Man hands using tablet on blurred warehouse as background
Retailers are facing economic and geopolitical challenges that are very different from those they faced just a few years ago. Take the current Red Sea crisis. According to JP Morgan, 30% of global container trade passes through the Suez Canal, and the resulting shipping delays are upending global supply chains.
While the full threat to global trade will become clearer in the coming months, already, some retailers, manufacturers and their supply chain partners are warning of the significant knock-on effects this will have on profit margins. Fast-moving, low-margin apparel retailers, for example, are particularly exposed to the Red Sea delays. They may struggle to keep pace with delivering garments to consumers who expect seasonal and on-trend collections to be available in an instant.
Disruption is not a one-time event
This recent trade disruption is just the latest in a series of unexpected challenges retailers have been facing in recent years. In 2021 and 2022, disruption in supply chains is estimated to have cost businesses $1.6 trillion in missed revenue opportunities per year. This staggering figure emphasizes that designing supply chains for resilience is no longer optional, but vital to retailers’ future success.
Right now, however, many retailers are in “response mode” and considering immediate actions, such as bringing in goods from closer locations so they can avoid longer-lead times and inflated prices that come with rerouting cargo ships around the Red Sea region.
Diversifying supply sources, exploring alternative shipping routes, and purchasing goods earlier, and in larger quantities, are also options retailers are exploring. However, existing trade routes are deeply ingrained in a retailer’s operations and deviating from these will likely come with higher costs.
Enter GenAI
Artificial Intelligence (AI) has been helping supply chains to be more efficient for years. Mining vast amount of past sales data to understand ‘what happened’ to then advise ‘what might happen’ and
‘what to do next’ to better meet demand have been tools retailers have used for decades. AI has a new member to the AI family, generative AI (GenAI). Since its entry, there’s been a huge amount of interest in GenAI’s potential for supply chains. And it’s easy to see why.
GenAI has the potential to impact much more than just the task at hand. There is value to be gained in everything from new product development, procurement and planning, manufacturing, and logistics, to after sales and services.
Just look at how GenAI can make digital twins – virtual representations of machines, products, or processes – much more powerful than they already are. Fed with real-time data, digital twins can help retailers test different response scenarios without impacting the actual day-to-day operations of the real-world supply chain. Retailers can then swiftly identify potential issues, such as bottlenecks, quality issues, or unforeseen shifts in demand, and proactively address them before they escalate.
Another area GenAI can propel, is supply chain nerve centers. Using cloud, data, AI, and analytics, they provide transparency, allowing retailers to identify risks by providing deeper visibility into their network of suppliers and manufacturers.
Both applications are fast becoming indispensable for detecting vulnerabilities in supply chains. They help retailers simulate how their supply chains behave under heavy loads, and build robust mitigation plans throughout their planning process.
The power of GenAI in Supply Chain
What makes GenAI so powerful? A recent report from Accenture
ACN
Then there’s the ways in which GenAI can make demand and capacity planning much more accessible. Many retailers already have analytics solutions to help with these tasks. But their applications typically provide very complex information. GenAI could help break this information down so that workers can query recommendations and receive explanations in everyday language.
GenAI can also provide access to tailored insights and automations based on chatbot interactions. That might include the introduction of GenAI-powered assistants for sourcing and procurement to help guide users to the right buying channel, support any call-off or spot-buy, and connect them with a professional stylist to manage the purchase.
In order fulfilment, GenAI has the potential to save considerable time and effort. For example, a GenAI-powered import/export document generator for shipping and export processes could tap an abundance of information and automatically fill in shipping and export documents.
The technology can also help with supply chain-related tasks such as Scope 3 emissions reporting by using generative AI to sift through millions of lines of spend data in multiple languages, before automatically mapping each line item to emissions factors, that procurement teams can the easily review.
GenAI to drive reinvention across the supply chain
Retailers have faced many curveballs over the past few years, and there will be more to come. It makes the need to build supply chain resilience to withstand future shocks, more important than ever.
Retailers have proven time and again that they are hardwired for change as a result of operating in an industry that has to deal with fluctuating inventories, variable workforce requirements, and fickle consumer trends. Now, with advanced technologies such as GenAI coming to the fore, retailers have an opportunity to position their organizations to lead the race for future growth.
Building resilience with generative AI
What makes generative AI so powerful? A recent report from Accenture outlines three capabilities the technology can enhance supply chain planning.
Firstly, generative AI can help supply chain managers make better decisions based on contextualized insights from unstructured data sources, such as text, pictures, videos, or event social media posts. Consider how forecasting could be improved by using generative AI to scan vast quantities of public online data sources to identify possible causes of future demand.
Generative AI can also provide access to tailored insights and automations based on chatbot interactions in everyday language. That might include asking a chatbot to help find a specific product and create a call-off or spot buy to a supplier if it’s not available. Other applications include auto-generating documents like purchase orders and inventory troubleshooting.
Then there’s the ability of creating relevant, context-specific text, code, images, or insights on demand, and at scale. Just think how generative AI could help produce auto-generated insights such as market trends and demand forecasts, or help support contract renewal negotiations with suppliers, as well as contextualized business operational performance metrics.
