Introduction to Management Science & Business Analytics 7th Edition | Hillier & Hillier | Complete Textbook with Spreadsheet Modeling & Case Studies

Master the essential quantitative decision-making tools for modern business success. This comprehensive 7th edition of Introduction to Management Science and Business Analytics by Frederick S. Hillier and Mark S. Hillier bridges two powerful disciplines—management science (operations research) and business analytics—to equip students, managers, and analysts with the skills to solve real-world business problems using data-driven approaches. What Makes This Edition Unique: Unlike traditional textbooks that focus solely on either management science or business analytics, this volume integrates both, showing how they complement each other across all three analytics stages: descriptive, predictive, and prescriptive. Key Features: Spreadsheet-First Approach – Learn by doing with Microsoft Excel. Every model is built directly in spreadsheets using SUMPRODUCT, Solver, and Analytic Solver add-ins—no advanced programming required. Case-Study Driven Pedagogy – Each chapter opens with a realistic case study (e.g., Wyndor Glass Co. product-mix, Goferbroke oil drilling decisions, Super Grain advertising campaigns) that grounds techniques in actual managerial dilemmas. New & Expanded in 7th Edition: Full integration of Business Analytics alongside traditional Management Science New chapters on predictive analytics (classification trees, KNN, naïve Bayes, logistic regression) Coverage of Analytic Solver for optimization, simulation, and data mining Updated application vignettes from real companies (IBM, Intel, General Motors, Chevron, DHL, Kroger, etc.) Full-color design with improved data visualizations Comprehensive Topic Coverage: Part 1 – Foundations: Nature of management science & business analytics Complete analysis process (problem definition → data → descriptive → predictive → prescriptive) Part 2 – Predictive Analytics Models: Classification & regression trees k-Nearest Neighbors (KNN) algorithm Naïve Bayes, logistic regression, multiple linear regression Time-series forecasting (exponential smoothing, moving averages, trend analysis) Part 3 – Linear Programming for Prescriptive Analytics: Product-mix, resource allocation, cost-benefit trade-offs Transportation, assignment, and network optimization problems What-if analysis and sensitivity analysis (shadow prices, allowable ranges, 100% rule) Part 4 – Integer & Nonlinear Programming: Binary integer programming for yes/no decisions (project selection, facility location, crew scheduling) Nonlinear programming with diminishing returns (portfolio selection, separable programming) Evolutionary Solver and genetic algorithms Part 5 – Uncertainty Models: Decision analysis (payoff tables, decision trees, Bayes’ rule, utility theory) Queueing models (M/M/1, M/G/1, M/M/s, priority queues) Computer simulation (random numbers, inverse transformation, risk analysis, optimization with simulation) Powerful Software Integration: Excel Solver for linear/integer programming Analytic Solver (by Frontline Systems) for advanced optimization, simulation, and data mining Queueing Simulator, Posterior Probabilities templates, and dozens of ready-to-use Excel files Student & Instructor Resources: Over 500 end-of-chapter problems Solved problems with complete solutions 42 end-of-chapter cases + 10 supplementary online cases 7 additional web-only chapters (PERT/CPM, inventory management, goal programming, etc.) Connect / SmartBook access for adaptive learning All spreadsheets available for download Target Audience: MBA and undergraduate business students in management science, operations research, or business analytics courses Professionals seeking practical spreadsheet modeling skills Instructors looking for a case-driven, analytics-integrated textbook Detailed Table of Contents (15 print chapters + 7 web chapters): Introduction Overview of the Analysis Process Classification & Prediction Models for Predictive Analytics Predictive Analytics Based on Traditional Forecasting Methods Linear Programming: Basic Concepts Linear Programming: Formulation & Applications The Art of Modeling with Spreadsheets What-If Analysis for Linear Programming Network Optimization Problems Integer Programming Nonlinear Programming Decision Analysis Queueing Models Computer Simulation: Basic Concepts Computer Simulation with Analytic Solver Plus web-only chapters: Solution Concepts for Linear Programming, Transportation & Assignment Problems, PERT/CPM, Goal Programming, Inventory Management (known/uncertain demand), Computer Simulation with Crystal Ball. Why Choose This Book? Whether you're a student preparing for a career in data analytics, a manager seeking to leverage data for better decisions, or an instructor looking for the most up-to-date, case-driven text, this 7th edition delivers. The dual focus on management science AND business analytics—plus the heavy emphasis on spreadsheet modeling—ensures you gain immediately applicable skills.