Industry 4.0 is changing the way we work across the supply chain.

Using AI, sensors, and Internet of Things (IoT) technology, a smart and data-driven distribution center can be developed. For example, by cross-referencing enterprise resource planning (ERP) systems with consumer trends data, AI technology can automatically order the correct amount of raw materials to fulfil orders, reducing waste and increasing profit.

As the complex web of distribution is opened to the benefits of AI, the supply chain could have a bigger economic benefit than any other application of AI in manufacturing.

Using this technology, distributors will no longer need to predict demand for products through guesswork but will instead merge datasets to make accurate predictions about the future, enabling them to make well-informed business decisions.

Inventory level efficiency
With insight into future demand, AI can also help with forecasting the demand of your suppliers, based on previous orders. This means crucial decisions can be made to optimize stock levels. For example, if your AI software lets a distributor know that many other distributors will want the same equipment in 12 months’ time, you would be sure to jump the queue and get ahead by ordering it much sooner than this.

Cost of goods sold
Why does it matter if inventory levels aren’t optimised? Well, it’s related to inventory level efficiency. Your cost of goods sold (COGS) will reduce since you don’t incur costs of holding inventory beyond its use. In 2015, the cost of over-stocking was $470 billion, and of under-stocking was $630 billion worldwide, according to IHL Group. Freeing up cash and storage space creates the potential for savings.

Lead times
As Industry 4.0 empowers your supply chain to manage different orders faster, lead times for customers will shorten. However, this increases the pressure to deliver on time, every time. To alleviate this, AI enables you to spot gaps in your inventory before it’s too late, and maintain long-lasting customer relationships, built on trust and reliability.

Applying these practices to a theoretical example provides an insight into the financial benefits AI can reap. Imagine a robot distributor — we’ll call them ‘Robo-bots’.

Robo-bots was taken by surprise by a recent shortage of components for the manufacture of its machines, facing huge unexpected lead times from its suppliers.

As demand for robots grows year on year, so does the order volumes of their essential components. The cause of Robo-bot’s delay was its supplier’s struggle to source harmonic drives, bearings and ball screws for use in its robots.

If Robot-bots had used AI software, things could be different. AI could scour data – such as robot demand, bearing supply and ball screw supply, much faster than a human could possibly do. Then, it could cross-reference this data with the company’s own order history, inventory and figures to flag up that the business was running out of components in advance.

Interestingly, this software can also be used to flag growing markets. In this instance, it could identify an increasing market for cleanroom robots. As per the insight, the Robo-bots could make the decision to order some of its usual robots with additional cleanroom adaptations.

Suddenly, Robo-bot’s stagnant top-line growth seems much more prosperous, thanks to the integration of intelligent and insightful software.

Of course, Robo-bots is a theoretical company with theoretical circumstances, but the message remains the same. Intelligent inventory management holds huge potential for improving a business’s top and bottom line.

AI does not only hold potential for machine builders, but also for resellers and distributors of industrial equipment. Consider a servomotor distributor as an example. The company has a regular order with a servomotor supplier, receiving a bulk order every quarter.

One year later, the distributor discovers that demand for these motors was not as expected, and a stockpile of servomotors has formed in the warehouse, taking up valuable storage space and cash.

To avoid this, the distributor could implement AI distribution software, to track inventory, market trends, sales and demand throughout the supply chain. If the demand isn’t there, the distributor could have made a more informed decision before partnering with the servomotor supplier.

Using AI for inventory management can help to avoid poor decisions, as well as inform new investments. However, this improvement won’t happen overnight. The success of this software will rely heavily on high data granularity, and businesses need to make sure they are building AI readiness now. Granularity is used to characterize the scale or level of detail in a set of data, of which AI is highly dependent on. The greater the granularity, the deeper the level of detail across the data.

Whether AI implementation is in the forthcoming plans or not, it’s a good idea to ensure data collection and storage is effective. If we are to untie the $1.1 trillion lost through inventory distortion worldwide, AI could provide the answer.