Industry 4.0 readiness is how readily organizations can use fourth industrial revolution technologies [1]. Industry 4.0 development changes the management of business operations and leads to new strategic thinking [2]. New business strategies are accompanied by new business models [3]. This strategic adjustment requires new configurations of products and processes [4]. Strategies no longer depend on the traditional competitive model. Instead, they are linked to customer expectations and experiences and focused on emerging consumer ecosystems [5]. Organizations are redefining their strategies for Industry 4.0. According to Ghobakhloo et al. [6], there must be strategic alignment with the new management paradigm. The technological trends of Industry 4.0 must be balanced with new models that integrate sustainable manufacturing [7], smart companies [8], and circular economies [9,10]. This strategic alignment should recognize clients’ needs, generate competitive advantages, and facilitate exploring and exploiting new opportunities [11]. The convergence of Industry 4.0 and artificial intelligence is revolutionizing organizational management by providing real-time information, automating repetitive tasks, and enhancing strategic decision-making. These technologies are increasing the efficiency, effectiveness, and adaptability of management in response to an ever-changing business environment.

For Tang and Veelenturf [12], automation is accompanied by self-management in the fourth industrial revolution, unlike the third industrial revolution, where automation requires programming. Self-management is complex and requires all process information to be available in real time. Real-time process information requires an interconnected ecosystem between all the individuals and objects within Industry 4.0 business models [13]. For Morawski and Ignaciuk [14], machines or processes must process and analyze all data to self-manage. Self-management requires multidisciplinary technologies that clarify the complexity of Industry 4.0 [15]. Interconnecting smart factories requires information and communication technologies (ICT) [16], such as cyber-physical systems (CPS), cloud computing (CC), the internet of things (IoT), and artificial intelligence [17]. Industry 4.0 will impact all human endeavors, including medicine, industrial maintenance, construction processes, agriculture, computer problem-solving, commerce, and society [18]. Industry 4.0 forces individuals to learn new skills and abilities, and technological transitions create new business models, jobs, and opportunities [19]. Industry 4.0 has various major benefits, including real-time integration of operations, cost reduction, energy efficiency, sustainability [20], increased flexibility, increased productivity, higher return on investment, and better performance management systems [21].

For Colli et al. [22], Industry 4.0 business models must adapt to the architecture of the digital world, which poses new challenges for organizational teams (hierarchical, projects, innovation). Additionally, more complex digital architectures require modification of strategic behaviors [23]. Furthermore, market instability forces structural and typological analysis of commercial strategic models and leads to better strategies [24]. Business strategies drive organizational design and dictate information system infrastructure [25]. Innovation strategies in many organizational structures are considered cost centers that are difficult to limit and measure. However, Industry 4.0 business models require separate growth incubators from the other organizational teams because they are essential for generating future value for customers and the company.

Industry 4.0 technologies are creating open innovation strategies. According to Sahi et al. [26], this model grows businesses by facilitating the exploration and exploitation of technological knowledge through corporate collaboration networks and synergy between suppliers, clients, or partners for business growth [27]. Open business models and innovation strategies are the definitive paths toward Industry 4.0 business models [28].

The new digital business models (Google, Instagram, Airbnb, YouTube, Meta, Uber, Spotify, etc.) have high market value. Conversely, traditional supply chains base their sustainability on experience, learning curves, constant investments in infrastructure, equal legal frameworks for all, etc. [29]. These supply chains should not ignore the need to adopt Industry 4.0. For Arromba et al. [30], digital transformation requires a model of critical management competencies, which considers customer relationship strategies, adaptation and collaboration strategies for agile and flexible responses, innovation strategies, and marketing strategies [31]. Digital transformation leads to measurable positive results and facilitates strategic business objectives [32]. Legacy industries must adjust their management strategies to utilize new digital architectures effectively and successfully compete with new digital business models [33].

Traditional supply chains require strategic adjustments for the adoption of Industry 4.0. Through SLR, it was possible to identify authors who have raised important strategic trends for Industry 4.0 [13,35,36]. These trends were grouped into subgroups to classify thematic axes, research topics, and strategic objectives. Then, through an SLR analysis and based on the experience and knowledge of the authors of this study, 7 strategic objectives, 10 thematic axes, and 28 research topics were determined. The seven strategic objectives identified are part of the organizational strategic alignment perspectives necessary for adopting Industry 4.0.

The knowledge and concepts within our seven objectives provide a clear vision and strategy for medium- and long-term Industry 4.0 adoption plans. These plans determine strategically aligned operating and cost projections for Industry 4.0.

New business models for Industry 4.0 must clearly define objectives, prioritize efficiency, and optimize value chains [16]. In digital business, technologies generate unique value propositions and experiences that differentiate companies and give them a competitive advantage. Business model design starts with planning and alignment with a company’s global strategy. Identifying and evaluating obstacles, critical factors, and internal and external barriers related to new digital businesses are part of the initial strategic analysis [2,38,39]. Implementing and managing digital services requires a new organizational structure, cultural change, new responsibilities, and greater emphasis on ICT strategic decisions.

