In an era where all the customers’ whims and nuances get delivered within minutes to the doorsteps, staying ahead of the customer sentiment while keeping the unit economics intact with optimal inventory in offline retail, is a Herculean task. Recent studies reveal the total cost for inventory distortion to be a staggering $1.77 trillion worldwide, and the lost sales owing to stock issues amounted to a whopping $349 billion for U.S. and Canadian retailers in 2022.

The retail industry is undergoing a huge paradigm shift and having the right inventory at the right place at the right time, data-driven decision-making, and understanding store and supply chain dynamics is vital for profitability. While all of these are crucial pillars, accurate forecasting or demand planning emerges as the most critical factor that is directly linked to profitability, inventory investments, customer satisfaction, wastage, and more. However, factors, such as manual adjustments to seasonal variations and static planning for inventory can affect the overall efficacy of demand forecasting solutions.

Below, we discuss some other factors that sabotage demand forecasting and ways to outsmart them for optimizing replenishment, minimizing inventory distortions, and unlocking greater cost savings without compromising customer satisfaction.

Adjusting forecasts manually to crank the inventory up and down for seasonal variations or unexpected events is prone to errors and delays. A standard 5% or 10% increase in inventory might lead to missed demand mapping, overstocks, markdowns, and empty shelves. These errors can ripple through the supply chain, affecting customer satisfaction and operational costs.

On the other hand, AI-powered demand forecasting processes disparate and unstructured data sets such as historical data, market trends, and predictive analytics to automate inventory decision-making. Retailers can automate repetitive tasks like data collection and analysis and choose from a set of highly configurable machine learning algorithms to dynamically refine forecasts based on real-time data inputs, actual store and supply chain level indicators, and disruptions.

Traditional forecasting relies on static tools like sheets and only uses the most recent data, some of which might be in silos, creating huge caveats for accurate demand planning. Ignoring the demand fluctuations, seasonality, and micro-trends, and adjusting inventory to outdated data can skyrocket inventory investments and stock issues.

Automation-powered forecasting solutions can capture real-time changes in demand, and anticipate fluctuations via predictive insights drawn from exhaustive data analytics. Retailers can leverage advanced analytics to identify seasonal patterns, trends, and promotional impacts on demand, which allows for continuous adjustment and optimization. They can dynamically adjust to SKU-store-level demand patterns and supply chain disruptions based on rich, data-driven, and reliable forecast insights, thereby unlocking precision at hyperlocal levels.

Now that you’re up to speed on the latest trends, it’s time to dive into effective strategies. Here are some winning tactics of successful brands to spark your 4th of July marketing efforts. This will help you harness the patriotic fervor to create impactful marketing campaigns that resonate with your customers and boost your sales.

Data in retail is fragmented, noisy, and sparse. The inability to integrate data from disparate sources, and refine it for drawing out actionable insights undermines the reliability of demand forecasts, leading to inaccurate inventory management and operational inefficiencies. Intelligent forecasting solutions come with built-in AI and ML capabilities to overcome these data hurdles and can generate highly focused and reliable forecasts within minutes. Empowered with the ability to choose from ensemble retail-tuned algorithms, businesses can revolutionize demand forecasting. They can view single forecasts, compare and revisit them, and aggregate them at the product, and hierarchy level as well. With advanced scenario modeling, they can easily build and pick the best model out of hundreds of choices and unlock greater control and precision over replenishment.

Retail sales influencers can be external as well as internal and ignoring them during demand forecasting can skew the results. However, manual as well as traditional forecasting methods are not equipped to incorporate external as well as internal influencers, and they cannot identify the right influences for specific products, categories, locations, etc.

On the other hand, AI-powered demand forecasting solutions come with built-in lists of external and internal influences that can be configured to business-unique factors and allow retailers to create forecasts according to the chosen factors. They can adjust the forecasts at the product location level for multiple factors at once and create highly refined and accurate plans for minimizing stock issues while keeping customer satisfaction intact.

