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No. 1 is the Pinnacle; But Artificial Intelligence Helps from the Enterprise Level to the Network Level

In a world where disruption is the new normal, companies want end-to-end supply chain resiliency.

Artificial intelligence and generative AI make such end-to-end supply chain resilience more than hype. From the enterprise level to the network level, supply chain leaders can achieve the necessary visibility and actionability.

Some AI applications operate just at the enterprise level – within the four walls of your organization. Others, like digital supply networks, go beyond your enterprise. Such AI-powered networks link multiple corporations together for an orchestrated end-to-end global supply chain.

And some AI applications operate both at the enterprise level and the network level.

The third part of my series on whether AI is more hype than reality focuses on 7 areas. AI and generative AI can provide true value in:

  1. Digital supply networks (network level activities)
  2. Visibility
  3. Actionability
  4. Operational predictions
  5. Demand forecasting
  6. Generative AI chatbots
  7. Tariff impact assessment

Part I: Generative Artificial Intelligence Can Boost Your Business

Part II: Hype Vs. Reality – AI Is Essential for Modern Warehouses

1. Beyond Enterprise Boundaries: Orchestrating AI-Powered Digital Supply Networks

Yes, AI can enable an integrated end-to-end approach that goes across business functions, from procurement to sales.

Yes, AI can analyze vast volumes of data, understand relationships, provide visibility into operations and support better decision-making. Yes, AI can optimize shipping routes, reduce lead times and enhance transportation efficiency.

But imagine having access to all of those benefits from supply chain partners outside of your enterprise. That’s what happens when you connect to a digital supply network for orchestration across your entire E2E supply chain.

AI-powered digital supply networks optimize all your suppliers, manufacturers and distributors, instead of just focusing on individual links.

Digital supply networks integrate AI, machine learning and cloud computing, removing boundaries between enterprises. This multiparty global network links it all together, from raw materials/components/parts to finished goods.

Your digital supply chain can continue with reverse logistics, from returns to recycling to the circular economy.

1A. From Macro to Micro: Navigating Digital Supply Networks

A digital supply network links all parties into one user interface. All suppliers, carriers, customers, manufacturers and distributors operate on one database. This single version of truth promotes cooperation between enterprises and allows real-time, simultaneous planning and execution.

These vast networks, powered by artificial intelligence, will yield double-digit improvements to numerous metrics.

Imagine reducing your cost of goods sold, cash-to-cash cycle, length of time between order and delivery, and total delivered cost across hundreds of suppliers in your E2E supply chain. Imagine how those exponential improvements would increase gross operating margins.

It’s possible with digital supply networks. The networks tie the macro view (your global E2E supply chain) with the micro view (enterprise-level daily operations). Data comes from every network source. The network links IoT sensors, GPS trackers, warehouse management systems, final mile providers, port operations, shippers, truckers and more.

Integrating that data allows for autonomous decision-making by your AI-powered systems. The system optimizes the entire network. Only serious issues require human intervention.

This is visibility paired with true actionability.

2. Visibility: AI’s Illumination, Real-Time Insights Across the Supply Chain

AI enhances supply chain visibility by providing real-time insights into every link of the chain.

A digital supply network’s single version of truth means all players know each party’s real-time constraints and execution status. This is unparalleled supply chain visibility. Uncover issues no matter where they are in your networks – 43 suppliers upstream or 36 suppliers downstream.

Imagine a pharmaceutical company tracking vaccine shipments globally. Artificial intelligence monitors temperature fluctuations, predicts delays and alerts stakeholders instantly. This visibility prevents stockouts, ensures compliance and boosts customer trust.

This is visibility that goes beyond vision. It includes the ability to act, often through your digital supply networks’ autonomous agents.

3. From Visibility to Actionability – AI’s Network Level Autonomous Planning and Execution

In digital supply networks, AI doesn’t stop at visibility.

The artificial intelligence/machine learning/cloud computing network autonomously and simultaneously plans and executes many decisions. The network can adjust/create orders, modify inventory policies, forecast transport capacity and optimize transport.

Again, these actions take place across multiple parties, not one enterprise.

This is the power of your digital supply network. AI-powered control towers capture, process and analyze structured and unstructured data. This provides real-time insights into supply disruptions, demand fluctuations and other critical events.

For instance, AI can promptly detect if weather conditions or port congestion delays shipments. AI algorithms can also identify inventory shortages or quality deviations.

Such insights are crucial for proactive decision-making.

  • Autonomous alerts: Anomalies trigger automated alerts to relevant stakeholders when anomalies occur. For instance, if a supplier misses a production deadline, the system notifies the procurement team.
  • Collaborative decisions: AI can facilitate collaboration among digital supply network partners. It can automatically trigger communication channels to address issues. For instance, if a supplier faces raw material shortages, AI can connect them with alternative suppliers.
  • Workflow resolution: AI initiates workflows to resolve issues. It might trigger reorders, adjust production schedules or allocate inventory from surplus locations.
  • Autonomous decision-making: Within established guardrails, AI can autonomously make decisions. For instance, AI may authorize expedited shipping without human intervention when a critical component is out of stock.
  • Learning and adaptation: Over time, AI learns from historical data and adapts. It becomes more effective at predicting, preventing and adjusting to issues. The “autonomous” part grows. Fewer decisions escalate to human supervisors for action.

