Utilizing Value Management to Increase Project Competitiveness

2018 ◽  
Author(s):  
Michael Tozer ◽  
Debra Tetteh-Wayoe
Keyword(s):  
Best Practices ◽  
Manufacturing Industry ◽  
Business Processes ◽  
Systematic Approach ◽  
Oil And Gas ◽  
Value Management ◽  
Capital Projects ◽  
A Value ◽  
Expected Performance ◽  
Oil And Gas Sector

With the current economic pressures being faced by the oil and gas sector, organizations are increasingly required to become more competitive on their capital projects. Enbridge has implemented the practice of Value Management (VM) to help achieve the needs and expectations of stakeholders with the least possible resources. VM is a systematic approach that is used by a multidisciplinary team to improve the value of a project (or aspects of a project) through the analysis of its functions, and is most effective when applied at the planning and development stages. A value study enables the expected performance (i.e. the desired functions) of a project to be clearly identified at the onset, and assesses a range of possible solutions/alternatives against the functions required by the owner. While VM is commonly used in the manufacturing industry, as well as on transportation and municipal projects, few examples of its application in the oil and gas sector were found. Enbridge researched a variety of VM best practices and created a framework that compliments existing company practices. This paper also highlights how the value methodology was recently applied to a capacity expansion project at the Front End Engineering and Design (FEED) stage. Our approach to the various elements of a value study will be discussed, including pre-workshop activities, the VM workshop, and post-workshop activities. Enbridge has seen significant benefits from the VM studies completed on projects to-date. Given the broad applicability of the value methodology, it is believed that our approach can also be successfully applied in other areas (e.g. improving business processes).

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

Risk Management, JSC Achimgaz Project Experience

2017 ◽  
Vol 6 (4) ◽  
pp. 3-9
Author(s):  
Е. Усова ◽  
E. Usova ◽  
Валерий Фунтов ◽  
Valeriy Funtov ◽  
А. Бутов ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Natural Gas ◽  
Management System ◽  
Business Processes ◽  
Oil And Gas ◽  
Risk Management System ◽  
Iso 31000 ◽  
Decision Making Processes ◽  
Oil And Gas Sector

The article is devoted to the introduction of risk management system in the activities of JSC Achimgaz, operating in the oil and gas sector and implementing the Project for the extraction of natural gas and condensate (hereinafter the Project). The analysis of implementation, its effectiveness within a system is discussed. According to the analysis the conclusion about the necessity of creating a unified system that integrates risk management into decision-making processes, key business processes and the culture of the organization, according to GOST ISO 31000-2010.

Digitalization Analysis of Engineering Projects Using the Oil and Gas Sector as an Example

2020 ◽  
Vol 26 (12) ◽  
pp. 688-696
Author(s):  
M. R. Usmanov ◽  
◽  
D. A. Fomenkov ◽  
M. A. Shushkin ◽  
◽  
...  
Keyword(s):  
Business Processes ◽  
Oil And Gas ◽  
Maximum Efficiency ◽  
Engineering Project ◽  
Optimal Decisions ◽  
Managerial Decisions ◽  
Modern Business ◽  
Analytical Tools ◽  
Engineering Projects ◽  
Oil And Gas Sector

he article outlines critical analysis of methods used in assessment of digitalization in companies and projects. Based on the results the authors infer the necessity of development of methods that might be used in analysis of engineering centers digitalization. The article presents new method that allows to estimate the level of intensity and effectiveness of different practices in digitalization. The method was tested in analysis of digitalization of engineering projects in oil industry. Digitalization is one of the main processes of changing modern business. Most companies actively declare the introduction of new digitalization tools in management. At the same time, these processes do not always lead to an increase in management efficiency. To make optimal decisions on the use of business digitalization tools, the development of appropriate analytical tools is required. The presented methodology includes: a set of metrics for the efficiency and activity of digitalization of an engineering project: grouping of metrics into three blocks; a method for evaluating the analyzed metrics, an algorithm for analyzing the digitalization of projects and making managerial decisions for their optimization. The technique presented in the article allows us to identify problem areas of digitalization in the business processes of the engineering center, as well as to determine in which directions the maximum efficiency was achieved. This methodology is aimed at making managerial decisions regarding the use of various tools for digitalizing business processes in the implementation of engineering projects.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals The data-driving management in oil and gas industry: examples and opportunities

2021 ◽  
Vol 6 (4) ◽  
pp. 137-146
Author(s):  
Andrey S. Bochkov ◽  
Mariia G. Dymochkina
Keyword(s):  
Decision Making ◽  
Best Practices ◽  
Business Processes ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Data Driven ◽  
Management Approach ◽  
Gas Industry ◽  
Data Driven Approach ◽  
Using Data

Background. Decision-making process in the oil and gas industry, traditionally extremely expensive, should be based on the point of maximizing the business value. Forecasting the effectiveness of investments of any business unit in oil and gas should be based on a data-driven management approach. The purpose of this article — to study methods and best practices of applying a data — driven approach to decision-making and analyze the possibility of scaling methods of best practices in the processes in oil and gas company. Materials and methods. Research a various case with data-driven management shows that using data-driven approach allows solving several tasks at once: to make a fast and quality decisions based on data that can always be checked, and the result can be analyzed; to reduce the costs by eliminating inefficient steps and increase the flexibility of the process; to form the correct attitude to data (data culture) and prepare for the implementation of the technologies of Industry 4.0. Analyze cases revealed two common and important things: engineering of business processes from the key performance indicators and the technological development. Results. In article discusses the topic of applying a data-driven decision-making approach in oil and gas companies using several examples of Gazprom Neft. These examples shows that better effect from the using of data-driven management is achieved by consistently modeling business processes for achieving maximum values; highlighting and fixing key business performance indicators and creating a digital monitoring of these indicators, which allows you to the achievement of goals. Conclusions. In the conclusion of the article there are recommendation about using data-driven management approach for various processes of an oil and gas company.

scholarly journals Oil and gas companies in the innovation conditions of economic development in Kazakhstan

2019 ◽  
Vol 135 ◽  
pp. 04025 ◽  
Author(s):  
Sayabek Ziyadin ◽  
Khakimzhan Malayev ◽  
Gulmira Yessenova ◽  
Anar Beyzhanova
Keyword(s):  
Global Economy ◽  
Manufacturing Industry ◽  
Oil And Gas ◽  
Cash Flows ◽  
Innovation Activity ◽  
Company Value ◽  
Main Development ◽  
Oil And Gas Companies ◽  
A Company ◽  
Oil And Gas Sector

The article shows that the implementation of oil and gas companies will be implemented in many sectors of the global economy, including the main development sector-manufacturing industry. In Kazakhstan, “positive” experience has been accumulated in connection with the implementation of the oil and gas sector, because it is one of the main aspect of the global industries in our republic. The purpose of this article is to identify trends in the innovative development of the manufacturing industry, as well as to systematize the basic elements of the development industry and economic growth of the country. The correlation analysis will show how the innovation activity of the companies influencing on the GRP of the country. The potential of the company contains any and all available resources: financial, labor, material, immaterial and others, as well as the ability of managers to manage these resources in order to create products and services and to maximize earnings. For the purpose of getting the full company value the company’s potential should be taken into account while applying the income method for valuation oil and gas companies, in calculating the discounted cash flows. The potential management can influence the value of a company and, consequently, the company management.

SCOR Implementation in Oil and Gas Company from an Emerging Market

2017 ◽  
Vol 8 (1) ◽  
pp. 93-107 ◽  
Author(s):  
Jibran Hafeez ◽  
Rameez Khalid ◽  
Shahid Mir
Keyword(s):  
Developing Countries ◽  
Supply Chain ◽  
Best Practices ◽  
Oil And Gas ◽  
Emerging Market ◽  
Business Success ◽  
Success Factor ◽  
Structured Interviews ◽  
Scor Model ◽  
Oil And Gas Sector

Measuring supply chain performance is an important business success factor in today's competitive environment and continuous improvement culture. Several models have been developed for this purpose, however, such models lack standardized language and are not well known in the developing countries. Supply Chain Council (SCC) developed Supply Chain Operations Reference (SCOR) model. This paper presents a case-based action-research for a step-by-step implementation of SCOR model. The case company belongs to oil and gas sector in a developing country. As-Is model was developed and analyzed for gaps. Reasons were identified using company documents and semi-structured interviews. To-Be model was then developed along with recommendations keeping into account the challenges faced by companies operating in emerging markets. The step-by-step SCOR implementation was found to be effective. It is further found that adapting the SCOR model for developing countries is a time-intensive effort and adapting the best practices can be a better option.

scholarly journals Discovering Business Processes from Email Logs Using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub Rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
Sina Zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations&rsquo; operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

Sign in / Sign up

Export Citation Format

Share Document