Demand forecasting is a key process in most companies more so for retail and FMCG companies. Demand forecasting has relied on the experience of forecasters and a few statistical models. Yet, there is a limit to the many factors human minds can decipher and translate into meaningful insights. Wrong forecasts have often led to inventory problems leading to markdowns or stock-out situations.
Use artificial intelligence (AI) has many benefits for demand forecasting. Below is a list of 5 ways in which AI is reshaping demand forecasting:
Most organizations base their forecast on the historical sales data adjusted for seasonality. This means that most companies fail to factor in most of the available internal and external data. Below are some of the examples of internal and external data
Internal data: sales data, product attribute data, promotions, and advertising
External Data: demographics, income, weather, competition pricing, etc.
AI brings together the internal, external, and contextual data leading to a more robust forecast.
Machine learning (ML) makes it easy for companies can find patterns in their historical data. This helps eliminate a large part of the assumptions in their prediction. In the future, machine learning systems will use the patterns they identify in the data to make better decisions.
AI systems can use data from many sources and can derive complex hidden relationships between them. Hence, they are a strong predictor of demand. After identification of the complex pattern in the historical data, the next step is to identify the effects of internal & external causal factors to amplify the demand signal and quantify the main demand drivers. Key demand drivers include prices, promotions, product lifecycle, and sales data.
Most organizations have functional silos. Different functions have accountability for price optimization, advertising, promotions, and demand forecasting. Demand forecasting is a horizontal process cutting across functions. Individual functional processes are not completely transparent to everyone. AI brings down these functional boundaries by consolidating data from various functions. Pricing, promotions, and advertising effectiveness are intermediate steps to demand forecasting. Any demand forecasting solution needs to incorporate all the intermediate processes. The image below shows Tuzo's approach to demand forecasting.
By analyzing the historical data, AI models can segregate the real demand and promotions-driven demand. Our product creates workbenches that are used for scenario planning for the impact of price or promotions on demand. Manufacturing and market constraints are factored into the optimal scenario.
AI-based demand forecasting has a significant impact on the revenues of a company. BCG estimates that across the projects it has implemented, AI has led to 2.5% higher revenues. The business impact of AI in demand forecasting is the most for travel, CPG, retail, and logistics companies, finds a McKinsey survey.
In our experience, ML-based demand forecasts provide an accuracy that is at least at the level of time series modeling or higher. This means that machine learning works better for demand forecasts by capturing a complex mix of historical data and market variables compared to traditional linear time series models. The use of AI/ML improves the accuracy of demand forecasts improves by an average of 64%.
Robust mathematical algorithms that recognize patterns and capture the intricate and hidden relationships between demand signals and data from source lists not only analyze huge amounts of data but also retrain models based on new information and adapt it to changing conditions and effects, resulting in more reliable forecasting. ML solutions lead to an increase in 5-15% prediction reliability compared to conventional methods. ML for demand forecasting works well for all time horizons (short, medium & long term). AI factors changing environments, volatile demand characteristics, and campaign plans for new products.
In an increasingly competitive world, most organizations are aiming to remove inefficiencies, increase revenues and stay relevant to the changing consumer needs. Demand forecasting using AI results in optimal inventories, the right level of trade spends, promotions and pricing. The revenue growth of 2.5% goes straight into the bottom line of the organizations.
Larger companies have adopted AI and are gaining an edge on small and mid-sized companies. Mid size companies lack resources and cannot afford expensive implementations by consulting organizations. As a result, mid-size companies are missing out on the benefits of AI. Tuzo is developing a no-code AI platform focused on revenue growth management. The platform is affordable, easy to use, and quick to realize the value of AI. Write to us at email@example.com for further information.