Industrie 4.0 versus Automatisierung

Jodlbauer, Herbert; Schagerl, Michael; Brunner, Manuel

doi:10.3139/104.111496

Cited by 12 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Organizational preparedness ensures that people, processes, and tools are well coordinated when incumbent manufacturers initiate DDBMI. Preparedness for change encompasses fulfilling various technological and infrastructural requirements, processes, and capabilities, as well as the strategic and managerial acumen necessary for initiating changes while considering various interdependent key aspects [55,56]. Organizational preparedness relies heavily on capabilities, given that companies require appropriate capabilities to address changes, challenges, and opportunities effectively.…”

Section: Organizational Preparednessmentioning

confidence: 99%

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Tripathi,

Bachmann,

Brunner

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.

show abstract

Section: Organizational Preparednessmentioning

confidence: 99%

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Tripathi,

Bachmann,

Brunner

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Within this context, [20] claim that a thorough analysis of the dimensions of Industry 4.0 is required, including the business objectives, the size of the company, its target market, and its strategy. Only after the overall maturity level is accurately assessed should the company start thinking about the goals for each area or department [23]. There are several maturity models related to I4.0, based on the literature;…”

Section: Models Proposed In the Literaturementioning

confidence: 99%

I4.0 assessment and roadmap: a case study in a medium-sized company in the consumer goods sector

Gomes¹,

Rodrigues²,

Bouzon³

2023

Braz. J. Develop.

View full text Add to dashboard Cite

The aim of this research is to define a maturity assessment model for SMEs and apply it to a company in the consumer goods sector, as well as to outline an I4.0 implementation roadmap considering the company's business strategies. The literature shows that there is a lack of studies focusing on applying maturity assessment models in SMEs, as they are more adherent to the large corporation scenario. Thus, this research assesses the developed maturity models and selects the one that can best be applied to the case study. Through a search in the main literature databases, we listed the main maturity models that can be applied in SMEs. Through an assessment of models and guidelines from other research, we selected the IMPULS maturity model for this case study. After applying the maturity model, the company studied was classified at maturity level 1, that is, the company is considered a beginner. Thus, focusing on the next 3 years of work, a roadmap was designed focusing on the 3 main dimensions with gaps in the assessment.

show abstract

“…Table 1 summarizes the major capability or competency diagnosis models related to smart factories. In addition to the six diagnostic models, other major diagnostic models related to smart manufacturing include Rockwell Automation's The Connected Enterprise Maturity Model [30], VDMA's IMPULS-Industry 4.0 Readiness [31], Fraunhofer Oberösterreich's Reifegradmodell Industrie 4.0 [32], PwC's Maturity Model-Industry 4.0 capabilities [33], and the Smart Manufacturing System Readiness Level [34]. This model measures the level of digital maturity of a company in the areas of market analysis (threat of new entrants, bargaining power of buyers, bargaining power of suppliers, competition with existing competitors, threat of substitutes), business (customer, culture/workforce, digital strategy, governance, digital market building, transition management, digital operations), and IT (interoperability, IT security, big data, connectivity) [8].…”

Section: Related Modelsmentioning

confidence: 99%

A Maturity Model for Diagnosing the Capabilities of Smart Factory Solution Suppliers and Its Pilot Application

Cha,

Lee,

Jun

et al. 2023

Systems

View full text Add to dashboard Cite

For the successful and sustainable deployment and diffusion of smart factories, both the capabilities of the adopters who operate the factories and the capabilities of the suppliers who supply information technology and equipment play very important roles. However, since the existing models for diagnosing the capabilities of smart factories are mainly focused on evaluating the capabilities of the manufacturing companies themselves, such as technological capabilities and digital transformation, there are not many models that diagnose the supply capabilities of suppliers from the perspective of demand companies. Unlike models that diagnose the level of smart factories, when diagnosing the capabilities of suppliers, various factors such as supply experience and management capabilities must be comprehensively evaluated in addition to the capabilities of the company itself. Therefore, this study proposes a new model to diagnose the capabilities of suppliers from the perspective of adopters who want to build smart factories and verifies the validity of the model by applying the model for a pilot diagnosis for 32 suppliers. In addition, based on the survey results obtained from both adopters and suppliers participating in the pilot diagnoses, this study proposes an institutionalization plan for capability diagnosis.

show abstract

Industrie 4.0 versus Automatisierung

Cited by 12 publications

References 1 publication

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

I4.0 assessment and roadmap: a case study in a medium-sized company in the consumer goods sector

A Maturity Model for Diagnosing the Capabilities of Smart Factory Solution Suppliers and Its Pilot Application

Contact Info

Product

Resources

About