This document discusses using decision support systems and linear programming models to optimize production planning at a tire factory in Najaf, Iraq. It presents the research problem, objectives, and data collected from the factory. A mathematical model is applied using linear programming to maximize profits based on constraints of available resources. The model results show the most profitable tire sizes to produce and determines shadow prices and surplus raw materials. Overall, the document examines how quantitative decision support tools can help managers optimize production plans.
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This research develop the managing within network and relationship mechanism in agribusiness
management through serious game. Agribusiness is represented as sand that work together in the market
(sandpile) to maintain networks and relationships. This research apply agent base model for predicting
activity network based on the parameters that exist in the collaboration. The result indicate that average
selling, average buying and market price (CK = 4) are not approach the value of the open market but
precisely coincide with eachother. Total bought and total sold are tend to be high value. This condition
suggests a very tight competition. The average selling, average buying and market price (CK = 0.01) are
approach the value of the open market. Total bought and total sold are not as high as total bought and total
sold, by using CK = 4, this condition shows the competition is not too tight.
All companies need to be more effectively than ever before. In the current financial climate, every dollar invested is important and know that your business is operating efficiently is an imperative need, but as a Manager not always easy to know if the decisions are really the best for your company.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Managing collaboration within networks and relationships in the serious game ...ijcsit
This research develop the managing within network and relationship mechanism in agribusiness
management through serious game. Agribusiness is represented as sand that work together in the market
(sandpile) to maintain networks and relationships. This research apply agent base model for predicting
activity network based on the parameters that exist in the collaboration. The result indicate that average
selling, average buying and market price (CK = 4) are not approach the value of the open market but
precisely coincide with eachother. Total bought and total sold are tend to be high value. This condition
suggests a very tight competition. The average selling, average buying and market price (CK = 0.01) are
approach the value of the open market. Total bought and total sold are not as high as total bought and total
sold, by using CK = 4, this condition shows the competition is not too tight.
All companies need to be more effectively than ever before. In the current financial climate, every dollar invested is important and know that your business is operating efficiently is an imperative need, but as a Manager not always easy to know if the decisions are really the best for your company.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Social Media its Impact with Positive and Negative AspectsEditor IJCATR
Social media is a platform for people to discuss their issues and opinions. Before knowing the aspects of social media
people must have to know what is social media? Social media are computer tools that allows people to share or exchange
information’s, ideas, images, videos and even more with each other through a particular network. In this paper we cover all aspects of
social media with its positive and negative effect. Focus is on the particular field like business, education, society and youth. During
this paper we describe how these media will affect society in a broad way.
Decision support systems, group decision support systems,expert systems-manag...clincy cleetus
concept of decision making,decision making process-intelligence phase-design phase-choice phase,types of decisions,meaning and definition of decision support systems(dss),evolution of dss,characteristics of dss,decision support and repetitiveness of decisions,objectives and importance of dss,classification of dss,components of dss,functions of dss,development of dss,support for different phases of decision making,benefits and risk of dss,group decision support systems, gdss software, gdss benefits and risks,expert systems,difference between dss and es, comparison between dss and es
El campo de DSS / BI esta evolucionando desde sus origenes como una herramienta primariamente de soporte personal y está rapidamente llegando a ser una comodidad compartida a traves de de las organizaciones
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
AN EXPERT SYSTEM FOR MAKE OR BUY DECISION IN MANUFACTURING INDUSTRYIAEME Publication
A computer-based system is designed to assist manufacturing industries in the make or buy decision, which is arguably the most fundamental component of manufacturing strategy. A model of make or buy decision was developed through review of literature and discussion with industry people. The system employs both case-based reasoning (CBR) and decision support system components. As part of the development process, interviews were conducted with managers in valve manufacturing industry in order to determine current make or buy practice and elicit opinions on how the decision-making process would be enhanced. The model consists of various checks as technology, capacity, sorting of parts by cost in descending and allocating capacity for parts which give maximum cost saving as first. A Knowledge Based System (KBS) was developed which incorporates these checks into the make buy decision. This system is used for analysis of capacity of machine used and idle capacity remaining for better performance of the industry. Expert system developed is also used to take decision about a new part /product to be manufactured inhouse or bought outside and save the time in decision making
