Manufacturing innovation for Industry 4.0: an innovation capability perspective

Astrid Heidemann Lassen, Maria Stoettrup Schioenning Larsen

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 25 December 2024

639

Abstract

Purpose

The number of small and medium-sized manufacturing companies that have successfully embraced the digital transformation envisioned by the Fourth Industrial Revolution (Industry 4.0) remains low. This paper argues that one reason is the significant innovation required in manufacturing systems to undergo such a transformation. This innovation demands capabilities vastly different from those traditionally employed for continuous improvements in manufacturing systems. The conventional development of manufacturing systems emphasizes resilience, robustness, and efficiency, typically thriving in stable and predictable conditions. However, developing a manufacturing system under highly complex and unpredictable circumstances requires new capabilities. We term this “manufacturing innovation”. At this stage, learning from successful cases is a valuable step towards unifying scattered evidence and developing coherent knowledge of how SMEs successfully do manufacturing innovation in the context of Industry 4.0.

Design/methodology/approach

We conducted a multiple case study involving seven small and medium-sized Danish manufacturing companies to investigate successful manufacturing innovation in the context of Industry 4.0. Cross-case analysis identified four critical propositions regarding the capabilities contributing positively to manufacturing innovation.

Findings

The research findings highlight various capabilities for successful manufacturing innovation in the context of Industry 4.0. They suggest that such significant digital transformation of manufacturing systems begins with radical innovations in enabling processes rather than core processes. A flexible approach facilitates it, often operationalized through iterative methods. Moreover, the accumulation of knowledge from previous manufacturing innovation initiatives forms a foundational basis for strategically approaching Industry 4.0, suggesting that experience in manufacturing development generally enhances the capacity to adopt Industry 4.0 technologies effectively.

Research limitations/implications

The results underscore the need for viewing digital transformation towards Industry 4.0 as a manufacturing innovation process, which relies on significantly different organizational capabilities than those supporting continuous manufacturing development. This insight has two implications for research in this domain; (1) Innovation process models must be developed to support radical systemic innovation, gradual learning and agile processes in manufacturing, and (2) Industry 4. 0 technologies enable new potential, but the actualization of this potential is dependent on organizational competences.

Practical implications

The findings also offer several practical implications. Identifying patterns of best practices provides much-needed inspiration and insight into how manufacturing innovation for Industry 4.0 may be approached. While we agree with studies showing that competencies are one of the biggest challenges for companies to get started, our results also suggest that by using a flexible approach, companies can build competencies gradually and as needed, which can yield the right results over time. Furthermore, the findings suggest that a specific starting point for manufacturing companies may be enabling processes rather than core processes. This new understanding of the types of solutions companies manage to progress with may suggest that the technologies here are more mature or that there is greater motivation to get started. This implication is supported by the result that a long-term strategy is needed, but that it must be operationalized into smaller solutions to avoid biting off more than they can chew initially. While other researchers have also pointed this out, we provide a deeper understanding of why it is necessary and how it can be operationalized.

Originality/value

The article is one of the first to make a qualitative study on multiple cases to understand how manufacturing companies successfully introduced manufacturing innovation for Industry 4.0.

Keywords

Citation

Lassen, A.H. and Larsen, M.S.S. (2025), "Manufacturing innovation for Industry 4.0: an innovation capability perspective", Journal of Manufacturing Technology Management, Vol. 36 No. 9, pp. 19-44. https://doi.org/10.1108/JMTM-09-2023-0414

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Astrid Heidemann Lassen and Maria Stoettrup Schioenning Larsen

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


Quick value overview

Interesting because: Despite the significant opportunities that Industry 4.0 offers, small and medium-sized enterprises (SMEs) have been slow to embrace the transformation, making it crucial to understand how to navigate this shift successfully. This research highlights the gap in existing literature on manufacturing systems, which often focus on stability and efficiency but overlook the need for innovation capabilities in the context of rapid technological advancements. The study provides valuable insights by examining seven successful cases, highlighting the unique complexities and requirements for manufacturing innovation in the Industry 4.0 context. It challenges traditional approaches to manufacturing system development and suggests a new perspective on how smaller firms can thrive in a rapidly evolving industrial landscape.

Theoretical value: This research introduces and expands on the concept of “manufacturing innovation capability.” It distinguishes this capability from traditional manufacturing systems development and process innovation, highlighting that the disruptive nature of Industry 4.0 demands unique strategies. The study identifies eight critical characteristics related to processes, resources, and values, leading to four central propositions on manufacturing innovation capabilities centered on gradual progression, adaptive processes, and the strategic accumulation of knowledge. These insights fill several gaps in the literature, particularly concerning how SMEs can manage the complexity and uncertainty inherent in Industry 4.0 transformations.

Practical value: The findings offer SMEs a blueprint for approaching Industry 4.0, emphasizing a flexible, iterative approach to innovation. The study suggests that SMEs should first focus on transforming enabling processes, rather than core manufacturing processes, to manage risks and build on existing knowledge bases. This approach allows SMEs to gradually develop the necessary competencies and navigate the complexities of Industry 4.0 without overwhelming their resources. Additionally, the research highlights the importance of balancing technological advancements with organizational development, advocating for continuous upskilling of employees, and fostering a culture of innovation. The emphasis on long-term strategic alignment over short-term financial metrics provides practical guidance for SMEs to prioritize sustainable growth and successfully integrate Industry 4.0 technologies.

1. Introduction

Manufacturing systems have evolved significantly over the decades, driven by continuous advancements in production technology, machine tools, information technology, materials science, and strategic organizational frameworks to minimize costs, enhance product quality and reliability, and maximize overall productivity and profitability. Manufacturing systems encompass the physical infrastructure, such as machinery, workstations, robots, and interconnected material handling equipment, and the informational architecture integrated through, e.g. production planning systems. Also, people and competencies are central parts of manufacturing systems, contributing crucially to their design, planning, operation, and ongoing refinement.

