Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming

Abstract

<jats:title>Abstract</jats:title><jats:p>Forecasting the demand of network of retail sales is a rather challenging task, especially nowadays where integration of online and physical store orders creates an abundance of data that has to be efficiently stored, analyzed, understood and finally, become ready to be acted upon in a very short time frame. The challenge becomes even bigger for added-value third party logistics (3PL) operators, since in most cases and demand forecasting aside, they are also responsible for receiving, storing and breaking inbound quantities from suppliers, consolidating and picking retail orders and finally plan and organize shipments on a daily basis. This paper argues that data analytics can play a critical role in contemporary logistics and especially in demand data management and forecasting of retail distribution networks. The main objective of the research presented in this paper is to showcase how data analytics can support the 3PL decision making process on replenishing the network stores, thus improving inventory management in both Distribution Centre (DC) and retail outlet levels and the workload planning of human resources and DC automations. To do so, this paper presents the case of a Greek 3PL provider fulfilling physical store and online orders on behalf of a large sporting goods importer operating a network of 129 stores in five different countries. The authors utilize the power of ‘R’, a statistical programming language, which is well-equipped with a multitude of libraries for this purpose, to compare demand forecasting methods and identify the one producing the smallest forecast error.</jats:p>

Industry 4.0 enablers in retailing: a literature review

“…Big data are used by both practitioners and researchers to develop analysis after the data identification in-store and online (Singh and Thirumoorthi, 2019), in particular using the information that customers leave online, on social network sites, on the retailers' applications, or on web-based tools. At the same time, Big Data are used in research to study research perspectives on consumer behaviour and engagement (Singh and Singhal, 2020) Forecasting Big data adoption in forecasting is still limited but advanced supply chains (Iftikhar and Khan, 2020), including the retail sector (Rathod and Kumar, 2021), have equipped themselves, for instance, with advanced systems to develop forecasting activities capable of scheduling logistics activities with a limited number of errors (Lalou et al, 2020).…”

“…In general, the use of Industry 4.0 innovations leads to a reduction in labour costs (Lee and Lee, 2020) in the industrial context and, indirectly, even in the retail sector, especially within the supply chain. Moreover, Big Data contributes to reducing lost sales and/or bad inventory (Fisher and Raman, 2018;Lalou et al, 2020) and IoT is useful in reducing logistics activities within the supply chain, integrating the activities of more companies involved, the retailer included. In any case, as in the industrial sector, the application of sensors to the business, organisational and management activity helps control the product costs (Maksimovi c et al, 2015).…”

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

“…Demand management is a topic of growing interest recently. Forecasting daily demand of products in the retail sector (Amalnick et al, 2019;Spiliotis et al, 2020;Abolghasemi et al, 2020;Lalou et al, 2020;Massaro et al, 2021), predicting potential customer demand or newly launched products (Kharfan et al, 2021) are amongst the most repeated topic in "demand forecasting" area. Moreover, there are lots of contributions in context of demand shaping (Safara, 2020;Verma et al, 2020;Lam et al, 2021;Kalinin et al, 2020) and demand sensing (Sathyan et al, 2021;Grzybowska et al, 2020;Jain & Kumar, 2020;Taghikhah et al, 2021;Türk et al, 2021;Shokouhyar et al, 2021) in recent publications.…”

Logistics Coordination Based on Inventory Management and Transportation Planning by Third-Party Logistics (3PL)

“…The second hypothesis (H2) is connected with checking the possibility of stock management operation automation by the 3PL entity in DN. The issue of supporting the picking operations in the warehouse by DF solutions is usually considered from the retailer's operations [49] or predicting the workforce demand [50]. In this paper, this issue will be considered as a possibility of time reduction by modifying the current stock analysis method.…”