U.S. Refining — Changing Supply and Slate Requirements

1994 ◽  
Vol 12 (2-3) ◽  
pp. 177-190
Author(s):  
Charles L. McSpadden
Keyword(s):  
Crude Oil ◽  
Raw Materials ◽  
Oil Quality ◽  
Petroleum Products ◽  
Refining Industry ◽  
Raw Material ◽  
Product Demand ◽  
Crude Oil Prices ◽  
Near Future ◽  
The U.S

With international refining industries facing a number of challenges in the near future, it seems clear that those who survive will be ones with the ability to turn serious problems into real opportunities. In the U.S. refining industry, challenges such as slow growth in product demand, increasing reliance on imported raw materials, and continued exposure to offshore exporting refineries will have critical effects on the ability of the industry to capitalize on available opportunities. Other challenges include the ability to tolerate continued declines in crude oil quality and the serious monetary questions related to compliance with environmental legislation, including air, soil, and water clean-up. This paper presents the challenges which the U.S. refining industry faces over the coming years, and seeks to address those issues which will impact the success or failure of the industry as a whole. The paper begins with a focus on the demand for U.S. petroleum products, encompassing brief historical data and forecasts of demand for the next few years. Closely related to demand is the subject of U.S. refinery operations, including product import and yield patterns. In this vein, the paper offers forecasts of crude runs to stills, as well as forecasts of capacity changes. Because profitability of U.S. refineries is affected by raw material costs, the paper next probes the possibilities resulting from world crude oil price fluctuations, considering the reemergence of Iraq as a market player. Forecasts of profit margins for U.S. refiners in 1998 are also offered. Turning to crude oil supplies and qualities, the paper examines the downward trend of U.S. crude oil production, providing a forecast of the decline by 1998. An associated trend, that of U.S. crude oil imports, is also evaluated, with a discussion of the origins of these imports included. The paper then presents a brief discussion of the principal recipient of Canadian crude oil exports, the U.S. Midwest (PADD II), encompassing statistics for refinery runs and deliveries of crudes. Volumes of Canadian crude exported to the region are also presented, as well as crude oil qualities in the region. Finally, heavy crude oil prices are examined because of the degradation of average crude oil qualities consumed by U.S. refiners. Spreads between light and heavy crudes are contemplated, with a forecast for the current-dollar WTI/Maya price spread provided.

scholarly journals Relationship between International Crude Oil Price and Indian Stock Market (NSE)

2011 ◽  
pp. 63-73
Author(s):  
Rajendra Mahunta
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Stock Exchange ◽  
Oil Prices ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
New Era ◽  
Crude Oil Prices

In this new era of economic growth, the exceptional increase in the crude oil prices is one of the significant developments that affect the global economy. Crude oil is an important raw material used for manufacturing sectors, so that increase in the price of oil is bound to warn the economy with inflationary inclination. The study examine the long-term relationships between CNX NIFTY FIFTY index of National Stock Exchange and crude price by using various econometric test. The surge in crude oil prices during recent years has generated a lot of interest in the relationship between oil price and equity markets. The study covers the period between 01.01.2010 and 31.12.2014 and was performed with data consisting of 1245 days. The empirical results show there was a cointegrated long-term relationship between CNX index and crude price. Granger causality results reveal that there is unidirectional causality exists and crude oil price causes NSE (CNX) but NSE (CNX) does not cause oil price.

scholarly journals FEASIBILITY OF THE UTILIZATION CONDENSATE AS RAW MATERIAL FOR PETROCHEMICAL

2018 ◽  
Vol 39 (3) ◽  
Author(s):  
Holisoh Holisoh
Keyword(s):  
Crude Oil ◽  
Petrochemical Industry ◽  
Raw Materials ◽  
Raw Material ◽  
Process Unit ◽  
Test Results ◽  
Economic Calculation ◽  
Hydrocarbon Product

