A knowledge-intensive methodology for explainable sales prediction

Sales prediction in food market is a complex issue that has been addressed in the recent past with machine learning techniques. Although some promising results, an experimental work that we describe in this paper shows some drawbacks of the above mentioned data-driven method and habilitates the definition of a novel methodology, strongly involving a piori knowledg

Regular and promotional sales in new product life-cycle: A competitive approach

In this paper, we consider the application of the Lotka-Volterra model with churn effects, LVch, (Guidolin and Guseo, 2015) to the case of a confectionary product produced in Italy and recently commercialized in a European country. Weekly time series, referring separately to quantities of regular and promotional sales, are available. Their joint inspection highlighted the presence of compensatory dynamics suggesting the study with the LVch to estimate whether competition between regular and promotional sales exists and how it affects product life-cycle. The study of sales under promotion with respect to regular ones represents a new way of dealing with promotional activities effects, whereas the innovation diffusion literature on new product growth has typically considered the effect of pricing and advertising through the generalized Bass model (Bass et al., 1994). In that model, the total amount of sales, regular plus promotional sales, is analyzed with a univariate approach, while price and advertising expenditures are used as exogenous inputs, without a feedback control. Conversely, exploiting the availability of two distinct time series and studying their interaction, our results show that competition has a symmetric character. Regular sales may access the residual market of those under promotion indicating the beneficial effect of promotional efforts, but the reverse effect is also present. Short-term forecasts on the evolution of the two series are then built with a two stage procedure based on an iterated SARMAX. The predicted values are further validated with observed real data. A comparison with Euler standard predictions is also performed

Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved.Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072S1477814785381

How Smart Operations Help Better Planning and Replenishment?

This chapter discusses various roles of smart information in Supply Chains (SC) of digital age and tries to answer an important question - What types of collaborative arrangements facilitate smart operations to improve planning, production and timely replenishment? We have conducted longitudinal case studies with firms practicing SC collaborations and also using smart information for operations. Based on the case analysis, the companies are further classified as 'smart planning' and 'traditional planning'. Research findings show the importance of aligning SC partnerships based on smart information requirements. These findings are based on case studies of Indian firms with global SC collaboration. We also discuss the role of Big Data for the companies using smart planning

Modern Centaurs: How Humans and AI Systems Interact in Sales Forecasting

Recent achievements of artificial intelligence (AI) have caused organizations to increasingly bring AI capabilities into their core business processes. Such AI-supported business processes often result in human+AI centaurs, which consist of an AI system, which performs most of the execution, and humans, who monitor this execution and occasionally provide additional inputs and overrides. Using sales data from Walmart, we conduct an online study to investigate if human supervision can improve upon state-of-the-art AI forecasts. Furthermore, we analyze the perceptions and behavioral intentions of the human participants over time. We find that human interventions consistently lead to less accurate forecasts and that participants initially underestimate the AI system’s accuracy and overestimate their own potential to improve upon the AI forecasts. However, perceptions quickly shift over the course of the study, causing the participants to perceive the AI system increasingly favorably, which also leads to behavioral changes and better overall system performance