5/5 (1)

In logistics and supply chain management, there is a never-ending quest for continuous improvement, driven by the need for constant cost minimisation and service optimisation (Prado-Prado, 2009). During my bachelor thesis at a large European freight operator, I performed research into the optimisation of the firm’s (collaborative) road freight transportation planning. The case involved a select number of UK depots, each with a distinct planning office and a varying number of daily customer orders; pickups or deliveries. My proposed solution followed a ‘traditional’, mathematical, Linear Programming methodology. However, Generative Artificial Intelligence (GenAI) solutions with similar functionalities are rapidly advancing and becoming increasingly accessible. This caused the company to initiate a pilot that combined my solution and GenAI transport planning software, with its sights set on an autonomous vehicle fleet in the future.

GenAI-driven route optimisation solutions leverage machine learning algorithms, through the analysis of large volumes of historical and real-time data to create optimised routes, factoring in time, distance, and cost (OptimoRoute, 2023). Critical Success Factors (CSFs) and capabilities of these GenAI models include system integration, data availability, demand forecasting, adaptive routing, multi-objective optimisation, and flexible, multi-faceted constraints (FarEye, 2023). Through these CSFs, the primary benefits achieved by GenAI routing models relate to cost and CO2 emission reductions (Gangil, 2023). Other prevalent benefits include real-time adjustable planning and enhanced customer service, time efficiency, safety, and resource utilisation (FarEye, 2023; Patel, 2023). During my short time there, the pilot of my thesis company already elicited some of these benefits, primarily improved resource utilisation.

While there is great enthusiasm from practitioners and researchers surrounding these models (Abduljabbar et al., 2019), their limitations should not be overlooked. Most GenAI-powered transport planning solutions are still in early stages of development. Therefore, freight operators should be cautious of the accuracy of the generated routes, especially when the quality and quantity of the input data are questionable (OptimoRoute, 2023; Patel, 2023). Additionally, road freight transportation planning is a complex process, with a very broad range of relevant variables and actors. E.g., traffic conditions, vehicle characteristics, storage conditions, and delivery timeframes. GenAI models may not be able to factor in all these aspects, potentially resulting in incomplete or suboptimal routes (OptimoRoute, 2023). When carefully considering these limitations, GenAI transport planning modules seem to have tremendous potential, and, while not without challenges, they provide an exciting glimpse into the future of logistics.

Citations:

Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189.

FarEye. (2023). AI Route Optimization & Route Planning Guide: How AI Routing Can Transform Route Optimization. FarEye. https://fareye.com/resources/blogs/ai-route-optimization

Gangil, A. (2023). Generative AI-based route and logistics optimization. InfoSys. https://blogs.infosys.com/digital-experience/emerging-technologies/generative-ai-based-route.html

OptimoRoute. (2023). An Overview of Route Optimization Techniques. OptimoRoute. https://optimoroute.com/route-optimization-techniques/

Patel, R. (2023). AI Route Optimization: Does it Really Make Delivery Operations Efficient? UpperInc. https://www.upperinc.com/blog/ai-route-optimization/

Prado-Prado, J. C. (2009). Continuous improvement in the supply chain. Total Quality Management, 20(3), 301-309.

5/5 (1)

As a hip-hop enthusiast, I, like many others, came across a ‘newly released’ song by Drake and The Weeknd, early 2023. This song, titled ‘Heart on My Sleeve’, quickly gained traction, but left fans confused as to why there was no communication surrounding its release on the artists’ official channels (Veltman, 2023). It soon became clear that neither the artists nor their labels were behind the creation and subsequent release of the song. Instead, it was created by an independent music producer using voice cloning Generative Artificial Intelligence (GenAI) technology, who subsequently published it on various prevalent streaming platforms like Spotify, YouTube, and Apple Music (Pearson, 2023). Before being taken down by the artists’ label (UMG) for copyright infringements, the song amassed over 600.000 streams on Spotify and over 20 million views on TikTok and Twitter (Singh et al., 2023), with a re-upload currently sitting at over 6 million views on YouTube (AzTech, 2023).

Voice cloning is one of the most widely known applications of GenAI in music, hosting a multitude of prevalent providers, such as Kits.AI, Vocaloid, Uberduck, and Speechify (Sandzer-Bell, 2023). Replicating one’s voice through GenAI entails training a machine learning model by providing an extensive range of diverse original voice audio clips, allowing it to learn the distinct vocal characteristics, styles, and nuances. The resulting voice model can then be used for voice synthesis or to overlay an existing voice (Wells-Edwards, 2022). From personal experience, I can say that creating a (somewhat) realistic voice model is surprisingly easy, although this seems to be strongly dependent on the quality of the input audio samples.

While many consider the ethical and legal considerations behind cloning living artists’ vocals, as exemplified in the scenario above, I am much more intrigued by the ethical implications of applying this technology to deceased artists. Imagine, you are a life-long Michael Jackson fan, and come across a recent AI-generated cover of ‘Love Again’ by Dua Lipa that uses his voice (which I strongly recommend) (Vargas, 2023). Are you excited to hear your favourite artist in a new and modern setting? Are you concerned for the artist’s legacy and integrity? Or do you fall somewhere in the middle of the spectrum? Due to its novelty and a lack of regulations surrounding the technology (Ciccarelli, 2023), voice cloning GenAI constitutes a unique space where ethical considerations heavily rely on the judgement and responsibility of creators and individuals.

Citations:

AzTech. (2023). Drake Heart on my sleeve Ft The Weeknd OFFICIAL AUDIO Best quality. YouTube. https://www.youtube.com/watch?v=7HZ2ie2ErFI

Ciccarelli, D. (2023). Are AI Voices Legal? Voices. https://www.voices.com/blog/ai-voices-legal/

Pearson, J. (2023). Viral AI-Generated Drake Song ‘Heart on My Sleeve’ Removed from Spotify, YouTube. Vice. https://www.vice.com/en/article/xgwx44/heart-on-my-sleeve-ai-ghostwriter-drake-spotify

Sandzer-Bell, E. (2023). AI-voice generators. Audio Cipher. https://www.audiocipher.com/post/ai-voice-generators

Singh, A., Hall, J., & Barnes, J. (2023). Listen: AI-generated Drake and Weeknd song streamed 20 million times causes music industry alarm. The Telegraph. https://www.telegraph.co.uk/world-news/2023/04/17/drake-weeknd-ai-generated-song-heart-on-my-sleeve/

Vargas, S. (2023). Michael Jackson – Love Again (Dua Lipa – AI Cover). YouTube. https://www.youtube.com/watch?v=J6LapydXoVE

Veltman, C. (2023). When you realize your favorite new song was written and performed by … AI. National Public Radio. https://www.npr.org/2023/04/21/1171032649/ai-music-heart-on-my-sleeve-drake-the-weeknd Wells-Edwards, B. (2022). What’s in a Voice? The Legal Implications of Voice Cloning. Ariz. L. Rev., 64, 1213.