Inter-SKU effects during sales and promotions can lead to double loss for retailers, if not managed well. Likewise, new product introductions can derail inventory planning if not considered during demand forecasting. Apart from leading to stock issues, they can spur secondary challenges like lost sales, and high operational costs, and dent customer loyalty.

AI/ML-powered demand forecasting manages cannibalization by dynamically balancing demand between products, considering promotions and availability. Retailers can launch new products without affecting revenue and sales across all product locations, or inducing inter-SKU effects, thereby unlocking intelligent replenishment, greater revenue, and efficient operations.

Accurate measurement of how much the customers will buy is ‘Step Zero’ for ensuring optimal demand fulfillment. It anticipates demand fluctuations, ensures the right amount of stocks at all times, and enables retailers to efficiently mitigate customer needs without ballooning the costs or inventory, and automation is the key enabler. By integrating disparate data sources, dynamic planning, incorporating all sales influencers in forecasting, and intelligent demand planning, retailers can improve shelf availability by 90%, and reduce OOS by 75%, inventory costs by 10%, and wastage by up to 30%, thereby unlocking greater savings, and unparalleled efficiencies.

Disclaimer: This blog does not indicate an ongoing business partnership with the brands, and all references are solely for illustrative purposes.

Also read: 9 Best Practices in Demand Forecasting for Grocery Retailers

With 95% of consumers planning to increase their spending, the trillion-dollar shiny and glamorous retail segment of health, beauty, and wellness is a ripe opportunity up for grabs. However, new geographic hotspots, diminishing boundaries between wellness and beauty, and rapidly evolving trends are setting the stage for adaptive and agile retail planning.

Is your replenishment strategy still relevant, or are you constantly pivoting to new challenges like promotions planning, trend-chasing, and hunch-based inventory?

With customers forming multiple tight-knit cliques of diverse purchase preferences across different retail segments, traditional historical-data-based replenishment no longer serves the purpose.

For retailers, it’s a unique time to pivot themselves and leverage intelligent process automation to strengthen their foothold in the industry with optimized assortments across all locations. Here is a strategic framework to achieve this.

29% of beauty purchase decisions are motivated by product availability, and 23% of them are driven by a good selection of products.

Further, the beauty shoppers in the ages of 18-24 and 30-40 have highly nuanced preferences encompassing adaptogen-infused products, dermatologist-backed ingredients, and more. Thus, the regional-level demand planning and replenishment strategy becomes ineffective in health, beauty, and wellness retail.

The inability to anticipate customer demand well in time leaves retailers gasping for more stock of quick-moving items while ending up with piles of slow-moving items. The high unit costs and vulnerability to markdowns and obsolescence make it important to have a smart and agile replenishment framework that drills down to the SKU level and then generates replenishment plans.

AI-driven demand forecasting solutions can factor in thousands of constraints – sales influencers, historical demand, regional parameters, category & channel-specific factors, and much more – to generate highly precise forecasts.

In 2024, skincare “efficacy” searches spiked by 700%, while 23% of shoppers were influenced by “product price comparison”.

While some customers are shopping across price points, some are enticed by the most-loved product in the influencer fraternity. Mere trend-chasing can easily spiral into overstock of slow-moving items and stockouts for quick movers. This means that demand planners need to mine and analyze a diverse and broader set of consumer, market, and purchase motivation data to optimize inventory.

AI-led replenishment planning solutions implement an analytics-driven approach for allocating inventory at all product locations. Retailers can choose from thousands of ensemble algorithms for each product-store combination and consider an unlimited number of customizable demand predictors.

This enables them to predict demand at a hyperlocal level and tune their replenishment plans based on a precise understanding of all constraints and demand influencers.

While online shopping has increased across health, beauty, and wellness retail segments, in-store buying and physical touchpoints still matter. As much as 40% of online retail includes a physical touchpoint.

According to the 2023 US Beauty Consumer Survey, as many as 54.2% of online beauty buyers say that trying on a new product during in-store shopping helps them discover new brands. Hence, having accurate inventory across all channels is critical for customer satisfaction, protecting revenue, and customer acquisition.

Inability to offer the right stock at the right location and right time can easily lead to customer churn, which is mostly irreversible, given the number of options available. This calls for replenishment planning with hyperlocal accuracy.

AI/ML-driven replenishment empowers retailers to offer highly tuned inventory as per granular demand patterns. Retailers can expand beyond core product categories and expand to adjacent categories or go deep in a specific category to gain spending share with customers.

Promotions have a substantial impact on retailer revenues. Studies show that 16% of beauty shoppers’ pre-shop motivation comes from “Deals.” Also, 23% of Gen Z and 28% of Millennials only buy products on discount.

However, the promotions in health, beauty, and wellness retail are particularly complex. A diverse set of promotions is running in parallel, making it hard for retailers to identify and capture the promotion-induced demand shifts and lifts, leading to cannibalization.

The cannibalization effect can eat away as much as 17% of the promotional gains, thereby causing retailers double harm. Intelligent replenishment solutions offer dynamic demand balancing for demand shifts and lifts during promotions. Thus, retailers end up ordering more promoted items and fewer unpromoted items, effectively cutting both overstock and stockouts.

The beauty and wellness sectors are converging, with a combined value of $140.7 billion in the US alone. Wellness alone has a market share of 82% of consumers in the US, 73% in the EU, and 87% in China.

While consumer focus on wellness and efficacy-backed products is increasing, emerging disruptions will ultimately affect retailer revenues. Further, retailers need to invest in replenishment frameworks that are agile enough to recalibrate inventory while keeping pace with trends.

AI-led demand forecasting and replenishment solutions forecast demand at the product-location level and tune order plans for inter-SKU plays while adapting to supply-side deviations. They empower retailers with holistic inventory optimization across warehouses, stores, and distribution centers, thereby facilitating precise planning and replenishment.

While the market opportunity in health, beauty, and wellness retail is huge, rapidly evolving demand patterns and trend-induced purchases are ruling the scene. Sticking to the same old one-size-fits-all replenishment strategy can cause retailers to lose out on anticipated gains.

Retailers can no longer afford manually cranked inventory decisions based on static sales or demand data. AI-driven inventory allocation tuned to hyperlocal nuances to offer wider assortments, while adapting to real-time demand patterns and agile recalibrations to supply-side deviations, is the right way to go.

The latest issue of Great Christmas Gazette is out and the gifts community is abuzz with the talks of holiday demand fluctuations, supply-side deviations, and snarling inefficiencies creeping from crevices of manual interventions.

The fulfillment teams are working overtime and gift shop shelves are teeming with new enticing and never-before-seen items. Suppliers are getting orders left, right, and center, and everything from warehouses to stores is in a festive frenzy.

However, Rudolph, the jolly retailer, found himself grappling with a mountain of inventory woes this year. With multiple stores and hundreds of suppliers, managing stock levels across different locations to cater to customer demand while ensuring optimal stocks is proving a difficult task. Without proper insight into the real-time inventory, demand patterns, market fluctuations, and supply-side operations, optimizing replenishment cycles became a nightmare.

While poring over dozens of sheets and purchase orders, Rudolph gets a visit from none other than Santa, who then describes how automation-driven retail and supply chain planning can rid Rudolph of all his woes.

Here are all the digs from the super engaging chat, straight from the North Pole!

The inability to anticipate demand accurately can significantly derail retail planning and supply-side operations and can result in multiple revenue-hurting scenarios. Common examples include overstocked shelves, stockouts, wastage, higher storage costs, capital lock-ins, mismatched assortment, and more.

Analyzing and predicting demand at granular levels, like stores, requires an in-depth understanding of the interplay of tens of different factors that vary from external to internal, customer preferences, seasonality, demand patterns, and so on. Traditional demand planning and forecasting systems crumble in such a scenario, leaving retailers with inaccurate forecasts, disgruntled customers, and thinning bottom lines.

AI-powered demand forecasting solutions, on the other hand, can analyze data from various sources, such as weather data, historical sales data, and social media trends, to provide retailers with accurate forecasts. This helps retailers order the right products in the right quantities and avoid stockouts or overstocking.

