In today’s changing environment, supply chains across the world are facing constant market disruptions from pandemics, geopolitical instability, and finally climate change. As a consequence,there is a growing number of firms that are adopting generative Artificial Intelligence (Ai) to boost visibility,efficiency and agility across their operations.
Generative AI poses itself as a different tool from the traditional automating tools, because it can generate new insights and scenarios based on large datasets, allowing organisations to make proactive, data-driven decisions.
An insightful real world case is Unilever, which has in the past years integrated AI-driven forecasting and logistics systems in all its global networks. Unilever AI-driven decision making strategy uses generative models that combine real-time demand data,weather information, and retail signals to optimize production and distribution (Unilever,2024).
The initiative reached more than 98% on-shelf product availability, at the same time also reducing waste and logistics costs. In addition, Unilever collaborates with startups through its 100+ Accelerator in order to implement predictive analytics and loT-based solutions that monitor and detect equipment failures before they disrupt operations.
From an Information Strategy point of view, Unilever sets an example of how firms can transform data into business actions and strategic assets. By linking data flows across suppliers, manufacturers, and retailers, businesses create adaptive,information-rich ecosystems capable of responding to changes faster than competitors (Raut et al., 2021).
On the other hand, recent research has highlighted that even an advanced decentralised learning model, such as federated learning, can expose sensitive information through inference attacks if not properly secured (Truong et al., 2020). Thus, information strategy nowadays must balance efficiency and innovation with responsible and ethical data management. In my opinion, Unilever’s case demonstrates that future supply chain management will not only be defined by efficiency, but also by intelligent, ethical, and privacy-conscious information systems that learn and evolve continuously.
References:
Raut, R. D., Mangla, S. K., Narwane, V. S., Dora, M., & Liu, M. (2021). Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains. Transportation Research Part E: Logistics and Transportation Review, 145, 102170. https://doi.org/10.1016/j.tre.2020.102170
Truong, N., Sun, K., Wang, S., Guitton, F., & Guo, Y. (2020). Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective. ArXiv.org. https://arxiv.org/abs/Unilever PLC. (2024, July 31). Utilising AI to redefine the future of customer connectivity.
Unilever; Unilever PLC. https://www.unilever.com/news/news-search/2024/utilising-ai-to-redefine-the-future-of-customer-connectivity/
Very interesting blog post, it really gave me a practical example of how companies can leverage Generative AI to increase operational efficiency across the value chain. Even if we study and hear a lot about AI integration, reading about how Unilever integrated multiple data sources to make real-time predictions is a great example of how AI theory turns into real results for businesses.
I believe that Unilever is not alone and over time we will see more companies following this path as AI tools become more accessible and cheaper to use.
It is interesting to question how can smaller FMCG companies leverage GenAI in their operations even if smaller budgets compared to Unilever. It would be nice to see more real examples of smaller companies trying to apply these tools in creative ways.