The year 2023 has brought with it great changes. Among them, we are experiencing the boom of a technology that some time ago we could only imagine, artificial intelligence.
Since the emergence of GPT chat at the end of 2022, tools have emerged to facilitate all kinds of tasks. This is only the beginning and from now on we will not cease to be surprised with what is to come.
This generative artificial intelligence is now disrupting all sectors. In this article, let’s see how it affects logistics.
First, what is Generative Artificial Intelligence?
Generative Artificial Intelligence is a branch of artificial intelligence that focuses on creating original and diverse content from input data or predefined algorithms.
These systems use machine learning techniques, neural networks, and evolutionary algorithms to learn patterns, styles, and structures in the data, to generate results that mimic or extend those patterns.
Generative AI applications span fields such as text generation, image creation, music, 3D design, among others, and have great potential to drive innovation and improve efficiency in various industries.
The most talked-about example: Chat GPT, which came to change the rules of the game.
ChatGPT developed by OpenAI is a generative language model based on the GPT architecture. GPT stands for Generative Pre-trained Transformer. This model, which we can currently use in your platform, is designed to generate coherent and contextual responses in text conversations.
This is a language model architecture that uses attention mechanisms and transformers to generate coherent and contextually relevant text. The GPT architecture is based on pre-training and fine-tuning, meaning that it first trains on a large body of text to learn general language patterns and structures, and then adjusts to specific tasks or domains with smaller, more specific data sets.
ChatGPT is an implementation of the GPT architecture designed to be used in text conversations to provide contextual and consistent responses to user questions and statements.
Other examples of Generative AI:
- Generative AI in Bing. An experience where AI augments Bing’s traditional search engine while being able to function as a separate chatbot. This is a GPT Chat competition that seeks to generate better search experiences, good answers, and a touch of creativity.
· Microsoft’s Copilot. Microsoft in alliance with OpenAI is preparing the Microsoft 365 Copilot that will soon be released to revolutionize office work integrated in Office tools.
Generative Artificial Intelligence applied to logistics.
Generative Artificial Intelligence can also be applied in logistics to improve operations efficiency and optimization. Let’s discover some of its possible applications:
- Optimized transportation routes: generative AI can be used to analyze historical and real-time data on traffic, weather conditions, and other factors that influence logistics. From this information, AI can generate optimized transportation routes that minimize transit time, fuel consumption, and associated costs.
Although AI has been experimented with for route optimization in transportation for years, generative AI has now provided a major boost in innovation. This also has a huge effect on driving autonomous cars.
o Artificial intelligence in the transportation industry and other applications in logistics:
- Supply chain planning: Generative AI can analyze demand patterns, fluctuations in production, as well as other key factors to generate optimized production and distribution plans. This enables companies to anticipate future needs, better manage resources, and reduce the risk of supply chain disruptions.
In the logistics industry, demand forecasting and inventory management are crucial to ensure efficient operations and meet customer needs. Traditionally, these processes have relied on historical data and manual analysis, which is time-consuming and error prone.
However, with the emergence of generative AI technology, logistics companies can leverage advanced algorithms and machine learning techniques to generate accurate predictions about future demand and optimize their inventory levels and production scheduling accordingly.
In addition, generative AI can help logistics companies identify potential supply chain disruptions, such as weather events or transportation delays. By providing real-time predictive insights and analytics, generative AI can help companies proactively respond and mitigate potential disruptions before they occur.
o Amazon’s Forecast Pro Quick Tour, a tool that uses machine learning algorithms to analyze historical data and generate accurate forecasts of future demand.
- Warehouse and distribution centre management: This tool can be used to analyze large volumes of data related to warehouse management, such as product locations, movement times and demand patterns. From this data, AI can generate optimized warehouse layouts that maximize efficiency in product handling and storage.
- Customer service: It can also be used to improve customer service by automating responses to common queries, freeing up customer service agents to deal with more complex problems.
These tools have the potential to revolutionize the way companies interact with their customers. By automating responses to common queries, chatbots and email automation can significantly reduce response times, freeing teams to handle more complex problems.
Voice assistants with generative AI technology can provide a more personalized and seamless experience for customers who prefer to use voice commands. It can even be used to analyze customer sentiment, identifying potential issues before they become major problems, allowing businesses to proactively address concerns and improve overall satisfaction.
Challenges and risks in the implementation of Generative AI
Generative Artificial Intelligence has great potential to revolutionize the logistics industry in the coming years. The ability of these technologies to analyze large amounts of data and generate optimized solutions from it will allow logistics companies to significantly improve their efficiency, reduce costs and time.
It is important to keep in mind the challenges and risks associated with implementing generative AI in the logistics industry and in any business in general. The issue of data security, privacy, and ethics in the use of these technologies will be crucial to ensure a sustainable and successful future for companies in the era of AI.
In this regard it must be said that, in terms of data security, it is important that adequate security measures are implemented to protect data against tampering, theft or loss. This could include measures such as data encryption, user authentication and limiting access to data to only those who need it to perform their job functions.
If we are talking about privacy, it is important that individuals’ rights to privacy are respected, and that applicable laws and regulations are complied with. For example, data anonymization techniques can be applied to protect the privacy of individuals, such as the use of pseudonyms or the removal of personally identifiable data.
In relation to ethics, it is important that the potential impacts of AI implementation on society are considered and that measures are taken to avoid any negative impacts. This could include evaluating the algorithms used for any potential bias and implementing measures to ensure that AI is used in a fair and non-discriminatory manner. One of the problems with AI tools is how we “teach” them, that is, what volume of data we use in their learning process. If this data is biased, then the results we get will be too. This implies that there may be gender, racial or any other bias in their answers. The ethics of data patterns has been one of the topics of debate for some time when we talk about AI.
There is, therefore, some unanimity that the main current problem of AI development is the dependence on large private companies, without public or state control. Many of these companies have large amounts of data and resources to invest in the development of advanced AI tools, which gives them a great competitive advantage in the market. In addition, the fact that these companies have a great deal of control over AI technology can raise concerns about concentration of power and lack of transparency.
However, there are also ongoing efforts to address these challenges. For example, some initiatives seek to foster collaboration between private and public companies to promote the development of more ethical and sustainable AI technologies. There are also efforts to develop regulatory frameworks and public policies that can guide the use of AI and protect the rights and privacy of individuals.
In addition, there are initiatives to encourage the development of more decentralized and distributed AI technologies that do not rely on a single company or centralized entity. For example, blockchain technology is being explored to allow data to be shared in a secure and decentralized manner, which could promote innovation and reduce dependence on large companies.
In summary, it is true that the reliance on large enterprises for AI poses certain challenges in terms of public or state control. However, there are ongoing efforts to address these challenges and encourage the development of more ethical, sustainable, and decentralized AI technologies.
