A revised cost-benefit algorithm for solving the expediting completion time of end product problem with defective materials in the product structure diagram

Chiu, Singa Wang; Shih, Chih-Chang; Chiu, Yuan‐Shyi Peter

doi:10.1243/09544054jem471

“…examined a model for stationary and random defective rates. Chiu et al (2007) proposed a revised cost-benefit algorithm for solving the expediting completion time of end product (ECTEP) problem with defective components/materials in the product structure diagram. They used adjusted the unit procurement cost and the required quantity to address the defective materials procured.…”

Section: Contemporary Trends In Eoq/epq Modellingmentioning

confidence: 99%

Inventory control in a two-level supply chain with learning, quality and inspection errors

Khan¹

2021

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A common measure of quality for a buyer or a vendor is the defect rate. Defects may represent an attribute, a dimension or a quantity. They may be classified as product quality defects or process quality defects. Product quality defects may be caused by human error which can de due to fatigue, lack of proper training, or other reasons. For example, an inspector may misclassify a defective fuel tank of a car as good. On the other hand, process quality defects maybe caused by a machine going out-of-control. While many researchers assume that the screening processes which separate the defective items are error-free, it would be realistic to consider misclassification errors in this process. Beside inspection errors, learning is another human factor that brings in enhancement in the overall performance of a supply chain. Learning is inherent when there are workers involved in a repetitive type of production process. Learning and forgetting are even more important in manufacturing environments that emphasize on flexibility where workers are cross-trained to do different tasks and where products have a short life cycle. Inventory management with learning in quality, inspection and processing time will be the focus of this thesis. A number of models will be developed for a buyer and/or a two level supply chain to incorporate these human factors. The key findings of this work may be summarized as 1. Inspection errors significantly affect the annual profit. 2. An increase in the unit screening cost reduces the annual profit to a great extent at slower rates of learning. 3. For the two-level supply chain we investigated, learning in production drops the annual cost significantly while the learning in supplier's quality results in a situation where there are no defectives from the suppliers. 4. Type II error may seem to be beneficial for a two level supply chain as the order/lot size goes down and thus affects the costs of ordering, production and screening. 5. Consignment stocking policy performs better than conventional stocking when holding costs go higher than a threshold value.

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“…examined a model for stationary and random defective rates. Chiu et al (2007) proposed a revised cost-benefit algorithm for solving the expediting completion time of end product (ECTEP) problem with defective components/materials in the product structure diagram. They used adjusted the unit procurement cost and the required quantity to address the defective materials procured.…”

Section: Contemporary Trends In Eoq/epq Modellingmentioning

confidence: 99%

Inventory control in a two-level supply chain with learning, quality and inspection errors

Khan¹

2021

Preprint

0

View full text Add to dashboard Cite

A common measure of quality for a buyer or a vendor is the defect rate. Defects may represent an attribute, a dimension or a quantity. They may be classified as product quality defects or process quality defects. Product quality defects may be caused by human error which can de due to fatigue, lack of proper training, or other reasons. For example, an inspector may misclassify a defective fuel tank of a car as good. On the other hand, process quality defects maybe caused by a machine going out-of-control. While many researchers assume that the screening processes which separate the defective items are error-free, it would be realistic to consider misclassification errors in this process. Beside inspection errors, learning is another human factor that brings in enhancement in the overall performance of a supply chain. Learning is inherent when there are workers involved in a repetitive type of production process. Learning and forgetting are even more important in manufacturing environments that emphasize on flexibility where workers are cross-trained to do different tasks and where products have a short life cycle. Inventory management with learning in quality, inspection and processing time will be the focus of this thesis. A number of models will be developed for a buyer and/or a two level supply chain to incorporate these human factors. The key findings of this work may be summarized as 1. Inspection errors significantly affect the annual profit. 2. An increase in the unit screening cost reduces the annual profit to a great extent at slower rates of learning. 3. For the two-level supply chain we investigated, learning in production drops the annual cost significantly while the learning in supplier's quality results in a situation where there are no defectives from the suppliers. 4. Type II error may seem to be beneficial for a two level supply chain as the order/lot size goes down and thus affects the costs of ordering, production and screening. 5. Consignment stocking policy performs better than conventional stocking when holding costs go higher than a threshold value.

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“…When products are produced inhouse (such as in the manufacturing sector) instead of being acquired from outside suppliers, the EMQ model (also known as the economic production quantity (EPQ) model) is often used to deal with the non-instantaneous inventory replenishment rate to minimize the expected production-inventory costs [2][3][4]. Despite the simplicity of EOQ/EMQ models, they are still applied throughout industry today [3][4][5][6] and are still the basis for the analyses of more complex systems (see, for example, references [7][8][9][10][11][12]). The classical EMQ model assumes that all items produced are of perfect quality.…”

Section: Introductionmentioning

confidence: 99%

Economic manufacturing quantity model with imperfect rework and random breakdown under abort/resume policy

Chiu

¹

,

Tseng

²

,

W.-C.

³

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part B:

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This paper studies the optimal production run time in an economic manufacturing quantity (EMQ) model with imperfect rework and Poisson machine breakdowns under abort/resume (A/R) control policy. In most realistic manufacturing processes, generation of defective items and random machine breakdowns are inevitable. In this study, a random defective rate is assumed and all defective items produced are reworked when regular production ends. The rework process is assumed to be imperfect. A proportion of reworked items fail during the reworking and become scrap. The system is subject to random breakdowns. The A/R inventory control policy is adopted when breakdowns occur, under such a policy, and the production of the interrupted lot will be immediately resumed when the machine is fixed and restored. Mathematical modelling is used and the integrated long-run average production-inventory cost per unit time is derived. Theorems on conditional convexity and on bounds of optimal production run times are proposed and proved. A recursive searching algorithm is developed for locating the optimal run time within the bounds, which minimizes the expected production-inventory costs. A numerical example with sensitivity analysis is provided to give insight into the operational planning and control of such a realistic production system.

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A revised cost-benefit algorithm for solving the expediting completion time of end product problem with defective materials in the product structure diagram

Cited by 3 publications

References 11 publications

Inventory control in a two-level supply chain with learning, quality and inspection errors

Inventory control in a two-level supply chain with learning, quality and inspection errors

Inventory control in a two-level supply chain with learning, quality and inspection errors

Economic manufacturing quantity model with imperfect rework and random breakdown under abort/resume policy

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