We have entered the fourth industrial revolution, Industry 4.0, which signifies a transformative shift towards interconnected, collaborative, and globally integrated manufacturing systems. This transformation aims to empower manufacturing systems to autonomously plan, operate, and execute processes with unprecedented efficiency and flexibility. The potential benefits are vast, ranging from enhanced operational efficiency and cost reduction through automation to the ability to offer mass customization and ensure superior quality and reliability through predictive analytics and data-driven insights (; ; ). As such, the term Industry 4.0 has become synonymous with adopting digital and real-time technologies in manufacturing processes and shifting to smart factories (). The key driver and fundamental characteristic of Industry 4.0 is digital transformation at a systemic level rather than at a single-process level. Systemic change is integrative as it affects multiple processes, technologies, and units within the organization and beyond.

However, empirical observations suggest that despite the considerable potential attributed to Industry 4.0 and widespread interest in adopting advanced technologies in manufacturing, few small and medium-sized enterprises (SMEs) have successfully transformed their manufacturing systems towards Industry 4.0 (; ; ). Scholars have extensively studied the barriers and challenges hindering SMEs’ adoption of Industry 4.0 (e.g. ; ; ), providing various insights.

One explanation is that SMEs often underestimate the complexity of Industry 4.0 solutions compared to earlier developments in manufacturing. A customary approach for companies is to prioritize the development of operational excellence by continuously refining execution capabilities. Operational excellence and incremental innovation/improvement are highly effective when companies follow predictable paths. This emphasizes established processes with lower uncertainty, serving as a cornerstone for maintaining competitive advantage in current markets—a critical element for ongoing success across most industries. However, in the era of Industry 4.0, marked by rapid technological advancements and disruptions, stability, and anticipated trajectories are seldom maintained. The inherent complexity stems from the systemic design requirements enabling process integration and autonomous decision-making based on data collection and data exchange in the manufacturing system (; ). This complexity leads to lower transparency, predictability, and increased risks in development processes. Consequently, the very approaches and capabilities that, under well-known conditions, have enabled success can now become vulnerabilities. As such, we argue that the digital transformation of manufacturing systems, with its inherent uncertainty and evolving knowledge base, demands distinct approaches and capabilities compared to incremental innovation. In this paper, we refer to this as “manufacturing innovation.”

What we find in the literature is that with rare exceptions (e.g. ; ; ; ; ; ), little appears on manufacturing innovation as a dedicated type of innovation. Instead, manufacturing innovation is most frequently addressed as, e.g. product innovation in manufacturing companies (e.g. ), development of stand-alone manufacturing technologies (e.g. ; ), or diffusion of technological innovation through eco-systems (e.g. ). This lack of dedicated knowledge on manufacturing innovation may also explain why manufacturing companies are struggling with the operational application of manufacturing innovation for Industry 4.0. Additionally, only a few articles provide insights into how SMEs successfully actualize manufacturing innovation for Industry 4.0. This is presumably due to the current lack of empirical evidence (; ).

This knowledge gap is interesting to address both from a theoretical and a managerial point of view. Theoretically, the explicit focus on innovation capabilities in manufacturing systems development will expand the knowledge base on manufacturing innovation as an independent concept requiring unique considerations. From a managerial point of view, such insights will inspire and encourage companies and provide best practices to approach manufacturing innovation. This article addresses the gap by examining seven cases of SME manufacturing companies that have successfully introduced manufacturing innovation. The research is guided by the following research question: “How do manufacturing SMEs successfully do manufacturing innovation for Industry 4.0?”

2. Theoretical foundations: manufacturing systems development and the need for innovation capabilities

When perceiving manufacturing system development through the lens of innovation management, it can be considered a form of process innovation. define process innovation as “the introduction of new elements into an organization’s production or service operations. These elements can include input materials, task specifications, work and information flow mechanisms, and equipment used to produce a product or deliver a service, all aimed at reducing costs and/or improving product quality” (p. 1). Despite the significant impact of process innovation on firm performance, it has received relatively little academic attention compared to product innovation. Most research has focused on product innovation, leaving process innovation largely overlooked (; ; ). Previous literature also indicates that process innovation primarily targets efficiency gains through cost reductions and increased production volumes.

While process innovation encompasses all business-related processes (e.g. ; ), manufacturing innovation distinctly focuses on the processes related to the manufacturing system. This aligns with , who defines manufacturing innovation as a particular type of process innovation: “a new method of producing an essentially established product by an essentially established process. Manufacturing innovation usually involves the installation of novel machinery and/or novel methods of controlling the manufacturing process. Very often, the efficient use of the innovation requires a variety of organizational changes, but changes in organization alone do not constitute manufacturing innovation.” (p. 247). also defines manufacturing innovation as a form of process innovation, specifically in production management (p. 30), a definition echoed by , who characterize it as innovation within the redesign of manufacturing systems (p. 411). This understanding is further reinforced by various scholars, such as and , who draw inspiration from definition of process innovation, tailoring it to manufacturing as “an organization-wide effort that involves fundamental rethinking and radical redesign of manufacturing related processes and systems to achieve dramatic improvements in manufacturing performance measures such as cost, quality, service, and speed” (, p. 480). Such definitions of manufacturing innovation emphasize a much more transformative nature than what is traditionally associated with process innovation. Moreover, how to do manufacturing systems development with high complexity, low transparency and predictability, and increased risks are underexplored.

