This research investigates the links between information quality, service quality, system quality, and supply chain performance in Saudi Arabia's manufacturing sector. The goal is to provide insights into the elements that impact supply chain outcomes and to identify critical performance drivers. Data was acquired from a sample of manufacturing organizations, and the correlations were analyzed using a structural equation modeling technique. Information quality, service quality, and supply chain performance are all shown to have substantial positive correlations. Higher levels of information quality and service quality relate to enhanced supply chain performance in the manufacturing sector. However, system quality was shown to have a comparatively smaller influence on supply chain performance, suggesting that investments in information management systems and service delivery methods may offer higher returns. The reliability and validity of the measuring scales employed in this research were evaluated and determined to be strong, assuring the precision and consistency of the findings. These results add to the current research by identifying the unique elements that influence supply chain performance in Saudi Arabia's manufacturing sector. The study has many implications for practitioners in the manufacturing sector as it discusses the significance of investing in information management systems, providing high-quality services, and continually evaluating and improving supply chain performance. Organizations may improve their competitiveness and create better supply chain results by concentrating on these areas. Additional elements and possible moderating or mediating variables may be investigated in future studies to acquire a better knowledge of the dynamics that impact supply chain performance. Overall, this research offers significant insights for practitioners and decision-makers in the manufacturing industry, directing them towards more appropriate methods to maximize supply chain performance and achieve long-term success.

This paper addresses the sustainable closed-loop supply chain (SCLSC) design problem regarding selecting a supplier under total quantity discount with demand uncertainty and logistic flow uncertainty. The proposed model considers the three pillars of sustainability: the economic, environmental, and social realms. The model deals with the costs incurred by products-related manufacturing and minimizes the carbon dioxide emissions resulting from different manufacturing processes, as well as the attendant rate of injuries among the workers. Python edition 2019-07 software with the SCIPY solver was used to solve the model, using a sequential least squares programming algorithm (SLSQP) to obtain optimal solutions. A numerical study was conducted to validate the model. A sensitivity analysis was conducted to address the effects of both types of uncertainty on the optimal solution. It was found that the effect of a high rate of demand uncertainty is more severe than the effect of the uncertainty of the flow logistics in the reverse direction since the former generated a lower value of the optimal solution than the worst-case scenario generated by the uncertainty budget. Moreover, the higher the weight of environmental and social objectives, the higher the proportion of recycled products from the total production. This study proposes a robust optimization model for an SCLSC that considers two types of uncertainty: the uncertainty budget that is used for the logistics flow in the reverse direction for refurbished and redesigned products and the box of uncertainty that is used to address the demand uncertainty.

The pharmaceutical supply chain (PSC) plays a crucial role in ensuring the timely and reliable availability of essential drugs while maintaining high-quality standards. Balancing the triad of cost, time, and quality is paramount in optimizing the complexities of this supply chain. In this research, a multi-objective PSC optimization model is developed to maximize the key business factors. The dynamic nature of the PSC can significantly compromise the effectiveness of the decision making process. To deal with this challenge, a robust possibilistic flexible programming approach (RPFPA) solution methodology is proposed. This methodology provides a robust and flexible framework to tackle the uncertainties within the supply chain. To validate the proposed model and methodology, a computational analysis of a case study is conducted. The results of the analysis demonstrate the effectiveness of the model and methodology in addressing the uncertainties and complexities of the PSC. Specifically, the findings reveal that by accepting a 23.8% increase in costs, decision-makers can achieve a desirable level of robustness in their decisions. Moreover, the study identifies that the assignment of higher priority to cost objectives leads to more centralized decisions within the supply chain, while a greater emphasis on quality objectives results in a more decentralized approach. By employing the proposed approach, decision-makers can efficiently deal with the complexities and uncertainties inherent in the PSC, making well-informed choices that balance cost, time, and quality.