# Smart-enhanced organisational functions: A framework comprising capabilities, barriers, and evolutional path

Paulo Eduardo Pissardini, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Elias Ribeiro da Silva

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## To cite this version:

Paulo Eduardo Pissardini, Moacir Godinho Filho, Mario Henrique Callefi, Gilberto Miller Devós Ganga, Elias Ribeiro da Silva. Smart-enhanced organisational functions: A framework comprising capabilities, barriers, and evolutional path. Expert Systems with Applications, 2024, 255, pp.124530. 10.1016/j.eswa.2024.124530. hal-04918996

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# Smart-enhanced organisational functions: A framework comprising capabilities, barriers, and evolutional path

Paulo Eduardo Pissardini<sup>a, b</sup>, Moacir Godinho Filho<sup>c, b</sup>, Mario Henrique Callefi<sup>d, e</sup>,
Gilberto Miller Devós Ganga<sup>b</sup>, Elias Ribeiro da Silva<sup>e</sup>

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## ARTICLE INFO

Keywords:

Barriers

Capabilities

Connected products

Smart production

Smart industrial products

## ABSTRACT

Integrating smart industrial products (SIPs) into production processes introduces new dynamics of capabilities and challenges within organisations. However, a prevalent issue is the ad hoc adoption of SIPs without a cohesive strategy, resulting in isolated solutions that do not effectively enhance core organisational functions. Our research fills this critical gap by identifying and combining the capabilities and barriers posed by SIPs. Our findings allow us to propose a framework comprising the stages each main organisational function needs to develop to move from a traditional to a completely autonomous decision-making function. This evolutional path requires that each organisational function acquires selected capabilities and overcomes specific barriers. Our approach encompasses a multi-method methodology, commencing with a systematic literature review (SLR) encompassing 44 papers, leading to the identification of 20 capabilities and 23 barriers. Subsequently, through rigorous content analyses and expert interviews, we formulate the framework. This paper contributes to academia by providing a framework with an evolutional path that main organisational functions may follow according to the capability they acquire from the SIP and which barriers should be surpassed at each level. For managers in practice, the proposed framework enables a better understanding of the stage of their operations and which capabilities will take their main organisational function from traditional (level 0) to autonomous (level 3) stages.

## 1. Introduction

The last decade has presented disruptive advances concerning smart products, especially with the emergence of Industry 4.0 in 2011, supported by the German government and led by the German Academy of Science and Engineering (Lee et al., 2022). In their early days, smart products were mainly the subject of philosophising in technology think tanks or were used for cutting-edge marketing technology at trade fairs (Raff et al., 2020). However, smart products have become a tangible reality for both the customer and the organisational environment, enabling new ways to develop and commercialise goods. Besides, the increasing adoption of disruptive technologies and SIPs has transformed organisational standards and product features across various sectors.

However, many companies are adopting SIPs without a clear strategy, resulting in isolated solutions that do not directly contribute to their core organisational functions. Moreover, the lack of systematic and strategic identification of capabilities and barriers related to SIPs hinders the evolution of organisational functions towards fully autonomous decision-making processes.

In an organisational context, this tendency of the evolution of products towards smart products has entailed the necessity for further studies to deeply understand these products' impacts on the manufacturing environment since this new industrial stage is affecting competition rules, industry structure, and customer demands (Bartodziej, 2017). In this context, our primary motivation in this study is to develop a comprehensive framework that systematically identifies and

<footnote><sup>a</sup> Department of Industrial Engineering, Amapá State University, Macapá, Amapá, 68900-070, Brazil</footnote>

<footnote><sup>b</sup> Department of Production Engineering, Federal University of São Carlos, Washington Luís Road, s/n, São Carlos, SP, 13565-905, Brazil</footnote>

<footnote><sup>c</sup> Department of Supply Chain Management and Decision Sciences, EM Normandie Business School, Metis Lab, 20 Quai Frissard, 76600 Le Havre, France</footnote>

<footnote><sup>d</sup> Chair of Factory Planning and Intralogistics, Chemnitz University of Technology, 09125, Chemnitz, Germany</footnote>

<footnote><sup>e</sup> University of Southern Denmark, Department of Technology and Innovation, Alsion 2, Senderborg 6400, Denmark</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: Paulo.pissardini@estudante.ufscar.br (P.E. Pissardini), mgodinhofilho@em-normandie.fr (M. Godinho Filho), mario.callefi@mb.tu-chemnitz.de (M.H. Callefi), ganga@dep.ufscar.br (G. Miller Devós Ganga), elias@iti.sdu.dk (E. Ribeiro da Silva).</footnote>
combines capabilities and barriers associated with adopting Smart Industrial Products (SIPs). This framework aims to provide key organisational functions with a clearly defined and structured evolutionary pathway, facilitating the transition from traditional to fully autonomous decision-making functions. This paper focuses on the following organisational functions: Accounting, Sustainability, Maintenance, Production Planning and Control and Quality Management.

While the field of smart products has gained popularity in research, there remains a lack of consensus and clarity regarding the definition of a smart product (Raff et al., 2020). Addressing this gap, our paper defines Smart Industrial Products (SIPs) precisely as follows: "Products incorporating elements of the physical, connected, and smart dimensions, integrating physical and digital components. These products feature embedded systems enabling smart functionality, including the recognition, analysis, and autonomous exchange of customer data about the customer, goods in production, processes, and the environment in real time. Furthermore, SIPs offer innovative integrated services through self-improvement of their configuration to optimise performance and maximise organisational value." This definition was synthesised by analysing key aspects of SIPs outlined by various authors (Hoffman & Novak, 2015; Holmström et al., 2009; Kärkkäinen et al., 2003; Luo et al., 2023; Rijsdijk & Hultink, 2009). It is important to highlight that this topic has several practical and academic contributions and has been studied recently worldwide (for example, Yang et al., 2024; Li et al., 2024; Huda et al., 2024; Martín et al., 2024, among a lot of others).

Our research is focused on identifying the capabilities and barriers associated with SIPs for organisational functions, culminating in a framework that delineates the evolutionary path these functions traverse to become smart, based on their acquired capabilities. Capabilities can be understood from two different perspectives. The first perspective comprises the capabilities that the product needs to have to be considered smart or intelligent for the final customer, also referred to as smart consumer products (Michler et al., 2020). These products include smartwatches, smart bands, smart cars, smartphones, etc. Raff et al. (2020) proposed a framework containing these capabilities. The second capability perspective considers the abilities that an organisation (specifically its main organisational functions) acquires when adopting SIPs in its processes to create value for itself and its customers. Our study focuses on this second perspective, having a technology-enabled capability perspective rather than a simple technology adoption approach. In our research, we also address barriers, in line with Sarkar and Bhuniya's (2022) assertion that comprehending these obstacles is crucial, even when organisations are equipped with the capabilities offered by SIPs.

Our research identifies the capabilities and barriers entailed by SIPs for organisational functions, proposing a framework that presents the evolutionary path followed by an organisational function to become smart, according to the level of the acquired capability. Although some research has been carried out on capabilities and barriers related to SIP adoption, there is a gap in the literature regarding this topic. SIPs require a completely new technology infrastructure, known as the "technology stack," which acts as a conduit for data exchange between the product and the user, incorporating data from various business systems, external sources, and interconnected products (Porter & Heppelmann, 2015). Researchers such as Bueno et al. (2022) and Pardo et al. (2020) have emphasised the transformative potential of embedded technologies in creating and capturing value. This technology stack serves as a multi-functional platform within SIPs, facilitating data storage, analytics, application execution, and ensuring secure access to products and the associated data flow (Porter & Heppelmann, 2015). While it is evident that enabling technologies empower SIPs to function effectively, the specific impact of these capabilities on various organisational functions remains a subject that requires further investigation.

Aiming at contributing to bridging this research gap, our paper collects and critically analyses the existing knowledge in the SIP field, presenting and clarifying which capabilities impact each organisational function, identifying the barriers faced by companies in implementing

SIPs in their operational environment and organising these capabilities and barriers in a framework that reveals an evolutionary path for the functions impacted. Therefore, this study addresses the following main research question:

*RQ: What evolutional path should an organisational function follow to become completely autonomous?*

To answer this RQ, first of all, three sub-questions should be answered, stated below:

*SRQ1*. What are the capabilities provided by SIPs for each of the main organisational functions?

*SRQ2*. What barriers do organisations face in implementing SIPs, and at what moment do these barriers directly impact SIP adoption?

*SRQ3*. What differentiates a traditional organisational function from a smart-enhanced organisational function?

Concerning the innovation aspects of this research, our framework offers a practical guide for companies interested in adopting SIPs efficiently and effectively. Therefore, the main contributions of our paper are threefold. First, we propose a novel definition for the SIP construct, expanding the definition proposed by Raff et al. (2020). Second, we identify in the literature the capabilities enabled by SIPs and the barriers to adopting smart products in the industrial context and validate them with experts. This rigorous analysis ensures a thorough examination of the literature and the experts' knowledge in the field, making our findings robust and reliable. Third, we propose an evolutionary path that companies should follow to improve the level of decision-making in their different traditional functions. This proposed path is not merely theoretical since experts with empirical knowledge about smart products validated it. Thus, our framework is grounded in practical strategies and actions organisations can implement at each stage. Based on these motivations and contributions, our research provides valuable insights for the academic and professional community, driving the successful implementation of SIPs in organisations across diverse sectors and preparing them for a smarter and more strategic approach to their operations. Moreover, for managers in practice, it provides an enhanced understanding of the current stage of their operations, the necessary capabilities to achieve autonomous decision-making and which barriers will be faced according to the autonomy level of an organisational function. In addition, the multi-method approach was considered to answer the proposed research question, contemplating a systematic literature review (SLR) and interviews conducted with experts. This combination of methods fits the need to ensure an exhaustive literature review to identify capabilities and barriers, as well as interviews with experts to ensure the validity of the findings. Finally, to the best of our knowledge, our proposed framework is the first framework found in literature, which delineates the evolutionary path each main organisational function needs to develop to move from a traditional to a completely autonomous decision-making function.

Our paper is organised as follows. Section 2 presents the literature review and the research gap this work seeks to fill. Section 3 presents the research method. Section 4 presents and discusses the results. Finally, Section 5 provides concluding remarks and suggestions for future research.

