The training of suppliers and inbound quality inspectors is a common strategy to increase the quality performance of the supply chain but, under budget constraints, these actors compete for a limited amount of training hours. The proposed model aims to allocate the available training hours so as to minimise a total quality cost function composed of prevention, appraisal, and failure costs; it also sets the inspection rates defining the inspection policies assigned to suppliers. The relationship between decision variables and costs is expressed through organisational and individual learning-forgetting curves, for suppliers and quality inspectors respectively, and the effect of the training hours on quality improvement is measured in terms of failure rates. To the best of our knowledge, a total quality cost model with such decision variables is new in the related literature, as it is a model including both organisational and individual learning-forgetting phenomena. A nonlinear optimisation approach was adopted to solve this complex problem. The experimental section includes a decision trees analysis of simplified scenarios in order to interpret the model functioning, as well as a complex numerical example to extrapolate managerial insights.
|Data di pubblicazione:||2019|
|Titolo:||Quality cost-based allocation of training hours using learning-forgetting curves|
|Autore/i:||Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Rimini, Bianca|
|Digital Object Identifier (DOI):||10.1016/j.cie.2019.02.020|
|Codice identificativo Scopus:||2-s2.0-85061626377|
|Citazione:||Quality cost-based allocation of training hours using learning-forgetting curves / Lolli, Francesco; Balugani, Elia; Gamberini, Rita; Rimini, Bianca. - In: COMPUTERS & INDUSTRIAL ENGINEERING. - ISSN 0360-8352. - (2019), pp. 1-12.|
|Tipologia||Articolo su rivista|
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