Artificial Intelligence

Role of AI in Logistics & Supply Chain Management

AI in Logistics

Artificial Intelligence (AI) has been used in supply chain for a while and yet there is greater urgency for companies to effectively leverage its potential. A research by Gartner suggests that the level of machine automation is set to double in the next few years- while the expedition to gather more and more data has begun only recently.

So how is AI applicable to logistics?

With so much data involved in a logistics chain that it is difficult to comprehend all with the assistance of AI. With the application of algorithms, the processor is able to examine and store the data and then act, respond and intervene accordingly. AI also has the capability of analyzing past data and learn from it to enable businesses with better recommendations using predictive analytics.

One of the possibilities that AI can realize in logistics is that of increased automation for e.g. AI-powered robots that can be used to automatically restock empty warehouses but also predict when they will run low. Even if they may not be precisely accurate. One benefit of all the data analysis is the real-time information gained as well as complete visibility of warehouse stock.

In fact, Carousel the leading provider of personalized logistics predicts that one of the biggest growth area where the artificial technology makes a noteworthy difference to the bottom line is the intelligent forecast systems and providing value-added services more easily and at a cost-efficient rate.

Artificial Intelligence trends in logistics

There are currently two trends in AI and logistics industry – they are – Anticipatory Logistics and Self-Learning Systems.

Anticipatory Logistics

Anticipatory logistics are grounded on predictive algorithms running on big data. This permits logistics professionals to improve efficiency and quality by estimating demand in advance before the customer places an order.

The foremost influencer for anticipatory logistics is customer’s lack of patience with long delivery times. Customers always want their online shopping experience to be matched with the convenience of fast delivery. In this area, anticipatory logistics benefits all parties involved in the supply line – by anticipating demand, permitting businesses to spike up their resources before the demand shoots up.

For e.g. AI predicting that consumer demand for the latest mobile device model is going to spike – the manufacturer will then increase the production of that particular model accordingly.

AI Data

This will in turn inform the trucking companies in advance of how much transportation capability is needed to deliver all the mobile devices and when (dates). The retailer will know when to order adequate stock, increase advertising efforts and display space on the store shelves and in general prepare for a boom in online shopping.

Anticipatory logistics also serves well on the supply chain risk management front. AI tools predict maintenance needs and probable risks- quite similar to transportation/disruption management predictions. Manufacturing and transportation industries utilize AI technology to vehicle and factory maintenance. In such a case, predictive maintenance is based on the sensor data gathered from smart machines and vehicles.

KONUX, a Munich-based IIoT company – integrates smart sensor systems with AI-based analytics in order to monitor infrastructure and other assets’ condition and thus enable predictive maintenance. One of their solution includes switch monitoring which assists rail operators in monitoring and examination of switch health. The system helps observe the progressing mechanical wear of switches and detect anomalies in time. This prevents railroad switch errors.

Self-Learning Systems

“Machine learning uses massive computing power to recognize patterns in data that humans could never see, and then learns from every new piece of data it receives to get smarter and more accurate in real time,” says JOC.com.

Machine learning or rather self-learning is a very familiar concept in industries like digital pattern detection, eDiscovery and sensor data analytics. Even if the logistics industries have been particularly slow in the uptake of machine learning, other intuitive companies are adopting self-learning systems.

Machine learning leverages data across multiple systems and data sets. In the context of logistics – the system combines all the data in the carrier network

The power of machine learning comes from leveraging data across multiple systems and data sets. We can combine all the data we have in our carrier network with outside data sources like GPS systems, historical pricing performance and FMCSA to help shippers more accurately predict demand, analyze trends in supply chains, monitor seasonal calendars, and track daily patterns within lanes.

Self-learning logistics systems improve their algorithms as they get more data over time. The system works by recognizing patterns in data, analyzing them, and issuing accurate reports or actions.

Popular usage cases for machine learning and logistics are decoding handwritten text, such as the messy handwriting often found on envelopes. The post office already makes wide use of these self-learning logistics, as do major shipping companies like UPS and FedEx.

In the logistics industry, we are using machine learning to make quicker and better decisions that help shippers optimize carrier selection, rating, routing, and quality control processes that save costs and improve efficiencies. With its ability to gather and analyze thousands of disparate data points, machine learning can help you solve a problem you don’t know is there.

For example, if you’re looking at lane planning, a traditional analytical model would look at a fixed set of assumptions. Analytics based on machine learning can consider dynamic attributes like weather or traffic and self-evolve over time to recognize patterns that humans would not see.

A newer development in self-learning systems is intelligent warehouses. These systems recognize repeated trends and incidents, analyze the repeated data, connect the data to specific entities such as orders or customers, and launch pre-pack instructions.

Another common example is AI and robotics that check on stock levels to reorder and restock as needed. Over time, self-learning enables the system to improve its algorithms for even more accurate responses.

Conclusion

AI has got everyone excited because it has the ability to not only increase efficiencies and service clients at a lower cost, but it can also create a more agile business model that is both flexible and scalable in response to changing market needs.

Logisticians have, up until now, only been able to predict based on (sometimes out-of-date) inventory levels and static (as opposed to live) sales data. With AI however, logisticians can get a much more accurate picture of exactly what is happening and this means forecasting will no longer be just an estimation.

AI therefore offers the potential for considerable efficiencies, while customers could also benefit from more reliable stock information, time-savings and a greater customer experience overall.

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