There are no cowboys in supply chain management. The stakes are too high.

One mistake at any point could cause a disruption up and down the supply chain and leave an embarrassing number of people–sometimes entire countries–at both ends feeling understandably frustrated and angry.

It is for this reason that the accepted wisdom across the industry has always been to take intentional but measured risks, rather than swing for the fences.

So, you can imagine how many stakeholders felt when generative AI and its potential for revolutionary change came on the scene.

Is this just another “hype cycle” technology that has emerged from the peripheries to hijack the popular consciousness? Or are we looking at game-changing technology the likes of which the industry has never seen?

In this article, we discuss how the latter point of view has proven to be true, and share real-world examples of how businesses around the world have used generative AI to do what–just a year ago–was believed entirely constrained to the world of sci-fi and wishful thinking.

What Does the Supply Chain Industry Typically Look Like?

The supply chain landscape can sometimes appear dystopian despite the best intentions of stakeholders and actors. For example, since the coronavirus pandemic, supply chains have been under enormous pressure, and every effort to move the needle and normalize things has been met with challenges between ecosystem partner networks, tariff wars, actual wars, and massive turnover in human capital. One can even argue that the massive changes that are underfoot are fueling the enormous turnover in human capital.

In addition to that, many supply chains remain in need of significant innovation and reinvention, despite the huge amounts of investment that has gone into the industry.

For example, critical supply chain elements remain functionally siloed, making it practically impossible to predictably execute end-to-end delivery strategies and endeavors.

More so, important data and applications are often confined within departmental boundaries, leading to fragmentation that renders even the best enterprise resource planning (ERP) moot in the face of the plethora of orders and commitments that span the disparate systems.

All these lead to a less-than-optimal customer experience, unnecessary costs, and latency execution. And given the usual gradual approach to any sort of change or evolution in the supply chain industry, there simply hasn’t been any real hope for change on the horizon yet…that is until the unveiling of generative AI.

What Is Generative AI? How Does It Differ from Regular AI?

AI has been a bit of a buzzword over the last decade, so it’s not exactly new. All the sudden interest and hype has to do with generative AI, which is a subset of the AI landscape.

The difference between generative AI and traditional AI can be summed up simply: while traditional AI is able to carry out specific tasks based on predefined rules, patterns, and other data, generative AI is capable of going beyond this by creating entirely new sets of data and content with human-like efficiency.

Think of generative AI like your super-creative friend who could take a few pieces of information from you and create something entirely new and original –these could be pictures, text, music, video, and so on.

The Impact of Generative AI on Supply Chain Management

The cloud-based architecture of generative AI endows it with the unprecedented ability to connect data end-to-end, bypassing the siloes that have heretofore plagued supply chain management.

For the very first time in the history of the industry, there is a real potential for innovation beyond the usual constraints that have defined the supply chain.

Thanks to generative AI, internal and external stakeholders can have instant access to accurate information they need to plan and direct operations at any level of the supply chain.

Here’s a snapshot of what’s now possible:

Source: Research Gate

  • Improved Accuracy in Demand Forecasting

    Because of generative AI’s ability to gather, analyze, and visually display large volumes of historical data, market trends, and other key external factors to improve demand forecasting. Generative AI enhances demand forecasting accuracy by leveraging its pattern recognition capabilities, processing vast amounts of data, providing real-time insights, eliminating human bias, and adapting to changing conditions. What this means for supply chain managers is that like never before they can be endowed with the ability to own, shape, and scale their operations to whatever their business requires.

    This is a good thing because there has never been a greater need for agile, competitive, and resilient supply chains than today.

  • Advanced Inventory Management

    Generative AI is able to improve inventory management by leveraging data gathering and data analytics. The AI can ingest high volumes of data at frightening speeds from various sources across the board like radio-frequency identification (RFID), visual analytics, warehouse management systems (WMS), enterprise resource planning (ERP), etc., to predict critical events like stockouts, overstock situations, and even ideal reorder points.

    This gives supply chain personnel the ability to stay abreast of their business as it ebbs and flows throughout the year or from quarter to quarter.

  • Cost Reduction

    The cost reduction potential of generative AI in supply chain management is massive. For example, one of the most sought-after features of generative AI is its machine learning ability that allows it to learn and take over the execution of repetitive tasks. This reduces labor costs and frees up personnel to focus on high-level endeavors that actually move the needle for the business.

    Other cost reduction opportunities ushered in by generative AI include decreased inventory costs–like inventory holding costs from overstocking, and also lower transportation expenses, thanks to route optimization.

  • End-to-End Visibility

    Where there was previously siloed information that was hard to access, not to mention analyze, generative AI has made it possible for individuals to interact with structured and unstructured data like never before. Generative AI has the unprecedented ability to track shipments, monitor supplier performance, and identify bottlenecks like supplier disruptions and political instability, among others.

    Supply chain managers are now able to keep their fingers on the pulse of all that affects their business. Thanks to AI, they can identify potential risks and be proactive with mitigation strategies where needed.

  • Quality Control

    Generative AI is able to enhance and enforce quality control in products and processes across the supply chain by identifying and highlighting defects in the products early on in the production process or distribution process.

    Furthermore, this quality control can also be extended to personnel. Thanks to the end-to-end visibility and the large store of interconnected data and insights afforded by generative AI, training that used to take months only takes a few days.

    Digital assistants can ensure quality control by simply weaving through the mountain of data to gain key insights by way of simple conversations with generative AI like, “Where do I have excess inventory?” or “How is my vendor performing on the job?”, “Could you create and send 100 invoices?”.

  • Route Optimization

    Generative AI has done away with the complexity that came with trying to make sense of hundreds of disparate reports and data sources, by establishing a common platform from which the entire supply chain can be orchestrated.

    Thus, route optimization has never been easier, thanks to generative AI. Supply chain companies can now use this AI to generate optimal transportation plans that reduce delivery times, minimize fuel consumption, and boost customer satisfaction.

    In addition to that, the generative AI is dynamic, capable of altering its plans in response to real-time circumstances and disruptions. This dynamic response of AI makes for a more resilient supply chain.

  • Predictive Maintenance

    Generative AI helps in predictive maintenance by gathering and analyzing equipment data to identify patterns that indicate at when maintenance is required. Supply chain managers can even train their AI models to predict maintenance based on historical data, so that there are no bottlenecks during operations.

    Where their equipment is concerned supply chain companies can now look forward to reduced down time and increased productivity, which leads to greater revenue overall.

  • Improved Customer Experience

    Due to the positive influence of generative AI on so many key aspects of supply chain management, it goes without saying that it also enhances the customer experience. Customers of supply chain companies that leverage AI can look forward to a host of perks that make life easier like accurate order tracking information, timely deliveries, detailed and helpful reports, and minimal errors

Real-World Use Cases of Generative AI in Supply Chain Management

With AI forming the core foundation of their supply chain, certain enterprises have been able to deploy generative AI in innovative ways that allow them to be agile and to scale as circumstances demand.

According to Ernst & Young, some of these companies include a leading US retailer that leveraged generative AI to severely shorten the time needed to negotiate cost and purchasing terms with vendors.

The scheme worked so well that when asked, over 65% of vendors said they preferred to negotiate with the bot rather than with a human!

Companies have even been known to use generative AI tools to negotiate with each other!

Another industrial leader in Europe was able to accelerate the time from ideation to commercialization by developing their own AI models with their own data sets. Specifically, this company was able to partner with a leading tech company to use generative AI to optimize their factory automation and product lifecycle management. Their efforts were able to significantly shorten the product development lifecycle by boosting the overall efficiency using automated inspection processes.

Similarly, one of the biggest supply chain companies in the US is leveraging its own customized AI platform to optimize picking routes within its warehouses. This has boosted productivity by about 30% and cut operational costs related to the better use of space and the handling of materials.

In addition to this, they have been able to use the generative aspect of their proprietary AI for deeper customization, based on prevailing circumstances. For example, they may optimize with certain routes or deliveries prioritized, or optimize to use less fuel, and so on.

Finally, IBM is another example of how generative AI can positively impact supply chain performance. They incorporated conversational AI into their platform to make it easy for all players to access critical information using natural language. This created a dashboard that provided instant feedback on every aspect of the supply chain, making it easy for them to respond to any disruption and disturbance.

The result of their efforts was that despite all the uncertainty ushered in by COVID-19, IBM was still able to fulfill 100% of its orders and deliver on all its promises.

What Does the Future Hold for Supply Chain Management and Generative AI?

Supply chain management will continue to be the cautious field that it has always been, because of the high stakes involved. But many players are waking up to the fact that the playing field has changed ever since generative AI came into play.

As generative AI evolves into a more advanced tool with greater capabilities, you can rest assured that we’ll see an increasing number of use cases all over the world as logistics companies leverage the technology to innovate their supply chains to drive more value at lower costs.

If you’re interested in learning more about how generative AI can take your supply chain management to the next level, reach out to Ziffity today so we can talk more.