A Framework to Support Industry 4.0: Chemical Company Case Study

Cortés, Daniel; Chavarría-Barrientos, Dante; Ortega, Arturo Santamaría; Falcón, Belén; Mitre, Leopoldo; Correa, Rodrigo Coloma; Moreno, Jaime; Funes, R.; Molina, Arturo

doi:10.1007/978-3-319-99127-6_33

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…By integrating the value chain and enabling information transparency, Industry 4.0 facilitates smartness, including the smart factory (Radziwon et al ., 2014; Wang et al ., 2016), the smart supply chain (Wu et al ., 2016) and the smart product (Nunes et al ., 2017; Salkin et al ., 2018). Through real-time information on production elements, smart capabilities enhance autonomy, flexibility, decision-making and productivity (Alani and Alloghani, 2019; Barreto et al ., 2017; Cortés Serrano et al ., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Driving smartness for organizational performance through Industry 4.0: a systems perspective

Abiodun

Rampersad

Brinkworth

2023

JMTM

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PurposeThe internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.Design/methodology/approachThis study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.FindingsThe study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.Research limitations/implicationsThis study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.Practical implicationsThe study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.Originality/valueExisting studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

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Section: Literature Reviewmentioning

confidence: 99%

Driving smartness for organizational performance through Industry 4.0: a systems perspective

Abiodun

Rampersad

Brinkworth

2023

JMTM

View full text Add to dashboard Cite

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“…Thus, severe industrial accidents could seriously damage the surrounding environment and urban areas, which the government must consider in industrial planning (Gomez et al 2008). Even though the chemical industry has played a key role in the development of manufacturing, countries are now using new economic models, such as the circular economy and Industry 4.0, to achieve sustainable levels of growth and development (Cucciniello-Cespi 2018;Cortés Serrano et al 2018;Keijer et al 2019).…”

Section: The Chemical Industry and Azerbaijan's Economymentioning

confidence: 99%

Dutch disease-led de-industrialization in the Azerbaijan’s economy

Niftiyev

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Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

Alamäki

Aunimo

Ketamo

et al. 2019

Collaborative Networks and Digital Transformation

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This case study focuses on an experiment analysing textual conversation data using machine learning algorithms and shows that sharing data across organisational boundaries requires anonymisation that decreases that data's information richness. Additionally, sharing data between organisations, conducting data analytics and collaborating to create new business insight requires inter-organisational collaboration. This study shows that analysing highly anonymised and professional conversation data challenges the capabilities of artificial intelligence. Machine learning algorithms alone cannot learn the internal connections and meanings of information cues. This experiment is therefore in line with prior research in interactive machine learning where data scientists, specialists and computational agents interact. This study reveals that, alongside humans, computational agents will be important actors in collaborative networks. Thus, humans are needed in several phases of the machine learning process for facilitating and training. This calls for collaborative working in multidisciplinary teams of data scientists and substance experts interacting with computational agents.

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A Framework to Support Industry 4.0: Chemical Company Case Study

Cited by 5 publications

References 15 publications

Driving smartness for organizational performance through Industry 4.0: a systems perspective

Driving smartness for organizational performance through Industry 4.0: a systems perspective

Dutch disease-led de-industrialization in the Azerbaijan’s economy

Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

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