Purchase or rent? Optimal pricing for 3D printing capacity sharing platforms

Online sharing platforms have attracted considerable research and management attention across a number of industries, including travel, real estate, and cloud computing. They also have great potential for the 3D printing (3DP) industry, offering users the choice between owning or renting 3DP capacity. For matching supply and demand, capacity pricing is crucial. In this paper we consider two fundamental questions concerning pricing: (i) What is the optimal pricing strategy for a 3DP capacity sharing platform? (ii) How do usage level and printer heterogeneity affect consumers’ choice between in-house printing (owning) and outsourcing (renting)? Using queuing analysis, we derive the structural properties of the solutions to the problems. Furthermore, we conduct numerical studies using real-world data to generate managerial insights from the analytical findings. A key finding is that governments should focus on encouraging technological progress to lower the printers’ prices in order to improve the well-being of the industry. When considering two types of printers, we find that it is more beneficial for the platform if the high capacity printer dominates the market, as the platform then retains the prominent role in “redistributing” the 3DP capacity.</p

Fairness In Supply Chain Relationships The Value And Consequence For Reputation And Sustainability

The purpose of this research is to examine a notion, which is commonly perceived as subjective and endogenous known as fairness (also referred to as justice or impartiality) in the supply chain context. The principal aim of supply chain relationships is to create an avenue where competitive advantage can be achieved both as individual firms and as a chain through working collaboratively on supply chain operations and tasks. By collaborating with autonomous firms, concerns arise about whether the benefits, rewards and risks of relationships are apportioned in a fair (just) and satisfactory manner. This is evident in today’s supply chains where chain partners portray opportunistic and unethical behaviours using their bargaining power negatively and betraying partner’s trust. A number of studies have reported the significance of fairness in supply chain relationships, particularly promoting collaboration and improving relationship performance. Nonetheless, the significance of fairness in supply chain relationships has been a rather neglected area in the supply chain literature. Therefore, this study aims to fill some of the gaps that are present in the literature. Through a socio-economic lens, this study will probe the issue of fairness in supply chain relationships using the social exchange and equity theories as the analytical lens. Conceptualizing fairness into three main types (distributive, procedural and interactional), this study aims to understand the concept in the business to business relationship setting. A particular focus steers towards how perceiving fairness affects the development of relationships between buyers and suppliers in the supply chain. This aspect is of significant value because a good relationship between supply chain partners is a crucial antecedent for any stable exchange relationship. To fully understand the worth of fairness in this context, there is a need to consider the consequence for long-lasting relationships. As a result, this research considers two critical factors that have been neglected in the pertinent supply chain fairness literature such as firm reputation and relationship sustainability. Through in-depth executive interviews, interesting findings were revealed concerning the role that fairness perception plays in moulding a sustainable relationship between businesses and creating a positive firm image. The results of the authors’ exploratory work are presented as quotations to provide the body of the relevant subject. Findings show that the notion of fairness in the inter-organizational relationship context is a double-edged sword with prospective positive and negative effects on relationship development process between supply chain partners. Fairness is also a very ‘sensitive’ subject that many firms elude from, but largely impacts on an organization’s behaviour towards its partners. It is critical that when managing relationships with other firms in a supply chain, fairness should be at the forefront of the relationship banner by managers

Fairness In Supply Chain Relationships The Value And Consequence For Reputation And Sustainability

The purpose of this research is to examine a notion, which is commonly perceived as subjective and endogenous known as fairness (also referred to as justice or impartiality) in the supply chain context. The principal aim of supply chain relationships is to create an avenue where competitive advantage can be achieved both as individual firms and as a chain through working collaboratively on supply chain operations and tasks. By collaborating with autonomous firms, concerns arise about whether the benefits, rewards and risks of relationships are apportioned in a fair (just) and satisfactory manner. This is evident in today’s supply chains where chain partners portray opportunistic and unethical behaviours using their bargaining power negatively and betraying partner’s trust. A number of studies have reported the significance of fairness in supply chain relationships, particularly promoting collaboration and improving relationship performance. Nonetheless, the significance of fairness in supply chain relationships has been a rather neglected area in the supply chain literature. Therefore, this study aims to fill some of the gaps that are present in the literature. Through a socio-economic lens, this study will probe the issue of fairness in supply chain relationships using the social exchange and equity theories as the analytical lens. Conceptualizing fairness into three main types (distributive, procedural and interactional), this study aims to understand the concept in the business to business relationship setting. A particular focus steers towards how perceiving fairness affects the development of relationships between buyers and suppliers in the supply chain. This aspect is of significant value because a good relationship between supply chain partners is a crucial antecedent for any stable exchange relationship. To fully understand the worth of fairness in this context, there is a need to consider the consequence for long-lasting relationships. As a result, this research considers two critical factors that have been neglected in the pertinent supply chain fairness literature such as firm reputation and relationship sustainability. Through in-depth executive interviews, interesting findings were revealed concerning the role that fairness perception plays in moulding a sustainable relationship between businesses and creating a positive firm image. The results of the authors’ exploratory work are presented as quotations to provide the body of the relevant subject. Findings show that the notion of fairness in the inter-organizational relationship context is a double-edged sword with prospective positive and negative effects on relationship development process between supply chain partners. Fairness is also a very ‘sensitive’ subject that many firms elude from, but largely impacts on an organization’s behaviour towards its partners. It is critical that when managing relationships with other firms in a supply chain, fairness should be at the forefront of the relationship banner by managers

