Published on

January 5, 2024

Machine Learning

What is ML-Powered Demand Forecasting?

Explore how ML-powered demand forecasting enhances accuracy, tackles volatility, and optimizes supply chain operations.
Julia Dunlea
VP of Marketing
Machine Learning

Imagine a world where businesses could predict the future with uncanny accuracy. A world where companies could anticipate customer demands, optimize their inventory, and stay ahead of the competition effortlessly. Well, that world is closer than you might think, thanks to the power of machine learning (ML) in demand forecasting.

Accurate demand forecasting is the cornerstone of successful retail operations. It influences inventory management, shapes customer expectations, and sets the stage for a competitive edge.

Demand forecasting is a complex function of various factors like customer interest, economic conditions, seasonal trends, and geographic location. Traditional forecasting methods often fall short when dealing with these varying influencing aspects. Fortunately, artificial intelligence (AI)-powered demand forecasting has emerged as a solution to this problem, revolutionizing the way businesses operate and make critical decisions. 

Gone are the days of relying solely on traditional methods, educated guesses, or gut feelings to predict demand. Machine learning (ML) offers a robust solution to the intricate puzzle of demand forecasting. ML not only improves accuracy but also handles complex datasets and automates the process, making it a game-changer in the retail landscape. 

This article will serve as your comprehensive guide into the pivotal role of demand forecasting in commerce, offering insightful guidance on harnessing ML for demand forecasting. Let's delve into this cutting-edge technology and explore how it's transforming the retail landscape!

What is demand forecasting?

Demand forecasting enables businesses to foresee future demand patterns for their products or services based on a range of factors, such as historical data on sales, market trends, and customer behavior. It is not about mere guesswork; instead, it relies on data-driven analysis to make well-informed projections.

The accuracy of demand predictions usually increases as the time horizon narrows – in other words, it's easier to predict what might happen next week than to forecast the demand five years from now.

That being said, the importance of demand forecasting cannot be overstated for a plethora of reasons, including: 

  • Effective inventory management: This ensures that businesses have sufficient stock to meet customer demand without overstocking and tying up resources.
  • Meeting customer expectations: A well-executed demand forecast facilitates the maintenance of a competitive edge and promotes business growth. 
  • Successful business planning: Demand forecasting is a key component of any organization, helping them anticipate future demand and plan their supply chain activities accordingly.
  • Risk management and modeling: Most forecasting methods provide not just a prediction, but a typical range of demand. For instance, a forecast might predict a demand range of 10 to 20 purchases. This range can be factored into broader risk management decisions.
  • Studying the impact of potential environmental changes: If a company plans to introduce a new product similar to an existing one, the forecasting model can be used to study the effect of price changes and help determine the starting price. However, it's important to note that the validity of such a model relies heavily on the similarity between the new and existing products.
  • Market trend prediction: Seasonal fluctuations and economic indicators are typically incorporated into these models. Even though the economic indicators can significantly increase the complexity of the machine learning model, including them can enhance the accuracy of the forecast, making it a valuable tool for strategic planning.

The importance of demand forecasting in retail

Demand forecasting profoundly impacts many of the following aspects of retail sales and management, highlighting its significance for every retailer's strategy.

Inventory management

Accurate demand forecasting is key to efficacious inventory management. By predicting future demand, retailers can ensure they have enough stock to meet customer needs without overstocking and blocking unnecessary capital. 

This helps in avoiding stockouts, reducing carrying costs, and improving customer satisfaction. ML models offer an additional advantage by providing a confidence interval for the forecast, such as predicting between 10 and 20 purchases, which can be incorporated into risk management strategies to maximize expected value.

Supply chain management

By forecasting demand, retailers can plan production schedules, raw material procurement, and distribution activities to match expected customer demand. 

This helps in minimizing lead times, improving efficiency, and reducing costs, leading to a more streamlined and cost-effective supply chain.

Pricing and promotion strategies

Retailers will be enabled to adjust prices based on anticipated demand fluctuations, seasonal trends, or market conditions. 

Even better, forecasting helps in planning promotional activities such as discounts, sales, or special offers to stimulate the market during low-demand periods or to clear excess inventory. Data collected after a pricing change can be used to estimate the impact of other pricing strategies.

New product introduction

When introducing new products or planning expansions, demand forecasting helps estimate the potential demand for these offerings. By assessing market trends, customer preferences, and historical data, retailers can make informed decisions regarding product development, marketing campaigns, and sales projections. 

For instance, if a retailer is introducing a product with a similar feature set (like a water bottle in a new color), the model can estimate how price increases are likely to affect demand.

Capacity planning

For retailers with physical store locations or service-based businesses, demand forecasting is crucial for optimal resource allocation and capacity planning. 

This includes planning staff schedules, managing store layouts (for larger retailers), and estimating service requirements to meet anticipated customer demand.

Financial planning and budgeting

Estimating future sales volumes and revenues will allow retailers to create accurate financial projections, set performance targets, and allocate resources accordingly. 

This will also facilitate effective budgeting, investment planning, and risk assessment, ensuring the financial health and sustainability of the business.

The role of machine learning in demand forecasting

Machine learning, a subset of artificial intelligence, has revolutionized demand forecasting. It involves using AI algorithms that can learn from and make predictions or decisions based on data.

Unlike traditional methods, machine learning models can process vast volumes of data rapidly, capturing subtle patterns and correlations that escape the human eye. 

By gleaning insights from diverse data sources like sales records, social media trends, weather data, economic indicators, and more, ML algorithms can provide a more comprehensive and accurate picture of future demand.

