Digital Transformation and Operational Efficiency in Fast-Moving Consumer Goods Companies: A Systematic Review of Industry 4.0 Applications

A systematic review was conducted in accordance with the PRISMA 2020 guidelines, with the aim of identifying empirical evidence on the influence of digital transformation and advanced technologies on operational efficiency in FMCG companies. The process was designed to ensure transparency, reproducibility and methodological rigor throughout all stages of identification, screening, eligibility assessment and final inclusion of studies [25, 26].

The search was carried out in the Scopus database, selected for its comprehensive coverage of scientific publications in the fields of business, management and technology. To operationalize the search, a Boolean equation was constructed to reflect the core concepts of the review: TITLE-ABS-KEY (“digital transformation” OR “Industry 4.0” OR “automation” OR “artificial intelligence” OR “robotic process automation” OR “computer vision”) AND (“FMCG” OR “fast moving consumer goods” OR “consumer goods”) AND (“operational efficiency” OR “performance” OR “productivity”). This formula made it possible to capture studies situated at the intersection of emerging technologies and operational performance.

The initial search generated 1,572 records. To ensure relevance and recency, filters were applied to include only articles published between 2019 and 2024, written in English, classified under the Business, Management and Accounting subject area, available in open access and published as final-version scientific articles. After automatic filtering, 427 records remained. Duplicates and studies lacking thematic relevance were removed through a title and abstract review, resulting in 41 articles selected for full-text assessment. Sixteen of these were excluded for not meeting the methodological or thematic criteria, resulting in a final sample of 25 empirical studies. Figure 1 presents the PRISMA 2020 flow diagram summarizing the complete selection process, and Table 2 details the inclusion and exclusion criteria applied at each stage.

The methodological quality of the included studies was appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, which provides structured criteria for evaluating both qualitative and quantitative designs. This assessment considered the clarity of the research objectives, the appropriateness of the study design, the adequacy of sampling procedures, the validity of data collection instruments, the transparency of analytical methods and the consistency between the evidence presented and the conclusions reported. All selected studies met the minimum quality thresholds established by the JBI criteria.

Table 2. Inclusion and exclusion criteria applied in the review

Once the eligible studies were confirmed, a structured data extraction process was implemented. A coding sheet was developed to ensure systematic extraction and organization of key analytical variables. The coding categories included: (a) the type of digital technology analyzed, such as AI, automation or computer vision; (b) the country in which the study was conducted; (c) the size of the firms examined; (d) the methodological approach adopted (quantitative, qualitative or mixed); and (e) operational efficiency indicators, including error reduction, cycle time, forecasting accuracy or resource optimization. Table 3 outlines the variables and coding rules applied during this stage. This structured approach facilitated a consistent comparative analysis of heterogeneous evidence and supported the synthesis presented in the Results section.

Overall, the methodological design ensured a rigorous and transparent review process, enabling the identification of dominant technological trends, recurring operational impacts and contextual determinants that shape the effectiveness of digital transformation initiatives within FMCG companies. Although the selected studies vary in their specific topics, all of them met the predefined eligibility criteria by providing empirical or systematized evidence relevant to digital transformation processes, operational performance or organizational efficiency. Their inclusion is justified because each contributes conceptually or empirically to understanding how technological innovation influences efficiency outcomes within FMCG environments or comparable production settings.

Following the recommendations of PRISMA 2020, a structured data extraction process was carried out. For each included study, key analytical variables were coded to ensure consistency and transparency in the synthesis. The variables were selected according to the objectives of the review and the dimensions most frequently used in studies evaluating digital transformation and operational efficiency.

Table 3. Coding sheet used for data extraction (variables and coding rules)

Table 3 presents the coding scheme used, including the variables extracted, their definitions, coding options, and an illustrative example. This coding sheet provided the basis for organizing the evidence and conducting the subsequent analysis.

The evidence synthesized in this review shows that digital transformation is a fundamental element in strengthening operational efficiency in FMCG companies, although its contribution is not uniform and depends on technical, organizational and contextual factors that determine the magnitude of its effects. The selected studies agree that technologies such as AI, advanced automation, computer vision and big data analytics have enabled the optimization of essential processes in areas such as planning, production, quality inspection and logistics management. However, the results also reveal significant variations between companies and countries, suggesting that digital transformation, although necessary, is not sufficient on its own to guarantee consistent improvements in efficiency [28, 29].

A central aspect of the discussion concerns understanding how the operational nature of the FMCG sector influences the effects of digitalization. Various studies indicate that this sector presents high levels of turnover, short product life cycles and volatile demand, which force companies to manage processes with greater precision and speed than those observed in other industrial environments [1, 18]. For example, AI systems applied to demand forecasting allow real-time adjustments to production volumes to prevent both stockouts and overproduction, a particularly critical task for perishable or seasonal products. The literature presents cases in which predictive models based on machine learning have reduced forecasting errors by up to 20%, improving coordination between procurement, storage and distribution. These benefits are not trivial in a sector where small variations in planning can generate significant losses due to product expiration, returns or additional logistics costs [30, 31].

Computer vision is another example of a technology whose operational impact has been documented in concrete terms. Studies such as those by Tang et al. [17] and Majerník et al. [32] show that automated inspection of production lines makes it possible to detect microdefects in packaging, incorrect printing, deformities or adhesions that previously required manual inspection. These findings illustrate in a tangible way how digitalization can affect specific functions of the FMCG sector that require high levels of precision [32, 33].

