Methodology for the Assessment of Structural Complexity in Global Production Networks

Schuh, Günther; Potente, Till; Varandani, Rawina; Schmitz, Torben

doi:10.1016/j.procir.2013.05.012

Cited by 25 publications

(7 citation statements)

References 4 publications

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“…Latest scientific approaches which deal with complexity directly or indirectly do not have a consistent definition of complexity [35][36][37][38]. Due to the variety of definitions of complexity that do not allow unambiguous determination, in everyday speech the term complexity is often equated with the term complicacy [11,[39][40].…”

Section: Complexity Dimensionsmentioning

confidence: 99%

Method for a Systematic Evaluation of Advanced Complexity Management Maturity

et al. 2014

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Dealing with the strongly increasing complexity of the company itself and its environment has become a key competitive factor. Companies can only face the progressively increasing external complexity in global markets with an appropriate "healthy" internal complexity. It inevitably has to be adapted to market demands. If the internal complexity is too low, the external complexity cannot be mastered sufficiently. The complexity management in the company is therefore not effective. If the internal perspective is too high, the company thus has unnecessary efforts and the complexity management is not efficient. The complexity within socio-technical organizations such as e.g. value networks or industrial companies is characterized by the difficulties and turbulences encountered in daily business and can be described by four dimensions: variety, heterogeneity, dynamics and non-transparency. Most companies have not introduced or implemented a complexity management system in order to deal with these issues yet. Many companies do not know if the used management activities are efficient, effective and adequate. Therefore, companies have to be reviewed and evaluated regarding their complexity management maturity. Maturity models can be used to support the analysis and assessment of skills and development levels of products, processes or organizations. Such competence models are using defined levels of maturity, which can be used to describe the different achievable skill levels. Maturity models for the purposes of evaluation issues have several benefits such as finding vulnerabilities and identifying improvement measures, a better control over costs and time or an earlier and more accurate predictable release and introduction of complexity management activities. This paper presents basics of an advanced Complexity Management as well as an approach for a systematic evaluation of advanced Complexity Management maturity, describing the different levels and taking into account recommendations to increase the degree of maturity.

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Section: Complexity Dimensionsmentioning

confidence: 99%

Method for a Systematic Evaluation of Advanced Complexity Management Maturity

et al. 2014

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“…Latest scientific approaches which deal with complexity directly or indirectly do not have a consistent definition of complexity [22][23][24][25][26][27]. Due to the variety of definitions of complexity that do not allow unambiguous determination, in everyday speech the term complexity is often equated with the term complicacy [11,[28][29].…”

Section: Complexity Dimensionsmentioning

confidence: 99%

Complexity Patterns in the Advanced Complexity Management of Value Networks

Jäger¹,

Kluth²,

Schatz³

et al. 2014

Procedia CIRP

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The way of dealing with the strongly increasing complexity of the company itself and its environment has become a key competitive factor. Complexity factors in a variety of different business areas require an advanced Complexity Management. Therefore, knowledge regarding the specifics of the respective complexity, the so-called Complexity Footprint, is decisive to meet requirements and to derive measures by using appropriate instruments. The current Fraunhofer IPA empirical study "advanced Complexity Management -the new management discipline" with more than 190 industrial participants shows, that companies expect a future increase in complexity, but not yet have the tools to deal with it. Furthermore, complexity management is mostly focused on the complexity field product and here in product modularization and variety management. The importance of ideal complexity, of product profitability in response to product complexity in connection with complexity in process and organization is mostly ignored.Within this paper the different activities and instruments of advanced Complexity Management are presented. This includes the approach of complexity patterns in value networks including production and supply chain as well as the summary of several complexity patterns to the Fraunhofer IPA Complexity Footprint. First an up-to-date survey on complexity in value networks is given. Then, the Stuttgart complexity comprehension is introduced. To define the external and internal complexity in socio-technical systems like value networks, the differences are presented. The difference between complicacy and complexity is given, within the complexity dimensions variety, heterogeneity, dynamics and opacity. After this, complexity fields such as goods and services, process and organization as well as their several subfields connectivity and interdependency are established. Examples for complexity in each field are given to highlight the different appearance of complexity. Following, the advanced Complexity Management is introduced and finally the Fraunhofer IPA Complexity Footprint is introduced. Within this Complexity Footprint the complexity patterns in value networks are located and a description is given.

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“…• the need to sustainably empower the workforce (Learning Factory) (LF) as indicated by Narkhede et al [10], • the need to develop an autonomous and intelligent process management (PM) (Smart Factory) (SF) as presented by Lee et al [11], • the need to cope with increasing complexity of value-stream networks (VSN) as researched by Schuh et al [12], • the necessary paradigm shift towards strategic alignment as pointed out by Covey [13].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

Schmidt

Villalba-Díez

Ordieres-Meré

et al. 2020

Sensors

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Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the “HOSHIN KANRI TREE” (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain’s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.

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Methodology for the Assessment of Structural Complexity in Global Production Networks

Cited by 25 publications

References 4 publications

Method for a Systematic Evaluation of Advanced Complexity Management Maturity

Method for a Systematic Evaluation of Advanced Complexity Management Maturity

Complexity Patterns in the Advanced Complexity Management of Value Networks

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

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