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Maturity Models in the Age of Industry 4.0 – Do the Available Models Correspond to the Needs of Business Practice?
Abstract
Maturity models (MMs) enable users to identify the
need for change and to derive the necessary measures
to accompany the change process. Existing literature
reviews indicate that the number of available models
has increased sharply in recent years. At the same
time, it is found that the number of model applications
does not keep up with the pace of development.
Against the background of the current digitization
trend, this article empirically investigates which
models are actually used in business practice. We find
that the degree of application is very low. Moreover,
we also examine user-related model requirements,
reasons for employing MMs, and the purpose of using
MMs, which can support the user-centered
development of future MMs.
... The 4.0 maturity model must correspond with the business models that exist in particular economies [99]. The key components of business process maturity models are based on business models [98]. ...
... et al. (2016)[98]. According toFelch et al. (2019),[99] the MM are adopted to Industry 4.0. The key constructs of the project model in Industry 4.0 are based on principles of PM and change management process[100,101]. ...
... et al. (2016)[98]. According toFelch et al. (2019),[99] the MM are adopted to Industry 4.0. The key constructs of the project model in Industry 4.0 are based on principles of PM and change management process[100,101]. ...
The aim of the article is to present the author’s concept of steel market enterprises’ maturity for functioning in Industry 4.0. The model is based on the assessment of key technologies or pillars of the new industrial concept. The proposed assessment includes five levels of investment maturity under the conditions of the fourth industrial revolution (a five-point assessment scale was used to measure the degree of maturity of developed enterprises by implemented projects of smart manufacturing (SM). The maturity assessment proposal is based on direct research. The research was carried out in the segment of enterprises from the Polish steel sector. The tool used for the research was a questionnaire survey. The research was carried out with 79 selected steel enterprises in Poland for the pilot study. On the basis of the research, it was established that the segment of enterprises in the Polish steel market are at the third level of maturity in the five-level scale of the model, where level 1 is the “preliminary” level, and 5 represents the optimal maturity level. Within particular pillars of Industry 4.0, according to all respondents, the biggest changes in terms of implementing the industrial concept took place in connection with the use of Internet and mobile technologies in the process of customer service, including EDI, an e-invoicing system. The second position belonged to investments in production automation with the use of individual machines (installation of sensors on devices and sensors for collecting data on the state of the machine). In addition, similar and relatively higher answers were given by respondents to questions about the development and compatibility of IT systems for production support, such as CAx, MRP, MES. The remaining technological pillars, such as data processing from machines, installations of real-time data; network and chain integration (end -to -end engineering); production automation with the use of interoperating machines (production nests); expansion of databases (Big Data) and visualisation of processes together with their optimisation using IoT were found to be initiated as activities in very large and large enterprises. The lowest rated scope of investment changes concerned the block chain in the steel sector. The research of investment of enterprises can be used to assess the maturity of enterprises in this segment of industry in Poland.
... Since the decision to grant a going concern has a major economic impact on the companies concerned and may be a basis for legal disputes, the explainability and transparency of the auditor's decision-making is also a criterion that must be considered when developing AI systems for the auditing domain (Kokina and Davenport, 2017). In order to help audit firms to start developing and using AI-based software solutions despite the multitude of regulatory requirements and the resulting implications, the use of Maturity Models (MMs) as a tool for strategic management is a suitable option (Felch, Asdecker and Sucky, 2019). MMs for different technologies and fields of application have been developed and discussed in scientific research for years (de Bruin, Rosemann, Freeze and Kulkarni, 2005). ...
... He analyses and structures MMs in a comprehensive literature study and finally develops a classification scheme. As digitisation has accelerated in recent years, the number of MMs proposed by the scientific community has also risen sharply (Felch et al., 2019).With regard to new technologies, e. g. AI, the use of MMs seems to be a suitable tool for the successful development and integration of these key technologies into the processes of a digitalised company (Felch et al., 2019). ...
... As digitisation has accelerated in recent years, the number of MMs proposed by the scientific community has also risen sharply (Felch et al., 2019).With regard to new technologies, e. g. AI, the use of MMs seems to be a suitable tool for the successful development and integration of these key technologies into the processes of a digitalised company (Felch et al., 2019). Lichtenthaler (2020) developed an AI Management Framework in which he defines appropriate maturity levels for the management of AI activities similar to the five stages of autonomous driving. ...
Artificial Intelligence (AI) is increasingly being used in various domains including highly regulated areas such as auditing. Although the use of AI in auditing may seem promising at the first glance, there are a number of implications that have so far prevented its broad application. By proposing the first Auditing Artificial Intelligence Maturity Model (A-AIMM), we assess the adoption and diffusion of AI in auditing by considering audit specific requirements. The resulting model contains eight different dimensions and five different maturity levels that foster audit firms in becoming AI-enabled organisations by providing recommendations for the further use of AI with their current capabilities. The development procedure represents a Design Science Research approach including a systematic literature review, a qualitative survey with audit experts and an iterative development process.
... • Maturity model (MM): It measures an organization's maturity based on a conceptualization of maturity levels and a target state to detect the need for change [50]. Considering these definitions, the following Boolean search string was used [("Industry 4.0" OR "Fourth Industrial Revolution") AND ("maturity model" OR "readiness model" OR "assessment model" OR "diagnostic model" OR "capability model")]. ...
... The model proposed for this study has been developed using available theory on Lean and I4.0 and comparing existing models for I4.0 adoption. The design of an MM was selected since it allows measuring an organization's current maturity level to recognize the need for change toward integrating best practices for digitization, innovation, and value creation, thus achieving the desired adoption target for I4.0 [50]. ...
This paper proposes a maturity model (MM) to become a smart organization considering Lean as a key enabler to drive I4.0 adoption. A systematic literature review on I4.0 and Lean concepts plus I4.0 adoption models was conducted through the PRISMA method based on articles from Scopus and Web of Science databases, and records from official websites (e.g., consulting firms) published between 2011 and 2022. Identifying the Lean and I4.0 relationship and comparing the models’ relevant characteristics allowed the development of the MM proposal. Although previous research refers to Lean and I4.0 collaboration, the opportunity to design a reference model for adopting both approaches was identified since their interaction enhances value creation. The comprehensive model supports structuring the types of Lean principles/methods/tools and I4.0 technologies and their action to link them and define which of them need to be implemented according to the maturity level chronologically. Additionally, the proposed MM provides an adoption roadmap that starts eliminating non-added activities in the initial stages for process improvement to integrate I4.0 enabling technologies later. The model makes it possible for practitioners to generate implementation and development processes oriented toward I4.0 adoption based on maturity levels in which Lean has the starting point at the first ones. Hence, it defines the enabling technologies to be incorporated and linked throughout the value chain, enhancing a Lean culture. This model will help organizations to become “smart” by allowing them to transition toward the best technology investment and continuously add value to their processes, people, and products. Moreover, the results will motivate researchers to study further the application of models for I4.0 adoption in which Lean is integrated to fill the gap with the I4.0 embrace caused by quickly changing industrial environments and the uncertainty and unknowledge of guidelines associated with incorporating new technologies.
... The organization readiness level is classified into five groups as initial, managed, defined, integrated and interoperable, and digital oriented. Existing literature reviews indicate that the number of available models has increased sharply in recent years [14]. At the same time, it is found that the number of model applications does not keep up with the pace of development. ...
... At the same time, it is found that the number of model applications does not keep up with the pace of development. Against the background of the current digitization trend, the work in [14] empirically investigates which models are actually used in business practice. They found that the degree of application is very low. ...
Banks and financial institutions in Sudan after ban lift are under increasing pressure to digitize services and operations so as to reduce digital gabs with its peer's institutions worldwide, and be ready for competency in the digital economy. Challenges with disruptive concepts such as IoT, physical systems, cloud computing, big data, artificial intelligence which are called Industry 4.0 with its business counterpart digital transformation face all banks in Sudan. Most banks and institutions are not ready for this digital transformation as they do not have the capabilities or digital literacy needed for digital transformation. In this paper due to the lack in such research work and the specificity of Sudan case which need specific criteria in assessing digital transformation readiness, we have proposed, implemented and testes a novel framework for assessing digital transformation readiness for banks and financial institutes in Sudan. This framework aims to assist banks and financial institutions by providing comprehensive guidance to assess their current situation and how to achieve higher digital transformation in order to maximize the benefits from digital technologies.
