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Background: Industry 4.0 is a burgeoning research area that has been addressed by many research entities. However, the literature shows that the industrial sector lacks the awareness and knowledge needed to comply with Industry 4.0 implications, particularly in developing countries. Methods: This study evaluates the status of small and medium enterprises (SMEs) in Jordan concerning Industry 4.0. Four criteria are assessed, including Industry 4.0 readiness, maturity, drivers, and barriers. Samples of SME respondents and Industry 4.0 experts are surveyed using an online questionnaire. Results: The results show that SMEs in Jordan are not mature enough nor ready to apply Industry 4.0. For the readiness dimension, SME respondents and experts agreed that the Jordanian SMEs’ status is between having initiatives in the pilot phase or implementing concepts to low degrees, except for autonomous workpiece and smart product aspects, in which Jordanian SMEs are behind due to financial and technological reasons. It was found that none of the Industry 4.0 investigated technologies have reached maturity levels. Customer requirements, cost reduction, competitors’ practice, productivity improvement, and quality improvement are found to be the major influencing drivers for Industry 4.0, while a lack of awareness and knowledge is found to be the crucial barrier hampering Industry 4.0 implementation. Conclusions: Jordan needs country-scale initiatives for the implementation of groundbreaking Industry 4.0 development, incorporating government agencies, industrial parties, and experts, relying on Industry 4.0’s readiness and practice status as a starting point, and considering the influential drivers and barriers to steer the development process.

PurposeThis paper focuses on quantifying and ranking the “7-Deadly” Wastes in the warehouse environment. The purpose of this paper is to develop a prioritized road map that would guide the implementation of the “7-Deadly” Wastes and their related waste-reduction practices in a warehouse environment for improving warehouse operational performance.Design/methodology/approachA four-stage approach was used to achieve the objectives of the study. A Delphi technique was employed to develop indicators of waste-reduction practices at the warehouse activities level and categorize them according to the “7-Deadly” wastes. Whereas categories ranked based on statistical analysis, a regression analysis was used to explore the impacts of this ranking.FindingsThe results of the regression analysis showed a significant influence of the ranked “7-Deadly” wastes on warehouse operational performance. The paper develops a road map for implementing waste-reduction practices in the warehouse.Originality/valueThe paper proposes a road map comprising warehouse waste categories. It develops an integrated approach to quantify, rank and prioritize initiatives to adopt the categories of the “7-Deadly” Wastes that would (not) affect warehouse operational performance.

The mechanical properties of extruded Polyvinylchloride (PVC) composites cannot be easily predicted due to the nonlinear nature of the relationship between the composite’s composition and the resulting after-production properties. In the work presented herein, supervised artificial neural network (ANN) modeling is used to predict and optimize three properties (tensile strength, ductility and density) of PVC composites having different weight percentages of virgin PVC, CaCO3, plasticizers, and recycled PVC. Different ANN models, designed and analyzed through factorial design and analysis of variance methodology, were evaluated using an experimental dataset which was designed according to the mixture design of experiments approach. The results show that the constituents-mechanical properties relationship of PVC composites’ can be accurately estimated using several ANN models including Levenberg-Marquardt/6-[18-9]3-3/Radial basis and Levenberg-Marquardt/6-[9-18]3-3/Log sigmoid models. The results also show that the ANN modeling is capable of determining the optimal weight percentages of the different PVC composite constituents in order to achieve a required composite property.

The design of manual-order-picking warehouses is a combination of interdependent decisions with enormous possible varieties in design components. The strong interrelationship between these componen...

Inkjet printing is an emerging technology with key advantages that make it suitable for the fabrication of stretchable circuits. Specifically, this process is cost-effective and less complex compared to conventional fabrication technologies. Inkjet printing has several process and geometry parameters that significantly affect the electromechanical properties of the printed circuits. This study aims to optimize the geometry parameters of inkjet-printed silver nanoparticle traces on plasma-treated polydimethylsiloxane (PDMS) substrates. The optimization process was conducted for two printed shapes, namely straight line and horseshoe patterns. The examined input factors for the straight line traces were: the number of inkjet-printed layers and line width. On the other hand, the number of cycles and amplitude were the examined input parameters for the horseshoe shape. First, the optimal number of layers and line width were found from the straight line analysis and subsequently were used in the optimization of the horseshoe pattern parameters. The optimization of the input parameters was carried out using the response surface methodology (RSM), where the objective of the optimization was to maximize the breakdown strain of the traces while maximizing the gauge factor and minimizing the ink cost. The results indicate that a 1.78 mm line width and one layer are the optimal geometry parameters for the straight line traces, while for the horseshoe pattern, the optimal parameters are one layer, a line width of 1.78 mm, amplitude of 4 mm and one cycle. The optimal straight line was designed to sustain up to 10% strain while the horseshoe pattern was designed to sustain up to 15% strain.

