A Multi-Objective Fuzzy Approach to Closed-Loop Supply Chain Network Design with Regard to Dynamic Pricing

During the last decade, reverse logistics networks received a considerable attention due to economic importance and environmental regulations and customer awareness. Integration of leading and reverse logistics networks during logistical network design is one of the most important factors in supply chain. In this research, an Integer Linear Programming model is presented to design a multi-layer reverse-leading, multi-product, and multi-period integrated logistics network by considering multi-capacity level for facilities under uncertainty condition. This model included three objectives: maximizing profit, minimizing delay of goods delivering to customer, and minimizing returned raw material from suppliers. This research gives financial incentives to encourage customers in order to return their used product. Considering that the remaining value of used products is the main incentive of a company to buy second-handed goods, a dynamic pricing approach is determined to define purchase price for these types of products, and based on that, the percentage of returned products were collected by customers. In addition, in this study, parameters have uncertainty features and are vague; therefore, at first, they are converted into exact parameters and, then, because model is multi-objective, the fuzzy mathematical programming approach is used to convert multi-objective model into a single objective; finally, the model by version 8 of Lingo is run. In order to solve a large-sized model, a non-dominated sorting genetic algorithm II (NSGA-II) was applied. Computational results indicate the effect of the proposed purchase price on encourage customers to return the used products

Recognition of nonrandom patterns on supply performance by employing statistical monitoring

This paper introduces a practical methodology of assign able signals and Run chart tests for identification of nonrandom patterns of supplier performance by statistical monitoring. The assumption of normal distribution is one of the important factors to implement a control chart in industry and service. It is supposed that natural data shows lack of any nonrandom pattern signals or out of control points on control chart. The data of supplier’s on-time delivery for automotive industry has been gathered and illustrated on control chart by employing appropriate transformation and assignable signals and run chart were tested on the control chart accordingly. The results show that tests were able to identify nonrandom patterns of supplier performance data. Out of control signals were removed from the control chart and show that on-time delivery performance was increased accordingly. The control chart with natural pattern can be used as pilot for monitoring supplier delivery over time and improve supplier delivery performance

Improving purchasing performance by implementation of QMS process management approach in a manufacturing company

Process oriented approach in quality management system has been introduced with ISO9001:2000. The international standard promotes the adoption of a process approach when developing, implementing and improving the effectiveness of a QMS to enhance customer satisfaction by meeting customer requirement. The advantage of process approach is to link all parties in scope of business of the organization from suppliers, internal departments of organization to gather to make B2B communication and integration. In this paper, the process approach is defined based on QMS requirement. Supply department was proposed for implementation of process. Purchasing process was designed as a linkage between supplier and internal company departments. Performance indicators were developed and measured accordingly. It shows that process management can improve delivery capability, quality and monitor price of supplied parts by suppliers

Recognition of Nonrandom Patterns on Supply Performance by Employing Statistical Monitoring

Abstract: This paper introduces a practical methodology of assignable signals and Run chart tests for identification of nonrandom patterns of supplier performance by statistical monitoring. The assumption of normal distribution is one of the important factors to implement a control chart in industry and service. It is supposed that natural data shows lack of any nonrandom pattern signals or out of control points on control chart. The data of supplier's on-time delivery for automotive industry has been gathered and illustrated on control chart by employing appropriate transformation and assignable signals and run chart were tested on the control chart accordingly. The results show that tests were able to identify nonrandom patterns of supplier performance data. Out of control signals were removed from the control chart and show that on-time delivery performance was increased accordingly. The control chart with natural pattern can be used as pilot for monitoring supplier delivery over time and improve supplier delivery performance