Performance measurement for offline inspections under variable interactions and inspection errors in low-volume production

The assessment of the performance of inspection strategies is a crucial element in the design phase of product quality inspections of manufacturing companies. The aspects that inspection designers need to consider include: (1) the typology of quality inspection, (2) the inspection variables involved, (3) the potential interaction between variables and (4) the presence of inspection errors. In particular, low-volume inspection design is critical due to the lack of historical data and the inadequacy of traditional statistical approaches. By considering these issues, this paper proposes a novel approach to support inspection designers in the prediction of offline quality inspection performance. The development of a probabilistic model based on the analysis of the possible variable interactions and inspection errors and the definition of some performance measures may successfully help designers in the early design stages of inspection process planning. The approach is supported by a practical application in the Additive Manufacturing field.

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

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.

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

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.