Keywords

1 Introduction

Smart Manufacturing is an emerging 4th Industrial Revolution (4IR) phenomena as a result of the convergence of various Information and Communication Technologies (ICT) that improve manufacturing factors such as productivity, quality, delivery and flexibility [1,2,3,4]. Smart manufacturing relates to enabling industries and their manufacturing ecosystems to connect and adopt novel efficiency gains along their value and supply chains [5]. Smart Manufacturing as a new technological innovation is also seen as a key driver of improved manufacturing operations [6, 7] and a catalyst of manufacturing intelligence [8].

However, despite all the mentioned benefits, SMMEs are not ready for the adoption and implementation of smart manufacturing as compared to larger firms [9,10,11,12]. Jun et al. [9] attribute this to the SMME environment, their underdeveloped capabilities, and practical limitations such as cost and personnel. Muller and Voigt [10] suggest that the challenge is with SMME business model constraints, while Luff [12] and Safar et al. [11] highlight the issue of lack of resources as the primary constraint.

There have been other systematic reviews [5, 13,15,16,17,18] to understand the factors inhibiting smart manufacturing adoption and implementation by SMMEs. Those reviews focused on different dimensions [5, 13,15,16] and contextual perspectives [17, 18]. For example, Mittal et al. [5, 13, 14] focused specifically on maturity models, while Hamdi [16] and Moeuf et al. [15] focused on Enterprise Resource Planning (ERP) and organisational dimensions respectively. Nowotarski and Paslawski [17], and Sommer et al. [18] focused on Germany and the construction sector.

In this paper, the objective is to understand the level of SMME readiness/adoption for smart manufacturing and how this has been researched from the perspective of innovation. The rationale for the research objective was derived from Nooteboom [19, 20] and Tornatzky and Klein’s [21] work on SMMEs underpinned by Roger’s diffusion of innovations theory [22]. Building on the diffusion of innovation theory [22], Nooteboom [19, 20] identified the first stage of the SMME adoption process as awareness; adequate knowledge about the innovation to be adopted. While awareness is generally a given for large firms and technology-oriented SMMEs, it is a key barrier for technology-following and non-technological SMMEs with regard to readiness for adoption [19, 20, 23].

The adoption and diffusion of new technological innovations is contingent on three characteristics: the relative advantage (benefits) of the technological innovation, the complexity of the technological innovation and the compatibility of the technological innovation [21]. Relative advantage is the degree to which an innovation is perceived as being better than the idea it supersedes, complexity is the degree to which an innovation is perceived as relatively difficult to understand and use, while compatibility is the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters [21].

The systematic review specifically focuses on the context of the research (low-income or high-income country), the awareness (adequate knowledge), benefits (relative advantage), the technological innovations and readiness/adoption as important dimensions of interest. Based on the objective this review differentiates itself from previous similar reviews [5, 13,15,16,17,18].

The paper therefore sought to find answers from the review for the following research questions (RQ):

  • RQ1: What is the state of awareness (adequate knowledge) of SMMEs in relation to smart manufacturing?

  • RQ2: What are the benefits of smart manufacturing in relation to SMMEs?

  • RQ3: Which technologies are seen as key to the concept of smart manufacturing in relation to SMMEs?

  • RQ4: What is the state of smart manufacturing readiness and adoption by SMMEs?

The rest of the paper is structured as follows, Sect. 2 presents the review methodology. Section 3 presents the results of the review and the research gaps. The conclusions, limitations and future work are presented in Sect. 4.

2 Methodology

The study followed Lage and Junior’s [24] systematic review protocol, and used the reporting approach of Amui et al.’s [25]. The SLR considered publications, which followed the formal introduction of Industry 4.0 that is from 2011 onwards. The time period for article selection was October 2018 to January 2019, while the analysis of the articles was from February 2019 to July 2019. A schematic representation of the adopted review protocol is shown in Fig. 1 below.

Fig. 1.
figure 1

A schematic representation for the adopted methodology and results framework for this study

Full size image

2.1 Identification of the Research Articles

The first step was identifying the research articles through; (1) the construction of the search terms, (2) identifying the relevant data sources and (3) inclusion and exclusion criteria.

2.2 Construction of the Search Terms

The construction of the search terms was primarily derived from RQ1, RQ2, RQ3 and RQ4. These search strings were constructed based on the Unit of Analysis (SMMEs), Technology Artefact (Smart Manufacturing/Industry 4.0/Smart Factory) and Phenomenon of Interest (Adoption/Trends/Benefits/Technologies/Issues). The full details of the 33 constructed search strings are presented in Appendix 1 found on http://dx.doi.org/10.13140/RG.2.2.19652.17285.

2.3 Identification of the Data Sources

Smart Manufacturing (industry 4.0) is based on the convergence of various technologies and the integration of multiple disciplines and domains such as mechanical engineering, electrical engineering, computer science and other related fields [1, 26]. The multidisciplinary nature of smart manufacturing meant that the review needed to perform the search in similar multidisciplinary research databases. The study therefore chose the four that are most commonly used by multidisciplinary researchers [27,28,29,30,31,32,33]; Web of Science (WoS), Academic Search Premier (ASP), ScienceDirect and Google Scholar.

2.4 Inclusion and Exclusion Criteria

Using the constructed search terms, 279 articles were identified from the four databases (Fig. 2) in the first iteration. This first iteration was based on the following inclusion criteria:

Fig. 2.
figure 2

A schematic representation of the inclusion and exclusion process

Full size image

  • Articles that mention smart manufacturing or industry 4.0 or smart factory within their content or title

  • All industries or sectors (not just manufacturing)

  • Peer reviewed journal or conference articles

  • Articles written in English

In the second iteration, after removing duplications from different databases, 177 articles were excluded based on the review of titles and abstract. These articles were excluded based on the following exclusion criteria:

  • Articles that merely mention some of the search terms, but do not solely focus on smart manufacturing, industry 4.0 or smart factory concepts

  • Articles focusing on one specific application of a particular technology in relation to smart manufacturing or industry 4.0 or smart factory

  • Articles that do not focus on SMMEs

  • Articles that merely mention SMMEs

In the final round of the iteration process, 72 articles were excluded based on the analysis of the full text of the articles. The remainder of the articles (30 peer reviewed journals and conference papers) were determined to be relevant for this review (See Appendix 2 Table 10 on http://dx.doi.org/10.13140/RG.2.2.19652.17285 for the articles selected for this review). The primary exclusion criteria was the following:

  • Articles that do not discuss any of the four study dimensions (awareness: adequate knowledge, benefits, technologies and readiness/adoption) discussed in Sect. 1 within their content

2.5 A Structured Classification and Coding Framework

To enable a structured and effective analysis of the body of knowledge contained within the sourced articles, a modified classification and coding framework utilizing numbers and letter codes to classify the articles was utilized (Table 1). This framework is adopted from Amui et al. [25] who developed it based on other systematic review efforts [34,35,36,37,38].

Table 1. Classification and coding framework
Full size table

3 Analysis and Discussion of Findings

The thirty selected research articles were coded according to Table 1, with the resultant coding results shown in Table 2 below.

Table 2. Results of the codification framework
Full size table

3.1 Context and Origin

The majority of the articles focused on high-income countries (1A) in comparison to low income countries (1B) as shown in Table 2. The majority of the research studies in relation to smart manufacturing (Table 2) originated from Europe (6B) conducted by German universities and research institutions. Only one study by a Moroccan university originated from the African (6E) continent [16]. See details in Appendix 2 Table 11 on http://dx.doi.org/10.13140/RG.2.2.19652.17285.

The findings suggest that there is a research gap in this regard, and therefore a research opportunity for smart manufacturing for SMMEs in low-income countries, specifically in the African continent. The resource constrained environments of low-income country contexts often results in completely new and contrasting technological innovations as has been shown in the mobile money sector in low-income countries [39].

3.2 What’s in a Name: Smart Manufacturing vs 4IR

In this study, no article used smart manufacturing (2A) and/or smart factory (2C) as the main theme on their own (Table 3). Both these terms were used interchangeably with Industry 4.0 as shown in Table 3. Industry 4.0 (2B) is also not used interchangeably in this study and seems to be the most preferred term (Table 3). This could be as a result of the majority of the researchers/studies emanating from Germany. The findings suggest that the manufacturing 4IR discourse has primarily been through the lens of Industry 4.0 concept applied in a non-contextual approach to country specific problems and strategic initiatives [16, 40]. However, due to definitional differences of these terms (smart manufacturing/industry 4.0/smart factory) [5], there is a research gap and a related opportunity to contextualize 4IR concepts such as smart manufacturing to country specific problems and strategic initiatives.

Table 3. Usage of the 4IR terms
Full size table

3.3 Research Methods

The majority of the articles utilized case studies/interviews (3E) as research methods (Table 2). The case studies/interviews were in most cases used as part of the mixed research methodology approach to support conceptual/theoretical research (3C) articles (Table 2). Further analysis on the types of research methods and how they were utilized in the reviewed articles is shown in Table 4. The findings illustrate the need to create new substantive research theories that can contextualize smart manufacturing research to relate better to context specific needs.

Table 4. Research methods
Full size table

3.4 Economic Sector

While the majority of the articles (4A) focused on the manufacturing sector, a significant number of articles (4C) studied SMMEs in a generalized manner (Table 5). Although SMMEs share a number of similarities, they are heterogeneous in nature as they operate in different sectors [41]. The findings suggest that more sector specific research needs to be done with regards to SMMEs.

Table 5. Economic sector
Full size table

3.5 Dimensions

To answer the framed research questions posed in Sect. 1 (introduction), the four dimensions (1) awareness (adequate knowledge), (2) benefits (relative advantage), (3) technologies and (4) readiness/adoption (awareness, relative advantage, complexity and compatibility) shown in Table 1, were analyzed in accordance with the codification results of Table 2.

Smart Manufacturing: Awareness (adequate knowledge)

RQ1 sought to establish the state of awareness (knowledge) of SMMEs in relation to smart manufacturing. While the majority of the studies (16 articles) did not mention or discuss this dimension, those articles that did mention/discuss (14 articles) this dimension did not provide any substantive details in relation to the awareness dimension (Table 6). See more details with regard to further analysis in Appendix 2 Table 12 on http://dx.doi.org/10.13140/RG.2.2.19652.17285.

Table 6. Awareness
Full size table

The findings indicates that there is little comprehensive research focusing on investigating SMME levels of awareness in relation to smart manufacturing. Given that the awareness stage has very critical and significant implications to smart manufacturing readiness/adoption based on the innovations theory [19, 22], there is a critical need for research to focus on the awareness of SMMEs in relation to the opportunities, challenges and demands of smart manufacturing.

Smart Manufacturing (industry 4.0): Relative Advantage (Benefits)

RQ2 sought to identify the benefits (relative advantage) of smart manufacturing in relation to SMMEs. The majority of the research studies did not look into the relative advantage dimension to identify explicitly or implicitly any potential benefits of smart manufacturing for SMMEs (Table 7). Only 5 articles discussed and or identified the relative advantage of smart manufacturing (See details in Appendix 2 Table 13 on http://dx.doi.org/10.13140/RG.2.2.19652.17285).

Table 7. Benefits (relative advantage)
Full size table

The findings points to the need for research studies to focus on explicitly identifying the practical relative advantage of smart manufacturing for SMMEs based on concrete and comprehensive research.

Smart Manufacturing (industry 4.0): Technologies

RQ3 sought to identify technologies that are seen as key to the concept of smart manufacturing in relation to SMMEs. The majority of the research studies identified and discussed smart manufacturing technologies from a general applications context and not specific to SMMEs or how they relate and or can be applied in SMME context (Table 8). Only 10 articles identified technologies that are seen as key to the concept of smart manufacturing in relation to SMMEs (See details in Appendix 2 Table 14 on http://dx.doi.org/10.13140/RG.2.2.19652.17285).

Table 8. Technologies
Full size table

The findings reveals a research gap in identifying specific smart manufacturing technologies that are relevant to SMMEs from a sector or industry perspective. This research gap leaves SMMEs exposed and vulnerable to smart manufacturing anecdotes and sales pitches. The availability of empirically proven research on smart manufacturing technologies per sector and or industry would assist SMMEs which usually do not have the resources to do feasibility studies.

Smart Manufacturing (industry 4.0): Readiness/Adoption

RQ4 sought to understand the state of smart manufacturing readiness and adoption by SMMEs based on the innovations theory [19, 22]. Readiness and adoption of smart manufacturing by SMMEs is contingent on four key dimensions (1) awareness (adequate knowledge), (2) relative advantage (benefits), (3) complexity and (4) compatibility [19, 22]. Only a few research articles looked into the issue of awareness (Table 6), relative advantage (Table 7) and complexity as shown in Table 9, while 90% of the research articles indicates that smart manufacturing is currently incompatible with SMME characteristics due to lack of expertise, skills and resources (Table 9). See detailed analysis of complexity and compatibility in Appendix 2 Table 15 on http://dx.doi.org/10.13140/RG.2.2.19652.17285.

Table 9. Adoption/readiness
Full size table

The findings reveal that, research gaps in relation to understanding the impact of awareness, relative advantage and complexity needs to be addressed to be able to assess the state of smart manufacturing readiness and adoption by SMMEs. The findings, further reveal that compatibility seems to be emerging as one of the major factors negatively affecting smart manufacturing readiness/adoption for SMMEs. This situation may have far reaching implications in the context of low-skilled and resource constrained low-income countries.

4 Conclusion, Limitations and Future Work

The primary objective of this work was to understand the level of SMME readiness/adoption for smart manufacturing and how this has been researched from the perspective of digital technologies innovation guided by four research questions (introduction section). Firstly, the findings suggest that smart manufacturing research on SMMEs is limited in low-income countries and also not contextualized to context specific demands such as sector and or industry specific challenges and preconditions. These includes smart manufacturing technology applications and how they relate or can be applied in SMME context. Secondly, the findings suggest that the issues of SMME awareness, relative advantage and complexity of smart manufacturing DT innovation have received very little research attention. This is critical for readiness/adoption in relation to technology-following SMMEs [20]. Lastly, SMME characteristics (lack of skills, expertise and resources) are incompatible with smart manufacturing. This renders technology-following SMMEs not ready for the adoption of smart manufacturing.

Future context specific research investigating smart manufacturing in settings such as resource constraint low-income countries, non-technology based sectors/industries such as the manufacturing and technology-following SMMEs is recommended. More research to evaluate the impact of SMME awareness, relative advantage, complexity and incompatibility of smart manufacturing for former non-technology (but now technology-following) SMMEs should also be considered in future studies.

The systematic review is limited in its time horizon and its emphasis on low-income/high-income context. Further research is needed that uses other regional aspects.