The 5 Phases of AI-Powered Supply Chain Automation

The internet, smartphones and now, artificial intelligence. 

The latest in a line of disruptive technologies, AI is changing the way businesses operate. And despite a reputation for lagging behind other industries in digital transformation, the supply chain sector is not immune. The rise of AI in supply chain management is happening. 

But realising the benefits of AI in supply chain management goes hand in hand with coming to grips with your supply chain data. Without a good dataset as the foundation, even the most sophisticated AI and data science initiatives will fail to be useful. This is why, even if implementing automation and AI in your supply chain isn’t a priority right now, it’s important to prepare by building a robust supply chain data infrastructure. 

In this article, we’ll dig into what supply chain automation means, outline the applications and benefits of automation in supply chain management and provide some practical tips to set your organisation up for an automated future. 

What is supply chain automation?

Supply chain automation refers to the implementation of technology to execute supply chain activities quicker, cheaper or to a higher standard than they are being done currently. 

Basic supply chain automation is typically powered by rules-based logic, with more advanced automation programmes likely to feature AI and machine learning technologies. 

How is AI used in supply chain management?

The potential applications of automation and AI in supply chain management range from simple, rules-based task automation to fully autonomous optimisation whereby AI is executing decisions independently of human operators. 

‍

Let’s look at the various applications in more detail:

Simple task automation

Automation in its simplest form involves programming technology to execute a particular task when a set of conditions are met. This level of supply chain automation does not typically involve AI technology, instead relying on rule-based logic.

Example: triggering email alerts when a container ETA changes

Diagnostic reporting 

At this stage, you’re analysing historical data and building reports to understand where things are going well and where they’re going wrong. Insights derived from these reports often inform the priorities of supply chain operators. 

Example: aggregating historical demurrage charges by port to understand where goods are getting stuck

Predictive analytics 

This is typically the stage where AI tools are introduced into the supply chain tech stack. Historical and current data will be analysed to conduct scenario analysis and generate a prediction of likely outcomes.

Example: modelling the financial impact of changing suppliers, routes or carriers

Prescriptive recommendations

At this stage, your AI-enabled tech stack understands your objectives and is functioning as a ‘recommendation engine’ whereby it is suggesting actions to supply chain operators. 

Example: suggesting alternative carriers based on shipping rates, ETA reliability and carbon emissions 

Autonomous optimisation

Autonomous optimisation takes recommendations a step further by automatically and continuously implementing them with little to no human involvement. Machine learning and inputs from supply chain operators can help improve the quality of these optimisation actions over time. 

Example: automatically adjusting shipping routes based on real-time port congestion data

Benefits of automation in supply chain management 

In most cases, the cost of the technology required to support supply chain automation is less than the equivalent cost of paying humans to execute all the tasks manually. 

In a similar vein, automation also means that tasks get completed faster. In the context of supply chain management, accelerating the time needed to make a decision and execute a response to a delay or disruption means you can get your goods moving faster.

Technology is also better equipped to make sense of huge volumes of data than humans are. As such, computers are often able to make better recommendations than humans can, in a fraction of the time, and repeat the analysis that led to those recommendations as frequently as desired. This culminates in higher quality, on-demand intelligence for decision makers. 

How to automate supply chain management?

1. Take control of your supply chain data

AI models are only as good as the data that feeds them.

But supply chain data management is complicated by the fact that the data infrastructure is highly fragmented and siloed. Data is controlled by a wide range of supply chain actors including forwarders, carriers, 3PL partners, warehouses, hauliers and others. As a result, data often lives in a complex mess of spreadsheets and carrier portals and is rarely aggregated in a way that supports AI-powered automation. 

Even when spreadsheets are aggregated, they only capture a ‘moment in time’ snapshot. But supply chains are dynamic entities, and understanding how data points are changing over time is also essential to understanding supply chain performance. When you amass a historical supply chain dataset, you’ll be able to understand what has happened in your supply chain to arrive at the situation you find yourself in today and make better decisions in the future. 

Recognising this reality, the best way to set yourself up for the successful adoption of AI and automation in supply chain management is to take control of your supply chain data. By taking ownership, unifying and organising  your data in a single source of truth, you’ll be building up a robust historical data set that can be used to train your AI and automation models (whenever automation does become a priority). 

2. Acknowledge the strategic importance of the supply chain function

The supply chain function isn’t just an operations centre – yet many people perceive it to be. Instead, it should be viewed as a strategic function at the core of a company’s nervous system that has a meaningful impact on the bottom line. 

Beyond reducing your cost of goods sold, a well optimised supply chain can provide a range of strategic benefits including the ability to bring new products to market faster than the competition and respond quickly to changing consumer demand. 

Realising the benefits of automation and AI in supply chain management goes hand in hand with shifting perspectives within the supply chain function itself as well as the wider organisation. 

3. Just get started!

Building an automated supply chain is not a project that will ever be complete. Rather it’s a constantly evolving process. 

The time will never feel right and the data in your ERP will never be perfect, so the best thing you can do is to get started on your journey now. Doing so will allow you to start realising the efficiency and reliability benefits of automation and build a case for further AI down the line. 

‍

The future of the supply chain inevitably lies in AI-powered solutions that streamline operations and drive unprecedented efficiency and performance – the only question is how quickly will we get there? 

Get in touch to learn how Beacon can help you get started on your supply chain automation journey. 

‍