Are Autonomous Supply Chains Wishful Thinking? 

An autonomous supply chain is one where minimal to zero human intervention is required. Instead, each component – from sourcing raw materials to manufacturing, logistics, and distribution – is completed using advanced technologies like artificial intelligence (AI), Internet of Things (IoT), blockchain and hyper-automation.

With complete autonomy, there’s no longer a need for people to manage, optimise and execute tasks, the machines do it all (and probably a lot better too).  

It’s a futuristic notion that is as tantalising as it is frightening. But is it realistic? Perhaps. But the road to autonomy has its fair share of technical, human and ethical hurdles that make it a complex vision to fully realise, at least in the immediate future. 

That said, the technologies needed to build an autonomous supply chain exist, albeit in disparate or semi-mature parts.  

Artificial intelligence can predict demand with startling precision. It can sift through incredibly large sets of historical data, to generate highly accurate demand forecasts which is then used to optimise inventory levels, reduce holding costs and minimise the risks of stockouts or excess stock. In the case of an autonomous supply chain, this information would trigger automated procurement processes, ensuring the right products are ordered at the right time.  

Internet of Things (IoT) devices can track goods in transit. GPS trackers can provide information on locations, dynamically rerouting goods in the event of delays. Environmental sensors can keep tabs on the condition of goods, providing real-time alerts should products be exposed to extreme temperatures. All of this information can be used by an autonomous supply chain to adapt to changing conditions, boost transit efficiencies, and maintain quality standards.  

Blockchain can ensure the authenticity and integrity of transactions by creating a secure ledger of transactions that can only be transparently accessed by authorised personnel. While still in its maturity phase, blockchain offers security, transparency, and automated compliance which could all be critical components of a fully autonomous supply chain. 

Robotic Process Automation (RPA) is already sweeping through the procurement function, automating repetitive tasks that were once the responsibility of humans. But given that RPA’s capabilities are generally limited to specific, rule-based tasks and often require human intervention for more complex, decision-making processes it’s not quite there yet. 

Hyperautomation could potentially fill the gaps left by RPA’s limitations. Coined by Gartner, hyperautomation extends automation to more complex, decision-making tasks to support a more nuanced, intelligent approach to automation. For example, hyperautomation uses technologies like Optical Character Recognition (OCR) to automatically process documents and accelerate administrative functions.

In demand planning, it can predict inventory needs in real-time by analysing variables. Or it can provide precise delivery times and adapt to live traffic conditions for fleet management. More than just doing tasks faster, it takes on decision-making, predictive analytics, and real-time adaptability – all essential traits of an autonomous supply chain. 

Combined, these technologies could make an autonomous supply chain feasible. But there is not yet a cohesive system that has achieved the perfect interplay between these technologies. Not only will it be complex to build, it will also need to be unwaveringly exact – any malfunction or incorrect decision-making could have a disastrous domino effect across the supply chain. 

The autonomous supply chain is a double-edged sword for supply chain professionals. On the one hand, you are freed from tedious tasks, able to work on more engaging and fulfilling tasks. On the other, there’s the chance your role will change or worse, become obsolete. 

But there is some consolation. No matter where technology takes us, there will always be a need for the nuance and understanding that only human experience offers. Employees with a deep understanding of a business will always be able to provide the level of context that a machine simply can’t. 

Which brings us to the third point – ethical considerations. An autonomous supply chain will require a considerable amount of trust in the technology running behind the scenes – a trust that many may not be willing to give quite yet.  

Questions abound. In an autonomous supply chain – where decisions are made by machines – who is accountable should an error or malfunction occur- the developers, operators or the AI itself? Are current regulations in place and ready to manage the challenges posed by autonomous systems, and if so, are they evolving to keep pace?

Finally, what measures are in place to ensure AI systems don’t perpetuate systemic biases? Let’s hope the answers to these questions are fully fleshed out before the autonomous supply chain matures. 

An autonomous supply chain seems technologically feasible and well on the way to fruition. But are the human, ethical, and regulatory challenges ready for what’s to come? Only time will tell.