Today, most DTC brands are caught in a forecasting crisis. Consumer behaviour has become increasingly unpredictable, with spending patterns shifting dramatically due to inflation and economic uncertainty.
What worked yesterday simply doesn't work today.
The reality for Indian brands is harsh in terms of forecasting. Many founders still make inventory decisions based on intuition or last year's data, resulting in stockouts during peak sales or warehouses full of unsold products.
Both scenarios drain your cash flow and compromise brand growth.
You can't afford to guess anymore, you need to implement proper demand forecasting in supply chain management to help you predict what customers want, when they want it, and how much they'll buy.
In this blog, we’ll break down practical demand forecasting methods to help you move from reactive inventory management to predictive planning.
What Is Demand Forecasting in Supply Chain Management?
Demand forecasting in supply chain management helps predict future consumer demand so you can make the right decision regarding optimising your entire supply chain to be able to meet and fulfil the projected demand surges.
The process involves analysing both internal factors, such as promotional activities, pricing strategies, and marketing campaigns and external elements like economic conditions, seasonal patterns, and competitor actions to estimate how much of each product people will want to buy during specific periods.
When done right, demand forecasting enables you to optimise inventory levels, cut storage costs, and ensure product availability when consumers need them.
Most brands typically use a three-stage forecasting approach
- Environmental forecasting (projecting broader economic indicators)
- Industry forecasting (analysing sector-specific trends)
- Company-specific sales forecasting
Why Demand Forecasting Matters in E-commerce
You can no longer decide the growth of your brand on gut instincts, in these times where customer’s shopping behaviour is so unpredictable due to the growing number of factors of influence, basing your brand growth on data-based forecasts is important.
Inventory Optimisation and Cost Management
Accurate demand forecasting of future sales is important as it impacts your costs and profit, making it a logical starting point for all business planning.
Because when your forecasts are precise, it helps you maintain optimal stock levels, avoid both excess inventory costs and stockout situations, allowing you to optimise your inventory better.
Customer Satisfaction and Service Levels
Customers expect your products to be available when they want to make a purchase. Often, brands suffer from inaccurate forecasts that lead to stockouts, which directly affect customer experience and the potential loss of sales.
But if your forecasts are accurate, it’ll ensure all products (popular, new launches, etc) remain in stock, so shoppers can purchase them without having to leave your website empty-handed.
Supply Chain Coordination
Properly executed forecasting builds better coordination across the entire supply chain network and helps suppliers, manufacturers, and retailers to work with an aligned forecast.
This coordination becomes especially important when managing retail logistics operations, where multiple touchpoints need to synchronise based on demand predictions.
Especially in the case of brands that often work with multiple suppliers and need to synchronise product availability on various sales channels.
Seasonal and Trend Adaptation
Seasonality and constantly evolving consumer buying trends are a big challenge most brands face, this is where demand forecasting comes in as a bridge, helping you anticipate these fluctuations by using data.
Be it preparing for festival seasons, capitalising on viral trends, or managing end-of-season clearances, accurate forecasts provide you with the foundation for data-backed decision-making.
Common Demand Forecasting Methods in Supply Chain Management
Demand forecasting models in supply chain management are broadly categorised into qualitative and quantitative approaches, each with distinct advantages depending on your brand context and available data.
Qualitative Forecasting Methods
- Expert Opinion
The expert opinion method involves gathering insights from experienced professionals within your company.
- Market Research and Customer Surveys
Market research involves directly surveying customers about their future purchasing behaviour and intentions. This helps you understand their preferences and buying patterns, which you can use for new product launches or market expansion.
- Sales Force Composite
This model uses insights from your sales team who interact directly with customers, and provides you with demand estimates based on their understanding of customer behaviour and market conditions in their respective territories.
- Delphi Method
The Delphi method is based on the hypothesis that two people's opinions are more accurate than one and is mainly used for mid to long-term forecasts where related data are insufficient.
Under this model, the consensus agreement is reached among a group of experienced professionals for review and requires careful management to prevent bias and ensure representative participation.