RPA is another key element for understanding differences in reported outcomes. The literature indicates that automation systems such as collaborative robots or RPA solutions have replaced repetitive tasks traditionally carried out by operators, ranging from labelling to inventory verification [21, 29]. For example, some companies use autonomous mobile robots to transport inputs between production zones, reducing manual movements and downtime. There are also documented cases of automation in order preparation, where intelligent algorithms assign optimal routes and priorities perishable products. However, not all companies experience similar improvements, as automation also introduces challenges related to staff training, role reassignment and cultural resistance to interacting with intelligent systems [7, 34].

When analyzing the factors that limit the effectiveness of digital transformation, the literature shows that the lack of internal digital capabilities is one of the most relevant obstacles. Studies such as those by North et al. [12] and Kusnandar et al. [20] indicate that many companies do not have personnel with competencies in data analysis, programming, digital systems design or advanced technology maintenance. This is observed more markedly in small and medium-sized enterprises, which cannot afford high investments or hire specialists in emerging technologies. In these cases, advanced systems are often implemented partially or superficially, reducing their potential benefits. For example, some studies show companies that acquire AI systems but lack sufficiently robust data infrastructure, which prevents the models from being fed with accurate and up-to-date information. As a result, the systems fail to generate reliable predictions and end up being underutilized [3, 35].

Differences between geographical contexts also generate important variations. Research conducted in developed countries tends to report substantial improvements in efficiency, especially when traceability technologies, IoT sensors and intelligent storage systems are integrated [18]. In these settings, companies use integrated automation networks to control inventories in real time and coordinate logistics routes using predictive data, reducing delivery times and waste. In contrast, studies carried out in emerging economies, such as those by Annarelli et al. [36] and Okrah [37], indicate that many companies lack stable connectivity, digital infrastructure and financial capital to acquire or maintain advanced technology. Cases are documented in which production lines continue operating with manual or semiautomated processes, considerably limiting the effects of digital transformation on operational efficiency. This confirms that the impact of emerging technologies is strongly conditioned by the socioeconomic environment and not solely by the technical characteristics of the tools [2, 4].

The literature also shows that the effects of digitalization on internal labor dynamics should not be underestimated. The transition towards more automated production models often generates tensions related to job security, the redefinition of roles and the need to acquire new competencies [38]. In some cases, the incorporation of digital systems has created uncertainty among line workers, who perceive that their functions could be replaced by algorithms or robots. These tensions affect the acceptance of technology and can slow its implementation. Studies agree that when organizations invest in training and clearly communicate the objectives of digital transformation, these forms of resistance decrease and operational benefits increase [9].

Another relevant aspect identified in this discussion concerns the role of explainability in AI systems. Although various studies highlight the potential of AI to optimize processes, none of the selected research analyses the use of explainable models or mechanisms of algorithmic transparency [39, 40]. In sectors where errors may have significant consequences, such as the food or hygiene products industry, the lack of interpretability limits trust in automated systems. For example, when a classification algorithm detects a defect in a production line but does not explain its reasoning, supervisors may be reluctant to make decisions based on results that they do not fully understand. This gap reveals a relevant opportunity for future research aimed at developing explainable AI systems adapted to mass manufacturing environments, where transparency could increase the effectiveness and adoption of these technologies [41].

It is important to note that this gap is not anecdotal but systematic. Among all the studies included in this review, none explicitly reported the implementation of explainable AI techniques, nor did they include any methodological component related to model transparency. This absence across the entire sample of reviewed studies reinforces the claim that explainable artificial intelligence (XAI) remains largely unexplored within the FMCG-focused digital transformation literature [28].

In addition, the literature reviewed suggests that the link between digitalization and sustainability deserves greater attention. Although studies such as those by Lombardi et al. [33] and Bor et al. [42] indicate that digitalization can reduce waste, optimize resource consumption and promote cleaner processes, they also warn that the acquisition and operation of advanced technologies involve environmental costs associated with hardware production, energy consumption and electronic waste management. For example, some computer vision systems require high-resolution cameras and dedicated servers to process images, which can significantly increase energy consumption if not managed appropriately. This tension between operational benefits and environmental impacts suggests the need for digitalization strategies that integrate sustainability criteria from the design, implementation and evaluation stages [29].

The transversal integration of digital systems also emerges as a determining factor for success. Studies show that companies that implement technologies in isolation obtain limited benefits, whereas those that adopt integrated models achieve broader improvements in efficiency. For example, when companies combine AI-based demand forecasting systems with real-time inventory platforms and automation technologies in production, cycle times decrease and synchronization between areas improves noticeably [15]. However, integration requires significant investment and advanced organizational capabilities, which explains why many companies remain in the early stages of digital maturity.

In addition, the implications derived from these findings operate at two distinct levels. At the managerial level, professionals should priorities workforce reskilling, strengthen digital capabilities, promote integrated technological adoption and ensure that digital initiatives are aligned with organizational strategies. At the policy level, governments and regulatory bodies must address structural barriers by investing in digital infrastructure, fostering national training programmes and providing incentives that facilitate technology adoption. Clarifying these two layers of implications is essential for understanding how digital transformation can advance at both organizational and societal levels.

In summary, this review confirms that digital transformation in the FMCG sector must be understood as a multidimensional process in which advanced technologies, human skills, organizational structure and contextual conditions interact to determine outcomes. Technological innovations, although promising, do not automatically guarantee improvements in operational efficiency if they are not accompanied by strategic management that considers the particularities of the sector, talent development and the structural limitations of each context. Future research could explore in greater depth the application of explainable AI, the development of digital maturity models adapted to FMCG companies and the environmental impacts of digitalization, in order to promote a holistic approach that facilitates the transition towards more efficient, responsible and sustainable production systems [43-46].