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The suppliers, which are one of the most important actors in the supply chain, have a significant effect on the performances of their customer firms. Hence, supplier performance evaluation has become a competitive tool in today's goods and service producing industries. This paper presents a supplier performance evaluation process developed using the Six Sigma Define – Measure – Analyse – Improve – Control (DMAIC) methodology. The proposed process has been applied in a central services company, where the suppliers had never been evaluated before. To this end, first of all, the evaluation criteria have been defined through brainstorming and meetings within the company, then weighted with Analytical Hierarchy Process (AHP), and categorized according to Kano's Model. Afterwards, suppliers have been scored and classified based on a proposed methodology using a modified version of Kano's model, and thus, the proposed process has proven to be useful in real life industrial applications.

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The emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers' variant to encode a log sequences and adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.

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