Controllable Energy Consumption in a Sustainable Smart Manufacturing Model Considering Superior Service, Flexible Demand, and Partial Outsourcing

Abstract

The waste of energy in the present era is a dangerous signal for the future. All categories of consumers should come forward to moderate energy use and prevent wastage. This study focuses on a controllable energy consumption-based sustainable inventory model incorporating variable production rates, improved service, partial outsourcing planning, defective production, restoring reworkable items, disposing of non-reworkable items, and energy-saving steps. Reducing unusual energy consumption in production systems reduces carbon emissions and maximizes the system’s profit. An improved service level attracts customers, increases demand, and improves product reputation. Separate holding costs of reworked, defective, and perfect-quality items are considered for every lot delivered and reworked. The demand in the market is related to price and service. A traditional optimization technique examines the global optimization for the profit function and decision variables. Numerical illustrations as well as concave 3D graphs validate the analytical results and provide a sensitivity analysis for different parameters. The model is validated through special cases and comparison graphs.

System Dynamics and Sustainable Solution: The Case in a Large-Scale Pallet Manufacturing Company

“…For this purpose, FUCA and TOPSIS multicriteria analyses were used, as in the works of Duc [42], who demonstrated that the FUCA method could be used in mechanical processing for multicriteria decision making that did not require data standardization. To select the best scenarios from the simulation and sensitivity analysis, eight decision variables were considered, which competed based on the maximum and minimum optimization criteria according to Bachar et al [26]. These authors used a traditional technique to examine the global optimization of the profit and decision function variables in an energy consumption project of an intelligent manufacturing company, where the demand was considered flexible.…”

“…[12,13] Use of the multicriteria method for the selection of the best scenarios Selection of the most critical variables to apply a multicriteria decision making TOPSIS and FUCA. [14,[26][27][28] Mathematical models and metaheuristic algorithms…”

Remanufacturing and Product Recovery Strategies Considering Chain-to-Chain Competition and Power Structures

“…We refer to [8,[13][14][15][16] for a comprehensive review. There is also some other literature on environmental protection and sustainability from the perspective of energy consumption, transportation hazard, and new technologies, such as Bachar et al [17], Hota et al [18], and Sarkar et al [19]. However, we consider product recovery strategies with remanufacturing.…”

Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review

“…They discuss mathematical models involving variables from different sources to maximize profit. Mishra et al (2020) [75] considered the situation characterized by the need to limit carbon emissions; Bachar et al (2022) [76] investigated the use of partial outsourcing to satisfy customer demands; Padiyar et al (2022) [77] considered problems of product deterioration and inflation that introduce uncertainties into inventory management, and considered variables from suppliers, producers, and retailers. Sarkar et al (2022) [78] focused on the use of emerging technology in supply chain management, namely, radio frequency identification (RFID), to optimize warehouse layout.…”