How to effectively learn AI Prompting, with the 'AI for Production Planners (Prompt Course)'?

Start improving schedule reliability and throughput with AI-guided production planning

This course gives production planners, schedulers, and operations leaders a practical way to apply AI and conversational tools to day-to-day planning decisions. It focuses on repeatable prompt workflows that turn messy data into clear schedules, capacity plans, MRP insights, inventory actions, bottleneck fixes, and supplier or workforce coordination steps-while keeping quality, cost, safety, and environmental goals in view.

What you will learn

• How to build AI-assisted schedules that respect routings, work centers, setup times, changeovers, and due dates, and how to adjust quickly as priorities shift.
• Ways to compare finite vs. infinite capacity assumptions, test scenarios, and align capacity with demand using AI-assisted reasoning and structured outputs.
• MRP improvements that connect demand signals to BOMs, lead times, safety stock, and planned orders, with clear exception lists for action.
• Fast bottleneck identification using throughput, queue time, and utilization indicators, plus suggestions to relieve constraints without causing new ones.
• Inventory policies that balance service and cash, including ABC/XYZ thinking, reorder point logic, and mitigation for intermittent demand.
• Quality control planning that links risk (FMEA-style thinking) to inspection steps, sampling, SPC cues, and practical nonconformance handling.
• Lean strategy prompts to reduce waste, shorten lead time, stabilize flow, and support continuous improvement without disrupting commitments.
• Forecast guidance that weighs seasonality, promotions, and variability, with ways to sense changes earlier and prioritize what matters.
• Supplier coordination prompts for realistic lead times, capacity signals, ASNs, and resilient order plans that handle disruptions.
• Workforce management prompts for skills matrices, shift plans, cross-training options, and overtime trade-offs.
• Production cost analysis that surfaces material, labor, overhead drivers, and scenario comparisons to protect margin.
• Safety and compliance prompts that keep standards visible in plans, checklists, and change impact assessments.
• Technology integration approaches for ERP/MRP/MES data, spreadsheets, APIs, and dashboards so outputs fit your current toolchain.
• Environmental impact assessment that ties production choices to energy use, scrap, emissions, and practical reduction steps.

How the course works

The course is structured around modular prompt workflows. Each module explains the planning goal, the data typically used (for example: BOMs, routings, work centers, on-hand and open orders, demand history, supplier lead times, quality metrics), and the decision outputs you want (for example: schedule lists, capacity summaries, planned orders, exception queues, risk registers, and action plans). You'll learn how to frame requests so the AI produces clear, auditable, and operations-ready results.

• Clarify the objective: what decision you need, what horizon, which constraints must never be broken.
• Provide structured inputs: formatted text, tables, or summaries that reflect your ERP/MRP exports or spreadsheet views.
• Request the output format you need: tabular lists, prioritized actions, checklists, or scenario comparisons.
• Apply validation steps: sanity checks, constraint checks, KPI deltas, and explicit risk flags before implementing changes.
• Iterate: ask for refinements, "what-ifs," and alternative plans until the result is actionable.

Using the prompts effectively

• Set context clearly: plant, product families, shift patterns, key constraints, and critical customers.
• Pin the rules of the system: finite/near-finite capacity assumptions, lot sizing policies, setup families, and quality checkpoints.
• Standardize input structure: consistent column names and units; short data dictionaries help the AI "read" your exports correctly.
• Ask for decision-ready outputs: provide the target format so you can paste results into your ERP, MES, or spreadsheet.
• Run controlled comparisons: baseline vs. proposed schedule, side-by-side capacity views, and material coverage before/after.
• Add quality gates: request exception lists, constraint violations, and confidence notes, then verify against source data.
• Document changes: keep a log of prompt settings, inputs, and outputs for audit and learning.
• Respect confidentiality: avoid pasting sensitive data unless permitted; use masked or sample data during practice.

