Inventory management is all about having the right items on hand at the right time to meet customer demand while controlling costs and minimizing waste and loss. Companies with best-in-class inventory management practices don’t guess how much stock to buy, and they keep a steady flow of raw materials, work-in-progress items, and finished goods moving from manufacturing to consumer, over a variety of distribution channels.
But no one stays best in class by resting on their laurels. Companies need to keep on top of trends in inventory management, understand the drivers behind them, and determine whether it makes sense to be an early adopter — or let someone else work out the bugs.
What Is Inventory Management?
Inventory management is the process of acquiring, storing, and selling or using the four main types of inventory: raw materials; works in progress (WIP); finished goods; and maintenance, repair and operations (MRO) stock.
4 Main Inventory Types Explained
Businesses of all sizes have four main types of items in inventory. These items represent a company’s end-to-end process and cover the majority of steps in an operation, whether the final product is for end-user usage, part of a B2B supply chain, or a perishable such as a baked good. The following breaks down these types in greater detail:
- Raw materials: Raw materials include both nonperishable materials, such as sand, wood, or wool, and perishable materials, such as raw fruits, vegetables, grains, or meats used to make processed foods.
- Work in progress: WIP are goods that are in progress but not yet ready to sell, such as sheets of glass, window frames, fabric, or flour.
- Finished goods: Finished goods are ready-to-sell items, such as a window, suit coat, or a loaf of bread. Finished goods may be either intermediate items headed for another manufacturer, such as fabric to a clothing maker or bread to a sandwich shop, or a consumer good destined for a retailer or direct-to-consumer (D2C) sale.
- Maintenance, repair and operations: MRO items are necessary to keep the production line up and running, like tools or spare parts, or consumables to get products to their destinations, like paint or packaging.
All companies — and especially those in inventory-intensive industries, such as manufacturing, retail and food service — must avoid tying up more cash in inventory than necessary while minimizing waste and shrinkage. Successful companies accomplish this using inventory models.
Why Are Inventory Models Important?
An inventory model is the system a business uses to determine the optimal way to produce its goods. The inventory model or models in use govern areas including, but not limited to, the order frequency for a balanced level of raw materials or MRO stock, deciding how best to track and store items awaiting production or transit and how to fill customer orders quicky and with high accuracy. Factors when selecting a model include the industry, any special considerations around the production lifecycle and which model leaders think will best maximize the investment in goods and raw materials
Understanding inventory models will help businesses maximize resources, manage costs, and deliver quality goods to customers on time, and is the first step in effective inventory management. That's because each model has a specific technique to help leaders determine how much stock to have on hand. For instance, companies with more complex manufacturing and supply chain processes use methods such as just-in-time (JIT) and materials requirement planning (MRP) to balance inventory. Popular models like economic order quantity (EOQ), economic production quantity (EPQ), and days sales of inventory (DSI) are also useful.
While smaller businesses tend to manually track inventory using spreadsheets, larger corporations benefit from using either specialized enterprise resource planning (ERP) software or a specialized inventory management application. Current ERP software often comes with artificial intelligence (AI) capabilities, allowing AI agents to automate data entry, assess incoming data for signs of low stock or fraud, provide insights on patterns impacting inventory or related logistics, or collaborate across different data sources.
Once a company has settled on a model, it’s time to seek a competitive advantage. And that requires some out-of-the-box thinking, advanced planning, and leveraging advances in technology and process.
Top 14 Inventory Management Trends
Keeping abreast of the latest trends in inventory management is essential no matter what business you’re in. Many of these trends are focused on helping companies assess where to invest resources, while others will get you more buy-in from stakeholders, better use of data and a roadmap to growth. In many cases, AI tools and capabilities can reduce the amount of effort involved with these tasks. The following review key trends impacting inventory management today:
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Automated Guided Vehicles (AGVs) and Automated Mobile Robots (AMRs)
Customers are demanding ever-faster deliveries, so businesses are increasingly looking for ways to work more efficiently. Automated guided vehicles (AGVs) and automated mobile robots (AMRs) are tools to help warehouse operators collect products from decks and pallets. While AGVs have been around for a while, AMRs are newer on the block and take advantage of cutting-edge AI technology.
