Commodity Model Research Articles

Designing a robust logistics model for perishable emergency commodities in an epidemic outbreak using Lagrangian relaxation: a case of COVID-19

Designing a robust logistics model for perishable emergency commodities in an epidemic outbreak using Lagrangian relaxation: a case of COVID-19

Automated pricing and replenishment decisions for vegetable products based on evaluation optimization models

Based on the commodity information of the supermarket in the annex, the detailed data of historical sales flow, the wholesale price of vegetable commodities and the recent loss rate of vegetable commodities, and through the data analysis of each category and each single product, the automatic pricing and replenishment decision-making model of commodities is established. Use the optimization evaluation algorithm to formulate the total daily replenishment and pricing strategy of each category and each single product. In order to solve the first problem, firstly, the outliers in the original data of Annexes 2 and 3 are cleaned, normalized, feature selected and dimensionally reduced. Secondly, a quarter is taken as a sales cycle of supermarkets, so as to find the proportion of sales volume of a certain category in the same quarter of three years to the total sales volume, and give the distribution law of sales volume of different categories, the results are shown. Considering different periods again, the daily sales volume distribution law is calculated by taking one day as a sales cycle, and the results are shown. Finally, the Pearson grade correlation coefficient is used to judge the relationship between the processing indicators, and the matrix heat map is obtained. According to the two results, it was concluded that there was a significant positive correlation between the sales volume of mosaic and cauliflower vegetables, and a significant negative correlation between the sales volume of nightshade and aquatic root vegetables. In view of the second problem, firstly, considering the functional relationship between the total sales volume and the cost pricing, the correlation analysis and linear fitting were carried out to obtain the linear relationship between the sales price of each category and the maximum value of the sales volume of each category in July of the previous year can be described as Through further nonlinear fitting and optimization problem solving, the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) are shown in Table 1 and Table 2, which makes the supermarket have the largest revenue In response to the third question, based on the known data, we can analyze the data requirements for each data: we need to know the sales volume of various vegetables during this period, we need to determine the purchase cost of each vegetable, we need to understand the past pricing strategy and response, and we need to know the inventory of various vegetables on June 30. On this basis, a multi-objective dynamic programming model is established, and the total number of saleable items is 30 by using the greedy algorithm to obtain the replenishment quantity of single items on July 1, and the pricing strategy is further solved by using the linear equation fitted in problem 2. In response to the fourth problem , on the basis of the existing sales, wholesale price and loss rate data, in order to better formulate the replenishment and pricing decisions of vegetable products, supermarkets also need to consider and collect the following 12 aspects of relevant data to assist in planning the pricing and replenishment decisions of vegetable products, such as: customer preference and satisfaction survey, seasonality and availability of vegetables, competitor information, inventory costs and storage conditions, historical sales data and trend analysis, customer flow and purchase period, nutritional value and health benefits of vegetables, Socio-economic factors, external environmental factors, policy and regulatory factors, technological and innovation factors, and supply chain and logistics information to ensure more comprehensive and accurate decision-making. Among them, the analysis of historical sales data and trends is mainly carried out.

Automatic Pricing and Replenishment Strategy Model of Vegetable Commodities

In the fresh supermarket, the general vegetable goods as a short shelf life and rapid deterioration of products, its inventory loss is very serious. In the process of sales, its quality tends to deteriorate with the increase of sales time. In view of the short shelf life of fresh products, this paper establishes an automatic pricing and replenishment decision model of vegetable commodities based on prediction and optimization algorithm. Through the historical sales and demand replenishment of each commodity, the distribution law and mutual relationship of vegetable categories and individual products are analyzed, and the future pricing and replenishment plan of supermarket is formulated. At the same time, it looks forward to the future, and puts forward some opinions on the decision-making process of profit maximization.

Electricity Pricing with Continuous-time Commodity Model and Non-monotonic Load Curve

Electricity Pricing with Continuous-time Commodity Model and Non-monotonic Load Curve

Value Chain Map of Coffee Commodities in Central Aceh Regency in the Implementation of Inclusive Business Model

