How Artificial Intelligence Can Improve the Logistics Industry

#Machine Learning
September 4, 2018 4 min read

For a long time, logistics providers have depended on research and analytics to collect and understand the huge data they collect. But lately, data volumes continue to grow by leaps and bounds, making the old ways of doing business outdated. It is for this reason artificial intelligence is finding numerous applications in logistics industry networks within the supply chain.

In the logistics industry, artificial intelligence refers to new computing methods, like machine learning, deep learning, and natural language processing. These new techniques help in both streamlining and automating processes from start to finish. In other words, computers can now be used to parse data, give analytics, and generate events depending on their conclusions. In the grand scheme of things, the new technologies mean a total reconfiguration in the logistics industry and the way business is carried out in 2018 and beyond.

Artificial intelligence exists in two main categories: augmentation and automation. In augmentation, AI helps humans to carry out their tasks commercially or personally without complete control of the work’s output. Automation, on the other hand, is where AI works autonomously without human intervention.

Here are ways in which the development of artificial intelligence can improve the logistics industry.

Enhance precision and effectiveness in supply chain processing

Introducing AI and processing can help to attain precision and efficacy in supply chain processing. Initially, the logistics industry did not want to adopt new technologies due to the sheer number of workers. However, a lot has changed, and there has been a tremendous improvement in the shift from the standard logistics system to anticipatory logistics. By adopting an anticipatory logistics system, companies can now easily spot a rise or fall in demand and manage the production volume accordingly. In fact, anticipatory logistics emerged when customers started losing patience; they wanted fast delivery of their orders.

Processing and analyzing big data

Over 20 years since the advent of the Internet, several logistics, organizations still rely on spreadsheets and other outdated systems to manage their data. This causes these companies to struggle with big data.

Artificial intelligence comes in handy to harness all data in supply chains, analyze it, identify patterns and provide insight to every section of the supply chain. Supply chains generate high volumes of data on a daily basis, and this data is the most underutilized asset in the industry. The data is both unstructured and structured, and by using artificial intelligence, companies are able to exploit it.

Providing intelligent advice

Artificial intelligence platforms with integrated business intelligence (BI) and industry consulting intelligence (CI) give profound insight into carriers, customers, and operations. With this actionable intelligence, business owners can make real-time decision-making based on the situations taking place in various business departments, units, and systems. The AI platforms provide this advice in a centralized and cohesive way hard to achieve the human way.

Taking action proactively

The latest and most advanced forms of AI not only analyze and advise, but they also execute. The capabilities to evaluate, analyze, give recommendations, and execute the required changes for many business and logistical situations affecting the shipping industry make it possible for companies to readily carry out supply chain risk management, put corrective measures in place, and reduce operational delays.

Autonomous vehicles for shipping

Lately, artificial intelligence in logistics and shipping has become a significant focus in supply chain management. Quicker and more accurate shipping decreases transport costs and lead times. It also reduces labor expenses, contributes to eco-friendly operations, and most importantly, increases the gap between various competitors. McKinsey conducted a cross-industry study in 2017 and discovered the early adopters of artificial intelligence in transport showed profit margins of more than 5 percent. On the other hand participants in the sector that did not adopt AI were in debt.

Elon Musk’s new brainchild, the electric toy, which is an electric semi-truck, shows how automated vehicles are going to impact the logistics industry and supply chain management space. While there are concerns about this technology replacing human labor and causing unemployment, the use of autonomous vehicles is vital for any industry. Although the law forbids truck drivers from working over 11 hours a day without an eight-hour break, using advance transport management software, it’s possible for a driverless truck to drive almost 24 hours. The implication is AI and automation would double the output of the United States transport network at 25% of the cost.

Automating and accelerating the customs brokerage

The main problem with today’s customs declarations is their reliance on complex manual processes, which require one to have knowledge of industries, regulations, and customers. This effort-intensive process requires cross-referencing of information and validation of information from customer documents, carrier documents, government-specific forms, and regulatory bodies. All of this requires close attention to detail, and human employees can hardly concentrate consistently throughout a workday. The outcome can be costly mistakes; businesses may incur demurrage costs and non-compliance fees for holding goods in customs for too long.

AI changes all that. It can automate and expedite the brokerage process while saving costs and improving the error margin.


    Machine learning for supply chain planning

    Supply chain planning (SCP) is an essential part of the supply chain management (SCM) strategy. In the modern business world, it’s crucial to have intelligent work tools in addition to the logistics management software to help establish concrete plans.

    If applied within supply chain planning, machine learning can help significantly when it comes to forecasting inventory, supply, and demand. If used correctly though, the supply chain management working tools, machine learning, could improve the optimization and agility of decision-making in the supply chain.

    By using machine learning technology, the SCM professionals in charge of SCP would provide the best possible outcome based on intelligent algorithms and analysis of big data. This capability would accelerate the delivery of goods and balance demand and supply. In supply chain management artificial intelligence, human analysis won’t be necessary, but rather, AI sets the action for success.

    Machine learning for better warehouse management

    The success of supply chain planning heavily depends on effective warehouse management. In spite of demand forecasting, understocking and overstocking (supply flaws) can be disastrous to any consumer-based organization. However, blending machine learning and supply chain can make a difference. Machine learning gives endless forecasting bringing to bear a regularly self-improving output. Such capabilities could make warehouse management better. 

    Final words 

    Although it’s easy to brush off artificial intelligence development, this technology has significant implications in business. If used correctly, it can catapult forward-thinking organizations way ahead of their competition.   

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