Business Process Optimization With Data

Data-Driven Business Process Optimization: From Mathematical Modeling to Real-World Applications

What you'll learn

Build mathematical models to represent business problems and solve them using optimization methods.

Apply optimization techniques to real cases such as supply chain management, production planning, scheduling, and inventory control.

Use data-driven methods like process mining, bottleneck analysis, and KPI dashboards to analyze and improve workflows.

Implement advanced business process applications including facility location, routing and scheduling, manpower planning, and goal programming.

Requirements

Basic understanding of business processes will be helpful, but no prior knowledge of optimization is required.

Familiarity with Python is recommended for coding sections, though step-by-step explanations are provided.

A willingness to learn analytical and quantitative methods for business decision making.

Description

In this course, we focus on how data and optimization methods can improve the way businesses run their processes. You will start with the foundations of optimization, operations research, and data science, and then move into practical applications that directly impact industries.We will cover essential methods such as mathematical modeling, production planning, scheduling, inventory theory, manpower planning, and supply chain optimization. Each of these is explained through structured examples and projects, so you can see how theory connects to real decisions.The course also goes into advanced business applications like facility capacity planning, facility location decisions, goal programming for multiple objectives, routing and scheduling, and assembly line balancing. These are common challenges in manufacturing, logistics, and service systems, and you’ll learn practical ways to approach them.Since data is central to modern decision making, we also explore portfolio optimization, prescriptive analytics, and sequential decision analytics. On the process analysis side, you will learn process mining, bottleneck analysis, value stream mapping, and business process reengineering—methods that help you analyze and improve workflows.We do not stop at analysis; the course connects to real business needs with KPI dashboards, real-time process monitoring, predictive process analytics, and A/B testing. Sector-specific modules show how these ideas apply to finance, customer service, healthcare, and retail operations.Finally, we address modern tools and approaches, such as robotic process automation, digital twins, cloud-based optimization, API integrations, and Lean Six Sigma with Python. You will also learn how agile process design and data-driven change management support continuous process improvement.By the end of the course, you will have both the theoretical knowledge and the practical understanding to apply data-driven optimization in real business environments. Whether you work in supply chain, production, services, or analytics, you will gain tools that can help improve efficiency, cut costs, and support better decision making. Let's get started.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Core Concepts

Lecture 2 Optimization & Data Science

Lecture 3 Operations Research & Machine Learning

Section 3: Routing

Lecture 4 VRP Time Windows - Code Lesson

Lecture 5 VRP Time Windows - Output

Section 4: Projects Specific to Business Processes

Lecture 6 Production Planning with Streamlit

Section 5: Sequential Decision Analytics

Lecture 7 Dynamic Inventory Model

Section 6: Business Process Analysis & Modeling

Lecture 8 Bottleneck Analysis

Business analysts, industrial engineers, and operations managers who want to apply optimization in real projects.,Data scientists and Python developers interested in combining analytics with decision science.,Professionals in supply chain, production, logistics, or service industries looking to improve efficiency with data-driven methods.,Students or researchers in operations research, management science, or business analytics seeking applied skills.