Point of View

How Generative AI will Transform Supply Chain Industry


If your supply chain isn't ready for GenAI, you've got a problem

The genius of the Internet is that it's unbreakable. If an unforeseen event closes one route, data packets quickly find another. That hasn't been possible for logistics, until now.

Just as TCP/IP revolutionized digital communications, making the Internet possible, GenAI will transform physical supply chains. It will enable logistics providers to optimize real-world processes, predicting demand more accurately, automating labor-intensive tasks and improving overall efficiency.

It will provide the intelligence and agility required to route cargo around obstacles in near real time.

Companies that embrace AI now will be able to deliver dynamic, accelerated, self-healing supply chains for their customers. 

What is GenAI and what does that mean for logistics?

GenAI creates new data based on the distribution patterns in data it has seen before, either in training or production. This data can be text, images, numbers — or something else.

In addition to creating new data in forms that are useful to their human operators, GenAI is also able to understand instructions written in plain English.

What does this mean for logistics and supply chain professionals?

  • By integrating and understanding more natural-language sources, in real time, GenAI can build more detailed, accurate and useful models than other technologies, at speed.
  • Using synthesised data, generative AI can compensate for gaps in the historical data, allowing it to predict and mitigate supply-chain disruptions and other problems before they happen.
  • They can use supply chain simulation and what-if analysis to explore various scenarios, test factors, and make informed decisions without real-world risks.

Where will GenAI make the biggest difference?

In the medium to long term, almost no part of the supply chain will be left untouched by generative AI. The ability to assimilate unconventional sources of data, create new models and make accurate predictions faster will transform the whole sector.

But the areas in generative AI will be adopted and have the biggest impact in the shortest time:

  • Demand forecasting: dynamically integrating a vast range of natural-language sources, and accurately synthesizing missing data, GenAI will dramatically improve demand forecasting.
  • Warehouse optimization: continuously optimize warehouse contracts, layouts and operations to maximize efficiency, minimize cost and reduce waste.
  • Supplier selection: using GenAI, procurement can go beyond the normal RFP process and factor real-world supplier performance and interactions into the selection process.
  • Fraud detection: by creating a more detailed understanding of baseline 'normal', GenAI can spot behavioral deviations that indicate fraud faster and earlier.
  • Product innovation: by processing market data, customer feedback, and competitor insights at scale and in near real time, GenAI can also accelerate product design and innovation.

The resulting insights help companies conceive, design, and bring to market more innovative products and services, better able to meet specific customer needs and align with evolving market trends. GenAI assists in product development, refining product specifications, and simulating various application scenarios.

Some of the additional use cases in which generative AI can have the greatest impact are:

Sustainability and environmental impact: Generative AI helps supply chain managers reduce their environmental impact by optimizing processes at every stage, from energy consumption to waste reduction.

By analyzing historical data and patterns, it generates models that enhance process and energy efficiency, minimize fuel consumption, and reduce emissions. It does this by optimizing logistics, in particular route planning, and selection between different modes of transportation.

Generative AI can also help streamline inventory management and devise eco-friendly packaging solutions. This reduces material usage and waste in the supply chain.

Reverse logistics and returns management: By analyzing historical return patterns and customer feedback, generative AI can predict anticipated return volumes and reasons.

This provides the business with a head start, enabling it to design and optimize reverse logistics processes and routes. The technology can also support decision-making on topics such as whether to repair, refurbish, recycle, or dispose of returned products.

This helps reduce handling costs and waste and extends the average life of the product categories in question.

Another use case likely to show a rapid return on any investment in generative AI includes emissions reduction through improved operations planning. This is particularly true with last-mile logistics.

Because they're by nature multi-modal and unpredictable, last-mile logistics have traditionally been difficult to optimize for reduced emissions.

Using generative AI, companies will be able to more accurately model demand fluctuations in advance. This will enable them to have exactly the right mix of vehicles in any location and to optimize their entire last-mile operation for time, emissions and cost simultaneously.

This, in turn, will mean better utilization rates, fewer empty miles and lower fuel consumption. That leads to lower emissions, lower costs and a better customer experience.  

How to start the process of adopting generative AI, now?

Given the advantages we've outlined, supply chain divisions of every organization have every reason to adopt generative AI. But how can companies adopt generative AI efficiently, in a way that avoids pitfalls and generates the best returns in the shortest possible time?

Follow these five steps:

Step 1: Define objectives and use cases
Ensure you thoroughly understanding the company´s unique challenges. Then determine how generative AI can address them. For the energy and logistics sectors, objectives and use cases may include improving demand forecasting accuracy, optimizing transportation routes, reducing energy consumption, or enhancing inventory management.

Step 2: Assess data and infrastructure
Understanding the data landscape is crucial, including assessing the quality, quantity, and relevance of the available data. Also, evaluate the existing IT infrastructure to ensure it can handle the computational requirements of GenAI models and efficiently process and store the data.

Step 3: Form a cross-functional team
Introducing GenAI to critical business processes requires an experienced team with multidisciplinary skills. These include data science, machine learning, and domain-specific knowledge. Ultimately, this team is the spearpoint to developing and implementing the appropriate solution to meet defined objectives and use cases and build confidence across the organization.

Step 4: Select tools and models
Your team, key stakeholders and external technology partners must work together to choose the right AI frameworks, tools, and models. This is crucial for effective implementation.

You can select specific generative AI models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) based on the identified use cases. These models, applied correctly, are particularly effective in generating data that mimics the characteristics of the given dataset.

Step 5: Integration and maintenance
Integrating GenAI into existing workflows and systems requires smooth communication between the AI solution and other components of the supply chain.

It's essential to monitor the performance of the deployed models and continuously collect feedback to make necessary improvements.

Ongoing maintenance and updates are also vital to adapt to evolving industry needs. Only through continuous optimization can you improve modeling accuracy and address unforeseen issues.

Next steps

To yield the best possible returns, generative AI must be deployed in a way that is highly tailored to the specifics of your operations and business model.

The best way to achieve this is to work with a technology partner that has an established record of success in the travel and logistics sector.

IBS Software is a leading SaaS solutions provider to the travel industry globally, managing mission-critical operations for customers in the aviation, hospitality, and energy resources industries.

An established provider of planning and operational platforms to the travel, transportation, and logistics industry, IBS is currently testing generative AI features on its existing platforms. Find out more about how IBS Software can help you use generative AI to achieve your goals. 


Related Posts

Comment for this post has been locked by admin.