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Supplier Selection is one of the most studied areas in management and decision sciences. However, it is still a highly problematic subject since decision-makers take an educated guess after a certain stage in real life practices. In order to overcome the problems, decision makers should focus their attention on the first stage of the selection process, criteria determination, as the quality of the selection phase heavily depends on the first stage. Additionally, strategic fitness in supplier selection is also not much considered in the literature and real life practices. However, looking for conformity of supplying organizations with corporate strategies of buying organization is crucial for the success and leadership of the buying organization. Therefore, this paper intends to determine the most influential corporate strategy based supplier pre-qualification criteria. Data acquisition phase of the Delphi technique was used for determining criteria and fuzzy relational maps was used for relating criteria with corporate strategies. All data were collected from a global Tier-1 manufacturing company in the automotive industry. The results show that the most important strategy based criteria were mainly about organizational and managerial characteristics of the company. Cost and price, which are considered very important in the literature and in real life practices, were determined as moderately important in strategic context. Companies need to focus their attention on criteria such as technical qualification of employees, continuous improvement systems, and communication abilities when pre-qualifying suppliers

The roof matrix represents correlations among engineering characteristics (EC) in the house of quality (HoQ) in Quality Functions Deployment (QFD). Correlations are usually measured qualitatively and omitted in the analysis. However, ignoring them may cause duplication of effort, decreased product performance, and unsatisfied customer requirements (CR). Hence, this paper intends to propose an approach to considering the correlations quantitatively. Fuzzy Cognitive Maps (FCM) were used for this purpose. Additionally, Axiomatic Design (AD), for examining relationships between CRs and ECs, and Fuzzy Analytic Hierarchy Process (FAHP) with the Extent Analysis (EA) were used for checking the consistency of the evaluations. The proposed approach was applied in a sheet metal die-making company for ranking CRs and ECs. Results show that FCM enables analysing the quantitative roof matrix practically. The square roof matrix that supports FCM's adjacency matrix structure successfully represents asymmetric relationships among ECs. Integrating the correlations into the analysis resulted in a change in the final ranking. It also helped determine the most manageable ECs, better satisfiable CRs, and most critical/least manageable ECs.

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The rapid increase in bad loans caused banks to be more cautious and selective for minimizing their risks. However, although banks make the lending decision by using quantitative financial criteria, the final decision step is usually intuitive/judgemental when the offer does not meet the expectations of the customer. In this case, the success of the decision directly depends on the experience of the bank personnel or unit manager. Such an application may lead to decisions that do not comply with a specific standard/rule and result in default. One of the ways to eliminate these drawbacks is to standardize and automate the final decision phase within the framework of some subjective rules determined with common sense by the top management of the bank. Fuzzy logic allows quantitative analysis of subjective judgments and qualitative criteria. In this context, the aim of the study is to examine how fuzzy logic approaches can be used in the final step of lending decisions. A hypothetical lending decision was modeled and resolved using fuzzy linguistic qualifiers, fuzzy propositions, and fuzzy logic control systems. As a result, the fuzzy rule base of the control system that was visualized with MATLAB made it possible to examine the impact of different levels of subjective evaluation scores on the final decision. Also, decision fields and customer groups could be created with the pseudo-code surface images.