Hence, in this paper, we take a point of departure in theory on organizational capabilities. In general, organizational capabilities refer to what an organization can or cannot do. Since the early 1990s, there has been ongoing discussion about the nature and importance of organizational capabilities. Organizational capabilities have been described as the abilities of firms to deploy their resources to achieve desired outcomes (; ). While the concept has been widely discussed in the literature, it remains somewhat ambiguous (). It is, however, widely acknowledged that companies must continuously develop new organizational capabilities, termed “dynamic capabilities,” to remain competitive. The concept of “dynamic capabilities,” introduced by , refers to a firm’s ability to renew resources and competencies in response to environmental changes. Numerous scholars have further developed this idea (e.g. , ; ; ; ). Amongst others, argue that dynamic capabilities and innovation capabilities are closely interlinked and that one could perceive innovation capability as a particular dynamic capability. This paper follows this line of reasoning and considers innovation capabilities as a central and dynamic organizational capability. This is also supported by , who assert that innovation capability is likely the most crucial capability a firm can have.

state that innovation capability is the “potential to generate new ideas, identify new market opportunities, and implement marketable innovations by leveraging existing resources and capabilities.” In particular, the latter part of this definition seems highly relevant when considering manufacturing innovation. In fact, most technologies needed to transform manufacturing systems digitally are already at a sufficient maturity stage – the innovation challenge concerns leveraging resources and capabilities to convert this knowledge into new solutions. conceptualize innovation capability as the ability to activate and combine knowledge, resulting in innovative products, services, processes, and systems. define innovation capability as the potential to create novel and valuable products or knowledge. describe it as the ability to continuously transform knowledge and ideas into new products, processes, and systems to benefit the firm and its stakeholders. From such definitions, we defer that innovation capabilities consist of reinforcing practices and processes within the firm that allow for generation, activation, combination, and transformation of knowledge into newness.

To understand how innovation capabilities unfold in organizations, identified four dimensions: employee knowledge and skills, technical systems, managerial systems guiding knowledge creation and control, and the values and norms associated with these processes. and further condensed this to three building blocks of organizational capabilities for innovation:

  • (1)

    Processes are the methods and activities used to transform inputs into higher-value outputs, including the organization’s patterns of interaction, coordination, communication, and decision-making.

  • (2)

    Resources include tangible and intangible resources such as people, equipment, technology, product design, brand, information, finances, and relations with external partners.

  • (3)

    Values are the criteria used for decision-making or the mindsets of the decision-makers

Our empirical research takes its point of departure in this understanding to identify how manufacturing SMEs do manufacturing innovation for Industry 4.0.

3. Methodology

To capture empirically how manufacturing SMEs do manufacturing innovation for Industry 4.0, we use case research to explore the innovation capabilities applied by SMEs that have succeeded in introducing Industry 4.0. Case study research is particularly suited for this purpose as manufacturing innovation in the context of Industry 4.0 is a contemporary phenomenon that has only been scarcely conceptualized. The suitability of case studies is described by as “Particularly well-suited to new research areas or research areas for which existing theory seems inadequate. This type of work is highly complementary to incremental theory building from normal science research. The former is useful in early stages of research on a topic or when a fresh perspective is needed, whilst the latter is useful in later stages of knowledge” (pp. 548-549).

The research focus further calls for the use of literal replication in case studies as the primary technique of reliable knowledge creation, as the focus is on identifying patterns of innovation capabilities. We ensure comparability by establishing homogeneity in the cases through the overall characteristics of the companies.

3.1 Case selection

This research is based on a multiple case study. The cases are selected purposively with the expectation of comparability, therefore strengthening our findings (). The selection criteria we applied for the cases are:

  • (1)

    Manufacturing company: We selected case companies with industry-standard classification NACE Rev2 C20-28. The companies have a high degree of commonality in (1) process structure complexity (high level of mechanization of production processes, some level of systemization of production processes and activities, and some degree of interconnection of production process tasks and stage; (2) product line complexity (multiple product categories with medium complexity of end-products; (3) manufacturing strategy (low cost) and (4) current employee competences profiles (mix of unskilled and skilled).

  • (2)

    Small or medium-sized enterprise (SME): This criterion is applied as we want to study manufacturing innovation in companies of this size due to the struggles in this context. We operationalize SMEs following the EU definition of staff headcount <250.

  • (3)

    Successful manufacturing innovation: This criterion is applied as we aim to understand the capabilities used to do manufacturing innovation. There is little joint agreement on a precise definition of success (). However, this study defines it as a combined process and output measure of actively progressing with, or already having implemented, a manufacturing innovation for Industry 4.0, which solves a complex problem. As the progress with manufacturing innovations for Industry 4.0 is generally low, we also consider the act of progress to be an expression of success. We further applied measures of perceived success, operationalized as the respondents’ perception of the manufacturing innovation: (1) meeting the envisioned goal of the innovation and (2) providing learnings for further innovation.

Based on a sample of 84 manufacturing companies that all participated in a large-scale research project on implementing Industry 4.0, we identified seven manufacturers that fulfill these criteria. The case companies are presented in with an emphasis on highlighting their comparability.

3.2 Data collection

The research of the seven cases employs a variety of case study tools, including documentation, interviews, and direct observations, to ensure the validity and reliability of the results. Documentation of the companies’ Industry 4.0 innovation strategy and digital maturity assessment sets the general context for understanding the specific company, while interviews yield particular knowledge on the research subject. To understand the capabilities of manufacturing innovation, we collected data specifically related to this. To this end, we follow categorization of innovation capabilities as resources, processes, and values. This data is collected through semi-structured interviews with key informants in each company and on-site visits to all companies where observations of the manufacturing innovations have been carried out and the company has presented future manufacturing innovations. Finally, observations offer firsthand insights into the dynamics and interactions within the case companies.

Triangulating these tools achieves a comprehensive understanding, blending broad contextual awareness with targeted specifics. Moreover, combining different tools helps mitigate the weaknesses inherent in each. For instance, observations are used to counterbalance potential biases in interviews, where respondents might tailor their answers to please the interviewer. In addition to formal research tools, informal conversations are also essential additions. These conversations uncovered personal perspectives, moods, and latent conflicts, enriching the research process and refining the questions posed in formal interviews. An overview of the data collected for each case is presented in .

