Inventory Analytics Course

Lesson 1 • What Inventory Is and Why It Matters

Defines inventory as a business asset and explains its role in balancing supply and demand. Establishes the vocabulary used throughout the course.

Lesson 2 • Key Performance Indicators for Inventory

Defines turnover ratio, days on hand, fill rate, and service level as core metrics. Connects each KPI to operational and financial decision-making.

Lesson 3 • Inventory Data and Record Accuracy

Explains how data quality underpins every analytical model introduced later. Covers cycle counting, reconciliation, and shrinkage tracking.

Lesson 4 • Inventory Cost Components

Breaks down holding, ordering, and shortage costs and shows how they interact. Provides the cost lens applied in every subsequent analytical model.

Lesson 1 • Qualitative Forecasting Methods

Covers expert judgment, market surveys, and Delphi method for new or sparse data situations. Complements quantitative methods when historical data is limited.

Lesson 2 • Forecast Accuracy and Error Measurement

Defines MAE, MAPE, RMSE, and bias metrics to evaluate and improve forecast quality. Accurate error measurement drives model selection and safety stock calculations.

Lesson 3 • Quantitative Time-Series Methods

Teaches moving averages, exponential smoothing, and Holt-Winters models for stable demand. Students apply each method to real datasets and compare outputs.

Lesson 4 • Understanding Demand Patterns

Identifies trend, seasonality, cyclicality, and randomness in demand data. Accurate pattern recognition is the prerequisite for choosing the right forecasting model.

Lesson 1 • Economic Order Quantity Model

Derives the EOQ formula and its assumptions for minimising total ordering and holding costs. Serves as the baseline model against which all extensions are compared.

Lesson 2 • Periodic Review Systems

Explains the order-up-to model and fixed-interval replenishment as alternatives to continuous review. Highlights trade-offs in review frequency, safety stock, and administrative effort.

Lesson 3 • Safety Stock Determination

Quantifies safety stock using demand variability, lead time variability, and target service levels. Directly applies forecast error metrics from Chapter 2.

Lesson 4 • EOQ Extensions and Variants

Covers production order quantity, quantity discounts, and backorder models as EOQ adaptations. Expands applicability to manufacturing and discount-driven purchasing contexts.

Lesson 1 • Newsvendor Model and Critical Ratio

Solves single-period stocking decisions using the critical ratio and marginal cost logic. Applies directly to perishable goods, fashion items, and event-based inventory.

Lesson 2 • Service Level Trade-off Analysis

Quantifies the cost of achieving incremental service level improvements using fill rate curves. Enables data-driven negotiation of service targets with stakeholders.

Lesson 3 • Inventory Optimisation Under Constraints

Applies budget, space, and supplier constraints to stock level decisions using linear programming concepts. Bridges statistical models with real-world operational limits.

Lesson 4 • Probability Distributions in Demand

Maps demand patterns to normal, Poisson, and negative binomial distributions. Correct distribution selection is foundational to all probabilistic inventory models.

Lesson 1 • Prescriptive Analytics and Optimisation

Translates analytical outputs into recommended order quantities and replenishment schedules. Closes the loop between analysis and operational execution.

Lesson 2 • Data Sources and Integration

Maps inventory data flows from ERP, WMS, and POS systems into a unified analytical layer. Data integration quality directly determines the reliability of all downstream analytics.

Lesson 3 • Inventory Dashboard Design

Designs role-specific dashboards that surface the right KPIs for planners, buyers, and executives. Effective visualization accelerates decision-making and exception management.

Lesson 4 • Predictive Analytics and Demand Sensing

Uses machine learning signals and leading indicators to improve short-term demand predictions. Extends classical forecasting with real-time data inputs for faster response.

Lesson 1 • Policy Performance Review Process

Defines a structured cycle for measuring policy outcomes and triggering parameter updates. Continuous review ensures policies remain aligned with changing demand and supply conditions.

Lesson 2 • Segmented Policy by Product Category

Assigns differentiated replenishment policies to ABC-XYZ segments identified in Chapter 1. Ensures high-value and high-variability items receive proportionate management attention.

Lesson 3 • Defining Inventory Policy Parameters

Specifies reorder points, order quantities, and safety stock as formal policy parameters. Converts model outputs into operational rules that planners can execute consistently.

Lesson 4 • Regulatory and Compliance Considerations

Addresses traceability, expiry management, and audit requirements relevant to regulated industries. Embeds compliance into policy design rather than treating it as an afterthought.

Lesson 1 • Collaborative Planning, Forecasting, and Replenishment

Applies the CPFR framework to align forecasts and replenishment plans between trading partners. Shared planning reduces forecast error and inventory duplication across the chain.

Lesson 2 • Supplier Lead Time Analytics

Measures and models supplier lead time variability to refine safety stock and reorder points. Accurate lead time data reduces excess buffer stock across the network.

Lesson 3 • Vendor-Managed Inventory Programmes

Explains VMI structures where suppliers manage replenishment using shared demand data. Reduces buyer administrative burden while improving supplier production planning.

Lesson 4 • Demand Signal Sharing and Visibility

Uses point-of-sale and inventory position data shared upstream to reduce the bullwhip effect. Builds on multi-echelon concepts from Chapter 4 with practical implementation guidance.

Lesson 1 • Network-Level Inventory Optimisation

Optimizes stock placement across distribution networks using multi-echelon models and risk pooling. Extends single-location models to enterprise-wide inventory positioning decisions.

Lesson 2 • Inventory Reduction and Working Capital

Identifies excess and obsolete stock drivers and quantifies working capital freed by reduction. Links inventory analytics directly to cash flow and return on assets.

Lesson 3 • Omnichannel Inventory Challenges

Addresses inventory pooling, fulfillment routing, and phantom inventory in omnichannel retail. Applies analytics to balance stock across physical and digital demand channels.

Lesson 4 • Machine Learning in Inventory Optimisation

Applies reinforcement learning and deep learning to dynamic replenishment and demand forecasting. Positions students to evaluate and deploy next-generation inventory AI tools.