Latest news with #DileepKumarRai
Forbes
16-07-2025
- Business
- Forbes
The Future Of Forecasting: How AI Can Help Industries Predict Demand For Products That Don't Exist Yet
Dileep Kumar Rai is a Global Supply Chain Optimization Expert, Oracle Fusion Cloud architect, and demand forecasting leader. Launching a new product in today's market isn't just risky; it's like setting sail into uncharted waters during a storm, with no compass and relying solely on your instincts to guide you. Throughout my years of working alongside leaders in the automotive, tech, fashion, FMCG and pharmaceutical industries, I've uncovered a widespread yet underappreciated challenge: the 'no-data' dilemma. When introducing something genuinely new to the market, the usual tools—such as ERP forecasts, historical sales curves and even seasoned intuition—often fall short. The result? Overproduction, lost revenue or empty shelves. My Industry Finding: The 'No-Data' Dilemma Through extensive research and direct collaboration with industry innovators, I have identified four recurring pain points that keep executives awake at night: This isn't just an abstract problem; it's a daily operational reality that costs companies millions in missed opportunities and excess inventory. The Cross-Industry AI Forecasting Framework To address this, I developed a proprietary and adaptable AI/ML-powered forecasting framework that combines the art of human judgment with the science of machine learning, tailoring it to each industry's unique dynamics. The Secret Sauce: Understanding The Framework Consider launching a new product as similar to crafting a new recipe: • Data Ingestion: My 'pantry' is stocked with both internal ERP staples and the freshest external ingredients: social buzz, competitor actions, and economic factors signals. • Feature Engineering: Here's the spice rack mixing predictors like launch buzz, product attributes and campaign reach to create a distinctive flavor profile for every launch. • Tailored ML Models: These reflect the cooking techniques of XG-Boost for FMCG, LSTM (long short-term memory) for fashion, and Bayesian models for pharma, each adapted to the industry's unique texture and volatility. • Scenario Simulator: This is my test kitchen, where I experiment with different launch 'recipes,' adjusting prices, channels and competitor responses to determine which flavors succeed. • Executive Dashboard: The tasting table provides real-time insights, confidence intervals and scenario comparisons, empowering leaders to select the best options before introducing them to the market. Industry-Specific Flavor Profiles No two industries have the same palate. This framework adapts like a master chef: Key Drivers: Seasonality, social buzz Modeling Approach: LSTM combined with NLP to capture fast-changing trends and consumer sentiment. Key Drivers: Economic cycles, supply chain lag Modeling Approach: DeepAR paired with time series boosting to handle production volatility and macroeconomic shifts. Key Drivers: Retail promotions, regional preferences Modeling Approach: XGBoost with promotion uplift models to track short-term promotional lifts and geographic variability. Key Drivers: Clinical trial data, prescriber behavior Modeling Approach: Bayesian Models combined with ARIMAX to incorporate clinical data uncertainty and prescribing trends. The Unique Ingredient: Comp Chaining For New Product Launches A cornerstone of my framework is a technique I call Comp Chaining. This method enables us to 'borrow' the sales history of analogous products and blend it with new launch scenarios, even when there's no direct precedent. Here's how Comp Chaining works: • Choose A New Product: Select a target new product and a source-comparable (Comp) product. • Align Sales Histories: Shift the company's sales timeline to match the new launch, week by week. • Adjust For Differences: Use ratios and statistical adjustments to account for differences in initial orders, program length or market conditions. • Blend With AI Forecasts: Where the Comp's history runs out, machine learning fills in the gaps, seasoning the forecast with real-time external data. • Simulate And Refine: Run multiple scenarios, adjusting the blend until the forecast matches the unique profile of your new product. This approach doesn't just copy the past; it creates a new recipe, tailored for today's market and tomorrow's challenges. How To Bring This Framework To Your Organization Final Thoughts: Turning Uncertainty Into A Strategic Ingredient Forecasting demand for new products was once a tedious and uncertain game of guesswork. This framework demonstrates that it doesn't have to be that way. By blending real-world data, AI and Comp Chaining, you can turn uncertainty from a risk into your secret ingredient for growth. In a world where innovation cycles are accelerating and consumer attention is fleeting, the question isn't whether you can afford to add this flavor to your forecasting. The real question is: Can you afford not to? Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
