February 09, 2026
Automation and operational efficiency with packaging machines in e-commerce
Kelly Rui Liu; Sarah de Oliveira
Summary prepared by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing.
This study analyzed the impacts of implementing packaging machines in an e-commerce company, evaluating their effects on operational efficiency, cost reduction, and the quality of logistics processes. The research quantified and qualified the transformations resulting from the transition from a manual packaging system to an automated model, integrated with artificial intelligence systems, in order to provide concrete evidence on the benefits and challenges of this modernization.
The relevance of the research lies in the growth of e-commerce, which pressures logistics operations for greater agility, precision, and economy. In this scenario, packaging is a critical step; bottlenecks and human errors can compromise the supply chain and customer satisfaction. The literature points to automation as a strategic solution. Studies such as Gross et al. (2022) demonstrate that automated equipment aims to meet the growing demands of the sector, ensuring greater precision and economy. Automation is, therefore, a determining factor for the sustainability and scalability of operations in a digital market.
The transition to automated processes is linked to the concepts of Industry 4.0, which advocates the integration of digital technologies to create intelligent production systems. De Souza and Bonette (2019) highlight that, through technologies such as the Internet of Things (IoT), equipment can interact, optimizing workflows and reducing operational failures. This approach allows for rigorous control over production, from precise weighing and sealing to packaging standardization, which facilitates stages such as storage and transportation. Automation represents a strategic reconfiguration that aligns logistics operations with consumer demands for speed and reliability.
The reduction of operational costs is one of the main drivers for the adoption of automation. Ouverney and Martins (2018) state that the replacement of manual processes with intelligent systems reduces the dependence on labor for repetitive tasks, allowing for a more strategic allocation of human resources to activities with higher added value. In addition to payroll savings, automation impacts the reduction of material waste, such as plastic films and labels, and the decrease in rework. This resource optimization improves profit margins and contributes to more sustainable operational practices.
Despite the benefits, the implementation of automated systems presents challenges. Socca Junior (2024) points out that initial investment costs and the need to adapt internal processes can be significant barriers. The success of the transition depends on training the team to operate and maintain the new equipment. Change management becomes essential, requiring planning, adequate training, and transparent communication to mitigate resistance. Computational modeling, as suggested by Leal et al. (2024), can be a valuable tool to simulate scenarios, predict obstacles, and optimize the implementation process, minimizing risks and maximizing return on investment.
The methodology adopted was an applied research, with a mixed approach (qualitative and quantitative) and descriptive purpose, using the case study as an investigative strategy. This choice, aligned with Gil (2021), is effective for understanding complex phenomena in their real contexts. The study was conducted at Empresa Alfa (fictitious name), a medium-sized e-commerce company for household products in São Paulo, which recently migrated from manual packaging to an automated system. The company selection followed rigorous criteria, according to Lakatos and Marconi (2021), including: consolidated presence in e-commerce, order volume that justified automation, and a clear history of manual processes before the change. This scope allowed for a robust comparative analysis between the pre- and post-automation scenarios, maintaining the confidentiality of the participants.
Data collection was structured in three complementary stages. The first was a documentary survey of operational reports and performance indicators (KPIs), such as packaging time, error rate, and costs. The second involved a structured questionnaire applied to 50 logistics area employees. The third stage consisted of semi-structured interviews with managers to capture qualitative perceptions about the transition. For the analysis, quantitative data were treated with descriptive statistics (means, frequencies, percentage variations), while qualitative data were subjected to content analysis to identify patterns. The integration of these two datasets, a practice recommended by Lakatos and Marconi (2021), allowed for a rich interpretation of the results, cross-referencing performance metrics with professionals’ experience.
The results reveal a multifaceted and positive impact of automation. In terms of operational efficiency, the data indicated an approximate 18% increase in overall sector productivity. This metric was corroborated by employee perception, where 70% reported that the time to pack orders decreased considerably, and 90% agreed that automation improved team productivity. This gain in agility is fundamental in e-commerce; processing speed is a critical factor. Automation eliminated repetitive manual tasks and optimized the workflow, allowing for the processing of a larger volume of orders with the same structure.
The quality and standardization of processes also showed expressive improvements. Around 87% of respondents perceived a significant improvement in packaging standardization. This uniformity enhances product presentation and generates logistical benefits, such as optimization of storage space and reduction of damages. The precision of automated systems resulted in a 22% decrease in rework, with 65% of employees reporting a sharp drop in corrections and repackaging. This reduction in operational errors aligns with the conclusions of Socca Junior (2024), who associates automation with an increase in production reliability.
In cost and waste reduction, the results were equally positive. The analysis of operational reports pointed to a 15% decrease in material waste, such as plastic films and labels. This saving is confirmed by the perception of 73% of collaborators, who indicated a reduction in input consumption. The precision of the equipment avoids the excessive use of materials and minimizes losses, generating financial impact and contributing to a more sustainable operation. Consequently, 77% of participants believe that the company achieved significant financial gains, corroborating the analysis by Gross et al. (2022) on the profitability of investments in packaging technologies.
