The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations' GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain.

Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations

Material handling equipment (MHE) selection is a rapidly increasing multi-criteria decision-making (MCDM) issue with a plethora of factors affecting the selection process. The proper selection of MHE is a crucial issue for industrial organization’s efficiency and productivity in the global market. The selection of the most suitable MHE for a specific engineering application is a costly as well as time-consuming process in which many candidate alternatives available on the market are considered as preliminary choices. Despite the facts that there are already a large number of mathematical methods available to evaluate, choose, and rate the alternatives for a given engineering application, this paper investigates the ability and applicability of two MCDM methods, namely, Additive Ratio ASsessment (ARAS) and COmplex PRoportional ASsessment (COPRAS). The present research article investigates three real-time MHE selection problems, i.e., conveyor selection, automated guided vehicles (AGV) selection, and selection of robots. All the three MCDM problems are evaluated and alternative rankings are proposed using these two methods, while the objective weighted system, entropy is used to assess the relative weights of the criteria. The current outcome rankings are also validated by using other six MCDM methods and comparing them to the findings of the previous researchers. Each example provides a listing of all the possible options from optimum to worst MHE that could potentially improve the previously proposed rankings and sensitivity analysis is computed to demonstrate that these two MCDM methods are robust enough and capable of being used effectively in solving MHE selection problems. Finally, conveyor 3, AGV 5, and ASEA-IRB 60/2 robot are found to be the best choice, while conveyor 1, AGV 3, and Unimation PUMA 500/600 robot are the worst alternative for the three MHE selection issues respectively.

Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques

In this study, ultrasonic-assisted drilling was performed on different heat-treated (T6, T1) AA6061 to investigate the effects of cutting parameters on forces, surface roughness, and tool wear TiN-coated and uncoated HSS-G drill bits were used in the experiments The results obtained in the experiments were evaluated according to the general full factorial design and the effect graphs of different drilling parameters were generated In addition, ANOVA tables were created to evaluate the results SEM and OM micrographs were used to investigate the tool wear and chip formation Finally, a multi-response optimization was performed to optimize drilling parameters for minimum cutting force and surface roughness According to, optimum drilling parameters were determined as AA6061-T6 for material, uncoated HSS-G for a drill bit, 1520 rpm for spindle speed, and highest ultrasonic vibration frequency at 225 kHz In addition, it was observed that ultrasonic vibration has the effect of reducing built-up edge formation on the drill bit

Investigation of the effects of ultrasonic assisted drilling on tool wear and optimization of drilling parameters

Performance of swarm intelligence based chaotic meta-heuristic algorithms in civil structural health monitoring

Although there have been many studies on the drillability of various grades of stainless steel, there is no scientific research on the drilling of ferritic stainless steel. Also, the burr at hole exit means the need for secondary machining operation and indirectly increases the production cost. Thus, this study focused on the modeling and minimizing burr height (Bh), thrust force (Fz) and surface roughness (Ra) during drilling of AISI 430 ferritic stainless steel with uncoated carbide drill under dry condition. Bh, Fz and Ra based on different cutting speed and feed rates were measured during drilling tests, and then cutting parameters were optimized by applying Taguchi based grey relational analysis. Moreover, the mathematical models were created by employing the response surface method to predict the machining outputs. The thrust force and the surface roughness decreased while the burr height increased with the increase in cutting speed. Uniform burr formation with drill cap was observed for all machining parameters under dry environment. The effect levels of feed rate and cutting speed on burr height were determined as 54.82% and 44.67%, respectively. These result shows that cutting speed is as important as the feed rate during the drilling of the ferritic stainless steel. In the current study, the best suitable levels of feed rate and cutting speed were detected as 0.12 mm/rev and 45 m/min for minimizing Bh, Fz and Ra. The coefficients of determination obtained by RSM indicated a relationship in high level between the cutting parameters and machining outputs.

Modelling and multiresponse optimization for minimizing burr height, thrust force and surface roughness in drilling of ferritic stainless steel

FPA based optimization of drilling burr using regression analysis and ANN model

ABSTRACT This paper proposes a novel plan-do-check-act (PDCA) based group decision making model to evaluate the GSCM performance of manufacturing organizations. This research employs an integrated fuzzy multi-criteria decision-making (MCDM) approach in which the fuzzy-analytical hierarchy process (FAHP) method determines criteria weights and the fuzzy-technique for order preference by similarity to the ideal solution (FTOPSIS) method ranks the organizations. Five qualitative criteria are selected from the extant literature, which encompass both environmental, operational, and economic aspects of sustainability. Data is collected by developing a questionnaire, establishing a decision-makers’ committee, and carrying out a survey. To illustrate the industrial application of the proposed model, this study considers a real-world case study in the Indian manufacturing sector, in which three organizations (Organization A, Organization B, and Organization C) are selected from three distinct industrial segments, namely the leather industry, the pharmaceutical industry, and the steel industry, respectively. The result reveals that the environmental impact of harmful substances released from production is the most influential parameter. The result also reveals that Organization C is the benchmark organization and its strategies can guide other organizations for GSCM performance improvement. Moreover, the proposed model is capable of handling vagueness and ambiguity in decisions.

A PDCA based approach to evaluate green supply chain management performance under fuzzy environment

Green supply chain management framework for supplier selection: an integrated multi-criteria decision-making approach

The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.,The literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.,The result shows that “Total CO2 emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.,This research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.,The methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.,The design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.

Selection of environmental-conscious sourcing: an empirical investigation

The aim of the research is to select the best experimental setup and optimal process parameters of EDM process Mathematical models are developed combining ENTROPY-COPRAS methodologies with ENTROPY-TOPSIS methodologies to solve a multi-response parameter optimization problem Initially, ENTROPY method is used to calculate the weights on the output parameters of the experimented result Thereafter, preference value of each parameter influences the decision matrix Finally, COPRAS and TOPSIS are used to calculate the rank of the experiment performed for optimal process parameter analysis The result shows that experiment no 5 is the optimal experimental setup, and corresponding values of the output parameters are peak current of 45 A, pulse duration of 261 µs, dielectric level of 80 mm and flushing pressure of 03 kg/cm2 according to COPRAS method, and using TOPSIS method, it revealed that experiment no 6 is the optimal experimental setup with the corresponding optimized values as peak current of 45 A, pulse duration of 520 µs, dielectric level of 40 mm and flushing pressure of 05 kg/cm2

Madhab Chandra Mandal

Papers

FPA based optimization of drilling burr using regression analysis and ANN model

A PDCA based approach to evaluate green supply chain management performance under fuzzy environment

Green supply chain management framework for supplier selection: an integrated multi-criteria decision-making approach

Selection of environmental-conscious sourcing: an empirical investigation

Optimum Experimental Setup of EDM Using Entropy Coupled MCDM Techniques