Traditionally, AGVs rely on magnetic strips or wires to follow fixed paths through a warehouse, meaning that they aren’t great fits for facilities that change their floorplans or have a lot of people moving around. AMRs are among a new class of “collaborative robots,” and they don't need to rely on fixed routes to navigate a space. Instead, AMRs use a combination of sensors and AI-powered tools for navigation, decisions, and handling tasks or obstacles. They can even be “paired” with a human worker.
Both types of vehicles reduce the time it takes to move items around the warehouse and free up human staff for other tasks to help fulfill orders faster. Because AMRs don’t require additional investments in infrastructure, such as the wires to guide AGVs, they can be more cost effective and scalable than you might think while being relatively easy to put to work.
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Artificial Intelligence
When it comes to warehouses and inventory management, systems with AI and agentic AI capabilities work hand-in-hand with those IIoT initiatives. The problem is that a lot of the data manufacturers and retailers collect now isn’t structured to fit neatly in a spreadsheet: Think product images, videos shot as those AMRs move around warehouses, various SKU formats and the data produced by all manner of sensors and scanners. AI agents can examine inventory to spot out defective products or packaging and notify appropriate parties so that customers only get quality items. Plus, the nature of inventory means that your data set is continuously growing and changing. All that makes it difficult to analyze.
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Cloud-Based Solutions
The ability to track inventory in real-time can be a game-changer for any business. Because cloud-based solutions allow all of your company’s data to be unified in ways that allows for both anywhere/anytime access by staff and direct sourcing for AI agents, , decision-makers can respond to and solve inventory issues more quickly. And while many SMBs may still have some legacy on-premises applications, upgrading to cloud platforms, like software as a service (SaaS), delivers tangible benefits: Lower upfront costs since there’s no hardware to buy, faster implementation, consistently up-to-date software, and better security and resilience than most organizations can build on their own. In addition, cloud platforms often include AI tools, including simplified paths to developing custom AI agents.
From an inventory management perspective, situating data in a central location simplifies adding new warehouse locations, even doing pop-up fulfillment centers in stores. Centralization enables a GPS location project, where you track on-the-move pallets, containers or delivery vehicles in real time to predict when items will arrive at their destinations. AI tools and agents can then process that data for real-time tracking as well as ongoing analysis to find the reasons for recurring delays.
Furthermore, the AI tools in inventory management software can integrate with your finance and accounting and order management systems for cross-functional analysis and granular tracking of inventory down to the SKU or barcode, whether items are in a warehouse or in transit.
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Distributed Inventory Management
Distributing inventory across multiple warehouses can reduce transportation costs and speed up delivery times — if you can put the right products in the right places and consistently dispatch items from the warehouse closest to the customer.
Success requires data analysis to see where orders are coming from versus where stock is located, the flexibility to set up distribution centers in the right sites based on data, and the technology to direct suppliers to properly split up shipments. AI agents can optimize distribution by connecting data across warehousing, sales, and logistics for more effective actions and management.
In most cases, when a company is managing more, smaller warehouses versus a few huge locations, it can more tightly manage inventory.
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Predictive Picking
Again, this trend depends on data analysis — in this case, using unstructured data to predict behavior by recognizing interdependencies and patterns. Predictive picking software can use AI tools to initiate fulfillments before an order has even been placed. Success depends on unifying data, such as planned marketing campaigns, weather and seasonality to predict customer orders with a high degree of accuracy; cross-functional AI agents can connect this data for a broader analysis.
If that sounds complicated, it is. Success at scale requires a lot of data and powerful analytics tools. But most manufacturers and retailers can start down the predictive path by analyzing historical data to spot demand surges for specific products, beyond the obvious like candy in late October or pool chemicals in May. Then, they can deploy AI-powered analytics to figure out why there was a spike and whether it’s likely to recur. If so, the company can have enough stock on hand and design a fulfillment process that minimizes shipping times and touches. And that data can itself eventually feed into a predictive picking program.
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Success Strategies for Trend Adoption
Which trends make sense for your business? That depends on your company's strategic objectives, budget, size, and appetite for technology. Businesses should weigh the cost/benefit against other long-, medium- and short-term projects on the table, and ensure there is an executive sponsor who can define the plan's success.