The human factor was central to the analysis. The data show that the transition management was successful, with 83% of employees reporting having received adequate training. Although 60% faced initial difficulties, 90% reported being fully adapted to the new model at the time of the survey, suggesting that the training was effective. The active participation of 80% of employees in the transition process was also a key factor, promoting engagement and reducing resistance, in line with the recommendations of Leal et al. (2024) on the importance of continuous training.
The impact of automation has extended throughout the entire logistics chain and customer experience. Approximately 78% of employees noticed an increase in the speed of order preparation and shipment. Furthermore, 70% reported a decrease in returns and complaints due to packaging errors. This data demonstrates that the improvement in internal quality has translated into a more reliable service for the consumer. The improvement in workflow control, indicated by 87% of collaborators, suggests that automation has provided greater operational visibility and predictability, aligning with the analyses of De Souza and Bonette (2019).
The overall evaluation of the initiative by collaborators reinforces the success of the implementation. A majority of 83% consider that automation brought more benefits than problems. Only 7% pointed to a predominance of difficulties. Confidence in technology is high, with 93% of employees showing themselves favorable to replicating automation in other sectors, recognizing its potential to increase efficiency. This positive perception indicates not only the technical effectiveness of the solution but also the consolidation of an organizational culture open to innovation.
The findings of this study are in line with international literature. Kustiyawan et al. (2023), for example, demonstrated that the optimization of packaging machines can increase the Overall Equipment Effectiveness (OEE) from 30.49% to 67.67%, a performance leap that mirrors the productivity gains observed at Empresa Alfa. Similarly, Zhao et al.’s (2024) research found that increases in the density of industrial robots result in growth of labor productivity, validating that technological intensification is a driver of efficiency. The transition from a manual to an automated process represents a drastic increase in technological density, justifying the observed gains.
The articulation between quantitative and qualitative data allowed for a detailed panorama of the transformation. Automation not only accelerated processes and reduced costs, but reconfigured work routines, demanded new skills, and strengthened quality control. Success at Company Alfa can be attributed to an integrated approach, which combined technology with careful change management planning, including employee training and involvement. This synergy between technology, processes, and people was fundamental to overcoming initial challenges and consolidating long-term benefits.
The data analysis found that packaging automation promoted significant and positive impacts on the organization’s operational, logistical, and economic dynamics. Gains in efficiency, packaging standardization, rework reduction, and waste minimization were widely confirmed. The combination of technology and professional training emerged as an essential factor for the success of the transition, with the broad acceptance of automation by employees evidencing the organizational maturity to absorb innovations. It is concluded that the objective was achieved: it was demonstrated that the implementation of packaging machines was decisive in raising efficiency levels, reducing operational costs, and qualifying the logistics processes of the analyzed company. The integration of technology, strategy, and human training proved central to the initiative’s success, reaffirming automation’s role as a structuring instrument for sustainable growth in companies operating in the e-commerce environment.
References:
Almeida Junior, E. S; de. Reduction of setup time applied to the packaging of a chocolate factory. 2024. Course Conclusion Work (Bachelor of Mechanical Engineering) – Federal Technological University of Paraná, Pato Branco, 2024.
De Souza, J. V. Z., & Bonette, L. R. (2019). Impacts of industry 4.0 and its operational autonomy in automation processes, through the internet of things. Revista Gestão Industrial, 15(4).
Gil, A. C. (2021). How to Do Qualitative Research. São Paulo: Atlas.
Gross, F. et al. (2022). Automation of the Bacon Packaging Process in Cubes in a Cooperative in Western Paraná. Revista Pleiade, 16(35), 68-78.
Kustiyawan, I.; Roestan, M. R.; Riani, C. (2023). Automated Packaging Machine Analysis with the Overall Equipment Efficiency Method. International Journal of Industrial Engineering & Production Research, 34(4), 1–14.
Lakatos, E. M., & Marconi, M. A (2021). Methodology of Scientific Work. 9th ed. São Paulo: Atlas.
Leal, L. K. B. et al. (2024). Computational analysis and modeling of the adhesive tape manufacturing process in a company in the Manaus Industrial Pole: an application in Flexsim® software. Exacta, 22(1), 197-237.
Ouverney, K., & Martins, D. M. S. (2018). Automation of Software Implementation Process using Docker and microservices – A Case Study. Seminars of Course Conclusion Work of the Bachelor of Information Systems, 3(1).
Socca Junior, J. R. (2024). Supply chain management: impacts on organizational competitiveness and implementation challenges. Revista Tópicos, 2(13), 1-17.
Zhao, H.; Li, X.; Wang, Y.; Chen, Z. (2024). Impact of industrial robots on labour productivity: Empirical evidence from 17 Chinese industries. Technological Forecasting and Social Change, 198, 1–12.
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