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Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM).

This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed.

The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness.

MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises.