AI Use Cases

Artificial Intelligence – Exemplary Use Cases

The following use cases help to explore how AI can transform supply chain management and after-sales operations. From vendor scheduling to stock optimization, these examples highlight the tangible benefits of AI-driven strategies, including cost savings, increased transparency, and improved resource allocation. 

1. Use Case: Supply Chain – Vendor Scheduling 

Vendor schedulers play a crucial role in ensuring uninterrupted operations at OEMs (Original Equipment Manufacturer) by “keeping the line running.” This involves closely monitoring both ends of the supply chain, identifying potential bottlenecks in advance, and proactively addressing issues. To achieve this, they need high levels of visibility across a complex supply chain network, often managing hundreds of part numbers simultaneously.

An AI-powered network, integrated with relevant data sources, can extract and analyze critical information from available datasets. By leveraging insights from experienced vendor schedulers and company documentation, the AI interprets and acts upon data effectively. As new challenges arise, the system can be updated using natural language instructions, eliminating the need for programming skills and enabling continuous learning and adaptation.

This AI-driven approach not only improves the accuracy and speed of decision-making but also empowers vendor schedulers to focus on strategic tasks rather than repetitive manual processes. By identifying and addressing issues proactively, the AI minimizes downtime, optimizes inventory levels, and ensures timely delivery of materials.

Additionally, the system enhances collaboration across the supply chain by providing real-time updates and actionable insights to all stakeholders. This level of transparency fosters better communication and coordination, enabling faster responses to disruptions and demand fluctuations.

As the AI continuously learns from new data and feedback, it evolves to become more intuitive and aligned with the organization’s unique supply chain dynamics. This adaptability ensures that the system remains a valuable tool even as business needs and external conditions change over time.

By integrating such a solution, companies can achieve a competitive edge, reduce operational costs, and strengthen their ability to meet customer demands reliably.

Example Scenario:

For an OEM with annual supply chain costs of $50 million:

• Inventory Optimization (20%): $2–3 million savings 
• Downtime Reduction (30%): $3–6 million savings 
• Expedited Shipping (15%): $0.5–1 million savings 
• Procurement Efficiency (7%): $1–2 million savings 
• Scheduler Productivity (15%): $0.75–1.5 million savings 

–> Total Potential Savings: $7.25–13.5 million annually

2. Use Case: After Sales – Stock Optimization 

After sales and production plants operate on separate, unconnected SAP systems, resulting in a lack of centralized transparency regarding key factors such as requirements, inventories, stock levels, and critical data like trends, historical usage, and consumption patterns. This disconnect makes it challenging for aftersales to meet short-term demands efficiently and accurately forecast future needs. At the same time, production plants often require aftersales parts to address bottlenecks, but the lack of visibility prevents seamless coordination.

Despite having high inventory levels and capital commitments, both aftersales and production plants struggle to identify and redistribute excess stock proactively. As a result, critical shortages are often addressed reactively, leading to costly last-minute interventions and inefficiencies.

The goal is to create full transparency across inventories, requirements, and tied-up capital, enabling a comprehensive, bi-directional analysis between production plants and aftersales. This will allow for the proactive identification of opportunities to redistribute excess stock, reduce fast-track processes, and optimize the capital tied up in both aftersales and production plants. Additionally, the functionality and added value of the prototype will be validated, ensuring its effectiveness and readiness for series production.

The benefits of this approach are:

• Optimized inventory levels and improved resource allocation through reduction of excess stock
• Reduction of tied-up capital across the entire supply chain
• More precise demand forecasting and better alignment between production and actual needs
• More efficient and responsive supply chain through cross-plant transparency

This approach optimizes stock levels, reduces excess inventory, and improves resource allocation. It also significantly cuts tied-up capital, freeing resources for other uses. Enhanced demand management leads to more accurate forecasting and better alignment with production needs. Full transparency across plants and aftersales boosts supply chain efficiency. Lastly, the prototype’s functionality will be validated, ensuring its real-world effectiveness.

Example Scenario:

–> Total Potential Savings: ~5% –> Stock reduction from a covered inventory of $40 Mio –> rd.$2 Mio 

EFS Consulting helps leverage the strength of AI using domain expertise 

EFS Consulting: Strengths & Customer Benefits 

• Domain expertise  
• Deep understanding of AI potentials and limitations 
• Solution tailored to specific needs 
• Suppliers and technology agnostic 
• Predictable costs for transparent implementation 

The advantages of customized solutions include not only the cost factor – more cost-effective development and better performance – but also a modular and open AI architecture that ensures independence. 

We bring everything together in a two-phase process: prototype development followed by series production. Our customized solutions, compared to generic tools like ChatGPT, provide faster, more efficient performance, and, most importantly, full control over your data. 

The advantages of tailored solutions are clear: they are faster and provide better performance. Contrary to common expectations, a tailored solution can actually be more cost-effective, as it utilizes only the necessary resources in a targeted manner. Moreover, our architecture is modular and open, ensuring independence from outdated tools as technology evolves.  

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