How AI and ML can bring value to Supply Chain and Logistics

Given the interconnected nature of today's global supply chain, any disturbance at any point can have far-reaching consequences. The likelihood of and damage from disruptions in the supply chain can be minimised with the right set of systems and procedures in place. 

Connectivity and visibility in the supply chain are simplified by cutting-edge technology like artificial intelligence and machine learning. Therefore, more companies are adopting their use to foresee and counteract supply chain difficulties. 

What exactly is Artificial Intelligence? 

In order to allow computers or robots to perform activities typically performed by people, #artificialintelligence (AI) models human intelligence processes. 

A.I. used to be something exclusively found in fiction. However, this practise and technology is now embedded in conventional culture. AI is now used in many different areas, such as navigation apps, facial recognition systems, digital personal assistants, and even home appliances like robot vacuum cleaners. 

By digesting more data and showing more patterns and trends than humans, AI is able to adapt to new circumstances and learn new information almost instantly. 

Businesses are increasingly relying on AI technology to help them swiftly and accurately make important strategic choices. 

Some examples of how this might look in #supplychain management are the creation of demand projections to guarantee stock availability and the planning of transportation routes to minimise wasted time and fuel. 

What is machine learning? 

The field of #machinelearning (ML) falls under the umbrella of AI. It makes use of self-learning algorithms, software, or systems. Over time, ML models learn by observing patterns and outliers, and then they can make predictions. 

It analyses the data on a regular basis and makes recommendations based on the patterns it finds. This may involve figuring out more efficient routes for picking items in a warehouse, anticipating potential problems with automated equipment in order to avoid breakdowns or monitoring packages as they travel through the supply chain in order to find the most efficient path. 

Improvement opportunities that a human might overlook or take too much time to spot might be highlighted through machine learning. Problems are less likely to arise if addressed before they even become an issue, which helps with that. 

The value of AI and Machine Learning in logistics and Supply Chain 

Artificial intelligence is a versatile technology with many potential applications. Logistics and supply chains, which deal with intricate tasks like stocking shelves, moving products between storage facilities, and monitoring traffic conditions in real time, are one of the most promising areas. 

In this blog, let us explore a few machine learning and AI use cases that hold great promise for the logistics, transportation, and supply chain industries. 

1. Supply Chain Management  

There are many ways in which SCM can make use of AI. It's important to note that AI systems are well-suited for optimising processes based on vast amounts of relevant data because of their ability to manage such data quickly and efficiently.  

Real-time 360-degree visualisations and supervision are another method in which AI can be used in supply chains. Automation of this sort enables businesses to keep a close eye on the performance of its workforce, machinery, or transportation fleet, to spot issues before they become serious, and to react swiftly when anything crucial happens. These methods and technologies are ideal for use in quality assurance, in addition to their utility in other areas such as management of resources and evaluation of potential dangers. 

Businesses open to new technology can reap several rewards from implementing AI in supply chain management. Businesses can save money by using AI to streamline their logistical processes and cut down on the time it takes to deliver goods and services to customers. 

2. Matching Shipments Intelligently 

To choose which shipping company is most suited to transport their products, shippers tap on a variety of professional networks. On these sites, both shippers and carriers can advertise their transport options, including routes, capacities, pricing, and other details that may attract potential business partners. Algorithms with machine learning capabilities can offer the best match between the parameters posted by shippers and those provided by various freight firms based on historical data acquired from past transactions. 

Transportation management systems (TMS) can better meet the demands of its clients by employing ML technology to provide informed suggestions. This eliminates the need to manually search for information and produces more accurate results than current approaches. 

Shipping plans involving a single firm's fleet, or the fleets of its subcontractors can benefit from the same method. With limited resources, the business may want to know which fleet units and carriers are the greatest fit. This is a difficult task since there are many factors to consider, including the routes to be taken, the commodities to be transported, and the time frames for making these deliveries, all of which must be coordinated to avoid conflict with other deliveries scheduled for the same period. 

TMS systems can use ML algorithms to determine the best possible pairings to use, given on the information at hand. During the manual selection process, the system is able to learn from past results and apply that knowledge to future planning, all while spotting any human mistake in the process. 

3. AI in Inventory Management 

Stock levels must be calculated by inventory management teams, who face the challenge of meeting customer demand without over- or under-stocking. 

To get a full picture of your warehouse and supply chain, AI may employ algorithms to deliver better data and analysis. Artificial intelligence and machine learning's scenario-testing capabilities provide the best ways to keep up with demand. 

Since the research is performed on a daily basis, you'll know exactly where your approach needs to be adjusted to account for the dynamic nature of the market. For instance, processing orders can be sped up or slowed down in accordance with demand. Possessing such knowledge allows one to make well-considered choices, cut down on wasteful spending, and improve efficiency. 

4. Forecasting demand with Artificial Intelligence 

In supply chain management, demand forecasting is crucial. Improved stock availability and lower holding costs are the result of ideal inventory levels, which may be determined with the help of more precise forecasting. 

To uncover previously unseen patterns in demand data, machine learning models do not rely solely on sales data. In doing so, they will be able to anticipate potential disruptions in the supply chain by anticipating and analysing external causes. Adopting preventative actions will yield better outcomes and lessen the financial burden to the company. 

5. Optimization of Transportation Routes 

Artificial intelligence has a direct impact on the trucking industry through route optimization, allowing for more efficient use of drivers' time and fuel by reducing the distances between pickup and drop-off points and allowing businesses to respond more quickly to changes in regional demand by shifting delivery windows accordingly. To further optimise resource allocation, cut fuel consumption, and maximise vehicle usage, logistics firms can take advantage of state-of-the-art solutions that allow them to manage and coordinate hundreds of trucks concurrently. 

With the use of machine learning models, AI-powered transportation management systems can anticipate client demand, then match it with available transport capacity, and even combine certain deliveries. With this information, logistics firms may better anticipate the impact of unforeseen occurrences on delivery times, such as road closures caused by accidents. 

Final Thoughts 

The widespread adoption of AI systems built on deep learning algorithms by businesses eager to improve their operations is something we anticipate happening in the not-too-distant future. These ML models are meant to improve the big data processing and decision-making capabilities of businesses. 

Businesses will likely boost their usage of AI-based apps as a result of the rising level of competition in the market and the rapid evolution of the global economy. With the help of these innovations, we can make better, more rapid decisions, which in turn boosts productivity. The greatest value of deep learning algorithms comes from the increased automation it enables, which has a big effect on lowering costs across the board in logistics and supply chains (transportation cost reduction). We think the potential applications of this technology across several fields to the benefit of society are virtually limitless. 

Talk to our experts to learn how to streamline your operations and embrace the data-driven culture by utilising our centre of excellence (COE) and solutions for artificial intelligence (AI), hyperautomation (HA), analytics (Analytics), and other exponential technologies.