BACKGROUND: An efficient inventory control system would help optimize the use of resources and eventually help improve patient care. OBJECTIVES: The study aimed to find out the surgical consumables using always, better, and control (ABC) and vital, essential, and desirable (VED) technique as well as calculating the lead time of specific category A and vital surgical consumables. METHODS: This was a descriptive, record-based study conducted from January to March 2016 in the surgical stores of the All India Institute of Medical Sciences, New Delhi. The study comprised all the surgical consumables which were procured during the financial year 2014-2015. Stores ledger containing details of the consumption of the items, supply orders, and procurement files of the items were studied for performing ABC analysis and calculating the lead time. A list of surgical consumables was distributed to the doctors, nursing staff, technical staff, and hospital stores personnel to categorize them into VED categories after explaining them the basis for the classification. RESULTS: ABC analysis revealed that 35 items (14%), 52 items (21%), and 171 items (69%) were categorized into A (70% annual consumption value [ACV]), B (20% ACV), and C (10% ACV) category, respectively. In the current study, vital items comprised the majority of the items, i.e., 73% of the total items and essential (E) category of items comprised 26% of all the items. The average internal, external, and total lead time was 17 days (range 3-30 days), 25 days (range 5-38) and 44 days (range 18-98 days), respectively. CONCLUSIONS: Hospitals stores need to implement inventory management techniques to reduce the number of stock-outs and internal lead time.
Overconfidence is a universal psychological behavior. Overconfidence on demand awareness will have a significant impact on operation decisions. The supplier estimated the demand with excessive precision which influences the inventory financing decision-making deeply. We built the demand function based on the supplierâs overconfidence. Then we established the retailer, supplier, and the Bankâs profit function, respectively. Through the analysis of the bilevel Stackelberg game, we obtained the order quantity of the retailer with the capital constraint, the wholesale price of overconfident supplier, and the loan-to-value ratio of Bank, and we analyzed the influence of overconfidence on the decision variables. We have several findings as follows. First, the overconfidence makes the decisions of the retailer, supplier, and Bank deviate from the rational decisions. Second, the space of the market profit will affect the decision variables in the joint decision-making. Third, the financing supply chain (including the Bank and supply chain) should have a positive attitude towards the overconfidence of the supplier. Forth, in the joint decision-making, the supplier need determines the buyback price according to the capital demand; and in the decentralized decision-making, the supplier should try to use high buyback price strategy.
Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.
The purpose of this study is to apply the α-Sutte Indicator and ARIMA in forecasting data. α-Sutte Indicator is a new forecasting method that was developed in 2017 by Ansari Saleh Ahmar. To see the accuracy of these methods, the forecasting results of the α-Sutte Indicator will be forecasting methods compared to other items, namely: ARIMA. Based on the results of forecasting, it is found that α-Sutte Indicator has MSE and MAE values that are lower than other methods (ARIMA). This is supported by MSE data from α-Sutte Indicator smaller than ARIMA(1,1,1).
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called "latent source model". The Bayesian regression for "latent source model" was introduced and discussed by Chen, Nikolov and Shah (2013) and Bresler, Chen and Shah (2014) for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.