... Maturity models can map business transformation from an initial to an anticipated state by identifying characteristics that determine step-changes from a lower level of maturity to a higher level (Paulk et al., 1993). The origin of maturity modelling was in software engineering; however, it has seen application in a wide range of sectors, including but not limited to the medical sector, education, e-governance, supply chain management and construction (Santos-Neto and Costa, 2019;Felch et al., 2019). Aligned with that, maturity models have remained predominant even in construction related literature to assess maturity across a variety of areas such as project controls (Jawad and Ledwith, 2021), BIM (Yilmaz et al., 2019), risk management (Hartono et al., 2019), health and safety (Musonda et al., 2021), offsite (Dang et al., 2020) and lean construction (Rodegheri and Serra, 2020). ...
... The SMCeMM is about improving industry standards collectively; however, a maturity model with integrated systems is very progressive and educating the broad spectrum of contractors would remain critical (Felch et al., 2019). Spreading out informants across different peripheries of informant groups contributed to the validity of the maturity characteristics across the different sizes of general contractors (Okoli and Pawlowski, 2004;Miles and Huberman, 1994). ...
Purpose-Industry 4.0 is characterised by systemic transformations occurring exponentially, encompassing an array of dynamic processes and technologies. To move towards a more sustainable future, it is important to understand the nature of this transformation. However, construction enterprises are experiencing a capacity shortage in identifying the transitional management steps needed to navigate Industry 4.0 better. This paper presents a maturity model with the acronym "Smart Modern Construction Enterprise Maturity Model (SMCeMM)" that provides direction to construction enterprises. Design/methodology/approach-It adopts an iterative procedure to develop the maturity model. The attributes of Industry 4.0 maturity are obtained through a critical literature review. The model is further developed through knowledge elicitation using modified Delphi-based expert forums and subsequent analysis through qualitative techniques. The conceptual validity of the model is established through a validation expert forum. Findings-The research defines maturity characteristics of construction enterprises across five levels namely ad-hoc, driven, transforming, integrated and innovative encompassing seven process categories; data management, people and culture, leadership and strategy, automation, collaboration and communication, change management and innovation. The maturity characteristics are then translated into assessment criteria which can be used to assess how mature a construction enterprise is in navigating Industry 4.0. Originality/value-The results advance the field of Industry 4.0 strategy research in construction. The findings can be used to access Industry 4.0 maturity of general contractors of varying sizes and scales and generate a set of recommendations to support their macroscopic strategic planning.
... A model is needed, which specifies how maturity is measured or calculated and what data is used for this purpose. In the academic literature and in more practice-related publications, a wide variety of different models is suggested [2] [8] [20] [30]. As we show with some examples, in most cases the suggested and employed models lack empirical evidence and are rather simplistically formulated and/or make some ad-hoc assumptions regarding the model parameters used. ...
... In reality, more complex and in particular also nonlinear relationships could exist between basic variables and derived maturity assessments. This also reflects the requirements for organizations to individualize their maturity assessments, as shown by Felch et al. [8]. For that reason, a ML approach could also consider more general functions describing related connections. ...
... Typically, articles do not distinguish between the assessment method and the actual maturity model and do not or only partially provide the proposed assessment instruments (e.g., questionnaires), impeding reproducibility and practical dissemination (Mamoghli et al., 2018;Tarhan et al., 2016). Contradicting the requirements of practitioners (Felch et al., 2019), many maturity models also entail only limited prescriptive features and managerial implications, as they follow a rather descriptive approach (Felch and Asdecker, 2020;Van Looy et al., 2011). Usually, the focus lies on rather generic BPM capabilities, disregarding process characteristics (Andriani et al., 2018;Van Looy et al., 2017). ...
... Consequently, prescriptive features and managerial recommendations are scarce. Along this line, the quantitative study of Felch et al. (2019) indicates that practitioners highly require maturity models that include process orientation, quick application, simplicity, customizability, and a structured improvement plan. That also reflects the motivation of our study since the need for a practical maturity model incorporating the IT and BPM perspectives was particularly raised during a research project with an industry partner. ...
Business Process Maturity Models (BPMMs) receive growing attention within digital transformation to evaluate the maturity level of business processes regarding specific capabilities. However, existing models are primarily descriptive, with limited prescriptive features and guidance to improve the current state. Besides, the assessment methods of popular BPMMs do not adequately reflect the indispensable role of information technology (IT), i.e., IT systems and information flow, for business process management (BPM) and process performance. Motivated by a research project with an industry partner, we apply the practice-oriented Action Design Research methodology to introduce our comprehensive and prescriptive “BPMMxIT” maturity model that integrates the IT and BPM perspectives. This study details the development and application procedures and provides generalised design principles to evaluate process performance and capability maturity for digital transformation. Transparency and reproducibility are facilitated by extensive supplementary material. Finally, theoretical implications and pivotal directions for organisational practice and further research are outlined.
... Despite the importance of SC aspects and SMEs regarding Industry 4.0, a review of 20 maturity models for Industry 4.0 by Felch et al. (2019) revealed that only three out of 20 maturity models consider the aspect of SCs and only superficially do so. Wagire et al. (2020) provided an overview of 12 maturity models in the context of Industry 4.0. ...
... Instead, it presents results of a broad sample of SMEs and large enterprises and compares both groups regarding specific strengths and characteristics. It further contributes to the missing SC perspective of extant maturity or readiness models that also consider SMEs, in which company-external perspectives are only marginally evaluated (Felch et al., 2019;Wagire et al., 2020). By presenting both internal and external perspectives on maturity towards DT and Industry 4.0, a combined perspective is generated that allows to compare SMEs and large enterprises regarding specific characteristics, as presented in Table 1. ...
Purpose
While there are several readiness assessments regarding digital transformation (DT) and Industry 4.0 in extant literature, this study aims to contribute to (a) a better understanding of digital readiness in supply chain (SC) aspects and (b) elaborate on differences between small and medium-sized enterprises (SMEs) and large enterprises.
Design/methodology/approach
The study is based on 409 companies that participated in the Digital Readiness Check (DRC) in the region of Salzburg (Austria) and Bavaria (Germany) – an online assessment for self-evaluating the digital readiness of companies.
Findings
The study's results provide insights for the categories of strategy, employees, initiation of business transactions and SC. These are further differentiated for SMEs and large enterprises.
Research limitations/implications
The study is limited to two regions in Austria and Germany, based on a self-evaluation of companies in a single point of time perspective. For future research, the results of this study should be expanded for different regions. Further, the results could be validated regarding external observations and measuring results at a later point of time.
Practical implications
The DRC may help companies in benchmarking themselves and gaining a better understanding about categories that must be improved, especially regarding SC aspects of DT.
Originality/value
The DRC extends extant literature regarding the differentiation between SMEs and large enterprises as well as focussing on SC aspects of DT.
... Most of these models are complex, less pragmatic and also do not take into account the changing goals of the organization (Hizam-Hanafiah et al., 2020). Felch et al. (2019) conducted a detailed analysis in terms of the model's applicability to business practice. They suggest that not all Industry 4.0 readiness models are relevant or applicative. ...
... They suggest that not all Industry 4.0 readiness models are relevant or applicative. Some of these models are designed for specific industries and others are generic (Felch et al., 2019). They further suggest that empirical validation of these models has not been conducted. ...
Purpose
Organizations use Industry 4.0 readiness models to evaluate their preparedness prior to the implementation of Industry 4.0. Though there are many studies on Industry 4.0 readiness models, the dimensions of readiness differ. Besides, there is no study empirically validating the readiness model in different sectors or types of organization. The purpose of this study is to conceptualize the dimensions of the Industry 4.0 readiness model and subsequently evaluate the criticality of these dimensions in manufacturing, service, small and medium-sized enterprises (SMEs) and large enterprises (LEs).
Design/methodology/approach
The study uses an exploratory sequential mixed method design. In phase one, 37 senior managers participated through a purposive sampling frame. In phase two, 70 senior managers participated in an online survey.
Findings
The results of the study indicated that the Industry 4.0 readiness model has 10 dimensions. Further, the criticality of the dimensions as applied to different sectors and type of organizations is put forward. This study will help manufacturing, services, SMEs and LEs to evaluate Industry 4.0 readiness before commencing the deployment of Industry 4.0.
Practical implications
The findings can be very beneficial for Industry 4.0 practitioners and senior managers in different organisations to understand what readiness dimensions need to be considered prior to implementation of Industry 4.0 technology.
Originality/value
This paper makes an attempt to conceptualize the Industry 4.0 readiness model and utilizes an exploratory mixed method for critically evaluating the dimensions related to the model.
... Maturity models (MMs) are tools that offer organizations in different domains the possibility to either determine their decisive maturity within a pre-defined process or activity, or to offer guidance on how to improve this maturity and thereby improve performance. Due to their popularity in research and practice, many MMs have evolved in recent years; however, their applicability within organizations remains limited (Felch et al. 2019). Among others, reasons for these limitations can be found in their ad hoc development approaches (Pereira and Serrano 2020), leading to MMs that are missing conceptual grounding in their structures. ...
... Although numerous guidelines exist addressing the developmental process of MMs (i.e., Becker et al. 2009;De Bruin et al. 2005;Mettler 2010;Pöppelbuß and Röglinger 2011;Solli-Saether and Gottschalk 2010), they either focus on the development of MMs in specific domains like IT management (Becker et al. 2009) or business process management (Pöppelbuß and Röglinger 2011), or their recommendations remain limited in their interpretability and validity as they do not provide terminology or structural concept models for the development process (Bley et al. 2020;Plattfaut et al. 2011). Since most previously developed IT MMs still follow an ad hoc approach in their development process (Pereira and Serrano 2020), they often differ in the terminology used, their descriptive or prescriptive principles, and/or the extent of provided factors and their influence on degree of maturity, leading to limited comprehensibility and applicability in practice (Felch et al. 2019). Possible explanations can further be found in an assumed ones-size-fits-all approach to MMs for different classes of organizations, as well as the assumption of a single linear path rather than several different paths to maturity (Lasrado et al. 2015), thereby leading to an inflexible and non-realistic illustration of a complex reality. ...
Maturity models are regarded by researchers and practitioners as supportive tools for organizations to improve performance in various domains. The development process, however, still lacks theoretical and conceptual knowledge on how to design maturity models in complex domains like digitalization, which is why their applicability is often limited. To address this shortcoming, we develop an information systems design theory for maturity models based on configurational theory. We argue that configurational methods can depict domain-driven complexity and, furthermore, are able to identify sector-and size-specific evolutionary paths of organizational maturity, which enables the developer to derive more realistic and useful maturity models. We provide design principles for a method and model artifact in complex domains and demonstrate their applicability in an exemplary instantiation of design theory. Furthermore, we discuss artifact mutability and testable propositions, which are so far missing in the field of maturity model design research.
... Maturity models have a long history and models are developed for various purposes. Many maturity models have also been developed by consultants and associations (e.g., Anderl et al. 2015;Felch, Asdecker, and Sucky 2019). Maturity model research has been applied in more than 20 domains, but it is still heavily dominated by software development and software engineering models (Wendler 2012). ...
... Business maturity models provide information about a company's current status and how to improve it (Röglinger, Pöppelbuß, and Becker 2012) and offer a simple but effective tool to measure companies' capabilities and contribute to transformation and the development of competencies in companies by initiating a change process (Mettler, Rohner, and Winter 2010;Wendler 2012). They can also be used in developing a company's future vision and path, as benchmarking tools to compare firms with each other to set development goals or as self-review frames and managerial tools for selfimprovement action (Felch, Asdecker, and Sucky 2019;Leino et al. 2017;Röglinger, Pöppelbuß, and Becker 2012). Many business maturity models have roots in cmm (Paulk et al. 1993;Wendler 2012), and have adopted cmm's five-level approach (level 1 -initial, level 2 -managed, level 3 -defined, level 4 -quantitatively managed and level 5 -optimised), which describes an evolutionary path of increasingly organised and systematic maturity stages. ...
Small and medium-sized enterprises (SMES) play a key role in national economies around the world but face pressure to sustain their competitiveness in domestic and global markets. SMES should check their position periodically and figure out what they need to do next. Maturity models are suitable tools for documenting SMES’ current state, for developing the company’s future vision and path and for comparing capabilities between companies. This study’s aim is to obtain an overview of existing maturity models focused on SMES by conducting a systematic literature review (SLR) of the publications on business maturity models from the lens of SMES. As a result of this study, a growing trend for business maturity models for SMES is identified and future research opportunities for SME maturity research are suggested
... In addition to scientific maturity models, there are also a large number of maturity models developed by consultants, associations and initiatives, mostly having four to eight dimensions and five levels of maturity (e.g. Anderl et al., 2015;Felch, Asdecker, & Sucky, 2019). Maturity models offer organisations a simple but effective tool to measure their capabilities (Leino et al., 2017;Wendler, 2012) and contribute to organisational transformation and the development of competencies in organisations by initiating a change process (Mettler, Rohner, & Winter, 2010). ...
... Felch, Asdecker, & Sucky, 2019; Silventoinen, Pels, Kärkkäinen, Lampela, & Okkonen, 2013). Maturity models are especially suitable tools for (1) documenting the current state, (2) developing the future vision and providing guidance on that development path and (3) comparing the capabilities between organisations (Felch & al., 2019;Leino et al., 2017). ...
Digitalisation has been identified as one of the major trends changing society and business. However, companies are not making the most of all the opportunities that digitalisation has to offer. In the digital transformation process, it is important to start with assessing the current state. Digital maturity assessment can be used to analyse the current level of digital readiness and performance of an organisation. Micro-enterprises are the smallest group of enterprises and numerically the dominant enterprise type in economies. They differ from larger enterprises in terms of organisational characteristics, such as their unique attitude towards digital tools and application deployment. Furthermore, micro-enterprises are lagging behind in exploring the possibilities that come with digitalisation. This article describes the design process, usage possibilities, and initial experiences of a digital maturity model, especially from the viewpoint of micro-enterprises. As result, this article presents a micro-enterprise-focused self-evaluation framework providing holistic digital maturity status.
... The first step to develop this strategy is the assessment of how prepared organizations are to adopt I4.0 technologies (Antony et al., 2021;Krishnan et al., 2021). Maturity assessment models are one of the most common tools to understand firms' digital readiness level (Felch et al., 2019). These tools help companies understand their development and progression within the digitalization journey, providing them with an exhaustive overview of the changes required to facilitate technology implementation, across multiple departments of the organization (Antony et al., 2021;Proença and Borbinha, 2016;Schumacher et al., 2016). ...
The successful adoption of Industry 4.0 technologies by firms requires them to formulate a digital strategy and implementation roadmap. An established approach to assess firms’ needs towards digitalization is through maturity models. While there is a large number of maturity models in the literature, they present several limitations related to their generalizability and theoretical foundations. Our study aims to build and empirically validate an Industry 4.0 digital maturity model, based on the Technology-Organization-Environment framework. We conducted a systematic literature review of 55 digital maturity models, which we synthesized to create an integrated digital maturity assessment model. We tested our model through a focus group with industry experts and 24 companies from various manufacturing sectors.
Our review suggests that existing digital maturity models have underestimated the relevance of the Environment dimension. Our empirical data suggests that companies often invest in digital technologies without considering critical organizational and environmental constraints.
... Maturity models generally come from two types of sources [13]: scientific models and models developed by consulting firms. It is also possible to organize digital maturity models into two groups: self-assessment tools and audit tools. ...
... The model for measuring progress-digital maturity-is based on the assessment of digital capabilities primarily within these common business dimensions such as customers, strategies, technologies, operations, organization, and culture (Valdez-de-Leon, 2016;Williams et al., 2019;Felch et al., 2019;Eremina et al., 2019;Deloitte, 2018;Mydyti and Kadriu, 2021a;Rogić et al., 2022). ...
Background and purpose: This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies.
Methodology: Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design.
Results: Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented.
Conclusion: This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results.
... Despite myriad barriers and challenges, the attempts have subsequently triggered many digital transformation initiatives pursuing the I4.0 adoption benefits. The activity often involved experts to assist a firm on setting up I4.0 strategy and direction [5]. ...
With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0 (I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state. Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart technology, products and services have been treated as the popular evaluation dimensions which will define the state of an industry’s maturity level.
... Source: Prepared by the author During the interviews, it was detected that most organisations were unaware of the existence of tools that could assess their level of digital maturity, and more specifically, the use of maturity models as a starting point for a correct assessment of their technological positioning in the Industry 4.0 area (Machado et al., 2019) and subsequent construction of an implementation roadmap. After the explanation on how maturity models work, based on the analysis developed by Colli et al. (2018) and Felch et al. (2019), all interviewees considered them to be an asset to be taken into account, although they pointed out that, since it is a tool of strategic application, the decision to use it should be made by boards of directors (in the case of large companies) and general management (in SMEs). ...
