The digital flip: How generative AI could be your new logistics partner

Glenn Steinberg
13 November, 23

Generative AI has been on everyone’s lips since OpenAI’s ChatGPT appeared in late 2022. It’s a fast-evolving space, too. However, for now, generative AI is a co-pilot, not an autopilot. People will move from creators to editors in what I call the “Digital Flip”.

Traditionally, the human-machine relationship has been driven by people executed processes, data presentation and the power of technology. With generative AI, that paradigm has flipped. Now technology, powered by data, is intelligent enough to execute process and make decisions on its own, with people playing a supervisory role to validate quality — that is the Digital Flip.

A simple example is having your email system serve up answers to the emails you receive with the human editing the answer before it goes out. The human evolves from creator to editor.

Generative AI is a technology that may help to create a more efficient version of you. And it will have an impact on supply chains and logistics, and the people working in them.

Let’s explore how that might happen.

How is Generative AI different and what value does it bring to logistics and the supply chain?

Traditional AI has played a significant role in supply chain operations over the years, with its ability to detect patterns in historical data to manage demand volatility, supply constraints, production scheduling and dynamic distribution, among other tasks.

However, the limitations of traditional AI have become apparent, as it often relies on structured data and pre-defined rules to make decisions. With the exponential growth of data and the increasing complexity of supply chains, there is a growing need for more advanced AI technologies, such as generative AI, that can learn from a large, diverse collection of data and generate new insights that go beyond traditional rule-based approaches.

Generative AI has the potential to transform supply chain operations by disrupting current AI capabilities and providing faster speed to insights.

Here’s one example already in use: One of the biggest logistics companies in the US is using a proprietary AI platform to optimize picking routes within its warehouses, boosting workforce productivity by about 30% while slashing operational costs through optimized space and materials handling. The generative component offers users the ability to communicate with the tool using natural dialog, and to ask deeper questions that can lead to new ways of thinking and a greater propensity for innovation.

Generative AI across supply chain networks

Future use cases could include feeding in all of the data you have about warehouse locations, transport endpoints, demand patterns and data about the world (for example, what other suppliers are doing, weather patterns or demographic data), and asking for recommendations on an optimal network design.

Risk management is a very promising area too, particularly in preparing for risks that supply chain planners haven’t considered. In some circumstances, generative AI’s capabilities could also carry out some of the suggested risk mitigations autonomously.

Last-mile optimization is another area rich in potential. For logistics operations, one of the major challenges is routing in real time, specifically navigating unexpected factors such as traffic, weather, or priority of deliveries. Generative AI could solve this, just as in the example above, but for an entire fleet of delivery vehicles, with drivers able to talk to the routing technology to instruct it or ask questions.

Help your people get ready to adopt new ways of working

In each of the examples above, the Digital Flip explains the sequence of and relationship between technology, data and people. Preparing your workforce now is critical.    

Generative AI technology will make some day-to-day activities obsolete but will also free employees up for more strategic activities and decision-making. It’s important to consider the emotional impact of the workforce — the fear of job loss, resistance, or general uncertainty.

One way to drive engagement and buy-in is to show people how generative AI can help make them more efficient, evolving and improving their day-to-day experience. And of course, people will need to be reskilled for new responsibilities — it’s important to provide the right learning environment and make training programs available at all levels of the organization.

Be mindful of risks and limitations

With any new technology, going all-in is not an advisable approach. Companies will need solid governance and responsible frameworks to build confidence. Regulation is coming, but for now, it’s up to us to tread carefully.

Bear in mind that these systems are not designed to provide deterministic answers out of the box. To begin with, generative AI should be used to create a “first draft,” keeping humans in the loop to oversee and edit the answers and suggestions it provides. After all, it’s the Digital Flip, not the “Digital Exclusion”.

Another major risk area is exposing generative AI models to your corporate data. Data security should be watertight, treated just like any other internal data system.

Some public providers, such as GPT-4, allow you to create a private instance of the model to better safeguard enterprise data. However, the integration of these systems into existing tech stacks will require thoughtful consideration and testing to address gaps in data, technology and governance.

Next steps

Generative AI is already in the mainstream consciousness, but we’re just at the beginning of the journey of how we harness it. We should accept that it will impact supply chain logistics, but keep your focus on the value that it could bring to your organization.

Here are three recommended steps.

  • Avoid “shiny object syndrome”. Generative AI is a powerful and exciting tool, but it’s not a strategy. Identify a business problem you want to solve or a capability you want to build, and experiment with it accordingly.
  • Embrace the Digital Flip, keep an open mind, and guard against the risks. Trial initiatives to learn more and drive quick wins. Use the pilot results to justify the additional investment needed to scale and deliver value.
  • Coordinate collaboration across the organization. Discuss the implications of how you’re planning to use generative AI, identify who will be impacted and enabled by it, and deliver the right communication and training to drive adoption.

Following these steps will help you create a roadmap for adopting generative AI more widely and bring your people with you on the journey.

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