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Effective information system (IS) has been one of the key factors that shaped the supply chain management (SCM) developments in the last few decades. The literature on surveying IS in SCM is somehow scarce, particularly in developing countries. The purpose of this study is to investigate the current use and future adoption direction of Supply Chain Management Information Systems (SCMIS) in Jordan. Data from 64 manufacturing and service firms were collected. The results show that i) the overall implementation of SCMIS is very poor, ii) the currently implemented SCMIS, and the expected to implement, are mostly focusing/will focus on separated functions within firms, iii) integration with suppliers’ and customers’ system, resistance to change from employees, and insufficient vendor support; are the main problems facing the adaptation of SCMIS, and iv) for improving SCM activities; enhanced information provision and closer cooperation between firms and associated governmental agencies are needed. The study is concluded with recommendations for SCM policymakers, firms’ managers, and SCMIS vendors.

The study focuses on the monitoring of the performance of a photovoltaic system, influenced by the climatic characteristics of a particular geographical area, in which the photovoltaic system is installed. This paper presents the results obtained by monitoring, from February 2017 to January 2018, the 5 MWp photovoltaic system that was installed on the campus of Jordan University of Science and Technology (JUST), Jordan. The tilt angle is 15°, and the azimuth angle is 0°. The values of energy and power generated, final yield, reference yield, photovoltaic system efficiency, performance ratio, and cell temperature losses were analyzed and correlated to the climatic characteristics of the site, such as insolation, ambient temperature, and wind speed. System Advisor Model was employed to give model simulation results. A comparison of the actual performance and the simulated one was given. An economic analysis was also carried out and showed that the annual internal rate of return was 30.11%, and the payback period was 4.32 years.

Purpose: This paper presents an implementation of the Six Sigma DMAIC approach implementing lean tools and facilities layout techniques to reduce the occurrence of different types of nonconformities in the carpeting process. Such carpeting process can be found in several industries such as construction, aviation, and automotive. Design/methodology/approach: The improvement process was built through a sequential implementation of appropriate interconnected tools at each phase of the DMAIC approach. Utilized tools included: Pareto analysis, control charts, Ishikawa chart, 5-whys, failure mode and effect analysis, process capability ratio, value stream mapping, and strategic layout planning. Findings: The carpeting process capability, quality of the product, customer satisfaction, and cost of poor quality were significantly improved. Explicitly, the sigma level was improved from 2.297 to 2.886 and the defects per million opportunities (DPMO) was reduced from 21615 to 3905. Originality/value: This paper has approved the capability of the Six Sigma DMAIC approach to analyze, investigate, and remove the root causes of the carpeting (preparation-installation) process nonconformities .

An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (