As global trade dynamics continue to shift, retailers and wholesalers are finding themselves in a new era of volatility. Once beneficiaries of globalization, the consumer goods sector is now facing increased uncertainty as protectionist policies and tariff expansions raise the stakes across supply chains. 

According to a recent PwC report, these tariffs could impact the consumer product sector by up to $134 billion. That’s five times the current tariff load of $27 billion. And the apparel industry is bearing the brunt of it. To remain competitive and profitable, businesses must embrace a more precise, tech-enabled and forward-thinking approach.

5 Tariff Strategies to Avoid the Cost of Getting it Wrong

One of the most underestimated consequences of tariffs is the cost of getting it wrong. Overbuying, inaccurate forecasting, or delayed inventory movement now come with a heavier price tag. Goods that were once slightly overstocked and marked down for clearance are now inventory liabilities that were more expensive to import in the first place.

As Arielle Knutson, CEO of athleticwear brand Oiselle, said to a Reuters reporter, “Ordering the right amount of product—and not being stuck with too much cash tied up in inventory—is key. It’s an almost impossible needle to thread.”

It might feel impossible, but it isn’t. Here are five tariff strategies wholesalers and retailers can take to offset the impact of tariffs:

1. Ditch “Excel Hell” for a Dynamic AI Platform

Managing critical functions through disconnected spreadsheets is no longer sustainable. Excel may have once worked for forecasting and inventory planning, but it can’t keep up with the pace, complexity and volatility created by shifting tariff policies and global supply chain disruptions. Relying on siloed data leads to misalignment across teams, delayed decisions and costly mistakes. If merchandising, planning and supply chain teams are all working from different files or outdated reports, it’s nearly impossible to respond in time to tariff-driven cost increases or sudden inventory shifts. Integrated, AI-powered platforms replace “Excel hell” with a centralized system of truth—eliminating manual processes and enabling faster, smarter decision-making.

These tools analyze real-time data across departments including merchandising, demand planning, sourcing and logistics, to generate predictive insights that help retailers buy smarter, reduce overstock and respond swiftly to market changes.

2. Nail Down Forecasting Accuracy

Historically, imprecise forecasting has plagued the industry, leading to overstocking, stockouts and wasted resources that impact profitability. With tariffs, the cost of being inaccurate is magnified. To succeed, retailers must embrace planning tools that offer better insight into what sells, where and when. Advanced inventory planning systems, powered by AI, allow retailers to align supply with demand precisely. By analyzing sales data and market trends in real time, AI-driven systems can accurately forecast demand and ensure inventory is purchased with purpose. 

3. Start Earlier

Timing is everything. Beginning the product lifecycle earlier gives businesses more room to negotiate with suppliers and avoid price shocks. One 7thonline client saved an average of $1 per garment simply by starting the production planning process one week earlier than competitors.

Early actions also allow businesses to diversify sourcing, reroute logistics and hedge risk before it becomes a crisis. If one supplier faces an increase in tariffs, retailers have more time to react; they can negotiate, find cost savings elsewhere or go with an alternative supplier.

4. Don’t Let Excess Inventory Drain Your Margins

Tariff-inflated products that don’t move fast enough get marked down, wiping out margins even further. Excess inventory takes up space, ties up capital, increases carrying costs and in a tariff-laden landscape, represents money spent on goods that may never sell at full price.  The key to reversing this trend is smarter buying from the start, backed by real-time visibility into demand signals. With integrated inventory management platforms, retailers can continuously monitor sales data and adjust open-to-buy budgets accordingly. 

5. Give People What they Want

Rather than planning for supply, retailers need to plan for demand. That starts with better buying strategies that prioritize precise assortment planning and allocation; the ability to identify the right products and match them to the right store locations, in the right sizes, is critical for the consumer experience. Inventory management solutions ensure retailers are stocked with high-demand products and can adapt to shifting consumer behavior. AI provides smarter allocation recommendations based on the propensity to sell for each product at each store location. 

Read the original article on Fashion Mannuscript here.

