Demand Forecasting of Retail Sales Using Data Analytics and Statistical Programming
| Autor: | Orestis K. Efthymiou, Stavros T. Ponis, Panagiota Lalou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010407 polymers
Entrepreneurship Operations research HF5001-6182 business.industry Data management statistical programming Workload 010501 environmental sciences Demand forecasting 01 natural sciences General Business Management and Accounting decision making 0104 chemical sciences Task (project management) retail sales demand forecasting third party logistics (3pl) operators Data analysis Business Decision-making business Human resources data analytics 0105 earth and related environmental sciences |
| Zdroj: | Management şi Marketing, Vol 15, Iss 2, Pp 186-202 (2020) |
| ISSN: | 2069-8887 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|