Role of AI in supply chain management

Organisations are beginning to utilise AI to manage distribution costs, streamline communications between suppliers, and improve factory workflows.

Let’s face it, supply chains involve hundreds, if not thousands, of suppliers, manufacturers, and other companies. With so many moving elements, it can be extremely challenging to keep up with projects, especially as we begin to utilise modern, but often complex, technologies.

Some organisations are employing AI workflows to manage patterns and relationships within large datasets, optimising processes. Alongside tracking, documentation, forecasting, security, and production management capabilities, AI is proving to be a beneficial asset.

Benefits of AI in supply chain management

• Fewer errors and improved relationships: AI has the ability to see every moving cog at once, reporting on their whereabouts and actions within seconds. Due to this constant, in-depth view of the supply chain, AI helps to prevent errors and improve relationships with suppliers.

With full visibility and smart algorithms, AI prevents human error and machine mistakes faster. On top of this, AI systems can analyse the root causes of problems, preventing future hiccups.

• Better efficiency: By fully organising and evaluating processes, AI ensures each step of the supply chain is entirely optimal.

Additionally, predictive analytics means that demand is met at exactly the right time, optimising warehouse capacity and helping maintain employee workflows.

• Keeping people and products safe: AI keeps both employees and products safe across the whole supply chain.

By monitoring work and transport environments, AI suggests safety-driven changes and ensures employees are following the safety guidelines set out by the organisation.

• Reduced costs: AI can learn from complex and unpredictable situations, and perform tasks like counting, tracking, and recording inventory more precisely and with less human input. AI can also spot and resolve bottlenecks in the supply chain, increasing efficiency.

By improving efficiency and mastering repetitive tasks, AI lowers the expense of running a complex supply chain.

• Improved sustainability: The more a supply chain is optimised, the less input is needed from the organisations involved, reducing strain and negative environmental impacts.

AI not only streamlines processes, but can identify areas of poor sustainability, suggesting ways to improve. For example, optimising travel routes to reduce fuel usage and preventing wasted product by accurately predicting future demand.

What is supply chain sustainability and what role does AI play?

Sustainability in the supply chain is becoming a priority for more organisations. This is partly due to the increase in laws and legislations surrounding sustainable supply chain practices and a growing customer preference for products that are made sustainably.

Organisations should make note of and uphold a series of environmental and social sustainability practices at every stage of the supply chain.

Supply chain sustainability involves processes to prevent pollution, unnecessary water usage, greenhouse gas emissions, deforestation, and other impacts that come from manufacturing and distributing products and services. Supply chain sustainability is increasingly vital for the following reasons:

• Consumers, investors, and partners demand and expect positive sustainability practises
• Long-term impacts on ecosystems and the planet need to be prevented
• Supply chain resilience and security is vital and helped in part by sustainable processes
• Law and legislation around environmental best practises

AI plays a vital role in making supply chains more sustainable by optimising resource use, reducing waste, improving efficiency, and enhancing transparency. AI can help monitor and analyse environmental data, design better transportation and delivery systems, and verify the quality and origin of products to ensure sustainability every step of the way.

The future of AI in supply chain management

One of the key applications of AI in supply chain management is demand forecasting, which is the process of predicting customer demand for products or services based on historical data, market trends, and other factors. Demand forecasting enables supply chain managers to plan production, inventory, and distribution strategies more effectively and reduce costs and risks.

Future advancements in AI for demand forecasting may include the use of deep learning models that can handle complex and nonlinear relationships between multiple variables, such as weather, seasonality, price, promotions, and consumer behaviour. These models can also incorporate external data sources, such as social media, web traffic, and news events, to capture real-time changes in demand patterns, streamlining supply chains.

By improving visibility, transparency, and collaboration among different stakeholders, AI will revolutionise the future of supply chain management.