3. Producción
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Item type:Publication, An introduction to data envelopment analysis(Springer Science+Business Media, 2021-12-11)Following the seminal work of Farrell (1957), Charnes et al. (1978) introduced DEA as a deterministic and nonparametric efficiency evaluation tool. DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units, DMUs (Charles et al., 2016, 2018; Tsolas et al., 2020). DEA is a data-oriented technique (Zhu, 2020) that is used to construct an empirical production frontier to measure efficiency. Note that the original DEA program of Charnes et al. (1978) is based on the CRS specification of technology and is used to measure the technical and scale efficiency of DMUs. However, Banker et al. (1984) extended this program to the case of VRS to estimate purely technical efficiency. Over the past three decades, DEA has been widely used to evaluate the relative efficiency of production firms, the nature of the returns-to-scale, and the productivity changes. The DEA literature has seen a wide variety of applications across a plethora of domains, having become a powerful management science tool (Charles et al., 2018). In this chapter, we briefly review the fundamental concepts in DEA, along with the basic technologies and programs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stochastic network data envelopment analysis(Springer Science+Business Media, 2021-12-11)Most real-life production processes are multi-stage in nature. Characterization of such processes via concepts such as technical efficiency is considered important to firm managers for the stage-specific analysis of their business decisions in improving their performance. Therefore, it is imperative to estimate the efficiency of a firm not only for the network production system but also for its sub-processes to locate the sources of inefficiency. In this chapter, we deal with production processes characterized by a two-stage network structure that links their stage-specific processes with intermediate products (measures). In this two-stage production process, the first stage uses input resources to produce intermediate products, which are all, in turn, used as inputs in the second stage to produce final outputs. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fuzzy data-driven scenario-based robust data envelopment analysis for prediction and optimisation of an electrical discharge machine's parameters(Elsevier Ltd, 2022-05-01)An electrical discharge machine (EDM) has a high impact on production management, with its process having many advantages over conventional machining processes, including the ability of the machine to create very high-quality material that is intricate to inner industrial sections. This study investigates the impact of EDM machining parameters on the volumetric flow rate, electrode corrosion percentage, and surface roughness. These machining parameters are increasingly important for the quality of the final product, leading to higher customer satisfaction and greater market share of the company. Due to dynamic changes in the machine's parameters and production environmental changes, using an uncertain model is inevitable. To investigate the machining data under uncertainty, a mathematical modelling approach based on the fuzzy possibility regression integrated (FPRI) model is developed. One advantage of the proposed model is that it is able to predict the surface roughness, volumetric flow rate, and corrosion percentage of the electrode. An adaptive-network-based fuzzy inference system (ANFIS) is applied to achieve the optimal levels of each output. Since the results and numbers obtained from the neural network are uncertain and their distribution is not clear, a robust data envelopment analysis approach (RDEA) is employed to select the best tuned-level of the parameters. The findings confirm the accuracy and reliability of the proposed method for prediction and optimisation of the EDM's parameters and encourage further tests for other production and supply chain applications.