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

Strategic recommendations for new product adoption in the Chinese market

This study extended current understandings of the relationships among domain specific innovativeness (DSI), the desire for unique consumer products (DUCPs), perceived new product characteristics (PNPCs), and Chinese consumers’ new product adoption behavior. It also investigated the indirect effect of vicarious learning behavior on Chinese consumers’ acceptance of new products. Data was collected in Shanghai, China. The results demonstrated that DSI and PNPCs were the primary drivers of new product adoption. The study also showed that PNPCs played a mediating role in the relationship between vicarious learning and the adoption of new products by Chinese consumers. The results confirmed the predictive power of DSI and how PNPCs affect Chinese innovative buying behavior. The results also suggest that PNPCs facilitate Chinese consumers’ new product learning behavior. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

Preparation of Monodisperse Iron Oxide Nanoparticles via the Synthesis and Decomposition of Iron Fatty Acid Complexes

Iron fatty acid complexes (IFACs) are prepared via the dissolution of porous hematite powder in hot unsaturated fatty acid. The IFACs are then decomposed in five different organic solvents under reflux conditions in the presence of the respective fatty acid. The XRD analysis results indicate that the resulting NPs comprise a mixture of wustite, magnetite, and maghemite phases. The solvents with a higher boiling point prompt the formation of larger NPs containing wustite as the major component, while those with a lower boiling point produce smaller NPs with maghemite as the major component. In addition, it is shown that unstable NPs with a mixed wustite–magnetite composition can be oxidized to pure maghemite by extending the reaction time or using an oxidizing agent

Unraveling the Role of the rssC Gene of Serratia marcescens by Atomic Force Microscopy

100學年度研究獎補助論文[[abstract]]The product and direct role of the rssC gene of Serratia marcescens is unknown. For unraveling the role of the rssC gene, atomic force microscopy has been used to identify the surfaces of intact S. marcescens wild-type CH-1 cells and rssC mutant CH-1ΔC cells. The detailed surface topographies were directly visualized, and quantitative measurements of the physical properties of the membrane structures were provided. CH-1 and CH-1ΔC cells were observed before and after treatment with lysozyme, and their topography-related parameters, e.g., a valley-to-peak distance, mean height, surface roughness, and surface root-mean-square values, were defined and compared. The data obtained suggest that the cellular surface topography of mutant CH-1ΔC becomes rougher and more precipitous than that of wild-type CH-1 cells. Moreover, it was found that, compared with native wild-type CH-1, the cellular surface topography of lysozyme-treated CH-1 was not changed profoundly. The product of the rssC gene is thus predicted to be mainly responsible for fatty-acid biosynthesis of the S. marcescens outer membrane. This study represents the first direct observation of the structural changes in membranes of bacterial mutant cells and offers a new prospect for predicting gene expression in bacterial cells.[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]GB