2 research outputs found
Demand Prediction Using Machine Learning Methods and Stacked Generalization
Supply and demand are two fundamental concepts of sellers and customers.
Predicting demand accurately is critical for organizations in order to be able
to make plans. In this paper, we propose a new approach for demand prediction
on an e-commerce web site. The proposed model differs from earlier models in
several ways. The business model used in the e-commerce web site, for which the
model is implemented, includes many sellers that sell the same product at the
same time at different prices where the company operates a market place model.
The demand prediction for such a model should consider the price of the same
product sold by competing sellers along the features of these sellers. In this
study we first applied different regression algorithms for specific set of
products of one department of a company that is one of the most popular online
e-commerce companies in Turkey. Then we used stacked generalization or also
known as stacking ensemble learning to predict demand. Finally, all the
approaches are evaluated on a real world data set obtained from the e-commerce
company. The experimental results show that some of the machine learning
methods do produce almost as good results as the stacked generalization method.Comment: Proceedings of the 6th International Conference on Data Science,
Technology and Application
Demand Forecasting Tool For Inventory Control Smart Systems
With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future time periods. It plays an important role in the field of inventory control and Supply Chain, since it enables production and supply planning and therefore can reduce delivery times and optimize Supply Chain decisions. This paper presents an extensive literature review about demand forecasting methods for time-series data. Based on analysis results and findings, a new demand forecasting tool for inventory control is proposed. First, a forecasting pipeline is designed to allow selecting the most accurate demand forecasting method. The validation of the proposed solution is executed on Stock&Buy case study, a growing online retail platform. For this reason, two new methods are proposed: (1) a hybrid method, Comb-TSB, is proposed for intermittent and lumpy demand patterns. Comb- TSB automatically selects the most accurate model among a set of methods. (2) a clustering-based approach (ClustAvg) is proposed to forecast demand for new products which have very few or no sales history data. The evaluation process showed that the proposed tool achieves good forecasting accuracy by making the most appropriate choice while defining the forecasting method to apply for each product selection