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

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

In vivo evolution of biopsy-proven inflammatory demyelination quantified by R2t* mapping

A 35-year-old man with an enhancing tumefactive brain lesion underwent biopsy, revealing inflammatory demyelination. We used quantitative Gradient-Recalled-Echo (qGRE) MRI to visualize and measure tissue damage in the lesion. Two weeks after biopsy, qGRE showed significant R2t* reduction in the left optic radiation and surrounding tissue, consistent with the histopathological and clinical findings. qGRE was repeated 6 and 14 months later, demonstrating partially recovered optic radiation R2t*, in concert with improvement of the hemianopia to ultimately involve only the lower right visual quadrant. These results support qGRE metrics as in vivo biomarkers for tissue damage and longitudinal monitoring of demyelinating disease

Non-leisure time physical activity is an independent predictor of longevity for a Taiwanese elderly population: an eight-year follow-up study

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to determine the relationship between leisure time physical activity (LTPA) and non-leisure time physical activity (NLTPA) on mortality among the elderly in Taiwan.</p> <p>Methods</p> <p>This is a prospective observational cohort study. We analyzed the mortality data from a cohort of 876 non-institutionalized community-dwelling men and women aged 65 years or over, who were recruited by stratified clustering random sampling from Tainan city and participated in the 1996 Elderly Medication Survey. Information about activities and other variables were collected by structured interviews at baseline in the participants' home. The Cox proportional hazards model and crude death rate were applied to estimate mortality risk.</p> <p>Results</p> <p>Among the 876 participants, 312 died during the follow-up period (1996-2004). In the unadjusted Cox regression model, subjects aged over 75, having difficulty in carrying out activities of daily living (ADLs), a BMI less than 18.5, a history of diabetes mellitus or stroke, without LTPA or being inactive in NLTPA, were found to have a higher risk of eight-year mortality. With the adjustment for age, gender, education level, habitual smoking and drinking, living status, BMI and medical history, the mortality was found to be higher among the sedentary subjects, either defined by lack of LTPA or NLTPA, with the hazard ratio of 1.27 (95% confidence interval [CI] = 0.97-1.66) and 1.45 (95% CI = 1.07-1.97), respectively. Furthermore, when both LTPA and NLTPA were put into the model simultaneously, NLTPA (HR = 1.40; 95% CI = 1.03-1.91) but not LTPA (HR = 1.21, 95% CI = 0.92-1.59) significantly predicted mortality during eight-year follow-up. In addition, subjects who were actively engaged in NLTPA had a lower mortality risk especially in subjects without performing LTPA.</p> <p>Conclusions</p> <p>NLTPA is an independent predictor of longevity among older people in Taiwan. A physically active lifestyle, especially engaged in NLTPA, is associated with lower mortality risk in the elderly population. We thus suggest that encouraging older people to keep on engaging in customary NLTPA is good for their health.</p

Interpersonal Relationships among University Safety Professionals: The Impact of a Safety Departmentf

PresentationForming strong interpersonal relationships enables an organization or individual to achieve more favorable outcomes. The objectives of this study were to examine the frequency of interpersonal interactions among safety professionals (SPs) employed at Taiwanese universities and the factors that affected this frequency. To accomplish these objectives, we mailed questionnaires to a simple random sampling of 200 university SPs. Moreover, an interpersonal relationship scale was developed in this study; exploratory factor and internal consistency analyses revealed that the scale was valid and reliable. Results derived from the questionnaire revealed that in SP interpersonal relationships, general affairs department personnel, laboratory or internship unit supervisors, and teaching staff ranked highest in frequency of interactions. Multivariate analysis of variance results showed that establishing a safety department exerted a statistically significant effect on SP interpersonal relationships. SPs employed by universities with safety departments interacted more frequently with both internal and external relationships. Therefore, we suggest that universities without a safety department establish such a department to strengthen the labor safety and health structure, thereby benefitting SPs in fulfilling responsibilities to promote safety and health management

Aberrant ATRX protein expression is associated with poor overall survival in NF1-MPNST

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are aggressive soft tissue sarcomas that can occur sporadically or in the setting of the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. These tumors carry a dismal overall survival. Previous work in our lab had identified ATRX chromatin remodeler

Genetic and Functional Analysis of the DLG4 Gene Encoding the Post-Synaptic Density Protein 95 in Schizophrenia

Hypofunction of N-methyl-D-aspartate (NMDA) receptor-mediated signal transduction has been implicated in the pathophysiology of schizophrenia. Post-synaptic density protein 95 (PSD95) plays a critical role in regulating the trafficking and activity of the NMDA receptor and altered expression of the PSD95 has been detected in the post-mortem brain of patients with schizophrenia. The study aimed to examine whether the DLG4 gene that encodes the PSD95 may confer genetic susceptibility to schizophrenia. We re-sequenced the core promoter, all the exons, and 3′ untranslated regions (UTR) of the DLG4 gene in 588 Taiwanese schizophrenic patients and conducted an association study with 539 non-psychotic subjects. We did not detect any rare mutations at the protein-coding sequences of the DLG4 gene associated with schizophrenia. Nevertheless, we identified four polymorphic markers at the core promoter and 5′ UTR and one single nucleotide polymorphism (SNP) at the 3′UTR of the DLG4 gene in this sample. Genetic analysis showed an association of a haplotype (C–D) derived from 2 polymorphic markers at the core promoter (odds ratio = 1.26, 95% confidence interval = 1.06–1.51, p = 0.01), and a borderline association of the T allele of the rs13331 at 3′UTR with schizophrenia (odds ratio = 1.19, 95% confidence interval = 0.99–1.43, p = 0.06). Further reporter gene assay showed that the C-D-C-C and the T allele of the rs13331 had significant lower activity than their counter parts. Our data indicate that the expression of the DLG4 gene is subject to regulation by the polymorphic markers at the core promoter region, 5′ and 3′UTR of the gene, and is associated with the susceptibility of schizophrenia