Firms are eager to adopt new technologies, such as Artificial Intelligence (A.I.), Cloud Computing, Big Data, etc., as they witness successful business applications. As one of the disruptive technologies, Blockchain technology (BCT) has been drawing attention stemming from cryptocurrency proliferation (e.g., Bitcoin and Ethereum), for which Blockchain serves as the backbone. However, the public is haunted by the bewilderment between cryptocurrencies and BCT. Furthermore, the burgeoning of Metaverse and non-fungible tokens (NFT) has raised BCT to another notch. This study conducts a holistic literature review on BCT features, implementations, and business implications. In particular, by reviewing and analyzing 2265 up-to-date articles that reveal BCT’s applications across various fields, this Blockchain-centered study reveals the research status and delineates future research directions. It is shown that, among various characteristics of BCT, traceability is the main characteristic fueling BCT’s application in supply chain management (SCM). We further find that the BCT-related research has been extremely growing in SCM, healthcare, and government, while declining in the areas of banking and cyber security. Geographically, the top countries with BCT-related publications are China, U.S., and India. Finally, it is emphasized that BCT-related research in environmental sciences and agriculture have potential to be explored.

This paper considers a continuous-review, single-product, production-inventory system with a constant replenishment rate, compound Poisson demands, and lost sales. Two objective functions that represent metrics of operational costs are considered: (1) the sum of the expected discounted inventory holding costs and lost-sales penalties, both over an infinite time horizon, given an initial inventory level; and (2) the long-run time average of the same costs. The goal is to minimize these cost metrics with respect to the replenishment rate. It is, however, not possible to obtain closed-form expressions for the aforementioned cost functions directly in terms of positive replenishment rate (PRR). To overcome this difficulty, we construct a bijection from the PRR space to the space of positive roots of Lundberg's fundamental equation, to be referred to as the Lundberg positive root (LPR) space. This transformation allows us to derive closed-form expressions for the aforementioned cost metrics with respect to the LPR variable, in lieu of the PRR variable. We then proceed to solve the optimization problem in the LPR space and, finally, recover the optimal replenishment rate from the optimal LPR variable via the inverse bijection. For the special cases of constant or loss-proportional penalty and exponentially distributed demand sizes, we obtain simpler explicit formulas for the optimal replenishment rate.