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Course Overview and Learning Objectives

Welcome and Course Orientation

Learning Outcomes and Success Criteria

Foundations of Dynamic Lot Sizing

Inventory Cost Structures and Net Requirements

Comparative Overview: EOQ, Silver-Meal, Least Unit Cost, and Wagner-Whitin

The Wagner-Whitin Algorithm: Theory and Structure

Problem Formulation and Optimality Properties

Dynamic Programming Recurrence and Complexity

Constructing the Cost Matrix and Optimal Policy

Implementation and Solution Methods

Coding the Wagner-Whitin Algorithm

Mixed-Integer Programming Formulation and Solvers

Extensions, Practicalities, and Enterprise Integration

Practical Extensions and When to Use Wagner-Whitin

Data, Governance, and KPI Alignment in End-to-End Planning

Quizzes

Quiz 1: Cost Structures and Assumptions

Quiz 2: Heuristics versus Exact Methods

Quiz 3: Formal Problem Statement

Quiz 4: Bellman Recurrence and States

Quiz 5: Complexity and Dominance

Quiz 6: Cost Matrix Construction

Quiz 7: Implementation Details

Quiz 8: MIP Formulations

Quiz 9: Practical Extensions and Policy Choice

Quiz 10: Enterprise Integration and KPIs

Wagner‑Whitin Mastery: Optimal Dynamic Lot‑Sizing for Enterprise Planning is a focused, practitioner‑grade course that teaches you how to model, solve, and deploy the classic Wagner‑Whitin dynamic lot‑sizing algorithm across real enterprise planning workflows. If your business faces non‑stationary demand, fluctuating costs, or frequent re‑planning cycles, this course shows how to achieve provably optimal purchase or production schedules that minimize total setup and holding costs over a finite horizon. Going beyond formulas, you’ll connect theory to practice: translate demand forecasts into net requirements, construct the cost matrix, implement the O(T^2) dynamic program with smart pruning, and compare exact solutions with heuristics such as Silver‑Meal and Least Unit Cost. You will also learn when a Mixed‑Integer Programming (MIP) formulation is advantageous, and how to benchmark DP versus solver performance. Finally, you’ll embed the method into MRP/ERP environments with appropriate governance, cost parameterization, and KPI tracking—cost‑to‑serve, fill rate, inventory turns, and plan stability. By the end, you’ll have a robust toolkit to choose the right policy for each scenario, from rolling‑horizon planning with safety stock overlays to handling capacity and minimum order quantities—turning advanced inventory theory into measurable business impact.

Wagner‑Whitin Mastery: Optimal Dynamic Lot‑Sizing for Enterprise Planning

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            <p>Enterprise planners face a recurring question: when and how much to order across a finite, time-varying horizon to minimize total cost while preserving service. This course provides a rigorous, practice-ready treatment of dynamic lot-sizing with a concentrated focus on the Wagner‑Whitin algorithm—an exact dynamic programming method that delivers provably optimal replenishment schedules under deterministic demand. Designed for learners with foundations in operations research, basic dynamic programming, and inventory theory, the curriculum connects theory to enterprise realities, translating optimality properties into decisions that move KPIs such as cost-to-serve, inventory turns, fill rate, and plan stability.</p>

<p>We begin by clarifying cost structures and assumptions—setup versus holding costs, discrete time buckets, and base-case constraints such as no backlogging and unlimited capacity—then position Wagner‑Whitin against EOQ, Silver‑Meal, and Least Unit Cost to show when heuristics falter in non-stationary demand. The heart of the course develops the formal problem statement, Bellman recursion, and the zero-inventory ordering structure, followed by construction of the cost matrix C(i, j), policy backtracking, and complexity analysis with O(T²) baselines and pruning. You will implement the algorithm with clean data structures, cumulative sums, and robust tests, and contrast it with mixed‑integer programming formulations that leverage strong cuts and facility‑location insights. Finally, we extend to rolling horizons, service levels, safety stock overlays, capacity and MOQ constraints, and show how to embed the method into MRP/ERP workflows with sound data governance. Quizzes reinforce each milestone, and a capstone project aligns solution quality with enterprise KPIs to ensure the method is both optimal on paper and effective in production planning.</p>
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