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Artificial intelligence is undoubtedly a popular topic nowadays, with an increasing number of applications in many industries and processes. We are all familiar with some of its most popular applications, such as voice and image recognition, however, its relation to logistics is not as well known to the public. According to a study from IBM, 46% of supply chain executives anticipate that AI a will be their greatest area of investment in the coming years (Butner & Lubowe, 2017). But how exactly is AI impacting logistics, and what are the benefits of it? 

Logistics is a part of the supply chain process concerned with the movement of resources such as goods and supplies from one location to another. It involves not only cross company B2B logistics, but also internal logistics and deliveries to the customer. For this movement of resources, different transportation vehicles are used, such as trucks, ships, containers, and planes, which all emit high greenhouse gas rates. In 2015, the CO2 emissions from logistic transport accounted for 21.7% of the total of CO2 emissions.(Vidová et al., 2012). Therefore, making the logistic process more efficient is key for improving its sustainability. How can AI help with this? 

Logistics is getting smarter every day with the incorporation of sensors and GPS in most transportation devices. Data is getting collected throughout the entire supply process, enabling companies to gather real time information on the location of their product, the transportation issues, and the delivery status among others. But not only the system is internally connected: companies are increasingly involving the clients in these systems to ensure better customer experiences (Chmielewski et al., 2021). For example, the Port of Rotterdam implemented in 2020 an application for customers that tracks ship containers and determines expected arrival and departure times for vessels (Port of Rotterdam, 2020.). While this type of tracking might seem basic in B2C logistics, such as when tracking a package from Amazon, this kind of application is still not widespread in B2B logistics.  

With all the gathered data, AI is helping companies to take decisions about real time route optimization and trip combinations to ensure efficient and less polluting shipments, while also making deliveries faster. It is also helping to forecast demand in order to better optimize vehicles and operation costs, and to automate warehouses for the reception and sending of the deliveries.  

In a sector where fast decision making is key for smooth operation, the support of AI is bringing improved efficiencies in a process that was usually based on history-based forecasting, shifting it to a prediction-based system of orders and issues. This end-to-end approach will hopefully help improve the planning and performance of logistics, ultimately reducing fuel consumption and optimizing fleet routes. (Tim Gaus et al., 2018). 

References:

Butner, K., & Lubowe, D. (2017). AI is reshaping the supply chain. In IBM Institute for Business value. https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=GBE03836USEN 

Cmielewski, J., Daher, M., & Ghazal, O. (2021). Intelligent automation in transportation. Deloitte Insights. https://www2.deloitte.com/xe/en/insights/focus/transportation/the-role-of-intelligent-automation-in-the-movement-of-goods.html 

Port of Rotterdam Authority introduces track & trace containers | Port of Rotterdam. (2020). Retrieved October 6, 2021, from https://www.portofrotterdam.com/en/news-and-press-releases/port-rotterdam-authority-introduces-track-trace-containers 

Tim Gaus, Ken Olsen, & Mike Deloso. (2018). Advanced Artificial Intelligence and Supply Chain Planning. Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/industry-4-0/artificial-intelligence-supply-chain-planning.html 

Vidová, H., Babčanová, D., Witkowski, K., & Saniuk, S. (2012). Logistics and Its Environmental Impacts. May 2016, 1007–1014. https://doi.org/10.3846/bm.2012.129 

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In the novel 1984, George Orwell described a dystopian future where mass surveillance and propaganda enabled a totalitarian government that completely forbade freedom of speech. Citizens were surveilled through screens, cameras and microphones that constantly tracked their moves and thoughts. What once was a science fiction story is increasingly becoming a reality in the current ultra-connected society, where smartphones and cameras are simply everywhere. 

 Consumers are starting to grow aware of the of the amount of data collected by companies and institutions: according to Forbes, 69% of consumers are concerned about the amount of personal data gathered online (Goswami, 2020). However, these privacy concerns have not yet impacted much our online activity patterns: for example, a survey by Deloitte shows that 91% of consumers accept terms and conditions statements on the internet without reading them (Cakebread, 2017). 

Some people argument that they have no fear of online surveillance or data privacy issues, because simply they have “nothing to hide”. But this argument is flawed: not having anything to hide shouldn’t mean not having the right to privacy, the same way as not having anything to say doesn’t mean not caring about free speech. Our data can be used by governments and companies in potentially non ethical ways, and this data is increasingly easier to obtain through some technologies: for example, in China facial recognition is aiding in surveilling the population to enforce the law and is programmed to automatically flag the faces of ethnic minorities to track their movements in detail (Hill, 2021). Another example, the Cambridge Analytica scandal that showed how data obtained from Facebook influenced the result of the US elections in 2014 (Nicholas Confessore, 2011). These are only a few examples of how the misuse of data can go far in terms of human privacy rights violations, and the impact that this can have in societies and entire countries. 

In this context, companies have to be increasingly considerate of the use they make of personal data and the ethical and legal implications it can have. There is a rise in interest of lawmakers to regulate this matter, and there will be an increase in regulation of data privacy in the next years. Businesses will have to adapt their strategies to a more restricted use of consumer data as customers become more aware of their data protection rights, as we can start seeing in moves such as Apple’s removal of third-party cookies in their browsers, that Google followed with a statement promising to do the same by 2023 (Kris Jones, 2021). 

Meanwhile, it’s important for everyone to become aware of the risks in data protection and keep in mind the individual responsibility in data sharing: remember, you might not have anything to hide, but there is always something to lose. 

Sources:

Cakebread, C. (2017). Deloitte Study: 91 Percent of Americans Agree to Terms of Service Without Reading. Business Insider. https://www.businessinsider.com/deloitte-study-91-percent-agree-terms-of-service-without-reading-2017-11?international=true&r=US&IR=T 

Hill, K. (2021). Facial Recognition: What Happens When We’re Tracked Everywhere We Go? – The New York Times. New York Times. https://www.nytimes.com/interactive/2021/03/18/magazine/facial-recognition-clearview-ai.html?utm_source=pocket_mylist 

Kris Jones. (2021). Disruption Required: A Privacy-First Approach To Navigating The New Third-Party Data Rules. Forbes. https://www.forbes.com/sites/forbestechcouncil/2021/09/16/disruption-required-a-privacy-first-approach-to-navigating-the-new-third-party-data-rules/ 

Nicholas Confessore. (2011). Cambridge Analytica and Facebook: The Scandal and the Fallout So Far – The New York Times. The New York Times. https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html?searchResultPosition=7 

Goswami, S. (2020). The Rising Concern Around Consumer Data And Privacy. Forbes. https://www.forbes.com/sites/forbestechcouncil/2020/12/14/the-rising-concern-around-consumer-data-and-privacy/