A common challenge for many CG and retail organizations is the proliferation of disparate forecasting solutions, all with narrow purviews of data, that have been built for specific use cases such as pricing, promotions or inventory. It is not uncommon for enterprises today to have five or six different forecasting platforms, all of which suffer from some degree of error given the siloed understanding that these individual tools have.
Machine learning-based forecasting: Relevance in rapidly changing environments
There are many factors that influence demand forecasting accuracy, both externally and internally. And often, a sizable number of causes and critical data points go unnoticed by traditional forecasting tools. However, with the integration of machine learning (ML) algorithms, forecasting tools have become more advanced to provide proactive and predictive insight into consumer behavior and preferences, and enable better decision making.
As an example: Winterwear sales could have been higher last year because of the early onset of winter. And, due to the delay in procurement, a certain percentage of customer demand remained unfulfilled.
Forecasting for demand this year should not just be based on last year’s sales. An ideal approach would consider other factors like:
- Weather department’s prediction of a longer fall and a less severe winter than last year.
- Unfulfilled demand of last year.
- People working from home and consequent change in style preference.
- Changing channel preferences.
- New product launches by competitors.
- The emergence of more competitors in specific categories.
Accounting for the number of variables and the magnitude of change can be overwhelming for analysts working with legacy tools. Here, ML-based predictive analytics can help ease decision-making by considering the following:
1. External factors: Weather trends and events, competition, fashion lifecycle, changing consumer behavior and preferences and more recently, the COVID-19 pandemic.
2. Internal factors: Unfulfilled demand and unsold inventory from last year, channel performance and volatility experienced over the past few months of the pandemic, available shelf space, new product launches, etc.
The next generation of demand forecasting
When done right, AI-powered demand forecasting can help companies both save and make money by being better tuned in to the needs of their customers. There are unified demand forecasting platforms accounting for all relevant factors resulting in a far more accurate enterprise-wide view.
Advanced demand forecasting tools help CG brands and retail organizations dynamically interpret, adjust and shape ever changing consumer behavior, whether it is helping a retailer figure out the right price point, determine the right promotional offer, or optimize the inventory investments in a more meaningful and accurate way.