Cleaner Production in Multivariate Supply Chain Networks: Sustainable Business Future Through a “Roll of Dice”

The importance of supply chain management in analyzing and later catalyzing economic expectations while simultaneously prioritizing cleaner production aspects is a vital component of modern business management for sustainable growth. This article lays down a self-consistent foundation of optimizing supply chain modules based on an inherent stochastic optimization routine that intrinsically incorporates all four modes of uncertainty cited in popular literature—environmental uncertainty, demand uncertainty, economic uncertainty, and social uncertainty. Quantifying uncertainty through a subset of variables that are inherently stochastic, our theoretical model provides the “missing links” that calibrate supply chain functional modules against external stochastic fluctuations, originating from different interconnected nodes of a supply chain network. The key deliverable here is the specific endogenous interactions of uncertainty components that best optimize the cost function constrained by conditions specific to the supply chain network. Starting from a multiparameter, multivariable cost function defining the supply chain kernel, we deliver a mechanism to assess the viability of a sustainable business by first ranking the (stochastic) variables in order of their subjective importance (may change from one SME to another) and then optimizing the cost kernel, a “free energy” construct, subject to constraints specific to that industry, to identify conditions (as equations) that characterize market sustainability of a business venture. The solution architecture can be described in two distinct parts. In the first part, the ranking is initially obtained through an analytical hierarchical process (AHP) structure, the deterministic part of which (“SuperDecisions” software) ranks these variables based on eigenvectors and corresponding eigenvalues of a deterministic constrained optimization model. Model predictions are then generated introducing the stochastic nature of the variables in the supply chain. These model predictions are ratified against actual SME data and e-waste recycler data to emphasize both the usefulness of the modeling strategy as well as the importance of SDGs and cleaner production in business strategies in two different scenarios. The results feature the time evolution behavior of the variables, which identifies the “operation windows” for supply chain managers. These operation windows highlight the effectiveness of different business strategies, identified as initial and boundary conditions, leading towards a sustainable production regime of operations management. Our model almost unerringly predicts the timeline at which a policy change can reinvigorate product sales while simultaneously coping with business. With a predetermined cost function, new time-dependent policy solution together with periodic monitoring can eliminate adverse impacts on the cost of both demand and environmental uncertainty in a single intervention without compromising the profit margin for SMEs. The findings from our study adhere to cleaner production strategies as under SDG—8, 9, 11–15, and 17.