Operations management Study Guide

📖 Core Concepts Operations Management (OM) – Designs & controls the production of goods/services; integrates with supply‑chain, marketing, finance, HR. Strategic vs. Operational Decisions – Strategic: long‑term (facility location, technology). Operational: short‑term (daily schedules, order release). Production System Types – Continuous (chemical, cannot reverse) vs. Discrete (distinct items, can be assembled). Lean & JIT – Eliminate waste (Muda), overburden (Muri), unevenness (Mura); produce only what is needed, when it is needed. Quality Management – Uses statistical process control (control charts) to separate common‑cause from special‑cause variation. Capacity Planning – Matching resources to variable demand; uses queuing theory for service systems. 📌 Must Remember EOQ optimal order size: $Q^ = \sqrt{\dfrac{2DS}{H}}$ (D = annual demand, S = ordering cost, H = holding cost per unit). OEE = Availability × Cycle‑time Efficiency × Quality Rate – key lean KPI. Order Winners vs. Qualifiers: Winners differentiate; Qualifiers are minimum requirements. Push vs. Pull: Push = forecast‑driven; Pull = demand‑driven (kanban triggers). Six Sigma Goal: ≤ 3.4 defects per million opportunities (±6σ). MRP steps: Sum → Split → Shift. Service Characteristics: simultaneous production/consumption, perishable, intangible, non‑transferable ownership. 🔄 Key Processes EOQ Calculation Estimate D, S, H → compute $Q^$. Order size that minimizes total holding + ordering cost. MRP Net‑ting (Sum‑Split‑Shift) Sum: Aggregate gross requirements from MPS & dependent demand. Split: Apply lot‑size rules (EOQ, EPQ, etc.). Shift: Offset by lead time to obtain planned order releases. Lean Kanban Flow Downstream station removes a card → signals upstream to produce one lot. Fixed number of cards = max WIP → use Little’s Law $L = \lambda W$ to control lead time. Six Sigma DMAIC Define: Problem statement & goals. Measure: Collect data, calculate process capability. Analyze: Identify root causes (Ishikawa, Pareto). Improve: Implement solutions, reduce variation. Control: Deploy control charts, sustain gains. Service Capacity Planning (Queuing) Choose appropriate queue model (e.g., M/M/1). Compute utilization ρ = λ/μ; ensure ρ < 0.85 to keep wait times reasonable. 🔍 Key Comparisons Push vs. Pull – Push: schedule based on forecast → risk of excess inventory. Pull: produce when downstream demand occurs → lower inventory, higher responsiveness. EOQ vs. EPQ – EOQ: assumes instantaneous replenishment. EPQ: assumes continuous production rate, adds production time factor. Job Shop vs. Transfer Line – Job Shop: low volume, high variety, dynamic bottlenecks. Transfer Line: high volume, low variety, fixed bottleneck station. Make‑to‑Stock vs. Make‑to‑Order – MTS: produce before demand (high inventory). MTO: produce after order (low inventory, longer lead time). ⚠️ Common Misunderstandings “Lean = Just‑In‑Time only.” – Lean also targets waste elimination (Muda, Muri, Mura) and uses tools like 5S, SMED, value‑stream mapping. “Control chart limits = tolerance limits.” – Control limits reflect process variation; tolerances are customer/specification limits. “Higher capacity utilization always better.” – Utilization > 85 % can cause excessive waiting, reduced flexibility, and higher defect rates. “Six Sigma eliminates all defects.” – It reduces defects to a statistically acceptable level (3.4 ppm), not absolute zero. 🧠 Mental Models / Intuition Little’s Law: $L = \lambda W$ → Inventory = Throughput × Flow Time. Visualize WIP as a “water tank”: more flow (throughput) or slower flow (longer time) raises the level. Bottleneck Theory: System output ≈ capacity of the slowest station. Improving any other station yields diminishing returns until the bottleneck is shifted. Pull Signal = “Kanban Card” – Think of a grocery store shelf: when an item is removed, the shelf signals the back‑room to restock. 🚩 Exceptions & Edge Cases EPQ with scrap or rework – Effective production rate $P{eff}=P(1 - \text{scrap rate})$ modifies the EPQ formula. Service queuing with batch arrivals – M/D/k or G/G/1 models may be needed; simple M/M/1 assumptions break down. Lean in high‑mix environments – Over‑reliance on single‑piece flow can cause excessive change‑over; SMED and cellular layouts mitigate. 📍 When to Use Which EOQ – When demand is steady, ordering & holding costs are known, and lead time is constant. EPQ – When items are produced in‑house continuously and inventory builds while production runs. Kanban (Pull) – For high‑volume, low‑variety lines where WIP limits are critical. MRP – When product structure is complex (multiple levels of dependent demand). Queue Theory (M/M/1) – For single‑server service processes with exponential inter‑arrival & service times. 👀 Patterns to Recognize Repetitive high‑utilization → Bottleneck emergence – Look for stations with > 85 % utilization in schedules. Spike in defect rate + new operator – May indicate a special‑cause variation (training issue). Inventory buildup at one stage only – Signals a local bottleneck or mismatched lot‑size. Demand forecast error > σ → Need larger safety stock or move to a pull system. 🗂️ Exam Traps Choosing EOQ when demand is seasonal – EOQ assumes constant D; seasonal demand requires time‑varying order sizes. Confusing control limits with specification limits – Control limits are derived from process data; spec limits are external requirements. Assuming all JIT systems are pull – Some “JIT” implementations still use push forecasts for upstream activities; the key is the decoupling point. Treating “capacity utilization = efficiency” – Efficiency compares actual to standard usage; utilization compares output to maximum capacity. Selecting MRP for purely independent demand items – Simple reorder point (ROP) may be sufficient; MRP adds unnecessary complexity. --- Use this guide to scan key ideas, recall formulas, and spot typical exam pitfalls quickly.