Quantitative Forecasting Methods
- Time Series Analysis
Time series models analyse past sales with effective demand for a particular product under normal conditions to predict future requirements.
It helps you identify trends, seasonal variations, and cyclical patterns within your sales data.
- Simple Moving Average
It calculates the average demand over a specific number of recent periods, helping you to remove the oldest values from forecast data and add new values, which can be used to show seasonality in demand.
- Exponential Smoothing
It assigns different weights to historical data points, giving more importance to recent observations. The model says that the emergence of new information, the weighted value or the importance of the new data is more than the weighted value or importance of the old data.
- Trend Projection
This method uses statistical techniques like least squares regression to identify and project trends. It focuses on the graphical representation of the data by analysing the least-squares value and gives you an overview of the future trends of demand.
- Associative Model/Causal Models
Causal or associative models build relationships between demand and external factors such as economic indicators, weather patterns, or marketing activities.
The model assumes that the variable being forecasted is concerned with the other variables that are prevailing in the market environment, such as the state of the economy or interest rates.
How to Choose the Right Forecasting Method
Selecting the appropriate forecasting method will depend on multiple factors specific to your company context and requirements.
Data Availability and Quality
The foundation of any forecasting method lies in data availability and quality. For new products or brands with limited past data, qualitative methods like expert opinion or market research work best.
Because qualitative forecasting is simple, cost-effective and works well for small-scale companies in early stages.
On the other hand, brands with enough historical data can use quantitative methods for more accurate predictions.
Although these are complex in nature and expensive, they provide a reliable method for long-term planning and are generally preferred by mid to large-scale companies.
Forecast Horizon
The most common forecast horizons are short-term and long-term forecasting. In a short-run forecast, seasonal patterns are of much importance. It may cover three months, six months or one year. It provides information for tactical decisions.
Similar longer-term predictions range from one year or more as they incorporate broader economic and market factors.
Product Lifecycle Stage
Product maturity affects forecasting method selection, as established products with stable demand patterns benefit from time series analysis, and for new products, you'll need to conduct market research or take expert opinion.
Resource Constraints
Consider your available resources, both human and technological.
Qualitative methods are often "easy and cheap" to implement and help save cost, whereas quantitative methods might require a proper investment in technology and training.
Accuracy Requirements
Different brand decisions require varying levels of forecasting accuracy.
Critical production planning decisions may justify the use of advanced forecasting systems, and routine inventory replenishment might work best with simpler forecasting methods.
Market Stability
Stable markets with predictable patterns favour quantitative methods, whereas volatile or fast-moving categories require more flexible qualitative approaches that can quickly process new information.
Tools & Technologies for Demand Forecasting
Nowadays, most e-commerce companies use sophisticated tools and technologies to improve forecasting accuracy and efficiency.
Information Technology Integration
IT plays a significant role in demand forecasting as this process deals with a large amount of data and is performed regularly.
The integration of IT systems helps you create more accurate and timely forecasting by processing data that would be impossible to handle manually.
Advanced Forecasting Algorithms
Modern demand forecasting in supply chain management applications makes it quite easy to evaluate the performance of the various forecasting algorithms in contrast to historical figures to find out the one that provides the best fit to the stated demand patterns.
Real-time Data Integration
Contemporary forecasting systems integrate real-time sales data, market information, and external factors to continuously update predictions.
A sound demand planning module must be associated with both the customers' orders and the customers' sales information to help you incorporate the latest information when forecasting demand."
Challenges in Demand Forecasting for Indian E-commerce
Brands face several forecasting hurdles that require a deep understanding of multiple factors involved in the forecasting process.
Extreme Seasonality and Regional Variations
Unlike Western markets with predictable Christmas, New Year, black friday, etc, peaks, the Indian e-commerce market experiences multiple festival seasons throughout the year, each bringing new demands and surges.
What sells well in Delhi might struggle in Chennai due to different cultural preferences, climate conditions, and local festivals.
Infrastructure Limitations
The gap between planned inventory and actual availability of your products often differs due to transportation delays and carryover inventory.
It is important for you to understand the difference between logistics and supply chain management so you can figure out whether it occurred due to poor demand prediction or supply chain failures.
Price Sensitivity
Indian customers show high price sensitivity across most e-commerce categories, so even a small price change might create large demand fluctuations in your forecast.
Cash-on-Delivery Complexity
High COD orders create return rate uncertainties that often change demand calculations, this is because original order volumes don't translate directly to actual sales when returns vary by region, product category, and customer segment.
Adding another layer of unpredictability that standard forecasting models don't necessarily account for by default.
Limited Historical Data
Often, brands work with insufficient historical data for proper quantitative forecasting. This is where qualitative methods are necessary, especially when records of customer data are limited.
Best Practices for Effective Demand Forecasting
Here are some practical tips you must consider when forecasting demand for your brand.
Set Clear Forecasting Goals
Define what objectives your forecasts will support, it could be
- Inventory purchasing decisions
- Production planning timelines
- Promotional activity planning
- Seasonal staffing requirements
Build Cross-Functional Teams
Create teams including sales, marketing, operations, and procurement people, to prevent problems like when "two individual supply chain partners made different forecasts and acted accordingly, causing supply-demand mismatches.
Segment Products Intelligently
It is important to understand and identify customer segments for different forecasting approaches.
You can segment your products based on demand characteristics such as
- Demand volatility and predictability patterns
- Seasonal intensity and timing
- Price sensitivity levels
- Customer type differences
- Product lifecycle stage
Track Forecast Performance Systematically
Calculate forecast errors at all levels, by product, region, time, and customer segment.
Use common methods such as Mean Absolute Error (MAE),
Mean Squared Error (MSE),
and Tracking Signal for calculating numbers, and additionally implement error tracking to identify patterns and root causes, other than simple accuracy numbers.
Combine Statistical and Intuitive Inputs
Quantitative methods provide you with consistency, but sometimes it is better to depend on human instinct (market intelligence and expert judgment) as well in forecasting.
Be sure to also factor in your marketing insights about campaign effectiveness, sales feedback about consumer conversations, and external intelligence about competitive activities.
Assign Clear Ownership
Often, only the marketing team is considered accountable for forecasting, but all your departments in the supply chain must work together because overall accuracy should be a shared responsibility.
For this, you must assign clear ownership for different forecasting aspects to ensure collaborative input for maximum accuracy.
For example, marketing might own promotional forecasts, operations own capacity constraints, and sales own customer-specific predictions.
Plan for Forecast Uncertainty
Set up contingency planning to address different demand scenarios in case your original forecast goes wrong.
Build supply chain flexibility to respond to potential forecast errors. This could include supplier agreements for urgent orders, flexible workforce arrangements, or safety stock policies that account for uncertainty.
Account for Supply Constraints
Recognise that demand forecasting accuracy has limited value if your supply chain constraints prevent meeting predicted demand.
In fact, several forecast errors occur mainly due to production limitations rather than demand prediction errors.
So, carefully consider supply chain constraints when projecting forecast targets, ensuring your logistics management functions align with your demand predictions. If your supplier capacity is limited, focus on accuracy on achievable demand levels instead of total market potential.
To Warp It Up
Going forward, growing a brand will become a data-first process where demand forecasting in supply chain management will play an important role to ensure reliable and constant brand growth.
Here, you must apply relevant forecasting models that best suit your unique state of business, which will help you make the right decision regarding what will likely sell the most, when and at what frequency.
FAQs (Frequently Asked Questions On Demand Forecasting In Supply Chain Management)
What is demand forecasting with an example?
Demand forecasting predicts future customer demand using historical data and market analysis. For instance, an Indian fashion brand analyses past Diwali sales data to estimate the kurta inventory needed for the upcoming festive season.
What are the three levels of demand forecasting?
The three levels are environmental forecasting (economic indicators), industry forecasting (sector-specific trends), and company-specific sales forecasting. This approach helps brands understand macro conditions, market dynamics, and individual business performance patterns.
How can new D2C brands forecast demand without historical data?
New brands should use qualitative methods like market research, expert opinions, industry benchmarks, and similar product data from comparable companies to estimate initial demand patterns.
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