Many industries are searching for the “new normal,” even five years after the global pandemic rocked their foundations. Supply chains have experienced exceptional hardship due to the pandemic, suffering from snowballing challenges spurred by safety regulations during the lockdowns in 2020 and 2021. Strict reductions in the workforce led to shipping delays and material shortages, revealing the fragile nature of the supply chain infrastructure. These myriad disruptions within the manufacturing and distribution ecosystem had such profound effects that the United States government released a statement in July 2021 addressing the matter to the public. Many within the industry would likely agree something is seriously wrong when consumers ask questions about the supply chain.
Fortunately, once governments began lifting their lockdown restrictions, the burdens of the pandemic began to ease. Shortages have diminished significantly. The 2024 holiday season points to a return to normalcy, with only a small percentage of retailers expressing concern over inventory challenges this year — a significant improvement from the 39% that expected challenges in 2021. Unsurprisingly, this return to normal has been partly driven by record-breaking venture capital investments in supply chain technology. Collectively, venture-funded supply chain technology companies attracted billions of funding in 2021.
In the wake of the supply chain crisis, many companies have begun to examine ways to fortify their processes and increase resilience in the face of global challenges. Representing about 10% of the global GDP, the supply chain and logistics industry must be able to afford to remain laggard in its operations. While the pandemic was not necessarily predictable, the world is undoubtedly undergoing significant changes due to various factors, such as shifts in geopolitics, climate change, and accelerating technological innovations across industries. One such innovation that has captured the imaginations of companies and investors everywhere made its mass market debut in November 2022 with the release of ChatGPT: large language models (LLMs.) Often used interchangeably in conversation about Generative AI, or GenAI, LLMs are a subcategory of GenAI that deals specifically with enabling software to engage with natural language instead of relying on code alone. The supply chain and logistics industry benefits tremendously from these newfound technological capabilities, avoiding future calamities and improving operations across the vertical.
Supply Chain Technology Lagged Well Before the Pandemic
The pandemic may have amplified the need for supply chain technology overhaul, but the industry’s weaknesses were apparent long before the lockdowns. Even major stalwarts in global logistics continue to rely on disparate systems, unstructured data, and manual processes to keep goods on a healthy cadence of production and distribution to meet consumer demand. Essential information remains under the discretion of humans to record, transmit, and analyze, leaving endless room for error — not to mention billions in value from unprocessed data streams — on the table.
Because the global economy relies so heavily on an uninterrupted supply chain, as the events spurred by the pandemic demonstrated, implementing new technologies can be a struggle for companies in this vertical. Technology adoption can prove challenging even under ideal circumstances due to the high cost of equipment and software, upskilling workforces, and strategic complexities associated with operational changes. Before GenAI emerged, workflow automation had already become a part of enterprise operations, offering potential improvements in scalability, efficiency, and accuracy compared to manual workflows. However, while workflow automation became a fixture in several industries, supply chain and logistics could have been more successful with its adoption.
Workflow Automation in Supply Chain: A Brief History
Workflow automation has been around for decades and pioneered by tech giants like Microsoft, Oracle, and SAP. The beginnings offered simple automation for repetitive tasks, following strict rules that could be coded into software programs. Early workflow programs were like assembly line robots for data streams; they were designed for one task and had to be constantly monitored and repaired by highly specialized software engineers to keep them running. Continuous innovation within automation software introduced complex logic, API integrations, and computer vision AI into the mix, greatly expanding its use cases and improving overall performance. These improvements birthed entirely new software categories, such as Process Mining and Robotic Process Automation (RPA).
Despite these improvements, the supply chain industry remained largely unaffected by the technological innovations within workflow automation software. While the long-term cost savings of automation can be significant, large enterprises dealing in manufacturing and distribution logistics require substantial up-front investment. Due to reliability and security concerns, even with the rise of cloud computing, expensive on-premise equipment and software were still necessary for handling large data streams. Supply chain operations also rely on particular functions, meaning that most one-size-fits-all software solutions must be a better fit for the industry’s requirements. The challenge of unstructured data — texts, emails, and other disparate documentation — also persisted, making automation within the supply chain industry a steeper hill than most were willing to climb.
GenAI in Supply Chain Technology Changes Everything
Although the rise of vertical-specific software addressed the need for tailored solutions for the supply chain and logistics industry, the problem of unstructured data was one that legacy approaches to automation could not overcome. This is where recent advances in GenAI and LLMs are poised to play a major role in supply chain technology innovation and adoption.
Historically, workflow automation software has operated asynchronously based on deterministic software programming techniques with defined input-output relationships and explicit rules and conditions. This evolved into probabilistic automation for early AI applications, providing only incremental improvements. Yet, with the advent of GenAI and LLMs, software is undergoing a paradigm shift, adding intelligent automation to its capabilities. In the supply chain context, LLMs’ ability to process natural language and conduct inference-based reasoning renders workflows reliant on human reasoning and unstructured data sources susceptible to automation for the first time. The same can be said for other verticals that rely heavily on human communication and natural language, like financial services, healthcare, legal, and customer support. This presents enormous opportunities for supply chain software companies specializing in LLM-based solutions, laying the foundation for a new era of industrial automation led by companies such as Parade, Loop, Vooma, Happyrobot, and Linc AI.
“Generative AI is disrupting every industry, and the supply chain stands to gain a lot when someone gets the formula right,” says Kevin Finn, a technology investment banker specializing in supply chain software at Ignatious, a boutique M&A advisory firm.
While several incumbent technology companies invest millions in upgrading their SaaS offerings to better leverage GenAI capabilities, Finn believes that challenger companies have unprecedented opportunities within this vertical.
“It’s still too early to say which companies will present the strongest offerings to the market,” says Finn. “Yet startups that move quickly can make waves, earn venture investment, and may realize better outcomes.”
Published By: Aize Perez
