Production Planning & Facilities

Forecast, stock, schedule and lay out. Demand forecasting, inventory models and safety stock, production planning, MRP and scheduling, and facility layout with assembly-line balancing.

• Demand Forecasting — Predicting demand to plan production, inventory and staffing. Learn qualitative vs quantitative forecasting, time-series components (level, trend, seasonality, noise), naive, moving average and weighted moving average, exponential smoothing and choosing α, Holt's trend and Holt–Winters seasonal methods, causal regression, error measures (MAD, MSE, MAPE, bias and tracking signal), and machine-learning forecasting — with Python comparing three methods on real-looking data.
• Inventory Management: EOQ, Safety Stock & Newsvendor — How much to order and when. Learn why inventory exists and what it costs, ABC analysis, the EOQ model and its robustness, quantity discounts and production (EPQ) variants, reorder points, lead-time demand and safety stock for service levels, continuous (Q, R) vs periodic (T, S) review policies, the newsvendor model for perishable items, and inventory KPIs — with Python for EOQ, safety stock and the critical ratio.
• Production Planning, MRP & Scheduling — From monthly plans to minute-by-minute sequences. Learn the planning hierarchy (S&OP, aggregate planning, MPS, MRP, shop-floor scheduling), aggregate strategies (chase, level, mixed), MRP logic (BOM explosion, netting, lead-time offsetting, lot sizing), capacity planning (RCCP, CRP), single-machine sequencing rules (FCFS, SPT, EDD, CR), Johnson's rule for two machines, Gantt charts and finite scheduling — with Python comparing dispatching rules and applying Johnson's rule.
• Facility Layout & Assembly Line Balancing — Arranging work for smooth flow. Learn layout types (process, product, cellular, fixed-position), from–to charts and load–distance analysis, systematic layout planning (SLP) and relationship charts, material handling, cycle/takt time, precedence diagrams, line balancing heuristics (largest candidate rule, ranked positional weight), efficiency and balance delay, mixed-model lines and U-lines — with Python balancing an 8-task line.