Supply Chain Optimization Engine
Price volatility, supply uncertainty, demand unpredictability – our models empower you to navigate even the most challenging supply chain scenarios armed with AI-powered, prescriptive recommendations.
Our clients
High stock levels, lost sales and burdensome manual processes are costly consequences of an inadequate inventory management process. With targeted automation and AI-powered recommendations, we empower you to optimize your inventory levels, reduce costs, and deliver a seamless distribution service.
Our bespoke approach to forecasting demand
Off-the-shelf forecasting solutions use simplified mathematical and demand distribution assumptions that fit the average case – not your specific case. This generates recommendations based on unstable historical events and low forecasting reliability. Instead, at Rewire, we apply machine learning algorithms to perform bottom-up and top-down demand modelling. This allows you to explore supply chain scenarios and their cost implications in a highly detailed and accurate way. From accounting for outliers to planning for peaks in demand for new products.
Scaling AI across your entire supply chain
Our supply chain optimization engines are developed using cutting-edge prefab components that we rapidly configure and customize in alignment with your strategy and circumstances. This approach means you can start experiencing value fast, and we can scale your AI deployment rapidly. In a few months, you could expand from a solution covering one product in three countries to 10 products across 30 countries.
AI use cases within supply chain management
AI use cases
In the post-pandemic environment, supply chain has emerged as a key differentiator and driver of enterprise value — even more so than brand or product.
Rewire drives compounding value over time for supply chain operators using our AI supply chain optimization engine. We focus on two key areas to achieve this.
Demand planning & forecasting
Operating assumptions based on average demand alone do not allow for adequate planning given the complexity of modern supply chains. Our models are engineered and trained to:
- Predict probabilities of different demand scenarios instead of average demand
- Model full demand distribution to mitigate and manage uncertainty
- Estimate lost sales based on historical out-of-stocks
- Include a wide range of features: trends, peaks and lows, campaigns, seasonality, and substitution
Cost optimization
Cost-based optimization in supply chain management (SCM) is crucial for minimizing expenses, maximizing profit, and maintaining efficient operations.
- Determine optimal stock levels by balancing cost and risk trade-offs
- Map the risks of overstocking and understocking to their associated cost components
- Transition from crude, default safety stock to cost-optimal inventory levels
Our track record speaks volumes
4%
Increase sales revenue
Year one revenue increase of 4%, generating €16m through AI-powered forecasting.
19%
Improvements in forecasting
A prediction accuracy increase of 19%, delivering a €9m year one cost reduction through algorithm-based inventory levels.
18%
Reduction in working capital
Year one working capital reduction of 18%, driven by a 20% reduction in inventory costs, while maintaining stock availability.
Implementing AI in the supply chain
Identify & prioritize
Focus on strategic, high-impact opportunities to address business challenges, prioritizing based on business importance and AI feasibility
Design, scope & plan
Design for scale and business change by validating opportunities and defining solution requirements for data, algorithms, and application
Implement an MVP
Quickly achieve initial impact by testing a proof-of-concept and implementing a Minimum Viable Product (MVP) to ensure optimal value creation
Scale the solution
Scale rapidly when value potential is proven by expanding and refining the solution to meet increased demand, either across product lines, regions, or both.
Data and AI consulting
5 reasons to partner with Rewire
Frequently Asked Questions
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:
- Data quality and availability – data quality is crucial as AI and ML operations depend on accurate, well-structured data to deliver real commercial impact.
- Integration with legacy systems – integrating AI with older legacy systems can be both complicated and costly, sometimes requiring upgrades to your IT infrastructure.
- 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.
- 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.