The utilization of condensate is currently not optimal yet, where most of its production are mixed with crude oil which results in significant shrinkage in oil volume. As a hydrocarbon product, condensate can be utilized as a raw material for petrochemical industry, fuel and solvent. Currently raw materials of petrochemical industry in Indonesia are imported from other countries. To optimize the utilization of condensate it is necessary to conduct a characteristic test of several types of condensate and then conducted a utilization analysis based on their number and its nature. Paraffin, Olefin, Naphthenic and Aromatic (PONA) test results showed that the six samples of condensate in the test had a fairly high paraffin content of above 60%. The highest paraffin content was 82.84% for condensate B and the lowest was 61.4% for condensate E. The six condensate samples contain higher paraffin, which are suitable for use as raw material for petrochemical olefin.The results of the economic calculation of the construction unit Olefin Process Center with a capacity of 100,000 BPSD, which is an IRR as 22.8 %, the NPV as of US $ 1,801,491,951.12 , POT for 4.1 years, and PI as 1.87. Developing of Olefin Process Unit Center being constructed in Indonesia is economical worthy.

Product Marketing

2007 ◽  
Author(s):  
James Wei
Keyword(s):  
United States ◽  
Raw Materials ◽  
The United States ◽  
Census Bureau ◽  
Raw Material ◽  
Relative Advantage ◽  
Standard Industrial Classification ◽  
Economic Activities ◽  
Agricultural Chemicals ◽  
The U.S

A marketer should follow the maxim of the 4th century BC strategist Sun-zi, who said “Know self, know opponents, hundred battles, hundred victories.” We are the chemical processing industries (CPI), which is a collection of firms that manufacture and sell a range of products that involve chemistry and employ many chemical engineers. The buyers are consumers, businesses, governments, and foreigners. When we consider selling a product to a buyer, we pay particular attention to profitable and growing markets where our product has a relative advantage over competition. It takes a bold pioneer to introduce a new product that requires the creation of a new market. Let us study the sellers of chemical products, which are collectively called the CPI. These manufacturers are skilled in the use of chemical reactions and separations to make their products, and they employ many chemical engineers and chemists, often in highly responsible positions. Many of the firms in the CPI are also our suppliers of raw materials and intermediates, our customers for our products, and our competition in making and selling their products. The Statistical Abstract of the United States is published annually by the U.S. Census Bureau, which groups all the economic activities in the United States into 11 divisions by the Standard Industrial Classification (SIC). The manufacturing division is divided into 20 sections designated by two-digit numbers. The manufacturers that involve chemistry intensively are listed in table 9.1, by two 2-digit numbers, such as: 20 Food, 28 Chemicals, and 29 Petroleum Refining. The table lists the number of establishments, the number of employees and value of shipment in 1996. The SIC 28, “Chemical and Allied Products,” is the basic supplying industry to the other sectors. Table 9.1 also gives the subdivision of SIC 28 into three-digit subsectors, such as: 281 Industrial Inorganics, 283 Drugs, and 286 Industrial Organics. The subsectors of 281 and 286 form the core of the Chemical and Allied Products, as they provide raw material and intermediates for the rest of the subsectors, such as 282 Plastics and 287 Agricultural chemicals.

Research on Optimization of Inventory Management Based on Demand Forecasting

2014 ◽  
Vol 687-691 ◽  
pp. 4828-4831
Author(s):  
Jia Xi ◽  
Ping Ba Sha
Keyword(s):  
Inventory Management ◽  
Raw Materials ◽  
Continuous Production ◽  
Research Result ◽  
Demand Forecasting ◽  
Raw Material ◽  
Production Cycle ◽  
Demand Forecast ◽  
Product Demand ◽  
On Demand

