How to Meet Unconstrained Demand in a Constrained World – Part 2
- Consumer Goods & Services
Fallon Wolken
- 0
- 4 minutes read
Key Benefits of Using AI and Tools for Demand Planning
- Improved Forecast Accuracy: AI-driven demand planning tools leverage vast datasets and advanced algorithms to predict demand with higher precision.
- Increased Agility: AI can dynamically adjust demand forecasts in response to real-time data, allowing for quick adaptation to market changes.
- Reduced Inventory Costs: Optimized demand forecasts lead to better inventory management, reducing the need for excessive safety stock and lowering storage costs.
- Enhanced Customer Satisfaction: Meeting unconstrained demand without delay or shortages ensures a higher level of customer satisfaction, leading to increased loyalty.
- Data-Driven Decision Making: AI tools provide actionable insights, enabling demand planners to make informed decisions based on data rather than intuition.
- Efficient Resource Allocation: By automating demand planning tasks, human resources can focus on strategic initiatives, improving overall productivity.
- Sustainability: Optimized demand planning reduces waste by minimizing overproduction and reducing spoilage, which is critical for sustainability efforts in the CPG industry.
By deploying real-time demand monitoring and adjustments, CPG companies can use AI tools to track real-time sales data, adjust forecasts instantly and alert demand planners to sudden changes in demand. This allows for dynamic and near real-time adjustments to production and inventory levels, ensuring demand is met without excess, leading to a reduction in response time to fluctuations in demand, decreasing the risk of lost sales due to demand spikes and lower waste from excess inventory.
Customer segmentation and personalization uses AI to segment demand based on customer demographics, purchasing behavior and regional trends, allowing for personalized marketing and promotion strategies. This ensures that supply is closely aligned with demand across various customer segments and results in increased customer loyalty through targeted promotions, maximization of the profitability of products and better alignment of production with demand at a granular level.
The use of AI-based scenario planning and simulation by CPG companies creates demand simulation models that allow planners to test multiple scenarios (e.g., new product launches, price changes, or competitive actions) and their impact on demand across the portfolio. This demand shaping empowers planners to optimize inventory levels under various conditions and allows companies to prepare for market fluctuations, improves strategic planning by reducing uncertainties and enhances decision-making by simulating different market conditions. Demand shaping levers include traditional promotional activity, advertising or social media influencers, and the tactic can be used to better balance constrained demand and supply to improve profitability.
Inventory and replenishment optimization will always be hot button topics – and topics that keep CPG executives up at night. AI algorithms help optimize inventory replenishment processes by predicting when and where stock levels should be refilled based on forecasted demand, demand error and replenishment. This approach minimizes out-of-stock situations while preventing overstock, reduces carrying costs associated with excess inventory, ensures product availability across channels and lowers transportation costs by optimizing replenishment cycles. These benefits, in turn, boost sales growth potential and increase market share.