3.3 Data reduction and analysis

To build explanations, the data treatment process consists of two primary cycles of data coding to condense data, followed by the process of pattern matching, as suggested by . In the first cycle, the data is coded one case at a time using an explorative open coding approach, considering the theoretical sensitivity reached through the preliminary conceptual understanding of innovation capabilities. In the second cycle, the themes identified in the first cycle are condensed into fewer variables through axial coding to improve cross-case comparison. The findings of the seven cases are compared, searching for patterns of similarity. Following that, relationships between variables are explored to identify foundational innovation capabilities. In line with the recommendations by , the data presentation is done in two steps: (1) data tables/figures and (2) narrative examples. This increases the chain of evidence. The analysis of the results leads us to infer four propositions about how SMEs do manufacturing innovation for Industry 4.0.

4. Empirical findings and analysis

Through the first and second cycle analyses, the data revealed eight dimensions capturing manufacturing innovation in the case companies and how they work with this (see ). The consideration of the relationship between these dimensions further elucidated three foundational aspects of the innovation capabilities required for SMEs to work successfully with manufacturing innovation for Industry 4.0. These are subsequently discussed in light of the conceptual insights on processes, resources, and values for innovation capabilities. The findings and analysis are presented below.

4.1 Manufacturing innovation for Industry 4.0

Initially, the empirical analysis identifies eight dimensions capturing manufacturing innovation in the case companies and how they work with this. The eight dimensions are described here:

High Complexity emerged as a significant theme. This complexity arises from integrating diverse systems, addressing data security concerns, and requiring specialized technical expertise to manage and maintain these systems—the need to view digital systems as ecosystems across and beyond the company was defining.

Radical Innovation was identified as another defining dimension. The case companies highlight that their focus for manufacturing innovation is to improve efficiency, productivity, and competitiveness significantly. However, this also requires that they navigate substantial changes in their operational paradigms.

The pattern of Technological and Human Development underscores the interplay between technological advancements and organizations’ human and cultural aspects. The case companies point out that challenges relate to technologies and perhaps even more to organizational culture, legacy systems, and competencies.

Technological Innovation is also a core pattern, as all cases revolve around adopting and integrating advanced technologies into traditional manufacturing processes.

The emergence of Strategic Initiatives as a pattern highlights that the case companies work with manufacturing innovation not only as an operational activity but also view it strategically with implications for the company’s future directions. They develop comprehensive digital roadmaps, conduct strategic reviews, and formulate digital ecosystem strategies to guide them through technology adoption and integration complexities.

User-centered approaches were also identified as a common approach to manufacturing innovation. The companies emphasize engaging users in the refinement of digital solutions. Users are both internal and external users and customers. The empirical data showed that understanding user needs, prototyping solutions, gathering feedback, iterating on designs, and continuously improving the user experience was central.

The pattern of Iterative Improvement and Agile Project Management reflects that companies utilize agile methodologies and data-driven insights in their approach to manufacturing innovation. The iterative innovation process, supported by agile practices, enables them to break down complexity into smaller bits and continuously learn from these. The ability to adapt quickly to changing conditions and continuously refine their technological knowledge is central.

Finally, Cultural Transformation and Change Management emerged as a critical dimension. The companies try to foster a culture of innovation and describe how this requires a comprehensive change management process. They work on creating awareness, building commitment, empowering employees, and aligning leadership practices with the manufacturing innovation goals.

4.2 Capabilities for manufacturing innovation for Industry 4.0

The eight dimensions collectively provide a nuanced understanding of the practices and imperatives involved in manufacturing innovation for Industry 4.0. The analysis also revealed that each dimension is multifaceted and that addressing these dimensions holistically is crucial for organizations. Hence, the following analysis explores the relationships between the variables to identify and discuss foundational capabilities enabling manufacturing innovation.

4.2.1 Processes: handling systemic and technological complexity through gradual development

This first foundational pattern captured various characteristics of manufacturing innovation in the context of Industry 4.0 and how this creates particular premises for best practices on the processes followed.

The problems to be solved using Industry 4.0 technologies are highly complex and involve radical innovation of the technical solutions and the organizational systems that will utilize the new solutions.

The manufacturing innovations in the seven cases range from an off-the-shelf intelligent tool cabinet to a new, intelligent production line that solves complex problems. While some manufacturing innovations are new to the industry, others have been implemented before and are strictly new to the individual company. presents an overview of the manufacturing innovations in the seven cases.

The companies plan to expand their solutions with new features in all seven cases, meaning they will not reach a final solution soon. Instead, these solutions are initial steps towards gradually building an Industry 4.0 manufacturing system. For instance, in Case B, the first step was to build a product configurator capable of configuring all the company’s products. This step has been completed, and the configurator is operational. The following steps involve connecting the configurator to a webshop and generating data for production, enabling future operations to be managed from this data. This incremental approach, introducing small, isolated manufacturing innovations and then extending the solution, aligns with findings that digital transformation occurs through “a series of incremental interlocked steps to achieve radical change cumulatively” (p. 811). The Vice President of Supply Chain and Manufacturing in Case B reflects this sentiment: “We know that we can use this [Industry 4.0] for something, but we have difficulty pinpointing which initiatives will lead to which gains.”

With Industry 4.0, additive manufacturing and collaborative robots may transform core manufacturing processes and enhance productivity (). However, a cross-case comparison indicates that most innovations are radical changes to enabling processes rather than core processes—solutions that significantly alter processes supporting production. There are various reasons for this. In Case D, the company aims to use Industry 4.0 technologies to double production capacity without increasing the number of employees. They chose to implement an intelligent tool cabinet over other projects because the production engineer driving the implementation was enthusiastic about it, and the COO wanted to leverage this enthusiasm to foster the adoption of more complex Industry 4.0 solutions. In Case F, the company selected a solution considered “low-hanging fruit” by management to gain organizational buy-in. This approach suggests that these organizations view the gradual development of capabilities for manufacturing innovation as crucial for long-term goals.

outlines how the innovation process is structured in the seven cases. In Case B, the company began its digital transformation by enhancing how data for the manufacturing system is generated. Once this phase is complete, the company will modify the manufacturing system so that each workstation can read product data and translate it into operations. Thus, the Industry 4.0 manufacturing system will evolve gradually, a method used in all seven cases. This approach breaks down the Industry 4.0 manufacturing system into minor, manageable problems.

Early decisions on solution design can affect future design options and have significant long-term consequences, which are difficult to predict due to the complex nature of an Industry 4.0 manufacturing system. By making gradual changes, feedback from earlier steps can inform subsequent solution designs. The Site Technology and Asset Manager in Case F acknowledges the necessity of splitting the transformation process into smaller steps for brown-field development: “The ‘big bang’ [for Industry 4.0] does not exist unless you build a factory from scratch.”

Adopting a gradual transformation to Industry 4.0 can positively impact an organization’s innovation capability by leveraging accumulated knowledge (). To determine if accumulated knowledge from manufacturing innovation has influenced the companies’ innovation processes, we examined their level of experience with manufacturing innovation related to automation and Industry 4.0. Although our analysis did not reveal a direct relationship between experience with prior Industry 4.0 projects and the adoption of new Industry 4.0 projects, prior research suggests such a relationship might exist (). However, our findings did suggest a connection between the existing level of automation in the companies’ manufacturing systems and the degree of novelty in their manufacturing innovations (see ).

We observed that companies with more manual process handling are more likely to introduce innovations that are “new to the company.” In contrast, companies with more automated systems are more likely to introduce innovations that are “new to the industry.” This implies that companies with extensive experience in automation may be more willing to take higher risks, and accumulated knowledge from previous manufacturing innovation projects could positively influence the novelty of subsequent innovations. This further underscores the value of a gradual transformation to Industry 4.0.

All the problems addressed in the cases are complex. Research suggests that understanding complex Industry 4.0 problems and identifying suitable solutions requires several iterative cycles between these activities. Consistent with this, the cases applied an iterative innovation process for managing manufacturing innovation, manifested in different ways. For instance, in cases A and B, the companies started and paused the projects multiple times. Each resumption marked a new iteration of the project. In cases D, E, F, and G, iterations involved the sporadic engagement of end users throughout the innovation process, who provided insights on operations and assisted in evaluating technical possibilities.

Six companies developed prototypes to simulate and test their solutions. Prototyping in manufacturing innovation is an iterative process that continually refines the alignment between process structure, technology, and organization. For example, in case G, the company created a simple, inexpensive sensor solution to demonstrate the potential of a more robust solution. Through prototyping, the company learned about the technology’s possibilities and limitations while simultaneously initiating the organizational transformation process.

Case C did not initially create a prototype of the production line. Instead, the company considers the new production line a prototype featuring many built-in Industry 4.0 elements. The company plans to explore these features and apply the acquired knowledge to other production lines. This approach is also seen in cases E and G. In case G, the company expects to generate value from the solution while treating it as a prototype to inform future production line requirements: “We expect to get a new production line at some point, and this has to be [Industry] 5.0 or 6.0.” (Plant Manager, case G).

In case G, the prototype is built on the same production line intended for the final solution. In contrast, case A is built on a machine rarely used, allowing testing in an authentic setting with minimal disruption to daily operations.

Although six cases involved prototyping, the role of the prototype varied, reflecting different learning approaches in the innovation process. In cases A, F, and G learning about requirements, solution design, and technical possibilities primarily occurred before investing in the solution. The prototype was a mock-up to test the final solution’s validity. In contrast, cases B, C, D, and E invested in smaller-scale solutions and used their implementation and operation to learn about possibilities and limitations, applying a learning-by-doing approach.

4.2.2 Resources: accumulating human resources and organizational experience

This second foundational pattern emphasizes that introducing manufacturing innovations for Industry 4.0 impacts the manufacturing system’s technical and human aspects, affecting shop floor workers and managerial staff alike. It highlights that the innovation capability comprises tangible and intangible resources.

Best practices influence the likelihood of successful adoption, such as top management involvement in the innovation process to ensure the availability of resources, decisiveness, and alignment with strategic goals. Additionally, correctly timing and methodically involving users of the manufacturing innovation—often shop floor employees in the context of Industry 4.0—is crucial. provides an overview of how employees and management are involved.

Top management is involved in the innovation process in all seven cases. In some companies, a top manager directly manages the project; in others, an engineer serves as the project manager with supervision from one or more top managers. Some companies employ a formal governance structure, where responsibility and decision-making are structured. In contrast, others use a more informal approach, which may reflect their level of experience in organizing manufacturing innovation. In most companies, the promoter of the solution comes from top management, likely due to Industry 4.0 being conceptual and challenging to operationalize (). This also underscores Industry 4.0 as a strategic initiative for these companies.

Employee involvement varies across companies. Some involve employees throughout the process, while others wait until they have a nearly finished solution. Balancing the timing of involvement can be challenging. For example, in case E, shop floor employees are involved early to provide input on solution requirements. In contrast, in cases A and B, users are involved late in the process, which management later regretted: “We skipped the basics – getting people on board. I have taken some steps back now [to get people on board].” (Production Manager, case A).

The companies all try to foster a culture of innovation and describe how this requires a comprehensive change management process. They work on creating awareness, building commitment, empowering employees, and aligning leadership practices with the manufacturing innovation goals.

4.2.3 Values: strategic process relying on agility

This third foundational pattern captures best practices for how manufacturing innovation is approved by the case companies, thereby capturing the mindset and criteria decision-makers use when considering manufacturing innovation.

These practices highlight strategic approaches and emphasize learning and agility. This involves forming an innovation strategy, estimating financial potential, committing resources, and planning the process. provides an overview of these characteristics in each of the seven cases.

All cases have developed an Industry 4.0 strategy to guide future initiatives, marking the beginning of their Industry 4.0 journey. For example, in case D, the strategy focuses on doubling production with existing capacity: “We want to produce double the number of products with the same capacity level.” (COO, case D).

Each company has also crafted a business case or financial justification influencing investment decisions. In cases A, D, and G, top management estimated costs and benefits, although strategic value often outweighs precise financial forecasts: “The premise of this project is that it cannot worsen the situation. It can only get better […] so we are more interested in the result of the project [i.e. the savings derived from the solution].” (CEO, case A).

In cases B, C, and E, there is a stronger emphasis on return on investment (ROI) calculations. Case B, for instance, combines ROI with complexity assessments to prioritize projects. The use of rough estimates rather than detailed ROI calculations might stem from the challenge of quantifying benefits, especially for complex solutions ().

Funding for projects is secured in all cases, but resource constraints have caused delays in three cases where operational management has taken precedence over innovation time. This challenge is common in SMEs, where adding new roles may not be feasible (). To mitigate this, an iterative approach is adopted, as seen in case F, where multiple solution versions are implemented iteratively to deliver immediate value and evolve as requirements clarify: “We started on a small scale. We took a small step in developing the solution and implemented it. Afterward, we defined the next step and implemented it. So, we never had any loose ends. If we had taken an approach trying to embrace everything from the beginning, the development and implementation phases would have been never-ending.” (COO, case D).

This iterative approach ensures steady progress without overwhelming resources. Case D highlights its effectiveness: “If we had tried to do everything from the start, the development and implementation phases would have been never-ending.” (COO, case D).

5. Discussion and conclusion

While Industry 4.0 presents opportunities for companies of all sizes, the adoption rate among SMEs remains low. To increase the number of successful transformations, it is crucial to understand how SMEs should approach this shift and what capabilities are needed. This article has examined seven successful cases to answer the question: How do manufacturing SMEs successfully do manufacturing innovation for Industry 4.0?”

The analysis demonstrates that manufacturing innovation for Industry 4.0 calls for developing particular innovation capabilities related to processes, resources, and values. We suggest that such manufacturing innovation capability is distinct from the current best practices in the literature on manufacturing systems development or process innovation.

We find a pattern related to processes showing that the companies handle systemic and technological complexity through gradual development. The empirical analysis shows that in all cases, the manufacturing innovation involved a higher level of complexity than previous developments in the manufacturing systems. This complexity arises from systemic relationships not only within the existing manufacturing system but also with potential future innovations for Industry 4.0, which may still be unknown. Of particular interest in this context is that our empirical results suggest that the companies primarily focused on radical transformations of enabling processes, unlike the first three industrial revolutions, which were driven by radical innovations in core manufacturing processes (e.g. ). Several factors may explain this finding. One could be that companies may perceive the risk of this type of innovation as lower because it does not interfere significantly with product design or strategic decisions related to manufacturing capabilities. Another could be that although the vision of Industry 4.0 requires substantial changes to core manufacturing processes, companies may find that the most immediate and understandable benefits are related to transforming enabling processes. Both interpretations of the results suggest that part of the manufacturing innovation capability is that companies navigate complexity by designing innovation processes that actively reduce risks and increase relatedness to the existing knowledge base. Also, our empirical results suggest that the more experience a company has with manufacturing innovation, the more advanced the company’s new manufacturing innovation, emphasizing that accumulated knowledge is an essential foundation for the strategic approach to Industry 4.0.

This finding aligns well with our initial conceptual observations that manufacturing innovation for Industry 4.0 is of a much more transformative nature than what is traditionally associated with process innovation and manufacturing systems development, hence requiring different capabilities. We propose that a critical aspect of manufacturing innovation capabilities is:

Proposition 1.

Manufacturing Innovation for Industry 4.0 is radical by nature and is best approached in small steps, focusing on enabling processes before core processes and emphasizing continuous knowledge accumulation to inform future initiatives.

Different opinions exist in the literature on approaching the transformation to Industry 4.0. For instance, argues that the transformation should be approached as an emergent, step-by-step process powered by accumulated knowledge and emerging opportunities. suggests that the transformation should be planned from start to end, followed by execution. While such an approach has benefits such as transparency and resource planning, our results suggest that SMEs benefit from an emergent approach. The inherent complexity of manufacturing innovations for Industry 4.0 imposes a need for exploration, so an iterative innovation process is preferable (; ). Our results empirically support this conclusion and add another perspective, as the companies use several means to achieve flexibility in the innovation process in addition to using an iterative approach. Some companies are, for instance, less focused on structure in the process and instead use a learning-by-doing approach to obtain the required knowledge to solve the manufacturing innovation. Some companies use prototyping, creating a small-scale solution to learn and understand solution requirements, whereas others learn during the installation. Based on this, we infer our second proposition:

Proposition 2.

Manufacturing Innovation for Industry 4.0 requires adaptive process capabilities. These processes include starting and pausing projects as needed, using prototypes to simulate and refine solutions before full-scale implementation, and implementing in phases to manage complexity and ensure steady progress.

Considering resources in focus for the companies, we find a central overall pattern emphasizing the accumulation of human resources and organizational experience. Manufacturing companies often lack explicit competence strategies related to Industry 4.0 (). Hence, accumulated knowledge from other manufacturing projects may serve as a competence-developing activity supporting manufacturing innovation in the context of Industry 4.0. If, for instance, the organization is used to continuously being introduced to new technology in the manufacturing system, the organization may have less resistance to such changes over time, improving the chances of successful adoption of future manufacturing innovations (). Furthermore, continuing to initiate and implement new manufacturing innovations may also improve a company’s ability to transform a conceptual vision of the manufacturing system into concrete solutions, which many companies find difficult in Industry 4.0. As such, the analysis showed that the accumulation of experience served several purposes: creating awareness, building commitment, empowering employees, and aligning leadership practices with the manufacturing innovation goals. This implies that continuous focus on gradually building competencies and knowledge is vital. This insight aligns with contemporary calls for developing socio-technical process models and perspectives on Industry 5.0 (e.g. ; ). Our third proposition is therefore:

Proposition 3.

Manufacturing Innovation for Industry 4.0 involves balancing technological advancement with organizational development initiatives. While securing necessary technological resources is crucial, equal emphasis must be placed on nurturing human capital, including training and upskilling programs, to ensure that employees have the necessary skills to operate and leverage new technologies effectively and foster a culture of continuous innovation.

The highly emergent approach to manufacturing innovation, however, comes with the risk that the initiatives will result in “islands” of solutions that are not integrated. Integration is a necessity for realizing the systemic potential of Industry 4.0. Hence, the manufacturing innovation initiatives need to be anchored jointly. Therefore, management involvement and capabilities are needed to ensure the strategic direction for Industry 4.0 initiatives. When exploring the values applied by the decision-makers in the companies, the analysis showed that agility also affected decisions made on investments and the short- vs long-term valuation of initiatives. This capability at the strategic level of the companies leads to our final proposition on manufacturing innovation for Industry 4.0:

Proposition 4.

Manufacturing Innovation calls for companies to prioritize long-term strategic alignment and learning over short-term financial metrics in their approach to Industry 4.0. They must value innovations’ strategic impact and potential transformative benefits, allowing flexibility in financial justifications to focus on achieving long-term goals and operational excellence.

The four propositions encapsulate how processes, resources, and values intersect in guiding the manufacturing innovation capabilities needed to adopt and implement Industry 4.0 initiatives. Several of the findings draw direct links to innovation management, highlighting the significant benefits of integrating knowledge of innovation capabilities into the field of manufacturing systems development. We argue that this integration is essential to understand further how to manage manufacturing systems development characterized by high complexity, low transparency and predictability, and increased risks.

5.1 Implications of findings

In sum, we address a problem shared by practicing managers and the research community: how to manage manufacturing innovation for Industry 4.0 so that firms can better realize its potential benefits.

Conceptually, the findings add to research in organizational capabilities by developing a manufacturing innovation capability concept. The importance of such a capability has been addressed as a side issue in prior influential work on capabilities, but it has not received specific attention. The results underscore the need to view digital transformation towards Industry 4.0 as a manufacturing innovation process that relies on significantly different organizational capabilities than those supporting continuous manufacturing development. This insight has several implications for further research.

First, innovation process models specifically for manufacturing must be developed to support radical systemic innovation, gradual learning, and agile processes. Such research will contribute to filling several conceptual gaps: One gap in the literature on manufacturing systems, where radical innovation is rarely addressed. This body of literature emphasizes resilience, robustness, and efficiency, thriving in stable and predictable conditions. Another gap this would target is in innovation management literature, where capabilities for process innovation are largely overlooked (as also identified by , , ). Secondly, Industry 4.0 technologies enable new potential, but our results suggest that the actualization of this potential is dependent on organizational capabilities. Hence, an implication of this research is the suggestion that research seeking to explain the drivers and barriers of Industry 4.0 technology adoption will benefit from a socio-technical perspective. This finding resonates with similar aspirations of a human-centric approach to technologies presented in work and policy on Industry 5.0 ().

The findings also offer several practical implications. Identifying patterns of best practices provides much-needed inspiration and insight into how manufacturing innovation for Industry 4.0 may be approached. While we agree with studies showing that competencies are one of the biggest challenges for companies to get started, our results also suggest that by using a flexible approach, companies can build competencies gradually and as needed, yielding the right results over time. Furthermore, the findings suggest that a specific starting point for manufacturing companies may be enabling processes rather than core processes. This new understanding of the types of solutions companies manage to progress with may suggest that the technologies here are more mature or that there is greater motivation to get started. This implication is supported by the result that a long-term strategy is needed but that it must be operationalized into smaller solutions to avoid biting off more than they can chew initially. While other researchers have also pointed this out, we provide a deeper understanding of why it is necessary and how it can be operationalized.

Figures

Analytical construction of capabilities for MI

Figure 1

Analytical construction of capabilities for MI

Level of automation of existing manufacturing system combined with degree of innovation of the manufacturing innovation in the cases

Figure 2

Level of automation of existing manufacturing system combined with degree of innovation of the manufacturing innovation in the cases

