurate forecasts. As the inventory replenishment is built on top of highly accurate demand forecasting, and the models are constantly trained on historical data, trends, and demand patterns, the retailers are able to optimize the inventory in a strategic and efficient manner, thereby unlocking multiple business benefits, including:

• 10% Reduction in Inventory Costs
• 75% Reduction in Out-of-Stocks
• 10-30% Reduction in Wastage
• 99% Shelf-Availability

For more information about how Order Right optimizes inventory while tackling core planning issues such as inter-SKU effects or in-store cannibalization, supply side disruptions, and more, please get in touch with our experts for a personalized free demo session today!

With a staggering 150 Million+ convenience stores and a record 160 million transactions per day in the US alone, convenience store retail emerges as a delectable opportunity. However, 42% of the customers shopping at C-stores consider “having the right stock” to be one of the most important factors for a delightful experience. Add the constantly fluctuating pool of suppliers, micro-trends affecting sales, and highly evolving demand patterns to the mix, and retailers are left with nothing short of a mind-bender.

The traditional replenishment planning approach crumbles in the face of the challenging C-store landscape due to the inability to predict demand across multiple locations, optimize inventory ROI, and plan promotions that don’t cannibalize sales. So, how can retailers break out of the vicious cycle of having too much stock or empty shelves, and how can they entice customers with loyalty programs without compromising margins? How can they simplify new product introductions and supplier collaboration at a scale?

Let’s dive into the answers!

As many as 88% of customers consider the experience provided by a brand to be as valuable as its products or services. However, the short product lifecycle in quick-moving C-store retail and highly fluctuating demand patterns make it hard to decode customer delight. Traditional inventory management optimization lacks agility and fails to adapt to quick demand changes or anticipate key metrics such as “what” or “how”.

On the other hand, intelligent replenishment built on top of AI-driven demand planning overcomes these bottlenecks. Retailers can generate highly precise order plans with a few clicks while factoring in diverse, complex, and granular sales influencers for C-store retail. They can optimize inventory for specific locations, stores, channels, products, etc. while reducing inventory costs and improving accurate shelf availability.

C-store retail has extremely complex and elusive sales influencers or parameters, such as time of the day, season, holidays, local events nearby, new product launches, short-term trends, and many other locality-specific factors. So, mastering inventory planning requires a careful analysis of all these factors which might be highly subjective and far beyond the computational expertise of traditional replenishment solutions.

Replenishment planning solutions with the in-built intelligence of AI and ML algorithms can easily generate precise order plans for specific locations while adjusting to specific sales influencing parameters at scale. Thus retailers can optimize inventory across multiple locations without having to switch interfaces while adapting to customer preferences and historical sales data at an ultra-granular or hyperlocal level.

Loyalty programs, bundled offerings, and promotions (discounts and sales) are some of the most challenging aspects of C-store strategizing. The inability to analyze and map the effects of such offerings in advance is one of the major driving forces behind this. As the local parameters influence the demand patterns and inventory, and products are replaced at a quick pace, promotions become trickier to manage.

AI and ML-driven solutions inherently incorporate data from multiple sources, such as promotions, historical sales, and more, and all the order plans are generated accordingly. Thus, the retailers can easily optimize the predictive replenishment for specific locations in a data-driven manner.

The emergence of quick delivery services that deliver food and other essentials in time-crunched delivery windows has given solid competition to the convenience store retail model. These quick delivery services offer a great assortment of goods and the convenience of getting everything delivered to your doorstep. Hence, offering variety is no longer a critical business differentiator. Retailers need to play smart when it comes to offering assortment as well as delightful personalized retail experience, such as offering bundled offerings, and loyalty programs, and aligning the promotions with the changing customer demand patterns.

Such careful planning of inventory operations while ensuring cost-effectiveness and streamlined management across the entire supply chain is a massive enigma. Integrating technology for multiple format retailing, optimizing replenishment specifically for high ROI products, at store, channel, category, or SKU level, and constantly adding/removing products for optimal profitability can emerge as a powerful solution.

Global consumer preferences have changed after the pandemic, willing to pay a sustainability premium of 9.7%. Wastage, supply chains with high CO2 emissions, and cost-intensive sourcing are making consumers disgruntled, and organic, locally sourced fresh produce is seeing higher demand.

