Frank Y. Chen

TL;DR: In this article, the authors quantify the effect of the bullwhip effect on simple two-stage supply chains consisting of a single retailer and a single manufacturer and demonstrate that the effect can be reduced by centralizing demand information.

TL;DR: In this paper, the authors demonstrate that the use of an exponential smoothing forecast by the retailer can cause the bullwhip effect and contrast these results with the increase in variability due to the using of a moving average forecast.

TL;DR: In this article, a periodic-review pricing and inventory control problem for a retailer, which faces stochastic price-sensitive demand, under quite general modeling assumptions, is studied, and it is shown that at the beginning of each period an (s, S) policy is optimal for replenishment, and the value of the optimal price depends on the inventory level after the replenishment decision has been done.

TL;DR: The bull-whip effect as discussed by the authors suggests that demand variability increases as one moves up a supply chain, and this increase in variability propagates up the supply chain and distorts the pattern of orders received by distributors, manufacturers and suppliers.

TL;DR: This work develops easily implementable heuristic algorithms and identifies both the absolute and asymptotic worst-case performance ratios of these heuristics and shows that most of these algorithms are optimal for the dispatching problem.