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Personalization
Personalization in inventory management focuses on a deep understanding of customers' buying habits, so you can stock and suggest relevant products to ensure a seamless experience based on past behavior. A robust inventory management system allows companies to tap into personalization data to boost sales. For example, a retailer may suggest additional products to customers browsing online or reaching check out, while a manufacturer could begin to stock complementary items, such as maintenance kits for the machinery it produces. AI tools are commonly used to synthesize that type of data for automated and fast personalized suggestions.
Sources of personalization data include the following:
- Demographic/persona data for individuals, such as job title or location.
- Company data points, such as employee count, revenue and industry.
- Behavioral data gleaned from your website or a customer’s order history: first-time vs. repeat buyer, content consumed, changes in buying quantity or SKU diversity.
- Contextual data such as time of day, week or month customer visits your site or whether an order is initiated from a mobile device or desktop.
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Creative Financing
Especially for new manufacturers, using creative financing to pay for inventory can deliver a competitive edge.
On the smaller side, think about sites like Kickstarter that enable “pre-tail” sales, where a maker can rack up retail revenue before the product is produced. These sales can fund purchases of raw materials and manufacturing capacity.
Larger manufacturers might look beyond a typical inventory loan, where the inventory itself is the collateral. Before looking for new financing, see if you can reduce your inventory carrying costs. AR loans or factoring, also called invoice factoring, entails either borrowing against your accounts receivables or selling your outstanding AR to a “factor,” which pays your company a percentage of the total value of those invoices, often 70% to 90%.
Companies with stock that’s not moving may also take steps to boost liquidity, including converting stale inventory to cash by offering appealing discounts or by bundling less popular items with strong sellers. Or, consider more flexible rental options instead of the traditional own or lease model — there are significant advantages to adding a subscription model or recurring revenue source to your business.
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Automation
Automation is where companies utilize trigger-specific actions without human intervention, or with very minimal involvement. By automating rote tasks, you free up employees to focus on more valuable projects, including those that promote growth and product quality improvement. The level of automation varies depending on the tool and underlying infrastructure; simple workflows can be based on triggers and thresholds while AI tools can introduce complex monitoring and decision-making for real-time automation.
For example, retail automation can include automatically updating stock counts when an order is processed through your sales platforms. You won’t be at risk of overselling, and the customer doesn’t have to wait to confirm an order. Other examples include automating SKU mapping, order fetching, updating real-time shipping rates and reorder notifications. AI can take this further by integrating other factors such as seasonality, weather, and competitor pricing to make snap decisions and suggestions outside of standard workflows.
Warehouse automation is a specific discipline focused on moving inventory into, around and out of warehouses with minimal human involvement. It takes a dual focus on digital and physical processes. At a basic level, warehouse automation merges automation, robotics, and data analytics. An example is a warehouse management system gathering information on the number of a given SKU that will ship in the next 24 hours and directing a worker to pick those items all at once to avoid repeat trips. More advanced warehouse automation could use AI, cameras, and sensors to help an AMR navigate a warehouse and compile an order without human help.
Moving toward real-time analytics powered by AI is a popular way for retailers to enable personalization, monitor changes in supply costs to recalculate stock levels and identify which suppliers aren’t up to the company’s standards.
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3PL
Third-party logistics, or 3PL, is where distribution and warehousing or other activity is outsourced to a third party. These services may help businesses reach more customers or operate more efficiently without incurring the costs that come with infrastructure development. Businesses have the option of outsourcing an entire logistics process or select operations. The key to success with 3PL is to connect all production sites, including the manufacturer and 3PL provider, such that they operate as a cohesive supply chain. AI oversight of live data and notifications can ease 3PL management.
More ecommerce means that returns, aka the reverse supply chain, represent a growing drain on profits. Contracting with a 3PL for returns handing could reduce costs because these firms tend to deliver economies of scale, including better rates with carriers, and processes tuned to execute returns as inexpensively and efficiently as possible.
