The Role of AI and ML in Building the Supply Chains of the Future
Global events such as the COVID- 19 pandemic demonstrate just how fragile international supply chains can be, with resilience becoming the go-to buzzword for many organisations since early 2020. Beyond the current pandemic, trade/tariff wars and geopolitical changes such as Brexit have created numerous logistical challenges such as a lack of transportation capacities, increasing rates, port congestion and container shortages. In fact, according to Gartner, the impact related to global trade operations, technology, political and supply shifts present the greatest disruptions after unpredictable events.
Economic and business uncertainty driven by the pandemic has also created a major challenge for logistics and supply chain managers to determine where and when to deploy resources. In addition to the typical peaks and troughs of demand at different times of the year, COVID has totally disrupted the way consumers purchase, and where they buy from. Nonetheless, customer expectations remain critical.
But leveraging historical data alone won’t cut it anymore for predicting consumer demands and trends. That’s why organisations are looking towards predictive forecasting leveraging technologies such as machine learning (ML) and artificial intelligence (AI) that provide more insight and capabilities for supply chain managers to think about demand.
Technologies like ML and AI are now being used by the supply chain in multiple areas of global trade operations, such as estimating time of arrival of a shipment, improving the restricted party screening processes or helping classify products. Most logistics and supply chain providers have already incorporated ML into their platforms or have it on their short-term roadmap.
Recent events have shown to the supply chain that it needs to double down on technologies such as ML and AI and rapidly pivot to meet changing demand and unforeseen levels of disruption. The Ever Given incident for example, involving an Evergreen ship blocking the Suez Canal back in March highlighted the fallibility of the global shipping network and need for a more diverse and agile supply chain that can quickly pivot to different modes of transport to avoid congestion.
The technology rethink
The logistical challenges created by the pandemic have also triggered a major rethink in the way organisations approach supply chains, particularly from a technology standpoint. In fact, BluJay research shows that 75 percent of global logistics organisations plan to take lessons learned from the pandemic to make changes to their supply chain practices. End-to-end visibility from warehouse to store is now essential to ensure a transparent and reactive supply chain.
But supply chain managers alone cannot achieve this kind of granular focus. This is made possible through harnessing data, networks, and applications to understand, monitor and swiftly adapt to changing circumstances. Digital supply chains that can be controlled easily through visual dashboards and data analytics are enabling a frictionless flow of inventory and orders across all modes and borders to meet growing customer expectations for seamless and faster deliveries. Customer service is now a critical part of the delivery experience. And supply chain companies are quickly realising this.
Over the next five years, BluJay research indicates that 67 percent of supply chain companies believe that customer experience (CX) will become the number one brand differentiator. As a result, the supply chain must move away from mass market methods, and instead offer a variety of options to customers. On a consumer level, this means providing different delivery options, including click and collect, free shipping or contactless delivery.
But the use dynamic routing powered by ML and AI to driver better CX can also be a very powerful tool to optimise the supply chain. Using AI and ML, dynamic routing can give supply chain firms the ability to consider plans B, C and D, offering alternative ways ocean and air freight cargo can be moved from source to customer when uncertainties like pandemic-related border closures happen.
The role of AI in dynamic routing
The visibility achieved though real-time data tracking is critical to achieving greater efficiencies, profitability and sustainability in the supply chain. Having that visibility can help ensure optimised routing to run the most efficient route possible to conserve energy and costs and help ensure customers are kept updated of the delivery process in real-time.
AI and ML will be key drivers of information visibility and resiliency. The applications of AI and ML can not only optimise stock inventory and cost margins, but they also play a role in dynamic routing so that items arrive to consumers in the shortest distance and timeframe possible.
Dynamic routing has empowered logistics providers to consider new plans and routes offering alternative ways ocean and air freight cargo can be moved from source to consumer when uncertainties like border closures, port congestion or blockages do occur, to avoid extending the delivery route and adding additional costs to the trip and a greater carbon footprint too. Importantly, AI and ML’s role in dynamic routing aids the customer experience because together they can manage consumer expectations through real-time comprehensive updates to provide transparency across the supply chain. This also means that the consumer has total visibility that their order may be delayed because of changes in the delivery schedule being impacted by a change in channel.
How artificial intelligence can create an “asset-less” state
The current adoption rate for ML and AI is still relatively low, but that won’t last. As data coming from supply chain sources increases, being able to leverage AI and ML through a
connected supply chain will help organisations become faster, more agile, and better equipped to respond to customers.
One of the key benefits of AI and ML is that they can enable supply chains to transition towards an “asset-less” state, matching the correct supply to demand in the given timeframe, resulting in a reduced product wastage. This can be achieved because AI and ML can accurately optimise stock inventory, minimising overproduction and potential errors.
This concept of the asset-less state refers to creating a supply chain that is as lean as possible. Using AI and ML to analyse past performance will help organisations optimise inventory so that they’re not carrying stock they don’t need and won’t necessarily be able to sell for a long time.
Businesses that optimise for visibility will not only be able to meet customer expectations, but will enable their own operations to run more smoothly and flexibly in the long term. With a robust connected network and the ability to react to real-time data, businesses will begin not only to make a difference to their customer acquisition and retention rates but impact the bottom line too while increasing sustainability within the supply chain.
The pandemic has forced many organisations to reconsider how they operate, both internally and externally. Internally, decisions on supply chain technology have often been driven by the needs of individual departments. As the critical nature of the supply chain has been exposed during the pandemic, however, supply chain managers are making their way into the boardroom and getting involved in directing technology decisions. Over time, these companies will reap the rewards and the supply chain manager’s influence will boost organisational resilience and reduce friction across the operational process.
A resilient supply chain that is agile and adaptable is far more valuable than one that is low-cost. Resilience comes through collaboration, since a network of partners enables much more agility, whatever disruption arises. Supply chain managers are wise to explore technologies such as ML and AI if they wish to access insights and capabilities to help them locate resources on-demand and provide capacity where and when businesses need it most.
By Katie Kincaid
