Automated Decision Making at the Warehouse

 Useful thoughts, makes much sense.

Automated Decision-Making in the Warehouse

Warehouse-within-warehouse pictured on tablet screen

Canva.com/yoh4nn

March 16, 2023  Sponsored by LogistiVIEW

Though warehouse management systems (WMS) are widely employed, they are not the ultimate answer to optimized warehouse operations. Moving to the next level — using automated intelligence for real-time decision-making and task-flow optimization — is now feasible.

While advanced process optimization has been employed in many manufacturing operations for some time, the added complexities of many warehouse operations have made this impractical until now.

In a manufacturing plant, there are typically one or more assembly lines that perform a limited number of tasks. In a warehouse — particularly in those handling e-commerce or consumer goods — an assembly line has to be essentially created for each of the hundreds or thousands of items being handled in real time, with the product mix continually changing.

Today, WMS requires people at all levels in the operation to make decisions based on complex information. Its capabilities are designed to ensure that data and inventory are not lost, providing detailed input, but not a clear story, of how that information should influence the next decision. Only now has computing power advanced to the level at which AI can successfully take over where manual decision-making has typically been required in most WMS systems.

Relieving Pressures on Decision Makers

When there’s too much data for a human to process in real time, managers can be pushed to make decisions that are sometimes based on a “gut” feeling or on historical practice, in situations that seem similar to circumstances they’ve previously encountered.

In reality, a number of decisions need to be made almost simultaneously, including:

•    What product needs to be released next?

•    What path should the order take through the warehouse?

•    Who should work on the order?

•    How should a scheduling exception be handled?  .... ' 

Uses of Digital Twins in the Warehouse

Thoughtful look  at Twin based modeling.  5 Cases.

Uses of Digital Twins in the Warehouse   in SupplyChainBrain

The digital twin of a warehouse — a real-time, 3D virtual representation of an actual facility down to the space, people, equipment and inventory — lets operators monitor activity, respond instantly to disruption and model future scenarios to improve performance. Here are five top use cases for a digital twin.

1  Manage volatility and complexity with a static workforce.

Chronic shortages in warehouse labor continue, leaving operators to navigate more complex order flow and heightened customer expectations for service reliability with the same or fewer workers, all as wages and operating costs rise.

The latest U.S. Department of Labor numbers for October 2022 tell the story: 482,000 job openings in transportation and warehousing, with 322,000 hires minus 294,000 quits, layoffs and discharges. Industrywide churn hovers around 50% annually. Worker-retention efforts focus on flexible hours, more varied work, less travel time and less repetitive physical stress, as much as on pay.

Automation is key to filling workforce gaps — not just robotics but also process automation to optimize workflow as companies begin many days uncertain about who will show up to do which jobs. Prioritizing, allocating and dynamically adjusting work to match demand requires control tower visibility across the entire facility.

“If I’m a warehouse VP, director or manager, I want to know what’s going on in my warehouse,” explains Bill Denbigh, vice president, product marketing for cloud-based supply chain software provider Tecsys Inc. “How busy am I? How full am I? Where are the most common pick locations? Where am I running out of space?”

A digital twin provides that baseline operating visualization. Underlying artificial intelligence and machine learning monitor operations in real time, map high-activity areas, flag disruptions and orchestrate movement of people and assets against defined objectives like order priority, throughput, travel time or cost per move. As a virtual copy, it can model “what-if” scenarios without disrupting workflow.

2  Help machines run smarter. 

As DCs become more capital-intensive with adoption of mechanization, automation and robotics, they become more like factories, with a growing share of ROI reliant on maintaining, orchestrating and fully utilizing capital assets.

A digital twin can manage both equipment and processes. It optimizes equipment performance and interaction — say, robotic arms loading a conveyor belt — but also monitors systems for predictive maintenance to minimize downtime. Twins also manage process flow, the movement of people, machines and product in the warehouse space. Near real-time simulation is a key differentiator for the technology. “Say you’re running a digital simulation using AGVs and realize you need to adjust dynamically to the reality of what’s happening on the floor to make them more efficient,” says Joe Vernon, principal business consultant with enterprise software and consulting firm EPAM Systems. “You can now do that over and over again, in very fast cycles.”

