Smart Inventory, Smarter Manufacturing: The Convergence of Automation and Supply Chain Management
Why this article is worth your time …
- From warehouses to production lines — Discover how automation is reshaping inventory management, from smart warehouses to AI-driven material movement on the factory floor.
- The role of AI in inventory optimization — Learn how AI-powered decision-making is streamlining stock monitoring, order fulfillment and workflow efficiency in manufacturing.
- Overcoming automation challenges — Explore the key hurdles manufacturers face when adopting automated inventory systems and the strategies to navigate brownfield constraints and workforce transitions.
Inventory management is now considered a prime candidate for automation as manufacturers target opportunities to reduce costs, accelerate product delivery and make the most out of experienced talent.
A major factor behind this shift are global supply chain complexities and rising customer expectations, which are prompting manufacturers to streamline critical inventory processes such as monitoring stock, parts reordering and optimizing the flow of goods.
Keith Chambers, Aveva’s vice president of manufacturing solutions, strategy and realization, spoke with Automation World about how manufacturers can benefit from the convergence of automation and inventory management.
AW: How has inventory management evolved with advancements in automation technologies?
KC: Inventory management has traditionally focused on warehouse storage, retrieval and material movements with the aim of improving warehouse efficiency. However, inventory management is now converging with manufacturing automation to enhance quality, productivity and cost-efficiency across the entire manufacturing facility. The driving force behind this shift is the shortage of labor and experience, which is fundamentally reshaping the cost-benefit equation in favor of greater automation use.
In this context, we see traditional manufacturing processes, which are largely automated today, expanding to include the automation of material movements from the warehouse to the production line and into the put-away of produced goods. The goal here is to minimize human interactions in these operations.
While automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) have been in use for some time, they are now better integrated into the overall production system as part of a push towards autonomous manufacturing. This integration is both cyber and physical, meaning these systems are "smart" in their operations, such as moving materials needed for specific orders to the right production line, and physically integrated in that they can automatically load materials into processing tanks, for example.
Another trend is the movement of the final stages of manufacturing into warehouses closer to the point-of-sale. For example, bulk packages are brought into a warehouse for final point-of-sale packaging based on current order fulfillment. This shift has led to the development of the warehouse execution system (WES), but it doesn't change the fundamental requirements for inventory automation.
AW: What are the key technologies required for automating inventory management?
KC: IIoT (industrial Internet of Things), industrial Ethernet, 5G networks, geolocation beacons, RFID tagging, bar codes and weighing systems provide the necessary sensing and infrastructure for autonomous inventory movements and management.
The next layer involves physical equipment capable of moving inventory. AGVs and AMRs transport materials between the warehouse and production floor, while robots can replace human workers managing material handling in production operations such as adding bagged materials into a tank. In certain industries, buffering inventory between production stages is also necessary.
The equipment varies depending on the process and is often considered part of the process. For example, the automotive industry requires automated inventory buffering systems like automated storage and retrieval systems (ASRS), which allow car bodies to be buffered and fed to the final assembly line in a different order than they come out of the paint shop.
To function autonomously, this equipment needs to be smart. That’s why AI is increasingly used to ensure safety by avoiding collisions, optimize pathing to determine the shortest distance between two points considering other mobile robot routes, and make various decisions around inspections and material handling, such as identifying dented cans or verifying the correct material.
While the goal of these systems is to operate autonomously, their actions must be orchestrated to be effective. For instance, a mobile robot needs to know what materials to collect and where to deliver them, and a packing robot needs to know the pallet stacking pattern for a product and the number of cases in an order.
Although Industry 4.0 envisioned this orchestration to be done from smart machine to smart machine, this has yet to be realized. Instead, it increasingly falls to MES (manufacturing execution systems) technology, which already orchestrates many necessary data sets. AI also assists with scheduling and planning, working with digital twins of manufacturing plants and warehouses to maximize throughput and efficiency.
AW: What factors should be considered when selecting the right technologies and tools for inventory automation?
KC: It's important to start by assessing the current level of automation in your production processes and applying lean principles to identify areas where waste is occurring. For instance, you should determine if the feed of raw materials to the machines is a bottleneck or if an increase in production is necessary before any benefits can be realized. These questions can typically be answered using OEE (overall equipment effectiveness) tools to analyze where production losses are happening.
Additionally, different processes and types of equipment vary in maturity within the automation space. For example, robotic palletizers and mobile robots for certain types of raw materials are quite common in many plants today.
AW: What common challenges do manufacturers face when adopting automated inventory management systems and how can they be overcome?
KC: Manufacturers often face significant challenges when adopting automated inventory management systems, particularly in existing brownfield factories. Unlike greenfield sites, which can be designed to accommodate mobile robots and other automation technologies, brownfield sites typically have limited physical space in warehouses and plant floors. This means that existing equipment may need to be moved and additional infrastructure, such as lighting for visual inspections, Wi-Fi and power may be needed. Wider or dedicated travel lanes and safety zones for mobile and stationary robots may also need to be set up.
Beyond these physical limitations, change management is a major challenge. The dream of lights-out production is rarely realized, and automating inventory can disrupt current processes for the people involved. This disruption can be a significant challenge, as changes to how material flows can have profound impacts on the process, throughput, cost and quality.
AW: What key performance indicators (KPIs) should manufacturers track to measure the effectiveness of their automated inventory management systems?
KC: To measure the effectiveness of automated inventory management systems, manufacturers should track several KPIs that reflect operational excellence in manufacturing, such as:
- OEE related to equipment utilization, performance and waste.
- WIP (work in process) levels.
- Inventory turnover.
- Order cycle time.
- Ontime, in full order production.
- Customer satisfaction/complaints.
AW: How does automating inventory management impact the workforce in manufacturing facilities?
KC: Manufacturers are exploring two distinct strategies based on the cost, availability and retention of workers. One approach is the "lights out" plant, which eliminates the need for plant floor workers, while the other focuses on enhancing the productivity and performance of the existing workforce. Inventory automation plays a role in both strategies, but the complexity varies. For instance, both approaches utilize mobile robots to automate the movement of palleted raw materials to the production side. However, a lights-out plant would employ robots to unload materials, while a more human-centric approach would involve a person working alongside a cobot to perform the task.
Industrial AI tools for optimization and generative assistants can level the playing field between the efficiencies of a lights-out plant and one where human workers are still essential. These tools can deliver similar gains and allow for deferred investment in new equipment and capital works, such as a new warehouse designed for mobile robots.
More warehouse automation coverage from Automation World:
Leaders relevant to this article: