How much should we stock for the next holiday? How many people should we hire to prepare for the upcoming peak season?
Can the promotion really drive the revenue growth? If we decrease the price of the product how much more sales would we expect? How much does the weather and temperature impact our demand? These are common questions businesses face on a daily basis, and sometimes wrong decisions would lead to unfavorable results.
Demand forecasting is the answer to tackle these challenges.
It uses past data and trends to predict future customer needs, like making an educated guess based on what’s happened before.
No one can predict the future with absolute certainty, so having the best tools is crucial. This is where Sophus comes in. Sophus offers advanced tools and insights that enhance the accuracy of demand forecasting.
By integrating the latest AI technology, Sophus helps businesses improve their demand projections and make better decisions.
Here’s how demand forecasting techniques work and how Sophus can support you.
Why is Demand Forecasting in Supply Chain Important?
Demand forecasting plays an important role in supply chain management by enabling organizations to anticipate and plan for future customer demand effectively.
Its importance lies in its ability to increase demand forecast accuracy and therefore better hedge against the investment in your supply chain, leading to improved return of investment, cost savings, and enhanced customer satisfaction.
Demand forecasting in supply chain brings these benefits:
- Better Inventory level: Prevents overstock and stockouts, saving costs and avoiding lost sales.
- Improved Production Planning: Helps schedule production and manage resources to reduce waste and ensure timely delivery.
- Efficient Logistics: Enhances transportation and distribution by better planning the right amount of resources in logistics.
- Informed Pricing and Marketing: Assists in setting prices and planning promotions based on future demand trends.
- Enhanced Collaboration: Aligns operations across suppliers, manufacturers, and retailers for better supply chain performance.
The Toolbox of Demand Forecasting Techniques
Demand forecasting in supply chain management involves a wide array of techniques, each with its own strengths and applications.
Here are some common approaches found in the forecaster’s toolbox:
Time Series Analysis
This category encompasses methods that analyze historical data patterns to make future predictions. Popular techniques include moving averages, exponential smoothing (Simple, Holt’s, Winter’s methods), ARIMA (AutoRegressive Integrated Moving Average), and seasonal decomposition (STL, X-12-ARIMA).
Causal Models
These methods establish relationships between demand and other influencing factors, such as economic indicators, promotions, or pricing. Regression analysis and econometric models are commonly used causal modeling techniques.
Artificial Intelligence (AI)Techniques
With the advent of big data and computational power, AIalgorithms have gained traction in demand forecasting.
Neural networks (feedforward, LSTM, etc.), random forests, gradient boosting machines (GBM), and support vector machines (SVM) are examples of these advanced techniques.
Judgmental Methods
In some cases, human expertise and market intelligence can complement quantitative methods. Market research, surveys, the Delphi method, and scenario analysis are judgmental approaches that incorporate human input.
Composite Approaches
These techniques combine multiple forecasting methods to leverage their strengths and mitigate individual weaknesses. Ensemble forecasting and Bayesian forecasting are examples of composite approaches that can improve forecast accuracy.
Other Techniques
Additionally, forecasters may employ leading indicators, input-output analysis, and simulation models to gain insights into demand patterns and future trends.
The choice of technique often depends on factors such as data availability, product characteristics, industry dynamics, and the desired level of accuracy. Effective demand forecasting in supply chain management often involves a combination of these techniques, tailored to the specific business context.
Forecasting at Multiple Levels
Demand forecasting in supply chain management often involves considering multiple levels of the product hierarchy or organizational structure.
Companies may forecast demand at the product family level, product level or SKU level from product perspective, from location/node perspective forecasts can often be done at the distribution center or store level. Each level of forecasting serves a different purpose and requires a tailored approach.
Top-Down vs. Bottom-Up Forecasting
Two common approaches to forecasting at multiple levels are top-down and bottom-up forecasting.
| Aspect | Top-Down Forecasting | Bottom-up Forecasting |
| Approach | Starts with a higher-level forecast (e.g., product family) and disaggregates to lower levels | Starts with forecasting at the lowest level (e.g., individual SKUs) and aggregates to higher levels |
| Consistency | Ensures lower-level forecasts are consistent with the higher-level forecast | May result in inconsistencies between higher and lower-level forecasts |
| Data Requirement | Useful when historical data is limited at lower levels | Requires detailed historical data at the lowest levels |
| Accuracy at Lower Levels | May be less accurate at lower levels | Provides more accurate forecasts at lower levels |
| Use Case | Suitable for high-level planning and when data granularity is low | Suitable for detailed planning and when data granularity is high |
Reconciling Conflicting Forecasts
When forecasting at multiple levels, it’s common to encounter situations where the top-down and bottom-up forecasts conflict or do not align. This can occur due to various reasons, such as different data sources, forecasting methods, or assumptions used at each level.
