Applied Decision Methods For Business

Applied Decision Methods For Business

This course explores the use of applied quantitative techniques to aid in business-oriented decision making. Emphasis is on problem identification and formulation with application of solution techniques and the interpretation of results. Included are probability theory; decision making under certainty, risk and uncertainty; utility theory; forecasting; inventory control; PERT/CPM; queuing theory; and linear programming. Prerequisite: MAT 201.

The use of statistical software is a required component in this course. It is expected that you already have a basic understanding of computers and Microsoft Excel. In-depth training is provided during the course on the appropriate use of software packages including TreePlan-Student-179 Excel Add-In, Excel QM, version 4, POM QM, version 4, and Analysis Tool Pack for Microsoft Excel.

Learning outcomes for the course include understanding different decision-making methods in a business environment, applying these methods to real-world problems, and interpreting and communicating the results effectively. The focus will be on application rather than on developing the mathematical models from scratch.

Paper For Above instruction

Applied decision-making techniques are crucial in the modern business environment, where managers face complex problems that require not only a deep understanding of quantitative methods but also an ability to interpret and communicate solutions effectively. This paper discusses the significance of applying quantitative decision methods, explores key concepts covered in the course, and emphasizes the importance of aligning analytical techniques with real-world business challenges.

In today’s competitive and fast-paced market, decision makers are inundated with large volumes of data and uncertain conditions. The application of probability theory, forecasting, and optimization models allows for more informed decision-making processes, minimizing risks and maximizing opportunities. For instance, probability theory helps in assessing risks and making decisions under uncertainty, which is fundamental in areas like investment planning, supply chain management, and product development (Shmueli & Koppius, 2011). Forecasting techniques enable businesses to anticipate future demand, optimize inventory levels, and streamline operations, thereby reducing costs and improving customer satisfaction (Makridakis, Wheelwright, & Hyndman, 1998).

Understanding and applying inventory control models, such as Economic Order Quantity (EOQ), facilitate managing stock levels efficiently, balancing costs associated with ordering and holding inventory (Harris, 1913). Project management tools like PERT and CPM help in planning, scheduling, and controlling complex projects, ensuring timely completion and optimal resource utilization (Kantor & Zec, 2001). Queuing theory finds applications in service operations such as call centers, hospitals, and retail outlets, where it helps optimize service efficiency and reduce customer wait times (Gross & Harris, 1998). Linear programming provides a framework for solving resource allocation problems by determining the best course of action when multiple constraints exist, which is common in production, transportation, and logistics (Chvátal, 1983).

The use of computer software—such as Excel and specialized decision analysis packages—enhances the efficiency and accuracy of solving complex quantitative problems (Turban, Sharda, & Delen, 2011). These tools allow managers to analyze large datasets, run simulations, and perform sensitivity analyses that inform strategic choices. Equipping students with the skills to formulate problems, analyze data, and interpret results ensures that they are prepared to address real-world business issues effectively (Hillier & Lieberman, 2010).

Implementation of these decision methods encourages a data-driven approach in organizations, fostering a culture of continuous improvement and operational excellence. When managers understand the assumptions, limitations, and appropriate applications of these models, they can develop better strategies for resource allocation, risk management, and process improvement (Shtub, 2002). Moreover, these methods support the development of critical thinking skills, enabling managers to evaluate alternative options with a quantitative perspective, ultimately leading to better decision outcomes (Evans, 2010).

In conclusion, applied quantitative decision methods play a vital role in enhancing managerial effectiveness and organizational competitiveness. As businesses encounter increasingly complex problems, the ability to utilize these tools for problem-solving and decision-making becomes essential. Ensuring proper understanding, application, and interpretation of these techniques empowers managers to make better-informed decisions that drive organizational success and sustainability in a dynamic environment.

References

• Chvátal, V. (1983). Linear programming. New York: Wiley.
• Evans, J. R. (2010). Business analytics: Methods, models, and decisions. Pearson.
• Gross, D., & Harris, C. M. (1998). Fundamentals of queueing theory (3rd ed.). Wiley.
• Harris, F. W. (1913). How many parts to make at once. Factory, The Magazine of Management, 10(2), 135-136.
• Hillier, F. S., & Lieberman, G. J. (2010). Introduction to operations research (9th ed.). McGraw-Hill Education.
• Kantor, R., & Zec, M. (2001). Project management: A managerial approach. Process Management Publishing.
• Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications (3rd ed.). Wiley.
• Shtub, A. (2002). Project management: Techniques in planning and controlling projects. CRC Press.
• Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553-572.
• Turban, E., Sharda, R., & Delen, D. (2011). Decision support and business intelligence systems (9th ed.). Pearson Education.