Search

Objective: Purchasing portfolio models have received a great deal of attention in both academic and practice fields as suitable purchasing strategies. Purchasing portfolio applies as a diagnostic and prescriptive purchasing tool. The core purpose of this study is to introduce a quantified portfolio for developing purchasing strategies that are aligned with competitive priorities. The quantitative method of this study relies on data mining (ordered clustering) and MADM (Best Worst method) to classify purchased items with the aim of creating a strategic fit in the supply chain and developing purchasing strategies in accordance with the competitive priorities of organizations.Methods: In portfolio models, the determination of the dimensions and the manner in which they are measured is important. In this study, firstly, the proper dimensions for commodity classification were introduced. These dimensions were competitive priority (as introduced in the literature review, including the costs, quality, speed, flexibility, and innovation), supply market analysis (an important dimension that should be considered in commodity classification analysis), and product features (that describe the characteristics of the commodities). Next, the proper criteria for each dimension were determined using the Delphi method. After that, the selected criteria using the Delphi method were weighted using the Best Worst method. In the following, purchasing items were classified using ordered clustering based on the PROMETHEE method. In this study, the clusters determined by PSO-K-means were ranked using the total unicriterion net flow of clusters in each dimension introduced in this study. Then, the preference profile was used to measure the preferential quality of each cluster on the different criteria in each dimension. The profile helps the purchasing managers with selecting the best proposed working methods for purchasing in each class of commodity by the preference profile. Finally, the proper working method and purchasing strategy were proposed for highly strategic commodities.Results: The approach and method presented in this study were implemented for 100 purchased items in a steel company. In this study, the company selected cost and quality as competitive priorities. The method construct bases on these competitive priorities and proper criteria for each dimension. The most appropriate purchasing methods for each class of high-strategic purchased items were presented taking into account the proposed methods and the opinion of experts. The strategies and working methods that were introduced in purchasing Chessboard were applied in this study.Conclusion: The approach of this research helps purchasing and supply managers to have more and more accurate choices of purchasing methods for each category of purchasing items. By considering the profile of preferences in the ordered clustering method based on the PROMETHEE principles it helps them to improve supply management and supply chain performance. Also, the alignment of purchasing strategy with business strategy could improve competitiveness.

Introduction: In recent years, rangeland degradation has reduced a wide range of ecosystem services for billions of people whose livelihoods depend on natural resources. Proper rangeland management can multiply the rangeland's capacity to benefit from ecosystem services. The dominant model of rangeland management is the preparation and implementation of pastoral plans. These plans, if not be properly selected and not be based on rangeland ecosystem services improving, in addition to exacerbate the downward trend of rangelands, also will result in the loss of capital. Therefore, by recognizing the effects of different management methods on ecosystem services, it is possible to change or modify rangeland management patterns. In the present study, we ranked rangeland management plans methods in Fars province using multi-criteria analysis in the context of rangeland ecosystem services. Material and methods: One of the major causes that hinder consideration of the value of ecosystem services in computation and evaluation are the heterogeneity of the different types of value measurement units, the impossibility of quantifying all values and the problems associated with the integration of quantitative and qualitative data. Therefore, given the capabilities of multi-criteria analysis methods in analyzing heterogeneous data, these methods are suitable tools for overcoming this problem and for making more reasoned and informed decisions. In the present study, fuzzy hierarchical analysis (AHP) was used to determine the weight of criteria and indices. Then, using the results of the first step as input of the PROMETHEE software, the rangeland remediation options were ranked using the PROMETHEE II analysis. Results and discussion: According to the literature on the subject and discussion with rangeland and environmental experts, 12 indicators were defined for four categories of ecosystem services. After determining the criteria, weight of criteria and indices were calculated using hierarchical analysis method. Paired comparisons of ecosystem services criteria indicated that regulatory and support services with a weight of 0.734 are more important than production and cultural services. The results of paired comparisons of ecosystem cultural service indices showed that biodiversity criterion was much more important than landscape, recreation and ecotourism criteria. Paired comparisons of sub-criteria of ecosystem services indexes showed that grazing capacity and profitability of livestock products with the relative weight of 0.555 and 0.224 were the highest importance among ecosystem services indices, respectively. In addition, rangeland water resource capability index with significant weight of 0.475 is in the first place of impact on ecosystem regulation and support indexes and soil erosion prevention index with the weight of 0.352 is ranked second. The final weight of each index was obtained by multiplying the relative weight of the criteria by the relative weight of the indices. Among the ecosystem services indexes, water resources weight was 0.35 which was the highest weight. After water resources, the role of the ecosystem in preventing soil erosion is the most important indicator evaluated. The third most weighted indicator is biodiversity. After data entry into PROMETHEE software, the results showed that among the various pasture management options, enclosure was ranked first. Therefore, the enclosure plan provides the most ecosystem services. 11 indicators had a positive effect on enclosure priority and the only indicator that had a negative effect on enclosure priority was access to water resources. To validate the results, sensitivity analysis was performed in three different scenarios. Results of sensitivity analysis showed that due to water shortage crisis and high importance of this index in rangeland ecosystem services, priority of rangeland options is not sensitive to changes in other indices. Conclusion: According to the results, the exclosure plan provides the highest ecosystem services in rangeland management and the least ecosystem services in medicinal plants. After exclosure, priority is with water projects includes springs refurbishment and pond construction. In order to better management of rangelands in Fars province and the necessity of ecosystem services consideration in rangeland plans, we can obtain valid evaluations by applying multi criteria methods.