To reconcile these conflicting forecasts, companies often employ techniques such as:
- Constrained Forecasting: This involves adjusting the lower-level forecasts to ensure they sum up to the higher-level forecast. This approach maintains consistency but may sacrifice accuracy at the lower levels.
- Proportional Reconciliation: In this method, the lower-level forecasts are adjusted proportionally to match the higher-level forecast. This preserves the relative proportions of the lower-level forecasts while aligning with the higher-level forecast.
- Optimization-Based Reconciliation: This approach uses mathematical optimization techniques to find the set of forecasts at all levels that minimizes the overall forecast error while satisfying the hierarchical constraints.
- Judgmental Adjustments: In some cases, forecasters may rely on expert judgment and domain knowledge to manually reconcile conflicting forecasts, taking into account factors such as market trends, promotions, or supply chain constraints.
Reconciling forecasts at multiple levels is crucial for ensuring consistency, aligning plans across the organization, and enabling effective decision-making in supply chain management.
Sophus X: Leading the Way in AI-Driven Demand Forecasting
Sophus X introduces an innovative AI-driven demand forecasting solution, revolutionizing the way businesses approach demand planning and inventory optimization.
This cutting-edge algorithm offers a comprehensive suite of features designed to enhance forecasting accuracy, adaptability, and decision-making capabilities.
Off-the-Shelf Software
Sophus’s new AI-driven demand forecast algorithm stands out as off-the-shelf software, eliminating the complexities associated with implementation. This user-friendly solution ensures that businesses can swiftly integrate and leverage the power of advanced forecasting without extensive setup hassles.
AI-based Algorithm
At the heart of Sophus’s algorithm lies a powerful AI based algorithm. This algorithm enhances the accuracy of predictions by considering multiple variables simultaneously, providing a comprehensive view of how different factors are influencing demand.
Forecast Probabilistic Output
Sophus doesn’t just stop at predicting demand; it goes a step further by offering a probabilistic output. This feature allows businesses to gauge the potential deviation or variability in demand forecasts, enabling more informed inventory planning and optimization to mitigate inventory-related risks.
User-Defined Causal Factors
One of the standout features of Sophus’s algorithm is its flexibility in incorporating user-defined casual factors. Businesses can integrate holidays, events, weather ,temperature and any other external regressors as variables, providing a more nuanced and context-aware forecasting model.
Product Life Cycle Consideration
Sophus understands that products have life cycles, and its algorithm takes this into account. By considering the phase-in and phase-out of products, the algorithm ensures that the forecasting model remains adaptive to changes in the product portfolio.
Impact of Price Changes
Acknowledging the impact of pricing on demand, Sophus allows users to input product price changes as a casual factor. This enables businesses to understand and anticipate the effects of pricing strategies on consumer demand, contributing to more strategic decision-making.
Scenario Building Capabilities
Using the intuitive design of Sophus X, the algorithm facilitates easy-to-use scenario building and configuration. This empowers users to explore various what-if scenarios, for example what if we stop a promotion event, what if we phase out a certain product earlier than planned, what if the temperature for the next week increases 10 degrees, for which users can easily build out and compare the outputs..
Final Words
Here you go with all the ins and outs of demand forecasting in supply chain management.
Understanding how to predict future demand could help you improve the forecast accuracy therefore improve inventory turns, better plan production, and improve logistics. This leads to better productivity and fewer issues.
At Sophus, we’ve empowered businesses like Hisense with advanced AI-driven demand forecasting solutions. By implementing our GBM algorithm, Hisense achieved a remarkable 70% accuracy in SKU-wise, weekly, and channel-specific forecasts.
This predictive tool adapts seamlessly across sales channels and SKU variations, requiring minimal customization. It doesn’t just interpret data; it guides strategic growth by uncovering sales drivers and optimizing supply chain efficiency.
Through our partnership, Hisense has transformed uncertainty into opportunity, ensuring products reach customers faster and enhancing overall business agility.
Ready to see the difference for yourself?
Book a demo now and take your demand forecasting to the next level!
FAQs
What is demand forecasting in SCM?
Demand forecasting in Supply Chain Management (SCM) is the process of predicting future customer demand to help businesses plan their production, inventory, and sales.
How can AI help with demand forecasting in the Supply chain?
AI can help with demand forecasting by analyzing large amounts of data quickly and accurately. It can identify and learn patterns and trends that the human brain or traditional statistical approach often can’t handle, leading to more accurate future predictions.
What is AI-driven demand forecasting in the supply chain?
Demand forecasting in supply chain management using AI involves using AI algorithms to analyze data and make predictions about future demand.
AI models learn from past data and capture all the patterns and trends on how certain factors would improve on the demand therefore to drive and improve the demand forecast accuracy over time.
