3 ways AI will transform supply chain for wholesalers and distributors
Posted by: electime 23rd August 2021
Philip Ashton, CEO of 7bridges, the AI-powered logistics platform
While financial services, marketing and eCommerce adopted AI and Machine Learning years ago, the logistics industry and in particular supply chain technology has been slower to digitise.
In 2021, the status quo is undergoing a period of disruption. AI-powered logistics and supply chain technologies are fueling a rapid transformation, enabling businesses to become more responsive, increase profit margins, and reduce their carbon footprint.
Here are three ways in which AI technology is due to rapidly transform the supply chain for wholesalers and distributors.
AI-powered procurement
Logistics are notoriously hard to procure – they look like a commodity but behave like a service. For this reason, many businesses have historically developed an exclusive relationship with a single 3PL, under the belief that this was the best way to receive preferential rates.
By bringing total transparency to the historical costs and performance of existing logistics providers, AI has since turned logistics procurement on its head. This is no small feat, and a task that for most businesses has been laborious (at best) or downright impossible until now, because of the unstructured data, inconsistent invoice formatting and incompatible pricing structures used by different logistics providers.
By applying AI and Machine Learning, invoice data from multiple partners can be normalised, and with a harmonised dataset, these technologies can also be used to audit historical charges and identify all of the errors and SLA breaches that would have gone undetected by the human eye. Where previously wholesalers and distributors would have struggled, these insights put wholesalers and distributors in a position of power, as they can use the data to renegotiate prices and service levels from their logistics providers (and of course, claim refunds where they’re due!).
The leverage AI provides extends to so much more than surfacing historical overcharges and SLA breaches however. One of the most exciting opportunities it creates is the ability to stress-test and simulate the impact of proposals from suppliers by creating a digital twin. A digital twin, which is a virtual depiction of a supply chain’s components – all of the warehouses, suppliers, and inventory, enables businesses to model a variety of scenarios – from sites going offline in bad weather to delays in shipments caused by disruptive events (like the Suez Canal saga).
When it comes to procurement, a digital twin enables you to predict the financial and service outcomes of using new and existing suppliers, so you can identify previously unknown opportunities and threats.
Reducing costs using dynamic carrier selection
Even businesses that run AI-driven logistics procurement exercises still have to keep a close eye on the performance of their suppliers. Direct logistics costs can rapidly eat into profit margins if they’re not kept under control.
AI can also help here in enabling businesses to deploy intelligent multi-carrier switching when they ship goods to their customers. Using AI in real time allows companies to take advantage of the best prices from carriers offering the highest performance on a route at a specific moment in time..
By using AI to build in this flexibility, it means that wholesalers can respond to surge orders without losing margin; as the AI can calculate the best route and provider in near real-time, taking account of the different locations of stock, external disruptions, and balancing cost with service levels. In fact, AI-powered carrier switching has become a huge competitive advantage for companies over the past 18 months. During the COVID-19 pandemic, a number of new surcharges have been levied by logistics providers (often without warning or transparency) – which for some companies has resulted in a 30% rise in shipping spend.
However, because AI-powered logistics technologies were able to identify these hidden additional charges and included them in calculations, businesses using AI to handle logistics were not hit with the same increased costs of shipping during the pandemic.
Addressing supply chain sustainability
The impact of climate change is becoming more apparent and many organisations are reviewing the sustainability of their operations – with supply chains a significant contributor to their carbon footprint (in Europe, the transport sector is responsible for nearly 30% of greenhouse emissions).
To significantly reduce their carbon footprint, businesses need to optimise shipments in the following ways:
- Select a fulfilment site that enables the shipment to travel on a route with the fewest miles spent in polluting transportation modes, at times when there will be least chance of delays (such as peak hour road traffic).
- Shipments should be packaged and packed together in a way that maximises vehicle load and reduces wastage.
While these may sound like simple calculations, in reality they are hugely complex – requiring access to multiple datasets from many sources. For a typical supply chain manager this level of complexity provides too many variables for someone in the role to calculate for themselves, or be able to adapt quickly to changing information. An artificial intelligence however can calculate the optimal fulfilment site, routing, logistics providers, packaging, packing formations, and times to send shipments with the lowest possible CO2 emissions.
Whether it’s improving procurement, reducing costs, or ensuring the future of green logistics, Artificial Intelligence and Machine Learning is already transforming the businesses of wholesalers and distributors able to invest in it.