12-06-2025
- Business
- Forbes
A Unified Framework For Inventory Optimization And Capacity Management
Dileep Kumar Rai is a global supply chain optimization expert, Oracle Fusion Cloud architect and demand forecasting leader. Modern supply chain operations present companies with two essential challenges: managing inventory levels properly to prevent excessive and insufficient stock and maintaining production capacity that matches changing market demands. While the separate management of these challenges creates siloed approaches, their combined impact determines customer satisfaction, operational efficiency and profitability. The core issue remains basic, yet resolving it is extremely difficult: • Excessive inventory leads to increased holding costs, which reduces profit margins and blocks essential working capital. • Insufficient inventory levels create stockout situations, which results in missed orders, revenue loss and harm to customer trust. • Production bottlenecks operate in the background to reduce throughput, which restricts a company from fulfilling orders even when inventory is present. Maintaining optimal inventory levels becomes critical in industries producing custom orders and storing products with high value and low sales velocity. The supply chain experiences multiple friction points because of procurement lead times, variable demand, multi-stage assembly and quality testing. I suggest the integrated inventory-capacity optimization (IICO) framework to tackle these interconnected challenges. This organized method aligns inventory policies with production conditions, employing a closed feedback loop to foster improvement. The framework consists of four core pillars: Knowing when and how much to reorder to meet demand without overstocking is at the heart of effective inventory management. We apply the classic QR Model (order quantity-reorder point model) to calculate: 1.1 Q = sqrt((2 * λ * S) / h) Where: 1.2. ROP. = dL + Z * σL Where: This model ensures optimal replenishment timing and quantity while incorporating variability and service level requirements. By integrating it directly into an ERP or procurement system, companies can automate purchase triggers when inventory approaches the calculated reorder point (ROP). Meeting demand is not just about having inventory—it's about the ability to process it. The next step is to measure capacity utilization across production or assembly stations: Utilization(U) = Actual Throughput / Machine Capacity A station operating near or at full utilization (>90%) may become a bottleneck, limiting overall throughput. This metric must account for both initial production and any rework or retesting loops that increase the effective load on machinery. Once high-utilization stations are identified, companies must determine: A bottleneck analysis quantifies the cost of capacity constraints in terms of lost orders and revenue compared to the investment required to expand capacity. This provides leadership with a clear ROI case for capital expenditure. Production systems seldom operate in a steady state. The IICO framework includes a feedback loop: This cycle ensures that capacity adjustments and inventory policies evolve as demand, process efficiency and supplier reliability change. While this framework originated from a real-world solution implemented within a high-stakes aerospace supply chain, its principles apply broadly to any operation where: The IICO framework provides a scalable, adaptable roadmap for synchronizing procurement, inventory management and production capacity in industries ranging from electronics to pharmaceuticals, automotive to luxury goods. By bridging inventory optimization and capacity management, organizations avoid a common pitfall: addressing stock levels without considering production constraints. Conversely, adding capacity without aligning inventory policies risks idle assets or ongoing shortages. A unified approach ensures: Operational agility is non-negotiable in a world of rising customer expectations and global supply chain disruptions. The IICO framework offers a structured, actionable pathway for companies balancing cost, capacity and customer satisfaction. This model transforms inventory and production management from reactive firefighting into proactive, strategic control by embedding predictive analytics, continuous improvement and cross-functional collaboration. Organizations ready to close the gap between inventory decisions and capacity realities will find in this framework not just a tool but a competitive advantage. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
