More from Mehmet Gumus's Recent Research

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Supply Diagnostic Incentives under Endogenous Information Asymmetry

Authors: Mohammad E. Nikoofal, Mehmet Gumus

Publication: Production and Operations Management, Forthcoming

Abstract:

This paper develops a dyadic supply chain model with one buyer who contracts the manufacturing of a new product to a supplier. Due to the lack of experience in manufacturing, the extent of supply risk is unknown to both the buyer and supplier before the time of contract. However, after the contract is accepted, the supplier may invest in a diagnostic test to acquire information about his true reliability, and use this information when deciding on a process improvement effort. Using this setting, we identify both operational and strategic benefits and costs of diagnostic test. Operationally, it helps the supplier to take the first-best level of improvement effort, which would increase efficiency of the total supply chain. Strategically, it enables the buyer to reduce the agency costs associated with implementing process improvement on the supplier. Besides these benefits, diagnostic test increases the degree of information asymmetry along the supply chain. This in turn provides the supplier with proprietary information, whose rent would be demanded from the buyer in equilibrium. Benefit-cost analysis reveals two key factors in determining the value of diagnostic test: (i) degree of endogenous information asymmetry between supply chain firms, and (ii) the relative cost of diagnostic test with respect to process improvement cost. Our results indicate that when both are high, the mere presence of diagnostic test can result in less reliable supply chain. This implies that when incentives are not properly aligned, information asymmetry amplified due to diagnostic test neutralizes all its benefits.

Published: 23 Jul 2018

Designing Risk-Adjusted Therapy for Patients with Hypertension

Authors: Manaf Zargoush, Mehmet Gumus, Vedat Verter, Stella Daskalopoulou

Journal Name: Production and Operations Management, Forthcoming

Abstract:

Hypertension has not been well studied by operations researchers from a clinical decision support perspective. Moreover, little personalized (i.e. patient-centric) guidance is available regarding the number and combination of antihypertensive medications. To fill this gap, we develop a Markov Decision Process (MDP) to characterize the optimal sequence (and combination) of antihypertensive medications under the standard medication dose. Our model is patient-centric as it takes into account a set of relevant patient characteristics such as age, gender, blood pressure level, smoking habits, diabetes status, and cholesterol level. Based on a set of intuitive assumptions, we prove that our model yields a series of structured optimal policies. Having calibrated our model based on real data and medical literature, we analyze these optimal policies and discuss their insights to the real practice. We also compare the benefits, in terms of quality adjusted life expectancy, QALE, obtained from our results with those obtained from British Hypertension Society (BHS) guideline.

Published: 20 Mar 2018