Effect of inspection performance in smart manufacturing system based on human quality control system

Kang, Chang Wook; Ramzan, Muhammad; Sarkar, Biswajit; Imran, Muhammad

doi:10.1007/s00170-017-1069-4

Cited by 56 publications

(30 citation statements)

References 47 publications

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“…The following abbreviations are used in this paper. Technology dependent demand rate (units/unit time) C(µ, λ) Unit smart production cost, a function of manufacturing quality performance parameter and variable production rate ($/unit/unit time) r 1 Raw material cost ($/unit) r 2 Raw material increment cost due to low manufacturing quality performance parameter ($/unit) Y Unit manufacturing yield loss cost due to low manufacturing quality performance parameter ($/unit/unit time) Hence the expected cost is strictly convex and achieves optimal minimum value at Q * = 2985.27, µ * = 1081.51, λ * = 5.54 × 10 −6 , and τ * = 0.006.…”

Section: Abbreviationsmentioning

confidence: 99%

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An Automated Smart EPQ-Based Inventory Model for Technology-Dependent Products under Stochastic Failure and Repair Rate

Asghar

Kim

2020

Symmetry

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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.

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Section: Abbreviationsmentioning

confidence: 99%

“…A smart production system, however, is intended for more productive human-machine interactions to expand production management. The productivity of the smart production system is affected by interactions between automated smart machine and skilled labor [1].…”

Section: Introductionmentioning

confidence: 99%

An Automated Smart EPQ-Based Inventory Model for Technology-Dependent Products under Stochastic Failure and Repair Rate

Asghar

Kim

2020

Symmetry

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“…Diabat et al [37] extended the basic production model with partial downstream down payment. Kang et al [38] explained the effect of smart manufacturing technology based on the human quality control system. Sarkar et al [39] extended the field of the imperfect production with a distribution free approach to calculate shortages during lead-time demand.…”

Section: Literature Reviewmentioning

confidence: 99%

A Single-Stage Manufacturing Model with Imperfect Items, Inspections, Rework, and Planned Backorders

et al. 2019

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Each industry prefers to sell perfect products in order to maintain its brand image. However, due to a long-run single-stage production system, the industry generally obtains obstacles. To solve this issue, a single-stage manufacturing model is formulated to make a perfect production system without defective items. For this, the industry decides to stop selling any products until whole products are ready to fulfill the order quantity. Furthermore, manufacturing managers prefer product qualification from the inspection station especially when processes are imperfect. The purpose of the proposed manufacturing model considers that the customer demands are not fulfilled during the production phase due to imperfection in the process, however customers are satisfied either at the end of the inspection process or after reworking the imperfect products. Rework operation, inspection process, and planned backordering are incorporated in the proposed model. An analytical approach is utilized to optimize the lot size and planned backorder quantities based on the minimum average cost. Numerical examples are used to illustrate and compare the proposed model with previously developed models. The proposed model is considered more beneficial in comparison with the existing models as it incorporates imperfection, rework, inspection rate, and planned backorders.

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“…But the main difference between an additive manufacturing and a smart manufacturing is that the additive manufacturing prefers more machinery controls than human labor, whereas the main theme of smart manufacturing systems is to make a human-factor related complex controllable management such that human-machine interactions can be used in the proper way to obtain the best complex products Kang et al ([5]) through skilled nature of labors. Kang et al [5] proved that the human interaction system must be controlled by a smart manufacturing system where the main performance of inspection is conducted by the three specified labors like skilled, unskilled, and semi-skilled labors. But the main issue is that they did not consider anything in the direction of energy.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

Sarkar

2019

Energies

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A smart production system is essential to produce complex products under the consumption of efficient energy. The main ramification of controllable production rate, amount of production size, and safety stocks is simultaneously optimized under proper utilization of energy within a smart production system with a random breakdown of spare parts. Due to the random breakdown, a greater amount of energy may be used. For this purpose, this study is concerned about the optimum safety stock level under the exact amount of energy utilization. For random breakdown, there are three cases as production inventory meets the demand without utilization of the safety stock, with using of the safety stock, and consumed the total safety stock amount and facing shortages. After the random breakdown time, the smart production system may move to an out-of-control state and may produce defective items, where the production rate of defective items is a random variable, which follows an exponential distribution. The total cost is highly nonlinear and cannot be solved by any classical optimization technique. A mathematical optimization tool is utilized to test the model. Numerical study proves that the effect of energy plays an important role for the smart manufacturing system even though random breakdowns are there. it is found that the controllable production rate under the effect of the optimum energy consumption really effects significantly in the minimization cost. It saves cost regarding the corrective and preventive maintenance cost. The amount of safety stock can have more support under the effect of optimum energy utilization. The energy can be replaced by the solar energy.

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Effect of inspection performance in smart manufacturing system based on human quality control system

Cited by 56 publications

References 47 publications

An Automated Smart EPQ-Based Inventory Model for Technology-Dependent Products under Stochastic Failure and Repair Rate

An Automated Smart EPQ-Based Inventory Model for Technology-Dependent Products under Stochastic Failure and Repair Rate

A Single-Stage Manufacturing Model with Imperfect Items, Inspections, Rework, and Planned Backorders

Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

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