## 2. Literature review

This section begins by discussing the smart products literature. Next, an overview of the SIP literature is presented. Finally, the research gaps are discussed based on the current literature analysis, and the study motivations are explained.

### 2.1. *An overview of the SIP literature*

Various technologies are critical in the Industry 4.0 landscape (Awan et al., 2022). Notably, smart products, such as IoT-based systems, have garnered significant attention (Kumar et al., 2022; Wang et al., 2023). Many studies on smart products have been conducted in recent years,
including the secure and reliable operation of smart products in various
domains (e.g., Song, Sun, Song, & Stojanovic, 2023; Song, Wu, Song, &
Stojanovic, 2023), prototyping in smart product design (e.g., Wang
et al., 2023; Zhang et al., 2023), and analysis of the use of a smart
product-service system (e.g., Chang et al., 2023; Feng et al., 2023;
Le-Dain et al., 2023; Yu & Sung, 2023).

A comprehensive literature review of smart products reveals four
fundamental types of research in this field: (1) analysed the role of smart
products in conjunction with other concepts. For example, Lopez and
Garza (2023) studied consumer bias related to artificial intelligence-
generated evaluations in the context of smart product usage. Huynh
Evertsen et al. (2022) analysed the circular economy innovations com-
mercialised by academic spin-offs, including smart products, and Ren
et al. (2024) studied the concept of a metaverse in the industrial context;
(2) described the development or use of a specific smart product (e.g.,
Chang et al., 2023; Cong et al., 2022); (3) analysed smart products
acceptance by consumers (e.g., Rochi, 2023; Sabatini et al., 2023); (4)
examined the utilisation of smart products in the industrial context (e.g.,
Kim et al., 2023; Liu & Ming, 2019).

Within the SIP literature, existing studies provide guidelines for
developing or implementing smart industrial product-service systems.
For example, Kim et al. (2023) provided a multi-level architecture for a
smart product-service system in the context of Industry 4.0. Similarly,
Liu and Ming (2019) proposed a thorough framework for identifying and
assessing the smart industrial product-service system requirements.
However, notably, the academic landscape predominantly focuses on
using smart products for the final consumer, while research dedicated to
the industrial sector remains comparatively limited.

## 2.2. Research gaps

Our study explores SIPs within an industrial context, an area still relatively uncharted compared to the extensively studied consumer-focused SIPs in the existing literature (e.g., Yang et al., 2024). Kim et al. (2023) highlight the importance of smart products in industrial settings, yet a comprehensive literature review reveals a notable gap in research dedicated explicitly to SIPs. While Akhtar et al. (2024) examine the potential of AI and Generative AI in smart product platforming, the integration challenges of different technologies within existing industrial systems are less emphasised. The prevailing research primarily focuses on developing and adopting smart product-service systems without adequately exploring crucial factors such as the capabilities SIPs enable and the barriers to their implementation. Akhtar et al. (2024) identified capabilities related to smart products but did not focus on the industrial sector. Additionally, our extends the analyses conducted by Li et al. (2024), who only investigated quality improvement and consumer valuation through SIPs. The existing literature also outlines a product's capabilities to be deemed 'smart' without addressing the specific capabilities that the SIPs enable.

In this regard, it can be observed that no research has analysed in
depth the benefits (capabilities) that a SIP can enable for industries, nor
is there any discussion on the difficulties these companies may face
when adopting a SIP (barriers). Therefore, to the best of our knowledge,
our study is the first to address the theme of SIP-enabled capabilities and
the barriers associated with adopting these technologies. By identifying
and deepening discussions about these SIP-related capabilities, valuable
insights can be gained to guide the effective adoption of these technol-
ogies in various organisational functions. Simultaneously, understand-
ing the adoption barriers to SIPs becomes essential, as it facilitates the
recognition of factors that prevent companies from successfully imple-
menting these technologies.

# 3. Research method

The main objective of this study is to provide a clear and structured
framework with the evolutional path that organisational functions may

follow when adopting SIPs, streamlining the transition from a tradi-
tional to a fully autonomous decision-making function. Thereby, we
performed a process comprising three different methodological steps.
The first step consisted of an SLR, where we identified each capability's
barriers, capabilities, and examples. Next, content analysis was con-
ducted to identify semantical similarities between capabilities and bar-
riers. This enabled us to classify the barriers according to when the SIP
was adopted, their related capabilities in the organisational function,
and their autonomy level. After that, we interviewed five experts
(practitioners and academics) to validate the classification and propose
the final framework containing the evolutional path. A summary of the
methods and steps is presented in Fig. 1.

3.1. Identifying the initial list of capabilities and barriers

The first step was conducting an SLR to enable the identification of an
initial list of capabilities and barriers. The justification of an SLR for this
first methodological step is that from the SLR, it is possible to respond to
specific research questions from a robust methodology for locating,
selecting, and evaluating existing studies (Denyer & Tranfield, 2009).

Based on the guidelines presented by Denyer and Tranfield (2009),
it was considered that the conducted SLR comprised three main stages:
planning, conducting, and dissemination. Thus, the research protocol
was defined first (Table 1), which included the main research points, the
string searched, the databases used, the research topics, the research
questions, and the inclusion and exclusion criteria.

The protocol was implemented in three academic databases: Scopus,
Engineering Village, and Web of Science, converging the most relevant
available academic articles on the research topic. After choosing the
databases, the search strings were defined. A broad search string was
specified because of the heterogeneous nature of smart products and the
absence of consistent terminology within the sector due to its novelty.
The main objective was to include the majority of studies in this field.
The search strings defined for this purpose are shown in Table 1.

This initial search yielded 24,956 papers. Only documents from 2010 to 2023 were selected, which included articles, press articles, or reviews written in English, and covering areas such as industrial, manufacturing, electrical, and electronic engineering, multidisciplinary, computer science, interdisciplinary, artificial intelligence, operations research, and management. It should be mentioned that duplicated articles were excluded. Considering this, the total sample was reduced to 2,779 papers. The initial year was chosen based on the emergence of Industry 4.0 in 2011 when most of the disruptive technologies that comprise smart products started to be commercially available and further adopted in the industry. Furthermore, only publications from peer-reviewed academic journals were considered, excluding articles from conferences and magazines. The justification for this decision not to consider conference articles is that many of these articles present partial results, anecdotal cases, or low rigor in validation (Rossini et al., 2023).

In the next step, the title and abstract of the remaining 2,779 articles were read to assess whether they met the inclusion and exclusion criteria previously defined. Due to the lack of terminological consensus in the field, in this step, each paper was individually analysed to verify whether the research focus was SIPs. After this, 2,648 papers were rejected based on the exclusion criteria. From the 131 remaining articles, the main findings, the theoretical framework, and the conclusions were read, applying the inclusion and exclusion criteria, resulting in 51 articles being selected. Since this work focuses on revealing the capabilities and barriers that SIPs entail for operations, only papers addressing these topics were included. In this process, the thematic positioning of each article is pivotal for the evaluation (Raff et al., 2020), and reports not showing any capability or barrier provided by SIPs were therefore not selected. Furthermore, articles focusing on any technical aspects of the product or emphasising product capabilities were not selected as the objective of this review is to assess the benefits of the process.
<figure><img src="image_4.png" /><figcaption>Fig. 1. Summary of the methodological approach and steps in this research.</figcaption></figure>

<table><caption>**Table 1**
Research protocol.</caption><thead><tr><th colspan="2">Research protocol</th></tr></thead><tbody><tr><td>Objective</td><td>To identify barriers and capabilities provided by smart industrial products (SIPs) for the main organisational functions</td></tr><tr><td>Guiding Questions</td><td>RQ1. What are the capabilities provided by SIPs for each of the main organisational functions?<br>RQ2. What barriers do organisations face in implementing SIPs, and at what moment do these barriers directly impact SIP adoption?<br>RQ3. What differentiates a traditional organisational function from a smart-enhanced organisational function, and what is the evolutional path such a function should follow?</td></tr><tr><td>Databases</td><td>Engineering Village, Scopus, Web of Science</td></tr><tr><td>Publication years</td><td>From 2010 to 2024</td></tr><tr><td>Document type</td><td>Articles, Articles in Press, Reviews</td></tr><tr><td>Language</td><td>English</td></tr><tr><td>Strings</td><td>((Smart AND Product) OR (Smart AND Products) OR (Connected AND Product) OR (Connected AND Products) OR (Smart AND Connected AND Product) OR (Smart AND Connected AND Products) OR (Intelligent AND Product) OR (Autonomous AND Product) OR (Autonomous AND Products) OR (Autonomous AND Device) OR (Autonomous AND Devices))</td></tr><tr><th colspan="2">Criteria explanation</th></tr><tr><th>I/E</th><th>Criteria</th></tr><tr><td>Exclusion</td><td>Non-related (NR)<br>NR-1: Articles considering smart product-service systems<br>NR-2: The definition of "smart product" is not related to the organisational environment</td></tr><tr><td rowspan="2">Inclusion</td><td>Loosely related (LR)<br>LR: Articles that cite "smart product" but do not discuss the impacts of the capabilities in operations</td></tr><tr><td>Closely related (CR)<br>CR: Articles featuring SIP studies, pointing out capabilities and their relationship with internal organisational functions</td></tr></tbody></table>

Following the review, another nine papers were identified in the references of the initial set of papers (i.e. a snowball approach), which were also read in their entirety. This step resulted in adopting five additional documents in the final sample. Thus, after applying the

inclusion and exclusion criteria, our final sample comprises 44 articles. When studying smart products' capabilities through a similar method, other studies (e.g., Callefi et al., 2022, 2024; Raff et al., 2020) have also arrived at a similar number of selected articles. A summary of the filtering process results is shown in Fig. 2. Moreover, a sample of the 44 selected articles is available in the Appendix.

## 3.2. Content analysis and expert interviews: Proposing the first draft of the framework for organisational functions' evolutional path

The framework is proposed based on content analysis and validated through expert interviews. The assessment elements comprised: 1) capabilities provided by the SIP for the organisation; 2) a brief description of these capabilities; 3) the function impacted (benefitted) by implementing the SIP; 4) a practical example (i.e. how the impact happens); and 5) the moment when the barriers directly impact SIP adoption.

Regarding capabilities, each of them represents a benefit that the process acquires when the organisation adopts the SIP. Thus, a codification and a description were provided to improve understanding and enable the grouping process for capabilities cited differently but with the same meanings. This process is based on how the capabilities impact the operations of the acquired ability. Subsequently, each report describes how the acquired ability changes a current process.

### 3.2.1. Content analysis

The content analysis procedure involved identifying capabilities associated with each organisational function, drawing upon practical examples cited in the analysed papers. Thus, each capability and barrier was codified and briefly described. These descriptions were then input into NVivo 11 software to analyse semantic similarities among constructs, following a procedure similar to that conducted by Bianco et al. (2023), who performed a semantic analysis of factor descriptions for categorisation. This step aimed to recognise instances where a capability or barrier might have been mentioned using different terminologies across various papers but held the same conceptual meaning. In addition, a sensitivity analysis was conducted to determine whether one or more of the analysed factors (capabilities and barriers) could be grouped into a single factor. In this regard, an adequate preliminary list of
<figure><img src="image_5.png" /><figcaption>Fig. 2. Summary of article selection.</figcaption></figure>

capabilities and barriers was guaranteed. Subsequently, three other researchers manually analysed the remaining barriers and capabilities. This thorough analysis sought to identify any remaining semantic similarities, ensuring the method's robustness.

Second, we also analysed self-citation effects to ensure the comprehensive coverage of relevant literature and identify potential biases in assessing capabilities and barriers. During this analysis, we observed that only two articles within our sample contained self-citations. These two studies did not address the same capabilities or barriers. Consequently, it can be inferred that there is no significant bias related to self-citation effects.

Third, we identified theoretical models and frameworks that pointed to classification stages for the functions according to the level each would have in terms of autonomy. For this analysis, we used an adaptation of the framework proposed by Oluyisola et al. (2020) as it proposes a classification for SIPs according to the autonomy level that their functions have. This model presents three classification levels according to the function's autonomy index, namely connected, transparent, and autonomous decision-making.

Fourth, we identified which words used in the reviewed capabilities descriptions were related to each framework level. Table 2 presents the levels, their respective definitions, and the keywords each capability definition or description should have to allow capability classification within an organisational function. These categories represent indicators related to the performance of capabilities in a practical context.

Fifth, after identifying the levels, we used Table 2 to identify the capabilities within each level. For instance, as later addressed, the capability MA3 comprises the ability to collect and carry information concerning the product during its lifecycle. This capability contains terms such as collect and carry; thus, these terms are associated with passivity and can be classified in the connectivity level of the maintenance function. Finally, the same procedure was used to identify the barriers to be classified at each level.

### 3.2.2. Expert interviews: The final framework proposition

The process of refining and verifying validity was conducted by interviewing five experts, including both practitioners and academics. This number of experts was chosen non-probabilistically via purposive sampling. This strategy has been used by several authors (Callefi et al., 2022; Lopes et al., 2023; Naeem, 2020). Furthermore, it was specified that the experts must be academic researchers with more than 10 years of experience in the field of production planning and control to satisfy

<table><caption>Table 2
SIP capabilities classification by category level.</caption><thead><tr><td>Category</td><td>Definition</td><td>Features that a capability must have to be classified within each category</td></tr></thead><tbody><tr><td>Connected</td><td>The connected category comprises the capability provided for an SIP when it is in its first stage of maturity (it is connected but does not have any level of integration or even autonomy in decision-making)</td><td>To be considered within this category, the capability description should contain terms such as data gathering, connectivity, or data acquisition</td></tr><tr><td>Transparent</td><td>The transparent category comprises the capabilities provided for an SIP when it is connected and integrated with other machines, systems, and resources.</td><td>To be considered within this category, the capability description should contain terms such as visibility, interoperability, real-time information management, M2M communication, connected, integrated, or networked</td></tr><tr><td>Autonomous decision-making (adaptability)</td><td>The autonomous decision-making category comprises all the capabilities that are provided by an SIP when it is in its highest stage of development, being connected, integrated, and making use of the data and the identified behavioural parameters of all the production systems to feed back to the SIP, continuously improving its configurations and predicting and planning for future events</td><td>To be considered within this category, the capability description should contain terms such as data usage, self-managing information, reconfiguration, algorithm application, analytics, change reaction, and historical data application</td></tr></tbody></table>

Source: Adapted from Oluyisola et al. (2020).

the criteria for selection. The experts' profiles are presented in Table 3.

Three aspects were considered while validating and refining the barriers and capabilities: title and description; correlation with the research objective, and association with the defined category. The interviews were conducted via videoconference and lasted an average of 50 min.
<table><caption>**Table 3**
Experts' profiles.</caption><thead><tr><th>Expert</th><th>Position</th><th>Academic education</th><th>Experience in PPC or Industry 4.0</th></tr></thead><tbody><tr><td>1</td><td>Researcher/ Professor/ Consultant</td><td>PhD in Production Engineering</td><td>20 years</td></tr><tr><td>2</td><td>Researcher/ Professor</td><td>PhD in Production Engineering</td><td>13 years</td></tr><tr><td>3</td><td>Researcher/ Professor/ Consultant</td><td>PhD in Production Engineering</td><td>13 years</td></tr><tr><td>4</td><td>Researcher/ Professor</td><td>PhD in Production Engineering</td><td>11 years</td></tr><tr><td>5</td><td>Researcher/ Professor</td><td>PhD in Production Engineering</td><td>15 years</td></tr></tbody></table>

# 4. Results and discussion

This section presents the results of this paper, showing the responses to the proposed research questions. We start with a brief descriptive analysis of the papers composing our final sample, later introducing the capabilities and barriers that allow us to introduce a framework for smart-enhanced organisational functions.

## 4.1. Descriptive analysis

This research identified 44 papers (Table A) that pointed out the capabilities or barriers SIPs provide for the process when organisations decide to adopt it in production. Of these, 18 papers (40.91 %) provided insights into capabilities and barriers to SIP adoption. Furthermore, 14 papers (31.82 %) focused solely on capabilities, while 11 papers (25.00 %) exclusively addressed barriers. Notably, one paper (2.27 %) neither demonstrated specific capabilities nor barriers; however, it was pivotal in spotlighting future challenges within the SIP field. This analysis reveals a trend of increasing publication count during the examined period, possibly catalysed by the assimilation of disruptive Industry 4.0 technologies. Another important aspect is the significant increase in the volume of articles published during the last five years, which accounts for more than 60 % of the papers in our sample. According to Wang et al. (2023), this tendency is due to practitioners' increased interest in SIP implementation. Fig. 3 shows the 44 papers' publishing distribution throughout time.

Considering the geographical applications of the studies (country of the corresponding author), Fig. 4 shows that publications are

widespread in four continents (America, Asia, Oceania, and Europe). Most of the research, however, is centred in Europe (54.55 %), Asia (29.55 %), America (13.64 %), and Oceania (2.26 %). Another point to note is that authors from developed countries wrote most of the studies. Nevertheless, no emerging country had more than one article identified. It seems that the SIP research field follows the worldwide trend, in which the majority of Industry 4.0 research is concentrated in developed countries (Oesterreich & Teuteberg, 2016).

## 4.2. Analysis of capabilities and carriers for SIPs

### 4.2.1. Capabilities provided by SIPs for the main organisational function

In this SLR, the term capability was adopted to consider the ability that the SIP adoption brings to the process. In Table 4, we present the 20 capabilities identified by the SLR. Table 4 also presents, for each capability, the description, the authors who highlighted each one, a practical example (i.e., how the impact happens), and the main function impacted. In this process, the capabilities were classified within each organisational function. To classify the capabilities, we analysed the description of each one with the terms associated with each organisational function. This content analysis was performed using NVivo 11 software. Table 4 presents all the capabilities identified and their functions and provides examples of these capabilities, illustrating how they improve the current operations in a real operational environment. The examples were extracted from the papers composing the final sample of this SLR.

As shown in Fig. 5, Production planning and control (PPC) was the main function impacted, containing 13 out of 20 capabilities identified (65 %), followed by quality management (QM) with five capabilities (25 %), and maintenance management (MM) with three capabilities (15 %). Accounting management (AM) and sustainability (SUS) functions were impacted by one capability for each function (5 %). The total percentage exceeds 100 % because two capabilities directly affect two functions simultaneously.

From this analysis, we identified some strengths and weaknesses of the literature concerning SIP barriers and capabilities. Regarding strengths, the practical nature of the works allows us to find examples of applications and their impacts, as well as the temporal moment of impact of structural barriers when adopting an SIP. On the other hand, concerning a weakness in the literature, we perceive that different authors address different capabilities according to specific contexts of SIP applications, resulting in a somewhat myopic managerial perspective.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Growth of publications in the SIP field.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Distribution of the papers by geographical location.</figcaption></figure>

We believe this systematic literature review helps to bridge this gap once it organises the field, proposing a list of capabilities and barriers provided by SIPs for the main organisational functions.

### 4.2.2. Barriers faced by organisations in implementing an SIP

Initially, 23 barriers were identified (Table 5), demonstrating many factors influencing SIP implementation. The barriers were classified into three groups according to the temporal moment of their impact on the SIP implementation process. The classification process was undertaken through content analysis (using NVivo 11), considering the association among the terms contained in each barrier's description and the level it impacts. Table 5 contains the barriers and their main characteristics.

### 4.2.3. Smart-enhanced organisational functions: Characteristics and evolution

The smart-enhanced organisational function term may comprise more than one level within the function, given that several authors (e.g., Porter & Heppelmann, 2015; Romsdal et al., 2021; Tomiyama et al., 2019) have classified SIPs according to pre-defined categories that are impacted by specific capabilities. In this step, the identified examples were fundamental to classifying the capabilities. Fig. 6 presents the proposed framework showing the relationship between capabilities, barriers, and organisational functions. The relationship between these three categories of variables provides an evolutional path for organisational functions. This framework identifies the necessary capabilities for each organisational function according to the SIP level and the barriers that must be overcome.