Objective: Purchasing portfolio models have received a great deal of attention in both academic and practice fields as suitable purchasing strategies. Purchasing portfolio applies as a diagnostic and prescriptive purchasing tool. The core purpose of this study is to introduce a quantified portfolio for developing purchasing strategies that are aligned with competitive priorities. The quantitative method of this study relies on data mining (ordered clustering) and MADM (Best Worst method) to classify purchased items with the aim of creating a strategic fit in the supply chain and developing purchasing strategies in accordance with the competitive priorities of organizations. Methods: In portfolio models, the determination of the dimensions and the manner in which they are measured is important. In this study, firstly, the proper dimensions for commodity classification were introduced. These dimensions were competitive priority (as introduced in the literature review, including the costs, quality, speed, flexibility, and innovation), supply market analysis (an important dimension that should be considered in commodity classification analysis), and product features (that describe the characteristics of the commodities). Next, the proper criteria for each dimension were determined using the Delphi method. After that, the selected criteria using the Delphi method were weighted using the Best Worst method. In the following, purchasing items were classified using ordered clustering based on the PROMETHEE method. In this study, the clusters determined by PSO-K-means were ranked using the total unicriterion net flow of clusters in each dimension introduced in this study. Then, the preference profile was used to measure the preferential quality of each cluster on the different criteria in each dimension. The profile helps the purchasing managers with selecting the best proposed working methods for purchasing in each class of commodity by the preference profile. Finally, the proper working method and purchasing strategy were proposed for highly strategic commodities. Results: The approach and method presented in this study were implemented for 100 purchased items in a steel company. In this study, the company selected cost and quality as competitive priorities. The method construct bases on these competitive priorities and proper criteria for each dimension. The most appropriate purchasing methods for each class of high-strategic purchased items were presented taking into account the proposed methods and the opinion of experts. The strategies and working methods that were introduced in purchasing Chessboard were applied in this study. Conclusion: The approach of this research helps purchasing and supply managers to have more and more accurate choices of purchasing methods for each category of purchasing items. By considering the profile of preferences in the ordered clustering method based on the PROMETHEE principles it helps them to improve supply management and supply chain performance. Also, the alignment of purchasing strategy with business strategy could improve competitiveness. [ABSTRACT FROM AUTHOR]