Value Creation in the Digitally Enabled Knowledge Economy

North, Klaus; Maier, Ronald; Haas, Oliver

doi:10.1007/978-3-319-73546-7_1

Cited by 7 publications

(11 citation statements)

References 47 publications

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“…The employees in Smart Factories may be concerned about their future role and tasks as there will be continuous shifts in human-machine interaction (HMI), where the mechanized counterparts of a human worker in the future may be different CPS, a robot or an AI application. The roles that workers execute may be the controller of a machine, peer or teammate up to their replacement by an intelligent machine [30]. Ansari, Erol and Shin (2018) differentiate HMI into human-machine cooperation and human-machine collaboration in the Smart Factory environment: "A human labor on the one side is assisted by smart devices and machines (human-machine cooperation) and on the other should interact and exchange information with intelligent machines (human-machine collaboration)" [14].…”

Section: Knowledge Integration: Employee Levelmentioning

confidence: 99%

“…One approach is a "mutual learning" between human and machine, supported by human acquisition, machine acquisition, human participation and machine participation leading to the execution of shared tasks between human and machine [14]. North, Maier and Haas (2018) envision that "in the future expertise will be defined as human (expert) plus intelligent machine", with the challenge being how they learn and work together [30]. A possible system, showcasing this synergetic collaboration is the implementation of cobots, passive robots that are tailored for collaboratively inhabiting a shared space for the purpose of operating processes together with humans [31].…”

Section: Knowledge Integration: Employee Levelmentioning

confidence: 99%

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Knowledge Integration in Smart Factories

et al. 2021

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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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Section: Knowledge Integration: Employee Levelmentioning

confidence: 99%

Section: Knowledge Integration: Employee Levelmentioning

confidence: 99%

Knowledge Integration in Smart Factories

et al. 2021

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“…To take advantage of the great opportunities generated by digitalisation, firms must rethink the way they manage knowledge, their cognitive processes and practices, with implications for all the firm's business models and governance mechanisms (North et al , 2018a). Firms will be forced to identify new mechanisms of knowledge production and sharing.…”

Section: Learning and Decision Making In The Digitised Economymentioning

confidence: 99%

“…With this in mind, North et al (2018b) introduced the concept of “Knowledge 4.0.”, defined as the fourth and most recent stage of knowledge development, a “digitised knowledge society”, i.e. a societal stage where the adoption of digital technologies is pervasive and consistently contributes to the creation of value, and where the focus of knowledge management is significantly extended and supported by technology.…”

Section: Learning and Decision Making In The Digitised Economymentioning

confidence: 99%

Digital transformation in the economics of complexity: the role of predictive models in strategic management

Iscaro

Castaldi

Maresca

et al. 2021

JSMA

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Purpose This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics. Design/methodology/approach To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model. Findings Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge. Originality/value From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.

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“…Indeed, machine learning can provide automatic knowledge extraction from manufacturing big data to increase production efficiency, reduce management costs (O'Donovan et al, 2015), and drive technological innovation. In this context, the paradigm of Knowledge Management 4.0 (Ansari, 2019) emphasizes the business value creation achieved by extracting and providing accessibility to manufacturing domain-specific knowledge obtained by coupling human experiences and data-driven approaches (North et al, 2018). As an example, the data obtained through IoT devices can be analyzed via a machine learning approach to detect production anomalies (Alfeo et al, 2020), while their management can be supported by considering past human-driven maintenance operations to collect best practices and improve the maintenance processes (Navinchandran et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Technological troubleshooting based on sentence embedding with deep transformers

2021

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In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine.

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Value Creation in the Digitally Enabled Knowledge Economy

Cited by 7 publications

References 47 publications

Knowledge Integration in Smart Factories

Knowledge Integration in Smart Factories

Digital transformation in the economics of complexity: the role of predictive models in strategic management

Technological troubleshooting based on sentence embedding with deep transformers

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