Anattemptto forecast demand and buildforecasting modelsis made in the article. The modern business is conducted in the contextof fierce competition. Adequate decisions require deep, comprehensive assessment of the situation and a reliable forecast of developments. Proven methodsof forecastingby extrapolation of the previous results to the future are poorly suited for qualitative changes in theeconomy, especially in a complex, dynamic,and uncertain environment. Acompany that has managed to forecastthe situationcorrectlyearnsadditional profit in comparison with the one that abstained from forecasting. Afirm that made the wrong forecastloses the most. The simplest forecastis to extrapolate the current situation to the future. This technique may be well suited fora stable and clear situation, but it begins to fail in a dynamic and uncertain environment, as well as in the contextof structural adjustments
Keys to Succeed in Implementing Total Preventive Maintenance (TPM) and Lean S...IJMTST Journal
Competition is global and it continues to get more intense, with changes in technology, introduction of new and differentiated products and techniques. These changes are faster than what can be implemented. Profits are no longer driven by prices but with costs.[1] Customers have access to just about anything at their finger tips. The expectation like quick response, lower prices, flexible orders and quality products, is increasing every day from the customers. Our OEM’s (Original Equipment Manufacturers) are searching for new methods of doing business and they expect their suppliers, like us to do the same. The challenge in front of us is how we respond effectively to these changing trends in the industry for our survival & growth. Change is the only certainty and the above is very much applicable to any business to achieve and sustain competitive edge. It is evident that organizations, which are innovative and visionary, have successfully implemented the change, realizing its business strategies would lead to their long term survival
Elimination of rework in V Cap by Using Six Sigma MethodologyIJERD Editor
The purpose of this project was to gain a strong understanding of Six Sigma management philosophy, concepts, and practices and to apply this knowledge to creating a Six Sigma academic course or training program. This was done through three main methods: preliminary research and data collection, the creation of a design model for Six Sigma academic course program establishment, and the creation of a Six Sigma academic course program syllabus. The preliminary research consisted of conducting a Six Sigma knowledge survey with the students of Worcester Polytechnic Institute, interviewing a Six Sigma expert, and examining current Six Sigma educational programs in other universities, businesses, and organizations. As a result of our observations, we determined that Six Sigma has become a large part of many companies and should be implemented into more engineering programs at universities nation-wide, including Worcester Polytechnic Institute. This can be done through a project based course, as well as more Six Sigma based Interactive and Major Qualifying Projects.
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Churn customer, one of the most important issues in customer relationship management and marketing is especially in industries such as telecommunications, the financial and insurance. In recent decades much
research has been done in this area. In this research, the index set for the reasons set reason churn customers for our customers is of particular importance. In this study we are intended to provide a formula for the index churn customers, the better to understand the reasons for customers to provide churn. Therefore, in order to evaluate the formula provided through six Classification methods (Decision tree QUEST, Decision tree C5.0, Decision tree CHAID, Decision trees CART, Bayesian network, Neural network) to evaluate the formula will be involved with individual indicators
1Running Head MODULE 7 SUBMISSION6 MODULE 7 SUBMISSION.docxdrennanmicah
1
Running Head: MODULE 7 SUBMISSION
6
MODULE 7 SUBMISSION
Module 7 Submission
Student Name
School
Abstract
Introduction
Technology is very dynamic today and the business organizations are dependent on technological advancements. Business environment is changing and with the fast-paced aviation industry, there is the need to implement very good information systems technologies to improve the efficiency of their operations, hence improving their competitive advantage. The system improves communication, operations management, decision-making and record-keeping, which is very important in improving the way things are done within the organization.
Decision Supporting System
The aviation industry is a very competitive industry and it requires a lot of data analysis in order to continue improve service provision. The aviation industry is a very vast industry with so many players. Being a service industry, it is important to ensure that all the travelers receive the best services. An improvement in the efficiency, effectiveness and quality of services provided to the customers is not very easy. It requires thorough market analysis. This is the reason decision supporting systems are required. According to Toda (1998), a decision system is a very useful system, which helps in the collection, and the analysis of necessary information, on which a decision will be based. Airline management is not easy and it requires good decision making in order to improve both the safety of the passengers, as well as the profitability of the business. According to Ivanov & Netjasov, (n.d.), decision support systems have been used widely in the aviation industry. However, there are so many inconsistencies in the way they are used. Essentially, all decision supporting systems are aimed at improving the quality of decision making, while reducing the required time for decision making. Improvement of decision making through the use of Decision Supporting Systems (DSS), within the aviation industry is important. The success, safety, profitability and sustainability of an aviation organization is dependent on the quality of decisions being made.
In aviation maintenance, Decision Supporting Systems have a very big role to play. According to Zhang, Zhao, Tan, Yu, & Hua (2011), the Decision Supporting System can be useful in real-time acquisition and processing of data, which is essential in improving the decisions made by the leadership in the aviation organization. Provision of the historical data is also very important in improving the quality of decision made within the organization. Provision of the basis of a technical decision would be essential.
Having a good system to support decisions is very crucial in helping the technical team in the identification of issues or fault diagnosis. Faults in the organization systems or any issues affecting the crafts can have detrimental results if they are not well managed. According to Zhang, Zhao, .
A BENCHMARK MODEL FOR INTERNAL ASSESSMENT OF INDUSTRY USING FUZZY TOPSIS APPR...ijmech
Internal assessment is one of the most important factor of an industry, needs appropriate improvement
planning between the departments with development of Benchmark model among them. The proposed study
applies Fuzzy Technique for Order Preference by Similarity to Ideal Solution to rank different alternatives.
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competition between departments which are Human Resource, Finance, Production, Quality Assurance. To
remove the subjectivity, the linguistic data about the attributes is converted into a crisp score by using
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find the best alternative according to the industry’s requirement. Thus the endeavor has been made by the
authors to give a simple model for the evaluation of internal assessment of an industry
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To proposed a preference ranking order of the 3 models from best to worst.
To discuss different applicable areas of different MCDM methodologies.
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Decision support systems and their role in rationalizing the production plans
1. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
7
Decision Support Systems and Their Role in Rationalizing the
Production Plans: A Case Study on a Plant in Najaf
Mahmood. B. Ridha
Department of Business Administration, Al-Zaytoonah University
Amman, Jordan
Tel: 077-747-6064 E-mail: mahbedir@yahoo.com
Loay Alnaji (Corresponding author)
Department of Business Administration, Al-Zaytoonah University
Amman, Jordan
Tel: 079-870-3887 E-mail: Loay@Alnaji.net
Muiead A. Kalal
Department of Accounting, Kufa University
Kufa, Iraq
Tel: 00964-215-8-216
Abstract
This research focuses on how to analyze production plans based on quantitative indicators, enabling managers to
produce plans that produce the results that help make full use of resources to achieve the company’s goals, maximize
profits, and reduce costs to the lowest possible level. These concepts covered in this research, presented in three parts.
The first part covers the scientific methodology and literature review, the second part describes the theoretical side,
including presentation and analysis of DSS and the concepts of sensitivity analysis and production planning, and the
third part covers the application side, applying the discussed measurements in an organization to achieve results, and
recommendations.
1. Introduction
Production plans are of great importance in business productivity as a future course of action, determined by
methods of confrontation against conflicts, rivalries, and the possibility of achieving competitive advantage in the
market. The design of production plans is no longer linked entirely to inherited knowledge of the director or manager.
Due to rapid changes and complexity in the business environment, it is becoming more difficult for decision makers
to make the proper decisions inequality and quantity. Therefore, the role of decision support systems (DSS) have
become important. DSS enable decision makers to rely on quantitative methods to make their decisions, giving them
an edge over others who depend on experience (Yan, 2011). The same is applies in almost every field, from
pharmaceutical to education where universities try to collect knowledge provide its students with better education as
well be able to complete in the market. (Najim, Ghalib & Alnaji, 2013) (Alnaji, 2013).
2. Decision Support System concepts and theories
DSS emerged in the early 1970s as a concept by Morton who used the term Management Decision System as a step
in the development of management information systems to aid in decision making (Filip, 2008). DSS are interactive
systems enabling decision makers to interact with databases to generate a knowledge base that supports their
decisions in finding appropriate solutions to problems facing the organization (Power, 2003). Furthermore, DSS are
problem-oriented systems giving solutions to problems, replacing regular functional information systems that are
process oriented.
They can be viewed as a philosophy or an entry point to a solution rather than a specific methodology, regardless of
concepts. Characteristics and capacities of DSS can be summarized as:
Systems that offer solutions at all levels of administration
Systems that help provide successive and independent series of decisions.
Systems that depend on predefined models (operational, statistical, or financial).
2. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
8
3. Linear Programming
Linear programming is a simple model to understand. It does not require more mathematics than a number of simple
equations and a few variables. It can be used as a tool to determine the economic value of resources (Ridha, 2012).