Demand forecasting is the basis of the inventory management. Aiming at the problem of subjective forecasting method, we use quadratic exponential smoothing method to establish the mathematical model, to forecast sales volume of product A in every month in 2013. And based on demand forecasting, we put forward ABC classification management method to solve the inventory management issues. The research result of this paper has important implications in improving the inventory management level for many enterprises.Demand Forecasting and Inventory ManagementInventory management is an important part of enterprise management, and it directly affects the business situation of enterprises. A reasonable inventory can significantly enhance the comprehensive competitiveness of enterprises; too much or too little inventory settings would have a bad impact on the business, and some company even bog down because of inventory problems companies bogged down because of inventory problems [1-2]. To do inventory management, what should we do in the first step. The answer is demand forecast. When business scale reaches a certain level, it would need strict, systematic demand forecasting. The more accurate the demand forecasting is, the more accurate inventory planning would be, and more favorable for business enterprises.Few companies are able to be completely in accordance with the order production, and the vast majority of businesses are not waiting for orders after arrival, then determine how much raw material and manpower needed, and how to arrange production. Because it often takes a long production cycle, and no one is willing to wait a month to buy a bag of washing powder. Successful companies always make accurate predictions for product demand, and then put into production according to forecasting [3]. Due to their more accurate predictions, they can often carry out a reasonable plan and inventory management. Inventory forecasting, its essence is demand forecasting [4]. Demand forecasting provides important information for inventory management such as inventory amount, lead time, inventory turns. Demand forecasting is based on research and statistics, to make a scientific and reasonable inference for product demand. Product demand generally is within a certain period, certain market range, the number of consumers’ demand for a product. Demand forecasting results can help companies determine the amount of raw material inventory and products, and provide enterprise continuous production of raw materials needed, save liquidity and reduce inventory costs, improving the comprehensive competitiveness of enterprises.Product Demand Forecasting Model

scholarly journals Venezuela's Nascent Oil Industry and the 1932 US Tariff on Crude Oil Imports, 1927–1935

2009 ◽  
Vol 27 (3) ◽  
pp. 427-462 ◽  
Author(s):  
Brian S. McBeth
Keyword(s):  
Crude Oil ◽  
Negative Impact ◽  
Oil Prices ◽  
Oil Industry ◽  
Petroleum Products ◽  
Initial Development ◽  
Oil Companies ◽  
Competitive Edge ◽  
Crude Oil Prices ◽  
The Impact

ABSTRACTAfter a brief description of the initial development of Venezuela's crude oil industry, this paper examines the impact the 1932 US tariff on crude oil imports had on the country. The US tariff on crude oil imports stabilised domestic crude oil prices but prevented consumers from benefting from lower prices in refned petroleum products. The large us international integrated crude oil companies gained from higher crude oil prices for their domestic production while supplying their european markets with mostly cheap crude oil from their newly developed Venezuelan oilfelds. The tariff increased the Venezuelan oil industry's vulnerability to international events because it narrowed the competitive edge it had over domestic us crude oil production. consequently, the Gómez dictatorship in Venezuela at the time became more dependent on the oil companies operating in the country since they could reduce production considerably, or even leave the country as quickly as they entered with a negative impact on government revenues.

scholarly journals Time-Varying Relationship between Crude Oil Price and Exchange Rate in the Context of Structural Breaks

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2395
Author(s):  
Yue Liu ◽  
Pierre Failler ◽  
Jiaying Peng ◽  
Yuhang Zheng
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Structural Breaks ◽  
Oil Prices ◽  
Oil Price ◽  
Policy Uncertainty ◽  
Price Shocks ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
The U.S

This paper examines the dynamic relationship between crude oil prices and the U.S. exchange rate within the structural break detection context. Based on monthly data from January 1996 to April 2019, this paper identifies structural breaks in movements of oil price and examines the dynamic relationship between crude oil prices and the U.S. exchange rate movement by introducing the economic policy uncertainty and using the TVP-VAR (Time-Varying Parameter-Vector Auto Regression ) model. Empirical results indicate that shocks to crude oil prices have immediate and short-term impacts on movements in the exchange rate which are emphasized during the confidence intervals of structural breaks. Oil price shocks and economic policy uncertainty are interrelated and influence movements in the U.S. exchange rate. Since the U.S. dollar is the main currency of the international oil market and the U.S. has become a major exporter of crude oil, the transmission of price shocks to the U.S. exchange rate becomes complicated. In most cases, the relationship between oil prices and the U.S. exchange rate movements is negative.

scholarly journals Co-Integration of Oil and Commodity Prices: A Comprehensive Approach

