Abstract
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 μ ϵ [ μ m i n , μ m a x ] . 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.
Highlights
In today’s highly volatile and competitive economic situation, the demand rate of many products is not constant, especially for high-tech consumer electronic products
The per unit technology investment for raw material yield loss cost shows quite a unique trend, as with the increases in the yield loss cost value, reliability parameter decreases, the optimal investment decreases, and in the opposite case when yield lost cost increases, the production lot increases but the optimal investment decreases to reduce total expected cost, and especially with a 20% increase in yield loss cost, the production lot increases in a decreasing fashion due to lower the value of the manufacturing reliability parameter
The unreliable manufacturing system provides optimum values for all decision variables, and if the manufacturing unit is producing smart products, for example high-tech products/electronic products, our model provides the insight to choose investment options based on manufacturing system reliability
Summary
In today’s highly volatile and competitive economic situation, the demand rate of many products is not constant, especially for high-tech consumer electronic products. The design variable of the production system, called a reliability parameter, depends on manufacturing process quality factors, for instance, resource and technology limitations, work-ability, efficiency, and design complexities. The ever-growing product innovation urges the need for smart production inventory profit/loss models with investment in automated smart manufacturing technology to investigate the trade-off in process-quality improvements, productivity and system costs. This study examines an unreliable manufacturing system for a technology varying demand rate, where the performance-quality of the manufacturing process is enhanced with investment in advanced technology and varying production capacity. We develop an integrated smart production-maintenance policy based on demand variability and manufacturing unreliability This can be attained in various ways, for instance, through labor training in advanced skills, specialized equipment acquisition, and manufacturing procedural changes. The remainder of this paper is categorized as follows: Section 2 provides a comprehensive overview of the related literature; Section 3 addresses problem formulation; Section 4 develops mathematical modeling of the proposed system; Section 5 illustrates the developed mathematical model with numerical experiment and sensitivity analysis of model parameters; Section 6 concludes the study and suggests some future extensions
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