Supply Chain Technology: Types of Practical Supply Chain Tech

The backbone of any physical brand is its supply chain, a network of raw material producers, manufacturers, shippers, warehouses, retailers, and—at the end—a buyer. A typical supply chain includes information systems designed to keep track of everything, giving managers deep visibility into their operations while helping their business stay agile amid economic and market shifts.

Advances in technology, such as artificial intelligence and robotics, have made these systems more effective but also harder to understand. Learn more about how technology is changing supply-chain management, and pick up insights from Aerflo cofounders John Thorp and Buzz Wiggins, who used supply chain technologies to expand their complex, multilayered supply chain.

What is supply chain technology? 

Supply chain technology refers to the array of digital innovation tools, hardware, and supply chain software used to oversee the movement of goods from raw materials to the final consumer. It encompasses everything from the cloud computing platforms that host your inventory management data to the Internet of Things sensors that can provide real-time shipment tracking.

The goal of implementing these advanced technologies is enhanced efficiency, cost reduction, and a better customer experience. In an era of more frequent supply chain disruptions, technology acts as a control tower, helping supply chain professionals identify risks and make informed decisions.

Types of supply chain technology

• Forecasting tools
• Internet of Things
• Barcode systems
• Additive manufacturing
• Cloud computing and SaaS
• Advanced data analytics
• Digital twins
• Robotics
• Digital procurement platforms

To build a resilient system, it helps to understand the different supply chain solutions. Here’s a look at the technologies reshaping the supply chain landscape.

Forecasting tools

Forecasting software is designed to help businesses anticipate customer demand and align their production schedules accordingly. Traditionally, these tools rely on quantitative statistical models—such as moving averages or regression analysis—to project future needs based on past sales cycles and seasonal trends. By centralizing data from across the organization, these platforms allow managers to visualize potential stockouts or overstock situations months in advance.

AI supply chain management simplifies forecasting. By analyzing past sales data and historical data, AI demand forecasting tools can sense demand in real time by analyzing external variables like weather patterns, social media sentiment, and even geopolitical shifts. This lets businesses optimize inventory management, ensuring they have enough stock without overinvesting. Ultimately, although AI can process data faster than humans, it cannot yet account for the uncertainty of a sudden tariff rate change or a shipping delay.

For a company like Aerflo, which makes portable carbonation devices, forecasting is a constant battle against the unknown. “The only thing that’s certain about a forecast is that it’s going to be wrong,” cofounder John Thorp says. “You’re trying to forecast what happens in nine months when you don’t even know what’s going to happen tomorrow.” AI demand forecasting bridges the gap by identifying patterns in consumer behavior that humans might miss, reducing the need for constant manual intervention. And for Aerflo, the human-led strategy of building flexibility into the plan is what prevents a wrong forecast from becoming a business-ending event.

Internet of Things

The Internet of Things connects physical objects to the web. In a supply chain, this can mean placing sensors on pallets or inside containers to collect data on location and condition. This provides real-time information, letting companies mitigate risks—like a shipment getting lost or damaged—before they result in a bigger or total loss.

Barcode systems

An effective barcode inventory management system helps businesses maintain accurate records across the supply chain. In a business model where components—like the carbon dioxide capsules that inject gas into Aerflo’s water bottles—are returned and refilled, tracking unique serial numbers via digital scanners ensures a specific part has been through the necessary cleaning and inspection cycles. This allows the control tower to know exactly which canisters are ready for circulation and which are still in the cleaning and inspection queue.

Additive manufacturing

Additive manufacturing—also known as 3D printing, when material is incrementally added to a product—is moving beyond the prototyping phase and onto the shop floor. It allows on-demand production of custom components that would otherwise be subject to long lead times from overseas vendors.

Aerflo used 3D printing to bypass a 60-day lead time for its packaging. Although typical box inserts are made of folded cardboard, Aerflo’s engineering team found that designing a 3D-printed insert using biodegradable filament was more efficient. 

