Knowledge that Transforms

We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is back-ordered, and any excess inventory is carried to the next period. In contrast to the classic inventory literature, we assume that the information of the demand distribution is not known a priori, and the decision-maker observes demand realizations over the planning horizon. We propose a nonparametric algorithm that generates a sequence of adaptive ordering decisions based on the stochastic gradient descent method. We compare the [Formula: see text]-period cost of our algorithm to the clairvoyant, who knows the underlying demand distribution in advance, and we prove that the expected [Formula: see text]-period regret is at most [Formula: see text], matching a lower bound for this problem.

With rapid development of live-streaming e-commerce, an increasing number of firms are collaborating with live-streamers (e.g., online influencers and celebrities) to host online live-shows for selling their products. To reduce the firm’s risk and motivate it to stock more, live-streamers can commit a minimum sales volume and/or the effort devoted to attracting fans and promoting the upcoming live-show. In this article, we study the roles of such sales and effort commitments on the performance of a “supply chain” in which a seller sells its product via a live-streamer. Product demand at the live-show is random while depending on the live-streamer’s pre-show effort and the seller needs to decide the amount of inventory for sale at the live-show. We consider three scenarios, in which the live-streamer makes sales-only, effort-only, and hybrid commitments, and compare the decisions and profits of both parties. We show that the commitment on sales volume or effort is mutually beneficial to the seller and the live-streamer. Moreover, the hybrid commitment can motivate the seller to stock more inventory and induce more effort from the live-streamer compared to sales-only or effort-only commitment. The live-streamer always prefers the hybrid commitment whereas the seller may prefer the sales-only commitment, for example, when the penalty is at a medium level.

This study investigates the determinants of salary for operations management scholars. Is it as some opine that the only thing that matters is the publication count in “A” journals? How do the full range of activities: research, teaching, service, and administration, affect salary? The present research seeks to shed light on these questions and is predicated on a data set that consists of the research, teaching, and service outcomes, along with salary, for a full census of operations management faculty at 22 public universities (227 faculty) for base, 9-month salary and a subset of 15 universities (150 faculty) for total annual compensation. The results demonstrate partial support for the hypotheses that only “A” publications are financially rewarded, with publications in Production and Operations Management having the highest correlation with salary. The salary reward for publishing in “A” journals is unaffected by number of coauthors. Publications in other academic journals, top-tier practitioner journals, and top journals in sister-fields, and measures of impact, such as citations, H-index, and research awards provide no additional explanatory value of salary. Likewise, service and teaching awards do not add explanatory value. Female Full Professors are paid roughly $23,000 less in base salary than their male counterparts. Non-publishing factors that significantly contribute to salary include changing university affiliations (only for Associate and Full Professors), taking on administrative duties, as well as certain qualitative measures such as achieving Fellow status at the Production and Operations Management Society.

We describe the process used to identify the ten most influential papers published in the Production and Operations Management ( POM) journal in its first thirty years. We also briefly discuss and highlight the selected papers.

Government plays a critical role in developing and adopting new products with social benefits. We study how the government should mix two different subsidies—a technology-push subsidy, which awards manufacturers for cost-reducing R&D efforts, and a demand-pull subsidy, which directly rewards customers—when multiple firms compete in the presence of spillover. We develop a model with the government and two firms: The government first announces a subsidy policy, then the two firms decide R&D investments to reduce production costs and sell the products. We analyze how the optimal subsidy policy and resultant market outcomes change in the social benefit of the product and the spillover level. We find that the government should use a different subsidy policy depending on the social benefit of the product. When the social benefit is low, no subsidy should be given, letting the two firms make discretionary R&D efforts without any inducement. When the social benefit is modest, the government should only give a push subsidy and let the firms lead production adoptions with cost-reducing R&D. When the social benefit is large, the government should provide both subsidies, but the dependence on the pull subsidy increases. We also study how the spillover level influences the optimal subsidy. As knowledge spillover increases, we find that the government should increase the push subsidy to offset the losses incurred by the cost leader. We contribute to the literature by offering policy insights on how the government should design subsidies to maximize the adoption of products with social benefits.