... Para ello, es necesario determinar primeramente el estado actual respecto al nivel de madurez para poder entender que hay y que falta por hacer. La descripción de la madurez digital de una empresa puede contribuir a la transformación organizacional y la renovación de sus competencias ya que documentan el status quo de la empresa, y este, funciona como guía inicial para el cambio organizacional [17]. ...
In recent years, the economic, technological, and sociocultural environments have been in constant change. Customers are more demanding and aware of market trends. Companies innovate their processes through the implementation of technologies that allow them to optimize operations and facilitate business-client interaction. However, many companies, especially small and medium-sized enterprises, face difficulties in adapting to new business models. There are even companies that are not willing to change the traditional way they carry out their daily business operations, which can present more challenges in today's market. The digital transformation has caused companies to restructure their business models in recent years. Currently, with the arrival of COVID-19, many businesses have been affected due to the lack of digitization in the processes. Small and medium-sized companies were forced to embark on E-Commerce in order to reach their customers and survive the competition. Still, not all companies have managed to properly implement a digital transformation strategy to redesign their structures and operations. In Panama there are still no studies focused on the degree of digitalization of small and medium-sized companies. This research aims to describe the degree of digitalization of local companies in the province of Chiriqui - Panama
... In addition, although a large range of various maturity models has been developed, their usability and actual effectiveness are arguable. Felch, Asdecker, and Sucky [20] claim that particularly scientific maturity models in practice fail to meet users' requirements. This drawback is due to the lack of customizability and adaptability of such models. ...
The fast-growing market for the adoption of IoT technologies poses serious challenges for companies providing IoT solutions. These challenges require constant technological and managerial improvement from the companies. To select the right direction for improvements, managers need appropriate tools for analysis and decision making. Recognised tools of this type are maturity models. Currently, maturity models developed for IoT adoption are mainly oriented to the business to business (B2B) market, while business to consumer (B2C) companies also need such a reliable tool for business improvement. Thus, this work is intended to fill the gap in existing research through the development of a maturity model for IoT adoption focused on the B2C market. To achieve this goal, we based our model on the scientific literature as well as on practical experience gained by leading companies in the market of IoT solutions. Moreover, the development and validation of the maturity model are carried out in close collaboration with two reputable European experts with extensive practical experience in this field. The result is a maturity model, which is a balanced, practice-oriented tool for assessing the maturity of the IoT solutions implementation and accounting for the specificity of the B2C market.
... Source: Prepared by the author During the interviews, it was detected that most organisations were unaware of the existence of tools that could assess their level of digital maturity, and more specifically, the use of maturity models as a starting point for a correct assessment of their technological positioning in the Industry 4.0 area (Machado et al., 2019) and subsequent construction of an implementation roadmap. After the explanation on how maturity models work, based on the analysis developed by Colli et al. (2018) and Felch et al. (2019), all interviewees considered them to be an asset to be taken into account, although they pointed out that, since it is a tool of strategic application, the decision to use it should be made by boards of directors (in the case of large companies) and general management (in SMEs). ...
... Felch ve diğ. [27], geçmişi 1970'li yıllara dayanan ve son dönemde önemli ölçüde ivme kazanmış olgunluk ölçüm modellerinin hangilerinin erişilebilir olduğunu ve pratikte ne derece kullanıldıklarını araştırmıştır. Bu olgunluk ölçüm modellerinin bilinirlik ve kullanılabilirliğinin araştırılması için internet üzerinden kısa bir anket ile veri toplanmıştır. ...
... [40] analyzed various industry 4.0 maturity models. [41] have identified the most widely used Industry 4.0 maturity model. This paper aims to give an overview of available Industry 4.0 maturity models and empirically test their dissemination in business practice. ...
As an inevitable process, digitalization has become a priority for many companies. The measurement of digital maturity is the first step toward adequately executing this. Although digital maturity models (DMM) have been developed for different sectors in the literature, such studies in the defense industry are lacking due to sector-specific dynamics. This study aims to close this gap and proposes a digital maturity model specific to the defense industry. In this study, a novel model was developed that combines the SF-AHP and SF-TODIM methods due to the uncertainty and hesitancy contained in the evaluation. The validity of the presented novel model has been demonstrated in a prominent defense company in Turkey. According to the results, the most notable digital maturity dimensions are the evaluation of opportunities and alignment with stakeholders. In addition, the model indicates that the company owns the required soft skills, such as leadership, organizational culture, and strategic determination for digital transformation (DT). On the other hand, essential hard skills such as technology and operational competencies are yet to be improved. Lastly, sensitivity and comparison analyses are conducted to validate and verify the obtained results’ stability and robustness.
... We have therefore redefined MM so that it is compatible with a system similar to ours. Indeed, the literature proposes different Maturity Model levels to evaluate the digitalization of manufacturing systems [26][27][28]. ...
The use of information and communication technologies (ICT) in agriculture is far from their potential. In this article, we consider how to facilitate and systematize the process of transforming traditional agriculture into digital agriculture; Agriculture 4.0. Among the different technologies, we focus on the IoT aspects. In the article, we propose a new approach for the design of intelligent agricultural management and supervision systems. The proposed approach is illustrated as an example of application in the beekeeping sector. Indeed, this sector is affected by a crisis due to the disappearance of bees and the different actors need support to make their decisions. As an example of decisions that can be made, we can cite: treatment planning or policy planning. An architecture based on sensors and open data is proposed to help them make decisions. An implementation of it is shown; it is based on a device with sensors, as well as an interface to collect the data on beehives and show notifications and alerts to beekeepers. The proposed architecture is flexible, and it can be used in the context of different levels of technology maturity. The final objective is to develop a reusable architecture for Agriculture 4.0.
... However, these models are a management tool for reconfiguration, realignment, and renewal of organizational existing resources and capabilities. Moreover, Felch, et al. [25] argued that it is critical to study existing I4.0 readiness models, whether they originated from the practical or scientific community. Thus, Table 1 shows the existing I4.0 readiness models. ...
Industry 4.0 (I4.0) is a technological development in the manufacturing industry that has revolutionized Total Quality Management (TQM) practices. There has been scant empirical research on the multidimensional perspective of TQM. Thus, this study aims to empirically examine the effect of the multidimensional view of TQM (soft and hard) on I4.0 readiness in small and medium-sized (SMEs) manufacturing firms. Based on the sociotechnical systems (STS) theory, a framework has been developed and validated empirically through an online survey of 209 Malaysian SMEs manufacturing firms. Unlike the existing TQM studies that used structural equation modeling (SEM), a two-stage analysis was performed in this study. First, the SEM approach was used to determine which variable significantly affects I4.0 readiness. Second, the artificial neural network (ANN) technique was adopted to rank the relative influence of significant predictors obtained from SEM. The results show that the soft and hard TQM practices have supported the I4.0 readiness. Moreover, the results highlight that hard TQM practices have mediating role between soft TQM practices and I4.0 readiness. The ANN results affirmed that customer focus is considered an important TQM factor for I4.0 managerial readiness, advanced manufacturing technology for operational readiness and top management commitment for technology readiness. In a nutshell, the SEM-ANN approach uniquely contributes to the TQM and I4.0 literature. Finally, the findings can help managers to prioritize firms' soft and hard quality practices that promote I4.0 implementation, especially in emerging economies.
... The current situation makes it understandable that several research works declare that there is not a solution approach in the digital domain that is recognized as official, either from research or practice, despite the overwhelming offer [24]. It is also not surprising that their application has been rather limited [25] as the vision of the DT they provide is still not sufficiently complete and clear [26]. Considering that current solution proposals are mostly based on literature reviews of what already exists and studies of industrial cases, company surveys or personal experience of their authors, the approach of this work will be based on theory. ...
Companies around the world are finding themselves in a race against the relentless evolution of digital technologies that are accelerating innovation and creating a highly competitive environment. Many works in research and practice are trying to guide companies to a Digital Transformation (DT) that allows them to take full advantage of new technologies. However, generic solutions, that are mainly focused on technological aspects, make it clear that there is a lack of understanding of the whole scope of their implications. In this sense, this work takes a step back to analyze the origin of the shortcomings of current solution approaches. The results point out a lack of theoretical foundation on identifying the business dimensions implicated in a DT that define its scope. In this sense, this study contributes with a more comprehensive view of the DT process by using a formal approach to define the business dimensions involved in such a change based on the principles of the Socio-Technical Systems (STS) theory. As a result, this proposal goes beyond the purely technological views to (1) identify five business dimensions involved in the DT process: technology, processes, structure, competencies and culture and (2) recognize the key role of strategy and performance measurement, not as dimensions but as external elements that drive and control the DT process. A multiple case study of the DT process of three French manufacturers is presented to validate the proposition. General remarks and future research concerning the implementation of these dimensions conclude this study.