Researchers recently began exploring the role of mathematical models in decision making (Anderson, Camm,
Williams, & Sweeney, 2013). The use of linear programming models for medium-term production planning is
widespread. It may be used as a single-stage planning system to transform a yearly sales plan into a feasible
production plan that indicates, for each item (or group of items), which subperiods and what amounts should be
produced, such that a given objective function is at its optimum (Caixeta-Filho, vanSwaay-Nego, & Wagemaker,
2002; Hung & Cheng, 2002; Lawrence & Burbridge, 1976; Nichelson & Pullen, 1971; Stadtler, 1988).
4. Sensitivity Analysis
Sensitivity analysis plays an important role in linear programming. In some cases, knowing how an optimal solution
changes relative to perturbations in the input data is more important than simply computing an optimal solution. Data
for a given problem can never be absolutely accurate in real applications. Hence, it is crucial to keep track of how
optimal solutions, or the optimal value, change if the data changes. Because of this, researchers have investigated the
area of sensitivity analysis (Holder, Sturm, & Zhang, 2001;. Wendell, 2004; Julia L. Higle , Stein W. Wallace2003;
Sitarz,2010).
5. Research Methodology
5.1 Research Problem
In practice, organizations need to continuously evaluate and monitor production-planning processes; these plans are
supposed to be designed scientifically accurately and reflect the aspirations of the organization’s future. To achieve
organization objectives, it is very important for decision makers to support decision-making processes in production
planning by applying quantitative methods that help produce the best results with minimal cost.
5.2 Research Importance
The importance of this research comes from demonstrating the role of quantitative indicators in decision
management, as well as exploring the possibility of applying decision-support models in streamlining production
lines.
5.3 Research Objectives
This paper explores the role of quantitative models in decision making to enable plan managers to produce optimal
results. The objective of this paper is to shed light on two important models—linear programming and sensitivity
analysis—and their role in making decisions in a company.
5.4 Research Data
Research data were collected from a tire factory located in Najaf, Iraq. The factory categorizes the types of tires to
three categories:
1. Tires for small and medium-sized sedans.
2. Tires for small, medium, and large cars.
3. Tires for light and heavy tractors.
Each type of tires comes in different sizes. For the purpose of our research, the first type of tires was selected; the
first type is produced in the following sizes:
1. Tire size 145/13
2. Tire size 165/13
3. Tire size 175/70/13
4. Tire size 195/70/14
5. Tire size 195/75/14.
6. Tire size 185/75/14
7. Tire size 185/75/15
3. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
9
Table 1: The Cost and Profit Made From Each Type of the Tires
The material that goes into the making of the tires was divided into two types: primary resources and assistive
resources. Some of the material is solid, and some is liquid. Table 1 demonstrates the cost and profit made from each
type of the selected tires:
5.5 Applying a Mathematical Model
The mathematical model applied used j to represent the tire brand type taking values 1, 2, …, 7, x is the production
amount of that brand type where:
x1 is production amount of type 145/13.
x2 is production amount of type 165/13.
x3 is production amount of type 175/70/13.
x4 is production amount of type 195/70/14.
x5 is production amount of type 195/75/14.
x6 is production amount of type 185/77/14.
x7 is production amount of type 185/75/15.
Sale priceExpected profitMaking costTire size
16100 Dinar77315327145/13
25000 Dinar914315857165/13
21396 Dinar627315123175/70/13
24493 Dinar937315120195/70/14
27654 Dinar747320181195/75/14
31146 Dinar1057320573185/75/14
48099 Dinar767340426185/75/15
5. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
11
=
Constraint 25 0.21 0.21 0.21 0.35 0.35 0.35 0.54 <
=
474
Constraint 26 0.52 0.52 0.52 0.68 0.69 0.62 0.84 <
=
947
Constraint 27 0.25 0.26 0.26 0.37 0.38 0.39 0.45 <
=
412
To perform the analysis, Win Q.S.B and QM for Windows were used. Table 3 demonstrates the final results received
from running the model:
Table 3: The Final Results Received From Running the Model
Decision
variable
Solution value Unit cost or
profit (j)
Total
contribution
Reduced cost Basis status
X1 0 773.0000 0 -5,835.1250 at bound
X2 0 9,143.0000 0 -9,143.0000 at bound
X3 0 6,273.0000 0 -2,978.3750 at bound
X4 572.0000 9,373.0000 5,361.356.0000 0 basic
X5 0 7,473.0000 0 -4,091.2180 at bound
X6 111.0000 10,573.0000 1,173,603.0000 0 basic
X7 0 9,673.0000 0 -6,864.8740 at bound
Objective Function (Max.) = 6,534,959.0000
As can be seen from Table 3, the most profitable products with limited resources are:
1. X4: 195/70/14
2. X6: 185/75/14
The highest profit calculated is:
Objective Function (Max. 2) = 6,534,559 Dinar
5.6 Data Analysis and Discussion
From Table 3, the following can be concluded.
