Mindless Eating: Why We Eat More Than We Think

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Developing An Optimal Multivariate Forecasts Model For Supply Chain Inventory Management—A Case Study Of A Taiwanese Electronic Components Distributor

By reducing the volume of inventory and the ratio of obsoleted stock, enterprises can not only lower their cost and risk in a great amount, but also increase their flexibility of capital management. Thus, inventory issues are always taken seriously in enterprises’ supply chains. In the last decades, both industries and academia have come up with multiple solutions to avoid the damage caused by market volatility and to diminish the bullwhip effect. Examples include Toyota Production System (TPS), vendor managed inventory (VMI), collaborative planning, forecasting, and replenishment (CPFR) and so forth. However, little research has addressed the issue regarding with the optimal order amount given the forecast of customers’ demand. The issue is important because order amount is directly related with stock shortage and the inventory cost. To answer the question, this research aims to develop an optimal multivariate forecast model to determine how much and when we should order so that the inventory cost and the rate of stock shortage can be minimized. We will develop a decision support system (DSS) to implement our model. The bullwhip effect shows that if a retailer periodically updates the mean and variance of demand based on observed customer’s demand data, the variance of the orders placed by the retailer will be greater than the variance of demand. Lee et al. (2007) suggested information sharing and coordinate orders among the supply chain are solutions to alleviate the adversity of supply chain uncertainty that mentioned above, including the whiplash effect and dead stock risk. This research will develop an optimal multivariate forecasts to solve the problem. Multivariate forecasts use more than one equations if the variables, such as lead time, backlog and stock, are jointly dependent. We will compare our proposed model with exponential-smoothing forecasting model and a moving-average model to see which model is more applicable. We will also compare a correlated demand with a demand with linear trend to determine which one will be used in our optimal forecasting model. Decision Support System (DSS) can integrate analytical models responsive to the view point of a business process such as demand management. Thus, we will implement our analytical model using DSS. Even though several researchers have already developed DSS regarding with inventory management, like Achabal’s research in 2000 and Cakir’s research in 2008, few of them emphasize environmental dynamics such as demand uncertainty, significant seasonality, short product life cycle or high competitive intensity. Our model will address this issue by developing a multivariate forecasting model which considers multiple uncertainty factors. We will collect data from an electronic components distributor (ABC company). The data collection will be started at the beginning of 2016 and completed before March 2016. The data will enable us to test and refine our analytical model and make the DSS more feasible. We expect the DSS can support the ABC company to decide how much they should order and when is the best time for ordering in terms of reducing inventory. Therefore, the contribution of this research can be two-folded: first, to design a DSS that can actually help the case company to manage their orders more effectively, and, second, to find out variables that are related to inventory optimization in a dynamic environment and to develop an analytical model that is more general to be applied in other industires

SOFTWARE-AS-A-SERVICE (SAAS) INNOVATION IN THE CONTEXT OF SOFTWARE INDUSTRY: A RESOURCE ORCHESTRATION PERSPECTIVE

Cloud computing brings a paradigm shift in the software industry and changes the business model of software vendors (SV). Software as a service (SaaS), the most popular form of cloud computing, has been recognized as the fundamental change in the delivery, utilization, and management of software. While the transformation to SaaS requires changes within the organization, SVs must actively take action to attract customers to accept the SaaS business model, the so-called pull strategy. Drawing on the resource orchestration view, we propose that the antecedents (i.e., structuring cloud resources, developing service bundling capability, and leveraging cloud ecosystem) are related to the likelihood of an innovative SaaS, which, in turn, is associated with SaaS attractiveness to users. Our proposed research framework provides a guideline for SV to attract and persuade customers to adopt SaaS solutions actively

Low-energy electronic recoil in xenon detectors by solar neutrinos

Low-energy electronic recoil caused by solar neutrinos in multi-ton xenon detectors is an important subject not only because it is a source of the irreducible background for direct searches of weakly-interacting massive particles (WIMPs), but also because it provides a viable way to measure the solar $pp$ and $^{7}\textrm{Be}$ neutrinos at the precision level of current standard solar model predictions. In this work we perform $\textit{ab initio}$ many-body calculations for the structure, photoionization, and neutrino-ionization of xenon. It is found that the atomic binding effect yields a sizable suppression to the neutrino-electron scattering cross section at low recoil energies. Compared with the previous calculation based on the free electron picture, our calculated event rate of electronic recoil in the same detector configuration is reduced by about $25\%$. We present in this paper the electronic recoil rate spectrum in the energy window of 100 eV - 30 keV with the standard per ton per year normalization for xenon detectors, and discuss its implication for low energy solar neutrino detection (as the signal) and WIMP search (as a source of background).Comment: 12 pages, 3 figure

Local Activities of the Membranes Associated with Glycosaminoglycan-Chitosan Complexes in Bone Cells

Chitosan is a cationic polysaccharide derived from the partial deacetylation of chitin. Hyaluronic acid (HA), chondroitin sulfate (CS) and heparin (HP) are anionic glycosaminoglycans (GCGs) which can regulate osteogenic activity. In this study, chitosan membranes were prepared by glutaraldehyde crosslinking reaction and then complexed with three different types of GCGs. 7F2 osteoblasts-like cells and macrophages Raw264.7 were used as models to study the influence of chitosan membranes on osteometabolism. Although chitosan membranes are highly hydrophilic, the membranes associated with GCG-chitosan complexes showed about 60-70% cell attachment. Furthermore, the membranes associated with HP-chitosan complexes could increase ALP activity in comparison with chitosan films only. Three types of the membranes associated with GCG-chitosan complexes could significantly inhibit LPS induced-nitric oxide expression. In addition, chitosan membranes associated with HP and HA can down-regulate tartrate-resistant acid phosphatase (TRAP) activity but not CS-chitosan complexes. Based on these results, we conclude that chitosan membranes associated with HP can increase ALP activity in osteoblasts and chitosan membranes associated with HP and HA reduce TRAP activity in osteoclasts