Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

PurposeThe recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers.Design/methodology/approachTo achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers.FindingsThe findings reveal that the major barriers are in a four-level hierarchical relationship where “Insufficient SC strategic plans to ensure agility during crisis” acts as the most critical barrier, followed by “Poor information structure among SC contributors,” and “Inadequate risk management policy under pandemic.” Finally, the MICMAC analysis validates the findings from the ISM approach.Originality/valueThis study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.

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