Supply Weight Research Articles

A novel hierarchical fuzzy inference system for supplier selection and performance improvement in the oil & gas industry

ABSTRACT Evaluation of suppliers is essential to increasing competitive power, customer satisfaction, and profitability. Oil and gas companies can use this research to evaluate suppliers and map the potential path forward for future collaborations. Six supply chain managers in Iran designed HFIS for the oil and gas industry. Shannon Entropy was used to determine the relative weights of suppliers concerning overall uncertainty because the Oil and Gas industry uses many unstructured Key Performance Indicators (KPIs). Using Matlab Toolbox FIS, a future cooperation roadmap was developed. Experts suggested future collaboration with certain suppliers based on the HFIS results. The future cooperation strategy proposed by the framework is highly in line with their expectations. FIS results indicate that the proposed can help select the most appropriate suppliers for cooperation while providing a roadmap for weaker suppliers to improve their performance.

Design of The Performance Evaluation of Suppliers at UKM Weedang Uwuh Based on Multi Attribute Decision Making (MADM)

The development of small business is increasing with the presence of global competition. The current industrial ecology demands a continuous evaluation process to be able to survive and strive to improve quality products. Weeka Wedang Uwuh continuously improves its business system by paying attention to suppliers of raw materials. Supplier evaluation is carried out to guarantee sustainable quality and performance. Sustainable performance evaluation can produce good judgment if it is supported by a performance appraisal instrument. Supplier performance assessment will be useful to build a good and sustainable relationship between entrepreneurs and suppliers. The design of supplier performance evaluation is carried out using the QCDFR model approach namely, Quality, Cost, Delivery, Flexibility and Responsibility. The sub-criteria study that forms the QCDFR model is carried out using the interview method. QCDFR and Sub Criteria hereinafter referred to as KPI (Key Performance Indicator). KPI is an indicator of ongoing evaluation of the relationship between suppliers and entrepreneurs. The assessment of suppliers from entrepreneurs is carried out using the AHP approach which is able to show the weight of the entrepreneur's assessment of the ability of suppliers. The results of the Weeka Wedang Uwuh supplier performance evaluation model design results in a formulation to measure KPIs with criteria: quality of raw materials (size and condition of raw materials), ease of service (payment tolerance and information on availability and price changes, experience (average transaction value and average) average number of transactions), speed of response (changes in the amount and change in time and price of raw materials). Evaluation of the supplier's weight assessment for raw materials of ginger, sugar and sapphire resulted in the values of Mr. Sarno 0.501, Sugeng 0.709 and Mbak Wang 0.690. KPI measurement reinforces the results of the evaluation of weight assessment using AHP.

The multi-objective supplier selection problem with fuzzy parameters and solving the order allocation problem with coverage

PurposeThis study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many real-world supply chain problems are assumed as multiple objectives combinatorial optimization problems.Design/methodology/approachIn this paper, the authors propose a multi-objective model with fuzzy parameters to select suppliers and allocate orders considering multiple periods, multiple resources, multiple products and two-echelon supply chain. The objective functions consist of total purchase costs, transportation, order and on-time delivery, coverage and the weights of suppliers. Distance-based partial and general coverage of suppliers makes the number of orders of products more realistic. In this model, the weights of suppliers are determined by fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, as a multi-criteria decision analysis method, in the objective function. Also, the authors consider the parameters related to delays as triangular fuzzy numbers.FindingsA small-sized numerical example is provided to clearly show the proposed model. The exact epsilon constraint method is used to solve this given multi-objective combinatorial optimization problem. Subsequently, the sensitivity analysis is conducted to testify the proposed model. The obtained results demonstrate the validity of the proposed multiple objectives mixed integer mathematical programming model and the efficiency of the solution approach.Originality/valueIn real-life situations, supplier selection parameters are uncertain and incomplete. Hence, the fuzzy set theory is used to tackle uncertainty. In this paper, a multi-objective supplier selection problem is formulated taking into consideration the coverage of suppliers and suppliers’ weights. Integrating coverage of suppliers to select and allocate the order to them can be mentioned as the main contribution of this study. The proposed model considers the delay from suppliers as fuzzy parameters.

A novel efficient approach for supplier selection problem using the OWA-based ranking technique

Supplier selection is not only an important issue in the supply chain management but also a critical factor for affecting overall supply chain performance. However, most of the traditional calculating method does not consider the ordered weight of attribute values in a supplier selection problem. It will cause biased conclusions and decision-makers to make the wrong judgment. In order to effectively solve this issue, this paper proposed a novel OWA-based ranking technique to deal with the supplier selection problem. The proposed approach has three major advantages: (1) it has considered the ordered weight of attribute values, (2) it can deal with the insufficient and incomplete information in the supplier selection problem, and (3) the attribute rating values and attribute weights of suppliers, described by linguistic variables, are more flexible and reasonably reflect real-world situations. Finally, a supplier selection numerical example is used to illustrate the use of the proposed approach. After comparing the result that was obtained from the proposed method with both the grey possibility degree method and the grey-related analysis method, it was found that the proposed approach provides a more accurate and efficient ranking for dealing with the supplier selection problem.

Supplier selection: an analytic network process and imprecise goal programming model integrating the decision-maker's preferences

The supplier selection decision is very difficult and complex for the supply function manager. Supplier selection is considered as a multi-criteria problem that includes both quantitative and qualitative criteria. Moreover, the purchasing decision-maker (DM) considers several conflict objectives simultaneously such as maximising the total value of purchases, minimising the total cost of purchases and minimising the total rate of defective products. In this paper, we propose a new approach for selecting suppliers based on the combination of the imprecise goal programming (IGP) and the analytic network process (ANP). All the goals considered are interval-valued. The concept of satisfaction functions will be used to integrate explicitly the DM’s preferences. This approach allows us to respond to two key strategic questions: Which suppliers are the best? and How much should be purchased from each selected supplier? To answer the first question, we used the ANP method to evaluate suppliers. Then, the weights of suppliers obtained by the ANP are included in the IGP to determine the quantity purchased from each supplier. A numerical example illustrates the proposed approach.

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.