While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it ...offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts.We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out‐of‐sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best‐performing method (random forest) yields an out‐of‐sample MAPE of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.
•Advanced analytics for predicting customer behavior in the non-contractual setting.•Dynamic customer characteristics that derive from previous purchase information.•Machine learning algorithms for ...predicting future purchases.•Comparing logistic Lasso, extreme learning machine, and gradient tree boosting.
Predicting future customer behavior provides key information for efficiently directing resources at sales and marketing departments. Such information supports planning the inventory at the warehouse and point of sales, as well strategic decisions during manufacturing processes. In this paper, we develop advanced analytics tools that predict future customer behavior in the non-contractual setting. We establish a dynamic and data driven framework for predicting whether a customer is going to make purchase at the company within a certain time frame in the near future. For that purpose, we propose a new set of customer relevant features that derives from times and values of previous purchases. These customer features are updated every month, and state of the art machine learning algorithms are applied for purchase prediction. In our studies, the gradient tree boosting method turns out to be the best performing method. Using a data set containing more than 10 000 customers and a total number of 200 000 purchases we obtain an accuracy score of 89% and an AUC value of 0.95 for predicting next moth purchases on the test data set.
Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory ...placement, network planning, etc.). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporación Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition
•Deep learning algorithms achieve competitive results in sales forecast.•A single model is needed to generalize over all products, stores and time.•Random max time step trick can be used to avoid overfitting over specific timesteps.
Smart manufacturing, which is increasingly popular worldwide, is aided by time-series forecasting. As the volume of historical data increases, powerful forecasting techniques that reveal unknown ...relationships between past and future values are required to provide accurate forecasts of production and sales. Thus, in this article, a composite gate recurrent unit (GRU)-Prophet model with an attention mechanism was constructed to predict sales volume. In this composite model, Prophet model and GRU model with attention mechanism were used to capture linear and nonlinear features, respectively. The composite model was experimentally determined to be more applicable and to provide more accurate predictions than did recurrent neural network, long short-term memory, gate recurrent unit, Prophet, and autoregressive integrated moving average models. This article's composite model is suited to rapid changes in market demand and helps enterprises be more competitive in the field of smart manufacturing.
This research takes a retrospective view of the COVID-19 pandemic and attempts to accurately measure its impact on sales of different product categories in grocery retail. In total 150 product ...categories were analyzed using the data of a major supermarket chain in the Netherlands. We propose to measure the pandemic impact by excess sales – the difference of actual and expected sales. We show that the pandemic impact is twofold: (1) There was a large but brief growth at 30.6% in excess sales associated with panic buying across most product categories within a two-week period; and (2) People spending most of their time at home due to imposed restrictions resulted in an estimated 5.4% increase in total sales lasting as long as the restrictions were active. The pandemic impact on different product categories varies in magnitudes and timing. Using time series clustering, we identified eight clusters of categories with similar pandemic impacts. Using clustering results, we project that product categories used for cooking, baking or meal preparation in general will have elevated sales even after the pandemic.
The essential concepts of sellers and buyers are supply and demand. Organizations must be able to accurately predict demand in order to establish plans. Sales Prediction is based on predicting sales ...for various Big Mart stores in order to adjust the business strategy based on the projected performance. For Big Mart firms, in this paper present a novel approach to demand forecast. The Big Mart firms' business model, for which the model is executed, contains multiple shops selling the same product at the same time across the country where the company maintains a marketplace model. This proposed Supervised and Artificial Neural Network Algorithms produces reliable results when compared to other learning methods. Feature selection, data transformation, and data exploration will all play essential roles. This method is used on data from Big-Mart Sales, where data is discovered, processed, and enough relevant data is taken to help forecast correct future outcomes.
Online agricultural product trading has the characteristics of rapid and diversified transaction data; there is a fuzzy correspondence between sales volume influencing factors and sales volume ...levels. Based on this, this paper combines the data preprocessing technology of fuzzy membership and optimized deep learning algorithm, adding a self-encoding method with sparseness restriction, and proposes a deep learning sales forecasting model based on transformative computing with fuzzy membership-the super crown model (Super Imperial Crown Model, referred to as SICM). The model uses fuzzy membership to process the weighted relationship between sales influencing factors and sales rank, and uses a sparse autoencoder network to adaptively extract sample features; sales rank classification prediction uses Softmax classifier; BP fine-tuning is used to Achieve parameter optimization. Finally, use the collected transaction data to apply R software to simulate the optimized model and compare and analyze the comprehensive prediction performance. The results show that the super crown model can realize real-time and accurate dynamic sales classification prediction according to the characteristics of current online agricultural product transaction data, effectively overcome the imbalance of supply and demand caused by information imbalance, and promote the study of deep learning in the field of e-commerce transactions effect. Presented algorithm based on transformative computing techniques can be used in optimization of sales processes, management and analysis of sales markets.
