Both AI and Internet of Things (IoT) are powerful tools that can better enable warehouse and distribution center activities to keep pace with rapidly shifting supply chain dynamics. I see these two being used piecewise, in silos, in many warehouse operations & warehouse planning scenarios but the true power, as mentioned in the video above, comes from leveraging these together.
Remember that many of the fundamental problems that you are trying to solve for in the warehouses have not changed dramatically. It is just that the combination of AI and IoT provides you solution approaches that did not exist before. While IoT helps you capture data that could not be captured before, AI analyzes micro-decisions and optimizes them to a level not previously possible.
Smart or automated ?
An year ago, I wrote an article “Is your DC smart?” (link here: Is your Distribution Center Smart ?) that explained with an example, the difference between a Smart warehouse and an automated warehouse. In my opinion, implementing only IoT , without AI capabilities allow you to capture only a very small percentage of true benefits in the warehouse.
To capture the full benefits, you need a combination of new data sources, that’s IoT, coupled with better solutions, that’s AI, to make sense of the data, develop insights and act on that knowledge. The two are front and center for improved operational performance in warehouses.
The Demand driven DC
As many Supply Chains shift towards being Demand driven and as we start discussing “lot size one” in manufacturing, now is the time to leverage technologies to accommodate the current shift from forecast-driven to demand-driven DCs. But are our current Warehouse systems equipped to handle this shift ?
WMSs were built for the 90s
Even with a warehouse management system (WMS) in place, those decisions are made with set rules, set capacities and set resources. However, there’s nothing static about those orders that just dropped. Current conditions matter most going forward. Not preset rules. Now the systems need to intelligently balance capacity against resources, aiming to maximize utilization.
The replacement is not coming soon
However, a true, mature, commercial offering in this space does not exist yet (many are in the pilot though, including the startup that I am currently advising). Big names like JDA (now Blue Yonder), along with its partners, has committed $500 million in R&D over the next three years in this domain.
Building the capability
As discussed multiple times in the article so far, the three key aspects here are :
- Building IoT capabilities
- Building AI capabilities
- Integrating these two capabilities
We will briefly cover how we can build these capabilites in our warehouses operations.
IoT Data : Not a distant dream
Data granularity is a key enabler of more efficient operations. IoT data provides that level of granularity
I can confidently claim that many of the places from where IoT data is generated may already exist in your Warehouses. You were probably not familiar that these locations/assets were generating treasure trove of information. Examples ? Keep reading !
Materials handling equipment from conveyors to automatic guided vehicles and automated storage systems and the like all receive and send data about their activities. So do handheld devices from scanners to voice systems.
Most facilities are bringing in more and more data devices that are developing into a burgeoning IoT network. Many times, simple sensors provide information not previously available for decision making. Smart phones are part of that new network.
Data about people figures prominently, too. It matters where people are located at a given moment, what they are working on and how can they best be used. Real-time locator systems are moving into place to track people and their availability for specific tasks. In fact, several types of real-time locator systems are available, including smart phones, passive radio beacons and RFID.
There’s also the matter of people and robotics. Pairing capability to get both the right person and the right robot to fulfill an order using IoT data. It’s a matter of pairing up the locations and having the two work together to pick and make a run to packing.
Building out AI Capabilities: The real game changer
While access to data is becoming much simpler, most facilities lack the ability to decide how to use that data and what actions to take. It’s all a matter of bridging the gap between the forecast and what’s really happening in manufacturing. That’s where AI enters.
Simplified: AI in the warehouse learns and reacts to the current state, not just a set of pre-set rules.
AI and IoT are not two sides of the same coin but they have a very strong and effective symbiotic relationship. The more data about actions and interactions that AI receives, the more it can learn about how to adapt to current conditions.
While much of the IoT data comes from within the four walls, take the example of a late inbound load.
The DC is alerted by an IoT signal being managed by a control tower that a load will arrive late. AI takes that information and determines the optimal time to release and deploy a specific amount of labor to unload the truck. AI also determines what portion of that load should go directly to fill orders or to storage. Suddenly you have a new level of visibility and intelligence into how to make the DC operate most efficiently.
Getting to that point really does require the granularity of data that IoT delivers. Data granularity is the key enabler to allowing AI to learn as new situations present themselves. This particular form of AI is known as machine learning.
Machine learning can transform traditional industrial engineering . For example, engineers can build models and do time and motion studies to develop engineered standards. But it’s a time-consuming process. And, it’s locked into a point in time. Machine learning simply takes the data and uses algorithms to produce models on the fly based on current conditions. Furthermore, the model can adapt as conditions change. Better yet, AI learns with each iteration. It’s all in the name of reducing process optimization costs and improving resource plans.
Bringing IoT and AI together : Harnessing the true power
All of that is great. However, there is an even greater purpose to IoT and AI in the DC. The two technologies make it possible for a DC to move from being forecast driven to being demand driven. That is, when they are combined with WMS, warehouse execution systems and even work execution systems. Moving from forecast- to demand-driven operations is a huge but absolutely necessary pivot for DCs going forward.
It’s all a matter of coping with the current shift from manufacturing and distribution calling the shots in the supply chain. Increasingly, customers are in charge to the extent that they have now transcended low costs as the primary driver of supply chain efficiencies. As a result, a range of companies are investigating, piloting and fully integrating AI and IoT in warehouse operations.
Leading WMS product companies like Manhattan are integrating both IoT and AI in their warehouse execution package within their WMSs. Order streaming, robotics and distribution control have all benefited because the capability was introduced almost 18 months ago. A little over a year ago, JDA purchased Blue Yonder and its AI capabilities. That has become a backbone in JDA’s strategy to digitize predictive analytics to create what the company is developing—a prescriptive state for the self-learning supply chain. So much so that JDA decided to rebrand the entire organization as Blue Yonder.
It may be early in the use of IoT and AI in warehouse operations. And with all that’s calling for your attention day in and day out, it would be easy to overlook this development. But you would be better off if you didn’t let that happen.