... Several models have been proposed in the literature, and some surveys have been devoted to listing and analysing them. The number of identified maturity models varies from 10 in [12] to 15 in [13] and 30 in [14]. Sony and Naik have tried to identify the key elements that every maturity model should incorporate [15]. ...
The Industry 4.0 paradigm represents the fourth industrial revolution, embodied by the marriage between information and communication technologies and manufacturing. Assessment campaigns are conducted to examine the status of deployment of that paradigm, mostly through self-assessment questionnaires. Each campaign is typically limited in scope, involving just a group of companies located in a few countries at most. Such limitation does not allow an overall view of Industry 4.0’s diffusion. In this paper, we offer that panoramic view through a systematic literature review. The number of papers devoted to Industry 4.0 assessment grows steadily. However, many papers do not provide essential information about the assessment campaigns they report, e.g., not detailing the number, type, or location of companies involved and the questionnaire employed. We observe a large diffusion in Europe and Asia but not in the U.S., with the Top 5 countries being Malaysia, Poland, Italy, Germany and Slovakia. The campaigns uniformly cover small, medium, and large companies but not all industrial sectors. The choice of questionnaires is extremely varied, with no standard emerging.
... A possible reason is that many organisations do not provide details for implementing the models because they are considered a competitive advantage. On the other hand, Felch et al. (2019) emphasise that Readiness Maturity Models (MMs) are utilised to contribute to organisational transformation and renewal of competencies by initiating a change process. ...
The Fourth Industrial Revolution (4IR) has brought about opportunities and challenges to all sectors of the economy. The accelerated changes in the digital technology space drive most organisations and industries to adopt technology to increase competitiveness. In this paper, the exploratory research reviews preliminary literature of current 4IR readiness models to understand the landscape of the relevant knowledge. The aim is to provide a foundation for developing a 4IR readiness model for the services sector. This paper also proposes a conceptual model based systems thinking to guide developing a 4IR readiness model. The preliminary literature review has also revealed that opportunities exist in developing fresh new readiness measurement models for the services industry, including the banking sector.
... The readiness model is important as a starting point that assesses the current status of the organizations, gauge organizations' understanding of a change. In other words, it's such a management tool to contribute to reconfiguring, restructuring, and expanding a firm's capability (Schumacher et al. 2016;Felch et al. 2019). Hence, organizations have to establish the readiness model before making the decision for adopting IoT and BD to know if the company is ready for it or not. ...
Recently most companies have realized the importance of using digital technologies, especially the internet of things and big data, as it has a great impact on several industries such as: Fast moving consumer goods (FMCG), Transportation/Logistics, Automotive, Textiles and ready-made garments, Pharmaceuticals, etc. Before applying the
internet of things and Big data, companies must first assess the readiness to ensure successful implementation and avoid organization failure. The purpose of this research is to investigate the readiness of Egyptian companies to adopt the internet of things and big data. This is a work in process research that will investigate the readiness of
Egyptian companies to adopt the internet of things (IoT) and big data (BD). This paper will present the literature review and the theoretical framework of research which will be used to assess the readiness of Egyptian companies.
... But just as the technological options and possibilities grow, so does the complexity of a DT for manufacturers [3]. Substantial research work has been produced related to the concept, its strategic options, the technologies to use, as well as the models and frameworks to guide its application [4]. Government programs in many countries are also numerous in promoting the DT of manufacturers and with that, the growth of their economies [5]. ...
Digital Transformation (DT) represents a real challenge for companies worldwide, not only because of its complexity due to technology’s fast evolution, but also because of the lack of appropriate guidance. Available approaches are judged generic as they do not take into account the specific context of companies. In this sense, this work explores the influence of context in DT success and introduces a performance indicator to measure the impact of the company features that represent its specific context on the dimensions involved in a DT. As the second phase in a research project aimed to build a quantitative model that explains this relationship, this paper focuses on the application of the Impact Level (IL) factor in a real case scenario. The goal is to validate a previous theoretical analysis and also to identify changes in the results with a different characterization of company features. Relevant findings confirm the critical importance of Culture (f3) and R&D investment (f9) for DT success, but many differences arise from the comparative analysis that reveals the DT process as highly contextual. Future work will be focused on translating the insights of both studies into a quantitative model that presents the IL as an aggregator but also with the possibility to provide enough detail for better decision-making during the DT process.
... Maturity models are tools used to identify the best practices for the transformation of an organisation (Schumacher et al., 2016). They provide a structured approach to initiate and accompany short-term operational projects, as well as medium-term tactical changes and long-term strategic change (Felch et al., 2019). ...
... Especially in the complex era of digitalization, which has different implications for various sectors and company sizes, this approach offers tailored solutions, as it builds on equifinality for reaching the different maturity levels (Lasrado et al. 2015). Therefore, we answer the call for better MMs that will lead to higher applicability (Felch et al. 2019), with our findings offering ways to specifically address companies' requirements, thus making them more relevant for practice. Third, we contribute through the abstraction of the model artifact to design science research. ...
Phenomena like the digital transformation lead to constantly changing economic requirements for companies and their underlying business models. The concept of an innovation capability maturity model is a possible approach to adapt to these changes by presenting solutions for the improvement of innovation potential, which in turn leads to higher turnover. Since existing one-size-fits-all conceptual development approaches of maturity models cannot depict the characteristics for a specific class (size and sector) of companies, we apply a data-driven development approach for the class of small industrial companies. Divided into a knowledge and organizational dimension, our findings reveal a total of 21 factor combinations, representing different levels of innovation capability maturity. By applying a set-theoretic approach for the identification of factor configurations leading to low, medium, and high innovation capability maturity, we furthermore propose the conceptual approach for an empirically driven set-theoretic maturity model development, yielding a design science method artifact.
... In this context the evolutionary progress is divided into a sequence of levels or stages, which demonstrate a logical path from initial to a final state of maturity [42,43]. These models can be used for assessment of the maturity of different areas of interest, to identify strengths and weaknesses, to priorities measures and control progress, managerial tools for self-improvement action, and a tool for benchmarking to compare with competitors [27,39,[44][45][46]. ...
Small and medium-sized enterprises (SMEs) need to keep pace with large enterprises, thus they need to digitally transform. Since they usually lack resources (budget, knowledge, and time) many countries have their support environment to help SMEs in this endeavor. To be able to ensure the right kinds of support, it is crucial to assess the digital maturity of an enterprise. There are many models and assessment tools for digital maturity, however, they are either theoretical models, partial, vendor oriented, or suited for large enterprises. In this paper, we address the problem of assessing digital maturity for SMEs. For this purpose, we developed a multi-attribute model for assessment of the digital maturity of an SME. We followed the design science research approach, where the multi-attribute model is considered as an IT artifact. Within the design cycle, the decision expert (DEX) methodology of a broader multi-attribute decision making methodologies was applied. The developed model was validated by a group of experts and upgraded according to their feedback and finally evaluated on seven real-life cases. Results show that the model can be used in real business situations.
... Literature review reveals that there are several existing Industry 4.0 readiness models. The authors Felch & Sucky mentions that there are existing models which don't serve the need adequately or can be further developed, particularly in terms of business practice [Felch, Sucky 2019]. There has been a quick escalation in the number of Industry 4.0 readiness in the recent few years. ...
Background: Change readiness at organizational level is a key competence needed for Industry 4.0 readiness, and one of the most important critical success factors for managers in implementing Industry 4.0 initiatives. Methods: This paper conducts a critical literature review of 184 peer-reviewed academic journals and industry reports from 1990 to 2019, and identifies 30 Industry 4.0 readiness models. Results: A closer review of dimensions from these Industry 4.0 readiness models reveal that change readiness as a model dimension has not been sufficiently addressed. Supporting the conceptualization and operationalization of this new dimension, the literature review in this paper presents six change related dimensions, specifically change commitment factor, change efficacy, change management, individual fear of change, organizational change readiness and change leadership. Conclusion: This study after critical analysis of the literature proposes change readiness as a new dimension for Industry 4.0 readiness models. Furthermore, in terms of future research, change readiness as a new dimension for studying Industry 4.0 readiness models offers valuable implications for individuals and organizations.
Industry 5.0 is emerging as a novel approach in manufacturing, with focus on sustainability, resilience, and human centricity. This study investigates the complex and comprehensive Industry 5.0 concept, its relevance for the craft manufacturing paradigm and the role of technology in the industry transition towards Industry 5.0. Findings are based on empirical data from a research and development project with Norwegian leisure boat manufacturers, and results of previous research projects within the boat building industry. The boat building industry is a traditional hand-craft industry with high degree of manual labour. Findings show that this industry have several suitable conditions for developing strong capabilities related to sustainability, resilience, and human centricity. The investigated case companies, characterized as craft manufacturers, seem to have limited knowledge of both Industry 4.0 and Industry 5.0. Despite that previous research projects have included technological elements, advanced technologies are still scarce in production processes.
As artificial intelligence (AI) is increasingly used in various industries, it becomes crucial for organizations to enhance their capabilities and maturity in adopting AI responsibly. This paper employs a mixed-method approach that combines topic modeling with manual content analysis to provide a comprehensive review of the literature on AI maturity and readiness. The review encompasses an extensive corpus of 1451 papers, identifying the main themes and topics within this body of literature. Based on these findings, a subset of papers was selected and further analyzed to identify AI capabilities utilizing a sociotechnical lens. This further analysis led to the identification of foundational and responsible AI (RAI) capabilities. These capabilities have been integrated in a sociotechnical framework of capabilities for AI maturity models providing valuable insights for organizations and AI service providers and a basis for further research.
Digital maturity encompasses a company's performance on the path to digital transformation. Various barriers can hinder the improvement of companies on the path to digital maturity-such as organizational culture and various human elements. The starting point for this study was that, regardless of digitization, the human factor is becoming an increasingly important resource in organizations and that digital maturity models (DMM) also focus on these aspects. The purpose of this study is twofold: to investigate the emergence of organizational culture and the human factor in DMMs through a comparative analysis and conduct empirical research in Hungary. In this study, we also aim to investigate the different factors of digital maturity of Hungarian companies and find a correlation with digital maturity based on the characteristics of a learning organization. According to our hypothesis, companies with the characteristics of a learning organization achieve a higher level of digital maturity. To prove this, we surveyed 776 Hungarian companies using a structured questionnaire. Based on our survey, we confirmed the hypothesis using three variables: the business organization anticipates and predicts change, focuses on long-term impacts when making organizational decisions; employees can achieve personal success.
For a successful digital transformation, organizations must create an accurate roadmap and manage the process effectively. A digital maturity model is a critical success factor as it enables organizations to assess their current situation and create roadmaps aligned with their goals; however, a comprehensive systematic literature review covering the maturity models proposed by academia and consultancy firms is hard to find. Further, the existing models are sector-oriented, not organization-oriented, and do not consider the transformation journey holistically, but instead focus on model dimensions. This study first undertakes a comprehensive and up-to-date systematic literature review by applying the PRISMA approach using a bibliometric analysis tool capable of providing visual maps, then developing a unique holistic digital maturity model that covers several aspects of an organization’s digital transformation journey, from strategy to governance, and asking relevant questions. The hierarchical structure, comprising dimensions and sub-dimensions, presents content beyond the scope of other models. The results of the digital maturity assessment can be interpreted in parallel with the stages of the digital transformation. Consequently, the new holistic and sector-independent digital maturity model can be used by organizations in both the private and public sector.
The growth speed of top trending for global firms, the digital transformation (DT), has become steadier than ever by the advancement of digital technologies as well as the COVID-19 pandemic. In order to implement a digital evolutional path appropriately, the Digital Maturity Model (DMM) has been seen as a handy tool since they help companies evaluate their initial states and plan development road maps. So far, several DMMs have been developed for both cross-industry and/or specific sectors, including manufacturing. However, none of them is developed for the Electronics Industry (EI) firms despite the vital importance of the EI sector, especially in developing countries like Vietnam. These companies contribute a significant proportion of Vietnam's GDP, up to 40% in 2021. Moreover, none of them guide EI firms to embrace the Servitization model. This article presents a new DMM to digitally transform EI companies to align with the Servitization model by applying both the Product Service System (PPS) and Service Design approach. The DMM is developed with on the design science research methodology and uses its strategy of combining related models to form a new one to gain high quality. Experts from a joint-venture telecom equipment company under VNPT, the biggest telecommunication service provider in Vietnam, were chosen to join the DMM pre-evaluation phase of the development process. For further research, the authors expected that the newly developed DMM should be transferred and evaluated by more EI incumbent firms and then become a standardized DMM for the EI sector.
Objetivos: A economia mundial atravessa um período difícil fruto da conjuntura imposta pela pandemia COVID19, pelo que perceber o estado financeiro das empresas revela-se essencial. Os desempenhos das bolsas servem como um importante barómetro do sentimento económico vivido em cada país, assim como um instrumento de avaliação da confiança dos investidores. O presente trabalho pretende avaliar o desempenho e evolução da situação financeira entre 2010 e 2020 das empresas cotadas em Lisboa e Madrid.
Fundamentação teórica: Conforme Brealey et al. (2011), a análise financeira pretende avaliar o estado financeiro atual de uma organização e, assim, perceber a sua possível evolução. Para isso, Neves (2012) faz uso de técnicas que permitem interpretar a situação económico-financeira da empresa, transformando os dados das demonstrações financeiras em informação útil. Revela-se como uma das principais ferramentas para avaliar a sustentabilidade de uma empresa, isto é, a maximização do seu valor permitindo a sua continuidade (Hediger, 2010).
Metodologia: O quadro teórico desta investigação foi elaborado através da recolha dos indicadores tradicionalmente utilizados na análise económico-financeira, em concreto, rácios financeiros. A análise univariável passa principalmente pela construção de indicadores de Liquidez, Endividamento e Estrutura de Capital, Risco, Rendibilidade, Funcionamento e de Valor de Mercado. Serão também aplicados os modelos de análise multivariável identificados como mais eficientes para cada sector. Através da base de dados SABI da Bureau van Dijk, foi selecionada uma amostra das empresas cotadas em Lisboa e Madrid, com informação financeira disponível no período de 2010 a 2020. Foi também recolhida a informação financeira das Empresas Médias Setoriais. Seguidamente, foi efetuado o cálculo dos indicadores identificados e elaborada uma análise comparativa e evolutiva entre as entidades selecionadas.
Resultados e discussão: Este trabalho pretende avaliar as empresas da amostra em 6 áreas relacionando-as, através dos indicadores da análise univariável e dos modelos da análise multivariável, de forma a perceber a sua evolução e estado. A eficiência com que a empresa usa os seus recursos e a sua capacidade em gerar resultados são essenciais para o crescimento. Conforme Vasconcelos et al. (2019), observa-se que um sistema de gestão focado na criação de valor para o acionista leva a um melhor desempenho económico-financeiro e valorização de mercado. O desempenho bolsista é crucial para captação de investimento e consequente desenvolvimento da atividade das empresas. Após a avaliação destes fatores, pretende-se concluir em qual das bolsas as empresas apresentam melhores condições financeiras e de atratividade para os investidores, procurando observar de que forma o mercado as está a avaliar, ou por outras palavras, se numa delas as empresas se encontram sub ou sobrevalorizadas em relação à outra.
Conclusões e implicações da investigação: Através de uma análise setorial e do cenário macroeconómico dos dois países, este trabalho pretenderá avaliar os efeitos da crise de 2010 e oferecer uma visão acerca do estado económico-financeiro dos setores das empresas consideradas na amostra e a sua comparação com os dados setoriais publicados nos dois países.
Originalidade: Após a pesquisa, não foi encontrado um trabalho com o mesmo tema e horizonte temporal. Porém, foram encontrados trabalhos com temas semelhantes que seguiam, globalmente, a metodologia descrita.
Palavras-chave: Análise Financeira; Bolsa de valores; Desempenho; Criação de Valor.
The purpose of this paper is to conduct a comparison for Industry 4.0 maturity models in the context of Total Quality Management (TQM) principles. TQM principles have been used as criteria for evaluating alternative Industry 4.0 maturity models. Because of the critics about TQM concept for having an inadequate theoretical foundation and highly expert insight dependent nature, fuzzy logic principles have been used. In this study, linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used for ranking and Decision Making Trial and Evaluation Laboratory (DEMATEL) method has been used for criteria weighting to determine which maturity model mostly fits TQM principles. Four Industry 4.0 maturity models have been evaluated according to seven criteria and 33 sub-criteria. According to the results, the model of Schumacher et al. (2016) has the highest score among the compared models. Another result is among the seven main criteria customer focus has the most influence and relationship management has the less, and among 33 sub-criteria risk identification has the most influence and relationship management has the less. Findings represented in this study can serve academicians and practitioners to compare, develop and/or improve Industry 4.0 maturity models.