The new business models focused on digital services will be based on customer satisfaction, so the customer experience must be central to the digital strategy. Industry 4.0 requires companies to implement innovative strategies that constantly redesign the portfolio of products and services and improve customer-organization interaction throughout the value chain. The most effective way to formulate digital strategies is to involve corporate leaders (owners, shareholders, managers, boards of directors, or those in charge of digital processes) so the strategy is aligned with business values and objectives.

Traditional supply chains must constantly innovate to overcome challenges and stay competitive by embracing digital transformation. Industry 4.0 democratizes technology and makes all members an integral part of its operation. The focus of Industry 4.0 is the client and their requirements, which directs the implementation of digital technologies and operating systems.

The COVID-19 pandemic caused market and industry disruptions, forced many companies to look for new ways of doing business to survive, and accelerated the adoption of digital innovation strategies. Many companies and SMEs have had to quickly adapt to this new reality and accelerate their digital transformation. Depending on a company’s size, policy changes should be aimed at product or process innovation. Process innovation is the most appropriate for SMEs. According to Wamba and Queiroz [48], some companies, especially SMEs, hesitate to implement Industry 4.0 technologies because they require large investments in infrastructure and technology, impact their business model, and require specialist knowledge and skills.

The implementation of Industry 4.0 depends on the specific knowledge of each technology and its benefits. All hierarchical levels of the organization are responsible for acquiring this knowledge and skills, especially managers, who must design training policies and invest in technology. The periodic evaluation of digital ecosystems allows us to understand their potential and real benefits. The definition of policies and regulatory frameworks will facilitate knowledge transfer among Industry 4.0 participants, including smart companies, technology providers, and universities. Digital business models generate changes in organizational cultures and must be accompanied by robust knowledge management policies.

Skills and organizational capacities allow business activity development from the available resources. The strategy dictates the development and assignment of these skills and abilities [13]. Therefore, organizations must define internal and external competencies to optimize their strategic objectives. Supply chains with comprehensive sustainable manufacturing/circular economy/smart manufacturing models generate competitive advantages [95].

Few organizations have defined a transformation strategy for their digital ecosystems to adapt to Industry 4.0. Those organizations that develop digital strategies will gain competitiveness and market share in the future. Smart factories are changing the industrial landscape and require new capabilities for information processing. Data management, visibility, and availability allow faster decision-making. ICT and digitally interconnected ecosystems for real-time data processing are the main basis of Industry 4.0. Industry 4.0 technologies such as IoT, CC, CPS, additive manufacturing, augmented reality, etc., and artificial intelligence with machine learning and deep learning require new skills and human capabilities.

Digital skills facilitate product innovation and customization, value chain digitalization, process optimization, and data-driven decision-making. Together, these factors generate a competitive advantage among supply chains. The new technological skills should help achieve the maximum benefit with the least investment without losing sight of an organization’s needs.

According to Vereycken et al. [101] effective HRM implementation requires understanding the impact of Industry 4.0 on the workforce, analyzing workforce requirements, and measuring the impact on organizational effectiveness. Additionally, successful implementation depends on an organization’s ability to attract, retain, and develop its workforce and foster a culture of innovation and lifelong learning [99]. Implementing, operating, and maintaining digital technologies requires training or hiring HRM with novel skills and abilities. These HRMs need to be planned at all organizational levels. Organizational culture change must occur throughout the value chain and focus on digital ecosystems. Managing the Industry 4.0 workforce is complex; training models must be aligned with the workforce’s education, and well-being must be the center of transformation policies. Innovation strategies in the work environment must accompany the connection between the virtual model and the physical environment and workforce skills qualifications. Innovation and qualification ensure the operational digitization of the value chain. Likewise, it is necessary to facilitate a work environment that promotes an open mentality oriented toward learning, change, and experimentation.

Fast responses to changes in customer demand require effective use of technological resources [73]. The implementation of Industry 4.0 requires the gradual introduction of new technologies or systems [40]. Optimizing service levels in Industry 4.0 requires new strategies, technologies, and information systems. These strategies must evaluate the digitization of the value chain, assess whether the products/services are sustainable, and facilitate product customization by the client.

Interconnected ecosystems and absorption capacity define the service level in a smart factory. However, despite the multiple advantages of industrial digitization, several challenges remain, such as real-time availability and accessibility of data, data protection, data bias, storage, processing, integrated communication protocols, auditing, transmission speed, information security, and data quality.

Major outstanding concerns in preparation and evolution models include immature industrial digital technologies, integrating multiple pieces of equipment from different vendors with different communication capabilities and network technologies in a single ecosystem, and high cyber security standards. The service level depends on linking the virtual model and the physical environment with variables such as optimization of investment in technology, market and industry disruptions, digitization of the value chain and its ability to analyze big data, customer experience, client-organization integration, product portfolio innovation, digital ecosystem, business opportunities, product design, and human-centered processes, among others.