4. Operational Predictions – Forecasting Deliveries, Identifying Bottlenecks, Allocating Resources

At the enterprise level, generative AI models analyze complex data to predict operational outcomes. They can forecast delivery times, identify bottlenecks and optimize transportation routes.

Within your enterprise, such improvements allow you to save costs and better allocate resources.

This leads to better resource allocation and cost savings.

Using generative AI in your business along with AI-powered digital supply networks increases savings throughout your entire network. The system forecasts deliveries, identifies and remedies bottlenecks and optimizes transport routes for the entire network, not just one entity.

Resources are allocated to optimize the complete system, not just a specific link.

5. Demand Forecasting – From Local Gains to E2E Supply Chain Orchestration

At the enterprise level, generative AI algorithms can predict future demand patterns more accurately by analyzing historical data, market trends and external factors. 

This helps optimize inventory levels, reduce stockouts and enhance overall supply chain efficiency – within your enterprise.

Again, tying your enterprise into a digital supply network means you share in the benefits when your partners improve. Your digital supply network orchestrates inventory levels, solutions to stockouts and overall efficiency across the entire E2E supply chain.

6. Generative AI Chatbots – From Virtual Assistants and Repetitive Tasks for Supplier Contracts

Generative AI chatbots help at the enterprise and network levels.

Chatbots can handle inquiries about order status and resolve common questions. Chatbots could provide real-time updates on shipment status or help suppliers navigate procurement processes.

Generative AI also can act as virtual assistants to supply chain decision-makers. For example, a chatbot could find a specific spare part. If that part is unavailable, the chatbot could create a call-off or spot buy to a preferred supplier.

Beyond the enterprise level, you can deploy chatbots across digital supply networks. The bots enhance communications between suppliers and buyers. They can initiate conversations between supply chain partners, help onboard suppliers and negotiate prices across the network.

The bots can request additional information that can provide clarity or scale issues up to human decision-makers.

At the enterprise level, generative AI automates many repetitive tasks.

These include data entry, order processing and paperwork. Chatbots also boost supply chain operations by understanding natural language queries, providing accurate responses to your customers.

But chatbots also benefit supply chain networks by automating contract management. Imagine automatically drafting new agreements based on historical data. These business operation bots can analyze contract terms, identify patterns and generate customized contracts.

Imagine a chatbot creating a contract for a recurring supplier relationship, considering pricing terms, delivery schedules and quality requirements. This automation streamlines the process and ensures consistency in contract creation.

Freeing your staff from mundane activities allows supply chain leaders to focus on strategic decision-making and problem-solving.

7. Tariff and Trade Impact Assessment – Cost Modeling

At the enterprise level, generative AI can calculate operational costs by considering tariffs and trade policies. Including these variables helps supply chain leaders make data-driven decisions about sourcing, pricing and risk mitigation.

  • Cost modeling: AI can calculate operational costs by analyzing tariff rates specific to each product category. For example, the AI model can factor tariff costs into overall supply chain expenses. Supply chain leaders can then make data-driven decisions about sourcing strategies.
  • Sourcing strategies: Should you diversify suppliers to mitigate tariff risks? Or negotiate with existing suppliers to share the burden of tariffs? What if tariffs increase for a specific product category? The AI model can suggest alternative suppliers or production locations.
  • Risk mitigation: Generative AI can analyze historical data and geopolitical events, identifying potential disruptions. This helps supply chain leaders assess risks related to trade policies. Suppose a company relies heavily on a single supplier from a country facing trade tensions. The AI model can recommend diversifying sources or negotiating alternative arrangements to mitigate supply chain risks.
  • Customs processes and compliance: Customs processes play a critical role in international trade. Generative AI considers the complexities of customs documentation, import/export procedures and compliance requirements. Generative AI models can simulate scenarios, predicting delays caused by customs procedures and ensuring accurate cost calculations. This helps with reducing costs and resilient supply.

AI-powered Digital Supply Networks – THE Way to Competitive Advantage

So yes, make sure you use AI and generative AI to optimize, say, your warehouse operations.

AI can recommend efficient picking routes, minimize idle time and schedule maintenance. Warehouse managers receive actionable insights, leading to cost savings, faster order fulfillment and happier customers. A digital twin helps you model changes faster, quicker and more accurately.

But the chaos of the last few years has elevated supply chain leaders, in some instances, to the C-suite. Use that newfound respect to push for digital supply networks.

The fight between businesses is no longer supply chain vs. supply chain. The battle has moved to the realm of AI – it’s your digital supply network vs. the other digital supply network.

True success requires deploying AI and generative AI at the enterprise level and beyond.

I’d love to discuss how to get your organization into the battle. Let’s get your enterprise on the path to digital transformation and success. to competitive advantage and profitable growth.

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