Data Science to Supply Chain Management

In our contemporary world, data science has been widely applied to industries in nearly all perspectives. Data science carries with it a more intelligent and efficient way of production, reducing marginal cost and maximizing profit. Currently, supply chains are no longer only systems for keeping track of products along the chain, but they have become a way for companies to gain a competitive advantage and even build their own brand.
Discovering mathematical patterns behind supply chain data is essential for any business. Machine learning provides brand-new ways to capture the pattern without human intervention. Using statistical modeling and algorithms, it can find out a core set of essential factors with relatively high accuracy to improve the performance of the business. These factors influence inventory levels, supplier quality, demand forecasting and so forth. As a result, new knowledge and insights from machine learning are revolutionizing supply chain management. This article will walk through several examples of the application of machine learning on supply chain management.

One of the most challenging and essential aspects of supply chain management is demand forecast. Traditional techniques range from baseline statistical analysis techniques including moving averages to advanced simulation modeling.
One of the significant drawbacks of existing techniques is that the prediction focuses generally on historical demanding data. Therefore, the prediction accuracy is comparatively low and reaction time to the market is slow. However, machine learning predicts demand based on a wide spectrum of factors from different sources.

Take the beer industry as an example, intuitively seasonality is a crucial influencing factor to sales. Besides, sporting events significantly influence the sales in licensees (bars and arenas). Furthermore, if we take customers’ comment and ratings into consideration, after conducting a sentiment analysis, there is a relationship between sales and customers’ sentiment. Machine learning is more valuable at considering causal factors for new products that influence demand but were previously unknown.
Except for demand prediction, machine learning helps a business to further understand customers. If a business is collaborating with banks, it can acquire customer’s information when they use credit cards for purchase. It helps a business to conduct a customer segmentation and develop customized product to the target audience.

Currently, there is a trend that the sales of coolers and ciders are increasing while mainstream craft is declining because the young generation prefer RTD (ready-to-drink) than traditional beers. Knowing the demographic information helps brewing companies to optimize the product deployment to individual stores.