Data Science has become one of the most powerful technologies in the retail sector by providing fact-based and data-driven insights. Data Science technologies help retailers in enhancing their marketing strategies, operations, and financial performance. Retailers today are searching for ways to derive more customer intelligence and operational insights from their overflowing databases which are currently fulfilled by Data Science technologies. Data science plays a vital role in almost all sectors of retail such as assortment, recommendation, Logistics and Supply Chain Management, Demand Forecasting, Price Optimization for products, Predictive Maintenance, Churn prediction, and Data-Driven Product Management. Other products that are bought together with the required products by the customers lead to increase in sales.
One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand prediction methods for weather sensitive products at retail stores. We employ historical datasets from Walmart, whose customers and markets are often exposed to extreme weather events which can have a huge impact on sales regarding the affected stores and products. We want to accurately predict the sales of 111 potentially weather-sensitive products around the time of major weather events at 45 of Walmart retails locations in the U.S. Intuitively, we may expect an uptick in the sales of umbrellas before a big thunderstorm, but it is difficult for replenishment managers to predict the level of inventory needed to avoid being out-of-stock or overstock during and after that storm. While they rely on a variety of vendor tools to predict sales around extreme weather events, they mostly employ a time-consuming process that lacks a systematic measure of effectiveness. We employ all the methods critical to any analytics project and start with data exploration. Critical features are extracted from the raw historical dataset for demand forecasting accuracy and robustness. In particular, we employ Artificial Neural Network for forecasting demand for each product sold around the time of major weather events. Finally, we evaluate our model to evaluate their accuracy and robustness.
A Data science-based solution needs to address problems at multiple levels. While it addresses a business problem, computationally it is comprised of a pipeline of algorithm which, in turn, operates on relevant data presented in proper format. Contrary to the popular belief, almost all non-trivial data science solutions are needed to be built ground up with minute and interrelated attention to the details of the problem at all three levels. In the following we shall try to understand that with the help of a running example of aspects of a churn analysis solution. It is vital to understand that in most real-world cases we are re-purposing the data for building the solution.
Machine Learning Forecasting is attracting an essential role in several significant data initiatives today. Year ago, I have mentioned machine learning as top 7 future trends in supply chain. Big retailers, Supply chain, and logistics experts are using Machine Learning Forecasting to aid improve customer engagement and produce more precise demand forecasts better than traditional forecasting techniques. They can also use Machine Learning Forecasting to expand into new sales channels, improve customer service, reduce inventory and improve productivity.
The manufacturing business faces huge transformations nowadays. Due to rapid development of digital world and broad application of data science, various fields of human activity seek improvement. Modern manufacturing is often referred to as industry 4.0 that is the manufacturing under conditions of the fourth industrial revolution that has brought robotization, automation and broad application of data. The amount of data to be stored and processed is growing every day. Therefore, today's manufacturing companies need to find new solutions and use cases for this data.