Amazon Logistics is considered as a shipping and delivery service that is mainly meant for complementing and assisting existent logistics service providers such as USPS, UPS and FedEx. The Amazon Logistics department provides same day and 7-day options for delivery. It utilizes a varied host of third-party logistics partners all over the country.
In 2012, Amazon had acquired Kiva systems, which are Automated Guided Vehicle (AGV) systems. The acquisition of Kiva had created a spark within the logistics operational sector of the organisation (Yu et al., 2016). The Kiva systems are considered as a robotic system that could be implemented within the warehousing department in order to bring in efficiency among the various processes.
(Fig 1: The Kiva AGV systems used at Amazon Fulfilment Centres)
(Source: Yu et al., 2016)
This would also improve the logistics efficiency within their vast number of fulfillment centres. The present situation at Amazon logistics center is based on manual labour or vehicles that would load the materials onto trucks, which would be further be shipped to the customer (Li & Liu, 2016). This process takes a lot of time as it requires much effort to match the exact products, deliver the products to the right vehicle and then ship them to the desired location.
Evaluation of Current Business Operations and Processes
In the present context of Amazon logistics, although the company provides a faster means to supply the ordered goods within the proper time still there is a range of problems that is affecting the sector. Amazon has massively grown since their inception stage. The company has been in the process of expanding their operations in North America, AWS and International (Ritala, Golnam & Wegmann, 2014). The primary focus of the business strategy focuses on the investment of technologies, enhancement within their logistics applications, improvement of web services based on their fulfillment capacity, R&D activities in the logistics sector and also experimenting the operations in the logistics sector.
(Fig 2: The Shipping Costs as Percent of Net Sales)
(Source: Ritala, Golnam & Wegmann, 2014)
In the present scenario of shipping of products by the Amazon Fulfillment Centre, it has been seen that the company is opening small warehouses for supporting their Amazon Fresh and Prime facilities. These services are mainly meant for grocery delivery service. The inventory is the main factor of the business of Amazon (Angeleanu, 2015). The critical competency for logistics and transportation is to identify the needs of the business and provide solutions within the delivery.
Critical Issues and Key Problems in Present Situations
From the evaluation of the present business operations, it has been seen that Amazon faces critical problems in relation to load materials onto trucks. This work is often managed by manual labours, which incurs a lot of problems in management of time and making the products to reach to the proper destination within the estimated deadline (Christopher, 2016). In a report published in 2016, the company had raised typical concerns based on rapid expansion of straining management, intense competition and other dangers associated with the business.
In the present condition, it has been reported that Amazon mostly equips their warehouse workers with handheld scanners. These pickers move within the warehouse on a predefined path and thus collect items that are assigned to them for loading onto vehicles. The workers are responsible for scanning each item during retrieving them. The scanners monitor the time that is taken during each scanning of product. These pickers are also given a particular target to hit a particular number of scans within a single day (Bell, Bryman & Harley, 2018). This kind of manual working scenario increase the time of making the product to reach to the assigned shipping vehicle and further increase the time of delivery of the product to the customer.
Proposing Solutions to Identified Problem
The Amazon logistics operation has a major reputation in the global market. Hence, they are in the process of mitigating the current warehouse related problems. In the year 2012, Amazon had majorly acquired Kiva systems, which were Automated Guided Vehicles (AGV) (Domingo Galindo, 2016). These were specially designed robots that would providing huge facilities within the warehouse and logistics services offered within the company (Bechtsis et al., 2017). These robots are mainly responsible for picking and packing of goods within the warehouse, scanning the QR codes and thus arranging them onto the trucks that would make them reach to the specific locations.
(Fig 3: Estimated size of AGV within the market)
(Source: Bechtsis et al., 2017)
With the inclusion of AGVs within the warehousing operations, the company would fully aim to automate the processes of handling of materials and packaging of logistics (Keow & Nee, 2018). The robots would autonomously retrieve products from certain shelves and transfer the goods to a packaging station. After the product is properly packed, the AGV would load the product in the proper truck within the shortest possible time (Huang, Chen & Pan, 2015). This would help the customers to receive their products within the shortest possible time.