One at A Time: Multi-step Volumetric Probability Distribution Diffusion for Depth Estimation

Recent works have explored the fundamental role of depth estimation in multi-view stereo (MVS) and semantic scene completion (SSC). They generally construct 3D cost volumes to explore geometric correspondence in depth, and estimate such volumes in a single step relying directly on the ground truth approximation. However, such problem cannot be thoroughly handled in one step due to complex empirical distributions, especially in challenging regions like occlusions, reflections, etc. In this paper, we formulate the depth estimation task as a multi-step distribution approximation process, and introduce a new paradigm of modeling the Volumetric Probability Distribution progressively (step-by-step) following a Markov chain with Diffusion models (VPDD). Specifically, to constrain the multi-step generation of volume in VPDD, we construct a meta volume guidance and a confidence-aware contextual guidance as conditional geometry priors to facilitate the distribution approximation. For the sampling process, we further investigate an online filtering strategy to maintain consistency in volume representations for stable training. Experiments demonstrate that our plug-and-play VPDD outperforms the state-of-the-arts for tasks of MVS and SSC, and can also be easily extended to different baselines to get improvement. It is worth mentioning that we are the first camera-based work that surpasses LiDAR-based methods on the SemanticKITTI dataset

Load shifting of a supplier-based demand response of multi-class subscribers in smart grid

We propose a demand response (DR) solution approach for a real-time pricing model with multi-class users to determine the electricity supply mix. The model aims to address the problem of power consumption overloading in peak hours using the real-time information obtained from the interaction between suppliers and users in a smart grid. The proposed DR algorithm allocates the overloaded demand assigned to a supplier to other electricity suppliers in order to satisfy all users’ demand while the supplier ensures to maximize the utility or reserved demand of users. Furthermore, a priority approach based on different user groups is developed for allocating the extra demand to other suppliers. Numerical experiments have been conducted to analyze the performance of the algorithm and compare the real-time electricity price with the fixed price

Effect of Grain Coalescence on Dislocation and Stress Evolution of GaN Films Grown on Nanoscale Patterned Sapphire Substrates

Two types of nucleation layers (NLs), including in-situ low-temperature grown GaN (LT-GaN) and ex-situ sputtered physical vapor deposition AlN (PVD-AlN), are applied on cone-shaped nanoscale patterned sapphire substrate (NPSS). The initial growth process of GaN on these two NLs is comparably investigated by a series of growth interruptions. The coalescence process of GaN grains is modulated by adjusting the three-dimensional (3D) temperatures. The results indicate that higher 3D temperatures reduce the edge dislocation density while increasing the residual compressive stress in GaN films. Compared to the LT-GaN NLs, the PVD-AlN NLs effectively resist Ostwald ripening and facilitate the uniform growth of GaN grains on NPSS. Furthermore, GaN films grown on NPSS with PVD-AlN NLs exhibit a reduction of over 50% in both screw and edge dislocation densities compared to those grown on LT-GaN NLs. Additionally, PVD-AlN NLs result in an increase of about 0.5 GPa in the residual compressive stress observed in GaN films

The GPS Data Collection and Transmission Strategy for Floating Vehicle Technology

Floating vehicle equipped floating vehicle technology has been widely used to collect urban and inter-urban road network traffic data for network evaluation, traffic management and dynamic road guidance purposes. It has become one of the major technologies of Intelligent Transport Systems (ITS). Most of the commonly used floating vehicle devices receive floating vehicle data in a frequency of 1 Hz. To collect real time traffic data of the road network for the above application, floating vehicle data from floating vehicles has to be transmitted from vehicles to a traffic data management centre in short time intervals. With the increase in the numbers of floating vehicles, the cost on communication increases dramatically. This is often one of the major barriers limiting the application of floating vehicle technologies for real time traffic route guidance and many other applications. This paper proposes a floating vehicle - floating vehicle data transmission strategy, which can significantly reduce the data transmission cost with satisfied accuracy for real time traffic management and dynamic road guidance purposes

Comparative study on recognition of transportation system under real and UE status

Transportation system is a complex, large, integrated and open system. It’s difficult to recognize the system with analytical methods. So, two neural network models are developed to recognize the system. One is a back propagation neural network to recognize ideal system under equilibrium status, and the other is a counter propagation model to recognize real system with probe vehicle data. By recognizing ideal system, it turn out that neural network can simulate the process of traffic assignment, that is, neural network can simulate mapping relationship between OD matrix and assigned link flows, or link travel times. Similarly, if real-time OD matrix is obtained by probe vehicle technology, and then similarly results like link travel times can be obtained by similarly models. By comparing outputs of two models, difference about real and ideal transportation system can be found

Factors affecting the accuracy of analysts\u27 forecasts: A review of the literature

This study conducts a comprehensive review of the literature published during 1996- 2017 to identify the factors that affect the accuracy of financial analysts\u27 forecasts. We organize our review around three main groups, namely, (a) drivers of analyst forecast accuracy, (b) quality financial reporting, and (c) accounting standards. Among the several factors found, some factors (experience of the analyst, earnings quality, audit quality, IFRS adoption, and annual report readability) have a positive relationship with the accuracy of analysts\u27 forecasts while others (politically connected firms, firms audited by Non-Big 4, and international GAAP differences) have a negative relationship. Our findings contribute to future research by examining the factors affecting analyst forecast accuracy from different perspectives, which will prove to be useful for academicians, regulators, investors, and financial analysts