Encroachment in a three-echelon supply chain: manufacturer encroachment or distributor encroachment

PurposeThe purpose of this paper is to investigate manufacturer encroachment and distributor encroachment in a three-echelon supply chain consisting of an upside manufacturer, an intermediate distributor and a downside retailer.Design/methodology/approachIn this paper, the authors use the optimization theory to mathematize the proposed question and build a model. First, the authors consider sequential quantity decisions, where the encroacher decides on the direct selling quantity after determining the retailer's order quantity. Second, the authors relax this sequential decision process assumption by reconsidering a circumstance in which quantity decisions are decided simultaneously.FindingsIn contrast to previous studies, this study shows that in three-echelon supply chains, the upside firm is more likely to encroach compared with the downside firm. The “bright side” of encroachment exists for all players only when the encroachment cost is at a moderate level. However, in manufacturer encroachment under simultaneous quantity decisions, the “bright side” skips the distributor but benefits the retailer directly as the encroachment cost increases from zero to a certain level. The main reason lies in that the distributor loses its pricing power because the end-market has been disturbed by the simultaneous quantity decisions. A comparison of the results of sequential and simultaneous quantity decisions reveals the merit of simultaneous quantity decisions. The authors find that the intermediate role (the distributor in our model) in three-echelon supply chains may benefit more from simultaneous quantity decisions. That is, the distributor may achieve a better profit even in a market with intensified competition.Originality/valueThe findings of this paper contribute to the marketing science literature on encroachment. The majority of existing literature has focused on manufacturer encroachment in two-echelon supply chains. This paper innovatively investigates and compares manufacturer encroachment and distributor encroachment in a three-echelon supply chain.

3D cephalometric landmark detection by multiple stage deep reinforcement learning

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.

Structural Design Synthesis Through a Sequential Decision Process

This article illustrates that structural design synthesis can be achieved through a sequential decision process, whereby a sparsely connected seed configuration is sequentially altered through discrete actions to generate the best design solution, with respect to a specified objective and constraints. Specifically, the generative design synthesis is mathematically formulated as a finite Markov Decision Process. In this context, the states correspond to a specific structural configuration, the actions correspond to the available alterations that can be made to a given configuration, and the immediate rewards are constructed to be proportional to the improvement in the altered configuration’s performance. In the context of generative structural design synthesis, since the immediate rewards are not known at the onset of the process, reinforcement learning is employed to obtain an approximately optimal policy by which to alter the seed configuration to synthesize the best design solution. The approach is applied for the optimization of planar truss structures and its utility is investigated with three numerical examples, each with unique domains and constraints.

Jumper: Learning When to Make Classification Decision in Reading

In early years, text classification is typically accomplished by feature-based classifiers; recently, neural networks, as powerful classifiers, make it possible to work with raw input as the text stands. In this paper, we propose a novel framework, Jumper, inspired by the cognitive process of text reading, that models text classification as a sequential decision process. Basically, Jumper is a neural system that can scan a piece of text sequentially and make classification decision at the time it chooses. Both the classification and when to make the classification are part of the decision process which are controlled by the policy net and trained with reinforcement learning to maximize the overall classification accuracy. Experimental results show that a properly trained Jumper has the following properties: (1) It can make decisions whenever the evidence is enough, therefore reducing the total text reading by 30~40% and often finding the key rationale of prediction. (2) It can achieve classification accuracy better or comparable to state-of-the-art model in several benchmark and industrial datasets.