“Applied Operational Research with SAS,” authored by Ali Emrouznejad, is an essential guide within the domain of service operations, offering a rich compilation of techniques and methodologies harnessed through SAS software. The book systematically addresses various operational research (OR) methods and illustrates their practical application through SAS, providing readers with actionable insights and concrete examples. The following summary highlights the major points discussed in the book, structured to maximize the coverage of different aspects and practical advice.

Major Point:
Operational Research (OR) focuses on applying analytical methods to help make better decisions. SAS (Statistical Analysis System) is a powerful software suite for advanced analytics, multivariate analysis, and business intelligence.

Action:
Familiarize yourself with the basics of both OR and SAS. Begin with tutorials or introductory courses on SAS if you’re new to it, which will set a strong foundation for the practical applications covered later in the book.

Major Point:
Linear programming involves optimizing a linear objective function subject to a set of linear inequality or equality constraints. The book demonstrates how SAS can solve LP problems efficiently.

Example:
A transportation company might use LP to minimize the cost of delivering goods from several warehouses to various destinations.

Action:
Formulate your optimization problem clearly with an objective function and constraints. Utilize SAS procedures such as PROC LP or PROC OPTMODEL to solve the LP. Monitor error messages and diagnostics provided by SAS to refine your model.

Major Point:
Integer Programming extends LP by requiring some or all of the decision variables to be integers, which is critical for scenarios where variables represent discrete items or decisions.

Example:
A retail chain determining the number of stores to open in different locations may use IP to ensure decisions about store numbers are whole numbers.

Action:
Define your variables, constraints, and objective functions specifically in integer terms. Use PROC OPTMODEL in SAS, and leverage integer-specific options to solve the problem, ensuring realistic solutions where non-integer answers wouldn’t make sense.

Major Point:
Network models are crucial for problems involving networks such as transportation, logistics, and communication systems. These models can include shortest path, maximum flow, and minimum cost flow problems.

Example:
Finding the optimal routing for delivery trucks to minimize travel distance and time using network optimization techniques.

Action:
Map out your network problem clearly with nodes and edges. Use PROC OPTNET in SAS to solve network-related problems. Perform sensitivity analyses to see how changes in network parameters affect outcomes.

Major Point:
DEA is a non-parametric method in OR for measuring the efficiency of decision-making units (DMUs), such as businesses or public service providers.

Example:
Assessing the efficiency of different hospital branches by comparing inputs like staff numbers and operating costs to outputs such as the number of treated patients.

Action:
Implement DEA using PROC DEA in SAS. Collect comprehensive input and output data for the units under analysis. Regularly update the data to monitor performance trends over time.

Major Point:
Forecasting models predict future values based on historical data. SAS supports various forecasting techniques, including time series analysis, regression models, and exponential smoothing.

Example:
A retail business predicting future sales based on past sales data to plan inventory levels.

Action:
Implement forecasting with PROC FORECAST or PROC TIMESERIES in SAS. Use historical data to validate the model’s predictive accuracy. Adjust the parameters and configurations based on the validation results to improve forecast reliability.

Major Point:
Simulation involves creating a digital model to mimic the behavior of real-world processes, allowing experimentation without affecting the actual system.

Example:
Simulating customer queue systems in banks to determine optimal staffing levels that minimize waiting times.

Action:
Model your process with PROC SIMAN in SAS, defining the system’s components and rules. Run multiple simulations to explore different scenarios and identify optimal strategies. Validate the simulation with real-world data to ensure accuracy.

Major Point:
Queuing theory addresses the analysis of waiting lines, providing insights into system performance measures such as waiting time and queue length.

Example:
Analyzing the peak-hour traffic at a call center to ensure efficient customer service and minimal wait times.

Action:
Identify the key parameters of your queuing system (arrival rates, service rates, number of servers). Use PROC QSIM in SAS to model and analyze your queuing system. Implement improvements based on the simulation results to optimize performance.

Major Point:
Project scheduling involves planning and managing project timelines using techniques such as Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT).

Example:
Planning the construction phases of a building to minimize the total project duration.

Action:
Map out project activities, durations, and dependencies. Use PROC CPM in SAS for scheduling and critical path analysis. Adjust resource allocations and update schedules regularly to stay on track.

Major Point:
MCDM techniques help in making decisions that involve multiple, often conflicting criteria. Methods include Analytic Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT).

Example:
Choosing the best supplier based on criteria such as cost, quality, and delivery reliability.

Action:
Define and weight your decision criteria. Use PROC MCDM in SAS to analyze the trade-offs and prioritize options. Regularly review criteria and weights to ensure alignment with changing priorities.

Major Point:
Risk analysis helps in understanding and mitigating potential risks that can affect decision outcomes. Tools in SAS can model and analyze risk to support better decision-making.

Example:
A financial institution analyzing the risk of investment portfolios to manage financial exposure.

Action:
Identify potential risks and their impact. Use PROC QSIM and other relevant procedures in SAS to simulate risk scenarios and assess their probabilities. Develop and implement risk mitigation strategies based on the analysis results.

Major Point:
The book provides numerous case studies illustrating the practical application of OR techniques across various industries, using SAS as the analytical tool.

Example:
A case study on optimizing the scheduling of airline flight crews to minimize costs while ensuring compliance with regulatory constraints.

Action:
Study the provided case studies thoroughly to understand the application of different OR methods. Adapt the methodologies to your context, continuously refining your approach based on real-world outcomes and feedback.

Major Point:
Successfully applying OR techniques requires adhering to best practices and leveraging implementation tips provided in the book.

Example:
Ensuring data quality and accuracy before feeding it into SAS models to avoid erroneous results and misguided decisions.

Action:
Follow a systematic approach to data collection and validation. Keep abreast of updates and new features in SAS to enhance your modeling capabilities. Collaborate with cross-functional teams to enrich the decision-making process with diverse insights.

“Applied Operational Research with SAS” by Ali Emrouznejad is a comprehensive resource that bridges the gap between theory and practice. By systematically applying the techniques and actions derived from the book, professionals in service operations can significantly enhance their analytical capabilities, optimize processes, and drive better decision-making outcomes using the powerful tools offered by SAS. This structured approach ensures that the reader not only gains theoretical knowledge but also acquires practical, actionable skills relevant to their industry context.