In this study, machine learning algorithms (MLA) were employed to predict and classify the tensile strength of polymeric films of different compositions as a function of processing conditions. Two film production techniques were investigated, namely compression molding and extrusion-blow molding. Multi-factor experiments were designed with corresponding parameters. A tensile test was conducted on samples and the tensile strength was recorded. Predictive and classification models from nine MLA were developed. Performance analysis demonstrated the superior predictive ability of the support vector machine (SVM) algorithm, in which a coefficient of determination and mean absolute percentage error of 96% and 4%, respectively were obtained for the extrusion-blow molded films. The classification performance of the MLA was also evaluated, with several algorithms exhibiting excellent performance.

This paper presents a mixed integer programming model that aims to optimize the inventory and transportation costs for several drugs across three echelons: hospital pharmacy, warehouse and manufacturer. A continuous inventory review system is adopted to meet the rapid nature of the pharmaceutical supply chain. The model decides the quantities to be ordered of each drug in every echelon, in addition to the selection of warehouses and manufacturers that would best reduce the total cost of the supply chain.

Polyvinyl chloride (PVC) products are typically complex composites, whose quality characteristics vary widely depending on the types and proportions of their components, as well as other processing factors. It is often required to optimize PVC production for specific applications at the highest cost efficiency. This study describes the design and analysis of a statistical experiment to investigate the effects of different parameters over the mechanical properties of PVC intended for use in electrical wire insulation. Four commonly used mixture components, namely, virgin PVC, recycled PVC, calcium carbonate, and a plasticizer, and two process variables, type of plasticizer and filler particle size, were examined. Statistical tools were utilized to analyze and optimize the mixture while simultaneously finding the proper process parameters. The mix was optimized to achieve required strength and ductility, as per ASTM D6096 while minimizing cost. The paper demonstrates how statistical models can help ...

This paper explores the utility of supervised machine learning algorithms in predicting the tensile strength of high density polyethylene film produced by extrusion-blown molding process. Three algorithms were used: Artificial Neural Networks, Decision Tree, and kNearest Neighbors. Eleven input parameters, five materials related and six process related; were modeled in the algorithms. The application of algorithms demonstrated their capability in predicting the intended property of the extrusion-blown process products.

[EN] This paper presents a curriculum design approach for a Masters Programme in Telecommunications Management based on demand data obtained from surveying the needs of potential students of the proposed programme. Through online surveys disseminated at telecom companies in Jordan, it was possible to measure the demand for such a programme and to determine the required programme contents and specifications. The curriculum design is based on definition of programme outcomes and on using a house of quality approach (HOQ) to determine the list of courses required in the programme. Surveyed competencies are mapped to a long list of proposed courses in a HOQ in order to determine the importance of each of these courses. A final list of core and elective courses is then developed considering the contribution to programme outcomes and the academic standards., This work has been funded with support from the European Commission through Tempus Program, Project Number 511074. This publication reflects only the views of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

This study aims to optimally set the operational parameters of pulsed green laser welding with copper. Multi input parameters and critical-to-quality outputs were considered. The chosen inputs were power, pulse duration and pressured air flow, while the outputs were the maximum force handled by the welding joint and the penetration level of the laser weld. Design and analyses for single-response and two-response optimization were presented using response surface methodology, Analysis of variance (ANOVA), and overlaid contour plots. The results indicated that power and pulse duration have the most significant effect on both the maximum force handled and the penetration level. Air flow showed little to no effect on the force handled and critical effect on the penetration level.

Industrial thinking is being applied in efforts to try to stem the flow of migrants to Europe. In particular, industrial production in the Middle East is being subsidized by the European Union. This is being done with good intentions. However, positive outcomes are limited. In this paper, it is explained that limited positive outcomes are inevitable, because industrial thinking is too out-of-date and out-of-place to address the causes of mass migration in the Middle East. By contrast, “leapfrog” socio-technical systems could be applied to address the complex causes of mass migration, not as socio-economic crises, but as opportunities for mutual prosperity growth.

In the last two decades, many researchers have addressed the superior system performance resulted from the integration of process planning and scheduling functions. However, most of the published solution methods in this field fall short in three accounts. First, while integrating with scheduling, they ignore the checking of process planning feasibility with respect to tolerances allocation. Thus, operational tolerances may stackup beyond the blue print tolerances making the process plans infeasible. Second, they ignore the machines capabilities during the integration modeling which make these solution models practically inapplicable. Third, they focus on time consideration, such as makespan or lateness, and do not consider manufacturing cost related to operations-machines assignment. This paper presents an innovative model for the integration of process planning and scheduling in a job-shop environment. The model simultaneously serves three purposes: allocating operational tolerances while minimizing its manufacturing cost, minimizing the work in process inventory, and figuring the operation-machine assignments. The preemptive-goal programming method is used to solve the proposed multiobjective non-linear mixed integer model, and an implementation example is presented to demonstrate the effectiveness of the proposed modeling approach.

Efficiently solving the truck loading problem (TLP) is crucial for achieving urban logistics objectives. Many heuristics, mainly based on wall-building and layer-building concepts, are presented in literature to solve this NP-hard problem. The current research presents a two-step novel prioritization-stacking heuristic algorithm for solving the TLP. The new algorithm primarily aims to improve the truck space utilization and reduce inventory costs at origin and destination points. In particular, the current work considers developing countries conditions in which demand is uncertain, cost minimization is prioritized over customer service, and routing optimization is complicated by a lot of traffic jams. The performance of the proposed heuristic is evaluated by applying it to a real-life case study from the Jordanian market. Additionally, the stacking part of the proposed heuristic is benchmarked with a literature data sample. Both implementations have proved the validity of the suggested heuristic algorithm.

With the development of the Polyvinyl chloride (PVC) products in many applications, the challenge of investigating the raw material composition and reducing the cost have both become more and more important. Considerable research has been done investigating the effect of additives on the PVC products. Most of the PVC composites research investigates only the effect of single/few factors, at a time. This isolated consideration of the input factors does not take in consideration the interaction effect of the different factors. This paper implements a mixture experimental design approach to find out a cost-effective PVC composition for the production of electrical-insulation cables considering the ASTM Designation (D) 6096. The results analysis showed that a minimum cost can be achieved through using 20% virgin PVC, 18.75% recycled PVC, 43.75% CaCO3 with participle size 10 microns, 14% DOP plasticizer, and 3.5% CPW plasticizer. For maximum UTS the compound should consist of: 17.5% DOP, 62.5% virgin PVC, and 20.0% CaCO3 of particle size 5 microns. Finally, for the highest ductility the compound should be made of 35% virgin PVC, 20% CaCO3 of particle size 5 microns, and 45.0% DOP plasticizer., {"references":["H. Demir, M. Sipahioglu, D. Balkose, S. lku, \"Effect of additives on\nflexible PVC foam formation,\" Journal of Materials Processing\nTechnology, vol. 45, pp. 122 -143, 2007.","B. Li, \"Polymer Degradation and Stability,\" Progress in Polymer\nScience, vol. 28, no. 10, pp. 171 -195, 2003.","J. Disson J., S. Girois, \"Acrylic process aids for PVC: From theoretical\nconcepts to practical use,\" Journal of Vinyl & Additive Technology, vol.\n9, no. 4, pp. 177 -187, 2003.","E. Richter, \"Lubricants for film manufacture,\" Plastics Additives &\nCompounding, vol. 2, no. 11, pp. 14 -18, 2000.","M. Van-Den-Oever, C. Boeriu, R. Blaauw, and J. Van-Haveren,\n\"Colorants based on renewable resources and food-grade colorants for\napplication in thermoplastics,\" Journal of Applied Polymer Science, vol.\n92, no. 5, pp. 2961 -2969, 2004.","Annual Book of ASTM Standards, \"Designation: D 6096: Standard\nSpecification for Poly(Vinyl Chloride) Insulation for Wire and Cable,\n90┬░C Operation,\" ASTM International, PA, United States, 2002."]}