Benefits of ML in demand forecasting

  • Increases the accuracy of forecasts, thanks to its ability to learn from data and improve predictions over time. 
  • Handles large and complex datasets, allowing for more comprehensive and nuanced demand forecasts. 
  • Automates the demand forecasting process, saving time and resources. 
  • Adjusts to changing trends and patterns, which is crucial in the dynamic world of retail.

Tackling volatility in demand forecasting with machine learning

One of the most significant challenges in demand forecasting is dealing with the volatility caused by various influencing factors. These factors affect the accuracy of the ML model and lead to increased uncertainty and risk of stockouts or overstock. 

For instance, the COVID-19 pandemic significantly disrupted demand forecasting models in 2020 due to its unprecedented impact on consumer behavior and market trends.

Machine learning can help tackle this volatility. By identifying patterns and trends in large datasets, ML can account for these factors and provide more accurate, timely demand forecasts, even in volatile conditions. 

That being said, while machine learning can significantly improve forecasting accuracy, the forecasts often need to be interpreted by an expert human. This combination of AI and human expertise can result in highly effective demand forecasting strategies.

How to get started with machine learning for demand forecasting

1. Choose the right machine learning software

When venturing into ML for demand forecasting, selecting the right AI platform is essential. Consider factors such as ease of use, scalability, and compatibility with your existing systems.

Akkio is a no-code ML platform that stands out as an excellent option for demand forecasting with its time series forecasting capability and user-friendly interface. 

Akkio offers seamless integration and streamlines the entire ML process, from connecting datasets to exploring insights and routing them anywhere within your organization. Its versatility makes it an ideal choice for both data science teams and companies without extensive data science expertise.

2. Ensure data quality

Data is the lifeblood of machine learning. The quality of your data can significantly affect the accuracy of your demand forecasts. Retailers typically have access to a range of data sources, such as sales data, point-of-sale (POS) data, inventory data, and market research data.

However, ensuring data quality can be a challenge. Common issues include missing data, inconsistent data, and irrelevant data. These can skew your ML models and lead to inaccurate forecasts.

To ensure data quality, you'll need to undertake data cleaning, data validation, and data transformation. These processes involve removing errors, checking the data for accuracy and consistency, and transforming the data into a format suitable for analysis. 

Akkio can assist with these steps through its Chat Data Prep feature, which simplifies the data preparation process and ensures your data is ready for machine learning as easily as chatting with a friend.

3: Build and train the demand forecasting model

The process of building a ML-powered demand forecasting model involves several steps. These include defining the problem, selecting features, and choosing a machine learning algorithm. 

Let's break down these steps and show how you can accomplish them using Akkio:

  1. Preparing the model: Akkio allows you to easily upload .csv files directly into the platform.
  2. Explore the data using Chat Data Prep (optional): You can clean, merge, and explore data using the Chat Data Prep feature, which offers a comprehensive overview of your data.
  3. Setting up the model: The Akkio interface allows you to define prediction fields, date fields, and target fields for setting up the model. Once you've configured these settings, you can train your model. Akkio's platform makes this process straightforward and intuitive, even for users without a background in data science. 

4. Deploying the model

Deploying a machine learning model involves several steps. It starts with testing the model on new data to ensure it can make accurate predictions in real-world scenarios. The model is then integrated into your business process, where it can start making forecasts. 

When deploying a machine learning forecasting model, it's important to ensure the model is scalable and can handle real-time data. This allows the model to adapt to changing business needs and continue providing accurate forecasts even as your data grows.

With Akkio, deploying your model is straightforward. You simply need to select the endpoint for deployment. This could be a cloud-based integration, a web app, or an API endpoint. The platform guides you through the deployment process, making it easy to get your model up and running.

5. Monitoring the model

Once your model is deployed, it's crucial to monitor its performance to ensure it continues to provide accurate forecasts. This involves setting up a continuous integration/continuous deployment (CI/CD) pipeline that continuously compares model predictions to collected data. It's also important to consider how this data can be reintroduced into the model for retraining. 

One challenge of retraining machine learning models is catastrophic forgetting, where the model forgets things it has learned before. To avoid this, incorporating monitoring throughout the lifecycle of the ML model is recommended.

To monitor a demand forecasting model, set up alerts for significant changes in forecast accuracy. Regularly retrain the model on new data to ensure it stays up-to-date with the latest trends and patterns. This ongoing monitoring and maintenance ensure your model remains a valuable tool for demand forecasting. 

Akkio will simplify the deployment and monitoring of your ML model so you can harness the power of AI for demand forecasting, helping your business stay ahead of the curve and meet customer needs effectively.

Optimize your demand forecasting with Akkio

Demand forecasting is a critical aspect of business operations, influencing inventory management, operational efficiency, and overall profitability. The traditional methods of demand forecasting, while useful, often fall short in handling the complexities and volatility of today's dynamic market trends. 

This is where machine learning comes in to enhance the accuracy and efficiency of your demand forecasting! ML brings retail operations and provides resilience to volatility in the face of uncertain factors. It also automates the demand forecasting process, saving time and resources, and adapts to changing trends and patterns.

Akkio stands out as a powerful yet user-friendly AI platform for demand forecasting. Akkio’s robust features, such as predictive modeling, data preparation, and seamless integration capabilities, make it an excellent choice for businesses of all sizes and industries. It can handle large and complex datasets, allowing for more comprehensive and nuanced demand forecasts.

With Akkio, you can easily leverage ML by building, training, deploying, and monitoring AI-powered demand forecasting models! This will not only improve your business operations but also enhance decision-making and give you a competitive edge in the marketplace. 

Sign up with Akkio today and embark on your journey with machine learning for demand forecasting to unlock the full potential of your business!

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