Adopting and implementing the Industry 4.0 strategy to increase the overall performance of the organization became one of the main aims of organizations. However, ignoring the linkages between implementing strategic decisions and organizational internal factors/forces can endanger and shrink its performance, competitive advantages, and thus its strategic success. In this context, many companies failed to achieve the expected benefits of adopting the Industry 4.0 strategy. Therefore, the gained advantages of adopting the Industry 4.0 strategy should be sustained through perfect and comprehensive integration between Industry 4.0 concepts and the accompanying upgrades and changes in the organizational internal factors/forces. This will capitalize on organizations’ internal strengths and avoid weaknesses or turn them into strengths. In this paper, a conceptual model is proposed to investigate the relation between Industry 4.0 and internal organizational forces and examine their impacts on the sustainable competitive advantages of the organization. In the hypothesized model, three innovation capabilities (i.e., technological, economic, and commercial innovation) have been used to mediate the relation between the internal forces and the sustainable competitive advantages in parallel with Industry 4.0 adoption. The model and the proposed hypotheses have been simulated and tested using partial least squares structural equations modeling software called SmartPLS. The sample size used is 125 responses from different manufacturing fields. The results demonstrate the significant role that the internal organizational forces play in maintaining and sustaining the organization’s competitive advantages in combination with Industry 4.0.

Sustainable techniques in distribution centers, such as automation that reduces the land area needed, can be utilized. Automated Storage and Retrieval Systems (AS/RS) are used to efficiently manage the flow of pallets and carton cases in distribution centers. There are two types of AS/RS: one for pallets and another type for cases that are depalletized from pallets. Further enhancements on the system are obtained by investigating both warehouses together. This paper investigates an efficient approach that directly affects the conceptual design of automated distribution centers for the purpose of reducing the total costs. The tradeoff between the throughput (defined by the level of double handling) and warehouse capacity is investigated in this study by finding the best lot sizing rules for different classes of products (A, B, and C). These rules are to determine the method of moving carton cases from the first warehouse to the second one. The number of stacker cranes is determined based on the found throughput. The effect of double handling of pallets on the design is considered for the first time in this study. Analytical formulas and simulation were used to find the throughput and capacity based on the mentioned lot sizing rules. Then, an integer nonlinear model was developed to optimize the system. According to the results of the assumed data, the model can save up to 19.5%. The costs of stacker cranes were found to account for approximately 78.7% of the total costs in the best solution found. A decision support system has been developed to help decision makers find an efficient design of distribution center.

Industry 4.0 allows for greater flexibility in production processes so that products can be customized (i.e., mass customization). Innovative production techniques in an industrial liquid/yogurt filling machine (YFM) improved efficiency in the beverage industry. In this study, we have introduced the second phase designed control architecture of our YFM based on the concepts of industry 4.0 incorporating an NFC platform for improving customer satisfaction. Especially during this pandemic period, wireless technologies have been ubiquitous and pervasive for customized products. The basic components of the YFM have been described. High-level control architecture programmed fully automated filling operations, and the design stage of the development of a PFC-based controller for the YFM is elaborated. For the evaluation of the proposed control system, the operations of the electric/pneumatic input devices and actuators were simulated on FluidSIM-MecLab. The results of the simulation verify the design logic of the PFC-based controller. Comparisons were made between different production types using the developing YFM. A complex learning environment replicating a real production system to understand, learn, and apply modern manufacturing approaches has been developed. Through the creation of this YFM, the academic environment and industrial applications are combined. Consequently, the problem verification is becoming more realistic and more efficient than online (trial and error) automation programming.

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The short- and long-term volatility of oil and gas prices has a wide-ranging impact on both parties of petroleum contractual agreements, thus affecting the profitability of the project at any stage. Therefore, the government (first party) and the international oil company (second party) set the parameters of their contracts in a way that reduces the uncertainty. The effect of price fluctuations on economic indicators is investigated in this paper. The Taguchi method is used for the first time to find the best-agreement parameters, which are the “A” and “B” factors, in the standard Libyan agreement. There are four “A” components from “A1” to “A4”, and four “B” components from “B1” to “B4”. The purpose is to reduce the variability in the response variables, which are the company take (the percent of net cash flow for the international company) and average value of the second-party percent share of production (ASPS). The noise factors considered in this paper are oil, liquefied hydrocarbon byproduct (LHP), and gas prices. The method was applied to a case study of oil field development in Libya. The results showed that “A3” and “A4” were the most important control factors that affect the ASPS, while “B2” and “B3” are the most important factors affecting the company take. To obtain robust results, the most important factors to reduce variability were also determined. The effect of control parameters on the average NPV may be worth more than USD 22 MM in the 1-billion-barrel oilfield case study. The results showed that, for a given combination of “A” and “B” factors with a certain company take, the mean absolute deviation (MAD) of the NPV of the second party was reduced by 18% if the optimal combinations of the levels were used.