Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO₂ emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research.

Supplier selection is one of the most critical processes in supply chain management (SCM). Most small and medium enterprises (SMEs) face difficulties choosing the best supplier using conventional methods. A hybrid multi-criteria decision-making (MCDM) approach is proposed in supplier selection. This proposed framework integrates the Delphi technique as a data-gathering tool and Analytic Hierarchy Process (AHP) as the MCDM methodology for data analysis; both were used to select an effective supplier. This project applies the Delphi technique, allows experts to select the main criteria, and compares the trade-offs between the available alternatives depending on the main criteria. The criteria selected were price, delivery time, online ranking, rejection rate, and flexibility. Using the AHP approach, the criteria's weights were then assigned. The highest was for the price (43.84%), followed by the rejection rate (21.81%), online ranking (19.27%), delivery time (9.44%), and flexibility (5.64%). Lastly, a new framework was suggested using the weighted criteria collection for supplier selection.

Dimensional accuracy is important in fabricating miniaturized product in order to reduce the material waste and machining cost as well as to achieve a better quality product. This paper presents the analysis and modelling of dimensional accuracy in dry micro wire electrical discharge machining with control parameters of gap voltage and wire tension. The investigation was performed on stainless steel using integrated multi-process micro machine tools DT 110 with compressed air as the dielectric fluid and tungsten as the wire electrode. The dimensional accuracy was determined through kerf width differences of the machined slots. The kerf width was measured using scanning electron microscope. Full factorial was used to design the experiment while analysis of variance (ANOVA) was used to analyse the results as well as to evaluate the adequacy of the developed model. Based on ANOVA, both parameters; gap voltage and wire tension have high influence on kerf width differences. The optimum machining parameters for minimum kerf width differences were found to be 85 V gap voltage and 10 % wire tension. The developed model is adequate since the percentage error (2.13 %) is relatively small. It is recommended that different type of gases should be used for further investigation in order to determine the accuracy of the dry micro wire EDM.

This research presents the effect of high cutting speed on the surface roughness in the end milling process by using the Artificial Neural Network (ANN). An experimental investigation was conducted to measure the surface roughness for end milling. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted. The artificial neural network (ANN) was applied to simulate and study the effect of high cutting speed on the surface roughness.