کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4959313 1364858 2018 16 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Supplier quality improvement: The value of information under uncertainty
ترجمه فارسی عنوان
بهبود کیفیت ارائه دهنده خدمات: ارزش اطلاعات تحت عدم اطمینان
کلمات کلیدی
مدیریت زنجیره تامین؛ تحلیل ریسک؛ مدل سازی عدم اطمینان؛ تجزیه و تحلیل تصمیم گیری؛ تولید؛
Supply chain management; Risk analysis; Uncertainty modelling; Decision analysis; Manufacturing;
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی

•Decision support model to manage buyer risk in supplier development.•Value learning about capability to improve quality through supplier development.•Frame as Bayesian model representation of epistemic and aleatory uncertainties.•Derive computationally convenient expressions for practical implementation.•Find optimal supplier quality improvement investment may not increase with prior variance.

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 264, Issue 3, 1 February 2018, Pages 932-947
نویسندگان
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