Few companies have adopted technological innovation strategies that facilitate successful digital process transformation and generate new business models. Most organizations use technological innovation strategies for operational improvements and customer experience.

Industry 4.0 technologies can support production by implementing different capabilities depending on the needs of the production system. The ITC readiness concept defines how companies can exploit and benefit from technologies. According to Mittal et al. [75] a term adjacent to readiness is ripeness. Readiness can be distinguished from maturity; readiness is assessed before maturation, and maturity is assessed following technological implementation [107]. Developing a digitized lean manufacturing system is a viable business strategy for corporate survival in the Industry 4.0 environment [49].

Interconnected ecosystems depend on different internal and external knowledge sources. These collaborative networks promote business innovation and knowledge sharing about technology adoption impact, challenges, and benefits. Industry 4.0 requires open innovation strategies, technological investments, and internal and external ideas. These ideas depend a lot on the ability of everyone to transfer their knowledge to organizations, and this transfer depends a lot on HRM management practices. Idea contribution is essential for the success of technological innovation strategies.

Interconnected ecosystems depend on the type of digital business model and big data management. Digital ecosystems must be evaluated and analyzed using readiness and maturity models to optimize investments in technology. ICT computer security is a critical control point to protect big data from products and processes. Although Industry 4.0 requires vertical and horizontal integration and digitization strategies, it is important to highlight the common benefits of strategic alliances. Consequently, selecting a team of vendors with proven technologies is critical when building a network of partners. Connecting the virtual model and the physical environment must occur with the correct selection and implementation of technologies. Poor decision-making will slow the adoption processes and generate cost overruns. Digitizing each of the activities in the value chain should be the objective of any company. Companies must also focus on customer satisfaction, requirements, and human-centered design. Developing robust ICT digital interconnection ecosystems is essential for the survival of SMEs that aspire to be part of the fourth industrial revolution.

A concept introduced by Cohen and Levinthal [123], absorption capacity measures an organization’s ability to absorb external knowledge over time. A more current concept is an organization’s ability to detect, integrate, and take advantage of internal or external knowledge. Absorption capacity assigns value to an organization’s ability to capture, assimilate, and use knowledge to develop a competitive advantage [6]. Absorption capacity refers to an organization’s acquisition or assimilation of information and its ability to exploit it [124]. Big data analysis improves learning curves by adjusting management decision-making processes. According to Müller et al. [37], absorption capacity is important for organizational innovation. Information and communication technologies in Industry 4.0 (ICT 4.0) are essential in order to increase absorption capacity and facilitate the creation of innovation strategies [72].

Acquiring knowledge and information depends on a company’s ability to generate and manage big data acquired from its products and processes. As a result, the articulation of knowledge in Industry 4.0 is closely linked to the technological innovation strategies adopted by each company. This adaptation relies on various factors, such as the availability and transfer of technology, optimization of technology investments, entrepreneurial capacity, economic needs of the organization, and hiring expert personnel for technology adoption processes. These essential elements enhance absorption capacity, facilitating the development of new products, product customization, and process optimization. Absorption capacity can be measured using the four-dimensional scale suggested in [125] proposed and validated by [126].

The capacity for knowledge assimilation is partly determined by an organization’s structure, specifically its configuration and communication processes. Organizational structures with high levels of complexity typically offer limited opportunities for vertical communication processes. Likewise, horizontal communication processes with other business units may be reduced, often resulting in collaborative processes that are either forced or necessary due to the situation.

The capacity for transformation defines the internalization processes of external knowledge. Absorption capacity drives internal and external dynamics, accumulating knowledge and developing organizational learning processes. This process impacts the organization’s present and future activities, as external knowledge leads to internal knowledge development. An individual’s ability to transfer knowledge to the organization largely depends on developing policies at the HRM level. The implementation of ICT 4.0 drives new teaching and learning mechanisms. However, much literature is still needed to help define the processes required to transform this knowledge into innovation. The relationship between absorption and innovation capacity is partially mediated by organizational learning capacity [127]. The SLR suggests that this ability is complementary to the assimilative ability, and organizational structures aid in articulating and disseminating knowledge. Learning mechanisms serve as channels through which companies assimilate and internalize knowledge. Exploitation capacity measures the application of knowledge once information has been acquired, assimilated, and transformed. These activities allow for decision-making based on data and support the input for innovation strategies. Exploitation is when knowledge produces tangible outcomes such as new products or services. Customer organization and market interactions drive innovation processes and product portfolios. The SLR highlights the importance of R+D+I strategies in absorptive capacity. However, implementing these strategies requires economic investments, development times, and specialized human resources, which are often the main barriers to their development for most companies. Therefore, government policies that offer incentives and support are essential due to companies’ lack of capabilities and economic budgets to invest in these ideas.