Why shippers are at risk of missing out on the AI supply chains revolution

Artificial Intelligence (AI) has unquestionably been the business buzzword of 2023. Since ChatGPT burst onto the scene in late 2022 and demonstrated the new capabilities of Large Language Model (LLM) based systems, interest in the potential of these models has skyrocketed across industries. 

In the realm of supply chain management, AI has the potential to radically change the way businesses operate, offering significant advantages in operational efficiency, decision-making, and adaptability. However, when it comes to making these a reality, there are serious roadblocks in place for many shippers to solve in order to realise the benefits available. Businesses that can solve these will have the chance to race ahead, reducing costs, improving service levels and enhancing agility – those that fall behind will miss out.

We sat down with Tamir Strauss, Zencargo’s Chief Technology and Product Officer, on our podcast Freight to the Point, to understand the stakes and what it will take for shippers to get onboard with the next stage of supply chain evolution. 

The Year of the Bot

The advent of powerful AI models like ChatGPT, Bard and Llama has ignited fevered conversations in various industries, as these consumer-facing tools suddenly brought the potential of ideas that have been bubbling up behind the scenes for years into the public view. While simple AI tools, such as chatbots and machine-learning based analytics, have been on the market for some time, the reality was that their value was yet to be truly felt. 

The advent of conversational tools that could be used as easily by a data scientist and your grandparents changed all that.

We are hitting an inflection point in terms of the capabilities of these systems and these tools. There is a greater sense among corporates that if you don’t start to adopt these technologies and adopt them quite quickly, you will be left behind,” says Tamir.

The possibilities are real and immediate, with thousands of tools and capabilities able to be built on top of these models. Across finance, retail and logistics – any data heavy industry – there is a sense that these tools will be one of the key drivers of competitive advantage going forward, but the question remains of just how it will happen.

How can AI help the supply chain industry?

Modern supply chains are diffuse, complex and, often, subject to rapid changes in priorities. The challenge of squaring such a circle is the nature of supply chain management and recent years have made it clear just how challenging this can be in a volatile market.

The task of aggregating, analysing and acting on data from internal stakeholders, including finance, logistics and commercial teams, as well as external manufacturers, carriers, forwards and customers operators is a daily challenge for supply chain teams everywhere. AI tools that can ingest, understand and present complex information and make it available, understandable and usable for teams has the potential to change not just how businesses plan and execute their logistics operations, but also the speed and efficiency of global trade itself.

Demand forecasting and inventory optimisation

Demand forecasting is one of the most complex aspects of supply chain management, with traditional forecasting methods often falling short, leading to overstocking or understocking issues. This has been especially visible this year as cratering demand left many businesses overstocked over the festive season, with ordering remaining at low levels as shippers wait for economic conditions to improve.

AI offers a solution by turning a historically chaotic process into a science. “If we know where all the SKUs are, which boxes they’re in, which containers they’re in, then you’re giving yourself a chance to figure out… what should we do to meet the demand.” says Tamir. 

The ability to build demand models that combine historic demand data, up-to-date sales figures and upcoming orders has the potential to radically improve the way businesses manage inventory, reducing waste, stock outs and working capital commitments.

Real-time tracking and visibility

Visibility into the supply chain is essential for effective decision-making, but too often the task of bringing together the necessary data puts the system at a disadvantage. By the time that a shippers has chased the manufacturer, carrier or port to find out where a shipment is, it may have already moved on.

The ability of AI to draw data from multiple systems, categorise and present it in a structured way can reduce this gap, helping businesses make faster, more effective commercial decisions based on reliable information.

Insight and predictive analytics

AI’s power lies in its ability to learn from data, predict trends, and provide actionable insights. Despite the huge volume of data that shippers hold on their historic and current supply chain performance, the task of drawing tangible insights on what’s working and what isn’t can still be manual, tedious and riddled with inaccuracies. 

This makes it harder for shippers to learn long term lessons from their performance, keeping them in place, fighting fires and chasing information, rather than creating stable gains in their supply chains. The potential for AI in the supply chain lies in its ability to bring together diffuse data points to help simplify complex decisions that impact the bottom line and gross profit, streamlining processes that were once labour-intensive and error-prone.

The AI data challenge

While the potential of AI is huge, the reality is that it’s not a silver bullet. The best models in the world are still only as powerful as the data with which they’re fed.

“The core of AI and everything that it does relies on data. If you have all this information, if you collect all that information, if you reflect reality, if you’re able to store the data in a way that is accessible to machine learning and AI, then you can start driving predictions and improvements in efficiency.” says Tamir.

However, for many businesses, supply chain data is still not sourced or managed in a format that AI could even make use of due to:

• Siloed information in multiple systems, such as WMSs, ERPs or SCMs, without reliable integrations
• Manual information gathering via email and documentation that requires digitising and uploading to relevant platforms
• Systems operating at a lag where data isn’t available until it’s too late to be useful
• Incomplete or inaccurate information due to reporting or human error

Making the most of AI first requires creating a stable, up-to-date, representative digital view of your supply chain for models to then process and analyse. This is the core differentiator for businesses that will separate businesses that can leverage AI to its full potential and those working with partial or inaccurate insights based on low-quality data.

Don’t miss the boat on AI

From precise demand forecasting and real-time tracking to the intelligent optimisation of inventory and warehousing, the prospects of AI in supply chain are tantalising. But ambition has to be tempered with realism. Many businesses are yet to realise the existing potential of technology that has been on the market for years such as real-time tracking, integrated SCM systems and cloud documentation – these tools will be the foundation for what comes next.

As businesses continue to adapt and evolve, shippers will need to invest in the right infrastructure and relationships to set themselves up for AI success. 

Zencargo is the freight and technology partner for high-growth businesses worldwide, creating bespoke logistics solutions based on real-time data from across their supply chains. With our integrated SCM platform, we bring together insights from internal and external teams to create robust, accurate digital supply chain models, executed by our global network of freight experts.

To find out more about how you can prepare for the AI race to come, get in touch with our team.