To learn more about how your team can use AI to offset increased tariff costs, email us at info@7thonline.com or book a demo with our team.

Retail is in its AI era. From streamlining operations and optimizing supply chains to increasing personalization for shoppers, retailers have been eagerly jumping on the artificial intelligence bandwagon to modernize their brand and workflows. As the industry continues to embrace digital transformation and adopt innovative AI solutions, it’s important to remember—fragmented systems and duplicative functionality contribute to increased tech debt. 

Be wary of bolting new AI tools onto already complex tech stacks as it could create a sprawling mess of disconnected platforms and overlapping capabilities; simplify your AI stack before it becomes tech debt. Managing technical debt is crucial for maintaining agility and delivering exceptional customer experiences. Technical debt refers to the long-term costs associated with choosing suboptimal solutions that require future rework such as additional implementations. Accumulating technical debt can hinder innovation and agility, making it challenging for retailers to adapt to market changes effectively. 

Duds & Dupes: Your AI Tech Stack

Many retailers are expanding their technology stacks with a variety of AI platforms to support current and future business needs. As businesses grow, and needs change, retailers will seek out more solutions that address new concerns; however, sometimes this leads to patchy workflows and/or duplicative functionality. Streamline your technology stack by adopting a single AI system that aligns with your business needs and scales as you grow—analytics, personalization, forecasting, inventory optimization and more—not only simplifying your AI stack but also enhancing operational efficiency.

In order for AI to be truly impactful, it must be embedded into every step of your workflow. Instead of piecing together a patchwork of specialized AI tools, or worse—fake AI modules, select a system that offers comprehensive functionalities to avoid the pitfalls of managing multiple disparate tools: outdated data, low visibility, inaccurate forecasting, limited collaboration, etc.. 

Eliminating redundant components and embracing multi-purpose services also simplifies your AI stack. You don’t need separate assortment management systems for ecommerce and physical retail, or isolated demand planning for stores and wholesale accounts. Simplification not only cuts down on the resources needed for system upkeep but also accelerates the implementation of new features, ultimately enhancing your ability to respond to market demands and meet shopper expectations. As retailers continue to embrace digital transformation, choosing an all-in-one AI system can be the smartest way to stay ahead—without drowning in complexity.

The takeaway: you don’t need more AI tools. You need a better, unified one.

While adopting AI is essential for staying competitive, retailers must approach its integration thoughtfully—choose integrated and comprehensive solutions that closely align with business needs from the start. Unified AI platforms reduce complexity and technical debt, paving the way for sustained innovation and ensuring that your systems grow in tandem with your business objectives.

7thonline is a leading provider of cross-channel merchandise planning and assortment management solutions, empowering retailers and wholesalers to make smart decisions at every step of the retail workflow. With artificial intelligence and machine learning embedded into the core of the platform, 7thonline has been enabling leading retailers and wholesalers to optimize planning processes, demand forecasting and inventory productivity. To learn more, email us at info@7thonline.com or book a demo with our team.

For retailers that want to get ahead of their competitors, artificial intelligence will play a critical role going forward. Retailers globally lost an astonishing $1.77 trillion in 2023 due to poor inventory management according to IHL. However, AI stands ready to change that—if implemented correctly.

AI is already playing a major role in the retail industry, and its role will only increase over time. In a report entitled “Market Guide for Retail Assortment Management Applications: Short Life Cycle Products,” Gartner predicted that the top 10 retailers globally will tap into AI “to facilitate prescriptive product recommendations, transactions and forward deployment of inventory” by the end of 2025.

Smart allocation will be one area of particular importance for retailers, as getting the right products to the right place at the right time is critical for reducing or eliminating stockouts and price cuts caused by bloated inventory at a particular location.

How AI optimizes product allocation

Traditionally, product allocation is supply-driven; products are allocated based on static, predetermined rules set by internal teams: square footage of stores, sales volume, week of supply, etc. With artificial intelligence, allocation becomes dynamic. 

