Demand forecasting is an essential element of supply chain operations and warehouse management. With businesses getting back on track after COVID-19, the market has seen significant change and instability in consumer behavior as well as market trends. Consequently, predicting customer demand accurately has become much harder.
As traditional forecasting methods often fall short in dealing with the complexities of modern markets and complications caused due to COVID, the integration of Artificial Intelligence (AI) and Machine Learning (ML) demand forecasting techniques have been extremely efficient, offering unprecedented levels of accuracy and efficiency.
What is Demand Forecasting?
Demand forecasting is the use of analytics to predict future market and consumer behavior, make informed operations decisions, inventory planning, and policy regulation. It is done by considering variables like historical sales data, market trends, weather seasonal data, etc.
Demand forecasting helps the business make better-informed supply decisions that estimate the total sales and revenue for a future period.
It, therefore, plays a very important role in the success of businesses. By accurately predicting customer demand and efficiently managing inventory, organizations can avoid stockouts, reduce excess inventory, and optimise supply chain operations. AI brings a host of capabilities that can revolutionize these processes and enable businesses to make data-driven decisions for better outcomes.
Importance of Accurate Demand Forecasting
Accurate demand forecasting is an essential element in the operations planning and supply chain management of a company. Inaccurate forecasts lead to mismanagement in the whole supply chain causing inventory overload, excessive holding costs, and eventually, wastage of both time and resources. It can also cause stockouts on the other end leading to customer dissatisfaction.
AI-based demand forecasting, on the other hand, allows for much more accurate predictions, giving companies a competitive edge.
Limitations of Traditional Methods
Demand forecasting has been a part of operations for decades but since the advent of e-commerce and the instability in the market caused by Covid, traditional methods have shown some limitations. The accuracy in the predictions has gone down and these systems often struggle to keep up with the complex and non-linear relationships in the modern markets.
In addition to that, traditional forecasting methods and tools are manual which are also very time-consuming and prone to errors.
AI-ML Based Demand Forecasting
In response to the limitations of traditional forecasting methods, AI and ML based demand forecasting methods have been developed to automate the processes, make much more accurate predictions, and include a much wider range of variables that might affect the markets and consumer behavior.
Advantages of AI and ML in Demand Forecasting
AI models can handle much larger data sets and consider various demand drivers and variables. This offers much more flexibility to companies and allows for a much more holistic look into predicted market trends.
- Real-time Updates:
While traditional forecasting techniques are manual and require a lot of time for processing and generating actionable insights, AI-based models are not limited by human limitations and can incorporate new updates instantly and provide real-time updates making the predictions up-to-date.
- Automation: AI and ML streamline the process of demand forecasting, getting rid of time-consuming and inefficient manual processes of traditional demand forecasting.
- Increased Forecasting Accuracy:
Humans are prone to errors which also translate to manual traditional forecasting techniques. AI and ML models, on the other hand, make the process much more accurate. Apart from human errors, AI-based demand forecasting tools are also extremely good at understanding context and deriving references from historical data, making the predictions even more accurate.
- Improved Efficiency and Cost Savings
Another benefit of AI ML based forecasting is that it being an autonomous system, does not need human involvement, making the process cheaper and much more efficient.
Techniques of AI and ML Based Demand Forecasting
AI and ML forecasting models use multiple statistical techniques that have been developed as a system for predicting market behaviors. Two such AI forecasting techniques are:
Time Series Forecasting with ARIMA
Auto-Regressive Integrated Moving Average (ARIMA) model is one of the most well-known and widely integrated statistical methods for demand forecasting used by AI and ML models. According to Towards Data Science, it is a class of statistical algorithms that captures the standard temporal dependencies that is unique to a time series data.
Deep Learning for Demand Forecasting
Integration of deep learning algorithms is another well-known technique used in AI and ML based demand forecasting. Models such as neural networks are brilliant at recognizing complex patterns and non-linear variable relationships in the demand data of modern markets.
- What is AI-based demand forecasting, and how does it differ from traditional forecasting methods?
AI-based demand forecasting is the use of the potential of AI algorithms to make the process of demand forecasting much more efficient and accurate. It is different from traditional forecasting methods as it factors in many more variables and data points to make more accurate predictions.
- Is AI-based demand forecasting suitable for all businesses and industries?
AI-based demand forecasting is suitable for most businesses depending on the applicability and complexity of the demand data.
- How can AI-based demand forecasting benefit businesses?
AI-based demand forecasting can be beneficial for businesses as it provides extremely accurate predictions with much more efficiency, low costs, and marginally better accuracy.
- What challenges might businesses encounter when implementing AI-based demand forecasting?
The main challenges that businesses might encounter while integrating AI demand forecasting are, lack of quality data, integration complications due to non-compatibility, training of supply chain managers, AI’s potential biases, and transparency of calculations.
- How do AI-based demand forecasting handle seasonality and external factors?
AI-based demand forecasting tools factor in historical data and cyclic market behavior to handle seasonality. Other external factors are dealt by indexing individual components of those factors and analyzing them as per the statistical method used by that particular tool.
To conclude, AI and ML based demand forecasting have revolutionized the accuracy and efficiency of predicting customer demand and have enabled businesses to make better operations decisions in the era of unpredictable and unstable markets and trends.
Overcoming the limitations of traditional demand forecasting and integrating new and revolutionary statistical techniques, AI demand forecasting is helping businesses navigate the complex demand data of thriving and constantly changing modern markets.