The Dark Side of Customer Relationship Management in the Luxury Segment of the Hotel Industry

Today, service organizations are shifting their focus from transactional exchange to relational exchange for developing mutually satisfying relationship with customers. Extended relationships are reported to have a significant impact on transaction cost and profitability, and customer lifetime value. Serving the customers, in true sense, is the need of the hour as the customer was, is and will remain the central focus of all organizational activities. The hotel industry, especially the luxury segment hotels needs to be purely customer-centric and focus on the customer needs and duly fulfill them. Customers will not blindly accept poor service quality from a luxury hotel. They expect high quality of service in return for the money they spend on luxury hotels. This paper is an attempt to explain the dark side of CRM in the luxury segment of the hotel industry with the help of the "gap model" available in literature which suggests that gaps in service occur at various instances. The author explains that the gap model is a useful tool to explain the dark side partly. There is more to the dark side like privacy issues, unwillingness of customers to build a relationship with the service provider and changing tastes and preferences of the customer. Ritz- Carlton Hotel Company, L.L.C. has been chosen as a single case study and the research questions have been addressed for the industry at large using Ritz- Carlton as a classic example of superior service quality to the customers. Some simple measures to reduce the dark side have been mentioned, which addresses the third and last research question. The project would contribute as a useful guide to luxury hotels, giving them some valuable information on what the customer expectations are and if they are duly met then service gaps shall not occur. This paper shall provide scope for luxury hotels to improve their overall service quality and strengthen their position in the industry. The relevant existing theory has been reviewed and the subject has been explored, using the gap model (Parasuraman et al 1998) mainly. Based on the research findings and analysis, recommendation has been given to reduce the dark side at Ritz-Carlton and luxury hotels in general

Comparison of small molecules VEGFR inhibitors in the treatment of renal cell carcinoma

Vascular endothelial growth factors receptors (VEGFR) inhibitors play a vital role in the treatment of renal cell carcinoma. These are small molecules that predominantly exhibit anti-angiogenesis activity in conjunction with other anti-tumor effects. These drug therapies are approved for the use in patients as frontline agents or adjuvant therapy in renal cell carcinoma. However, VEGFR inhibitors are associated with undesirable adverse events, with some having a more manageable toxicity profile compared to others. As a result, choice of treatment poses a challenge for healthcare providers and patients. Nonetheless, these agents demonstrate improved disease/progression free survival (DFS/PFS) values and remain a critical component in the treatment of kidney cancer

The Effect of Uncertainties on Multi-Echelon Serial Supply Chains

Uncertainties are the major concerns in supply chain because existence of uncertainties degrades the performance of supply chain. Hence, business executives need to seriously focus towards controlling the effect of uncertainty on supply chain performance. In this study, a four echelon serial supply chain employed with reorder-point order-up-to level inventory replenishment (s, S) policy is modeled using system dynamics approach. Manufacturing systems adopting make-to-stock (MTS) and assemble-to-stock (ATS) manufacturing policy and operating under uncertain environment are modelled through system dynamics approach. A serial two-stage MTS manufacturing system is modelled through system dynamics approach and the behaviour is studied under the influence of uncertainty in demand, lead time, supplier’s acquisition rate, processing time and delay due to machine failure. Two different improved demand forecasting models are proposed to enhance the forecasting accuracy and reduce the bullwhip effect (BWE) and net-stock amplification (NSAmp). The first proposed model is the integrated approach of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) model denoted as ARIMA-GARCH to overcome the problem related to heteroskedastic nature of demand series. Second proposed model is the integrated approach of discrete wavelet transformation (DWT) and intelligence technique such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least square support vector machine (LSSVM) and multi-gene genetic programming (MGGP) to deal with non-linear, non-stationary demand series. Simulation study of multi-echelon supply chain indicates that target inventory significantly influence the BWE and it can be reduced through keeping target inventory at low level when there is low uncertainty in demand and lead time. From the analysis of manufacturing supply chain, it is observed that backlog at manufacturer’s end is significantly influenced by uncertainty in processing time and delay due to machine failure. The backup strategy adopted in manufacturing supply chain reveals that performance of manufacturing system is highly affected when uncertainty in supplier’s acquisition rate increases. The study proves that maintaining high service level at the bottom echelon is required to achieve high service level at the upper echelon of a supply chain. From the forecasting study, it is found that performance of the ARIMA-GARCH model outperforms the ARIMA model. Further, it is proved through case-study example

Amino acid selective unlabeling for sequence specific resonance assignments in proteins

Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective `unlabeling' or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly C-13/N-15 labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {(CO)-C-12 (i) -N-15 (i+1)}-filtered HSQC, which aids in linking the H-1(N)/N-15 resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i - 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to H-2 labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of N-14 at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies

Towards Resilient Cyber-Physical Energy Systems

In this paper, we develop a system-of-systems framework to address cyber-physical resilience, the ability to withstand the combined presence of both cyber attacks and physi-cal faults. This framework incorporates a definition of re-silience, a resilience metric as well as a resilient control de-sign methodology. The resilient control architecture utilizes a hybrid optimal control methodology combined with a dy-namic regulation market mechanism (DRMM), and is evalu-ated in the context of frequency regulation at a transmission grid. The framework enables the evaluation of both the clas-sical robust control properties and emerging resilient control properties under both cyber attacks and physical faults. The proposed framework is used to assess resilience of a Cyber-Physical Energy System (CPES) when subjected to both cyber and physical faults via DETERLab. DETERLab, a testbed capable of emulating high fidelity, cybersecure, net-worked systems, is used to construct critical scenarios with physical faults emulated in the form of generator outages and cyber faults emulated in the form of Denial of Service (DoS) attacks. Under these scenarios, the resilience and per-formance of a CPES that is comprised of 56 generators and 99 consumers is evaluated using the hybrid-DRMM control methodology