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| SN | Authors | Proposed system | Methodology |
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| 1 | Rahimifard and Newman [9] | A scheduling system for the simultaneous planning of workpieces, cutting tools, and fixtures in FMS | Discrete event simulation |
| 2 | Özbayrak and Bell [10] | A knowledge-based DSS for short-term scheduling of part-cutter assignment | Rule-based reasoning (RBR) |
| 3 | Buyurgan et al. [13] | Cutting tool selection and allocation in FMS | Heuristics of tool life to tool size ratio |
| 4 | Meseguer and Gonzalez [14] | A system for cutting tool planning integrated with CAPP | CAPP software |
| 5 | Petruse and Brîndaşu [6] | An augmented reality system for cutter planning and control | Augmented reality technologies |
| 6 | Arunachalam et al. [27] | A fuzzy MADM system to select complaint polishing cutters | Fuzzy logic and multiple-attribute decision-making |
| 7 | Sun et al. [28] | Two models for cutter delivery and cutter demand prediction in the metal-cutting process | Genetic algorithm (GA) |
| 7 | Li et al. [29] | A system to select cutter manufacturing materials | Analytic hierarchy process (AHP) |
| 8 | Saranya et al. [30] | Cutters selection system from a big relational database of machining operations | Artificial neural networks (ANN), GA, and fuzzy logic |
| 9 | Zhou et al. [31] | An ontology-based cutter configuration system for machining process | Ontological approach |
| 10 | Tomelero et al. [4] | A system for cutter planning and control at strategic, technical, and logistical aspects | Lean benchmarking |
| 12 | Kasie et al. [11] | A theoretical DSS model for stabilizing the flows of cutters, fixtures, and jigs | CBR, DES, and relational database management tools |
| 13 | Kasie and Bright [32] | A DSS for part-cutting assignment and control in turning operations | Neutrosophic CBR and best-worst method (BWM) |
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