دانلود مقاله ISI انگلیسی شماره 43246
عنوان فارسی مقاله

مدل سازی ریاضی و برآورد بیزی برای ممیزی در معرض اشتباه قفسه های خرده فروشی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
43246 2015 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Mathematical modeling and Bayesian estimation for error-prone retail shelf audits
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 80, December 2015, Pages 72–82

کلمات کلیدی
عملیات خرده فروشی - خدمات حسابرسی - خطای بازرسی - خطر گریزی - استنتاج بیزی
پیش نمایش مقاله
پیش نمایش مقاله مدل سازی ریاضی و برآورد بیزی برای ممیزی در معرض اشتباه قفسه های خرده فروشی

چکیده انگلیسی

Prevalent execution errors such as out-of-stock, inventory record inaccuracy, and product misplacement jeopardize retail performance by causing low on-shelf availability, which discourages not only retailers who have lost sales but also manufacturers who have worked hard to deliver goods into retail stores. Thus, external service companies are hired by manufacturers to conduct manual inspection regularly. Motivated by the practical need of shelf audit service providers, we use a general cost structure to develop a decision support model for periodic inspection. Some qualitative insights about the intricate relationships among inspection efficacy, cost factors, failure rate of shelf inventory integrity, and optimal decisions are derived from analytics assuming risk-neutrality. From simulation experiments we also find that managers' risk preferences have non-trivial impacts on optimal decisions. Based on a total cost standpoint high-quality inspection is predominantly preferred regardless of the level of risk aversion. Finally, we propose a Bayesian statistical model and a Markov chain Monte Carlo approach to estimate model parameters such that managers can make empirically informed decisions. Our major contribution lies in developing a mathematical model that is practically applicable and proposing a Bayesian estimation approach to rationalize unobservable model parameters, which are influential to optimal decisions but often arbitrarily assumed by decision makers.

خرید مقاله
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