Chapter 1

Copilot in Supply Chain Management

Although statistical AI models have long been used in areas such as inventory management and production planning, they have not yet led to significant changes in the way the manufacturing industry works. They have been limited to improving algorithms and automating routine tasks. However, the new generation of AI finally promises a fundamental change.

As part of Dynamics 365 Supply Chain Management, Copilot can optimize highly complex and labor-intensive processes. Users can:

  • combine data and AI to identify and mitigate risks in the supply chain
    improve the accuracy of demand forecasting,
  • support autonomous and self-regulated supply chains
    and intelligently automate processes.

There are also functions for optimizing inventory management and shortening storage cycle times. In practical applications, this results in innovative use cases that fundamentally change supply chain management.

Use case 1: Automated supply chain analysis

The initial situation

Managing autonomous, self-regulated supply chains is one of the biggest challenges in the manufacturing industry - due to numerous data sources, multiple cross-functional units and nested processes. Companies are often faced with the problem of harmonizing these different data and processes and making intuitive decisions when in doubt.

Change through Copilot

The complexity of supply chains results from the interconnectedness of numerous activities, players and global influencing factors, ranging from the procurement of raw materials to production and delivery. The new generation of AI can help to master this complexity by mapping multi-level network models from different, unconnected systems along the value chain.

With technologies such as reinforcement learning, these networks are evolving into adaptive, self-regulated systems that work towards goals such as increased resilience, profitability and customer service. They take into account historical trends as well as internal and external events and analyze multiple scenarios to determine the optimal course of action using techniques such as simulation and machine learning.

Business added value

The implementation of AI-supported assistants such as Microsoft Copilot enables the identification of patterns and anomalies in the supply chain, such as delivery delays or quality deviations. This enables the development of forecasting models that identify risks at an early stage and thus support preventive measures such as adjusting order quantities or initiating quality controls.

The advantages at a glance:

  • Increased production efficiency by reducing downtime
  • Reduction in warehousing costs through improved ordering process
  • Increased customer satisfaction through reliable deliveries
  • Continuous optimization of forecasts and processes using machine learning
  • Increasing corporate resilience through proactive risk management
  • Improve competitive position through rapid responsiveness to market changes

A practical example

The company "FutureTech" specializes in the production of high-tech components. The supply chain is complex, global and dynamic. Using Copilot for Dynamics 365 Supply Chain Management, FutureTech's supply chain manager discovers a potential delay with a key supplier in Asia caused by unexpected political unrest. Instead of rushing, the manager used Copilot to quickly analyze alternative suppliers and assess their impact on costs and delivery times.

Within a short time, Copilot has simulated several scenarios and recommends an order with a shorter delivery time from a reliable partner in Europe to avoid disruptions. For the orders already placed with the supplier in Asia, the delivery date is changed. Thanks to Copilot, FutureTech can prevent potential production stoppages and continue working smoothly without compromising customer satisfaction.

Aerial view of shipping containers for supply chain

Copilot in Microsoft Supply Chain Center

Use case 2: Optimization of inventory management

The initial situation

In inventory management, manufacturing companies are faced with the challenge of accurately calibrating stock levels to avoid supply shortages and minimize capital tied up in excess inventory. Traditional methods often rely on historical sales figures, which can lead to inefficient inventory management in dynamic market environments.

Change through Copilot

By using AI technologies such as Microsoft Dynamics 365 Copilot, it is possible to manage inventory intelligently. Copilot uses real-time data and advanced analysis methods to forecast actual demand. It takes into account seasonal fluctuations, market trends and consumer preferences and compares these with production cycles and delivery times. Adjustments to stock levels can be made automatically and in real-time to ensure optimal warehouse utilization.

Added business value

By integrating Microsoft Dynamics 365 Copilot into inventory management, companies can realize a number of benefits:

  • Reduce excess inventory and capital commitment due to more accurate demand forecasting
  • Avoidance of stock-outs thanks to real-time analysis of impending bottlenecks
  • Adaptability to market changes based on forecasts and analyses
  • Optimization of inventory turnover through more efficient inventory management
  • Sustainable operations management through the optimization of inventory management

A practical example

Let's stay with our example company FutureTech. Here, Microsoft Dynamics 365 Copilot is used to create precise demand forecasts using real-time data and advanced analytics.

Shortly before a public holiday, Copilot recognized an unexpected increase in demand for a certain electronic part. The production planners reacted immediately, adjusted orders and increased the production rate. Thanks to Copilot, Future Tech was able to avoid bottlenecks, optimize inventory turnover and ensure customer satisfaction through on-time deliveries.