How to Build Responsive and Dynamic Supply Chains With Next-Generation Demand Planning
Oracle | 5 min READ
What is demand planning?
In the process of serving its customers, each business across every industry must identify the volume of demand for their products or services across a period of time. The process that helps them identify these demand levels over the short and long term is called demand planning.
Demand planning therefore determines the logic that drives other downstream activities in the supply chain of a business, including capacity planning, safety stock strategy, warehousing, shipping, and so on.
Pradip Das
Pradip Das


Supply Chain Management Practice


What makes advanced demand planning capabilities indispensable today?
If the forecasts made in a demand plan fall short of the actual demand, the business loses on a part of the growth opportunity. On the other hand, if the actual demand falls short of the forecasts, the inventories are left with stale stock, which can generate significant losses in fast-moving supply chains. In addition, fluctuating demand levels and uncertainty resulting from geopolitical tensions has further injected volatility into the business landscape.
That’s why high-precision demand planning processes are crucial today. In addition to helping businesses manage the uncertainty, they also contribute to increased revenues and more agile supply chains. So, how can organizations improve their demand planning processes? The first step is to assess the current state of their demand planning capabilities – following which, they must leverage the right solutions to achieve improved forecasts.
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Five levels of demand planning maturity: where do you stand?
Organizations can assess the state of their demand planning processes by identifying where they fall in the following five levels of demand planning maturity :
Demand Planning Maturity Model
  • Reactive: This stage is characterized by siloed marketing, sales, finance, and operations teams, which employ their own forecasting processes. Typically, these teams generate forecasts based on experience, and are not accountable for the accuracy of their forecasts.
  • Anticipatory: The various teams of the organization still operate in silos, but the supply chain is now modeled with rudimentary customer forecasts.
  • Integrated: The organization starts making use of basic forecast accuracy metrics. While multiple teams exhibit some degree of collaboration, forecasting remains isolated to operations teams.
  • Collaborative: Inter-departmental silos have been broken down, and strong collaboration between teams leads to forecasts which take various indicators like marketing campaigns, sales data, operational plans into account. While the contribution of multiple factors to demand levels remains unknown, the organization is now focused on achieving improved forecast accuracy.
  • Advanced: Strong collaboration between departments, high-accuracy data-driven forecasts, and incentives to achieve continual improvements on demand forecasts drive a responsive and agile supply chain
By leveraging the right solutions, businesses can deploy an incremental strategy to drive their supply chains with an advanced maturity demand planning process.
Maturing your demand planning capabilities
In order to achieve an advanced level of maturity in their demand planning processes, it is crucial to adopt the right point solutions at the right stage. Following are the key solution areas that businesses should seek to improve at the corresponding stages of maturity.
Anticipatory: SKU segmentation and assumption tracking
SKU Segmentation
In large-scale businesses, not all SKUs sell at the same rate. Some move faster and some are more volatile to demand. Yet others sell in higher volumes. SKU segmentation solutions enable a business to classify their SKUs into four categories, based on their volume and volatility (as shown in the diagram below). This enables businesses to understand the gaps in their forecasting capabilities for each of the segments, and helps them formulate the right strategy to plug these gaps.
Volume Volatility
Assumption tracking
Demand forecasts will inevitably be supported by macro factors like market conditions, consumer trends, sentiments, and competitor activity rates that will change over time. For instance, economic growth, entry of new competitors in the market, promotional activities, or pricing changes are some examples of market factors which influence demand changes over time. These factors become an assumption that determine the applicability of the forecast. A systematic and structured mechanism to track changes in these assumptions over time is therefore crucial to building accurate demand plans. Moreover, it also enables the explanation of monthly changes in forecasts on the basis of changes in underlying assumptions.
Integrated: Multi-scenario planning with opportunities and vulnerabilities
Opportunities and vulnerabilities
At this stage of maturity, the introduction of opportunities and vulnerabilities in the demand plans can foster collaboration between multiple teams. Vulnerabilities and opportunities are essentially forecast errors that are caused by the uncertainty inherent in an assumption, or the likelihood of an event. While vulnerabilities represent potential downsides in the forecasted most-likely scenario and must be managed with a defensive strategy, opportunities represent the upsides and must be exploited. Such scenarios must be accounted for across teams in a collaborative approach to strengthen the demand plans.
Collaborative: Market-basket analysis and intelligent demand planning
Market-basket analysis with LEI
Leading economic indicators (LEIs) support long-term strategic plans by helping businesses identify the total demand. This is achieved by identifying key indicators which influence the business condition in an industry – these are then combined to build a predictive model which forecasts the business outlook for an industry across months. LEIs can be identified using multiple regression algorithms, and only those LEIs are chosen to build the predictive model, which influence the most variations across business cycles. Long-term forecasts achieved through LEI-based market-basket analysis helps reduce costs, improves efficiency, and improves the availability of working capital.
Intelligent demand planning
Intelligent demand planning solutions leverage Machine Learning (ML) techniques to identify demand patterns from historical data. These solutions, therefore, can be adopted after achieving a baseline of data maturity across the organization. While multiple ML algorithms are deployed to understand the factors that influence demand and to issue forecasts, MLP, or multi-layer perceptron performs the best against other neural network algorithms in short-term forecast accuracy. Integrating these solutions with interactive demand planning workbooks can help push the frontier of collaboration to the next level, and infuse data-driven demand planning across functions.
What next?
Legacy demand planning processes are no longer adequate to thrive in the dynamic economy of today – and this holds true for businesses across industries. By adopting these point solutions at the right stage, enterprises can work with better demand forecasts, and inject precision and responsiveness into their supply chain operations to realize more value at the bottom line.
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