Introduction of New Products in the Supply Chain : Optimization and Management of Risks

• Main Author: El KHOURY, Hiba
• Language: English
• Published: HEC 2012
• Subjects: [SHS:GESTION] Humanities and Social Sciences/Business administration; [SHS:GESTION] Sciences de l'Homme et Société/Gestion et management; Product rollover; Uncertain approval date; Planned stock-out rollover; Single product rollover; Dual product rollover; Risk sensitive optimization criterion; Conditional value at risk; Stochastic dominance; Stochastic comparisons; Bass demand; Product demand diffusion; Data-driven optimization
• Online Access: http://pastel.archives-ouvertes.fr/pastel-00708801; http://pastel.archives-ouvertes.fr/docs/00/70/88/01/PDF/EL_-_Khoury.pdf

Shorter product life cycles and rapid product obsolescence provide increasing incentives to introduce newproducts to markets more quickly. As a consequence of rapidly changing market conditions, firms focus onimproving their new product development processes to reap the benefits of early market entry. Researchershave analyzed market entry, but have seldom provided quantitative approaches for the product rolloverproblem. This research builds upon the literature by using established optimization methods to examine howfirms can minimize their net loss during the rollover process. Specifically, our work explicitly optimizes thetiming of removal of old products and introduction of new products, the optimal strategy, and the magnitudeof net losses when the market entry approval date of a new product is unknown. In the first paper, we use theconditional value at risk to optimize the net loss and investigate the effect of risk perception of the manageron the rollover process. We compare it to the minimization of the classical expected net loss. We deriveconditions for optimality and unique closed-form solutions for single and dual rollover cases. In the secondpaper, we investigate the rollover problem, but for a time-dependent demand rate for the second producttrying to approximate the Bass Model. Finally, in the third paper, we apply the data-driven optimizationapproach to the product rollover problem where the probability distribution of the approval date is unknown.We rather have historical observations of approval dates. We develop the optimal times of rollover and showthe superiority of the data-driven method over the conditional value at risk in case where it is difficult to guessthe real probability distribution