Advanced analytics and artificial intelligence are drivers of deep analysis and change in the perspective of businesses’ digital transformation. Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which provides guidance for businesses in terms of analyzing information and predicting in business. The key advantage of the application of the data mining approach in business is the impact by improving customers’ experience and decision-making. The aim of this research is to present a theoretical model to understand the researchers’ perspectives on data mining application in different business areas and digital transformation, and the discussion of some benefits and challenges of the data mining application in improving decision-making along the digital transformation of businesses. Moreover, this paper analyzes how the implementation of data mining techniques in business can lead to an increased efficiency and business productivity along their digital transformation journey.
This book constitutes the refereed conference proceedings of the 4th International Conference on Emerging Technologies in Computing, iCEtiC 2021, held in August 2021. Due to VOVID-19 pandemic the conference was helt virtually.
The 15 revised full papers were reviewed and selected from 44 submissions and are organized in topical sections covering Information and Network Security; Cloud, IoT and Distributed Computing; AI, Expert Systems and Big Data Analytics
The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.*
The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections:
Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice
Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing
Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus
Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19
Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains
*The conference was held online.
Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.
Son dönemlerde ortaya çıkan en önemli kavramlardan biri olan Sanayi 4.0, firmaların hem daha yüksek teknolojik seviyeye hem de daha yüksek verimlilik düzeyine ulaşmasına imkan sağlamaktadır. Sanayi 4.0 olgunluk modelleri ise, çeşitli göstergelerle/kriterlerle firmaların mevcut durumlarını ölçerek olması gereken yere nasıl ulaşacağını ve firmaların Sanayi 4.0’a hazır olma durumlarını değerlendiren modellerdir. Bu çalışmanın amacı, IMPULS olgunluk modeli ile lojistik firmaların Sanayi 4.0 olgunluk düzeylerinin değerlendirilmesine yönelik iki farklı yaklaşım önermektir. Bu amaçla, önerilen Ağırlıklandırılmış Olgunluk Puan Hesaplama Modeli Yaklaşımı (AHP-Olgunluk Puan Hesaplama Yöntemi) ve Çok Kriterli Olgunluk Modeli Yaklaşımı (AHP-TOPSIS ve AHP-VIKOR) ile firmaların sıralaması gerçekleştirilmiştir. Elde edilen bulgular, AHP yöntemiyle kriter ağırlıklarının hesaplanması sonucunda en yüksek ağırlığa sahip kriterin strateji ve organizasyon olduğunu göstermektedir. Ayrıca, önerilen her iki yaklaşımında benzer sonuçlar verdiği ve olgunluk düzeyinin ölçümünde kullanılabileceği sonucuna varılmıştır.
Günümüzde yeni teknolojilerin birçok alanda kullanılmaya başlanmasıyla bir dönüşüm süreci ortaya çıkmış ve bu süreç yaygın kullanımıyla Sanayi 4.0 adını almıştır. Sanayi 4.0 teknolojilerine geçme sürecinde firmalara yardımcı olmak ve firmaları standart yapısından kurtarmak amacıyla olgunluk modelleri gibi bir takım yaklaşımlara ihtiyaç vardır. Olgunluk modelleri firmaların kusursuzluk seviyesini tanımlarken, firmaların daha iyi konumlandırılmasına yardımcı olurlar. Bu çalışmanın amacı, Sanayi 4.0 olgunluk modelleri ile ilgili uygulamalı çalışmaları literatür araştırması yoluyla ele almaktır. Çalışma sonucunda, uygulamaya yönelik çalışmaların son yıllarda arttığı, literatürdeki modellerin birçok yönden eksikliklerinin olması sebebiyle araştırmacılar tarafından yeni modellerin önerildiği belirlenmiştir.
Purpose: To address the challenges regarding the concept of Industry 4.0 and the diversification methodology and based on the strategic guidance towards Industry 4.0, we propose a process model as a guiding framework for Industry 4.0 collaborative diversification vision, strategy and action building. In this paper we suggest a stage process model to guide and train companies to identify new opportunities for diversification within Industry 4.0. Systematically carrying out the stages will take a company to their individual specific vision and collaborative vision between different companies in the Industry 4.0 scenario.
Design/methodology/approach: This new collaborative diversification methodology involves industry within the pilot program; from the diversification and capacity assessment analysis of the company`s profile, skills and technologies that dominates, to identify the diversification opportunity map and its business modeling within the Industry 4.0 paradigm.
Findings: The application of maturity models to the Industry 4.0 may help organizations to integrate this methodology into their culture. Results show a real need for guided support in developing a company-specific Industry 4.0 vision and specific project planning.
Originality/value: Industry 4.0 promotes a vision where recent developments in information technology are expected to enable entirely new forms of cooperative engineering and manufacturing. The vision of industry 4.0 describes a whole new approach to business operations, and especially the production industries. To address the challenges regarding the concept of Industry 4.0 and the diversification methodology discussed above, and based on the strategic guidance towards Industry 4.0, we propose a unique process model as a guiding framework for Industry 4.0 collaborative diversification vision, strategy and action building.
In recent years, Industry 4.0 has been introduced as a popular term to describe the trend towards digitisation and automation of the manufacturing environment. Despite its potential benefits in terms of improvements in productivity and quality, this concept has not gained much attention in the construction industry. This development is founded in the fact that the far-reaching implications of the increasingly digitised and automated manufacturing environment are still widely unknown. Against this backdrop, the primary objective of this paper is to explore the state of the art as well as the state of practice of Industry 4.0 relating technologies in the construction industry by pointing out the political, economic, social, technological, environmental and legal implications of its adoption. In this context, we present the results of our triangulation approach, which consists of a comprehensive systematic literature review and case study research, by illustrating a PESTEL framework and a value chain model. Additionally, we provide recommendations for further research within a research agenda.
Manufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing – also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, the dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments.
World-class operations need to be efficient and cost effective. Maturity models are examined in this research as a tool for operational development in manufacturing companies. Several leading frameworks are analysed. These models provide baseline to develop companies operations and ultimately the creation of competitive advantage over competitors. The use of maturity models allows organizations to assess organizational methods and processes according to management best practices and against a clear set of external benchmarks. An industry example is presented from the ABB Corporation to showcase the usage of process maturity reviews in an industry context. To succeed in global competition, the ABB Corporation has strongly emphasized a culture of continuous improvement. Case company uses maturity models as a start of the continuous improvement loop, with aim to achieve world-class operations.
Context: The number of maturity models proposed in the area of Business Process Management (BPM) has increased considerably in the last decade. However, there are a number of challenges, such as the limited empirical studies on their validation and a limited extent of actionable properties of these models in guiding their application. These challenges hinder the widespread usage of the maturity models in the BPM field.
Objective: In order to better understand the state of the research on business process maturity models (BPMMs) and identify opportunities for future research, we conducted a systematic literature review.
Method: We searched the studies between the years 1990 and 2014 in established digital libraries to identify empirical studies of BPMMs by focusing on their development, validation, and application. We targeted studies on generic models proposed for business process maturity, business process management or orientation maturity, and selected 61 studies out of 2899 retrieved initially.
Results: We found that despite that many BPMMs were proposed in the last decade, the level of empirical evidence that reveals the validity and usefulness of these models is scarce.
Conclusion: The current state of research on BPM maturity is in its early phases, and academic literature lacks methodical applications of many mainstream BPMMs that have been proposed. Future research should be directed towards: (1) reconciling existing models with a strong emphasis on prescriptive properties, (2) conducting empirical studies to demonstrate the validity and usefulness of BPMMs, and (3) separating the assessment method used to evaluate the maturity level from the maturity model which acts as the reference framework for the assessment.
A worldwide movement in advanced manufacturing countries is seeking to reinvigorate (and revolutionize) the industrial and manufacturing core competencies with the use of the latest advances in information and communications technology. Visual computing plays an important role as the "glue factor" in complete solutions. This article positions visual computing in its intrinsic crucial role for Industrie 4.0 and provides a general, broad overview and points out specific directions and scenarios for future research.
