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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...

In manually operated warehouses, the travel distance of the order picker has profound effects on the warehouse cost and efficiency. Estimating this distance is difficult because the warehouse environment is a stochastic one, affected by a great number of parameters. Therefore, we present a comprehensive statistical study to assess how the different warehouse parameters and their interactions affect the travel distance. To estimate the travel distance, we simulate the different designs using agent-based modeling (ABM). Having 324 different designs, ABM has enabled us to build one computer model to simulate all the cases. The study shows that having one cross aisle only and using a class-based storage policy decreases the travel distance. Moreover, the results obtained show that choosing the best routing policy depends on the warehouse layout, which proves the importance of considering the interactions among the different parameters.

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.