Inventory replenishment is built on top of demand forecasting solutions and any errors in demand forecasting get snowballed in the replenishment phase. Further, optimal replenishment helps retailers reduce inventory costs, and wastage, cater to customer demand without overstocking, and improve shelf availability in a precise manner.

Achieving this manually or with the help of legacy inventory management systems doesn’t yield the required levels of accuracy across the entire value chain. Bias error, and no capability to measure and plan for demand shifts and lifts during promotions, the effect of seasonality, demand fluctuations, etc., are the major reasons for the same.

This is why retailers need to opt for inventory optimization instead of regular planning and management processes. AI/ML-driven inventory optimization enables retailers to allocate the right inventory with hyperlocal accuracy while incorporating multiple constraints and sales influencers. Retailers can adjust for any supply-side deviations such as lead time fluctuations, delays, fill rates, and more to ensure high shelf availability.

One of the most challenging aspects of retail planning is the siloed nature of tasks. Critical functions like demand forecasting, sales predictions, and inventory planning can steer the overall efficiency of retail operations wayward. The inability to analyze and model the effects of sales and demand patterns on inventory replenishment via an integrated interface makes it difficult to have a single source of truth.

The major reasons behind disparate functioning are legacy systems and lack of supporting technological infrastructure, which is often viewed as an additional business cost.

Investing in an integrated retail planning solution that offers a centralized operations management dashboard and combines both demand planning and inventory replenishment functions can do wonders in this regard. Retailers can model variations, get actionable insights related to all the functions in one place, and always have a single bird’s eye view of every function with the ability to go as granular as they want.

Another powerful tool that enables retailers to position inventory and plan assortments strategically is merchandising analytics. How the demand for specific items is looking like, are there any seasonal variations that are going to affect the demand in a particular category, and what items are vulnerable to wastage or overstocking – intelligent analytics capabilities have answers to all these and many such questions.

Now, investing in a standalone predictive analytics solution is only going to add another management interface with siloed data in the retail ecosystem.

So, we recommend opting for an integrated solution with comprehensive analytics capabilities. As the merchandise analytics capabilities are built-in and integrated with demand and inventory functions, retailers can take well-informed decisions and draw actionable insights from advanced data visualization.

Efficient and mutually fulfilling retailer-supplier collaboration is the foundation of successful retail operations. Retailer and supplier interactions, documentation, onboarding, product information, and finance management are all critical for fostering true supplier collaboration.

However, the majority of suppliers use third-party operations management interfaces, which means additional work for retailers and more redundancies in terms of individual tracking, processing, and more.

This is where automation-driven supplier collaboration frameworks come into the picture. Armed with built-in and customizable automated workflows for supplier onboarding, new product introductions, product cataloging, documentation, supplier/retailer data interchange, invoice and PO management, etc., these frameworks can transform retail supply chain management via truly seamless and effortless collaboration.

Automating retail and supply chain planning comes with amazing benefits. However, having the right approach, figuring out the right use cases, and incorporating a scalable, robust, and flexible technology suite is a dire necessity for successful endeavors.

So, while Rudolph benefits from these secret tips from dear Santa, why not take action and get started for an amazing holiday retail adventure this year?

Accurate inventory forecasting is the heart of retail planning and inventory optimization. Capturing demand accurately and optimizing inventory accordingly can not only reduce inventory costs but can also help retailers boost customer satisfaction, improve shelf availability, and increase inventory ROI.

With fierce competition, rapidly evolving customer preferences, and quick new product and brand introductions, retailers can no longer afford to plan inventory manually.

Studies reveal that the annual costs of holding excess inventory can reach up to 32% and recent economic changes, evolving priorities, etc., are pushing as many as 75% of customers to change their shopping habits.

So, how can retailers forecast demand, capture demand patterns, and evaluate the effect of seasonal variations on the inventory? What is inventory forecasting and what are the different techniques for the same? How important is it to forecast inventory and what are some of the best practices that retailers can benefit from?

Below, we explore the answers to all these questions.

