Outline
- Abstract
- Keywords
- 1. Introduction
- 2. Agile Supplier Evaluation Criteria
- 3. a Review of the Background of Ism, Ahp, and Topsis Techniques
- 4. Research Method
- 5. Contextual Model of the Research
- 5.1. a Review of Topsis and Ahp, and Fuzzy Calculation
- 6. Findings
- 7. Ranking Using Fuzzy Topsis and Ahp
- 7.1. Bnp = [(u1 − L1) + (m1 − L1)/3] + L1 (sun Et Al., 2010)
- 8. Discussion and Conclusion
- Future Recommendations
- 9. Uncited Reference
- References
رئوس مطالب
- چکیده
- کلید واژه ها
- 1. مقدمه
- 2.معیارهای ارزیابی تولید کننده هوشمند
- 3. مروری بر پیش زمینه شیوه های ISM، AHP و TOPSIS
- 4. روش تحقیق
- 5. مدل مفهومی تحقیق
- 5.1. مروری بر روش های TOPSIS و AHP و محاسبه فازی
- 6. یافته ها
- 7. رتبه بندی با بهره گیری از TOPSIS و AHP فازی
- 8. بحث و نتیجه گیری
- پیشنهاداتی برای آینده
Abstract
In the present competitive world, the organizations need to endeavor constantly so as to make progress as well as maintaining their current position through employing the appropriate strategies. Organizations surroundings have been undergoing rapid changes among which the different demands and the variety of customers are to be mentioned. The scarce and limited number of sources and facilities are also worth being cited as another example of an important restrictions placed on companies. One way to bring down these problems is employing agile suppliers and outsourcing appropriately. The current study results from two theses completed in the fields of agility and ISM. It begins with identifying the criteria to evaluate agile suppliers. Then these factors are ranked and categorized using the interpretive structural model. The results of this study depict that the delivery speed variable lays on the bottom level of the model outlet with quite high driving power. The delay reduction variable has the same characteristics. Next, using fuzzy hierarchical analysis method, the weight of the agility evaluation criteria of suppliers are measured and put as TOPSIS model input. Finally, six suppliers are rated using fuzzy TOPSIS method. The results of this study shows that the criteria with higher driving power and lower dependence have higher weight in AHP model. It is, therefore, necessary to focus on variables of the first and second level of model in order to increase suppliers’ agility. In this study, the weight of data has been determined using hierarchical analysis so as to increase the efficiency of the results of fuzzy TOPSIS technique. At the same time, interpretive structural model has been also employed to interpret the effects of the criteria on suppliers.
Keywords: Agile suppliers - Fuzzy environment - Hierarchical analysis - Interpretive structural model - TOPSISDiscussion and conclusion
In the knowledge age, the successful organizations are the ones which rapidly run novel strategies based on competitive advantages, and learning from market and customers they modify and improve their processes and customers if necessary. In the current study, first, the factors influencing agile supplier are given in different levels using interpretive structural model and then are given in a driving power and dependence graph.
The result of this process helps suppliers choose a more efficient way to increase the degree of their agility and competitive ability. In 2009, Kannan et al. has conducted a research which is relatively similar to this study but with different results; this could be possibly because of using AHP. ISM method results show that delivery time and lead time minimization variables are of the most important factors influencing suppliers’ agility. There is cost minimization factor in the next level. With taking a look at the graph of agility variable clusters, it can be seen that delivery time and lead time minimization variables are of high driving power whereas customer satisfaction and data accuracy variable have the minimum driving power and dependence. Also, the variables in linkage cluster have both high driving power and high dependence degree.
Delivery speed is among the factors, which was given the most importance in evaluating suppliers in the study by Agarwal et al. (2007), and accordingly, placing this variable in the first level of a JSM Model and its strong driving power depicts the significance of this index in terms of suppliers’ agility in the present study. The same is true of the variable of delay reduction time or JIT, because Muduli, Govindan, Barve, Kannan, and Geng (2013) pointed out the vitality of this variable in distinguishing the excel- ling supplier in their study in 2007. Similarly, the result of this study shows that this variable is placed in the first level of ISM and is of particular significance. The customer satisfaction and uncertainty minimization variables, which were introduced in the studies carried out by Gunasekaran (2003) and Prater et al. (2001), are considered as dependent variables in higher levels in this study. Correspondingly, the results of those studies which presented these variables as that of suppliers’ optimism and evaluation, proves the validity of results in this study.
In increasing suppliers’ agility through developing these variables their degree of independence must be considered. This is to say that with a partial increase in one of these variables, no change can be seen in suppliers’ agility. These variables must change at the same time with other variables from the same cluster and independent variables. Therefore, ISM model firstly focuses on delivery time and lead time minimization variables. In what follows, AHP method is used to determine the weight of each index so that it would be possible to categorize several suppliers from agility perspective using TOPSIS method. Upon considering decision making as a wide issue, fuzzy environment is used in this study.
After providing pairwise comparisons, their consistency is evaluated which proves that the value of 0.043 is true for certain rate of the consistency of pairwise comparison matrix. Considering the obtained weights and ranking these factors in terms of their weight, it could be seen that delivery time index is of higher weight and importance in this method. The second variable in this method is cost minimization. Weight ranking resulting from AHP method is similar to results from ISM ranking. Then using fuzzy TOPSIS, six suppliers are ranked and the results were given. In this ranking, two methods known as fuzzy calculation and ideal distance, and non -fuzzy calculations are used with the same ranking results. In this regard, it can be said that in this study mathematical calculations in a non-fuzzy environment for the purpose of ranking does not have considerable impact on the result with the distance to the ideal value being calculated different. Considering the stated results, it can be seen that organizations can use the above method in order to select supplier and concentrate on driving power variables derived in interpretive structural model to increase the suppliers’ efficiency and agility.
It is worth mentioning that the results of AHP model confirms those of ISM model inasmuch as the values of weight for each variable implies its importance which is to some extent shown in ISM model.