Supply Chain Integration Performance Measurement and Improvement in HMMB
| dc.contributor.advisor | Yitagesu, Yilma (PhD) | |
| dc.contributor.author | Tewoldual, Temesgen | |
| dc.date.accessioned | 2018-12-19T11:26:52Z | |
| dc.date.accessioned | 2023-11-18T06:26:49Z | |
| dc.date.available | 2018-12-19T11:26:52Z | |
| dc.date.available | 2023-11-18T06:26:49Z | |
| dc.date.issued | 2018-06 | |
| dc.description.abstract | Ethiopian metal manufacturing and machine building industries, is characterized by very low level of supply chain integration, poor export capacity, poor production capacity, and poor performance. Generally, the aim of this research study is to measure SCIP and finally to propose supply chain integration performance measurement and Improvement approach model to HMMBI. To accomplish the objective, the researcher conducted a literature survey review of Supply Chain integration, the current understanding of supply chain integration, factor of SCI, different supply chain integration performance measurement model and the study creates a framework for supply chain integration measurement to improve its performance. The collected data have been summarized using descriptive analysis method and analyzed by a statistical tool. Furthermore, the relationship in the SCOR model were tested using Spearman’s correlation coefficient, and the regression analysis was used to analyze causal relation and to check the fitness SCOR model with data. The majority responses in descriptive statistics results shows that average mean value 2.72 signifies agreement by the respondent this show that supply chain integration was very rarely practice in the case company. Furthermore artificial neural network algorithm analysis results a high number of neurons in the hidden layer indicate that SCI in HMMBI was very poor. The correlation result shows that there is strong correlation (ρ=0.78* - 0.921*) between supply chain integration and its performance factor metrics and the relationship is statistically significant. Regression analysis confirm that the relative contribution SCI metrics factors is 65.9%-100% of variability supply chain integration performance explained by each factor metrics at (planning100%, sourcing100%, making 89.3%, delivery 65.9%, and returning100%). From the whole metrics remaining 8.2% change in change can be attributed to other factors. In general, to check model fit the data different test was done on regression analysis DurbinWatson test, R square value test, adjusted R square value test, tolerance test, variance inflation factor test, b-value test, and multicollinearity test. The proposed factor metrics measure 90.8% of supply chain integration performance. Finally, the researcher proposed SCIP Measurement and Improvement Approach model for HMMBI based on SCOR model. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/15194 | |
| dc.language.iso | en_US | en_US |
| dc.subject | HMMBI | en_US |
| dc.subject | Supply Chain | en_US |
| dc.title | Supply Chain Integration Performance Measurement and Improvement in HMMB | en_US |
| dc.type | Thesis | en_US |