Strategic Asset Management: A Differentiating Strategy to Enhance a Competitive Advantage of Petrochemical Companies
Makama Ephraim Ntlaba,
Ozias Ncube
Issue:
Volume 3, Issue 3, June 2014
Pages:
118-127
Received:
17 April 2014
Accepted:
14 May 2014
Published:
30 May 2014
Abstract: This paper presents the results of a study of the benefit of implementing Strategic Asset Management (SAM) in line with principles recommended by PAS 55. SAS can assist organisations in the petrochemical industry to enhance their competitive advantage and subsequently become profitable. A survey design methodology is employed in this study. Four business sites of the case under study in South Africa, ranging from maintenance to production disciplines were sampled. Data is collected using questionnaires that were distributed to the four manufacturing sites, and 63 responses were received. Data mining was also conducted on SAP for the determination of seven KPI’s (Key Performance Indicators). The results reveal that the majority of the KPI’s are below target. This indicates deficiencies as far as effective SAM is concerned. This is also an indication of instability in operations resulting in inability to consistently and sustainably supply the market. Most respondents believe that SAM is the responsibility of all the members of the supply chain, and also that discipline cohesion, equipment reliability, pro-active maintenance, plant stability and safety are fundamental drivers of implementing SAM and condition monitoring. During the implementation process the significantly affected factors were observed to be operations, maintenance practices and employees. Management of the change and time presented challenges during the implementation process. Risk identification techniques and risk management techniques such as root cause analysis (RCA), failure modes effects and criticality analysis (FMECA), on-line condition monitoring and reliability centred maintenance (RCM) are the most preferred techniques. Results reveal that the majority of the employees are not familiar with PAS 55 and they believe it cannot be sustained. To support SAM, adherence to OEM instructions and competency of maintenance contractors are revealed to be the most important factors. Maintenance costs and SAP based KPI’s can be used to indicate the success of SAM. The main limitation of the study is that sampling was confined to a specific location’s facilities and therefore results cannot be generalised.
Abstract: This paper presents the results of a study of the benefit of implementing Strategic Asset Management (SAM) in line with principles recommended by PAS 55. SAS can assist organisations in the petrochemical industry to enhance their competitive advantage and subsequently become profitable. A survey design methodology is employed in this study. Four bu...
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Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company
Seyed Behnam Khakbaz,
Nastaran Hajiheydari,
Marziyeh Pourestarabadi
Issue:
Volume 3, Issue 3, June 2014
Pages:
128-134
Received:
27 April 2014
Accepted:
17 May 2014
Published:
30 May 2014
Abstract: Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.
Abstract: Today’s competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these co...
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The Relationship between the Growth of Exports and Growth of Gross Domestic Product of India
Mukherji Ronit,
Pandey Divya
Issue:
Volume 3, Issue 3, June 2014
Pages:
135-139
Received:
11 June 2014
Accepted:
24 June 2014
Published:
30 June 2014
Abstract: Does Economic Growth Promote Exports of a country or do exports lead to a higher growth? This paper tries to answer this question in the context of India, using a three step procedure of first conducting a Vector Auto Regression (VAR) analysis followed by a Granger Causality Test and an Impulse Response Function. Taking yearly data from 1969-2012, we find that growth of exports depends positively on growth of GDP with a year lag. Robustness checks show consistent VAR Results. Further the Granger Causality Test determines that GDP Growth causes Export growth in India. Finally Impulse Response Functions generated show that there are much higher responses of export through a change in GDP. So unanimously we find that India backs the theory of Growth Led Exports.
Abstract: Does Economic Growth Promote Exports of a country or do exports lead to a higher growth? This paper tries to answer this question in the context of India, using a three step procedure of first conducting a Vector Auto Regression (VAR) analysis followed by a Granger Causality Test and an Impulse Response Function. Taking yearly data from 1969-2012, ...
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