This study aims to describe the Value Chain of coffee commodities in Central Aceh Regency and analyze the Value Chain Map of coffee commodities in Central Aceh Regency in the implementation of the Inclusive Business Model. This research was conducted in Central Aceh District, Aceh Province. This research was conducted in March–August 2023. The data used is in the form of primary data and secondary data. The sampling method with non-probability sampling uses the purposive sampling method. The data analysis method used is qualitative data analysis. Data analysis techniques use the Value Chain Map approach. The value chain of coffee in Central Aceh Regency the first distribution pattern is farmers selling directly to middlemen to exporters and consumers, the second pattern is farmers to middlemen to cooperatives and consumers, then the third pattern is farmers to middlemen to MSMEs Coffee Processors and consumers. The implementation of an inclusive business model for coffee commodities in Central Aceh Regency is through a partnership or collaboration between coffee farmers and the Baitul Qiradh Baburrayyan Cooperative. The highest value for farmers who are members of cooperatives is in principle 2 of effective market linkage (3.8). The lowest value is principle 3 fair and transparent governance (3.0). The highest value in non-cooperative farmers is principle 1 chain-wide collaboration (2.5). The lowest value is the 6 principles of Measurement of outcome (2.0). The inclusive business model is very profitable for farmers who join cooperatives because farmers get premiums, higher prices, infrastructure development, coaching and capital to improve farmer welfare.

LSTM forecast of volatile national strategic food commodities

Using the Long Short-Term Memory (LSTM) forecast, this study suggested a short-term projection model for national critical food pricing commodities. The model was trained using historical time-series data from each commodity price over the previous three years. The results demonstrated that the proposed LSTM architecture model was generalizable to all commodities and performed well in the majority of cases. This result indicates that the model is resilient and can be used to forecast commodity prices and offer accurate forecasts for most of the ten volatile national strategic foods, with an error value of less than 0.01 and an accuracy value of >95%. The model, however, failed to recognize the pricing pattern in cooking oil and beef commodities, both of which had increasing trend patterns. This shows that the model may be unable to effectively estimate commodity prices in the face of fast price fluctuations. The magnitude and quality of the dataset hampered the investigation. The time period selected also influenced the study. Future research should employ a more extensive and diversified dataset to increase the model's performance, allow it to learn more patterns and make more accurate predictions, and could use a more extended lookup date to improve forecast accuracy. This would enable the model to account for more recent pricing changes. Despite the limitations, the results of this study are promising and could be used to develop a more accurate and reliable food price prediction model. Policymakers and stakeholders could use the model to make informed food prices and inflation decisions.

Conditional autoencoder pricing model for energy commodities

We propose a conditional latent factor asset pricing model for energy commodities (CAE) that uses a modified conditional autoencoder neural network to capture the non-linear relationship between latent factors and factor loadings. In addition to spot prices, we incorporate 127 macroeconomic and 598 energy information characteristics to extract the factor loadings. The empirical results demonstrate the high-quality performance of the model in out-of-sample testing. Furthermore, by analyzing characteristic importance, we find that energy information characteristics, particularly coal, electricity, and crude oil and natural gas resource development, play a dominant role in explaining the excess returns of energy commodities.

Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market

This paper suggests a Markov-switching model to evaluate commodity futures and spot dynamics, such that the diffusion coefficients and jump size parameter are associated with a hidden Markov chain. We improve the current models in the literature of the commodity markets by modeling the sudden jumps in the commodity prices through the hidden Markov chain. From the crude oil spot price in West Texas Intermediate, we estimate the parameters of proposed Markov-switching model based on expectation–maximization algorithm. To perform this task, we apply this estimation algorithm to the model discretized by Euler scheme and provide some convergence analysis for this discretization method. There are options, such as European options, which are written on the commodity futures. In this study, we evaluate them under the regime-switching model with various economic states. In the following, we calibrate the option prices resulting from the proposed commodity model to a set of observed European call options written on crude oil futures. For this purpose, we first apply an inverse Fourier transform and obtain a semi-analytical option pricing formula. Then, we use the fast Fourier transform method to compute option prices. Since the investors need to calculate Greeks in order to understand the risk involved in option investments, the Greek formulas of Delta, Rho, Theta, and Gamma are derived.

ASEAN-FTA Impact on Rubber and Crude Palm Oil Export: An Empirical Evidence from IMT Countries

This study applies the gravity model to explore whether the establishment and the agreement of the ASEAN Free Trade Area (AFTA) have an impact on the export increase of rubber and crude palm oil commodity trade for the case ASEAN member countries namely, Indonesia-Malaysia-Thailand (IMT). The study uses time-series data, first for Indonesia using the period of 1996-2017 with trading partners as much as 21 countries for crude palm oil (CPO) commodity and 22 countries for rubber commodity. Secondly, the CPO export model for Malaysia using data during 1997-2017 with 23 trading partners countries. Thirdly, export rubber model for Thailand using a period of 1999-2017 with 16 trading partners countries. The estimate the Indonesian and Thailand export model for rubber commodity, and second, the same model also is estimated export CPO for the Indonesian and Malaysian. Generally, we found that the regression results it appears ASEAN member countries have not been fully benefited from the enactment of the AFTA, especially on the export of Indonesia's main commodities such as rubber and crude palm oil which destined for trading partners countries. As for the export of Thailand rubber and Malaysian crude palm oil, both countries are still relatively slightly better because the AFTA agreement has fully benefited. During the study period, the results of this study indicated that these three countries trade both commodities more intensely to the non-ASEAN countries

