AI Supply Chain Optimization Engine

AI can significantly improve supply chain and inventory management by addressing key limitations of human decision-making in this complex context. Here’s how:

• Handling large-scale decisions: AI can effectively manage decisions across thousands of products, locations, and time horizons, which is beyond human cognitive capacity.
• Managing uncertainty: AI can better handle uncertainties like extreme demand pattern shifts, avoiding human biases such as recency bias or overconfidence in trend prediction.
• Balancing trade-offs: AI can efficiently analyze and balance multiple metric trade-offs for individual decisions (e.g., working capital costs vs. product availability), which is time-consuming and challenging for humans.

By leveraging AI alongside human decision-making, organizations can unlock additional profit, reduce waste and inefficiency, and improve sales and customer experience.

While AI could be deployed in numerous areas, we see the greatest impact and ROI in demand forecasting and inventory management. Our primary use cases (outlined above) demonstrate how AI can predict demand more accurately and reduce stockouts and excess inventory.

Once value is proven, other focus areas include AI-powered purchase planning to optimize order timing and quantities, while supplier selection tools ensure partnerships with reliable, cost-effective vendors. AI can also enhance route optimization in logistics and enable predictive maintenance on equipment to minimize downtime.

Starting with these areas helps streamline operations and secure quick wins that positively impact the entire supply chain.

Implementing AI into your supply chain presents several challenges. Four issues consistently arise in our work with clients:

1. Data quality and availability – data quality is crucial as AI and ML operations depend on accurate, well-structured data to deliver real commercial impact.
2. Integration with legacy systems – integrating AI with older legacy systems can be both complicated and costly, sometimes requiring upgrades to your IT infrastructure.
3. Lack of AI skills and talent – the shortage of data and AI expertise can slow progress. Investing in skills development and AI partnerships is key.
4. High initial costs – starting will small pilot projects can help manage expenses, build internal buy-in and and demonstrate value and impact early on.

With nearly 20 years of real-world AI consulting experience in supply chains, our team can support your enterprise in navigating these challenges. We focus on building your internal talent and developing a data and AI strategy to plug gaps and create scalable impact across your supply chain.

Forecasting has always been the backbone of supply chain management. Conventionally, supply chain managers will rely on ARIMA models and demand distribution modelling and while the models are demonstrably effective, they typically hit a performance plateau.

This is because they rely on historical data and work on the assumption that the future will be the same as the past. This isn’t really forecasting – it’s back-casting.

Given this, supply chain operations have become an effort in handling outliers and responding to events that destabilize demand.

AI builds on traditional models and enhances it by incorporating real-time signals such as online activity, weather patterns and economic indicators to predict demand shifts more accurately.

For example, Rewire has deployed AI in the supply chain to help companies reduce forecasting errors by up to 20% leading to better inventory allocation and reduced stockouts.

Generative AI and LLMs allow us to significantly enhance scenario planning and demand forecasting with supply chain management.

By using LLMs, we can enrich our existing data with external data, hypothetical scenarios and stress test models at scale in conjunction with our other models and methodologies.

LLMs and generative AI technologies are no ‘silver bullet’ on their own, but they add significant capability to AI based demand forecasting and supply chain management.

 Learn more about Rewire’s Gen AI consulting services.

AI helps mitigate human bias by making data-driven decisions at scale. Traditional decision-making often involves subjective judgments, which can lead to overstocking or understocking. AI provides a rational, objective perspective by analysing patterns and predicting future demand based on a wide range of factors. This enables businesses to optimise inventory levels, reduce waste, and enhance operational efficiency.

Here’s an example with one of our clients:

A demand planner at a car replacement parts company needed a new windscreen panel for a very expensive car. They had none in stock, so they had to wait for three weeks for the replacement part to arrive.

The planner, frustrated by the delays and disruption, purchased three more panels to avoid the situation occurring again. These panels remained unsold and sat in the warehouse until they were written off and sold at auction.

This one example is illustrative of the waste and inefficiency caused by human bias. With the right approach, AI can remove human bias at scale, balancing priorities and moving to real non-bounded optimization.

Scaling AI across global supply chains requires standardised processes, cross-functional collaboration, and robust data governance. Companies need to ensure seamless integration across different regions and business units while considering local market nuances. Establishing a phased rollout strategy, training super users, and leveraging AI to provide centralised decision-making support can help achieve successful scale-up.

AI can support sustainability by optimising logistics routes, reducing waste, and improving energy efficiency.

By analyzing transportation patterns, AI can suggest more efficient delivery routes, reducing fuel consumption and emissions. Additionally, AI-driven inventory management helps minimise overproduction and waste, aligning supply chain operations with net-zero requirements.