At the core of every AI-led supply chain transformation is a data-driven approach spanning data collection, transformation, and analysis. As ERP transformations become more result-driven, KPI focused, a data-driven approach takes center stage.
Out of the various supply chain processes, demand planning plays a central role, leveraging data across procurement, sales, manufacturing to generate accurate forecasts. The global demand planning software market was valued at $4.8 billion in 2024 and is forecast to reach $13.2 billion by 2033, growing at a CAGR of 11.7%1, highlighting the increasing importance of AI in demand planning and advanced forecasting capabilities.
While many organizations still rely on Excel-based planning processes, advancements in machine learning demand forecasting, predictive analytics in supply chain, large language models (LLMs), heuristic engines, and automated optimization have significantly reshaped what is possible. Modern systems such as Oracle Cloud Demand Management now provide hyperparameter tuning and advanced algorithms, enabling planners to improve forecast accuracy and inventory optimization while making faster, more informed decisions.
However, technology alone cannot guarantee better outcomes. Organizations must combine these capabilities with structured data-driven approach to unlock real value in supply chain digital transformation. Let us explore these-
Product Segmentation
By analyzing product sales and consumer data, segmentation should go beyond just ABC-XYZ and incorporate such as market variability, consumer behavior, launch frequency, and product churn. This should answer two critical questions upfront-
- Is the product forecastable?
- Is it even worth forecasting?
Value addition process Identification
By implementing audit trails, Forecast Value Add (FVA) evaluates whether each planning step, statistical models, overrides, consensus adjustments, improves the forecast or reduces accuracy.
FVA helps remove:
- Unnecessary manual interventions Planner bias
- Non-value-adding meetings
Bespoke Strategy
A bespoke strategy does not mean customizing the application itself. Instead, it focuses on enabling flexible forecasting approaches within platforms such as Oracle Cloud Demand Management and Oracle SCM Cloud, tailored to different data segments and business needs.
For example, short forecasting cycles in FMCG and fast-moving items with low variability may perform better with simple models such as moving averages. In contrast, intermittent demand, high variability, and new product introductions (NPI) require advanced techniques such as machine learning demand forecasting and predictive analytics in supply chain.
Bespoke Strategy allows users to-
- Gain faster insights into raw data
- Identify gaps in process, technology, personnel
- Calculate value-add KPI’s
- Respond more quickly to demand signals
One of the most impactful applications is AI in supply chain management, particularly AI-driven demand forecasting, by integrates weather patterns, events, and historical data. This highlights why a one-size-fits-all forecasting approach is no longer effective in modern supply chains.
Unified Data and Platform Need
As data volumes continue to outgrow traditional storage capacities, organizations are increasingly seeking a single system of record to store, transform, and analyse complex datasets. Oracle ERP Cloud addresses this need by providing a unified, integrated platform across supply chain functions. It also offers built-in data classification, advanced ML-based data modeling, and AI-driven analytics, enabling organizations to perform data analysis upfront and improve forecasting accuracy.
These can be achieved using-
1. Oracle Cloud Standard Demand Planning Segmentation Methods
- Dynamic Segmentation: Items and locations are automatically assigned to segments based on defined rules (e.g., cost, variability, volume) rather than static, manual grouping
- Enables management of New Product Introductions (NPI) alongside established products, improving forecast accuracy and inventory forecasting
2. Oracle Cloud Redwood-Enabled Audit Mechanism
- Oracle Cloud Demand Management (Redwood UX) provide an enhanced and intuitive audit trail for measure updates, allowing users to track changes to planning data directly within table cells
- Offers clear visibility into who made changes, when they were made, and the context, supporting better governance and data-driven decision making
3. Customized Hyper-Parameterized ML-Based Modelling
- Automated hyperparameter tuning in Oracle Cloud Demand Management enables AI in demand planning, by optimizing forecasting parameters and reducing Mean Absolute Percentage Error (MAPE)
- Configured within forecasting profiles to improve machine learning demand forecasting, inventory optimization, and overall demand planner efficiency.
4. Real-Time Adjustments Using External Demand Signals
- External demand signals in Oracle SCM Cloud are captured by importing external data (market, syndicated, or customer data) into custom measures, or by collecting internal signals like shipments/bookings history
- These signals enable real-time demand sensing, enhancing supply chain visibility, predictive analytics in supply chain, and overall responsiveness
As industries such as manufacturing, consumer products, and FMCG increase investments in demand planning systems, data fragmentation continues to grow. Organizations must first build a strong foundation with segmentation, governance, data structure, early constraint visibility, and KPI discipline before adopting advanced analytics and AI.
HTC helps organizations accelerate this journey by enabling AI-driven demand planning through Oracle ERP Cloud and Oracle SCM Cloud solutions. With a focus on improving forecast accuracy, inventory optimization, and supply chain visibility, HTC ensures that demand planning transformations deliver measurable business outcomes.
The future of demand planning is here. The real question is:
Is your data and process ecosystem ready to unlock it?
References:
1 https://marketintelo.com/report/demand-planning-software-market