The result is shorter simulation times for “smarter” equipment that can be pivoted quickly to adapt to changing conditions, in both simulations and actual operations. Systems can also generate real-time feedback from the floor on performance, constraints from narrow aisles or tight corners, or speed adjustments needed to align with human activity.     ... '

Digital Twins in the Warehouse

 Intro below.   We did something very  very early on using a number of simulated alternatives, as quite valuable.   Here seems to be taken to a new level.

How digital twins are transforming warehouse performance  in VentureBeat

The global Industry 4.0 market was worth $116 billion in 2021 and is predicted to rise to $337 billion by 2028. Many technologies are contributing to the incredible growth of Industry 4.0, but a standout among them is digital twin solutions. Specifically, digital twins are now being deployed to greatly improve warehouse automation operations with the end goal of increasing efficiency and reducing downtime. 

Digital twins can deliver virtual representations of a physical environment — proving extremely helpful to the warehouse industry. With a digital twin, new improvements and efficiencies can be tested virtually, without downtime or rearrangement of physical assets.

Warehouse operations are rapidly growing in complexity. Inventory is more diverse, as the massive expansion of ecommerce has brought an increase in the proliferation of SKUs. Logistics solutions are strained, as customers now expect lightning-fast fulfillment. Technology is more complex, as innovative new automation systems come to market, and managers must analyze the new systems to introduce those that bring the greatest benefit to their warehouse operations.

To win against competitors, smart companies are now building digital twins of their warehouse operations and using them to handle operational complexities and performance improvements.   .... ' 

Deep Learning in Robot Automation

 How do you automate Warehouse logistics?

Deep Learning in Robotic Automation and Warehouse Logistics  in NVIDIA Developer

By Troy Estes  Tags: Edge Computing, EGX, Manufacturing, Metropolis, News, Retail, Robotics

Advanced warehouses process up to hundreds-of-thousands of orders a day. Fulfilling these quantities requires a lot of inventory, physical space, and complex workflows to support the high volume of items picked.

In addition, micro-fulfillment centers are becoming increasingly popular to fill same-day delivery orders from customers. 

Operating these types of advanced facilities and machinery—along with a high volume of resources efficiently— requires skilled workers. While this workforce is shrinking, investing in edge computing and AI can help.

Shrinking Workforce

Modern warehouses feature robust automation. Autonomous machines and vehicles move products around warehouses, conveyor systems move goods into shipping containers, and use advanced 3D grids to provide pick-and-sort systems that pack products intelligently. But even with the current level of automation, modern warehouses are still labor intensive.

If boxes are different sizes, automated packing is difficult and often done manually. Some items are large and heavy, requiring a forklift and a driver to move. Finding and retaining employees is a challenge and COVID-19 protocols have exacerbated the problem. And while the workforce is shrinking, the ware Deep Learning for Advanced Industry house industry is growing rapidly. The next generation of warehouses needs to focus on automation to succeed. 

Deep Learning for Advanced Industry

Advancements in deep learning and edge computing provide intelligence and automate more processes of warehouses. Automated pick and place systems are an example of an operation advanced warehouses are implementing. In a pick and place system, an autonomous machine identifies an object among other objects in a bin, then selects, and places that object for packing elsewhere. 

For this system to be automated, many different deep learning models are required. That’s because some objects are very hard to detect with computer vision, like translucent, reflective, or non-uniform objects.

The walkthrough below outlines how deep learning models are used for an automated pick and place system.

First, the object grasp point needs to be identified. This is the simplest deep learning model, but an important function for automation. Once it is known where to grasp an object, it is necessary to understand how the object exists in 3D space. Therefore, a depth estimation model is needed that allows the pick and place system to understand the depth of the object in the environment.    ....'