Concerning accounting management, this function is essential to control organisations' net worth. Traditional assets management is the context where it is currently most straightforward to develop practically relevant means-ends propositions, at least regarding RFID technology. It transforms data into strategic actions that drive decision-making, analysing losses and profits (Meyer et al., 2009). Hence, to make improved decisions, data quality and temporal aspects are essential. An SIP may contribute to making this function a smart enhanced organisational function, providing data about itself regarding, e.g., production rate, machine utilisation, capacity, and load. This enables improvements in the decision-making process, e.g. when to substitute a machine, which capacity the new machine needs to have, and optimising asset investment by maximising the return on investment (ROI) and shortening the

payback (AM1). In addition, by introducing intelligence in SIPs, it becomes easier to share the status of the machine, including the condition of its main parts, and also facilitate the economic viability of the asset (Meyer et al., 2009; Yi et al., 2021). The highest level found in the literature on accounting management is the connected level. However, based on knowledge from previous levels, the potential evolution of technology, and the accounting function itself, we can infer that when the function reaches the transparent level, it will provide an even more detailed and transparent view of the organisational financial information. This includes automatically identifying positively correlated variables to support managers in making profitable decisions. At the autonomous decision-making level, the accounting function can evolve to play an even more strategic role within the company.

Concerning sustainability, this organisational function aims to reduce the organisation's environmental impact, migrating current operations to a more sustainable one. This migration has the social, economic, and environmental pillars as drivers. When this organisational function evolves to a connected level, data are automatically collected, and humans may make decisions with limited analytical capability (Tomiyama et al., 2019; Vendrell-Herrero et al., 2021). Afterwards, in the transparent sustainability function by real-time data integration, staff can identify new patterns in millions of pieces of data through analytics solutions (SUS1) and using embedded technology that allows information storage of the entire life cycle of the machine (Meyer et al., 2009). This new pattern may be converted into actions that reduce the entire organisation's environmental impact, such as reducing the amount of scrap and defects and improving energy management and other parameters that are not easy to identify without data integration. In addition, these capabilities only may be achieved by SIPs at a transparent level, that is, by SIPs that already have a connectivity level, which means that the organisations surpassed all structural barriers that hinder the advancement of the level of autonomy of an SIP. Although the transparent level is the highest level found in the literature, we can predict the autonomous decision-making level based on previous knowledge and characteristics of the level. Yet, SIPs can identify opportunities to expedite or delay a production order to make production possible when renewable energy is available (Antons & Arlinghaus, 2022b; Tomiyama et al., 2019; Turner et al., 2022). In summary, at this level, the SIP continuously learns from past events, improving present and future decision-making and achieving a circular economy.
<table><caption>**Table 4**

Capabilities provided by SIPs for the main organisational functions.</caption><thead><tr><th>Capabilities</th><th>Code</th><th>Description</th><th>Authors</th><th>Examples</th><th>Main functions impacted</th></tr></thead><tbody><tr><td>Improved efficiency in terms of asset investment</td><td>AM1</td><td>Ability to improve the efficiency in asset investment through data gathering concerning, for example, the use frequency, quantity, and other variables</td><td>3, 30</td><td>Bowen Engineering used a tool-tracking solution that combines both identification and tracking. The improved utilisation of assets and a significant reduction in tool investment (3) were achieved.</td><td>AM</td></tr><tr><td>The process becomes more sustainable.</td><td>SUS1</td><td>Ability to use integrated data to produce goods without causing environmental impacts or diminishing them</td><td>3, 23, 31, 40, 44</td><td>Energy consumption might be reduced by understanding the machine's condition. Better accuracy in predicting failures early will prevent unnecessary operation stoppage, guarantee the best resource use, and reduce waste. Understanding the usage over the life cycle will support better decisions for end-of-life treatment, thereby achieving a circular economy (36, 37). It is also the case that once end-of-life and recycling information is provided by SIPs, enhanced procedures may be followed in the act of disassembly to respect the optimal course of action in terms of overall sustainability and carbon reduction goals (44).</td><td>SUS</td></tr><tr><td>Improvement in maintenance accuracy, including preventive and prescriptive maintenance by viewing, drawing, and designing data</td><td>MM1 / PPC1</td><td>Ability to use integrated real-time information about the machine's use to anticipate breaking probability, improve maintenance planning and other factors related to the machine's efficiency</td><td>3, 6, 13, 23, 29, 30, 39, 40, 44</td><td>Patients need different types of care depending on their conditions. Attaching a Smart-It device to a mobile medicine cabinet can facilitate the health care of young patients with chronic diseases. Smart-It is a small-scale computing device connected to everyday objects to provide sensing, computing, and communication capabilities. The caregiver can track medicine use and drug compliance, reminding patients to take their medicine (23).</td><td>MM / PPC</td></tr><tr><td>Remote repair services</td><td>MM2</td><td>The integration between the SIP and the organisational systems allows staff to execute repairs on the SIP remotely</td><td>13, 31</td><td>Sysmex originally added connectivity to its instruments to allow remote monitoring but now also uses it to provide servicing. Service technicians can access just as much information about a machine when off-site as when on-site. Often, they can fix it by rebooting it, delivering a software upgrade, or talking an on-site medical technician through the process. As a result, service costs, equipment downtime, and customer satisfaction have improved dramatically (13).</td><td>MM</td></tr><tr><td>Collecting and carrying information about itself during its lifecycle</td><td>MM3</td><td>The ability of the SIP to collect data about its use to facilitate several services and its better use</td><td>5, 30, 31, 37, 42, 44</td><td>In the middle of the life phase, remote diagnostics seems to be one of the main promising services provided by SIPs. This diagnostics service facilitates services such as better problem diagnosis, improvement of condition-based maintenance and scheduling of service personnel, sourcing spare parts (38), and defect identification in product design and manufacturing procedures.</td><td>MM</td></tr><tr><td>Integration of sensing technologies into passive RFID tags</td><td>PPC2</td><td>Ability to use sensors to detect physical, chemical, or biological properties/ quantities, converting them into readable signals through the use of sensors, integrating data with the system</td><td>1</td><td>The Wireless Identification and Sensing Platform (WISP) project, carried out at Intel labs, has been used to measure quantities such as light, temperature, acceleration, strain, and liquid level in a particular environment (1).</td><td>PPC</td></tr><tr><td>Interoperability among various autonomous intelligent resources inside/outside the organisation</td><td>PPC3</td><td>Ability to communicate among different SIPs that work under different protocols, self-managing information to meet the PPC objectives</td><td>2, 13, 40, 41, 42, 43, 44</td><td>Attributes (e.g. location, type, quality, and version) and the physical operations through which it has been transformed can be stored. The item location can be more specific once there is no need to aggregate over fixed locations. This enables monitoring and controlling individual products where disturbances often happen (2).</td><td>PPC</td></tr><tr><td>Automatic reconfiguration of the SIP/ production line</td><td>PPC4</td><td>Ability to improve/ upgrade SIPs during the use phase according to the production</td><td>3, 16, 18, 24</td><td>SIPs in intelligent production lines and assembly will automatically reconfigure</td><td>PPC</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Capabilities</th><th>Code</th><th>Description</th><th>Authors</th><th>Examples</th><th>Main functions impacted</th></tr></thead><tbody><tr><td>Dynamic reconfiguring of the team's resources within the production process</td><td>PPC5</td><td>Ability to reconfigure the team's resources to provide agility to frequent changes in production, fault-tolerance to resource breakdowns, and adaptability to material flow variations through information integration</td><td>4</td><td>themselves in response to customised smart requirements, interacting with production engineers based on intelligent manufacturing technology, which seeks to improve production time and quality and to reduce costs in the physical end, achieving manufacturing process monitoring and production quality control in the cyber end for SIPs (24). When applied in a production system, this approach allows better and automated decision-making at the resource level based on the following strategies: hierarchical strategy; negotiated heterarchical strategy; and non-negotiated heterarchical strategy. Each one provides better decision-making according to specified criteria (4)</td><td>PPC</td></tr><tr><td>Complete integration among physical, cyber, and social environments with a high autonomy level, improving the performance of organisational information systems</td><td>PPC6</td><td>Ability of the SIP to autonomously make decisions acting in coordination with other SIPs, systems, and customers, improving information systems accuracy through automatic monitoring and context awareness</td><td>5, 8, 11, 17, 18, 27, 28, 29, 37, 41</td><td>A case study was performed to validate the proposed cyber-physical social system (CPSS). The agility, flexibility, responsiveness, and coordination capability of the CPSS-enabled personalised product production system was enhanced through multi-role interactions. The proposed CPSS platform and distributed production control mechanism were feasible (17).</td><td>PPC</td></tr><tr><td>Remote process control</td><td>PPC7</td><td>Ability to control the product through integration between the SIP and remote commands/ algorithms that are built into the device or reside in the product cloud</td><td>8, 20, 30, 36</td><td>In a production process, commands such as "if the pressure gets too high, shut off the valve" or "when traffic in a parking garage reaches a certain level, turn the overhead lighting on or off" can be added (8). In wind turbines, a local microcontroller can adjust each blade on every revolution to capture maximum wind energy. Each turbine can be adjusted to not only improve its performance but also to minimise its impact on the efficiency of those nearby (8). The integration of process and quality control aiming at implementing feedback control loops for the self-adaptation of process and product configuration is achieved by interacting with these agents. The benefits include increased production efficiency and scrap reduction from 3% to 5% (12).</td><td>PPC</td></tr><tr><td>Self-performance optimisation through the reuse of experiences to enhance its decisional performance</td><td>PPC8/ QM1</td><td>Ability to apply algorithms and analytics to both in-use or historical data, enabling a remarkable enhancement in output, utilisation, and efficiency. Through the expertise gained from past decision-making, SIPs make optimal choices that align production parameters with customer requirements, continuously feedbacking and improving it by applying self.* methods</td><td>8, 10, 12, 18, 29, 36, 43</td><td>GE's Brilliant Factories initiative uses sensors (retrofitted on existing equipment or designed into new equipment) to stream information into a data lake, where it can be analysed for insights on cutting downtime and improving efficiency. This approach doubled the production of defect-free units in a plant (13). Incorporating SIPs capable of providing real-time data regarding their state, progress, and current whereabouts in the value chain through accurate "process fingerprint" models of each manufacturing process, the connectivity and data provided by the sensor system enable automated tracking of the intelligent products' state and the creation of a seamless, item-level manufacturing history (batch-size-1) (26).</td><td>PPC</td></tr><tr><td>Machines linking together in systems</td><td>PPC9</td><td>Ability of networked machines to fully automate and optimise production</td><td>13, 20, 39, 40, 43</td><td>Adopting an autonomous product-driven control strategy in a production cell improves performance levels, especially in cases where disturbance factors occur (39). A system performs better in terms of robustness when it deploys an intelligent</td><td>PPC</td></tr><tr><td>Data collection capability is improved.</td><td>PPC10</td><td>Ability to collect and communicate data throughout the manufacturing process to continuously update the process plan</td><td>26, 30, 37, 39, 40</td><td></td><td>PPC</td></tr><tr><td>More robust and flexible production systems</td><td>PPC11</td><td>Ability to react to changes such as demand and production scheduling, and also to share information among SIPs as a way to bring robustness to the production process</td><td>2, 3, 5, 30, 36, 39</td><td></td><td>PPC</td></tr></tbody></table>

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<table><caption>Table 4 (continued)</caption><thead><tr><th>Capabilities</th><th>Code</th><th>Description</th><th>Authors</th><th>Examples</th><th>Main functions impacted</th></tr></thead><tbody><tr><td>Better complexity management associated with the combined offer of products and services within and between organisational units.</td><td>PPC12</td><td>Ability of an SIP to join and facilitate the complexity management of daily tasks associated with the provision of products and services of the goods produced</td><td>31, 32, 43</td><td>product approach in its operations (2, 40). Other demonstrations include the impact of information sharing among products and the system on the perturbations handling in FMS (41) and the effect of a role-based logic in an intelligent product approach on the real-time management of priority changes (42).</td><td>PPC</td></tr><tr><td>Increased visibility of ongoing operations</td><td>PPPC13/ QM2</td><td>Ability to have real-time information about ongoing operations, performing operational control</td><td>6, 7, 29, 30, 40, 43</td><td>Intelligent devices have advantages such as a high-speed start-up, power-saving features, excellent operability on the user interface, improving presentation quality and reducing meeting times (32). A company created an intelligent layer, allowing real-time information to make products like trucks and pallets smarter. Tracking technologies in products such as pallets and trucks were used to make them autonomous. Hence, every smart product should have access to all the information the tracking technology provides related to the physical object it represents. The negative impact of unexpected events in both its own and its customer's operations was mitigated, and the precision of information was improved in the enterprise resource planning system (7).</td><td>PPC/ QM</td></tr><tr><td>High-value aggregation</td><td>QM3</td><td>Ability of the process to make decisions based on generated/gathered data identifying new patterns in thousands of readings from many products over time</td><td>13, 28, 29</td><td>1) In a farm setting, data from humidity sensors can be combined with weather forecasts to optimise irrigation equipment and reduce water use. 2) In fleets of vehicles, information about each car or truck's pending service needs and location allows service departments to stage parts, schedule maintenance, and increase the efficiency of repairs. 3) Data on warranty status becomes more valuable when combined with product use and performance. Knowing that a customer's heavy use of a product will likely result in a premature failure covered under warranty, for example, can trigger pre-emptive service that may preclude later costly repairs (13).</td><td>QM</td></tr><tr><td>Ongoing quality management</td><td>QM4</td><td>Ability to identify and address design problems that testing failed to expose through operational quality control</td><td>13, 30, 36, 40</td><td>In 2013, batteries in two Tesla Model S cars were punctured and caught fire after drivers struck metal objects on the road. The road conditions and speeds leading to the punctures had not been simulated in testing. However, Tesla was able to reconstruct them through a software update to all vehicles that would raise their suspension under these conditions, significantly reducing the chances of further punctures (13).</td><td>QM</td></tr><tr><td>High process security</td><td>QM5</td><td>Ability to manage information about the process, such as a way to readily identify failures that can cause future severe damage</td><td>26, 29, 31</td><td>Jet engine blades are advanced products manufactured using several layers of materials sandwiched together using a thin heating profile and high pressure. An internal sensor monitoring the temperature curve could provide additional insights and reduce quality issues and scrap (26).</td><td>QM</td></tr></tbody></table>

Notes: 1. Atzori et al. (2010); 2. Meyer et al. (2009); 3. Meyer et al. (2011); 4. Borangiu et al. (2012); 5. McFarlane et al. (2013); 6. Takahara and Yasaki (2013); 7. Meyer et al. (2014); 8. Porter and Heppelmann (2015); 9. Hernández and Reiff-Marganiec (2014); 10. Bouazza et al. (2015); 11. Putnik et al. (2015); 12. Leitão et al. (2015); 13. Porter and Heppelmann (2015); 14. Duffy and Whitfield (2016); 15. Dawid et al. (2017); 16. Abramovici et al. (2017); 17. Ding and Jiang (2018); 18. Cena et al. (2019); 19. Silverio-Fernández et al. (2018); 20. Sorouri and Vyatkin (2018); 21. Juric and Lindenmeier (2019); 22. Keshavarzi and Van Den Hoek (2019); 23. Tomiyama et al. (2019); 24. Zhang et al. (2020); 25. Kahle et al. (2020); 26. Lenz et al. (2020); 27. Raff et al. (2020); 28. Pardo et al. (2020); 29. Hungud and Arunachalam (2020); 30. Yi et al. (2021); 31. Vendrell-Herrero et al. (2021); 32. Ruhul Amin et al. (2021); 33. Rathee et al. (2021); 34. Lin et al. (2021); 35. Thürer et al. (2021); 36. Imad et al. (2022); 37. Liu et al. (2023); 38. Oluyisola et al. (2022); 39. Antons and Arlinghaus (2022a); 40. Antons and Arlinghaus (2022b); 41. Tran et al. (2022); 42. Attajer et al. (2022); 43. Lei et al. (2023); 44. Turner et al. (2022).
<figure><img src="image_8.png" /><figcaption>Fig. 5. Capabilities per organisational function.</figcaption></figure>

The traditional maintenance management function is responsible for ensuring the proper functioning of all the assets used by the process to transform raw materials into finished goods and services or to support this process. This function may, in practice, use different approaches to perform maintenance of the assets, according to the moment, to the machine status, and to a myriad of other situations that may occur unpredictably in daily operations. Now, considering the many situations and scenarios that can arise in practice and the simultaneity and inter-relationship between events, maintenance management becomes a function that hardly operates at its optimum point. Moreover, implementing, evaluating, and managing indicators becomes very difficult for humans. In practice, the impact of an SIP on maintenance management may occur in different ways, according to the autonomy level of an SIP. Thus, an SIP that finds itself at a connected level may contribute to the maintenance management function by using data collected during its life-cycle (MM3) to facilitate several services and also to feed big data and analytic tools (Tomiyama et al., 2019), including a better end-of-life approach as before stated, achieving a circular economy. In sequence, by using an SIP at a transparent level, operations may use integrated real-time information of the machine's use, anticipating breakdown probability, and improving maintenance planning (MM1), for example, prescriptive maintenance (Vendrell-Herrero et al., 2021; Yi et al., 2021). Besides, SIPs allow remote repair services (MM2) (when integrated with organisational systems). This integration is achieved through digital twin technology and is the last stage before the autonomous maintenance level. With this capability, maintenance staff can monitor the machine's status in real time, and some defects may be repaired at a distance, changing only any parameter in the machine's configuration. Although in this function we found capability in levels connected and transparent, future perspectives may comprise the use of collected data concerning the entire lifecycle of a machine to autonomously improve maintenance indicators, allowing a prescriptive maintenance approach and remote repairing services. At this level, the maintenance function uses integrated historical data not only to prescribe maintenance needs but also to schedule the replacement of the part at the last possible moment before the component fails and the machine breaks down. The spare parts purchase requisition is also made considering lead time data versus the scheduled replacement, plus the safety stock period,

optimising the capital expenditure on slow-moving spare parts inventory to the maximum extent.

The PPC function comprises a series of decision-making processes to define what, how much, and when to produce and buy and the resources to be used (Vollmann et al., 2005). In the enhanced PPC function, connected SIP improves the organisation's data collection capability (PPC10), facilitating day-to-day management of complexities associated with the combined offer of products and services (PPC12). These capabilities allow the acquisition of other more advanced capabilities, such as the one acquired with transparent and autonomous decision-making SIPs. Besides, according to Vendrell-Herrero et al. (2021), organisations can better understand causal complexities regarding equifinality (different combinations of variables can be associated with the same outcome). This benefit helps organisations to solve problems and identify the relationship between variables that the limited human capacity to threat data cannot solve or identify. The only problem associated with this benefit is the computational power's necessity to threaten the data (Ruhul Amin et al., 2021). Therefore, firms that acquire an SIP connected level have an improvement in data collection but not necessarily in solving problems. Afterwards, regarding capabilities acquired at a transparent level, integration of sensing technologies into passive RFID tags (PPC2) (Atzori et al., 2010) allows organisations to acquire a series of other capabilities, such as remote process control (PPC7) (Sorouri & Vyatkin, 2018; Yi et al., 2021) and the optimisation of products' performance by the reuse of experience, enhancing decisional performance (PPC8). The transparency acquired in achieving this level presents both an opportunity and a challenge (Oluyisola et al., 2020). If, on the one hand, interoperability among various autonomous intelligent resources (MM1) and data collected may represent a source to feed analytics, enabling high precision of simulation models of production systems, on the other hand, standard data processing technologies are not capable of deriving insights from such big data (Lenz et al., 2020; Oluyisola et al., 2020). Furthermore, the dynamic reconfiguration of teams' resources within production processes enables organisations to respond quickly to some unexpected changes in the operational environment, such as fault tolerance to resource breakdown and adaptability to material flow variations (PPC5) (Borangiu et al., 2012), sometimes remotely controlling the production processes (PPC7) (Imad et al., 2022; Porter &
<table><caption>**Table 5**
Barriers faced by organisations in implementing SIPs.</caption><thead><tr><th>Barriers</th><th>Code</th><th>Description</th><th>References</th></tr></thead><tbody><tr><td>Communication difficulties</td><td>BA1</td><td>Refers to the different types of communication difficulties, such as vertical [among organisational levels over a short time (control) until the decision-making level (planning)] and horizontal levels [communications difficulties throughout the supply chain (suppliers to customers)].</td><td>1, 2, 3, 5, 6, 12, 15, 16, 22, 23, 25, 26, 29, 36, 37</td></tr><tr><td>Vulnerability in data security</td><td>BA2</td><td>Refers to the high vulnerability in the evolving IoT scenario. The kind of data being collected, with whom they are being shared, and how they can be manipulated by all levels (horizontal and vertical) can be decisive issues when an organisation decides to adopt an SIP.</td><td>1, 3, 6, 8, 9, 12, 13, 16, 17, 21, 22, 23, 25, 26, 34</td></tr><tr><td>Decreased planning efficiency when control is decentralised</td><td>BA3</td><td>The decentralisation of production control increases the system's robustness; however, its efficiency decreases once decentralised systems react to some unexpected events, such as a machine breaking down or failing and other disruptions.</td><td>2, 20, 39</td></tr><tr><td>Difficulties in integrating many different SIP</td><td>BA4</td><td>Refers to the way to operationalise IoT, allowing many systems with many components, built under different protocols, to work harmonically, adapting to the world's physical changes.</td><td>3, 30, 36, 37</td></tr><tr><td>Difficulties in aligning old industrial technologies with the implementation of a new SIP</td><td>BA5</td><td>Refers to the coexistence of new and old technologies, mainly in areas where transformation processes are evolutionary and not revolutionary (e.g. medicine).</td><td>5, 13, 16, 23, 25, 38</td></tr><tr><td>Lack of technological maturity</td><td>BA6</td><td>Many building blocks for supporting product intelligence are either in place or under development. Deployment on an industrial scale can also be considered within this barrier.</td><td>5, 37</td></tr><tr><td>Full adoption difficulties and lack of system stability (operational practicality and decision execution)</td><td>BA7</td><td>Initial deployments are likely to be partial rather than full deployments. In the case of full deployment, the complete stability of the entire system also needs to be considered.</td><td>5</td></tr><tr><td>Contractual and revenue-sharing challenges</td><td>BA8</td><td>Most companies fear having their ideas and confidential information leaked and prefer to compete individually.</td><td>5, 13, 19, 24, 25</td></tr></tbody></table>

<table><caption>**Table 5 (continued)**</caption><thead><tr><th>Barriers</th><th>Code</th><th>Description</th><th>References</th></tr></thead><tbody><tr><td>Social, psychological, and cultural individualistic behaviour barriers</td><td>BA9</td><td>Refers to the existence of resistance in process substitution or change, mainly if this process is rooted in the organisation. Cultural behaviour comprises the fears faced when people in the organisation need to share their knowledge and ideas.</td><td>5, 23, 24, 25</td></tr><tr><td>Lack of technical and economic feasibility in the early stage of utilisation</td><td>BA10</td><td>The term feasibility is understood as the possibility of an undertaking being achieved or is reasonable. In the context of the early stage of utilisation of an SIP's data, feasibility is a measure of the technical and economic viability.</td><td>5, 22, 24, 26, 36, 37</td></tr><tr><td>Complicated system introduction or even its affinity with already existing business systems</td><td>BA11</td><td>When connecting an SIP with business systems, procurement and deployment work for system introduction are necessary for each component of the service platform, network, and its own SIP. Most systems currently used by corporate users (e.g. Windows) are assumed to be devices' operating systems. Considering this situation, some failures associated with differences in operational systems and browsers could occur among each layer when connecting the SIP with existing business systems.</td><td>6, 12, 36</td></tr><tr><td>Tracking technology remains a fundamental challenge in transforming the abundance of operational information into accurate and timely control decisions</td><td>BA12</td><td>The commonly held perception that tracking technology will improve the ability to perform operational control can be equivocally wrong. Without specific mechanisms and technologies, such as big data and analytics, it would be impossible for human minds to deal with the large amount of data generated as well as to identify patterns among the data,</td><td>7, 37</td></tr><tr><td>Lack of infrastructure, including network and/or network latency, in some regions of the country where industries are based</td><td>BA13</td><td>Refers to the lack of network and multi-layer infrastructure required for new entrants in the market or not being available in the region where an organisation is set up.</td><td>8, 16, 22, 24, 25, 34, 36, 38</td></tr><tr><td>Uncertainty about ROI, payback, high costs and investments to (internally) develop an SIP</td><td>BA14</td><td>Uncertainty regarding ROI and payback are crucial issues. The investment can only be justified if the period is shorter and greater than the short natural life cycle of the goods produced. The total cost can involve factors</td><td>5, 9, 21, 22, 23, 25, 26</td></tr></tbody></table>

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<table><caption>Table 5 (continued)</caption><thead><tr><th>Barriers</th><th>Code</th><th>Description</th><th>References</th></tr></thead><tbody><tr><td>Self-adaptation difficulties among the SIP and its environment (plug-and-play technologies)</td><td>BA15</td><td>Refers to the lack of the product's ability to recognise standards within the process or how it is used (which is normally possible to achieve through other technologies, such as Big Data and Analytics) and to self-adapt to these newly recognised patterns. The product needs to readily deal with different technologies and software without needing installation or any other product upgrade.</td><td>12, 36</td></tr><tr><td>High energy consumption</td><td>BA16</td><td>The energy consumption of the SIP is proportional to the complexity of the operation it performs, and not all the products can store energy, requiring other technologies to capture it.</td><td>01, 12, 29, 34, 36</td></tr><tr><td>Risk of bad event propagation</td><td>BA17</td><td>The product can replicate not only learning obtained through good events but also that obtained from bad events.</td><td>12, 34</td></tr><tr><td>High complexity of management</td><td>BA18</td><td>The complexity of the product/service can directly influence the management tasks, the way it interacts with the good being produced, with the other agents of the process, and with its ecosystem.</td><td>12, 29</td></tr><tr><td>Lack of security and trust in using data</td><td>BA19</td><td>Refers to how much we can trust in the SIP's decision-making, as well as issues such as the use of the (private or shared) cloud and the level of trust concerning the data collected by a competitor's product that the organisation could not access.</td><td>12</td></tr><tr><td>High complexity of system management due to the increase in the number of agents used</td><td>BA20</td><td>Refers to difficulties related to the infrastructure robustness required when the number of agents (e.g. tags RFID, sensors) acting in a determined environment (and even outside it) significantly increases.</td><td>16, 30, 38</td></tr><tr><td>Cost strategy is not adherent to innovative SIP solutions</td><td>BA21</td><td>High focus on cost reduction and not on aggregate value for itself and its customers.</td><td>25, 36, 38</td></tr><tr><td>Cyber-attack vulnerability</td><td>BA22</td><td>The high level of network connectivity dependence can bring new cyber-attack threats.</td><td>33</td></tr><tr><td>Lack of adequate computational resources</td><td>BA23</td><td>Refers to insufficient computational power, battery, storage, and I/O bandwidth to host and manage IoT applications and services.</td><td>29, 34, 35, 36</td></tr></tbody></table>

Notes: 1. Atzori et al. (2010); 2. Meyer et al. (2009); 3. Meyer et al. (2011); 4. Borangiu et al. (2012); 5. McFarlane et al. (2013); 6. Takahara and Yasaki (2013); 7. Meyer et al. (2014); 8. Porter and Heppelmann (2015); 9. Hernández and Reiff-Marganiec (2014); 10. Bouazza et al. (2015); 11. Putnik et al. (2015); 12. Leitão et al. (2015); 13. Porter and Heppelmann (2015); 14. Duffy and Whitfield (2016); 15. Dawid et al. (2017); 16. Abramovici et al. (2017); 17. Ding and Jiang (2018); 18. Cena et al. (2019); 19. Silverio-Fernández et al. (2018); 20. Sorouri and Vyatkin (2018); 21. Juric and Lindenmeier (2019); 22. Keshavarzi and Van Den Hoek (2019); 23. Tomiyama et al. (2019); 24. Zhang et al. (2020); 25. Kahle et al. (2020); 26. Lenz et al. (2020); 27. Raff et al. (2020); 28. Pardo et al. (2020); 29. Hungud and Arunachalam (2020); 30. Yi et al. (2021); 31. Vendrell-Herrero et al. (2021); 32. Ruhul Amin et al. (2021); 33. Rathee et al. (2021); 34. Lin et al. (2021); 35. Thürer et al. (2021); 36. Imad et al. (2022); 37. Liu et al. (2023); 38. Oluyisola et al. (2022); 39. Antons and Arlinghaus (2022a); 40. Antons and Arlinghaus (2022b); 41. Tran et al. (2022); 42. Attajer et al. (2022); 43. Lei et al. (2023); 44. Turner et al. (2022).

Heppelmann, 2015; Sorouri & Vyatkin, 2018; Yi et al., 2021). Finally, the autonomous decision-making level encompasses four pivotal capabilities (PPC4, PPC6, PPC8, and PPC11). These capabilities represent the most critical, encompassing autonomy across the production system, horizontally (from supplier to customer) and vertically (from shop floor control to enterprise resource planning). This heightened autonomy offers opportunities to enhance operations by leveraging past experiences for future decision-making, facilitating fully autonomous production planning and control (Cena et al., 2019; Meyer et al., 2009, 2011).

The traditional quality management function aims to continuously improve the standards of products, processes, people, and the work environment. When enhanced by connected SIPs, the quality management function increases the visibility of ongoing operations (QM2) (Takahara & Yasaki, 2013), providing real-time information on ongoing operations. This allows organisations to control the quality of the product and production parameters in real time, preventing defective products from arriving in the next operations (Meyer et al., 2014; Yi et al., 2021). Consequently, the production rate of faulty products is severely reduced due to the real-time identification of the first product that is not inside the specifications. In the next level, the quality management function enhanced by SIPs at a transparent level ensures excellence in production control, providing high-value aggregation (QM4) (Imad et al., 2022; Porter & Heppelmann, 2015), identifying new patterns in thousands of readings from many products over time, and managing information about the process (QM5) (Lenz et al., 2020; Vendrell-Herrero et al., 2021), such as identifying failures that can cause future severe damage. At this level, the capabilities enabled by SIPs configure themselves as bases for a completely autonomous decision-making quality function. However, although the function threat data provides insights into the process, the SIPs are passive and do not react to improve parameters. A SIP may identify failure probability that tests do not identify and also anticipate any breaking likelihood that can negatively impact the production planning and control (Hungud & Arunachalam, 2020; Lenz et al., 2020; Vendrell-Herrero et al., 2021). Thus, transparent quality management functions positively impact all other processes and organisational functions. At the highest level, the quality management function improves products' performance (QM1) (Bouazza et al., 2015) using the information handling capability to improve machine and process parameters, aggregating value (QM3) (Pardo et al., 2020; Porter & Heppelmann, 2015) through manufacturing servitisation. Once a SIP becomes an integrating part of a more extensive system, the value proposition broadens (Pardo et al., 2020; Porter & Heppelmann, 2015). In the metallurgical industry, for example, real-time monitoring can identify the wear rate of a CNC (Computer Numerical Control) lathe tool insert, which increases the piece's dimensions, going beyond the specified parameters and deteriorating its surface roughness. Recognising this dimensional increase, the SIP self-corrects by advancing the tool a few hundredths of a millimetre to compensate for the wear. When the insert can no longer be advanced,
<figure><img src="image_9.png" /><figcaption>Fig. 6. Smart-enhanced organisational functions: relationship and evolutional path.</figcaption></figure>

the machine retracts the advancement due to the insert wear and rotates to another side (a triangular insert typically has three cutting edges for machining parts). The energy consumption of the machine, also monitored in real-time, can be compared with different rates of tool wear, machine speed, and the type of coolant fluid. Having this data, the efficiency of the SIP can be maximised, adding value to the organisation.

Table 6 summarises the current features of each managerial function according to the stage in which it is classified. The cells in *italics* represent future perspectives for each function. These future perspectives may be achieved soon due not only to the technological advancement of the leading disruptive technologies but also to future technologies still in development, which, when applied to SIPs, can lead to improved operational planning and control in the main organisational functions.

Regarding barriers to the adoption of SIPs, it was found that they can be classified into three main categories: (1) traditional function; (2) connected and transparent; (3) autonomous decision-making. The first group impacts functions at their lowest smartness level, the traditional level (before the SIP implementation). This group contains eight barriers (BA6, BA7, BA9, BA10, BA13, BA14, BA21, and BA23) that the traditional function should overcome to achieve the later levels. For example, lack of technological maturity (BA6) impacts the function at its lowest level, because without technical feasibility, it is not possible to start the SIP implementation (C. Liu et al., 2023; McFarlane et al., 2013).

The second group contains barriers (BA1, BA2, BA5, BA8, BA11, and BA15), which the connected or the transparent functions must overcome to achieve the level of autonomous decision-making. For example, at this level, communication difficulties (BA1) may hamper the integration between the SIP and other systems within organisations and the whole supply chain (Imad et al., 2022; C. Liu et al., 2023). These difficulties can evolve vertical communications between ordering coordination systems and other organisational systems, such as manufacturing execution systems (MESs), enterprise resource planning (ERP) systems, and other systems and horizontal communications (between SIPs).

Finally, in the third group, there are barriers associated with autonomous decision-making (BA3, BA4, BA12, BA16, BA17, BA18, BA19, BA20, and BA22). These barriers impact organisations after they surpass the first and second stages. As they are associated with decision-making issues, difficulties in integrating many different SIPs (BA4), for example, points to the challenges of having systems with many

components working together and their respective self-adaptation to real-world physical changes (Imad et al., 2022; C. Liu et al., 2023).

# 5. Conclusions

## 5.1. Main results and contributions

Using SIPs is essential to enhance operational efficiency. However, a knowledge gap remains, particularly concerning the terminological consensus on the evolution of SIPs and the capabilities and barriers linked to adopting this technology within organisations. The current literature fails to comprehensively address these aspects, mainly concerning the understanding of the capabilities enabled by SIPs and the barriers to using such technologies. In this regard, we proposed a way to bridge this gap by identifying an evolutionary path for the main organisational function to become completely autonomous. Any research with this aim was found to the best of our knowledge.

A multi-method approach was conducted to develop this framework, comprising three basic research steps, including SLR, followed by expert interviews and content analysis. In the first stage, we identified 20 capabilities categorised into three levels: connected, transparent, and autonomous decision-making. Additionally, we identified 23 barriers and categorised them into three groups: pre-implementation, during implementation, and post-implementation, considering their impact on various stages of SIP adoption. Thus, we proposed a framework that offers an evolutional path for traditional organisational functions, providing a clear trajectory for SIP adoption, both within and holistically.

This paper's main novel scientific contributions can be summarised in four key aspects. Firstly, through an extensive review of the main definitions found in the literature, our research provides a new definition for smart products directly related to the organisational context. Secondly, the capabilities provided and barriers faced by organisations are systematically analysed. Thirdly, the capabilities and barriers identified were validated by experts and classified through content analysis. The results revealed that these capabilities directly impact six different organisational functions, categorised into three levels of autonomy: connected, transparent, and autonomous decision-making. The structural barriers were also classified based on three temporal moments of SIP adoption: before, during, and after implementation. Finally, our
<table><caption>**Table 6**
Features and perspectives of different stages of smart-enhanced organisational functions.</caption><thead><tr><th rowspan="2">Management function</th><th colspan="3">Characteristics</th><th rowspan="2">Autonomous decision-making</th></tr><tr><th>Traditional</th><th>Connected</th><th>Transparent</th></tr></thead><tbody><tr><td>Accounting management (AM)</td><td>Asset management with a high period of inertia and a limited time horizon of the data.</td><td>Real-time data gathering allows organisations to know the machines' status regarding workload, demand forecasting, and other variables that influence and serve as a basis for new investments.</td><td>Data are integrated through the Internet of Things, and positively correlated variables may be automatically identified.</td><td>Through analytics, big data are analysed, and prescriptions for decision-making on new asset investments are significantly improved.</td></tr><tr><td>Sustainability (SUS)</td><td>Plan actions to reduce the environmental impact of the organisation.</td><td>Real-time data gathering allows humans to identify new patterns based on a limited analytics capability.</td><td>Real-time data integration allows staff to identify new parameters among data using analytics solutions. These new patterns can be converted into actions that reduce the environmental impact of the entire organisation, such as reducing the amount of scrap and defects and improving energy management and other parameters that are not easy to identify without data integration.</td><td>SIPs provide innovative solutions to improve the general sustainability of the entire process, including autonomous decision-making if any possible improvement is identified.</td></tr><tr><td>Maintenance management (MM)</td><td>The most common maintenance approaches comprise the use of preventive and predictive maintenance. The use of active approaches as prescriptive maintenance is limited because of the lack of data concerning the behavioural parameters of the entire production system.</td><td>Connected products collect data about machines' status. Maintenance staff must maintain them, create schedules, and sometimes react to unexpected machine failures and breakdowns.</td><td>Through connectivity, the transparent MM function may execute remote repair services in case of unexpected failures. Other reactive maintenance approaches, such as preventive and predictive, are also improved through better data visualisation and the ongoing monitoring of machines' status.</td><td>Enhanced MM can use the collected data concerning the entire lifecycle of a machine to autonomously improve maintenance indicators, allowing a prescriptive maintenance approach and remote repair services.</td></tr><tr><td>Production planning and control (PPC)</td><td>The traditional PPC function aims to reconcile production resources and customer demand. Some actions are taken according to particularities that evolve in each production system, specifically regarding the volume and variety of products produced.</td><td>The connected PPC function takes decisions based on real-time data gathering, which improves accuracy and builds a solid base for planners to consider future scenarios.</td><td>The network-integrated PPC function allows information interchange among different SIPs, resources, and systems, and the team may be dynamically reconfigured to meet PPC objectives.</td><td>PPC has complete integration among physical, cyber, and social environments, using data to improve SIPs' work parameters and reusing its experience to enhance its decisional performance.</td></tr><tr><td>Quality management (QM)</td><td>The traditional QM function improves the most diverse parameters of production, including the goods that are being produced. Based on data, sometimes collected through production sheets, the quality team plans actions to mitigate identified problems.</td><td>The connected QM function uses real-time data gathering to perform operational control.</td><td>The transparent QM function may identify and address problems that tests failed to expose and manage information about the process, such as ways to identify failures that can cause future severe damage.</td><td>The autonomous QM function can decide based on gathered data, identifying new patterns in thousands of readings from many products over time, reacting in many different ways, such as improving machines' status, product configuration, and other decisions that directly facilitate quality control.</td></tr></tbody></table>

Notes: Cells in *italics* represent organisational functions' future perspectives. Cells in regular font represent organisational functions' current perspectives.

framework presents an evolutionary pathway for the main organisational functions, outlining the barriers organisations must overcome to progress from one level to the next, eventually achieving autonomous decision-making.

In addition to the scientific contributions, this research holds significant practical implications. Firstly, the evolutionary pathway presented in our framework can assist organisations in gaining an in-depth understanding of the barriers they will face at each stage of SIP adoption. This understanding allows for more effective planning and adequate preparation to overcome these obstacles, which can reduce the time and resources required to implement these systems successfully.

Furthermore, our framework offers a clear vision of the capabilities that organisations will acquire as they overcome the barriers associated with each level of autonomy in organisational functions. This visualisation helps managers identify the tangible benefits that can be expected at each SIP adoption stage, facilitating the justification for ongoing investments in SIP technology. By categorising capabilities into three levels – connected, transparent, and autonomous decision-making – we provide a structure that allows organisations to evaluate their progress and set clear goals to advance towards full autonomy. This model offers a strategic roadmap and serves as a diagnostic tool to identify areas that need specific attention to reach the next level of

maturity.

Additionally, identifying and categorising barriers into pre-implementation, during-implementation, and post-implementation provide a detailed perspective of the specific challenges that may arise at different stages. With this information, organisations can develop targeted mitigation strategies for each phase, increasing the likelihood of a smooth and successful transition to more advanced systems.

In summary, our framework facilitates understanding the necessary steps for SIP implementation and provides a practical guide to overcoming associated barriers and maximising organisational capabilities. Therefore, our research innovates, offering valuable insights for managers and business leaders seeking to transform their operations by adopting advanced system integration technologies.

## 5.2. Limitations and future research

It is worth mentioning that, similar to any other research, this study has limitations. Some limitations are associated with the Systematic Literature Review (SLR) method. These limitations can be attributed to the subjectivity involved in defining the search string and selecting the literature based on inclusion and exclusion criteria. As a result, the authors' interpretation may have been influenced by the barriers and
capabilities presented in the final sample. Furthermore, although the literature search was conducted using a predetermined search string, it was limited to three databases. This means that papers indexed only in other databases were not included in the study.

towards an autonomous decision-making level across all functions more rapidly.

**List of Acronyms and Abbreviations:**

<table><thead><tr><td>AM</td><td>Accounting Management</td><td>ISM</td><td>Interpretive Structural Modelling</td></tr></thead><tbody><tr><td>CPPS</td><td>Cyber-Physical Production System</td><td>M2M</td><td>Machine-to-Machine Communication</td></tr><tr><td>ERP</td><td>Enterprise Resource Planning</td><td>MES</td><td>Manufacturing Execution System</td></tr><tr><td>ICTs</td><td>Information and Communication Technologies</td><td>MM</td><td>Maintenance Management</td></tr><tr><td>I/O bandwidth</td><td>Input/Output Bandwidth</td><td>MICMAC</td><td>Matrice d'Impact Croisé Multiplication Appliquée à un Classement</td></tr><tr><td>IoT</td><td>Internet of Things</td><td>QM</td><td>Quality Management</td></tr><tr><td>RFID</td><td>Radio Frequency Identification</td><td>SLR</td><td>Systematic Literature Review</td></tr><tr><td>ROI</td><td>Return On Investment</td><td>SUS</td><td>Sustainability</td></tr><tr><td>SCADA</td><td>Supervisory Control and Data Acquisition</td><td>WISP</td><td>Wireless Identification of Sensing Plattform</td></tr><tr><td>SIPs</td><td>Smart Industrial Products</td><td></td><td></td></tr></tbody></table>

Despite these limitations, this article makes valuable contributions to the field of SIPs, opening up a range of research opportunities related to the identified capabilities and barriers. It provides insights into organisations' benefits and challenges when adopting SIPs, thereby establishing a foundation for future SLRs that will contribute to a cumulative body of knowledge.

Considering the next steps, future studies have the potential to enhance the understanding of the operational aspects of capabilities that directly impact the Production Planning and Control function through a multi-method approach. This approach could involve an SLR, expert interviews, ISM, and fuzzy MICMAC analysis. By employing this methodology, a comprehensive framework could be developed, highlighting the inter-function relationships and the driving and dependence power of different capabilities.

Furthermore, studies involving specialists can be conducted to identify the driving and dependence power among all the identified capabilities. This study could investigate the relationship between organisational functions and the adoption of SIPs. It would provide valuable insights into how the capabilities acquired by one organisational function can drive or enhance the capabilities acquired by other functions.

Regarding barriers, future studies can employ a combination of Interpretive Structural Modelling and MICMAC approaches to identify a roadmap for SIP adoption. This would involve mitigating the main barriers faced by the primary organisational functions, proposing effective policies to overcome them, and facilitating the transition

**CRediT authorship contribution statement**

**Paulo Eduardo Pissardini:** Conceptualization, Methodology, Writing – original draft, Software, Visualization. **Moacir Godinho Filho:** Resources, Methodology, Validation, Writing – review & editing, Supervision. **Mario Henrique Callefi:** Conceptualization, Methodology, Writing – original draft, Visualization. **Gilberto Miller Devós Ganga:** Validation, Writing – review & editing, Supervision. **Elias Ribeiro da Silva:** Validation, Writing – review & editing, Supervision.

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Data availability**

No data was used for the research described in the article.

**Acknowledgements**

This study received funding from CAPES (a Foundation within the Ministry of Education in Brazil) under the funding code 001 and financial support from Economic Affairs and Climate Action (BMWK) under the funding code 01MK22001B.

**Appendix**

<table><caption>**Table A1**
Articles identified in the systematic literature review (SLR).</caption><thead><tr><td>#</td><td>Source</td><td>Publication Title</td></tr></thead><tbody><tr><td>01</td><td>Atzori et al. (2010)</td><td>The Internet of Things: A survey</td></tr><tr><td>02</td><td>Meyer et al. (2009)</td><td>Production monitoring and control with intelligent products</td></tr><tr><td>03</td><td>Meyer et al. (2011)</td><td>Intelligent Products: A Survey</td></tr><tr><td>04</td><td>Borangiu et al. (2012)</td><td>Distributed manufacturing control with extended CNP interaction of intelligent products</td></tr><tr><td>05</td><td>McFarlane et al. (2013)</td><td>Product intelligence in industrial control: Theory and practice</td></tr><tr><td>06</td><td>Takahara and Yasaki (2013)</td><td>Problems with using smart devices for business and efforts to resolve them</td></tr><tr><td>07</td><td>Meyer et al. (2014)</td><td>Intelligent products for enhancing the utilisation of tracking technology in transportation</td></tr><tr><td>08</td><td>Porter and Heppelmann (2015)</td><td>How smart, connected products are transforming competition</td></tr><tr><td>09</td><td>Hernández and Reiff-Marganiec (2014)</td><td>Classifying smart objects using capabilities</td></tr><tr><td>10</td><td>Bouazza et al. (2015)</td><td>A model for manufacturing scheduling optimization through learning intelligent products</td></tr><tr><td>11</td><td>Putnik et al. (2015)</td><td>Smart objects embedded production and quality management functions</td></tr><tr><td>12</td><td>Leitão et al. (2015)</td><td>Intelligent products: The grace experience</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A1 (continued)</caption><thead><tr><th>#</th><th>Source</th><th>Publication Title</th></tr></thead><tbody><tr><td>13</td><td>Porter and Heppelmann (2015)</td><td>How smart, connected products are transforming companies</td></tr><tr><td>14</td><td>Duffy and Whitfield (2016)</td><td>Smart Products through-life</td></tr><tr><td>15</td><td>Dawid et al. (2017)</td><td>Management science in the era of smart consumer products: challenges and research perspectives</td></tr><tr><td>16</td><td>Abramovici et al. (2017)</td><td>Reconfiguration of smart products during their use phase based on virtual product twins</td></tr><tr><td>17</td><td>Ding and Jiang (2018)</td><td>Incorporating social sensors, cyber-physical system nodes, and smart products for personalized production in a social manufacturing environment</td></tr><tr><td>18</td><td>Cena et al. (2019)</td><td>Multi-dimensional intelligence in smart physical objects</td></tr><tr><td>19</td><td>Silverio-Fernández et al. (2018)</td><td>What is a smart device? – a conceptualisation within the paradigm of the internet of things</td></tr><tr><td>20</td><td>Sorouri and Vyatkin (2018)</td><td>Intelligent product and mechatronic software components enabling mass customisation in advanced production systems</td></tr><tr><td>21</td><td>Juric and Lindenmeier (2019)</td><td>An empirical analysis of consumer resistance to smart-lighting products</td></tr><tr><td>22</td><td>Keshavarzi and Van Den Hoek (2019)</td><td>Edge Intelligence-On the Challenging Road to a Trillion Smart Connected IoT Devices</td></tr><tr><td>23</td><td>Tomiyama et al. (2019)</td><td>Development capabilities for smart products</td></tr><tr><td>24</td><td>Zhang et al. (2020)</td><td>Environment interaction model-driven smart products through-life design framework</td></tr><tr><td>25</td><td>Kahle et al. (2020)</td><td>Smart Products value creation in SMEs innovation ecosystems</td></tr><tr><td>26</td><td>Lenz et al. (2020)</td><td>Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life</td></tr><tr><td>27</td><td>Raff et al. (2020)</td><td>Smart Products: Conceptual Review, Synthesis, and Research Directions</td></tr><tr><td>28</td><td>Pardo et al. (2020)</td><td>Are products striking back? The rise of smart products in business markets</td></tr><tr><td>29</td><td>Hungud and Arunachalam (2020)</td><td>Digital twin: Empowering edge devices to be intelligent</td></tr><tr><td>30</td><td>Yi et al. (2021)</td><td>Digital twin-based smart assembly process design and application framework for complex products and its case study</td></tr><tr><td>31</td><td>Vendrell-Herrero et al. (2021)</td><td>Adoption and optimal configuration of smart products: The role of firm internationalization and offer hybridization</td></tr><tr><td>32</td><td>Ruhul Amin et al. (2021)</td><td>Cyber attacks and faults discrimination in intelligent electronic device-based energy management systems</td></tr><tr><td>33</td><td>Rathee et al. (2021)</td><td>Decision-Making Model for Securing IoT Devices in Smart Industries</td></tr><tr><td>34</td><td>Lin et al. (2021)</td><td>Evolutionary digital twin: A new approach for intelligent industrial product development</td></tr><tr><td>35</td><td>Thürer et al. (2021)</td><td>Self-Organizing Material Flow Control using Smart Products: An Assessment by Simulation</td></tr><tr><td>36</td><td>Imad et al. (2022)</td><td>Intelligent machining: a review of trends, achievements and current progress</td></tr><tr><td>37</td><td>Liu et al. (2023)</td><td>Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review</td></tr><tr><td>38</td><td>Oluysola et al. (2022)</td><td>Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study</td></tr><tr><td>39</td><td>Antons and Arlinghaus (2022a)</td><td>Data-driven and autonomous manufacturing control in cyber-physical production systems</td></tr><tr><td>40</td><td>Antons and Arlinghaus (2022b)</td><td>Distributing decision-making authority in manufacturing-review and roadmap for the factory of the future</td></tr><tr><td>41</td><td>Tran et al. (2022)</td><td>Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions</td></tr><tr><td>42</td><td>Attajer et al. (2022)</td><td>An analytic hierarchy process augmented with expert rules for product driven control in cyber-physical manufacturing systems</td></tr><tr><td>43</td><td>Lei et al. (2023)</td><td>Reinforcement learning-based dynamic production-logistics-integrated tasks allocation in smart factories</td></tr><tr><td>44</td><td>Turner et al. (2022)</td><td>Industry 5.0 and the Circular Economy: Utilizing LCA with Intelligent Products</td></tr></tbody></table>

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