Optimizing sustainable pencil and lithium-ion battery supply chain using stochastic feedforward neural network and blockchain techniques for payment transaction

Abstract

The global climate destruction from the production and transportation sector affects all life in numerous aspects. Since the eighteenth century, one of the essential items universally required by humans is a pencil. A manufacturer's assertion of eco-friendliness is examined while carrying out a triple-bottom-line analysis of various types of pencil production using old newspapers with green alternatives. The focus of this study is to achieve zero waste by recycling all waste through waste minimization, wastewater treatment, and reducing emissions by carbon capture. Wastewater contaminated with dye is degraded using a leaf extract from a tree named Pithecellobium dulce, and the remaining water is subsequently purified through wastewater treatment. This study uses three algorithms to find the lowest transportation cost such as (a) the second derivative test of an optimization technique in Mathematica 9.0 software, (b) the algebraic algorithm, and (c) the feedforward neural network. Optimal values are computed mathematically using the second derivative. In this study, electric vehicles equipped with lithium-ion batteries are employed to transport products between locations, and the stochastic artificial neural network technique is used to find minimum transportation cost and minimum integrated total cost in the pencil industry. Numerical analysis and sensitivity assessments provide a comparative solution for the model. Managerial insights are discussed to connect managers with emerging technologies, fostering sustainable and cost-efficient supply chain management. The payment transactions to each participant are authenticated via remix software. Altogether, using appropriate technologies, this study provides a cost-efficient supply chain with zero waste, lower emissions, and an efficient transaction approach.