Logistics involves the transportation of goods and services from the location of production to various markets through the use of liaisons. There are many ways in which companies access their customers when distributing their products and services. Each company has a procedure that enables its employees to communicate effectively to the external market. However, communication and interaction when collecting consumer information and data in some cases has challenges. The paper looks at the challenges that companies in the logistics industry face when dealing with their consumers (Bonev, 2012).
Firstly, companies that deal in retail have to access their products from companies who focus on wholesale trade. As such, they have to be in direct contact with the seller during the purchase and distribution of their products. On the other hand, retail companies have to maintain their relationship with their customers because they serve as their primary market. Before the sale of goods, customers have to have an understanding of the needs and requirements of their clientele. One of the challenges that retail companies face is that consumers are not educated on the products and services that are sold in the market. In turn, they are not able to give the retailers the correct feedback of their preference. Consequently, sellers are forced to trade in goods which may not perform well in the market (Dekker et al, 2004).
Secondly, some customers are not exposed to modern methods of communication. Moreover, companies that distribute products to remote areas cannot use IT services such as the internet to communicate with their clientele. In this case, reverse logistics is ineffective because suppliers of the products do not have the means of targeting the final destination of the product in the event that they need to review the pricing, mode of distribution, quantity, and quality of the product in question (Fleischmann, 2001).
Role of Technology
Technology plays a vital role in eliminating the challenges experienced during this process. One of the ways that technology improves efficiency is by connecting to regions that are located in offshore locations. Presently, most societies use the internet for communicating and interacting with people in different parts of the globe. In the case of logistics, traders use the internet to email, perform social network marketing, and access customer reviews on the products and services provided. In turn, customers are able to provide the required data and information on various aspects of a company’s brand (Bonev, 2012).
Technology also creates awareness of the latest trends and preferences in the market. Retailers take advantage of online forums, social networking sites, and blogs to discuss different products and services hence solving the challenge caused by distance (Fleischmann, 2001).
Why is Technology Important in Managing Reverse Logistics Processes?
Technology is significant in exposing logistic companies to new companies during the positioning and classification. Consumers can access all the features of the products before, during, and after production. As such, they are able to take part in the rebranding process through the click of a button. When managing reverse logistics processes, customer can give feedback of their level of satisfaction to retailers without having direct contact with the company. This increase the reliability, efficiency, and effectiveness of the companies (Dekker et al, 2004).
To summaries, companies can reduce the challenges they face by incorporating technology in all their operations. As a result, consumers who cannot be accessed easily can use this advantage to communicate the information and data required by companies needed to enhance trade.
- Bonev, M. (2012). Managing reverse logistics using system dynamics: A generic end-to-end approach. Hamburg: Diplomica Verlag.
- Dekker, R., Fleischmann, M., Inderfurth, K., & Van, W. L. N. (2004). Reverse logistics: Quantitative models for closed-loop supply chains; with 34 tables. Berlin [u.a.: Springer.
- Fleischmann, M. (2001). Quantitative models for reverse logistics. Berlin [u.a.: Springer.