How the modules connect

Production planning improves when analyses reinforce each other. This course shows how to link modules so improvements in one area don't create issues elsewhere.

• Forecasts inform MRP, which drives planned orders and inventory needs.
• MRP outputs and supplier feedback set realistic material availability for scheduling and capacity.
• Capacity plans and bottleneck analysis anchor the schedule; workforce plans close gaps.
• Quality planning and safety standards are embedded in work instructions and sequence decisions.
• Lean strategies streamline flow so schedules stick and WIP stays under control.
• Cost analysis reviews the impact of plan choices on margin and cash.
• Environmental assessment flags high-impact options and suggests lower-impact alternatives.

Outcomes and measurable value

While every plant is different, the course focuses on improvements you can measure and sustain. You'll learn how to set targets, collect baselines, and monitor progress.

• Schedule adherence and on-time delivery: fewer expedites and reschedules.
• Throughput and lead time: shorter queues at key work centers and faster order completion.
• Inventory health: fewer stockouts and excess; better alignment to demand variability.
• Material coverage: clearer exception lists so buyers and planners act early.
• Capacity balance: higher utilization at constraints without overloading non-constraints.
• Quality and safety: improved first-pass yield, fewer nonconformances, and compliance checks embedded in plans.
• Cost and cash: better material planning, lower overtime spikes, and fewer late penalties.
• Environmental metrics: reduced scrap, energy hotspots identified, and greener plan alternatives considered.

Who should take this course

• Production planners and schedulers who need faster, clearer decisions under changing priorities.
• Operations managers and supervisors who want repeatable planning routines for daily huddles and weekly S&OP touchpoints.
• Procurement and supplier coordinators who benefit from proactive material and capacity signals.
• Industrial engineers and CI leaders looking to support bottleneck relief and lean initiatives with AI-assisted analysis.
• Quality, EHS, and compliance teams who want planning artifacts that reflect standards and audit needs.
• Analysts who integrate ERP/MRP/MES data into planning dashboards and need AI-friendly formats.

What the course includes

• Module guides that explain planning goals, data needs, and decision outputs for each topic area listed in the course outline.
• Reusable prompt frameworks that map to common planning tasks, scenario analysis, and exception handling.
• Checklists for data preparation, constraint definition, validation, and action tracking.
• Reference sheets with key formulas, KPI definitions, and quick sanity checks.
• Troubleshooting notes that help you refine inputs and outputs when results look off.
• Optional capstone that connects demand, MRP, capacity, scheduling, and supplier/workforce plans into one cohesive planning cycle.

Data and tool readiness

You do not need to code to benefit from this course. Comfort with spreadsheets and ERP exports is enough. The course explains how to structure inputs, request specific output formats, and slot results back into your current tools. If you lack access to live data, you can practice with representative data shapes and then apply the same approach to your environment later.

Quality, safety, and responsible use

• Always validate: run checks for material coverage, capacity feasibility, and compliance impacts before issuing work orders.
• Stay within policy: follow data privacy and supplier confidentiality rules; mask sensitive fields when practicing.
• Record decisions: keep an audit trail of planning assumptions and changes for reviews and audits.
• Human in the loop: treat AI outputs as decision support; final judgment remains with experienced planners and leaders.

Why this course works

Production planners often juggle conflicting goals: service, cost, quality, safety, and sustainability. By standardizing how you ask for help from AI-and how you verify answers-you can shorten tedious analysis cycles, surface issues earlier, and communicate clearer plans to teams and suppliers. The result is steadier flow, fewer surprises, and quicker recovery when things change.

Ready to get started?

If you want a practical way to combine scheduling, capacity, MRP, bottlenecks, inventory, quality, lean methods, demand forecasting, supplier alignment, workforce planning, cost analysis, safety, technology integration, and environmental factors into one planning approach, this course brings it all together. The focus is simple: clear inputs, clear outputs, and repeatable routines you can use every day.