# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 1 Introduction
CODAS was selected for its capability to rank alternatives based on their performance across weighted criteria, utilizing both Euclidean and Taxicab distances for evaluation. This integration approach balances methodological simplicity with robust decision-making, ensuring the reliability of the results while avoiding unnecessary complexity. Its fuzzy extension enhances decision-making under uncertainty, complementing AHP by effectively managing alternative assessments. The integration of these methods provides a comprehensive framework for addressing the machine selection problem [11; 12]. Since 1975, there have been many fuzzy set extensions introduced by researchers, such as Type-2 fuzzy sets [13], Pythagorean fuzzy sets [14; 15], and Spherical fuzzy sets [16] to illustrate and protect uncertainty more reasonably. The Continuous Intuitionistic Fuzzy Set (CINFUS) is one of the recent extensions seeking to define continuous and uncertain information in real-world decision-making problems more accurately. It allows issues managed within a specific continuity scope to be more accurately modeled. CINFUSs, which have a more flexible structure than other fuzzy set extensions, permit more complicated systems to be modeled. Moreover, CINFUS, by classifying and considering decision-makers' behavior against uncertainty as risk-averse, risk-neutral, or risk-taking, also enables examination of the data including the uncertainty introduced by decision-makers [17; 18]. This research investigates how to combine lean manufacturing principles with digital transformation objectives in the machine selection process for managing inventory. Lean manufacturing prioritizes the removal of waste, enhancement of efficiency, and provision of value, whereas digital transformation facilitates real-time data integration, automation, and predictive analytics. The suggested decision-making framework integrates these principles through thoughtfully chosen evaluation criteria, ensuring alignment with goals related to operational efficiency and technological progress. This integration addresses the changing requirements of contemporary manufacturing settings, where agility, scalability, and cost-effectiveness are essential. Existing machine selection strategies frequently struggle with handling uncertainties and ambiguities inherent in decision-maker evaluations and criteria weighting. By integrating the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS), this study bridges the gap, offering a structured, reliable, and sensitivity-tested framework for managing multi-criteria trade-offs, thereby reassuring the audience of its comprehensive approach. The following key research questions (RQs) have been formulated to guide the study:  
RQ 1: How can integrating CINFU AHP and CODAS methods enhance decision-making under conditions of uncertainty and ambiguity compared to traditional methods? This question explores the value added by combining fuzzy multi-criteria decision-making techniques, with a focus on their ability to accommodate vague or imprecise data in expert evaluations and to ensure consistency in ranking alternatives. RQ 2: What are the most critical criteria influencing machine selection for inventory tracking within lean manufacturing systems? This question seeks to identify and prioritize the operational and technological factors, such as speed, setup time, ease of operation, energy cost, and maintenance, essential for selecting machines that align with efficiency and sustainability objectives. RQ 3: How does modifying the significance of criteria through sensitivity analysis affect the ranking of machine alternatives? This question investigates the framework's robustness under varying weighting scenarios, demonstrating its applicability in dynamic decision-making environments where priorities may shift. The rest of the paper is organized as follows: Section 2 reviews related work to reveal the gap in the literature.