scholarly journals Development of Decision-Making Support Systems to Design Chemical Process Equipment for Batch Production

2019 ◽  
pp. 048-058
Author(s):  
V.G. Mokrozub ◽  
◽  
E.N. Malygin ◽  
Keyword(s):  
Decision Making ◽  
Chemical Process ◽  
Support Systems ◽  
Process Equipment ◽  
Batch Production ◽  
Chemical Process Equipment ◽  
Decision Making Support

CABOT TANTALUM

Alloy Digest ◽  
1989 ◽  
Vol 38 (2) ◽  
Keyword(s):  
Chemical Process ◽  
Heat Treating ◽  
Electronics Industry ◽  
Process Equipment ◽  
Vacuum Furnace ◽  
Alloying Element ◽  
High Melting Point ◽  
Prosthetic Devices ◽  
Cabot Corporation ◽  
Chemical Process Equipment

Abstract Tantalum finds its largest use in the electronics industry, where it is used in filaments, filament supports, and capacitors. Metallurgical grade tantalum is used extensively in chemical process equipment. Tantalum resists corrosion by body fluids and is used in prosthetic devices. Its high melting point gives it utility in vacuum furnace components. It is also used as an alloying element in superalloys. This datasheet provides information on composition, physical properties, and tensile properties. It also includes information on corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: Ta-11. Producer or source: Cabot Corporation.

Intelligent Technologies in Decision-Making Support Systems

2020 ◽  
Author(s):  
F. F. Pashchenko ◽  
Bui Truong An ◽  
Tran Duc Hieu ◽  
A. F. Pashchenko ◽  
Nguyen Van Trong
Keyword(s):  
Decision Making ◽  
Support Systems ◽  
Decision Making Support

The role of decision-making support systems in IT service management processes

2014 ◽  
Vol 8 (2) ◽  
pp. 147-163 ◽  
Author(s):  
M. Mora ◽  
Gloria Phillips-Wren ◽  
Jorge Marx-Gomez ◽  
F. Wang ◽  
O. Gelman
Keyword(s):  
Decision Making ◽  
Service Management ◽  
Support Systems ◽  
It Service Management ◽  
It Service ◽  
Management Processes ◽  
Decision Making Support

Quality Factors for DMSS Assessment

2011 ◽  
pp. 272-286 ◽  
Author(s):  
Harold W. Webb ◽  
Surya B. Yadav
Keyword(s):  
Decision Making ◽  
Support Systems ◽  
Development Project ◽  
System Quality ◽  
Quality Factors ◽  
Systems Research ◽  
Software Quality Metrics ◽  
Quality Decision ◽  
Decision Making Support

The objective of this chapter is to demonstrate the use of a decision support systems research (DSSR) framework to improve decision making support systems (DMSS) quality. The DSSR Framework, which was developed to integrate theoretical constructs from various information systems areas into a coherent theme, can serve as a tool for DMSS developers. Developed to provide a unified reference to theoretical constructs used in theory building and testing, the DSSR framework can also be used as the basis for the identification and selection of a hierarchy of factors potentially affecting the quality of DMSS development. The chapter proposes that a unified set of quality factors derived from the DSSR framework be used in tandem with the generic software quality metrics framework specified in IEEE Standard 1061-1992. The integration of these two frameworks has the potential to improve the process of developing high-quality decision making support systems and system components. The usage of these frameworks to identify system quality factors is demonstrated in the context of a military research and development project.

Decision-Making Support Systems

2011 ◽  
pp. 978-984
Author(s):  
Guisseppi Forgionne ◽  
Manuel Mora ◽  
Jatinder N.D. Gupta ◽  
Ovsei Gelman
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Support Systems ◽  
Original Version ◽  
Executive Information Systems ◽  
Intelligent Decision Making ◽  
Knowledge Based ◽  
Challenges And Opportunities ◽  
Research Findings ◽  
Decision Making Support

Decision-making support systems (DMSS) are computerbased information systems designed to support some or all phases of the decision-making process (Forgionne, Mora, Cervantes, & Kohli, 2000). There are decision support systems (DSS), executive information systems (EIS), and expert systems/knowledge-based systems (ES/KBS). Individual EIS, DSS, and ES/KBS, or pair-integrated combinations of these systems, have yielded substantial benefits in practice. DMSS evolution has presented unique challenges and opportunities for information system professionals. To gain further insights about the DMSS field, the original version of this article presented expert views regarding achievements, challenges, and opportunities, and examined the implications for research and practice (Forgionne, Mora, Gupta, & Gelman, 2005). This article updates the original version by offering recent research findings on the emerging area of intelligent decision-making support systems (IDMSS). The title has been changed to reflect the new content.

Evaluation of Decision-Making Support Systems

2011 ◽  
pp. 131-140
Author(s):  
Gloria E Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Support Systems ◽  
The Internet ◽  
Dynamic Data ◽  
Human Decision ◽  
Broad Classification ◽  
Specific Contribution ◽  
Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

The Evaluation of Decision-Making Support Systems' Functionality

2011 ◽  
pp. 329-338 ◽  
Author(s):  
Giusseppi Forgionne ◽  
Stephen Russell
Keyword(s):  
Decision Making ◽  
Support System ◽  
Support Systems ◽  
Field Studies ◽  
Integrated System ◽  
Management Support ◽  
Individual System ◽  
Management Support Systems ◽  
Independent Effects ◽  
Decision Making Support

Contemporary decision-making support systems (DMSSs) are large systems that vary in nature, combining functionality from two or more classically defined support systems, often blurring the lines of their definitions. For example, in practical implementations, it is rare to find a decision support system (DSS) without executive information system (EIS) capabilities or an expert system (ES) without a recommender system capability. Decision-making support system has become an umbrella term spanning a broad range of systems and functional support capabilities (Alter, 2004). Various information systems have been proposed to support the decision-making process. Among others, there are DSSs, ESs, and management support systems (MSSs). Studies have been conducted to evaluate the decision effectiveness of each proposed system (Brown, 2005; Jean-Charles & Frédéric, 2003; Kanungo, Sharma, & Jain, 2001; Rajiv & Sarv, 2004). Case studies, field studies, and laboratory experiments have been the evaluation vehicles of choice (Fjermestad & Hiltz, 2001; James, Ramakrishnan, & Kustim, 2002; Kaplan, 2000). While for the most part each study has examined the decision effectiveness of an individual system, it has done so by examining the system as a whole using outcome- or user-related measures to quantify success and effectiveness (Etezadi-Amoli & Farhoomand, 1996; Holsapple & Sena, 2005; Jain, Ramamurthy, & Sundaram, 2006). When a study has included two or more systems, individual system effects typically have not been isolated. For example, Nemati, Steiger, Lyer, and Herschel (2002) presented an integrated system with both DSS and AI (artificial intelligence) functionality, but they did not explicitly test for the independent effects of the DSS and AI capabilities on the decision-making outcome and process. This article extends the previous work by examining the separate impacts of different DMSSs on decision effectiveness.

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