1. July 2025 DYNAMIC AI- DRIVEN INVENTORY PLANNING SOLUTION case study INNOVATIVE & COLLABORATIVE Presented by SDLC Corp www.sdlccorp.com Deep dive into the inventory systems of SDLC Corp

2. Case study EXECUTIVE SUMMARY BIn today’s competitive business landscape, traditional inventory systems struggle to adapt to volatile demand, lead time uncertainties, and multi- channel supply complexities. SDLC CORP, a premier AI development company, developed and deployed a dynamic AI-powered inventory planning solution tailored to a retail chain dealing with fast- moving consumer goods (FMCG). The project aimed to minimize stockouts, reduce holding costs, and improve forecasting accuracy. The AI solution dynamically adapts to market signals, customer behavior, and supply chain variations using machine learning models integrated into a cloud-native architecture. 001

3. Case study WHAT IS A DYNAMIC AI- DRIVEN INVENTORY PLANNING SOLUTION? Dynamic AI-Driven Inventory Planning refers to a system that uses artificial intelligence and machine learning models to make real- time, data-backed decisions about procurement, replenishment, stock rotation, and safety stock levels. Unlike static ERP-based systems, this approach adapts continuously by learning from: Historical sales data Seasonal patterns Real-time POS and SKU movement Supplier behavior and delays External factors like weather or economic indicators The goal is continuous optimization of inventory based on live market feedback. 002

4. Case study PROJECT OBJECTIVES 01 02 Minimize Stockouts & Overstock: Keep optimal levels in stock, reducing lost sales and markdowns. One of the major pain points was frequent stockouts of high-demand especially during seasonal surges or promotional periods. 04 Forecast Demand Accurately: Demand forcasting is the initail step to get the work started. Implement machine learning algorithms capable of recognizing patterns. 03 Improve Supplier Coordination and Lead Time Management: Lead time variability from vendors was another challenge, leading to delayed or misaligned stock arrivals. Incorporate supplier lead time data into AI models to calculate risk-adjusted reorder points.Create a supplier performance dashboard educe Human Dependency: Inventory planning was previously managed manually through spreadsheets and static reports, resulting in inconsistencies and delays. Automation replaces manual forecasting and stock calculation with AI-driven decision engines. 05 Create a Scalable & Configurable Architecture: Given the client’s ongoing expansion, the system had to be scalable, allowing:Easy onboarding of new warehouses, and regions. Custom configuration for different product categories. Real-time performance monitoring and cloud-native scalability. 003

5. Case study CREATION OF A SMART INVENTORY SYSTEM SDLC corp with its AI development services will help your business create a smart inventory system for your business to help you grow exponentially and minimize the error. 01 04 Dynamic forcast Real-time dashboard ime-series ML models with sliding windows. Power BI-based dashboards for inventory health, forecast variance, and alerts. 02 05 Self-learning Models AI-Driven Procurement Triggers Feedback loops updated forecasts based on live data. Automatic reorder point adjustments. 004

6. Case study LOGIC BEHIND THE SMART AI SYSTEM At the core of SDLC CORP’s intelligent inventory planning solution is a sophisticated logic engine that combines statistical models, machine learning algorithms, and real- time data processing. The logic is designed to forecast demand accurately, adapt to dynamic conditions, and drive automated inventory decisions. Here's how each component of the system logic works: Demand Forecasting Models Stock Replenishment Logic Predict future product demand across multiple SKUs with high accuracy. The system uses a hybrid model approach combining multiple forecasting techniques Maintain optimal stock levels by calculating when and how much to reorder. The system dynamically computes the Reorder Point (ROP) and Safety Stock Anomaly Detection External Integrations Make forecasts context-aware by incorporating external influencing factors. Makes the system proactive instead of reactive, helping businesses stay ahead of unpredictable variables affecting inventory. Detect unexpected events or irregular patterns in demand or stock behavior. Triggers alerts for human review or automated corrective actions like expedited orders or dynamic promotion adjustment. 005

7. Case study IMPLEMENTATION TIMELINE Week 1–2 Discovery & Planning Business process mapping and data audit Week 3–5 Data Engineering Data integration, cleaning, and lake setup Week 3–5 AI Model Development Model selection, training, hyperparameter tuning Week 11–13 System Integration API development and dashboard linkage Week 14–15 UAT & Iteration Testing, feedback loop, and model refinement Week 16 Go-Live Full deployment and real-time operation 006

8. Case study MEASURABLE OUTCOMES 23% decrease in stockouts 18% improvement in forecast accuracy (MAPE reduced from 24% to 6%) 30% lower inventory carrying cost 11% increase in product availability 16% fewer expedited shipping costs due to stock misalignment 2.3x faster procurement decision cycle 007 Strictly Confidential, For Recipient Only

9. Case study HOW SDLC CORP DELIVERED A TAILORED AI SOLUTION Client-Centric Discovery Phase Understand the client's business context, operational challenges, and goals at a granular level. conduct on site audits. Held stakeholder interviews with supply chain managers, data analysts, and warehouse supervisors. Mapped existing manual and semi-automated workflows for demand planning, stock checks, and vendor coordination. Identified pain points like demand unpredictability, lead time variance. Agile Delivery Model with Iterative Development Deliver value continuously while remaining flexible to evolving client needs. Prioritized features using a MoSCoW (Must, Should, Could, Won’t) model to balance innovation and business needs. Conducted weekly demos and feedback loops to ensure that stakeholders remained engaged and informed. Established a cross-functional team Customization Flexibility Build a flexible AI engine that adapts to the nuances of the client's products, regions, and vendor network. key customization is done in the basis of product, regional demand, lead time distribution to supplier, and other parties Open architecture supported plug-and-play integration with the client's existing systems. Training, Documentation & Handover Empower internal teams to understand, operate, and trust the AI system with minimal dependency on SDLC CORP post-deployment. Delivered on-site and virtual training workshops. Step-by-step guides for model retraining. Built a comprehensive user manual and API documentation for IT teams to maintain and scale the solution internally. 008

10. Case study CONCLUSION his case study highlights SDLC CORP’s core strengths as an AI development company custom AI model creation, end-to-end system development, and delivering measurable business value Through dynamic AI-driven inventory planning, SDLC CORP enabled its client to scale inventory management with intelligence, agility, and precision. From reducing stockouts to enabling predictive restocking, the solution represents the future of supply chain automation—powered by AI. Contact us sales@sdlccorp.com WWW.sdlccorp.com 009