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Hybrid Warehousing & Shipping
A hybrid warehouse combines multiple activities, some typical — storage, picking, shipping — and some not as common, as is the case when, for example, the line between retail location and warehouse blurs. For example, some big box stores have converted unused space to drop-ship locations. While this uses space efficiently, retail employees may need to be retrained.
We’ve also seen retailers partner with those 3PLs to store inventory and ship orders directly to end customers, adding a hybrid layer to traditional warehousing and shipping. Drop shipping, where a retailer never takes possession of stock but pays a manufacturer to send items direct to customers, can also have a hybrid flavor when a retailer chooses to stock a small number of popular drop-shipped items so it can offer premium shipping options. A creative approach to warehouse management means that businesses can offer those extra SKUs and lower their costs.
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Omni-Channel Inventory Control
It seems simple: Align your channels so that a customer can look online to see whether a given item is available in a nearby physical location, make the purchase then walk into the store and pick up the item. Oh, and make sure the cost of the item on the shelf is the same as what the buyer paid.
In reality, omni-channel inventory control requires coordination among store, distribution center and ecommerce operations to reconcile physical and online inventory and ensure price and discount or sale parity. A customer who purchases an item for $50 online, picks it up then decides to walk around the store and sees the same item on sale for $39.99 is likely to get right back in line for a price adjustment, and not cheerfully.
Still, to remain competitive, an omni-channel strategy is a must. To succeed, businesses need to make sure they have a connected supply chain, a near-real-time inventory reconciliation process to provide visibility, advanced demand planning, highly accurate order fulfillment, data analytics, and tracking and distribution centers close to where customers are.
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Blockchain
Most people think of blockchain as the underpinning for digital currencies like Bitcoin. But that’s just the beginning. A blockchain is simply a database that stores transactional data. Transactions, once created, cannot be modified, and a distributed ledger allows transparency, either to anyone or members of a private consortium.
There are a number of examples of companies using blockchain for inventory management and control. The top enterprise uses for blockchain now are supply chain tracking with tamper-proof audit trails, execution of smart contracts in the financial industry, and digitized and cross-compatible health records.
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Reporting & Analytics
A common thread with many of these trends is the use of real-time data analytics to make decisions, create a more customer-centric business model and minimize costs while boosting efficiency.
From an inventory perspective, becoming more data-driven allows businesses to make better demand forecasts, move toward just-in-time inventory replenishment, and get and provide near-real-time updates on supply/shipment locations and arrival times.
Much of this is powered by AI, which can sift through massive volumes of data for insights and analysis. Thus, businesses need to view it as a resource and use it to stay competitive. Best practices to become more data driven include the following:
- Collect data even if you’re not sure how you’ll use it now. Whether sensor data from IoT devices or images from a warehouse robot fleet, the more inputs you provide, the more accurate your eventual predictive analytics and reporting become.
- Opt for interconnected software and data sources. Siloes are bad for analytics, so look for systems that can interconnect with your finance and accounting, ERP, order, and customer management and other core software. Custom integrations are expensive. Look into AI agents that can reach across different data sources for contextual analysis.
- Insist that decisions are based on data, with reports to
back up assertions. Select the metrics, like logistics
KPIs or supply chain
KPIs, that matter to your company, and track them consistently.
All product companies can benefit from a deep dive into inventory management and controls.
- Take action based on insights. Teams that work to analyze data and issue reports but never see any movement based on their efforts will become discouraged. For manufacturers, even rudimentary data analytics can reveal suppliers that often fall short on quality or timeliness, production bottlenecks, or inefficient warehouse layouts. Add some machine learning and you can start taking predictive actions.
Improve Your Inventory Management With NetSuite Powered By AI
To maintain and even grow operations, inventory managers need to increase their skill sets and gain new data analytics and forecasting capabilities. Integrated business platforms like NetSuite ERP that include AI-powered inventory management can help you amass and analyze data so that you can embrace current trends, keep your profit margins strong, and maintain a high level of service for your customers.
Properly managing inventory can make or break a business, and the more technology advances, the more opportunities companies have to wring out insights. It’s important to pay attention to these trends to stay competitive, and cutting-edge AI tools such as NetSuite’s can automate data collection and analysis for real-time insights into stock, pricing, and industry trends.
