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

Alnaji, Loay

B. Ridha, Mahmood.

Kalal, Muiead A.

English

International Institute for Science, Technology and Education (IISTE): E-Journals

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). 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   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 price Expected profit Making cost Tire size 16100 Dinar 773 15327 145/13 25000 Dinar 9143 15857 165/13 21396 Dinar 6273 15123 175/70/13 24493 Dinar 9373 15120 195/70/14 27654 Dinar 7473 20181 195/75/14 31146 Dinar 10573 20573 185/75/14 48099 Dinar 7673 40426 185/75/15 Industrial Engineering Letters                                                                                                            www.iiste.org ISSN 2224-6096 (Paper) ISSN 2225-0581 (online) Vol.3, No.4, 2013  10 Table 2: The Variables and Constraints Entered Into the Model   X1 X2 X3 X4 X5 X6 X7   Maximize   773 9143 6273 9373 7473 10573 7673   Constraint  1 1.794 1.92 1.92 2.598 2.645 2.503 3.222 <= 4545 Constraint 2 0.326 0.326 0.326     0.465 0.445 0.43 0.666 <= 949 Constraint 3 0.939 0.308 0.308 1.682 1.72 1.598 2.053 <= 1736 Constraint 4 0.375 0.419 0.419 0.515 0.526 0.508 0.601 <= 1112 Constraint 5 0.639 0.862 0.862 1.119 1.141 1.065 1.436 <= 1540 Constraint 6 0.679 0.72 0.72 1.015 1.03 0.961 1.305 <= 1859 Constraint 7 0.146 0.171 0.171 0.225 0.23 0.216 0.283 <= 349 Constraint 8 0.05 0.059 0.059 0.079 0.079 0.75 0.1 <= 129 Constraint 9 0.069 0.081 0.081 0.106 0.108 0.102 0.132 <= 120 Constraint 10 0.003 0.003 0.003 0.004 0.004 0.004 0.005 <= 8.5   Constraint 11 0.03 0.03 0.03 0.039 0.04 0.39 0.053 <= 67 Constraint 12 0.045 0.052 0.052 0.071 0.071 0.067 0.093 <= 103 Constraint 13 0.017 0.023 0.023 0.029 0.03 0.028 0.036 <= 27 Constraint 14 0.011 0.012 0.012 0.016 0.016 0.016 0.02 <= 18 Constraint 15 0.02 0.026 0.026 0.034 0.035 0.032 0.044 <= 23 Constraint 16 0.006 0.007 0.007 0.009 0.01 0.009 0.012 <= 14 Constraint 17 0.001 0.001 0.001 0.001 0.001 0.001 0.001 <= 6.5 Constraint 18 0.003 0.004 0.004 0.005 0.005 0.005 0.006 <= 70 Constraint 19 0.016 0.012 0.012 0.018 0.018 0.018 0.03 <= 29 Constraint 20 0.003 0.003 0.003 0.006 0.005 0.006 0.006 <= 5.4 Constraint 21 0.006 0.006 0.006 0.013 0.014 0.012 0.012 <= 10 Constraint 22 0.023 0.027 0.027 0.037 0.036 0.034 0.05 <= 37 Constraint 23 0.266 0.0327 0.0327 0.405 0.413 0.393 0.483 <= 411 Constraint 24 0.345 0.452 0.452 0.593 0.602 0.562 0.735 < 595 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. 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.     Industrial Engineering Letters                                                                                                            www.iiste.org ISSN 2224-6096 (Paper) ISSN 2225-0581 (online) Vol.3, No.4, 2013  13 References Alnaji, L. (2013). The Change from Syllabus-Focused Curriculum Courses to Object-Focused Curriculum: A Case Study at Alzaytoonah University—Part I, Vol.4, No.4. 74-78. Anderson, D. R., Camm, J. D., Williams, T. A., & Sweeney, D. J. (2013). An introduction to management science: Quantitative approaches to decision making. Mason, OH: South-Western Cengage Learning. Caixeta-Filho, J. V., van Swaay-Nego, J. M., & Wagemaker, A. de P. (2002). Optimization of the production planning and trade of the lily flowers at Jan de Wit Company. Informs, 32, 35–46. Filip, F. G. (2008). Decision support and control for large-scale complex systems. Annual Reviews in Control, 32, 61–70. Holder, A. G, Sturm, J. F., & Zhang, S. (2001). Marginal and parametric analysis of the general optimal solution. Informs, 39, 394–415. Hung, Y.-F., & Cheng, G.-J. (2002). Hybrid capacity modeling for alternative machine types. IIE Transactions, 34, 157–165.  Julia L. Higle , Stein W. Wallace(2003), Sensitivity Analysis and Uncertainty in Linear Programming,Interfaces,Vol.33,No.4.53-60. Lawrence, K. D, & Burbridge, J. J. (1976). A multiple goal linear programming model for coordinated production and logistics planning. International Journal of Production Research, 14, 215–222. Najim A. Najim, Ghaleb A & Alnaji Loay. (2013). The Impact of the Key Dimensions of Entrepreneurship on Opportunities for the Success of New Ventures in the Greater Amman Municipality. European Journal of Business and Management, Vol.5,No.4.159-173. Nichelson, T. A. J., & Pullen, R. D. (1971). A linear programming model for integrating the annual planning of production and marketing. International Journal of Production Research, 9, 361–369. Power, D. (2003). A brief history of decision support systems. Retrieved from http://dssresources.com/history/dsshistoryv28.html Ridha, M. B. (2012). Linear programming model as a decision support system in knowledge management case study : Hospitals in Jordan. International Journal of Intelligent Information Processing, 3(4), 75–87. Sitarz,S,(2010), Standard sensitivity analysis and additive tolerance approach in MOLP, Ann Oper Res ,181: 219–232 DOI 10.1007/s10479-010-0728-8. Stadtler, H. (1988). Medium term production planning with minimum lot size. International Journal of Production Research, 26, 553–566. Yan, Y. L., & Davison, R. M. (2011). Using decision support systems in Chinese enterprises: A study of managerial information behavior. Information Development, 27, 15–31.  Wendell ,R .E,(2004), Tolerance Sensitivity and Optimality Bounds in Linear Programming, MANAGEMENT SCIENCE Vol. 50, No. 6, June 2004, pp. 797–803.     This academic article was published by The International Institute for Science, Technology and Education (IISTE).  The IISTE is a pioneer in the Open Access Publishing service based in the U.S. and Europe.  The aim of the institute is Accelerating Global Knowledge Sharing.  More information about the publisher can be found in the IISTE’s homepage:  http://www.iiste.org  CALL FOR PAPERS The IISTE is currently hosting more than 30 peer-reviewed academic journals and collaborating with academic institutions around the world.  There’s no deadline for submission.  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Decision Support Systems and Their Role in Rationalizing the Production Plans: A Case Study on a Plant in Najaf

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Decision Support Systems and Their Role in Rationalizing the Production Plans: A Case Study on a Plant in Najaf

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