Inventory forecasting is predicting the required inventory levels for a future period based on historical sales data, customer purchase trends, upcoming events, etc. Forecasting involves drawing well-calculated projections based on data-driven insights from previous data and multiple constraints influencing customer purchase behavior and demand.

Often used interchangeably with demand forecasting inventory forecasting spans beyond setting reorder points and order plan adjustments based on seasonal sales influencers. It encompasses identifying patterns and trends to adapt to dynamically evolving customer demands and market conditions to curate the right inventory at the right time to meet customer demand optimally.

While all three terms are interconnected and generally used interchangeably in the retail ecosystem, they are distinct processes

Inventory Forecasting

• Predicting future inventory requirements based on historical sales data, seasonal variations, and market trends
• The goal is to estimate optimal stock levels for meeting anticipated customer demand.
• Effective strategies include tracking and monitoring inventory KPIs – reorder points, average inventory, etc.

Inventory Replenishment

• Predicting future inventory requirements based on historical sales data, seasonal variations, and market trends
• The goal is to estimate optimal stock levels for meeting anticipated customer demand.
• Effective strategies include tracking and monitoring inventory KPIs – reorder points, average inventory, etc.

Demand Forecasting

• Predicting future inventory requirements based on historical sales data, seasonal variations, and market trends
• The goal is to estimate optimal stock levels for meeting anticipated customer demand.
• Effective strategies include tracking and monitoring inventory KPIs – reorder points, average inventory, etc.

There are multiple methods of inventory forecasting. Further, as retailers use different forecasting solutions with different capabilities, the boundaries among the techniques, algorithms, and processes are fleeting and evolving with every passing day.

Generally, inventory forecasting techniques can be categorized into the following four categories:

Trend Forecasting

• Shows trend projections based on changes in product demand over time
• Doesn’t always account for seasonality or sales data irregularities

Graphical Forecasting

• Identifies patterns and sloped trend lines to find out insights that might have been possibly missed
• Unable to handle complexities, heavily subjective, and reliant on historical data

Quantitative Forecasting

• Uses statistical methods and historical sales data to generate future inventory projections
• Demonstrates data dependency and inability to adapt to change

Qualitative Forecasting

• Involves market research and focus groups to predict future inventory requirements
• Doesn’t always account for seasonality or sales data irregularities

As evident from the above discussion, there is no single best inventory forecasting technique, as demand patterns are highly dynamic in nature, keep on evolving, and are affected by multiple factors.

Further, traditional and manual forecasting suffers from inefficiencies, inaccuracies, and poor scalability. Coupled with additional factors, like lead times, fill rates, and demand deviations stemming from in-store promotional dynamics, retailers find it difficult to generate precise inventory plans.

This is where AI and ML-driven inventory management optimization comes into the picture.

The AI/ML-driven inventory optimization solutions come with built-in intelligence of thousands of algorithms that can control and optimize for multiple factors and constraints, enabling retailers to unlock unparalleled precision and control over inventory.

Inventory optimization involves optimizing inventory at granular levels, precisely at the product-location level for multiple categories, stores, SKUs, and so on. What makes inventory optimization superior to standalone inventory forecasting and demand forecasting techniques is the fact that retailers can unlock immaculate precision and control over inventory based on accurate predictive modeling and real-time adjustments via a single interface.

ADA Global’s Order Right is one such powerful inventory optimization platform that comes with a built-in demand forecasting engine and ensemble AI algorithms to predict demand based on hundreds of customizable parameters.

Order Right (OR) enables retailers to generate accurate SKU-level order plans with unique retail-tuned replenishment planning solutions algorithms for intelligent demand-based replenishment planning.

Powered by Forecast Right which uses proprietary ML-based multivariate and algorithmic techniques to accurately and adaptively forecast demand, Order Right generates accurate SKU-level order plans for even the most challenging categories – from fresh and seasonal to new and promoted products with ease to help businesses unlock:

• 10% Reduction in Inventory Cost
• 75% Reduction in Out-of-Stock Instances
• 10-30% Reduction in Wastage
• 99% Increase in Shelf Availability

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.