Retracted: Research on Intelligent Recommendation Model of E-Commerce Commodity Based on Feature Selection and Deep Belief Network

Retracted: Research on Intelligent Recommendation Model of E-Commerce Commodity Based on Feature Selection and Deep Belief Network

Closed-form solutions via the invariant approach for one-factor commodity models

We utilise the invariant method for scalar linear (1+1) parabolic partial differential equations so as to effect reductions via invertible transformations to Lie canonical forms of the first and third types. This is performed for the one-factor commodity models that have parameters which are constants or dependent on the price of stock or the time. The unknown parameters of the equations are determined. New closed-form solutions for the simple transformed equations are deduced by utilizing the equivalence maps and known solutions of the two Lie canonical forms.

Use of Business Models of the Aggregator in the Market of Commodities (Petroleum Products)

One of the events of recent years is the development of Internet commerce. B2C Internet trading is carried out through various aggregators. The demands of buyers and sellers in complex markets for commodities have similarities to the B2C market. In the conditions of online trading, sellers significantly automate and increase sales volumes, and buyers receive a choice of offers and a wide choice of products. When choosing the form of an aggregator in the B2B market, the market features and the product properties have a special fixation. The oil products distribution market in Russia is actively introducing new technologies into business. The main criteria for segmentation in this market is a commodity. Petroleum products are natural goods with unique properties, the key characteristic of which is the natural quality standard. Thus, when developing an aggregator model for commodities, the sales process, providing access to real-time information on the volume of demand and supply, high speed of implementation and safety of cases, as well as the availability of necessary services are especially important. The article proposes a model of the B2B-hub aggregator in the oil products commodity distribution market to improve the efficiency of doing business for buyers and sellers.

The network of commodity risk

In this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to take into account well-known stylized facts shown in the time series of commodities as well as possible regime changes in their conditional variance dynamics. We rely on backtesting procedures to select the best model for each commodity. Second, we estimate the sparse Gaussian Graphical model of commodities exploiting the Graphical LASSO (GLASSO) methodology to detect the most relevant conditional dependence structure among and within the sectors. A novel feature of our framework is that GLASSO estimation is achieved exploring the precision matrix of the multivariate Gaussian distribution obtained using a Gaussian copula with marginals given by the residuals of the aforementioned selected models. We apply our approach to the sample of twenty-four series of commodity futures prices over the years 2005–2022. We find that Soybean Oil, Cotton, and Coffee represent the major sources of propagation of financial distress in commodity markets while Gold, Natural Gas UK, and Heating Oil are depicted as safe-haven commodities. The impact of Covid-19 is reflected in increased heterogeneity, as captured by the strongest relationships between commodities belonging to the same commodity sector and by weakened inter-sectorial connections. This finding suggests that connectedness does not always increase in response to crisis events.

Intelligent Decision Method of Multi-Agricultural Commodity Model Based on Machine Learning

China Agricultural Outlook publishes the outlook report on major agricultural commodities and the estimates of the supply–demand balance sheet of agricultural commodities in the next decade, so as to release agricultural information and guide the development of modern agriculture. A large amount of data has been accumulated in the work associated with agriculture outlook in recent years. This paper will optimize the agriculture commodity model through deep learning and create an analysis tool based on long short-term memory (LSTM) deep learning by comprehensively considering the key factors of supply and demand of agricultural commodities including the output, consumption and price and combining the impact of complex natural, social and economic factors. In this way, the close relevance of different varieties and multi-variable strong coupling of the analysis and prediction model of major agricultural commodities can be solved. The “random sampling” and “stress on causality” of traditional complex agriculture analysis models are replaced by the “whole data” and “emphasis on relevance over causality”. The intelligent decision method of agricultural commodity model based on deep learning proposed in this study can effectively improve the analysis efficiency and accuracy of multi-variety coupling model of agricultural commodities (at least by 15%), and enhance the intelligence of the supply–demand analysis and prediction, especially with the accumulation of future data, the prediction accuracy will continue to improve. Machine learning has been regarded as an effective method to provide forecast and early-warning of future agricultural development in a timely manner based on real-time monitoring of agricultural data.

