Wyniki wyszukiwania - BazTech

1

Exploring the critical success factors of a resilient supply chain

Azam Mehreen Kausar, Hasan Syed Mehmood, Qureshi Sheheryar Mohsin

Engineering Management in Production and Services

|

2023

|

Vol. 15, no. 1

41--56

EN

This study aims to identify and analyse critical success factors (CSFs) for an organisation aiming for a resilient supply chain. The methodology followed is the systematic analysis of big databases, such as Emerald, Science Direct, and Taylor & Francis, by using a specific set of keywords for filtering. The systematic literature review leads the author to the exploration of several CSFs, followed by their prioritisation by using principal component analysis. The paper highlighted eleven vital CSFs: top management commitment, development of an effective SCM strategy, logistics synchronisation, use of modern technologies, robust information and communication technology, information sharing with SC members, collaborative partnership, improved forecasting, trust development in SC partners, collaborative partnership, strategic partnership, development of reliable suppliers, continuous improvement in the preparedness and response practices, capacity building and training and staff development. The CSFs highlighted in the paper relate to all small and medium-sized enterprises (SMEs). This paper identifies the CSFs for developing a resilient supply chain that is comprehensive and has the potential to address uncertain circumstances. This work is the first of its kind on CSF assessment and categorisation in resilient supply chains.

2

Stability analysis of radial turning process for superalloys

Jiménez A., Boto F., Irigoien I., Sierra B., Suarez A.

Management Systems in Production Engineering

|

2017

|

nr 3 (25)

158--162

EN

Stability detection in machining processes is an essential component for the design of efficient machining processes. Automatic methods are able to determine when instability is happening and prevent possible machine failures. In this work a variety of methods are proposed for detecting stability anomalies based on the measured forces in the radial turning process of superalloys. Two different methods are proposed to determine instabilities. Each one is tested on real data obtained in the machining of Waspalloy, Haynes 282 and Inconel 718. Experimental data, in both Conventional and High Pressure Coolant (HPC) environments, are set in four different states depending on materials grain size and Hardness (LGA, LGS, SGA and SGS). Results reveal that PCA method is useful for visualization of the process and detection of anomalies in online processes.

PL

Wykrywanie stabilności w procesach obróbki jest podstawowym składnikiem procesu projektowania wydajnych procesów obróbki. Automatyczne metody są w stanie określić kiedy nastąpi niestabilność i zapobiec ewentualnym awariom maszyny. W pracy przedstawiono różne metody wykrywania anomalii stabilności w oparciu o mierzone siły w procesie toczenia promieniowego nadstopów. W celu określenia niestabilności proponuje się dwie różne metody. Każda z nich jest testowana na podstawie rzeczywistych danych uzyskanych podczas obróbki materiałów: Waspalloy, Haynes 282 i Inconel 718. Dane doświadczalne, zarówno w środowisku konwencjonalnym, jak i przy chłodzeniu wysokociśnieniowym (HPC), zostały ustawiane w czterech różnych stanach w zależności od wielkości ziarna i twardości materiału (LGA , LGS, SGA i SGS). Wyniki pokazują, że metoda analizy głównych składowych (PCA) jest przydatna do wizualizacji procesu i wykrywania nieprawidłowości w bieżących procesach.

3

Metody eksploracji danych, analiza skupień i analiza głównych składowych w badaniach monitoringowych wód powierzchniowych

Cieszyńska M., Wesołowski M.

Wiadomości Chemiczne

|

2009

|

[Z] 63, 7-8

575-600

EN

The paper presents overview of literature pointing up usefulness of two chemometric methods of data exploration: cluster analysis (CA) and principal component analysis (PCA) for interpretation of multidimensional data obtained during surface water monitoring programme. The exceptional importance of fresh water quality rests on the fact that it determines condition and development of all human beings as well as animals and plants. In sections 1 and 3 a concise description of the two methods of pattern recognition was presented. CA provides visual presentation of clustering mode of samples or variables in the form of tree-like scheme called dendrogram [3, 9]. Samples which exhibit considerable similarity fall into one cluster and at the same time they differ to the greatest extent from samples belonging to other clusters [1, 70-74]. PCA enables significant reduction of data matrix dimension without an excessive loss of information [5, 6, 82]. In addition, PCA provides intelligible graphic visualization of the data structure by scattering samples, described by many variables, on plane formed by the following principal components [74]. In CA methods of calculation the distance between different clusters or objects in a hierarchical dendrogram were also mentioned [75]. They define whether two clusters or objects are sufficiently similar to be joined together into one cluster. It was also denoted that among most commonly applied methods Ward's clustering method turns out to be the best and most effective for surface water monitoring results as it gives the largest number of correctly classified observations. The sections 2 and 4 present several examples of the two multidimensional chemometric techniques application in assessment of surface water quality. It was perceived that CA and PCA allow classification of sampling stations in relation to chemical composition of water and therefore permit to pinpoint areas with similar water quality [5, 82]. CA and PCA also help to indicate areas with distinctive chemical composition of water [6, 73, 90, 105]. They also help to identify factors which determine water quality at different regions [4, 5, 88, 113]. When applied for the results acquired from one watercourse at different sites CA and PCA enable to examine changes of water quality downstream [73, 80, 114]. The two chemometric methods are also useful tools for determination if season of sampling or specific weather conditions can influence variation of physicochemical parameters of water [90, 95, 103]. Other chemometric methods applied for monitoring results evaluation are mentioned in section 5 [74, 75, 116].