#18: AI-Driven Transformations: Rapid Value in Supply Chain Optimization

In the last edition of the newsletter we joined the visionary CEO - Emma Reynolds of our fictional mid-sized company Midcorp who built upon her success with AI in the customer journey and experience space to address another longstanding challenge - her supplychain woes.

This week we address some questions posed by one of you:

Which one or two "low-hanging fruit" scenarios would you recommend for MidCorp to tackle?

For the scenarios you've selected, which specific AI workflows—and their respective steps—could be implemented to bring about rapid and measurable value?

In response we'll explore how AI-powered workflows can bring rapid, measurable value to 2 specific use cases highlighted last time:

• demand forecasting and inventory management
• Supplier Relationship Enhancement

Workflow 1: Demand Forecasting

In today's dynamic marketplace, accurate demand forecasting is the cornerstone of supply chain excellence. AI-powered workflows offer a competitive edge. Let's revisit the essential steps:

Data Collection and Preprocessing: Start by gathering a rich dataset encompassing historical sales data, market indicators, and relevant external factors. This includes everything from economic indices to weather forecasts.

Feature Engineering: Define and engineer crucial features that influence demand, such as product attributes, pricing, promotions, and seasonality. For example, when forecasting the demand for electronic gadgets, include features like product specifications and consumer reviews.

Model Selection and Training: Choose a suitable forecasting model based on data characteristics. For instance, for short-term forecasting, consider an LSTM neural network. Train the model on historical data and fine-tune it for the best performance.

Prediction and Evaluation: Implement the trained model to make demand predictions. Evaluate its accuracy using metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). For instance, if the MAE indicates significant forecast errors for a specific product category, investigate further to refine your model.

Deployment and Integration: Seamlessly integrate the model into your inventory management and procurement systems. Ensure that it becomes an integral part of your decision-making process.

Monitoring and Continuous Improvement: Constantly monitor the accuracy of your forecasts and make adjustments as needed. Be prepared to update your models with fresh data to adapt to evolving market conditions.

Workflow 2: Inventory Management

Efficient inventory management is essential to minimize costs while ensuring product availability. Here are the key steps, further explained with examples:

Optimal Inventory Level Calculation: Demand forecasts are instrumental in determining optimal inventory levels. For instance, if the AI predicts an upsurge in demand for winter coats in October, you can proactively increase your inventory levels in September to meet the anticipated demand.

Order Replenishment and Procurement: AI can automate the replenishment process. For example, when your inventory of office supplies reaches a predefined threshold, the AI system can automatically generate a purchase order for restocking.

Inventory Tracking and Real-time Monitoring: Implement real-time tracking systems, such as RFID technology, to monitor stock levels accurately. This ensures you're always aware of inventory status.

Dynamic Pricing and Promotions: Adjust product pricing dynamically based on demand and inventory data. For instance, you can automatically apply discounts to products that are overstocked to stimulate sales.

Supply Chain Optimization: AI can optimize the entire supply chain, from route planning for shipments to warehouse management. By analyzing data on transportation costs, delivery times, and order volumes, AI can suggest optimal routes for each shipment, potentially saving both time and money.

Performance Analytics and KPI Tracking: Establish KPIs like inventory turnover rate, carrying costs, and customer service levels. For example, if the inventory turnover rate for a particular product category is lower than expected, investigate potential issues in demand forecasting or inventory management for that category.

Feedback Loop and Adaptation: Create a feedback loop between demand forecasting and inventory management. For example, if inventory levels for a product consistently exceed demand, consider adjusting the forecasting model or exploring new sales channels.

Workflow 3: Supplier Relationship Enhancement

Supplier relationships are a linchpin in a resilient supply chain. AI can bring substantial benefits in this area:

Supplier Data Collection and Integration: Gather data on supplier performance, pricing trends, and reliability. For instance, collect data on on-time delivery rates and quality metrics from suppliers.

Supplier Risk Assessment: AI can monitor external factors like market changes and geopolitical events that may impact suppliers. For example, if a key supplier's location is prone to natural disasters, the AI system can flag this as a potential risk.

Supplier Selection and Optimization: Leverage AI to prioritize suppliers based on performance data. For instance, if Supplier A consistently provides higher-quality components than Supplier B, you may choose to allocate more business to Supplier A.

Continuous Monitoring and Collaboration: Implement real-time monitoring of supplier performance, including factors like lead times and defect rates. Foster collaboration with suppliers to address issues proactively.

Feedback Loop and Improvement: Create a feedback loop with suppliers to identify areas for mutual enhancement. For example, if a supplier receives feedback about the quality of their products, they can take corrective actions to improve.

By embracing these AI-powered workflows, supply chain professionals can swiftly achieve measurable improvements in demand forecasting, inventory management, and supplier relationships. These transformations optimize operations, reduce costs, and elevate customer satisfaction, ultimately leading to a more robust and competitive supply chain.

?? Exciting Insights on Digital Transformation and Behavior Tech!

??Sanjay Ghoshal (Founder, worxogo) and Deepak delve into the crucial role of humans in digital transformation. Deepak highlights the need to integrate humans seamlessly with technology. He emphasizes that while machines are efficient, they lack human intelligence. The pandemic revealed the value of human insight in adapting to unexpected changes.

??? Building Resilience in a VUCA World ??

Deepak outlines the 4 pillars of resilience: people, process, technology, and information. He underscores how behavioral aspects drive resilience. Behaviors aligned with a common purpose enhance individual and company resilience. Process consistency yields better resilience too.

?? Leveraging Behavior Tech for Productivity ??

Behavioral technology has transformative potential. Deepak draws parallels between consumer and employee behavior. Just as positive reinforcement nudges consumers, it can boost employee performance. Wearables and smartphones could facilitate this digital-driven encouragement.

?? Maximizing Manager Effectiveness through Data ??

To combat information overload, digital tools can provide actionable insights. Deepak proposes heat maps accompanied by virtual prompts. Notifications and alerts can guide managers towards strategic actions, bridging the gap between analysis and execution.

?? Fostering Effective Manager Behaviors ??

Effective management hinges on behaviors. Deepak advocates for feedback mechanisms nudging managers to praise, communicate, and align behaviors. Sentiment analysis and behavioral data mining empower managers to reflect on their interactions.

?? Managing Digital Overwhelm for Frontline Teams ??

Deepak recalls simplifying measurements for tailored shirts. Similar reduction should be applied to information overload. Utilizing machine learning, relevant insights can be extracted, minimizing noise. Leaders must guide teams in filtering essential data for optimal performance.

?? AI's Future: Collaboration, not Replacement ??

Deepak dispels AI replacement fears as a natural human response to innovation. He draws historical parallels and envisions AI augmenting human capacities. Emotions might eventually be understood and replicated by AI, fostering collaboration.

?? Embrace the Potential of AI: Five Mantras ??

Deepak shares five AI mantras: Think Big yet execute incrementally, Collaborate with partners, Consider human impact, Plan for AI's evolution, and Envision AI's potential for human betterment.

?? Bright AI Horizons Ahead ??

Deepak's optimism shines through as he believes AI will enhance the human experience. Like past technologies, AI has both benefits and challenges. Society will harness AI's power for the greater good, opening doors to new dimensions of human existence.

SIGNOFF

Signing off for this week. Keep the feedback coming. Stay safe, Take care.

Stay tuned for more AI-powered insights and innovations in future editions of DEEPakAI: AI Demystified.