Research on Intelligent Recommendation Model of E-Commerce Commodity Based on Feature Selection and Deep Belief Network

Due to the complexity and uncertainty of customer demand behavior, it was often difficult to obtain satisfactory recommendation results by using the existing online commodity recommendation systems. Therefore, a network commodity intelligent recommendation model based on feature selection and deep belief network was proposed. Based on the basic structure and function of the existing recommendation systems, this paper expounded the interaction process between customers, e-commerce platforms, enterprises, and the recommendation system. By analyzing the internal relationship between customer demand and commodity recommendation, the relationship model between customer demand and commodity recommendation was established. After analyzing the characteristics of customers’ demand for goods, a data mining method was used to classify the characteristics of customers’ demand behavior, and a feature selection method based on deep belief network (DBN) was proposed to obtain the main information conducive to commodity recommendation. Finally, an e-commerce commodity recommendation algorithm based on feature selection and deep belief network was proposed. The experimental results showed that the network commodity recommendation model proposed in this paper can not only provide customers with satisfactory recommendation results but also has better performance than other traditional recommendation models. The recommendation model proposed in this paper can support different e-commerce website recommendation systems.

“For Sale by All Druggists”: Patent Medicine and National Market Access in Springfield, Illinois

Abstract Examination of newspaper ads and store records of available products combined with data generated from the archaeological record provide insight into how individuals and communities kept pace with national and global trends in medicine and advertising. Decades of archaeological investigation in Springfield, Illinois, by Fever River Research have yielded a rich data set that provides diverse insights into the community. The goal of this case study is to apply a commodity access model to the Springfield, Illinois, data to examine the accuracy of modern researchers’ ideas about the impact of market access on consumer choice. The combination of archaeological artifacts and archival data forms a compelling picture of a community that took advantage of unprecedented access to medicine and commodities during the rise of America's Gilded Age. In contrast to the original results of the commodity model, the results of this analysis indicate that the expansion of trade networks and interaction spheres may not have been the critical factor in consumer choice. Instead, social structures on a local level—between neighborhoods, competing stores, and the consumer—become more important in areas with diverse product access.

Can unconditional cash assistance improve household welfare? QUAIDS model for food commodities in Indonesia

ABSTRACT Expenditure patterns can be taken into consideration in determining the type of government assistance. Without adequate information on household expenditure patterns, cash transfers may not be effective in improving welfare. This study aims to examine the expenditure patterns of households receiving cash assistance in Indonesia. The expenditure pattern is estimated using the Quadratic Almost Ideal Demand System (QUAIDS). Four groups of expenditure types for food were examined: rice, staple food, beverages, tobacco and alcoholic beverages. The result showed that the largest predicted expenditure is for the purchase of staple food. Income elasticity for staple food is relatively highest than all other types of food. On the other hand, the price elasticity of tobacco and alcoholic beverages is relatively inelastic (lowest) compared to other food products. These results indicate that the provision of cash transfers for poor households has the potential to increase household welfare through increasing staple food consumption.

Accelerate Economic Growth of Lhokseumawe City with Application of Supply Chain and Main Strategic Commodity Model Based on Sharia During the Covid-19 Pandemic

Accelerate Economic Growth of Lhokseumawe City with Application of Supply Chain and Main Strategic Commodity Model Based on Sharia During the Covid-19 Pandemic

Potential impacts of concurrent and recurrent climate extremes on the global food system by 2030

The risk of food-supply instability is expected to increase along with the frequency and intensity of extreme agro-climatic events in many regions. Assessing the sensitivity of the global agricultural system to evolving extremes requires the probability of occurrence of such events to be estimated and their links with potential food supply and demand culminations to be established. From this perspective, in this article we implement a novel approach that can be used as a tool to inform decision-makers about the resilience of agricultural markets to climate extremes. By incorporating simulated climate-stress events into a partial-equilibrium model of interconnected agricultural commodity markets, we examine the complex manifestations of grain supply, demand and prices attributable to hazardous extremes. Market outcomes are further synthesized into coherently defined vulnerability and risk indicators. The proposed framework currently covers compound heat and water anomalies at the country level, potentially concurrent and recurrent, that impact annual crop yields and market balances in a recursive-dynamic manner until 2030. Our findings indicate that extreme-climate anomalies significantly distort expected market equilibria in the medium term. Moreover, extreme global prices may result either from climate anomalies in single key countries or from simultaneous events in many regions. Last but not least, trade and storage come forth as important alleviative mechanisms of the market uncertainty provoked by recurrent extremes.

A machine learning-based price state prediction model for agricultural commodities using external factors

In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states.