AI in Manufacturing: 5 Ways To Increase Resilience & Future-Proof The Supply Chain

Like all industries today, the manufacturing supply chain is on an unstable footing. In one of the world’s fastest innovating industries, the focus has been on how technology can improve operational efficiencies and resilience, save on costs, achieve their sustainability targets and grow customer bases. 

With pressures to produce and move goods at lightning speed, all eyes are on finding ways to upgrade, automate and orchestrate logistics. Many manufacturers are now looking towards digitisation to do so. This is driving the market for AI in the industry. So how can AI future-proof and enable supply chain resilience in manufacturing?

 

Improve environmental sustainability

With almost 60% of manufacturers citing environmental sustainability as a business imperative and pressure from all sides (including consumers demanding that all businesses take positive climate action), finding opportunities to improve sustainability credentials is crucial.

Utilising AI across supply chain and logistics, businesses can significantly reduce their carbon footprint by optimising shipments in multiple ways. 

For example, an AI like 7bridges’ LEO, can make complex calculations to identify which fulfilment site a manufacturer should use, the carrier(s) needed and the optimal packaging for shipments. The AI can do this by reading huge datasets, analysing the best possible outcomes and balancing these with business constraints. With this analysis complete it recommends the best way for the shipment to travel on a route with the fewest miles spent in polluting transportation modes, at times when there will be the slightest chance of delays (such as peak hour road traffic).

The result? Shipments can be packaged and packed together in a way that maximises vehicle load and reduces wastage. Fewer polluting miles are travelled. Carbon emissions are reduced and, by reducing wastage at every turn, spending can also be cut.

 

Demand planning and forecasting

The pandemic’s impact on the supply chain highlighted the need for better predictive tools. Demand forecasting is pegged as one of the top ten areas of technological investment for 2022. A recent study by IDC highlights that by 2023, 50% of all supply chain forecasts will be automated using AI, which is expected to improve accuracy by five percentage points. 

Manufacturing relies on steady, predicted demand to get products to market. AI can automatically link demand with supply so that businesses can optimise the manufacturing process. In practical terms, AI-powered demand planning allows companies to:

  • Cut storage and handling costs by only holding the stock needed at the time and reduce handling expenses 
  • Prevent missed sales and increase revenue due to stock-outs by ensuring on-demand access to goods
  • Reduce risk and increase resilience, rapidly adapting to changes in demand and avoiding potential bottlenecks with dynamic and predictive inventory optimisation

Streamlining working capital and stock balancing

The manufacturing industry has seen a boom in demand in recent years across all sectors, be it cosmetics, hardware, pharmaceuticals, clothes or cars. 

But meeting this demand remains challenging: a shortage of certain key parts or raw materials has led to huge price hikes. At the start of 2022, China and Hong Kong reintroduced strict Covid measures that are likely to further disrupt the flow of goods to global markets, and the cost of moving goods has not still not reduced to pre-pandemic levels (due to a toxic cocktail of cargo shortages and surging fuel prices across sea, air and land).

But securing access to these raw materials and key parts is just the first hurdle in meeting demand. As supply chains are required to be more agile than ever, manufacturers need to effectively balance their stock and working capital. This is especially challenging during turbulent times as we’ve seen in recent years. Many have had a hard time keeping up stock levels and lessons learned from the pandemic are compelling supply chain managers to find areas of improvement and efficiencies in inventory tracking and management.

That’s where AI can come in. Rather than risk incurring extra costs or lost revenue, AI like LEO can help manufacturers ensure they have the right stock in the right locations. Managing your working capital efficiently can help you increase customer service levels and speed without sustaining unnecessary costs.

 

Improve procurement and carrier selection

Procurement in the manufacturing industry is centred around efforts to manage risk, reduce costs and optimise supplier selection. Data spread across multiple systems with incompatible information and structures has historically meant that upgrading or improving the procurement function was almost impossible. 

AI can improve the procurement process, bringing total transparency to the historical costs and performance of existing providers, improving carrier selection and resilience, reducing pressure from manual actions, and lowering costs by securing the best rates when engaging new carriers. 

AI can also help in enabling businesses to deploy intelligent multi-carrier switching when they ship goods. 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.

One of the most exciting opportunities that AI creates in procurement and carrier selection is the ability to stress-test and simulate the impact of proposals from suppliers by creating a “digital twin”. This is a virtual representation of a supply chain’s components, including warehouses, suppliers, and inventory. It allows businesses to model various “what if” scenarios in current and future contracts. 

 

Logistics & transportation cost savings and optimisations

Since manufacturing operations and schedules can change in a moment due to any number of external factors, budget decisions around space or units can quickly be made redundant. These changes can lead to missed delivery slots or impact costs due to capacity inefficiencies. 

Transportation costs are hugely variable and can be a drain on profits if left unchecked. And with rising prices, reduced air and shipping capacity and increasing competition for available space, many manufacturers are struggling to regain control. 


Companies that utilise AI to process and analyse the vast amounts of structured and unstructured data within the logistic networks are better equipped to identify trends and make optimised decisions on where, when and how to route each order to ensure delivery is efficient, on time, and at the right price. Our proprietary AI is constantly learning to help manufacturers build more resilient supply chains and prepare for the future.





Build resilience into your manufacturing supply chain with our guide to Supply chain resilience for 2022 & beyond.

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