“This cardboard insert [from a vendor] is more expensive, and it takes 60 days to get here,” cofounder Buzz Wiggins says. By contrast, the team was able to design the 3D-printed plastic version in a single day. “We bought an extra 3D printer for our facility, and we now make these in-house.” This shift from sourced cardboard to in-house 3D printing didn’t just save time—it provided a more robust design that holds the carbonation system securely during shipment. 

Cloud computing and SaaS

Cloud-based software and software as a service (SaaS) platforms help smaller brands access the same high-level logistics tools as global giants. These platforms provide a single source of truth by syncing data across different departments in real time.

Aerflo utilizes a cloud ecosystem to keep its operations lean, integrating Shopify for real-time sales data and QuickBooks to manage cash flow so that every transaction is captured and categorized automatically. However, for inventory management, the company intentionally bypasses heavy enterprise resource planning (ERP) systems in favor of a hybrid approach: high-frequency manual counts recorded in Google Sheets.

For brands that scale beyond manual inventory management, the transition usually involves migrating to a comprehensive cloud ERP, like NetSuite. This type of platform acts as a centralized brain, managing everything from procurement to payroll. Additionally, specialized warehouse management systems like ShipStation and Manhattan Active allow for real-time pick-and-pack tracking and automated shipping label generation. These SaaS solutions eliminate the need for manual spreadsheets by providing a constant, automated update of stock levels across multiple warehouses.

Advanced data analytics

Advanced data analytics give companies deeper insights into their supply chain processes. Although many startups begin by managing operations via manual spreadsheets, advanced data analytics become essential as a company scales toward managing millions of individual product touchpoints. 

To handle this volume, growing brands typically lean on business intelligence platforms like Tableau, Microsoft Power BI, and Looker. These tools ingest raw data from across the supply chain—shipping logs, production schedules, and return rates—and transform it into visual dashboards. Companies use these analytics for diagnostic purposes, such as identifying exactly where a bottleneck occurs in a multileg shipping route, and for prescriptive purposes, like determining the optimal inventory levels for different regional warehouses to minimize shipping costs.

By moving data into a centralized data warehouse like Snowflake or Amazon Redshift, supply chain managers can run complex queries to find hidden inefficiencies, such as which specific manufacturing shifts result in the highest product defect rates.

Digital twins

In the context of supply chains, a digital twin is a dynamic, end-to-end replica of your entire logistics network. It pulls data from ERP systems, IoT sensors attached to shipping containers, and even external sources such as weather reports to create a model of how goods are moving. These models let businesses run simulations and conduct scenario analysis—assessing the potential effects of events like a port strike or a sudden tariff increase.

Aerflo faced a real-world version of this challenge when it encountered a double tariff on transparent Tritan plastic. As Buzz explains, Aerflo’s supplier in Malaysia ordered the material from the US for manufacturing, then shipped the finished part back to Aerflo in the US—with tariffs added at every stage. This dynamic forced the team to find a new supply chain configuration that made sense for that specific component. 

A digital twin lets companies model these scenarios before they happen, helping them build supply chain resilience. 

Robotics

Modern warehouses are increasingly populated by robotics and other automated systems. This includes everything from autonomous mobile robots (AMRs) that navigate warehouse floors to automated sorting belts. These technologies let companies focus on higher-level supply chain planning.

Digital procurement platforms

Digital procurement platforms have replaced the traditional, fragmented way of finding suppliers. These platforms let buyers vet global manufacturers, compare lead times, and manage contracts in a centralized digital location. By digitizing the request for proposal process, companies can compare multiple vendor bids side-by-side to assess, not just price, while also comparing sustainability certifications and production capacity.

Industry leaders like Thomasnet, SAP Ariba, and Coupa are common examples of platforms that provide this real-time visibility. For a hardware brand, these tools allow for strategic sourcing, where you can filter for manufacturers with specific expertise—such as high-pressure valve assembly.