Beyond World-Class Productivity

Sakamoto, Shigeyasu

doi:10.1007/978-1-84996-269-8

Cited by 18 publications

(6 citation statements)

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“…From this perspective, industrial engineering and quality management can be seen as being successful natural experiments in active inference that have taken place within diverse social organization in many different countries over many decades [14,15,[132][133][134]. However, as explained in Section 8 and summarized in Table 3 below, industrial engineering, quality management, and active inference constructs are not readily applicable to all types of social organization.…”

Section: Conclusion 91 Principal Contributionmentioning

confidence: 99%

“…Throughout the paper, theory-based practical examples are discussed that can provide common descriptions for scientists researching active inference and engineers who are interested in implementing active inference research in multi-intelligence social organization. They can provide widely applicable practical examples, because industrial engineering and quality management are applied in social organization in many different sectors around the world [14,15]. In order to be useful for the widest possible range of scientists and engineers, theory-based practical examples are provided of everyday social organization.…”

Section: Introductionmentioning

confidence: 99%

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Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.

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Section: Conclusion 91 Principal Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Fox

2021

Entropy

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“…In this systematic way, it is possible to evaluate the causes of losses and their related impact. Specifically, the novel classification has been conceived enriching the losses presented in the literature (Sakamoto, 2010; Naoum, 2016; ISO 22400, 2017), with some peculiarities emerged from the field.…”

Section: The Revised Overall Labour Effectiveness (Role)mentioning

confidence: 99%

“…Note that, in order to obtain regular data about all losses, there are several methods available for determining how people spend their working time (Sakamoto, 2010). Continual observation is frequently used, where people are shadowed for a period of time and their work activities are recorded.…”

Section: The Revised Overall Labour Effectiveness (Role)mentioning

confidence: 99%

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Revised overall labour effectiveness

Braglia

Castellano

Frosolini

et al. 2020

IJPPM

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PurposeThe paper proposes a new workforce effectiveness metric that is a sophisticated evolution of a pre-existing overall labour effectiveness (OLE) indicator. The KPI, named revised OLE (ROLE), provides a structured methodology to measure in a holistic way the losses relating to labour, maintaining some formal similitude to the overall equipment effectiveness (OEE)Design/methodology/approachA new structure of losses is proposed to overcome the drawbacks and the difficulties that usually affect the data collection stage, referring to directly measurable quantities or, when this is not the case, suggesting a viable method to quantify the loss. Besides, this approach facilitates the comprehension of labour-related issues, suggesting potential countermeasures. The novel ROLE indicator has been defined, based on this new structure, to evaluate the labour effectiveness in batch process industries. A real case study is provided, which explains the methodology and illustrates the capability of the corresponding KPI.FindingsThe present work analyses the labour performance indexes available in literature, with the aim of evidencing those aspects that can be properly observed and quantified and, at the same time, categorizing them to identify their possible drawbacks. A new structure of losses is derived, with respect to four different categories, which may help to measure the losses themselves more effectivelyOriginality/valueThe paper investigates some important KPIs dealing with labour performance and individuates some significant drawbacks. Then it suggests a new, inclusive structure of losses and a modified KPI that not only measures effectiveness but also allows to identify viable countermeasures.

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