Why is optimizing Warehouse Inventory Management a necessity of the hour? In the current business world, the small and medium businesses and the global conglomerates no longer work in watertight compartments. In this close fight among global conglomerates and SMBs, the possibilities of winning rest with the opponent who has the right resources, intelligent insights, and proper management skills.

Inventory control is one of the many crucial sectors of business. To play the field right, you require an inventory control system that ranges beyond fundamental reordering and stock monitoring to hold everything from end-to-end stock and business administration to demand to forecast and lead time analytics, reports, and more. Inventory management means creating a balance between demand and supply with the right quantity of stock at the right place, at the right time. 

Talk about AI and the Internet of Things in the warehouse, and the room can get very fast. Are we at that position already? Are these next-generation technologies for real in the warehouse or just a talking point from some “visionary?”

Quite easily, yes, we are at the point where AI and IoT are fundamental for the warehouse. Both are important new tools that better enable warehouse and shipping center activities to keep pace with rapidly shifting supply chain dynamics.

IoT makes no reason without Artificial Intelligence in the warehouse. You need a mix of new data sources, that’s IoT, linked with better solutions, that’s AI, to make reason of the data, develop insights and act on that knowledge. The two are front and center for increased operational performance.

In addition, specialists agree the two technologies are required to accommodate the current shift from forecast-driven to demand-driven DCs.

Some businesses interviewed for this story don’t have a business offering yet. Others are in pilot. Others have offered a product for a short time. 

Clearly, Artificial Intelligence and IoT are knocking at the warehouse door. It’s a knock you want to respond.

Building out the IoT

Let’s face it, warehouses and DCs are under stress like never before.

Orders drop in all day long, and it’s expected that the DC will take action quickly. The difficulty is figuring out how to best method those orders on time.

Even with a warehouse management system in place, those choices are made with practices, capacities, and resources. But, there’s nothing static about those orders that just dropped. Current positions matter most going forward. Not preset rules. Now the operations effectively balance capacity against support, aiming to maximize utilization.

So, where correctly is all that IoT data going to become from? Consider it or not, some of it is now in place in your facility.

Materials managing equipment from conveyors to automated guided vehicles, automated warehouse systems, and the like all receive and send information about their activities. So do handheld devices from scanners to voice operations.

Most facilities are producing in more and more data devices that are developing into a burgeoning IoT network. Many times, single sensors provide data not previously available for decision making. Smartphones are a section of that new network.

Data about somebody figures prominently, too. It values where people are located at a given time, what they are working on and how can they best be practiced.

Building out AI

While way to data is becoming much more straightforward, most facilities lack the ability to decide how to use that information and what actions to take. It’s all a subject of bridging the gap between the forecast and what’s really occurring in manufacturing. That’s where Artificial Intelligent enters.

Artificial Intelligence and IoT are not two sides of the same coin. But they do have a symbiotic connection. The more information about actions and cooperations that Artificial Intelligence gets, the more it can learn about how to adapt to current situations.

Getting to that point does need the granularity of data that IoT delivers. Data granularity is the critical enabler to allowing Artificial Intelligence to learn as new conditions present themselves. This special form of Artificial Intelligence is known as machine learning.

Bringing IoT and AI together

All of that is great. But, there is an even higher purpose to IoT and Artificial Intelligence in the DC. The two technologies make it reasonable for a DC to move from forecast-driven to demand-driven. That is, when they are connected with WMS, warehouse execution systems and even work execution methods. Going from forecast- to demand-driven processes is a huge but necessary pivot for DCs in the future.

It’s all a subject of coping with the modern shift from manufacturing and distribution, calling the cracks in the supply chain. Frequently, customers are in charge to the extent that they have now transformed low costs as the primary operator of supply chain capabilities.

As a result, many businesses are investigating, piloting and fully integrating Artificial Intelligence and IoT in warehouse operations.

Organizations are paying their due attention to integrating the two technologies with their current software packages. 

Understanding warehouse and inventory today with AI

According to the 2020 McKinsey Global Survey on AI, 50% of companies have adopted Artificial Intelligence in at least one business purpose. It has also been found that the goods or service-development and service-operations functions have the greatest rate of Artificial Intelligence adoption. The largest shares of businesses have announced revenue increases for inventory and parts optimization, pricing and advertising, customer-service analytics, and enterprises and demand forecasting within these functions.

Artificial Intelligence can offer aid in both physical jobs such as relocating and tracking things or more complicated methods in which advanced insights are required for an error-free preparation or demand forecast.