COMPARISON OF OWNER AND CONTRACTOR PERSPECTIVES IN ESTABLISHING COST CONTROL STRUCTURE

Author(s):  
Ruveyda Komurlu ◽  
Akin Er

Cost control is a part of cost management which is carried out by the project owner and the contractor throughout a project. However, the structures of the control function developed by each party generally differ since the purpose of the function and the level of the structure are not exactly the same. Contractors have several advantages while building a cost control system such as more detailed information about the project, more background, more dedicated personnel, specifically developed software etc. Therefore, contractors have a broader vision on the issue. Owners need to utilize cost control systems not only for common reasons but also due to some specific necessities. Unlike contractors, owners have to capitalize projects just before they put their investments into operation. This paper intends to focus the necessities prioritized by the owners and contractors on the issue. A comparison will be made to distinguish the differences between the cost perspectives of project owners and contractors. An assessment will be done of the matters that owners pay attention while establishing a cost control structure in light of the experiences practiced in oil and gas projects. Thus, the important points which should be better to consider by the owners' project control teams will be advised.

2011 ◽  
Vol 204-210 ◽  
pp. 1022-1025
Author(s):  
Jing Pu Chen ◽  
Wei Wei Wang

Objective cost management and Activity-Based cost management are both more advanced cost management methods, but they are difficult to overcome their own shortcomings. In order to overcome the defects of these two methods of cost management, in the meanwhile, to find a new way to perfect the cost control system in enterprise, the writer planned to make this study. By analyzing and summarizing the domestic and international research results based on the integration of the Objective cost management and Activity-Based cost management, the writer pointed out the lack of current research and raised from the development, design, procurement, production and service to the full range of “Target-Operation” cost management, and designed three functional modules, including target cost estimate module, operation chain optimization module, cost breakdown and control module and auxiliary module to improve the model's content. This study offers a new way for the managers of enterprises in the field of cost control system and strengthening enterprise competitive power.


2018 ◽  
Vol 5 (2) ◽  
pp. 134
Author(s):  
Luqman Arif Baihaqi ◽  
Imam Mas'ud ◽  
Yosefa Sayekti

This study aims to find out the system of business cost control of Tempe Sumber Mas. A Controling based difference between the calculation of the cost of production with cost of production of tempe using full costing. and This research uses Descriptive by Source and Technique Triangulation. The results of this study indicate that the cost control system used by the company is still simple and the calculation of the cost of production of tempe using full costing method is greater than calculate the total production cost used in the tempe production process. Keywords: Expense, Cost of Production, Full Costing, Cost Control


2011 ◽  
Vol 71-78 ◽  
pp. 4596-4599
Author(s):  
Bao Xia Cui ◽  
Aai Lin Zhang

This paper mainly analyze the cost management from basic concept, cost control method in construction and principal in cost management. The control focal point of the cost management is emphasized in every stage.


Auditor ◽  
2021 ◽  
pp. 40-45
Author(s):  
Nataliya Kazakova ◽  
Lyudmila Permitina

The article proposes the author’s two-level methodology of on-farm control based on the concept of sustainable development, which has practical significance and allows solving the problem of control, optimization of costs and production costs both at the pre-production stage and in the production process with an emphasis on control procedures for three types of costs: economic, environmental and social.


2019 ◽  
Vol 121 ◽  
pp. 05005
Author(s):  
Agatha Swierczynski

Corrosion is still responsible for large economic losses in many and very different industry sectors like e.g. marine, refinery and petrochemistry, oil and gas pipelines or of drinking water and appearing by hot gases and combustion products in steel and concrete constructions. There are only some examples chosen. The corrosion phenomena are still a huge astonishment because of some costly repair processes and because of large production losses. The corrosion control systems existing by now help to avoid or to minimize these losses but the question still is, if the existing control system can be working better or longer. If yes, what a key can optimize the corrosion protection depending on the sector’s requirements.


2019 ◽  
Vol 48 (05) ◽  
pp. 119-123
Author(s):  
Safwan Al Salaimeh

The software is a set of mathematical methods, and algorithms of information processing, which used in creating the control system. When designing control systems, Initial data for the design of control system. The tasks of the computerized control system are understood as a part of the computerized functions of the computerized control system characterized by the outcomes and outputs in specific form. control function is: commutative action for computerized control system, aimed to achieve a criterion goal. Depending on the properties of the process and their mathematical description can be combined into different classes; This paper shows the designing the mathematical models which need to computerized control systems (models (3) – (8)). In the same time this paper shows the main methods which were used to formulate the mathematical models as: • Stochastic and deterministic; • One dimensional and multidimensional; • Linear and nonlinear; • Static and dynamic; • Stationary and non – stationary; • With distributed and lumped parameters.


2019 ◽  
Vol 273 ◽  
pp. 02006
Author(s):  
Sveinung Johan Ohrem ◽  
HyungJu Kim ◽  
Mary Ann Lundteigen ◽  
Christian Holden

Control systems are an important and increasingly complex part of most industrial and non-industrial systems. As such, identifying and handling associated risks is increasingly important. Systems- Theoretic Process Analysis (STPA) is a relatively new hazard identification method developed to analyze modern, complex control systems. While traditional hazard analysis methods mainly focus on the failures of a system, STPA focuses on interactions among control commands and environmental conditions, so that potential non-failure problems, mainly caused by unsafe control actions, can be identified. Proportional-Integral-Derivative (PID) controllers are the most common conventional controllers (CCs) and are widely used in industry due to their simplicity. PID controllers are tuned for operation and based on the system behaviour, in a certain limited operating region. If the behavior and/or operating region of a system changes over time, the PID controller requires retuning to perform as desired and prevent loss of production, or accidents, due to inadequate control. Adaptive controllers (ACs) are able to self-adjust and adapt to changes in the system parameters and operating region, such that the overall control task is performed without the need for continuous re-tuning by an operator. The tuning of an AC is done once, at the time of implementation. This can be very helpful for both the efficiency and the safety of the control system. The interactions between the operator and the control system are reduced when the controller is able to self-adjust, potentially reducing the number of hazards. On the other hand, the complexity of ACs may introduce new kinds of hazards that do not exist when using CCs. In this paper, we compare CCs and ACs from both a control and a safety perspective using STPA. As a test case, we compare the efficiencies and hazards of a CC, and an AC applied to a pipeline-riser system subject to slug flow, a hazardous phenomenon occurring in mixed oil and gas pipes. This phenomenon is difficult to control since the behaviour changes drastically with different flow conditions.


2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.


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