Evaluation of the Problem
The impact of AGV for handling of materials within a warehouse includes some typical usages. With the inclusion of TQM techniques, the Kiva systems would help the company in transporting of the received materials to the different storage areas (Richards, 2017). The use of TQM techniques would help the organisation in realising the current problems and then rethink on solutions that could be provided to improve the working scenario. These systems would also be able to support the kitting operations and ensure just-in-time deliveries to the customers within the expected time (Van Meldert & De Boeck, 2016). The Kiva systems would help in transporting the finished goods to the particular shipping areas.
Amazon has set trends based on the usage of robotic systems for increasing the operational efficiency and thus save costs of business operations (Miš?evi? et al., 2018). From the inclusion of automated systems within the operational functions, it has been evaluated that the systems based on logistics functions would be improved. The AGV systems that would be installed within the logistics sector of Amazon, would incorporate real-time tracking facilities (Nicoletti, 2018). This would help the managerial and operations department to track the vehicles and thus ensure that there is no lack in the quality of provided services.
Some of the advantages that would be supported within the logistics business after the inclusion of Kiva AGV systems are:
Based on the evaluation of AGV after the inclusion within the logistics department, some of the specific recommendations that could be provided for the growth of the business efficiency are:
Hence, it could be discussed that with the inclusion of Kiva systems within the occupational workplace, it would result in bringing efficiency within the processes of work and also improve the productivity of the organisation.
Conclusion
From the discussion supported, it could be concluded that with the impact of AGV within the operational procedures of Amazon, it would reduce the impact of manual labour and ensure business productivity. This report puts emphasis on the use of robotic systems within the organisational prospects for saving operational costs and increasing efficiency. These end-user segments should employ automated systems throughout the entire supply chain management of the organisation. The entire circle of booking a product and making it reach to the customers is a highly needed service of Amazon. Hence, they should put major focus on the use of efficiency robotic system that would have the capability of transforming the workplace.
From the various kind of supported decisions, it could be expected that the present market for AGV would grow faster. The present discussed problems in Amazon lead to low kind of productivity as it requires manual labour to perform the needed tasks. The use of AGV technologies would reduce the number of manual labour from the workplace, bring in efficiency within the processes of work and also increase the supply of materials to the customers within the proper estimated time.
References
Angeleanu, A. (2015). New technology trends and their transformative impact on logistics and supply chain processes. International Journal of Economic Practices and Theories, 5(5), 413-419.
Bechtsis, D., Tsolakis, N., Vlachos, D., & Iakovou, E. (2017). Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. Journal of Cleaner Production, 142, 3970-3984.
Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press.
Christopher, M. (2016). Logistics & supply chain management. Pearson UK.
Huang, G. Q., Chen, M. Z., & Pan, J. (2015). Robotics in ecommerce logistics. HKIE Transactions, 22(2), 68-77.
Keow, M. A. K. S., & Nee, A. Y. H. (2018). Robotics in Supply Chain. Emerging Technologies for Supply Chain Management, 25.
Li, J. T., & Liu, H. J. (2016). Design optimization of amazon robotics. Automation, Control and Intelligent Systems, 4(2), 48-52.
Miš?evi?, G., Tijan, E., Žgalji?, D., & Jardas, M. (2018, January). Emerging trends in e-logistics. In Proceedings of the MIPRO.
Nicoletti, B. (2018). The future: procurement 4.0. In Agile Procurement (pp. 189-230). Palgrave Macmillan, Cham.
Richards, G. (2017). Warehouse management: a complete guide to improving efficiency and minimizing costs in the modern warehouse. Kogan Page Publishers.
Ritala, P., Golnam, A., & Wegmann, A. (2014). Coopetition-based business models: The case of Amazon. com. Industrial Marketing Management, 43(2), 236-249.
Van Meldert, B., & De Boeck, L. (2016). Introducing autonomous vehicles in logistics: a review from a broad perspective. Faculty of Economics and Business, Leuven, Belgium.
Yu, Y., Wang, X., Zhong, R. Y., & Huang, G. Q. (2016). E-commerce logistics in supply chain management: Practice perspective. Procedia Cirp, 52, 179-185.
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