2021 ◽  
Author(s):  
Mohcine Bakhat ◽  
Klaas Würzburg
Keyword(s):  
Raw Materials ◽  
Commodity Prices ◽  
Past Research ◽  
Food Prices ◽  
Cointegration Analysis ◽  
Raw Material ◽  
Complex Interactions ◽  
Crude Oil Prices ◽  
Long Run ◽  
Short Run

Past research has mainly applied linear cointegration analysis to study the relationship of crude oil prices with the prices of other commodities. However, recent methodological innovations in cointegration analysis allow for a more thorough analysis of the co-movement of commodity prices and detect asymmetric and thresholds co-movements. Following Enders and Siklos [1] and Hansen and Seo [2], we apply threshold cointegration analysis, detecting co-movements that earlier studies based on linear cointegration analysis could not detect. We find that adjustments to positive and negative deviations from the long-run equilibrium are asymmetric for copper, food and agricultural raw materials in the short-run. Moreover, the adjustments for aluminum and nickel are symmetric. The price Granger causalities behave as expected for metals and agricultural raw material prices. Food prices, however, behave differently. In sum, the results of this paper underscore the importance of consistently testing nonlinear cointegration and point out the complex interactions that take place between the markets of oil and other commodities.

scholarly journals PERENCANAAN KEBUTUHAN MATERIAL DENGAN METODE MATERIAL REQUIREMENT PLANNING

2016 ◽  
Vol 15 (2) ◽  
pp. 190
Author(s):  
SUHARTINI .
Keyword(s):  
Raw Materials ◽  
Raw Material ◽  
Performance Rating ◽  
Productive Capacity ◽  
Material Requirements Planning ◽  
Product Demand ◽  
Material Requirement Planning ◽  
Material Requirement ◽  
Many Sources ◽  
Material Ordering

Kegiatan produksi yang dilakukan oleh beberapa perusahaan masih menggunakan persediaan material yang banyak agar kegiatan produksi dapat berjalan lancar, akan tetapi kondisi ini dianggap tidak optimal karena banyak sumber daya yang ditanam, sehingga investasi hanya digunakan untuk keperluan kebutuhan material. Pemesanan material yang tidak didukung dengan data-data permintaan produk secara tepat, akan membuat persediaan bahan baku tidak dapat dipastikan sehingga material terkadang banyak dan kadang kala habis. Dalam menggunakan tenaga kerja dan fasilitas produksi yang tidak sesuai dengan kebutuhan permintaan, maka kegiatan produksi tidak berjalan secara efektif dan efisien. Sumber daya yang dimiliki oleh perusahaan harus diketahui secara detail, agar perusahaan dapat menggunakan kemampuan sumber daya yang ada secara optimal. Perusahaan dalam mencapai permintaan produk harus mengetahui kapasitas produksi yang dimiliki, sehingga kemampuan target produksi perusahaan dapat dicapai secara optimal, dengan melakukan pengukuran waktu standar pada operasi kerja dan menentukan performance rating untuk masing-masing operator. Dalam menentukan permintaan produk untuk periode berikutnya menggunakan metode peramalan jenis regresi linier dengan jumlah permintaan produk sebesar 1278 kg. Perencanaan kebutuhan material (MRP) pesanan dapat dilakukan pada hari ke-7 tiap bulan sesuai dengan jumlah masing-masing jenis bahan baku. Production activities which are conducted by some companies still use a lot of material inventory that production activities can run smoothly, but this condition is not considered optimal because many sources in the plant, so the investment is only in use for material. Ordering a material is not supported by data appropriately in product demand, it will make inventory of raw materials can not be sure so that the material is sometimes a lot and sometimes run out. The use of labor and production facilities are not accordance with demand, finally the production activities are not effective and efficient. Resources owned by the company must to be known in detail, so that the company can use capabilities of existing resources optimally. Company to achieve product demand should know the productive capacity, so that the ability of the company’s production targets can be achieved optimally, by measuring standard time at work operations and determine performance rating for each operator. In determining the demand for the product the next period using the method of linear regression forecasting the number of types of product demand of 1278kg. Material requirements planning (MRP) orders can be made on day 7 of each month in accordance with the number of each type of raw material.

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