Maturity models are a useful way for an organisation to assess their processes and the overall maturity levels of the organisation. However, finding the most appropriate maturity model is not an easy task especially for practitioners in industry. Hence, the purpose of this paper is to critically review, compare and contrast the existing maturity models in quality/operations management topics. This paper has reviewed the most common maturity models including but not limited to Bessant's continuous improvement capability model, Capability Maturity Model (CMM) and Capability Maturity Model Integration (CMMI) which is the most common model in the literature. The authors have observed a lack of maturity models for process management. Therefore, the future plan for this research is to develop a maturity model for a specific area in process management, which is Lean Six Sigma (LSS), as this is the main area of interest for the authors.
The Internet-of-Things (IoT) concept has been gradually developing, but it is unclear how extensive this concept is adopted within the supply chain domain. We derive an architectural framework to investigate four layers of ICT deployment. This framework enables practitioners and scientist to specify a status quo on different architectural levels and to identify possibilities for further improvement. Four extensive cases are investigated with this framework. One of the important conclusions is that "IoT" like technology and applications are pioneered in research programs, but operational logistic systems in diverse organizations primarily rely on less advanced technology, organizational structures-and work forms. This work can help in identifying gaps where IoT can strengthen future applications.
Purpose – The purpose of the paper is to investigate the relationship between supply chain maturity and performance, with specific references both to the business process orientation maturity model and to the supply chain operation reference model. Design/methodology/approach – Quantitative, survey based research was carried out with 478 Brazilian companies. Statistical analysis combined the use of descriptive statistics and structural equation modeling. Findings – Empirical results indicate a strong and positive statistical relationship between supply chain maturity and performance. The results also suggest that the deliver process maturity has a higher impact on overall performance than the other supply chain processes. Research limitations/implications – Quantifying supply chain maturity and performance is an opportunity for a company to align its performance measurements and process improvement actions with its broader policies and strategies. The use of this approach has been validated in several previous research studies in organizational self-assessment and business management. Practical implications – Maturity models are valuable frameworks for corporate leadership. This study provides solid statistical evidence that a company that has achieved a higher maturity level and implemented the maturity factors also has achieved superior performance. It also validates the application of these specific maturity factors in South America, specifically Brazil. Originality/value – This paper confirms and expands upon earlier research suggesting higher levels of process maturity were related to superior performance. This paper also examines the evolution of performance measurement systems, moving from a traditional approach to a more process oriented perspective by reporting on the origins of maturity models and presenting the main empirical contributions through the use of the business process maturity model and supply chain operation reference model.
Since the Software Engineering Institute has launched the Capability Maturity Model almost twenty years ago, hundreds of maturity models have been proposed by researchers and practitioners across multiple application domains. With process orientation being a central paradigm of organizational design and continuous process improvement taking top positions on CIO agendas, maturity models are also prospering in business process management. Although the application of maturity models is increasing in quantity and breadth, the concept of maturity models is frequently subject to criticism. Indeed, numerous shortcomings have been disclosed referring to both maturity models as design products and the process of maturity model design. Whereas research has already substantiated the design process, there is no holistic understanding of the principles of form and function - that is, the design principles - maturity models should meet. We therefore propose a pragmatic, yet well-founded framework of general design principles justified by existing literature and grouped according to typical purposes of use. The framework is demonstrated using an exemplary set of maturity models related to business process management. We finally give a brief outlook on implications and topics for further research.
Valid predictions for the direction of nonresponse bias were obtained from subjective estimates and extrapolations in an analysis of mail survey data from published studies. For estimates of the magnitude of bias, the use of extrapolations led to substantial improvements over a strategy of not using extrapolations.
The aim of this paper is the scientific development of a maturity model concerning the digital transformation of companies within the manufacturing industry’s supply chain. The rather “broad” and dispersed “mega-trend” of digitalization is expected to play an increasingly important role for companies as well as for the (digital) supply chain of the future. Such a model comprises the objective of addressing fundamental components, complementary innovations and relevant terminologies, like smart products, Cyber-Physical Systems (CPS) and Big Data Analytics. Scientific rigor is achieved through conducting grounded theory research and in-depth interviews as methods of data collection and evaluation. Furthermore, relevant aspects concerning the development and construction of maturity models are discussed, before a suitable and scientifically elaborated maturity model concerning digitalization emerges from the course of investigation and its value for economic practice as well as for the scientific community is specified.
Nowadays mechanical engineering products change from mechatronic systems to Cyber-Physical Systems (CPS). CPS are connected, embedded systems which directly record physical data using sensors and affect physical processes using actuators. They evaluate and save recorded data, use globally available services and interact with operators via multimodal human-machine-interfaces. In context of industrial production CPS change production processes radically. Due to the change of technical systems, equipment suppliers, especially companies of the mechanical engineering industry, face the challenges of a rising complexity and a nearly unmanageable amount of new solutions based on information and communication technology. The contribution at hand provides a reference architecture and maturity levels for CPS. The reference architecture serves as an universal blueprint to structure CPS and to visualize all components and relationships. Two sets of CPS maturity levels help companies to assess the status quo, to determine the target state and to define concrete actions for improving their systems.
The article focuses on the development of a theory. A discussion is presented about steps involved in developing a theory, such as seeing which factors logically should be considered as part of the explanation of the social or individual phenomena of interest. The authors assert that authors developing theories are considering these factors, they should err in favor of including too many factors, recognizing that over time their ideas will be refined. The article presents information about the importance of sensitivity to the competing virtues of parsimony and comprehensiveness.
The proliferation of cyber-physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of factory to implement flexible and reconfigurable manufacturing systems, i.e., smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products. Then, we give a classification of the smart objects into various types of agents and define a coordinator on cloud. The autonomous decision and distributed cooperation between agents lead the process achieving high flexibility. Moreover, this kind of self-organized system leverages on the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by the self-organized multi-agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent the deadlocks by improving the agents’ decision and the coordinator's behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies.
Maturity models are valuable instruments for IT managers because they allow the assessment of the current situation of a company as well as the identification of reasonable improvement measures. Over the last few years, more than a hundred maturity models have been developed to support IT management. They address a broad range of different application areas, comprising holistic assessments of IT management as well as appraisals of specific subareas (e. g. Business Process Management, Business Intelligence).
The evergrowing number of maturity models indicates a certain degree of arbitrariness concerning their development processes. Especially, this is highlighted by incomplete documentation of methodologies applied for maturity model development.
In this paper, we will try to work against this trend by proposing requirements concerning the development of maturity models. A selection of the few well-documented maturity models is compared to these requirements. The results lead us to a generic and consolidated procedure model for the design of maturity models. It provides a manual for the theoretically founded development and evaluation of maturity models. Finally, we will apply this procedure model to the development of the IT Performance Measurement Maturity Model (ITPM3).
Context: Maturity models offer organizations a simple but effective possibility to measure the quality of their processes. Emerged out of software engineering, the application fields have widened and maturity model research is becoming more important. During the last two decades the publication amount steadily rose as well. Until today, no studies have been available summarizing the activities and results of the field of maturity model research. Objective: The objective of this paper is to structure and analyze the available literature of the field of maturity model research to identify the state-of-the-art research as well as research gaps. Method: A systematic mapping study was conducted. It included relevant publications of journals and IS conferences. Mapping studies are a suitable method for structuring a broad research field concerning research questions about contents, methods, and trends in the available publications. Results: The mapping of 237 articles showed that current maturity model research is applicable to more than 20 domains, heavily dominated by software development and software engineering. The study revealed that most publications deal with the development of maturity models and empirical studies. Theoretical reflective publications are scarce. Furthermore, the relation between conceptual and design-oriented maturity model development was analyzed, indicating that there is still a gap in evaluating and validating developed maturity models. Finally, a comprehensive research framework was derived from the study results and implications for further research are given. Conclusion: The mapping study delivers the first systematic summary of maturity model research. The categorization of available publications helps researchers gain an overview of the state-of-the-art research and current research gaps. The proposed research framework supports researchers categorizing their own projects. In addition, practitioners planning to use a maturity model may use the study as starting point to identify which maturity models are suitable for their domain and where limitations exist.
The low rates of female participation in top management represent a puzzle, especially since some research suggests that the initial entry by women into top management in recent decades should have led to a positive social dynamic that made entry by subsequent women easier. We draw on the literature on majority-minority relations, gender in management, and social categories to theorize that the presence of a woman on a top management team may reduce rather than increase the probability that another top management position in the same firm will be occupied by a woman. Using twenty years of panel data on the top management teams of S&P 1,500 firms, we find robust evidence for such negative spillovers, which are especially strong for women chief executive officers and within similar job categories. We argue that our results are consistent with two mechanisms acting in concert: lack of solidarity among women managers and norms related to gender equity in management.
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