Pakistan Maintenance, Repair and Overhaul (MRO) sector companies are facing a significant competition among themselves. Aerospace spares supply chains are highly complex due to volatile and high demands. The dire predicament is further affected due to the highly competitive global market, characterized by other airlines and aerospace sector companies with more robust Electronic Supply Chain Management (ESCM) systems. Such a competitive environment demands more effective and efficacious supply chain management systems, implying a strong need by the MRO sector to embrace effective ESCM systems. This study was aimed at developing a framework for effective implementation of electronic supply chain management in aviation MRO sector of Pakistan to manage the ever-changing needs among the consumers as a result of tough competition and globalization. Study involved primarily quantitative analysis, however, four key independent/mediating variables were identified through content analysis. After content analysis, quantitative method was used to measure the impact of these variables on dependent variable (ESCM). For quantitative analysis, questionnaire was responded by 924 respondents from Pakistan’s aircraft MRO sector companies. Both SPSS and AMOS tools were used to analyse the impact of three independent and one mediating variable on ESCM. Results validation was also done through case study. Findings of study highlighted that all three independent variables (social influence, IT infrastructure and IT training) and mediating variable (IT competence) have significant effect on the dependent variable (ESCM implementation). Confirmatory factor analysis (structural equation modelling / AMOS) also confirmed a significant mediating effect of DEVELOPMENT OF FRAMEWORK FOR e-SCM IMPLEMENTATION ii IT Knowledge and IT Experience (Expanded factors of IT Competence) on ESCM implementation. The results indicate that significance of social influence has been central among the e-SCM adoption models. The research while analysing the impact of Information Technology (IT) variables on e-SCM, found that within IT infrastructure domain, key deficiencies among the MROs in Pakistan related largely to the human resource aspect. The study also highlights that role of IT deployment capability is not limited to the physical IT infrastructure. It must be balanced with managerial IT skills (e.g. IT knowledge management and experience in IT projects management) for effective linking of information flows across organizational boundaries. The research augments the existing literature that IT training can facilitate in ensuring seamless supply chain but this improvement could come through empowerment of employees specially those working in IT division. With regard to IT competence, this study highlights that its role cannot be underscored. IT competence was further expanded into Knowledge and Experience element. In the field of IT competence, the principal challenges were identified in IT experience domain, which seemed to be lacking within the Pakistan MRO industry. The study is unique in the aspects that e-SCM implementation in Pakistan’s MRO sector has not been reviewed in existing literature and secondly the study provides the structural composition of elements for e-SCM implementation in conjunction with Social Influence, IT Infrastructure, IT Training and IT Competence through an integrated framework. Study results may provide a guideline to the managers for increasing the scope and magnitude of e-SCM. The organizational capabilities identified in study results could provide a guideline to management while transforming their conventional supply chains into those based on the internet-based supply chain network.Improving upon the motivation, social impact value and IT competence/skill level through workshops, seminars, training programs and more investments for digitalization of systems could help in more smooth e-SCM implementation in MRO sector companies. Study highlights experience in IT projects along with general IT management in IT as the weak links in e SCM implementation. Findings of research work confirm that e-SCM implementation through combined effect of study variables are significant for enhancing companies’ performance (reduction in order lead times, better inventory management, life tracking etc.) as validated through case study as well. Limitations of study with future research directions have been discussed in last part of this thesis.
Tujuan penelitian ini untuk mendeskripsikan peningkatan hasil belajar mahasiswa melalui penerapan model pembelajaran kooperatif Word Square. Populasi dalam penelitian ini adalah 36 mahasiswa jurusan Pendidikan Ekonomi IKIP Gunungsitoli semester II. Penelitian ini merupakan Penelitian Tindakan Kelas dengan prosedur penelitian: persiapan, tindakan, observasi, dan refleksi. Berdasarkan hasil penelitian ditemukan bahwa penerapan model pembelajaran kooperatif work square dalam proses belajar mengajar mata kuliah Dasar- Dasar Akuntansi I dapat meningkatkan hasil belajar mahasiswa Pendidikan Ekonomi semester II IKIP Gunungsitoli. Hal ini terlihat pada hasil belajar siswa pada siklus I sebesar 58, 30% dalam kategori sedang, meningkat pada siklus II mencapai 83, 30% dalam kategori sangat baik. Sehingga disimpulkan bahwa model kooperatif Word Square berpengaruh positif terhadap hasil belajar yaitu peningkatan hasil belajar mahasiswa pada mata kuliah Dasar-Dasar Akuntansi I.
Missing information or missing data occurs when no data value is stored for any variable in the dataset. It is usually occurred and can have a substantial effect on the decisions. These days the treatment of the missing data is a real issue. In data quality, a severe threat is missing data which have a direct impact on the certainty of what is being presented to the end user. Imputation of missing values from predictive strategies created the idea that the influences accredited have some association with actual data. If these values did not have an association with actual data, it could produce uncertain results. There are different factual strategies of machine learning techniques like neural systems; genetic programming, and data mining strategies, which are utilized to impute such missing perceptions. In data analytics, predictive methodologies are exceptionally attractive because it can prompt better results. If there are missing data values in datasets certain machine algorithms cannot be applied to predict and forecast data. We can use different time series datasets of different organizations listed in the Pakistan Stock Exchange (PSX). The missing values can be discovered in these selected datasets. We have developed algorithms that can fill the missing dates in the datasets because some of the forecasting algorithms did not initially predict with missing values. The datasets with none missing values can be used for the training of some machine learning algorithms. The results based on different datasets of PSX can be used to forecast the next day value of the stock.