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

Automated decision-making eliminates or greatly reduces confusion, and instead employs software to recommend the actions a human needs to take — or automatically take an action based on current conditions in the operation and on defined success metrics.

An effective automated decision system has the ability to understand everything that's happening in the warehouse at a given point in time, the facility’s constraints and the ability to understand what the definition of success is.

These systems also enable the use of task flow optimization which applies constraint-based flow control to a warehouse for the purpose of producing maximum throughput.

To do this, task flow optimization manages humans, robots and automation with data that must be integrated from all the systems needed for complete operational visibility. With this capability, the system avoids problems that can result from human decisions that release too little or too much work to the floor. Instead, task-force optimization supports a balanced system by understanding and having the ability to physically model the entire warehouse and everything that has to happen in it. 

The result is maximized throughput and utilization while eliminating bottlenecks.

The goal of an automated system making decisions in real time is to achieve the so-called Goldilocks approach, which obtains the best possible result with all the available data.

For decades, warehouse managers have relied on WMS systems to provide the best available support for human decision-making. It’s been “good enough,” absent significant advances in computing technology. Now that the technology is available, it’s time to review and adopt new automated warehouse decision-making systems.

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