MGT1 Management Science PDF

MGT1 MANAGEMENT SCIENCE ENGR. REYVEN P. CULIS PMFC, CLSSYB, SO2 LEARNING OBJECTIVES Demonstrate the Define and explain List the characteristics Discuss the major Explain the features, complexity of decision management science of management limitations of to...

MGT1 MANAGEMENT SCIENCE ENGR. REYVEN P. CULIS PMFC, CLSSYB, SO2 LEARNING OBJECTIVES Demonstrate the Define and explain List the characteristics Discuss the major Explain the features, complexity of decision management science of management limitations of tools and process of making science management science Management Science MANAGEMENT MANAGEMENT Management is the process of planning, organizing, leading, and controlling an organization's resources—such as people, finances, and materials—to achieve specific goals and objectives efficiently and effectively. It involves coordinating activities, making decisions, and guiding an organization toward its strategic aims. MANAGEMENT SCIENCE  MANAGEMENT SCIENCE Management science is an interdisciplinary field that uses mathematical, statistical, and analytical methods to aid in decision- making, problem-solving, and the optimization of organizational processes. It applies quantitative techniques to analyze and improve various business operations, focusing on the efficient use of resources, forecasting, and strategic planning. Management science is defined as the application of scientific principles and quantitative methods to solve complex management problems and improve decision- making in business, industry, and other organizations.  SPECIAL CHARACTERISTICS OF MANAGEMENT SCIENCE Quantitative Approach: Emphasizes the use of mathematical models, statistics, and algorithms to solve problems. Decision-Making Focus: Aims to provide solutions and recommendations for complex decision-making scenarios. Interdisciplinary Nature: Combines elements from various fields such as economics, engineering, computer science, and statistics. Optimization-Oriented: Seeks to maximize efficiency and effectiveness in processes, resources, and outcomes. Problem-Specific Solutions: Tailors methods and models to specific organizational problems.  Areas of Operations Management: Optimizing production processes, inventory control, and supply chain management. Application of Project Management: Planning, scheduling, and resource Management allocation. Science Finance: Investment analysis, portfolio optimization, and risk management. Marketing: Demand forecasting, pricing strategies, and customer segmentation. Logistics and Transportation: Route optimization, distribution planning, and facility location. Human Resources: Workforce planning, scheduling, and performance evaluation. DECISION- MAKING  Decision-making is the cognitive process of selecting a course of action from multiple alternatives to achieve a desired outcome. It Decision involves identifying and evaluating options, weighing the pros and cons, and choosing the option that best aligns with one's Making goals, values, or needs. Decision- making can range from simple, everyday choices to complex, strategic decisions in various aspects of life, including personal, professional, and organizational contexts. STEP-BY-STEP PROCESS OF DECISION MAKING Steps in Decision Making Defining problem Collecting information Identifying alternatives Weighing alternatives Selecting the best possible option Planning and implementing Reviewing and evaluating the outcome Defining the Problem This step involves clearly identifying and understanding the issue or opportunity that requires a decision. It’s essential to frame the problem accurately to ensure that the subsequent steps address the correct challenge. For example, if a company’s profits are declining, defining whether the problem is due to decreased sales, rising costs, or both is crucial for finding the right solution. Collecting Information Gathering relevant data, facts, and insights is necessary to make an informed decision. This includes understanding the context, identifying stakeholders, and collecting information about the options available. The goal is to have a comprehensive understanding of the situation before evaluating alternatives. For instance, if you're deciding on a new marketing strategy, you might collect data on customer preferences, competitor strategies, and market trends. Identifying Alternatives This step involves generating a list of possible solutions or courses of action. It’s important to consider a range of options, including creative or unconventional ones. For example, if the problem is low employee morale, alternatives might include offering bonuses, improving working conditions, or providing professional development opportunities. Weighing Alternatives Once alternatives are identified, the next step is to evaluate each option by considering its pros and cons, potential risks, and benefits. This involves analyzing how each alternative aligns with your goals, values, or constraints. For example, when choosing between two job offers, you might weigh factors like salary, career growth, work-life balance, and company culture. Selecting the Best Possible Option After evaluating the alternatives, you choose the option that best addresses the problem and aligns with your objectives. The decision should be based on a balance of logical reasoning and, in some cases, intuition. For instance, after weighing the alternatives, you might decide to implement a flexible work schedule to improve employee satisfaction. Planning and Implementing With the decision made, the next step is to develop a plan to put the chosen solution into action. This includes outlining the necessary steps, assigning responsibilities, allocating resources, and setting timelines. Effective implementation requires coordination and communication to ensure that everyone involved understands their roles. For example, if you decide to launch a new product, this step would involve planning the production, marketing, and distribution processes. Reviewing and Evaluating the Outcome After implementation, it’s important to assess the results of the decision. Did the solution effectively address the problem? Were the expected outcomes achieved? This step involves monitoring progress, gathering feedback, and analyzing the results. If the outcome is not as expected, adjustments may be needed. For example, if a new sales strategy doesn’t lead to increased revenue, you might review the approach and make necessary changes. Linear Programming Purpose: To optimize resource allocation by maximizing or minimizing an objective (e.g., profit, cost) subject to constraints (e.g., labor, materials). Decision Analysis Purpose: To evaluate decisions under uncertainty, providing a structured approach to selecting the best course of action. Queuing Theory Purpose: To optimize service efficiency and reduce wait times by analyzing and managing queues in various settings. Game Theory Purpose: To analyze competitive situations where the outcome depends on the actions of multiple decision-makers, helping in strategic planning. Forecasting Purpose: To predict future trends using historical data, aiding in planning for demand, inventory, and resource needs. Inventory Models Purpose: To determine optimal ordering policies that minimize costs associated with holding, ordering, and stockouts. Network Models Purpose: To optimize flow and routing in logistics, supply chains, and project scheduling by solving network-related problems. Project Management Tools (PERT/CPM) Purpose: To plan, schedule, and control complex projects, ensuring timely completion within budget by identifying critical paths and task dependencies.  ENGR. REYVEN P. CULIS PMFC, CLSSYB, SO2 [email protected] | IE Department – CENAR University of Batangas Lipa City MGT1 MANAGEMENT SCIENCE ENGR. REYVEN P. CULIS PMFC, CLSSYB, SO2  LINEAR PROGRAMMING LINEAR PROGRAMMING Linear programming (LP) is a mathematical technique used to find the best possible outcome (such as maximum profit or minimum cost) in a given mathematical model. The model's requirements are expressed through linear relationships, involving a set of linear inequalities or equations.  Graphical Linear Programming and Simplex Linear Programming are two methods used to solve linear programming problems, but they differ in their approach, applicability, and complexity. Graphical Linear Programming is suitable for linear programming problems with two variables Simplex Linear Programming is suitable for linear programming problems with any number of variables. EXAMPLE Flair Furniture Co. produce two main products; chairs and tables; How many of each to make this month having the following conditions: LIMITATIONS: Make no more than 450 chairs Make atleast 100 tables TABLES (per CHAIRS (per HOURS table) chair) AVAILABLE Profit contribution $7 $5 Carpentry 3 hrs 4 hrs 2400 Painting 2 hrs 1 hr 1000 The main objective is to provide maximized profit for the establishment