Harnessing the Meteorological Effect for Predicting the Retail Price of Rice in Bangladesh

(1) Background: A binding recommended retail price has been used in several markets in a variety of forms, and the book market is a typical example. Publishers sell books to online retailers at a unit wholesale discount computed on the cover price. Retailers are then allowed to set the retail price. Therefore, if consumers regard the cover prices as reference points, then they may be more likely to purchase books if retail prices are lower than the cover prices. (2) Methods: We develop a Stackelberg game model for a book supply chain to investigates how reference price effects affect retailers and publisher’s pricing strategies. (3) Results: The results show that retailers will sell printed books at a discount only when the publisher’s wholesale discount rate is not high. Further, as the intensity of the reference price effects increases, (a) the lower boundary of the wholesale discount rate rises, (b) publishers’ profits increase and (c) retailers’ profits increase relative to the level of consumers’ e-books acceptance. (4) Conclusions: This result is related to the fact that the online retailer, such as Amazon and JD.com, like to invoke reference price effects in consumers’ minds by highlighting the printed book’s discount rate.

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Optimal Pricing Decisions for a Low-Carbon Supply Chain Considering Fairness Concern under Carbon Quota Policy

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18020556 ◽

2021 ◽

Vol 18 (2) ◽

pp. 556

Author(s):

Hao Zou ◽

Jin Qin ◽

Bo Dai

Keyword(s):

Emission Reduction ◽

Carbon Emission ◽

Reduction Rate ◽

Retail Price ◽

Low Carbon ◽

Close Attention ◽

Carbon Emission Reduction ◽

Carbon Supply ◽

Total Profit ◽

Fairness Concern

This research investigates the effect of fairness concerns on a sustainable low-carbon supply chain (LCSC) with a carbon quota policy, in which a manufacturer is in charge of manufacturing low-carbon products and sells them to a retailer. The demand is affected by price and the carbon emission reduction rate. The optimal decisions of pricing and carbon emission reduction rate are analyzed under four decision models: (i) centralized decision, (ii) decentralized decision without fairness concern, (iii) decentralized decision with manufacturer’s fairness concern, (iv) decentralized decision with retailer’s fairness concern. The results indicate that the profits in the centralized LCSC are higher than those in the decentralized LCSC with fairness concern. If a manufacturer pays close attention to fairness, the fairness concern coefficient will reduce the carbon emission reduction rate and the profit of the LCSC and increase the wholesale price and the retail price of the product. If a retailer pays close attention to fairness, and the preference of consumers for a low-carbon product is low, the fairness concern coefficient of the retailer increases the total profit of the LCSC and decreases the carbon emission reduction rate and retail price of the product. Otherwise, if the preference of consumers for a low-carbon product is great, the fairness concern coefficient of the retailer would lead to a lower retail price compared with the retail price in the centralized decision and decrease the total profit of the LCSC.

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Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea

Agriculture ◽

10.3390/agriculture10110513 ◽

2020 ◽

Vol 10 (11) ◽

pp. 513

Author(s):

Tserenpurev Chuluunsaikhan ◽

Ga-Ae Ryu ◽

Kwan-Hee Yoo ◽

HyungChul Rah ◽

Aziz Nasridinov

Keyword(s):

Deep Learning ◽

Topic Modeling ◽

Prediction Models ◽

Strong Relationship ◽

Retail Price ◽

Business Strategies ◽

Agricultural Commodities ◽

Statistical Machine Learning ◽

News Stories ◽

South Korean

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

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