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Purpose - Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design - This research is therefore based on the correspondence history of a large booking management system that examines the features of quotation requests from aspiring guests in order to learn and predict their actual booking behavior. Approach - In particular, we investigate the effectiveness of various machine learning techniques for predicting whether a request will turn into a booking by using features such as the length of stay, the number and type of guests, and their country of origin. Furthermore, a deeper analysis of the features involved is performed to quantify their impact on the prediction task. Findings - We based our experimental evaluation on a large dataset of correspondence data collected from 2014 to 2019 from a 4-star hotel in the South Tyrol region of Italy. Numerical experiments were conducted to compare the performance of different classification models against the dataset. The results show a potential business advantage in prioritizing requests for proposals based on our approach. Moreover, it becomes clear that it is necessary to solve the class imbalance problem and develop a proper understanding of the domain-specific features to achieve higher precision/recall for the booking class. The investigation on feature importance also exhibits a ranking of informative features, such as the duration of the stay, the number of days prior to the request, and the source/country of the request, for making accurate booking predictions. Originality of the research - To the best of our knowledge, this is one of the first attempts to apply and systematically harness machine learning techniques to request for quotation data in order to predict whether the request will end up in a booking. [ABSTRACT FROM AUTHOR]

in this paper we consider the purchasing operation tools used to catalog, request-for-quote (RFQ), order, prepare quotation and receipt of goods, and to study and design the Blockchain system for the B2B portal for E-procurement. The B2B portal system is implemented on the seller B2B information system infrastructure to facilitate the procurement operations on both the user and seller sides. Answers were collected from 19 e-procurement personnel from home hub buyer database who were anticipated to be end-users of the system. The raw data we analyzed were derived from the questionnaires distributed, completed and returned by 100% of the participants. An SPSS, Statistical Package for the Social Sciences, program was used to calculate the level of agreement concerning the users' opinions on which necessary features that facilitate the users of the B2B procurement portal should be developed and the satisfaction of trial users. The "End-users" opinion on which necessary features that facilitate the users of the B2B procurement portal should be developed shows that they strongly agreed that the "RFQ features that users can submit to request for quotation for the selected products under the desired buying options and conditions". The system prototype trials showed that the most satisfaction was on the "All proposed tools deliver high usability and capabilities". [ABSTRACT FROM AUTHOR]

Outsourcing is a major driver in the growth of virtual organizations (VOs) in the manufacturing domain. These outsourcing networks can be classified as VOs as they are created from different organizational entities for a specific purpose and exist for a specified period of time. This paper proposes the use of discrete event simulation (DES) as a means of evaluating proposed VO configurations within the request for quotation (RFQ) process. The work presented is targeted at manufacturing-based VOs that are created to carry out specific manufacturing tasks as outlined in an RFQ, rather than more general project-based VOs that are formed to carry out work within a less defined structure. Data from an existing virtual breeding environment (VBE), obtained during field research into outsourcing networks, is used to illustrate the use of DES to support VO creation. As DES requires a high level of skill to use, the design of an easy-to-use web-based application with an embedded simulation engine is described. [ABSTRACT FROM AUTHOR]

The impact of reverse auction adoption has been a very important issue recently and is the subject of many studies. We are extending research results in the field of empirical studies by examining problems with reliability of methods for cost savings calculation of electronic reverse auction adoption in business processes. Reliability of savings proclamation in research studies or promotional materials is crucial for understanding real impact, benchmarking studies or comparison of several auction strategies from different auction environments. From our experience and empirical examinations, the problem for savings calculations is based on reliability and suitability of comparative or sometimes called expected price as the core factor for savings calculations when comparing with winning price. Our empirical study was based on real data from 1198 reverse auctions in Slovak commercial sector. On the base of our study, the most frequent strategy for electronic reverse auction adoption is multi-phase auction with first phase using sealed bid format. We have identified that reliability of using comparative prices as parameter for cost savings calculations vary according to different auction situations or settings and number of participants, auction volume and ratio of new participants are very sensitive for the reliability of savings calculations. [ABSTRACT FROM AUTHOR]

The article announces that Rios Partners, an Arlington, Virginia-based management consulting firm, has been chosen to conduct a full, independent assessment of the Baldrige Performance Excellence Program in order to assist expand its reach and impact.

The Texas A&M University Penning Trap experiment () is an upcoming ion trap facility that will be used to search for possible scalar currents in T = 2 superallowed pure Fermi decays (utilizing β-delayed proton emitters), which, if found, would be an indication of physics beyond the standard model. In addition, will provide a low-energy, point-like source of ions for various other applications at the Cyclotron Institute at Texas A&M University. The experiment is centered around a unique, compensated cylindrical Penning trap that employs a specially optimized length/radius ratio in the electrode structure that is not used by any other facility. The radioactive beam, provided by the Texas A&M University Re-accelerated Exotics (T-REX) program at the Cyclotron Institute, will be prepared for loading in the measurement trap via a specially designed Radio Frequency Quadrupole (RFQ) cooler/buncher. These proceedings will describe the current status of the facility, paying particular attention to the design and initial characterization of the RFQ cooler/buncher. Future plans will also be discussed. [ABSTRACT FROM AUTHOR]

The global OTC markets have been very active in the past decade as many institutions have chosen to rely on growth in the OTC issuance to facilitate deal-making outside of the exchange regulated avenues. Products included in this category are bonds, converts, volatility and variance swaps, CDS contracts. This paper introduces the financial instruments used in connection with the OTC markets, presents and offers suggestions for setting up generic sell and buys side RFQ and market making systems and introduces the main concepts and components that need to be taken into account when developing such systems when targeting the growing E-Business focus of the market. [ABSTRACT FROM AUTHOR]

This research explores procurement strategies for multi-item requests for quotation (RFQs) in business-to-business (B2B) markets using responses from 825 purchasing professionals. The study first establishes procurement strategies that differ based on their level of strategic emphasis, i.e., the importance that is placed on the pursuit of four strategic objectives. Underlying objectives, which are obtained via factor analysis, include the focus on price, security of supply, internal procurement efficiencies, and bundle building. Next, cluster analysis is used to derive prototypical strategic approaches. The three cluster groups that emerge possess the same relative ranking of the four objectives, but differ based on the intensity with which these objectives are pursued. The clusters are labelled as the three strategic groups of strategists, opportunists, and responders. The research then explores, using an industrial buyer behavior lens, the impact of environmental antecedents in determining a particular strategy. Environmental variables include purchase importance, market uncertainty, supply base availability, buyer bargaining power, item experience, and supply base experience. Finally, the study tests the impact of procurement strategy on the buyer's perceived performance, suggesting that strategists, placing more emphasis on the pursuit of strategic sourcing objectives, achieve better performance than opportunists and responders. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to use advance demand information (ADI), taking the form of request for quotation (RFQ) data, in B2B sales forecasting. We apply supervised machine learning and Natural Language Processing techniques to analyze and learn from RFQs. We apply and test our approach in a case study at a large after-sales service and maintenance provider. After evaluation we found that our approach identifies ∼ 70% of actual sales (recall) with a precision rate of ∼ 50%, which represents a performance improvement of slightly more than a factor 2.5 over the current labor-intensive manual process at the service and maintenance provider. Our research contributes to literature by giving step-by-step guidance on incorporating artificial intelligence in B2B sales forecasting and revealing potential pitfalls along the way. Furthermore, our research gives an indication of the performance improvement that can be expected when adopting supervised machine learning into B2B sales forecasting. [ABSTRACT FROM AUTHOR]