AI Decision Support for Operations

Description

Operations run on thousands of small decisions: how many tents to reorder, how many pickers to schedule on Saturday, whether a jump in late deliveries needs action today. AI can support every one of them, and it can also mislead every one of them, with a confident forecast that ignores the season, an optimisation that nobody can explain, or a plausible cause for a problem that the data does not support. This course teaches you to use AI and analytics for operational decisions the way a careful planner would: frame the decision first, test every forecast against a simple baseline, put honest ranges on it, turn it into plans that respect real constraints, and explain every recommendation so the people who own the decision stay in charge.

What you will be able to do

• Classify operational decisions and choose between decision support and decision automation with numbers.
• Frame a decision with its objective, options, constraints and the costs of being wrong, and decide under uncertainty with expected value and the newsvendor rule.
• Build baseline and Holt-Winters forecasts, measure them with MAE, WAPE and bias, and attach prediction intervals checked with a backtest.
• Set safety stock and reorder points for a service level, size orders with the economic order quantity, plan staffing with linear programming and test plans against scenarios.
• Detect anomalies with robust scores, test AI-generated hypotheses with drill-downs, write faithful explanations and design review against automation bias.

How the course is taught

Every lesson follows the same rhythm: a real planning situation, a precise explanation with a plain-words summary, a prepared code demo you run in Google Colab, exercises that change service levels, costs and scenarios, and the key points to remember. You work on one realistic business throughout, the warehouse and purchasing of the fictional outdoor retailer Trailhead Outfitters, with three years of daily demand, supplier terms, staffing data and a year of operations that contains real incidents to find. You never write a program from scratch; you run, read and adapt explained code.

The analysis is honest about its limits. When the obvious method gives prediction intervals that are too narrow, the lesson shows it and fixes it; when a simple baseline beats a sophisticated model for some products, the advisor keeps the baseline. No AI account, API key or payment is needed: an offline stand-in assistant suggests causes and drafts explanations, including the invented reasons and false certainty you will learn to catch. Every output in the lessons comes from actually running the code.

What is included

Short lessons that move from understanding and framing operational decisions, through forecasting and planning, to anomalies, explanations and trust, followed by a guided project in which you build and test a replenishment advisor. You also get daily demand for Trailhead's products, the product list with prices and costs, supplier lead times and minimum orders, stock on hand, warehouse shift data, a year of daily operations with incidents in it, the offline stand-in assistant, the advisor code, starter and solution notebooks with self-checks for the project, and a quiz drawn from a question bank at the end of every module.

Who it is for

This course is for operations, supply chain, planning and purchasing staff who make recurring decisions about stock, staff and capacity, and for the managers who receive AI forecasts and recommendations and must decide whether to trust them. If you have ever ordered by gut feeling, been surprised by a stock-out the data could have predicted, or approved a recommendation you could not really explain, this course gives you the tools and the judgment to do better. You will leave with a working method, forecast, test, plan, check and explain, that you can apply to your own products next week. Enroll now and make every operational decision a well-framed, well-explained one.