Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the omni-channel. With machine learning, supercomputers learn from mining masses of big data without human intervention to provide unprecedented consumer demand insights.
Predictive analytics and advanced algorithms, such as neural networks, have emerged as the hottest (and sometimes controversial) topic among senior management teams. Neural network algorithms are self-correcting and powerful, but are difficult to replicate and explain using traditional multiple regression models.
For years, neural network models have been discarded due to the lack of storage and processing capabilities required to implement them. Now with cloud computing using supercomputers' neural network algorithms, along with ARIMAX, dynamic regression and unobserved components, models are becoming the catalyst for "machine learning-based forecasting."
According to an article in Consumer Goods Technology magazine, through pattern recognition there will be a shift from active engagement to automated engagement. As part of this shift, technology (machine learning) takes over tasks from information gathering to actual execution. Compared to traditional demand forecasting methods, machine learning-based forecasting helps companies understand and forecast consumer demand that, in many cases, would be otherwise impossible. Here are several reasons why:
Incomplete versus complete information and data. Traditional demand forecasts are based on time-series forecasting methods (Exponential Smoothing, ARIMA, and others) that can only use a handful of demand factors (e.g., trend, seasonality, and cycle). On the other hand, machine learning-based forecasting combines learning algorithms (ARIMAX, dynamic regression, neural networks and others) with big data and cloud computing to analyze thousands – even millions – of products using unlimited amounts of causal factors simultaneously up and down a company’s business hierarchy.
Traditional demand forecasting and planning systems are restricted to only the demand history, while machine learning-based forecasting can take advantage of limitless data, determining what’s significant, then prioritize available consumer insights (demand sensing) to influence future demand using “what if” analysis (demand shaping). Compared to traditional time-series forecasting systems, machine learning-based forecasting solutions identify the underlying demand drivers that influence demand, uncovering insights not possible with traditional time-series methods. Additionally, the self-learning algorithms get smarter as they consume new data and adapt the algorithms to consumer demand.
Holistic models using multiple dimensions versus single dimension algorithms. Traditional forecasting systems are characterized by a number of single-dimension algorithms, each designed to analyze demand based on certain data-limited constraints. As a result, much manual manipulation goes into cleansing data and separating it into baseline and promoted volumes. This limits which algorithms can be used across the product portfolio.
Machine learning-based forecasting takes a more sophisticated approach. It uses pattern recognition with a single, general-purpose array of algorithms that adapt to all the data. They fit many different types of demand patterns simultaneously across the product portfolio up/down the company’s business hierarchy without data cleansing handling multiple data streams (e.g., price, sales promotions, advertising, in-store merchandising and many others) in the same model — holistically -- without cleansing the data into baseline and promoted volumes.
For example, traditional forecasting systems have a specific purpose leading to multiple inconsistent forecasts across the product portfolio. With machine learning-based forecasting, the same algorithm is useful for multiple processes including pricing, sales promotions, in-store merchandising, advertising, temperature, store inventory, and others creating one vision of a realistic integrated forecast.
Partial versus complete use of item history. When creating demand forecasts, traditional demand forecasting and planning systems analyze the demand history for a particular product/SKU, category, channel and demographic market area. Machine learning-based forecasts leverage history for all items, including sales promotions, to forecast demand for every item at every node in the business hierarchy simultaneously.
Many feel the next generation of machine learning will also include cognitive computing where the supply chain becomes self-healing. This would improve upon machine learning by going beyond predictions to making decisions to automatically correct for anomalies in the supply chain.
Do you see machine learning-based forecasting supporting the next-generation demand management? Will it eventually lead to cognitive learning creating an autonomic self-healing supply chain; or are you still relying on cognitive dissonance to justify and maintain judgmental harmony within your current demand forecasting and planning process?
You can follow Charlie on: