Inventory management is rapidly evolving. Clipboards and spreadsheets have been replaced with powerful tools that leverage data, automation, and AI. This modern technology helps manufacturers keep pace with an ever-changing global supply chain and navigate economic uncertainties.
If your organization isn't integrating AI and automation for inventory management, you risk falling behind. These tools can help you meet rising consumer expectations and boost efficiency within your organization, while preventing critical errors and unplanned downtime.
In this article, we'll explore how AI and automation trends are shaping the future potential of inventory management and what this means for manufacturers who want to stay competitive in their industry.
Moving from Reactive to Predictive
Traditionally, most inventory management systems were reactive. Companies only placed new orders once the current stock dipped below a certain threshold. While many inventory managers practiced forecasting, their estimations relied heavily on historical data and observational trends. This worked in a stable environment, but couldn't account for unplanned disruptions.
With the advent of artificial intelligence (AI) systems, managers can shift from reactive forecasting to a predictive method. AI machine learning models can digest huge volumes of data, including:
- Sales trends
- Seasonal demands
- Supply chain disruptions
- Weather patterns
After parsing all this data, AI models can produce forecasts that update in real-time. Although there will always be unknown variables, these dynamic AI systems are nimble enough to make rapid pivots.
As they receive more data inputs, AI "evolves" to make inventory management predictions with greater accuracy. They learn from customer behavior and vendor relationships to fine-tune their models. When fully integrated, AI can also provide data to proactively optimize your pricing, supplier sources, and other strategies like product bundling to efficiently manage inventory turnover.
Using Automation for Smart Warehouses
Beyond predictive modeling, AI and automation lay the foundation for building smart warehouses. Many global manufacturers are using autonomous systems to revolutionize inventory management at the ground-level, transforming warehouses from static storage spaces into dynamic fulfilmment centers.
By utilizing autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and other AI-enhanced picking systems, inventory management is easier than ever to manage and scale.
These technologies can handle tasks like picking and packing orders, restocking shelves, and transporting goods across large facilities. They can also perform real-time cycle counts to ensure inventory counts are constantly up-to-date. Additionally, AI systems speed up component retrieval for assembly lines, which reduces downtown.
Automation can't fully replace an organization's labor needs. However, as rising wages and labor shortages make manual warehousing less stable, using automation can fill the gaps.
Internet of Things (IoT) Integration
Of course, we can't talk about AI and automation trends for inventory management without highlighting the role of IoT.
Short for Internet of Things, IoT refers to a network of physical objects embedded with sensors or other technological components that enable them to exchange data. These smart devices connect with one another, sharing data and enabling tasks to be automated across physical locations. A common example of IoT might be a thermostat system that utilizes cameras and other sensors to adjust the temperature depending on occupancy.
Within the context of inventory management, IoT can combine with AI to transform warehouses and other storage facilities into a robust, fully automated ecosystem.
Let's say your inventory includes perishable chemicals. IoT sensors on pallets can detect changes in temperature or volume that indicate quality is compromised. These sensors alert the system, which reroutes the pallet for immediate inspection while updating inventory records in real-time. Then, the production floor is notified and production schedules are adjusted to accommodate the inventory change.
Real-Time, Automated Replenishment
Another AI and automation trend that will shape the future of inventory management is real-time, autonomous replenishment. These systems are triggered automatically when inventory drops, placing new orders to anticipate production needs without human intervention.
The key advantage of an AI-powered replenishment system is that the minimum order threshold isn't static. You're not just programming software to reorder when inventory drops below 1,000 units. Instead, automated replenishment systems adjust themselves based on sales velocity, supplier lead times, and other production needs. These systems can also select different suppliers based on analysis of performance.
In high-stakes manufacturing, precision is priceless. An automated replenishment system driven by AI can minimize human error, reduce lead times, and help avoid both overstock and understock scenarios.
Challenges & Emerging AI Trends to Watch
Despite the advancements in AI and automation, adopting this technology into your inventory management system isn't quite "plug and play" – at least, not yet. For the time being, operators may face challenges. As you prepare to integrate these systems into your facilities, be mindful of:
- Data Quality: AI models are only as good as the data they receive. Make sure that your systems pull from a reliable database that's been verified for its accuracy.
- Integration: Many legacy systems struggle to keep up with modern technology like AI. You may need a custom-built API (application programming interface) or other software solutions.
- Cost: Depending on your needs, automation may be an expensive investment upfront. However, the ROI on this technology is generally very strong in the long-term economic landscape.
- Resistance to Change: Some internal teams may be resistant to changing their current system, so navigating the integration of AI and automation requires strong leadership and clear communication.
But if you're prepared to face these challenges, you'll be rewarded with faster fulfillment, lower carrying costs, more resilient supply chains, and other key advantages.
What's next for AI and its role in inventory management? Experts are predicting a few exciting trends to emerge:
"Digital Twins" for Inventory Simulation
Wondering how a delay with a supplier in Asia may affect operations in your warehouse on the West Coast? Using AI, you can develop a "digital twin" that replicates your inventory system electronically. Then, you can use this replica to run simulations of various scenarios – and prepare accordingly.
Using Blockchain for Traceability
Blockchain is essentially a decentralized, digital ledger. In tightly regulated industries, this technology is being used to track inventory from origin to delivery. Due to the decentralized nature of blockchain, it's highly transparent and virtually tamper-proof. This can help manufacturers and suppliers who must abide by special regulations or security protocols.
Decision Making with Edge AI
Another predicted trend is the adoption of edge AI. Unlike cloud databases, edge AI processes data locally. This reduces lag, as well as the risk of data breaches, and experts predict that it will become a vital tool for remote facilities that require tight latency requirements.
Managing Inventory for a More Efficient Future
AI and automation have the potential to do more than refine inventory management. When correctly integrated into your system, they can revolutionize it. Manufacturers who embrace this technology will boost efficiency and remain resilient in the face of economic uncertainty and supply chain disruptions.
The bottom line? Companies that invest in AI and automation for their inventory management will outpace the competition, while companies that don't risk falling behind. Using smart tech to power your inventory management is critical for future success.
