Artificial Intelligence Transforms the Face of Logistics

Smart cities, vehicles and devices are slowly becoming a permanent feature of the world around us. Artificial Intelligence (AI) is no longer just the stuff of sci-fi movies and books. As it turns out, a machine can be programmed to search for information and then to make decisions and even take action based on that information. Learning systems can improve the operation of many industries, including logistics, by helping to boost effectiveness and safety levels in transportation or warehousing. This being said, many people see that the fast evolution of intelligent machines poses some threats and express concern that AI will put us out of work.

What is AI and what can it do?

Artificial Intelligence is about intelligent behavior models and computer programs that mimic these behaviors. It involves engineering machines and computer software capable of carrying out autonomous functions of the human mind and applying human senses which cannot be described by an algorithm i.e. in areas such as decision-making when incomplete data is available, natural language analysis and synthesis (Natural Language Processing, NLP), logical and rational thinking, theorem proving, managing knowledge, preferences and information in robotics or expert and diagnostic systems. But how is Artificial Intelligence changing the face of logistics?

Chatbots support supply-related processes

Chatbots are computer programs called virtual assistants. They rely on AI technology to conduct conversations with people using natural language. These conversations resemble conventional text chat. A chatbot is a series of algorithms that recognize questions and then attempt to find matching responses. If a user asks a question that the chatbot doesn’t understand and can’t find a matching response to in the database, it will attempt to match the question to one of the „fail safe” responses or will try and learn from the user and then use the acquired information the next time a similar question gets asked. A chatbot possesses some information about the company and its products or services and can conduct any number of conversations simultaneously. It can rely on its own data bases or source information from external sources. A virtual assistant not only responds to questions, it can lead a conversation and guide it (linear dialogue) to topics related to a given company’s business or showcase its offering.

In procurement/ buying departments, chatbots should be used in routine, daily tasks such as responding to supplier queries during informal exchanges, generating buying requisitions (authorizing Procurement to purchase materials), responding to external queries about Procurement or suppliers, and receiving or completing paperwork such as invoices or payment claims.

Machine learning supports supply chain planning systems

Supply Chain Planning (SCP) systems form a part of Supply Chain Management (SCM). Machine Learning (ML) is a field that is preoccupied with artificial intelligence and aims to create automatic systems capable of progressively improving their performance with the use of accumulated experience (or data) and new knowledge acquired as its result. Self-learning systems enable more precise demand forecasting or delivery timing planning. They use historical data on production timings and logistics services supplier reliability. Machine learning also helps check real time product availability versus incoming customer queries and orders, allowing for more effective negotiations with customers and enabling them to be instantly notified when products will become available or offer substitutes instead.

Machine learning and warehouse logistics

Effective supply chain management is strictly dependent on the right warehouse logistics. In warehouse logistics, machine learning makes it possible to analyze huge amounts of data generated by warehouse management systems that cover things like order quantities, returns and inventory levels, helping to detect customer behavior patterns, data on what goods are ordered most often and when (e.g. on what weekday) or data about what goods are typically bought together (what goods are stocked close by in a warehouse to promote fast order picking). Speech synthesis and recognition techniques are also used to make warehouse logistics processes more effective. Workers equipped with ear pieces and microphones are notified of stock location and receive instructions, and then report the completed task to the WMS system. When it comes to inhouse transportation, Automated Guided Vehicles (AGV) are being used increasingly more. Thanks to machine learning, AGVs can „learn” to navigate a warehouse and automatically adapt to changes in their operating environment thus boosting work safety.

Smart road transportation vehicles

Autonomous HGVs that are able to drive themselves and respond to their external environment may end up revolutionizing the trucking industry. Smart trucks will help solve the problem with a shortage of professional truck drivers, help cut transport costs and delivery times, reduce a company’s environmental impact and increase road safety. Trucking is already becoming increasingly more automated thanks to the broader use of devices that are compatible with the Internet of Things. Autonomous HGVs can also form convoys (Truck Platooning), boosting the associated benefits even more. Companies like DAF, Daimler, Iveco, MAN, Scania or Volvo are working on developing smart truck technologies. Tests of such vehicles are currently underway. In mid-2016 driverless trucks drove to Rotterdam from several European locations. While a human was present inside each truck, his only job was to intervene in emergencies. Nonetheless, mass-scale production may begin from a few to over ten years from now.

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