CHAPTER ONE

INTRODUCTION

1.1Background to the Study

Logistics activities started about thousand years ago as organized trade, and gradually began to gain recognition as a valuable area of study during the early 1900s through the distribution of farm products (Grant et al., 2006). It was initially considered only as a function that supports organization’s business strategy and improves their time and space utility (Grant et al., 2006). Logistics has now evolved from a supportive function with little added value to a strategic function and a source of competitive advantage for businesses by creating value for customer through quality logistics services (Mentzer et al., 2008).

In today’s world of business and in the twenty-first century, most organizations are faced with the task of achieving the best possible results regarding efficiency and effectiveness in service delivery and profit maximization with available employees at their disposal. The strategic application of reverse logistics (RL) for the reclamation of products during their life cycle, and at the end of their useful life is gaining increased attention in today’s global business environment. The economic, and environmental implications of reclamation, reuse, and recycling to save landfill space, fuel, and costs are becoming strategically important for companies (Grant et al., 2006).

Business consumers in the digital age have become increasingly informed, challenging industrial marketing and sales teams to adapt their traditional marketing strategies to fully embrace buyers’ preferences and expectations. Forrester, in its latest research report, predicts that>20% of companies will begin to apply modern technologies in their industrial marketing platforms in order to optimize the engagement between business buyers and sellers (Robertson et al., 2018).

Technologies such as artificial intelligence (AI) and big data analytics have created unprecedented opportunities for companies to exploit their data assets for business-to-business (B2B) market initiatives. For instance, incorporating Lattice Engine’s predictive analytics into its marketing initiatives, the industrial marketing team at Akamai was able to better segment its customers and to send personalized messages, sextupling its lead-to-opportunity conversion rate (Anderson 2018). In addition, companies including Google, Amazon, Facebook, and Apple have all made great efforts in the field of industrial marketing through collecting and utilizing big data. All of this underlines the importance of  big data as a crucial factor in global marketing operations (Miguel & Casado 2016).

As e-commerce is widely used in the development of Internet and information technology, people can stay at home to buy their favorite products, so the online transactions gradually occupy an important position in people’s minds. Data also plays a key role in making different decisions about business supply chains and logistics operations that are closely related to the industrial marketing field. Supply chain management deals with creating and maintaining linkages between different entities with specific responsibilities, ranging from raw material procurement to end user product interactions. Logistics management ensures that relevant work support methods, such as traffic management, warehouse management, inventory management, packaging, and order tracking, are in place. Employing a large and diverse range of data in logistics and supply chain management, companies can understand the needs and preferences of their customers. Electronic commerce (e-commerce) giants such as Amazon, Flip kart, and Snap deal have been collecting and exploring data from customers, orders, inventory, and other information (Meena, 2017). The success of e-commerce companies now depends largely upon how efficiently they capture, store, and use data.

The advent of the big-data era has further strengthened the relationship between logistics distribution and e-commerce, and this presents new opportunities, such as the expansion of enterprise information, the sharing of distribution channels, and the integration of data resources. Particularly, e-commerce enterprises can accurately predict the future needs of customers and can fulfill personalized services to customers. In addition, they can organize and coordinate the distribution activities beforehand in a well-planned way, allowing for better selection and innovation of distribution modes. Now, companies can reduce the cost of logistics delivery, improve the efficiency of logistics delivery, and meet the diversified and high-quality delivery needs of customers.

According to Borak (2018), as an e-commerce enterprise’s logistics ability has become an important indicator of its competitiveness, the choice of a logistics distribution mode directly affects the enterprise’s quality and costs of distribution as well as its supply chain coordination. There are three primary logistics distribution modes for an e-commerce enterprise. These include self-built logistics, third-party logistics, and the joint distribution mode. In order to meet the development requirements of e-commerce and to improve customer satisfaction, it is imperative that e-commerce enterprises thoroughly understand and investigate the advantages and disadvantages of all kinds of logistics distribution modes and select the most appropriate one, so as to improve their users’ experience and to promote the sustainable and healthy development of e-commerce enterprises.

In the context of China, the information utilization rate of logistics distribution centers is low, and nearly half of logistics enterprises lack the support of information systems. Logistics delivery plans are usually formulated by staff through market research or experience, and they cannot meet the needs of the era of big data. Due to this lack of scientific and reasonable planning and analysis of logistics distribution lines, the cost of the modern logistics industry in China is too high, and the efficiency of the delivery of its goods is low, resulting in the waste of a large number of distribution resources (Bai & Sarkis 2018; Wang, Pauleen, &

Chan, 2013).

 Biswas (2018) highlighted that for the unprecedented growth of e-commerce in Africa, India some of the major factors are the emergence of retail as a dominant segment of the market, the dedication of government to ‘Digital India’, and an Internet user database of 400 million. Wang et al., (2019) suggested that the revenue of the e-commerce of India is expected to increase to 120 billion USD in 2020, increasing at a yearly rate of 51%. The reverse logistics involve replacements, disposal, and recycling, repairs, reselling returned products, exchanges and returns. The flow of reverse logistics also involves the complete ownership cost, energy used in the complete process, distance, percentage of cost recovery, handling cost per item, percentage of recycled material, disposition of cycle time, and amount of resold and returned products. Dutta et al (2020) tried out in developing sustainable e-commerce model for Indian markets. A multi objective optimization model is developed by them.

1.2 Statement of the Problem

Under Revolution 4.0, technology is starting to replace by the human resource and help to increase the efficiency of operation. Fast-pace of live increased the customer demand in increasing the efficiency and effectiveness of logistics, especially in reverse logistics. The customer wants the easiest way to return the faulty item at the lowest cost. However, online consumers lack the information for returning the product (Charles, 2014). There is limited information about product return in the internet shopping site and even unable to find at different sources such as Facebook and YouTube. Hence, limited information on returning product has restricted online consumers to return a faulty item. Also, the online consumers who bought items on social media are unable to access the return policy (Massey, 2018) as the online retailers did not provide the return policy for their customers. When online consumers would like to return a product, there are limited ways to get in contact with online retailers (Massey, 2018). Furthermore, the free return shipment sometimes is not available and must be burden by the online consumers and result in the increasing cost for them (Zumbach, 2016).

According to Pissed Consumer (2018), asserts the online shoppers unable to get refund or return of goods when they received a faulty item. Also, there is limited studies focus on the impact of reverse logistics on customer satisfaction in ecommerce. Therefore, the main aim of this study was to bridge this gap by analysing the process of reverse logistics in an e-commerce business.

1.3 Research Questions

1.3.1 Main Research Question

What are the analyses of the process of reverse logistics in an e-commerce business?

1.3.2 Specific Research Questions

1. What are the products distributed by Glotelho?
2. How are these products are been distributed?
3. What are the challenges faced in the distribution of the product?

1.4 Objectives of the study

1.4.1 Main objective

The main objective was to analyse the process of reverse logistics in an e-commerce business.

1.4.2 Specific Research Objectives

1. To identify the products distributed by Glotelho
2. To examine how these products are been distributed
3. To identify the challenges faced in the distribution of the product