Your Guide to AI in Supply Chain Planning

Supply chain planning can be an arduous task.

Incomplete, fragmented and poor quality data are pervasive issues. The master data itself can be a quagmire, often non-standardized across systems or business units, adding another layer of complexity.

The traditional reliance on historical data as the sole foundation for supply chain planning is no longer a viable approach, as it fails to address the dynamic nature of modern supply chains.

Decades of outdated technology ruled supply chain planning with legacy ERP systems and tools, leading to lengthy implementations and vendor dependency.

Today, artificial intelligence is driving a transformation in supply chain planning. AI-powered algorithms analyze vast data in real-time for predictive analytics in areas like demand forecasting, inventory optimization and route planning.

AI-driven decision-making is rising, thanks to low-code/no-code platforms that rapidly customize AI solutions. These platforms seamlessly embed AI into supply chain processes, enhancing decision speed and precision.

Leveraging AI in Supply Chain Planning

Demand Planning

AI and machine learning algorithms can be harnessed to boost forecast accuracy, providing a more comprehensive understanding of supply chain operations.

This technology facilitates consensus planning among different departments, even when navigating complex intersections of geography and customer hierarchy. It enables the identification of influential factors, trend capture, and automated demand sensing, utilizing real-time data, including market events and point-of-sale information. All of this culminates in precise supply and inventory planning.

Supply Planning

AI significantly improves supply chain planning by effectively balancing material and warehouse capacity constraints against demand plans. By leveraging AI's capabilities, organizations can generate accurate demand forecasts, optimize inventory and storage levels, prioritize order fulfillment, collaborate effectively with suppliers and proactively manage supply chain risks.

AI also enables scenario analysis, real-time monitoring and continuous improvement through machine learning — ensuring that customer commitments are met while enhancing overall supply chain performance.

Ultimately, AI-driven supply chain planning helps organizations align their operations with specific goals and customer satisfaction, giving them a leverage against competitors.

Logistics Planning

Optimizing transportation plans becomes effortless with AI. These plans are based on activities such as manufacturing, exploration and production. AI-powered software offers real-time information which helps optimize both short-term route plans and long-term fleet requirements.

Should-Cost Analysis

Leveraging AI empowers strategic price planning for goods and services while enhancing visibility into cost drivers and cost breakdown structures. This, in turn, leads to increased savings and operational efficiency.

The continuous linkage of an AI-powered software's price element library to market indices facilitates real-time cost evolution, thereby aiding in sourcing negotiations and budgeting guidance.

How to Get Started?

To begin AI-driven transformation in supply chain planning:

Understand Your Needs

Focusing on challenges unique to your organization is the key. Identify specific pain points, bottlenecks, and areas where improvements are needed. Dive deep into historical data to unveil patterns, variations, and opportunities for optimization. This data-driven approach will pinpoint precisely where AI can provide the most value. Involve key stakeholders from various departments, including supply chain, finance and operations. Their firsthand insights are invaluable for identifying pain points.

Select the Right AI Tools and Technologies

Ensure your data infrastructure can handle the volume, velocity, and variety of data needed for AI applications. Explore options such as data lakes, cloud-based solutions or data warehouses to support your AI initiatives effectively.

Thoroughly evaluate AI vendors and technologies that align with your supply chain needs. Prioritize solutions offering advanced forecasting, demand planning, optimization and real-time analytics capabilities. Look for software that offers customization.

Also Read: Supply Chain Planning Strategic Playbook

Change Management & Training

Even while focusing on tech solutions, companies must attend to vital supporting elements, such as organization, change management, and capability building.

Our research suggests this task is a common challenge: for example, only 13% of executives report that their organizations are sufficiently prepared to address their skills gaps.

To ensure adoption of new solutions, companies must invest in change management and capability building. Employees will need to embrace new ways of working, and a coordinated effort is required to educate the workforce on why changes are necessary, as are incentives to reinforce the desired behaviors.

Measure Progress and Iterate

Implement systems for real-time monitoring of AI-driven processes and outcomes. Regularly assess performance against established KPIs and be prepared to adapt strategies as needed. Leverage insights gained from AI to drive ongoing enhancements in your supply chain processes. Encourage cross-functional teams to collaborate on refining models and algorithms, ensuring a continuous cycle of improvement.

To Sum Up

In conclusion, the world of supply chain planning is undergoing a transformation thanks to artificial intelligence. AI brings precision and agility to the complex task of supply chain management. To start this journey, understand your organization's unique needs, invest in AI talent, and choose the right tools. Don't forget the importance of change management and training. AI offers continuous improvement, so measure progress and adapt as needed. Embracing AI isn't just a choice; it's a necessity to stay competitive in today's dynamic business landscape.

Here’s how GEP can be your partner for AI transformation in supply chain planning.