AI and the Future of Supply Chain Management


Encompassing multiple, often overlapping disciplines across the supply chain, artificial intelligence is set to empower supply chain management on a global scale.


As Forrester puts it, we’re six years into the Age of the Customer, when digitally empowered consumers place increased service expectations on every brand interaction.

The Chartered Management Institute suggests that emerging technology is driving up consumer expectations. It is also providing us with innovative ways of managing the supply chain to meet them.

Today, this looks like next-day delivery, flexible delivery options, more secure drop-off points, and customer-oriented returns policies – all for little or no additional delivery charge.


New technologies have put customers in the driver’s seat of the marketplace – giving them power over which brands will sink or swim in the digital age.


AI systems will typically demonstrate at least some of the behaviours associated with human intelligence such as planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation. Gartner saw it as one of the top 10 strategic technology trends for 2017, and virtual assistants such as Apple’s Siri are increasing their capabilities as AI applications.

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Amazon, for instance, is creating a ecosystem around its Alexa voice computing platform. Currently, the Alexa platform offers software development kits (SDKs) that allow third-party developers to build skills for the AI assistant and other manufacturers of hardware to integrate the Alexa assistant into their products.

Here are three use cases for driving benefits from AI across the supply chain.


Use Case 1: Guided procurement via chatbots


Guided procurement uses natural language processing and artificial intelligence to simplify the way suppliers, businesses, and customers interact.

In this capacity, chatbots can engage in conversation with suppliers. They can set and send requirements for suppliers around compliance, materials and invoicing. They can even manage general customer queries as well as more specific search and purchase requests. Earlier this month, globally recognised e-invoicing, e-procurement and financing services, Basware, launched Basware Assistant, a chatbot feature to achieve exactly this.

‘The best user interface is the one that you don’t need to use – it just runs in the background’, said Bhavin Shah, Director of Product Management for Basware. ‘By predicting what people might search for in real-time, and communicating that to them in the way they’d ordinarily speak, the system becomes naturally easy to use.’


Use Case 2: Warehouse and inventory management using machine learning


Machine learning applications extend into warehousing and inventory-based management. By providing an endless forecasting loop that continually analyses for the most accurate and impactful combinations of algorithms and data streams, machine learning technology is transforming organisations’ demand forecasting abilities at a time when this has never been more important.

Two of the largest retailers in the US have already implemented machine learning technology into their inventory management. ‘Over the summer of 2016, Lowe’s introduced its LoweBot in 11 stores throughout the San Francisco Bay Area. These autonomous retail robots […] create real-time data by using computer vision and machine learning to scan inventory and look for patterns in product or price discrepancies.’

Meanwhile, Walmart is continuing to test the use of proprietary drones across its warehouses, as first announced in June 2016. The retail giant has since reported that the technology is capable of completing an inventory check in 24 hours, a manual task that ‘can take about a month for employees’.

The inventory management capabilities that machine learning opens up for warehousing could be phenomenal, providing the competitive balance between flexibility and agility that continues to challenge supply chain management today.



Use Case 3: Autonomous vehicles for logistics and shipping


Transportation costs typically make up a significant portion of total supply chain costs, with key factors being drivers and fuel. As TechCrunch reported, ‘Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day.’

In 2016, a convoy of trucks demonstrated this by driving across Europe to arrive at the Port of Rotterdam without a single driver taking the wheel. More recently, a 40-tonne driverless truck has been successfully tested in Shanghai. Marking a major milestone in China’s automation push, the unmanned lorry was capable of travelling up to 80 kph (49.7 mph) safely while transporting cargo. Across the Pacific, GM Cruise Holdings’ self-driving fleet has just received a $2.25 billion capital infusion from SoftBank ahead of its 2019 launch.

Self-driving vehicles still have a way to go before mass rollout across the supply chain, but by eliminating the need for drivers and substantially improving the efficiency of fuel usage, autonomous fleets are set to transform the future of shipping and fleet management.

Designing a more intelligent supply chain

The integration of AI into the supply chain is set to increase. With Amazon leading the way, and use cases and success stories becoming more widespread, organisations are increasingly seeking to adopt AI technology in order to deliver the rising service standards expected by today’s customers.


The bottom line is customer satisfaction: improving the speed of deliveries, enhancing delivery happiness and adding to perfect order scores.


The challenge for supply chain directors is to design a supply chain that meets and even exceeds these uncompromising standards of service, and does so cost-effectively.

With technologies such as  AI and machine learning, organisations can develop supply chains that accomplish this. What barriers are preventing your company from adopting AI, and how can they be overcome?

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