Presentation of cases

Case characteristicsCase ACase BCase CCase DCase ECase FCase G
Company size (# of employees)Medium (82)Medium (190)Medium (120)Small (19)Medium (87)Medium (99)Small (31)
Type of productionDiscreteDiscreteDiscreteDiscreteDiscreteDiscreteDiscrete
Process structure complexityHigh mechanization of production processes, some systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, some systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, some systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, low systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, some systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, some systemization of production processes and activities, and some degree of interconnection of production process tasksHigh mechanization of production processes, high systemization of production processes and activities, and some degree of interconnection of production process tasks
Product line complexityMultiple product categories with low to medium complexity of end-productsMultiple product categories with medium complexity of end-productsMultiple product categories with medium complexity of end-productsMultiple product categories with medium complexity of end-productsMultiple product categories with medium complexity of end-productsMultiple product categories with low to medium complexity of end-productsMultiple product categories with medium complexity of end-products
Manufacturing strategyLow-costLow-cost/mass customizationLow-costLow-cost/mass customizationLow-cost/mass customizationLow-costLow-cost
Current employee competence profilesSome unskilled, most vocationally trained, few higher educationSome unskilled, most vocationally trained, few higher educationMost vocationally trained, few higher educationMost vocationally trained, few higher educationMost vocationally trained, few higher educationMost vocationally trained, few higher educationMost vocationally trained, few higher education
Status of innovation project at the time of data collectionThe innovation is implemented and is being used in operationsThe innovation is implemented and used in operationsThe innovation is undergoing final test runsThe innovation is implemented and used in operationsThe design and technical specifications of the solution are decidedThe innovation is implemented and used in operationsThe design requirements are chosen
Type of problemComplexComplexComplexComplexComplexComplexComplex
Manufacturing innovationHas mounted sensors on machines in production to measure OEE performance live. The operators can monitor the performance on tablets and get indications if machine settings need to be adjusted. Data from all machines is aggregated in a dashboard, which the production manager can use to track OEE performanceHas made a configurator for managing product variety. Currently, customers can use the configurator to design a productHas invested in a new intelligent production line where the whole process except feeding and packaging is hands-off. The production line’s design reduces changeover times from approximately two hours to 20 minHas installed an intelligent tool cabinet, which assists employees in picking the correct tool. The cabinet also keeps track of tool inventory levels and informs management if a reorder point is reachedIntroduction of an AGV in the production to move products to and from inventoryDashboard in the control room, which shows quality control results from production. The controller in the control room uses this information to assess whether adjustments are needed in the productionSensors on the production line shall collect OEE data to continuously monitor the line, identify problems, and improve efficiency
Prior solutionOEE monitoring was calculated when the production order was finished. Operator experience informed adjustments to machine settingsThree employees would design the product manuallyUse other production lines with longer changeover times and more manual handling in the production processEmployees would grab the tools they needed, and the need for reordering had to be controlled manually. Also, tools were often not returned after the end of use, and colleagues had to search the production to locate a toolAll movements of products in the production were performed with manually operated forkliftsAll results from one day’s quality control were collected into one file, manually transported, and handed over to the control roomIdentification and evaluation of problems on the production line was based on operators’ experience
Future extensions of the solutionIntegration to ERP system and more features in the dashboardThe following steps are to connect the configurator to a webshop and design it to generate data to produce the productSensors are built into the production line, so the company looks into which data is collected and how it can exploit it to improve the production line further. The company is also planning to introduce AGVs to the production lineThe tool cabinet will be linked to customer orders to ensure the correct match between product specifications and tool attributesThe current project focuses on introducing one AGV, but once the company has successfully installed this, it expects to invest in more AGVs to replace manual transportation in productionThe dashboard is continuously updated with new features based on inputs from operations and managementEventually, all information on whiteboards and paper in the production should be digitalized

Source(s): Authors’ own work

Extent of data collection

Data sourcesCase ACase BCase CCase DCase ECase FCase G
Industry 4.0 innovation strategy×××××××
Digital maturity assessment×××××××
Formal and informal data gathered from action research activities with the company focusing on their adoption of Industry 4.0 spanningFour years with recurrent activities every second monthFour years with recurrent activities every second monthEight months with recurrent activities every second weekFour years with recurrent activities every second monthThree months with recurrent activities every second week11 months with recurrent activities every month11 months with recurrent activities every second week
On-site visit with observations×××××××
Presentations of past and future manufacturing innovations×××××××
Semi-structured interview focusing on innovation approach withCOO and CEOQuality and Project Manager and Business Development Manager
Vice President of Supply Chain and Manufacturing
Production ManagerCOOProject Manager and Manufacturing Technology Development Maintenance ManagerSite Technology and Asset ManagerProduction Manager and Plant Manager

Source(s): Authors’ own work

Characteristics of manufacturing innovations

CharacteristicsCase ACase BCase CCase DCase ECase FCase G
Affected processEnabling processEnabling processCore manufacturing processEnabling processEnabling processEnabling processEnabling process
Degree of innovationRadicalRadicalRadicalRadicalRadicalIncrementalRadical
NewnessNew to companyNew to companyNew to companyNew to companyNew to industryNew to industryNew to industry

Source(s): Authors’ own work

Characteristics of innovation processes

CharacteristicsCase ACase BCase CCase DCase ECase FCase G
TrajectoryIterationsIterationsIterationsIterationsIterationsIterationsIterations
Has made a prototype× ×××××
Approach to learning about innovationLearning before doingLearning by doingLearning by doingLearning by doingLearning by doingLearning before doingLearning before doing
Open/closed approach to innovationOpenOpenOpenOpenOpenOpenOpen
Experience with manufacturing innovation (automation)A mix of automated and manual processes. Require manual handling between processesA mix of automated and manual processes. Require manual handling between processesA mix of automated and manual processes. Automated handling between some processesA mix of automated and manual processes. Require manual handling between processesA mix of automated and manual processes. Automated handling between some processesThe entire process is automated and hands-offThe entire process is automated and hands-off
Experience with manufacturing innovation (Industry 4.0)No prior Industry 4.0 solutions in operationsSome Industry 4.0-related projects have been implementedNo prior Industry 4.0 solutions in operationsSome Industry 4.0-related projects have been implementedSome Industry 4.0-related projects have been implementedOperations are hands-off and controlled by utilizing dataNo prior Industry 4.0 solutions in operations

Source(s): Authors’ own work

Characteristics of involvement of employees and management

CharacteristicsCase ACase BCase CCase DCase ECase FCase G
Promoter of projectTop managementTop managementTop managementProduction EngineerTop managementTop managementTop management
Top management involved×××××××
Employees involved in the processAffected employees are involved when the final solution is close to being finishedAffected employees are involved when the final solution is close to being finishedAffected employees are involved when the final solution is close to being finishedEarly in the process, before final solution is developedEarly in the process, before final solution is developedEarly in the process, before final solution is developedEarly in the process, before final solution is developed

Source(s): Authors’ own work

Characteristics of inputs in the innovation approach

CharacteristicsCase ACase BCase CCase DCase ECase FCase G
Has an Industry 4.0 innovation strategy×××××××
Has required resources (time) ××× ×
Has required resources (financial)×××××××
Financial assessment of innovationRough estimateROIROIRough estimateROIRough estimateRough estimate

Source(s): Authors’ own work

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Acknowledgements

This study was funded by the EU Regional Development Fund.

Corresponding author

Astrid Heidemann Lassen can be contacted at: ahl@mp.aau.dk

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