AI can drill down deep into the data to analyze and generate recommendations for site-specific assortments. Driven by demand drivers such as propensity to sell, AI systems can determine the best style and size mix within each category for each location and channel, and determine a sales index for individual products by analyzing historical sales data. 

To optimize product allocation, AI looks at the retailer’s sales history, including inventory levels and stockouts, to determine where that previous allocation strategy went wrong and then correct it. Effective allocation requires analysis of multiple data sets from each location, and only AI is capable of providing accurate analysis of all these data sets simultaneously.

In short, here’s how AI does all of this. The technology classifies and categorizes information based on visual, numerical or textual data. It then analyzes and modifies the retailer’s strategies, plans and allocations based on real-time data. It automatically generates analysis that makes faster response times possible and can help retailers retrieve relevant information quickly.

Real-time decision making

The advent of AI is empowering smart, real-time decision making across channels and locations. Before AI, retailers relied on spreadsheets and manual allocation methods that were time-consuming and error-prone—this is still common today.

Introducing AI into retail workflows transforms the planning process, making it effective and dynamic. With AI, retailers are armed with real-time analysis and smart recommendations that improve regional and store-level distributions; the industry-specific algorithms continually monitor real-time sales data to provide the most optimal recommendations.

Through this continuous improvement, AI can identify opportunity gaps in product ranges at each location, leading to additional sales—if those gaps can be filled. Without real-time understanding of inventory levels, retailers can refill stock right when it’s needed at the locations that require it.

Adapting to current conditions

Retailers face potential problems at both ends of the sales spectrum. On one side, they must deal with shifting consumer behaviors, but at the other end, they face the potential for supply chain disruptions that could prevent them from addressing those shifting behaviors.

However, AI is capable of continual monitoring and measuring actual performance against the plans created, creating a dynamic feedback loop. This feedback loop enables retailers to map their supply chains and connect parts of their business that were previously disconnected.

McKinsey estimates that retailers which implement technology to capture the variability of demand via forecasting can better predict customer behaviors, resulting in an average of a 3% to 4% increase in revenue. Through machine learning, as more data is collected and analyzed, these plans will become more and more accurate.

How to use AI to make predictions

According to McKinsey, autonomous supply chain planning can boost revenue by 4%, slash inventory by 20% and cut supply chain costs by 10%; AI enables retailers to continue meeting customer demand while reducing inventory (and its associated costs) because they can accurately predict demand across sizes, colors, locations and more.

McKinsey has found that companies have more success with implementing the technology when supported by four essential elements.

The first involves rewiring the organization by deploying real-time allocation dashboards, setting up a cross-functional supply chain organization and increasing the speed of critical decision making.

Secondly, McKinsey advises retailers to streamline their processes by enabling segmented business processes, promoting closed-loop planning and supporting “what-if” predictive scenario planning for allocation. Additionally, retailers should establish standard operating procedures to manage exceptions to typical situations.

Thirdly, McKinsey recommends digitizing the supply chain by leveraging scalable technologies and tools. Retailers are also advised to enable advanced analytics and AI-based planning solutions, integrate their technology across their supply chain and scale their technology to multiple regions and business units.

Finally, retailers should foster an accountability mindset and empower employees to make decisions based on the data and analysis received. Implementing AI also requires building talent with deep machine-learning capabilities—or outsourcing it.

Correct implementation of AI

When implemented correctly, AI is game-changing for retailers, but the key here is “correctly.” To achieve real value from AI, retailers need to move beyond buzzwords and embed AI into every step of their daily processes—rather than amplifying individual modules that are segregated from the rest of the system. 

For example, as a retailer, you need your open-to-buy system to communicate with your demand-forecasting tool, but AIs that doesn’t tap into all data sets simultaneously can’t provide accurate analysis or helpful information. Real AI should be able to provide robust analysis of multiple datasets without a data scientist’s expertise. 

Read the original article on The AI Journal here.

To learn more about our AI-native inventory management and demand planning solutions for retailers and wholesalers, email us at info@7thonline.com or book a demo with our team.