Expensive power cost is a significant concern in today’s manufacturing world. Reduction in energy consumption is an ultimate measure towards achieving manufacturing efficiency and emissions control. In the existing literature of scheduling problems, the consumption of energy is considered uncertain under the dimensions of uncertain demand and supply. In reality, it is a random parameter that also depends on production capacity, manufacturing technology, and operational condition of the manufacturing system. As the unit production cost varies with production rate and reliability of the manufacturing system, the energy consumption of the system also varies accordingly. Therefore, this study investigated an unreliable manufacturing system under stochastic production capacities and energy consumption. A stochastic production–inventory policy is developed to optimize production quantity, production rate, and manufacturing reliability under variable energy consumption costs. As energy consumption varies in different operational states of manufacturing, we consider three specific states of power consumption, namely working, idle, and repair time, for an integrated production–maintenance model. The considered production system is subjected to stochastic failure and repair time, where productivity and manufacturing reliability is improved through additional technology investment. The robustness of the model is shown through numerical example, comparative study, and sensitivity analysis of model parameters. Several graphical illustrations are also provided to obtain meaningful managerial insights.

With the ever-growing technology development, high-tech products such as mobile phones, computers, electromagnetic devices and smart devices are facing high design and production modification requirements with relatively shorter life cycles. For instance, every forthcoming smart phone goes out of production in a shorter period after its launch, followed by its next generation. The design of high-tech products requires high investments in smart and automated manufacturing technology to ensure higher production efficiency. For high-tech products with short life spans, the manufacturing performance-quality variable is an important design parameter that affects system reliability, production efficiency and manufacturing costs. Major performance-quality factors of a manufacturing system which affect productivity and reliability of the manufacturing process are discussed in this research. The study investigates an integrated smart production maintenance model under stochastic manufacturing reliability for technology dependent demand and variable production rate. The smart unit production cost is a function of manufacturing reliability and controllable production rate, as a manufacturing system can be operated at different production rates within designed limits $\mu ϵ[{\mu }_{min},$ ${\mu }_{max}]$ . Manufacturing reliability is increased through investment in smart manufacturing technology and resources. The integrated smart production maintenance model is formulated under general failure and repair time distributions and the optimal production maintenance policy is investigated under specific failure and repair time distributions. A mathematical model is developed to optimize the manufacturing quality-performance parameter, variable production rate, per unit technology investment and production lot size. The total cost function is optimized through the Khun–Tucker method. The mathematical model is also validated with numerical analysis, comparative study, and sensitivity analysis for model key parameters.