KS
K.L. Smith
2 records found
1
AI Enabled Dynamic Capabilities
Helping Large Organisations to Overcome Disruptions with a Capability Orchestration Framework
Due to globalisation and technological advancements, the world is becoming increasingly complex. Volatile, uncertain, complex and ambiguous (VUCA) environments have called for organisations to become more agile in order to survive and compete in such changing environments. Large
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Due to globalisation and technological advancements, the world is becoming increasingly complex. Volatile, uncertain, complex and ambiguous (VUCA) environments have called for organisations to become more agile in order to survive and compete in such changing environments. Large organisations are at particular risk for becoming stagnant due to operational inertia. In order to combat this and achieve agility, dynamic capabilities are developed. These capabilities allow an organisation to more effectively and efficiently change to incoming threats or opportunities. Such changes create uncertainty and insecurity amongst employees which translates into higher employee turnover and decreased performance. Stability therefore needs to be provided for individuals, while achieving dynamacy for organisations. This paradox of dynamic stability drives research into understanding relationships and effects caused by
disruptions. Covid-19 is used as an extreme use case in order to create these understandings. After primary and secondary research conclusions were developed, a conceptual framework was developed in order to orchestrate capabilities. This aims to help speed up the time taken for opportunities/threats to be translated into outcomes. This also aims to help improve the depth, diversity and accuracy of these outcomes.
disruptions. Covid-19 is used as an extreme use case in order to create these understandings. After primary and secondary research conclusions were developed, a conceptual framework was developed in order to orchestrate capabilities. This aims to help speed up the time taken for opportunities/threats to be translated into outcomes. This also aims to help improve the depth, diversity and accuracy of these outcomes.
Journal article
(2010)
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S.
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A.
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W.M.
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T.R.
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T.R.
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K.T.J.
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M.C.O.
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M.
Weber
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G.
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G.
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A.
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M.
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M.C.W.
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M.M.
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M.M.
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M.M.
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A.
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A.
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C.C.
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C.
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J.
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P.I.
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W.
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M.D.
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M.D.
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X.J.A.
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M.F.
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M.F.
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P.L.A.
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I.
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I.
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D.A.
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N.G.
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S.
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S.
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M.
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M.
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M.
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M.
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M.
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A.
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J.
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J.
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Singh
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S.
Ahuja
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S.
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S.
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P.
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S.
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M.
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M.G.V.
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A.
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A.
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A
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A
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A.
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A
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A
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A.
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A.
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R.D.
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D.
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A.
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T.
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A.
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M.
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M.
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R.T.
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R.T.
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Raj Thilak
Rajan
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Raj Thilak
Rajan
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Raj T.
Rajan
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Raj T.
Rajan
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A.
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A.
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R.
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S.
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M.
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M.
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S.
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A.
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C.
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L.
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L.
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S
Sarkar
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S
Sarkar
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M.
Costa
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M.
Costa
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M.
Costa
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M.
Marone
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M.
Marone
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M.
Marone
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S.
Chang
(author),
S.
Chang
(author),
J.
Chung
(author),
J.
Chung
(author),
D.H.
Kim
(author),
D.H.
Kim
(author),
G.
Kim
(author),
G.
Kim
(author)
Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic
...
Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation.
@en