•The SICM model has strong self-learning ability, which can realize the adaptive extraction of characteristic information of online agricultural product transaction data, effectively avoiding the traditional shallow classifier in the process of artificial cross-validation optimization through preset parameter values. The repetitive forecasting evaluation operation not only reduces the time for model training to the optimal level, but also can truly achieve real-time dynamic demand forecasting by inputting data at any time and producing results at any time, and achieves high forecasting accuracy.•In view of the “fuzzy mapping” relationship between the factors affecting the online sales of agricultural products and the sales classification level, the concept of fuzzy membership is introduced in the data preprocessing stage to reduce the sales forecast error.•The SICM model has good incremental self-learning update capability and generalization performance for agricultural product online transaction data with large capacity, diversity and high rate growth characteristics, and can be used as a long-term model for online agricultural products Real-time intelligent prediction of sales volume can also be extended to the entire e-commerce field.
•A novel AI-based causal framework for modeling and forecasting export sales is proposed.•A four-step genetic programming-based process is used to build a causal equation.•A highly accurate model and ...forecast are suggested for sales related variables.•Variable sensitivity analysis is used to analyze the variations of the target variable.•Insightful directions are suggested for further research in export sales forecasting.
Sales forecasting is important in production and supply chain management. It affects firms’ planning, strategy, marketing, logistics, warehousing and resource management. While traditional time series forecasting methods prevail in research and practice, they have several limitations. Causal forecasting methods are capable of predicting future sales behavior based on relationships between variables and not just past behavior and trends. This research proposes a framework for modeling and forecasting export sales using Genetic Programming, which is an artificial intelligence technique derived from the model of biological evolution. Analyzing an empirical case of an export company, an export sales forecasting model is suggested. Moreover, a sales forecast for a period of six weeks is conducted, the output of which is compared with the real sales data. Finally, a variable sensitivity analysis is presented for the causal forecasting model.
Many e-commerce platforms, such as AliExpress, run major promotion campaigns regularly. Before such a promotion, it is important to predict potential best sellers and their respective sales volumes ...so that the platform can arrange their supply chains and logistics accordingly. For items with a sufficiently long sales history, accurate sales forecast can be achieved through the traditional statistical forecasting techniques. Accurately predicting the sales volume of a new item, however, is rather challenging with existing methods; time series models tend to overfit due to the very limited historical sales records of the new item, whereas models that do not utilize historical information often fail to make accurate predictions, due to the lack of strong indicators of sales volume among the item's basic attributes. This article presents the solution deployed at Alibaba in 2019, which had been used in production to prepare for its annual "Double 11" promotion event whose total sales amount exceeded U.S. <inline-formula> <tex-math notation="LaTeX">\ </tex-math></inline-formula>38 billion in a single day. The main idea of the proposed solution is to predict the sales volume of each new item through its connections with older products with sufficiently long sales history. In other words, our solution considers the cross-selling effects between different products, which has been largely neglected in previous methods. Specifically, the proposed solution first constructs an item graph, in which each new item is connected to relevant older items. Then, a novel multitask graph convolutional neural network (GCN) is trained by a multiobjective optimization-based gradient surgery technique to predict the expected sales volumes of new items. The designs of both the item graph and the GCN exploit the fact that we only need to perform accurate sales forecasts for potential best-selling items in a major promotion, which helps reduce computational overhead. Extensive experiments on both proprietary AliExpress data and a public dataset demonstrate that the proposed solution achieves consistent performance gains compared to existing methods for sales forecast.
In the fashion retail industry, level of forecasting accuracy plays a crucial role in retailers’ profit. In order to avoid stock-out and maintain a high inventory fill rate, fashion retailers require ...specific and accurate sales forecasting systems. One of the key factors of an effective forecasting system is the availability of long and comprehensive historical data. However, in the fashion retail industry, the sales data stored in the point-of-sales (POS) systems are always not comprehensive and scattered due to various reasons. This paper presents a new seasonal discrete grey forecasting model based on cycle truncation accumulation with amendable items to improve sales forecasting accuracy. The proposed forecasting model is to overcome two important problems: seasonality and limited data. Although there are several works suitable with one of them, there is no previous research effort that overcome both problems in the context of grey models. The proposed algorithms are validated using real POS data of three fashion retailers selling high-ended, medium and basic fashion items. It was found that the proposed model is practical for fashion retail sales forecasting with short historical data and outperforms other state-of-art forecasting techniques.
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