How to integrate artificial intelligence into complex supply chains

Introducing an artificial intelligence application into an established network for the first time is always going to be a challenge. We are now at the point though, that many organisations have already delivered these kinds of projects. Leading manufacturing, retail, and logistics companies are already making use of artificial intelligence. Ikea is using it to forecast product demand, DHL is automating repetitive operations, and Amazon uses AI to maximise its profitability. What can we learn from these successful projects about how to integrate artificial intelligence into our complex supply chains?

Why use artificial intelligence to manage supply chains?

The latest developments in artificial intelligence technology represent fantastic opportunities to modernise and update inefficient business processes. Artificial intelligence can increase customer satisfaction, reduce labour costs and waste, and generally improve the bottom line.

Increase customer satisfaction

By predicting demand, artificial intelligence applications can ensure that products are available when customers need them. It can even analyse a customer’s purchasing history to make product recommendations and anticipate buying decisions. Once requested, an AI can optimise the transportation and delivery process, reducing the time taken for a customer to get their order.

Reduce labour costs 

Artificial intelligence can reduce labour costs by automating routine and repetitive tasks, allowing employees to focus on more complex operations. It can optimise supply chain processes, such as routing and scheduling, reducing the need for manual interventions. AI can also provide support to decision-makers by managing inventories and maintenance planning.

Reduce waste

As well as the other highlighted benefits, optimising inventories, asset utilisation, routing, and scheduling all act to minimise waste in complex supply chains. Artificial intelligence applications can additionally tackle unnecessary waste by monitoring, and addressing, product quality issues in real-time.

Case study: Amazon’s approach to supply chain optimisation

When optimising complex supply chains, Amazon recommends an approach based on hierarchical layers. The problem breaks down into these layers based on supply chain scope, decision-making timescales, and the type of uncertainty modelling. This helps to reduce the problem complexity and effort required. At the top-most hierarchical layer, the problem complexity comes from the large scope. While at the bottom-most layer, the complexity comes from the fine detail. The exact definition of each layer depends on the nature of the supply chain, but the effort required for each should be approximate.

Challenges faced when adopting artificial intelligence

Having an awareness of the most likely hurdles is critical to adopting any new technology. Artificial intelligence is not an exception to this rule.

Data quality and availability

Artificial intelligence applications rely on data. They need the right type of data, of the right quality, from the right places, at the right time. The algorithms will not only operate on this data but will train on it too. Poor data quality and availability will hold back any artificial intelligence project.

Network complexity

Complex supply chains increase the number of things to consider when integrating artificial intelligence. The supply chain may encompass many technologies, organisations, countries, or regions. Each of these may have a different approach to regulation, data accessibility, IT infrastructure, and employee training.

Change management

Adapting existing tried and tested supply chain practices to include artificial intelligence can have unpredictable consequences. Procedures, equipment, organisational structures, company culture, and employee roles may all experience change.

Approaches for successful integration

Integrating artificial intelligence into a complex supply chain is a well-worn path. Organisations that have achieved positive outcomes often share the details of their projects. The major factors that support a successful conclusion are as follows:

Make a plan

Develop a coordinated strategy (such as Amazon’s hierarchical layering) for identifying and managing the business case for each potential solution.

Get visible

Obtaining end-to-end visibility of the supply chain is essential. This not only includes the potential areas for modernisation, but any existing data silos, and the extended network as well. This visibility can form the basis of complete models of the supply chain.

Collect data

Real-time and historical data drive the decisions made by artificial intelligence applications. Start by collecting and storing data from the entire supply chain.

Automate

Many of the benefits of artificial intelligence are only realised with automation. The algorithms may need to make automatic adjustments to inventories, routing, and manufacturing processes.

Collaborate

Obtaining data and visibility of all aspects of the supply chain requires negotiation and integration with customers, suppliers, and other stakeholders. Consider how any improvement to the supply chain can benefit all the involved parties.