Ai replenishment optimizes inventory to reduce stockouts as well as out-of-stock events while increasing shelf availability and decreasing inventory spoilage by up to 30%. This is also a strong point in favor of C-store retail planning as compared to quick delivery services where the carbon cost of deliveries is very high.

C-store retailers have a wide supplier network that is constantly changing with suppliers moving in or out of the network, based on the newly introduced or discounted products. Disparate supplier collaboration platform interfaces lead to errors, tedious finance management, and clunky supply chain operations. Thus, retailers can land in supply-side complexities that can disrupt functioning and also leave a dent in finances.

Automated supplier collaboration solution facilitate quick supplier onboarding and efficient management of suppliers via in-built workflows for cross-functional decision-making. Capabilities such as EDI, digital cataloging, automated invoice reconciliation, and more can reduce paperwork, minimize errors, and ensure transparent status tracking.

The landscape of convenience stores is rapidly changing, with new demands on product offerings, a shift towards sustainability, and the need for seamless integration between in-store and online experiences. To stay ahead, C-store leaders must rethink strategies for retail planning, inventory demand prediction, and collaboration.

Also read: 5 Reasons Why Your Store Replenishment Might Be Ineffective

Research shows that the global convenience store market size is USD 663.5 billion, and the revenue projections sit at the USD 3.12 trillion mark for 2028. While the numbers paint a decent picture, stock issues, shrinking profits, spoilage, and loss of sales due to manual promotional planning keep on speed-bumping. Further, 42% of customers point out having the right stock and variety (28%) as the two most important factors for a positive C-store shopping experience.

Ensuring high shelf availability, quick and efficient adaptation to evolving demand patterns, and real-time views over demand shifts and lifts during promotions and discounts can transform inventory planning and boost customer satisfaction. However, these capabilities stem from a robust, intelligent, and self-learning technological infrastructure. But then again, where does one start, and what are the top automation use cases for convenience store inventory planning?

Let’s take a quick look at the answers.

A whopping $163 billion of inventory is tossed annually due to damage and oversupply. Overstock and out-of-stock are two major cost bleeds for retailers, especially in the convenience store segment, where fresh and ultra-fresh inventory is vulnerable to quick wastage and spoilage. Augmenting demand planning frameworks with advanced AI-driven capabilities can overcome stock issues and help retailers unlock significant inventory cost savings.

While inaccurate demand forecasting can inflate inventory holding costs by up to 30% and erode profit margins, AI-powered forecasting can reduce supply chain errors by up to 50%, reduce lost sales by 65%, and reduce warehousing costs by up to 40%. Another critical benefit of accurate demand forecasting is data-driven inventory planning that ensures optimal replenishment at all times while boosting shelf-availability and reducing stock issues.

Traditionally, demand planners have been working with sheets-based planning systems with basic automation capabilities patched together that result in a fragmented functioning of disparate components. Further, studies reveal that 40% of retailers cancel at least one in ten orders due to inaccurate inventory data.

They are manually cranking the orders up or down on the basis of random adjustments to demand influencers like seasonality, obsolescence, previous quarters’ or previous holidays’ patterns. This can lead to markdowns and wastage while bloating the inventory costs owing to the lack of clarity and visibility over location/product/supply-specific nuances.

Machine Learning-based retail replenishment algorithms are self-learning, adaptive, and exhaustive and can dynamically adjust to changes in demand patterns, external and internal sales influencers, promotions, supply-side disruptions, and more. Intelligent replenishment optimization solutions integrate data across all functions to draw actionable insights for accurate and responsive inventory planning aligned with real-time customer demand. They automatically choose and prioritize the most crucial sales influencers while taking any specific events, such as promotions, in consideration to adjust inventory to demand shifts and lifts.

Nearly 60% of retailers have less than 80% inventory accuracy and face data challenges.

Inaccurate inventory data or siloed data collected in disparately working components of the merchandising ecosystem prevent retailers from placing the right amount of inventory in the right place in a timely manner. This can prove fatal for profitability, especially in the C-Store segment, where managing sales events is already challenging owing to a poor understanding of SKU-level dynamics at specific store locations.

Intelligent inventory replenishment solutions optimize inventory at a product-store level for precision at the hyperlocal level based on granular visibility over demand patterns exhaustive understanding of sales influencers. Retailers can automatically generate highly optimal order plans for inventory at specific store locations, curbing in-store cannibalization, overcoming data challenges, and outsmarting supply chain disruptions.

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!