AI in Promising & Sourcing

AI and ML are revolutionizing order sourcing and promising. In this Order Management Guru discussion, experts dive deep into the rise of AI and machine learning (ML) and how it will impact order management systems (OMS). 

Order Management Gurus, Panel 2: AI in Promising and Sourcing

1. History and evolution of Sourcing and Promising for customer sales orders.
2. Projects that you have used Heuristic/AI/ML-based sourcing and promising.
3. Lessons learned and best practices.
4. Future of AI/ML in promising and sourcing.
5. Advice to the practitioners.

Panelists:

• Scott Vifquain, CTO, Tailored Brands
• Peter Stubbs, Senior Director, Product, Blue Yonder
• Devadas Pattathil, Co-Founder & Head of Product, Nextuple

Moderators:

• Abhishek Kalra, VP of Customer Success, Nextuple
• Gautham Acharaya VP of Strategic Initiatives, Nextuple 

The Rise of Complexity in Sourcing and Promising (00:08:20)

Sourcing and promising, the processes of determining the optimal fulfillment location and delivery date for customer orders, have evolved from simple rule-based systems to complex optimization challenges. This evolution has been fueled by:

• Omnichannel Fulfillment: The rise of omnichannel retail, with stores now acting as mini fulfillment centers, has significantly increased the complexity of sourcing decisions. Retailers must now consider factors like store inventory levels, capacity constraints, and proximity to the customer when determining the optimal fulfillment location.
• Delivery Speed and Efficiency: Customers expect faster and more reliable delivery than ever before, putting pressure on retailers to optimize their fulfillment networks and minimize shipping costs. This requires sophisticated algorithms that can consider factors like carrier rates, delivery timeframes, and potential delays.
• Data Explosion: The volume and variety of data available to inform sourcing and promising decisions have exploded in recent years. Retailers now have access to real-time inventory data, customer purchase history, product information, and even social media sentiment. Effectively leveraging this data requires advanced analytics and machine learning capabilities.

AI/ML: The Next Generation of Sourcing and Promising (00:12:08)

AI and ML are uniquely positioned to address the challenges of modern sourcing and promising. These technologies can:

• Uncover Hidden Patterns: Analyze vast amounts of data to identify patterns and correlations that humans might miss, leading to more accurate predictions and better decision-making.
• Automate Complex Decisions: Automate complex sourcing and promising decisions, freeing up human resources for more strategic tasks and improving operational efficiency.
• Adapt to Changing Conditions: Continuously learn and adapt to changing market conditions, customer preferences, and supply chain disruptions, ensuring optimal performance over time.

Key Applications: Where AI/ML Shines in Sourcing and Promising (00:19:43)

AI and ML are already being applied to various aspects of sourcing and promising, with impressive results:

• Dynamic Safety Stock Optimization: AI/ML algorithms can analyze historical sales data, seasonality, and even social media trends to predict optimal safety stock levels for each product and location. This minimizes the risk of stockouts while reducing unnecessary inventory holding costs.
• Markdown Avoidance: By predicting which products are likely to go on markdown, AI/ML can prioritize their fulfillment to customers who are willing to pay full price. This helps preserve profit margins and reduce waste.
• Capacity Planning: AI/ML can analyze historical data and real-time signals to predict store and fulfillment center capacity, ensuring that orders can be fulfilled efficiently and that labor resources are allocated effectively.
• Delivery Date Estimation: AI/ML can provide more accurate and reliable delivery date estimates by considering factors like carrier performance, shipping distances, and potential delays. This improves customer satisfaction and reduces inquiries to customer service.

The Trust Factor: Building Confidence in AI-Powered Solutions (00:33:05)

One of the biggest hurdles to AI/ML adoption is building trust and transparency. To overcome this challenge, retailers must:

• Prioritize Data Quality: Invest in data cleansing, validation, and enrichment to ensure the accuracy and reliability of AI/ML models. Garbage in, garbage out, as they say.
• Explain the "Why": Develop tools and techniques to explain how AI/ML models arrive at their decisions. This increases transparency and helps users understand the logic behind the recommendations.
• Prove the Value: Conduct A/B testing and simulations to demonstrate the tangible benefits of AI/ML solutions, such as improved fulfillment accuracy, reduced costs, and increased customer satisfaction.
• Embrace a Collaborative Approach: Involve stakeholders from across the organization in the development and implementation of AI/ML solutions to foster buy-in and ensure alignment with business objectives.

The Future of Intelligent Fulfillment: AI/ML Trends to Watch (00:43:52)

The future of AI/ML in order management is brimming with possibilities. Key trends to watch include:

• Closed-Loop Optimization: Integrating AI/ML across the entire order lifecycle, from demand forecasting and inventory planning to sourcing, promising, and post-purchase customer engagement. This creates a continuous feedback loop that drives continuous improvement and optimization.
• Demand Shaping: Leveraging AI/ML to influence customer behavior and shape demand based on profitability and operational efficiency. This could involve personalized recommendations, dynamic pricing, and targeted promotions that incentivize customers to choose fulfillment options that are most beneficial for the business.
• Composable Microservices: Adopting a microservices architecture to enable greater flexibility and scalability in deploying AI/ML solutions. This allows retailers to choose best-of-breed solutions and integrate them seamlessly into their existing OMS infrastructure.
• Conversational AI: Using natural language processing (NLP) to provide intuitive explanations of AI/ML decisions and recommendations. This makes AI/ML more accessible to business users and facilitates better understanding and adoption.
  
  
  

Beyond the Buzz: Practical Considerations for AI/ML Implementation

While the potential of AI/ML is undeniable, successful implementation requires careful planning and execution. Key considerations include:

• Data Infrastructure: Ensure you have the necessary data infrastructure in place to support AI/ML initiatives, including data storage, processing, and analysis capabilities.
• Talent Acquisition: Build a team with the necessary skills and expertise in AI/ML, data science, and software engineering.
• Change Management: Prepare your organization for the changes that AI/ML will bring, including new processes, roles, and responsibilities.
• Ethical Considerations: Ensure that your AI/ML solutions are developed and deployed ethically, considering issues like bias, fairness, and transparency.

By embracing the power of AI and ML, retailers can transform their order management systems into intelligent fulfillment engines that optimize operations, enhance the customer experience, and drive sustainable growth.