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 Ignoring the demand fluctuations, seasonality, and micro-trends, and adjusting inventory to outdated data can skyrocket inventory investments and stock issues.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.

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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.

Algonomy’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