# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
## 1 Introduction  
Today, the e-integration of digital solutions is essential to maintain a competitive advantage. Even if digital solutions developed to manage systems that are parts of the whole fulfill their tasks individually, they can fully ensure organizational integrity when they work together [1]. Although digital systems play a central role in collecting and organizing production data and using them for processes such as planning and analysis, they often lack real-time tracking to count raw materials and semi-finished goods in stock [2]. Despite advances, accurate counting of raw materials and semi-finished products continues to pose challenges. Traditional manual counting methods are prone to errors and inefficiencies, necessitating a comprehensive and integrated approach to address the plate counting problem effectively. These limitations can lead to inaccuracies in inventory counts and cause wasted resources, delayed production, and inefficiencies that directly impact operational costs and productivity [3]. For this reason, companies have adopted lean manufacturing principles to reduce waste, increase efficiency, and build flexibility in their production processes. Lean manufacturing provides a structured procedure to pinpoint and minimize waste but requires a complementary system to integrate and operationalize these principles in real time [4]. Connecting digital solutions with lean techniques presents a promising way to achieve high accuracy and control in inventory and production management. However, integrating digital solutions and initiatives of lean manufacturing techniques alone is insufficient to achieve optimal results [5]. A holistic approach that includes multifaceted decision-making and strategic planning is essential. A collaborative practice operating the Analytic Hierarchy Process (AHP) and Combinative Distance-Based Assessment (CODAS) methods presents considerable potential to tackle the complicated challenges of machine selection. AHP feeds a systematic framework for hierarchically analyzing and ranking machine alternatives, considering various factors [6]. In parallel, CODAS supports decision making by effectively ranking these alternatives, allowing for uncertainties and ambiguities that often arise in complex scenarios [7]. Combining AHP and CODAS yields a strong synergy, connecting AHP's structured examination of decision criteria via hierarchical levels and pairwise comparisons with CODAS's strength in addressing complicated, ambiguous and inconsistent information [8]. AHP enables the framework by representing the decision criteria and their relative importance. At the same time, CODAS enriches it by assessing and ranking alternatives on these criteria, accounting for inherent uncertainties and interdependencies. This collaborative approach allows decision-makers in manufacturing industry to guide the complexities of machine selection effectively, enabling well-informed consideration of alternatives [9]. The fuzzy extensions, in particular, improve the multi-criteria decision-making (MCDM) process, facilitating more effective navigation of the complexities in machine selection. The study utilized the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) for criteria weighting and Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) for alternative ranking. AHP was chosen for its effectiveness in accurately assigning weights to criteria through expert evaluations via pairwise comparisons. Although traditionally applied to multi-level hierarchies, AHP is also highly applicable for single-level criteria weighting, which aligns with the purpose of this research [10]. This study's criteria represent a single-level structure, eliminating the need for sub-criteria. AHP was used solely to derive the weights of these criteria, which were then input into the CODAS method for alternative ranking.