Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm

To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.

Impact of the Pilot Volume-Based Drug Purchasing Policy in China: Interrupted Time-Series Analysis with Controls

Centralizing procurement for prescription drugs has the potential to reduce drug spending by creating economies of scale and by improving purchasing power. In March 2019, the Chinese government launched a volume-based purchasing (VBP) pilot program using a competitive bidding process to purchase accredited generic drugs for which branded drug substitutes were available. We performed an interrupted time-series design to estimate the change in monthly drug purchase quantity and spending comparing 14 months before and 7 months after the VBP pilot. We obtained monthly prescription drug purchase data for all purchases from public medical institutions in the three large pilot cities (Beijing, Shanghai and Xi’an) and two non-pilot cities (Changsha and Zhengzhou) between January 2018 to September 2019. We used negative binomial regression and log-linked Gamma Generalized Linear Model for purchase quantity and spending respectively. We evaluated heterogeneity of impact by pilot city, drug type (selected or non-selected drugs), and therapeutic class (cardiovascular disease, mental disorder and cancer) separately. The implementation of the pilot reform was associated with a 132% (95%-CI: 104–165%, p < 0.001) increase in the purchase quantity of selected drugs in pilot cities compared to an 17% decrease (95%-CI: 9–25%, p < 0.001) in control cities. In contrast, the purchase quantity of branded and other drugs in pilot cities decreased by 38% (95%-CI: 27–46%, p < 0.001) and 77% (95%-CI: 71–81%, p < 0.001), respectively; while in control cities, these remained at similar levels. Overall, in pilot cities, there was a 35% (95%-CI: 28–41%, p < 0.001) decrease in the purchase spending for all drugs in the first post-policy month, from 8.1 billion CNY estimated in the absence of VBP down to 5.3 billion CNY; in control cities, the change was negligible. The largest reduction in spending occurred for drugs for the treatment of cardiovascular diseases. The evidence suggests a positive impact of the VBP pilot in reducing overall drug spending and increasing the use of accredited generics in three pilot cities. This overall trend is not observed in two non-pilot cities. Assessments of long-term impact of the VBP policy on additional key outcomes including drug prescriptions, drug utilization, patients’ health outcomes and payments on drugs are needed.

Economic Decision-Making Algorithm for Cross-Border Industrial E-Commerce Material Purchase Quantity Based on Markov Chain

With the deepening of globalization, cross-border industrial e-commerce has increasingly become a mainstream means of trade exchanges, however, with the continuous increase in business volume. How to accurately estimate the procurement volume of cross-border materials is a significant issue, which plays a vital role in quantifying economic efficiency. To this end, this study proposes a financial decision-making algorithm for cross-border industrial e-commerce material procurement based on the Markov chain. The algorithm first preprocesses the original logistics data, including data cleaning, missing data filling, and noisy data removal, represents the data as structured panel data, finally builds a Markov chain model based on the panel data, and then makes predictions on the new data. We verified the effectiveness of the proposed model on a simulated dataset and an actual dataset.

A Survey of incremental high-utility pattern mining based on storage structure

Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.

A Dragging-Down Effect: Consumer Decisions in Response to Price Increases

Four studies, across a range of domains, find a dragging-down effect in which consumers purchase fewer units of a product when a discount applies to more units. For example, consumers buy fewer peaches when each customer can buy up to three peaches at a discount than when each customer can buy only one peach at a discount or when there is no discount at all. In contrast to basic economic principles, this dragging-down effect implies that consumers purchase less (more) when the per-unit price is lower (higher). We propose and our results support an acceptability account: consumers will adopt the price-increase point (i.e., maximum discounted quantity) as their purchase quantity if that point falls within an acceptable range, and will ignore that point and purchase their initially preferred quantity instead if the price-increase point falls below the acceptable range. The current work enriches existing research on anchoring and pricing and carries implications for consumers, marketers, and policy-makers.

Pengaruh Terpaan Iklan Nacific di Instagram terhadap Keputusan Pembelian Produk Nacific pada Followers Akun @nacificofficial.id

This research discusses the effect of exposure to nacific advertisements on instragram on purchasing decicions for nacific prodctson followers of he @nacificofficial.id. This research uses quantitative approach and the analytical method used is multiple linear regression. The object of this research is Jang Hansol version of Nacific ad which about answers question about ‘Fresh Herb Origin’ product. Meanwhile, the subject is followers Nacific’s official account on Instagram, @nacificofficial.id who have watched the ad. Primery data collection using a questionnaire to 100 respondents. The results showed that exposure of Nacific ad on Instagram has positive influence on purchase decision of the advertised product. Thus, H1, H2 and H3 are accepted, that means frequency, duration and intensity have influence on purchase decision which have indicators: product choices, brand choices, supplier choices, time of purchase, purchase quantity and payment method

Fruit and vegetable expenditure disparities: evidence from Chile

Purpose The purpose of this paper is to explore fruit and vegetable (FV) procurement disparity across income groups. Design/methodology/approach This study uses mean comparison and quintile regression to explain FVs variations. Findings Households from the highest income quantile spend more than two times on FVs than households from the lowest quantile; however, this expenditure disparity is largely mitigated in terms of purchase quantity. This paper presents evidence that, rather than quantity discounts or income neighborhood, the type of store (traditional markets vs supermarkets) plays a relevant role in explaining the smaller gap in terms of purchase quantity. Research limitations/implications Traditional markets help low-income households access low-cost FVs. Social implications The authors generate evidence to show that traditional markets play a relevant role to supply affordable FV to low-income households. Originality/value The paper used a high-quality and uncommon data set. It is a topic of high social impact.

Optimal Option Purchasing Decisions for the Risk-Averse Retailer with Shortage Cost

To hedge against potential risks, this paper introduces the conditional value-at-risk (CVaR) measure into the option purchasing for the risk-averse retailer with shortage cost. We introduce two models for the risk-averse retailer to select the optimal option purchase quantity. It is found that both two optimal option purchase quantities to two models can be decreasing in the retail price and increasing in the option executing price under certain conditions. This is different from the optimal option purchase quantity for a risk-neutral retailer to maximize the expected profit. It is found that both two optimal option purchase quantities may be increasing or decreasing in the confidence level, which implies a retailer who becomes more risk-averse may purchase more or fewer options to hedge against potential risks. Under both two optimal option purchase quantities, it is proven that the retailer’s expected profit is decreasing in the confidence level. This confirms the fact that high return implies high risk while low risk comes with low return.