5.6.1 Indicators for the Objective Function Coefficients (Profit)
Looking at the Reduce Cost column in Table 6, if you add this value to the objective function coefficients that reflect
the expected profit in selling tires, especially for products that did not make profit with limited resources, this action
will prevent the product from being an unattractive product to a product with good profitability.
5.6.2 Total Contribution
From the total contribution column, we notice that X4, X6 and total profits are as follows:
X4 = 5,361,356
X6 = 1,173,603
Objective Function (Max) = 6,534,939
5.6.3 Shadow Prices
Shadow prices represent the amount of increase in expected profit if available material were increased by one unit
(Anderson et al., 2013). This measurement is of great importance for managers because it is used as an indicator
when making any decision to increase the quantity of available resources.
It is clear from Table 4 that the shadow price for Supplier 15, the amount for time machines (G.21), is 330,406.200,
but for other resources, the shadow price is zero, which means there is a surplus of these resources.
5.6.4 Lower Bound
The left-hand column in Table 4 represents the lower bound: the minimum of resources available. As can be seen
from Table 4, the lower bound for the first resource is 1,763,884 and for the second resource is 313,710 and so on.
6. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
12
Table 4: Model Results for Determining the Shadow Prices and Quantity of Surplus Raw Materials
Constraint Left-hand
side
Direction Right-hand
side
Slack or
surplus
Shadow price
1 C1 1,763.8890 <= 4,545.0000 2,781.1110 0
2 C2 313.7100 <= 949.0000 635.2900 0
3 C3 1,139.4820 <= 1,736.0000 596.5181 0
4 C4 350.9680 <= 1,112.0000 761.0320 0
5 C5 758.2830 <= 1,540.0000 781.7170 0
6 C6 687.2510 <= 1,859.0000 1,171.7490 0
7 C7 152.6760 <= 349.0000 196.3240 0
8 C8 128.4380 <= 129.0000 0.6520 0
9 C9 71.9540 <= 120.0000 48.0460 0
10 C10 2.7320 <= 8.5000 5.7680 0
11 C11 65.5980 <= 67.0000 1.4020 0
12 C12 48.0490 <= 103.0000 45.9510 0
13 C13 19.6960 <= 27.0000 7.3040 0
14 C14 10.9280 <= 18.0000 7.0720 0
15 C15 23.0000 <= 32.0000 0 330,406.2000
16 C16 6.1470 <= 14.0000 7.8530 0
17 C17 0.6830 <= 6.5000 5.8170 0
18 C18 3.4150 <= 7.0000 3.5850 0
19 C19 12.2940 <= 29.0000 16.7060 0
20 C20 7.0980 <= 5.4000 1.3020 0
21 C21 8.7680 <= 10.0000 1.2320 0
22 C22 25.9370 <= 37.0000 11.0630 0
23 C23 275.2830 <= 411.0000 135.7170 0
24 C24 401.5780 <= 595.0000 193.4220 0
25 C25 239.0500 <= 474.0000 234.9500 0
26 C26 457.7800 <= 997.0000 539.2200 0
27 C27 254.9300 <= 412.0000 157.0700 0
6. Conclusion
From the analysis conducted above, it is clear that the mathematical model can be adopted as the overall plan and can
be used to obtain quantitative indicators necessary to rationalize production plans for the future. Furthermore, DSS
enable decision makers to determine which product made the most profit using limited resources; in this case, for
example, the two products were Products 2 and 6. Finally, the analysis forms the basis for indicators enabling
administrators to determine which products produce a greater profit with minimal costs. Having said that, it is very
important for organizations to create technical and human requirements for the successful application of DSS
necessary to rationalize production-planning decisions. A recommendation the researcher proposes is the adoption of
a strategy by managers responsible for the operations of the production planning before launching the production
plan into service, managers should also use sensitivity analysis as a basis to provide quantitative indicators in the
rationalization of production plans.
7. Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.4, 2013
13
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