Operations Management (OM) is a core discipline in MBA programs, bridging strategy and execution to ensure businesses run efficiently and effectively. This comprehensive guide covers all major components of Operations Management you’d encounter in a typical MBA course. We explore fundamental concepts like process design, capacity planning, inventory management, quality control, and supply chain strategy. Along the way, we provide practical examples, actionable steps, and even code snippets where relevant, helping you translate theory into practice and boost productivity in any business setting.

Introduction to Operations Management

Operations Management is the art and science of turning inputs (materials, labor, technology) into valuable goods and services as efficiently as possible. It sits at the heart of every organization, coordinating resources and processes behind the scenes. For MBA students, mastering operations management means understanding how to design systems that deliver consistent quality, control costs, meet customer demand, and adapt to change.

Key roles and topics in an Operations Management MBA curriculum include:

Process Design & Analysis: How to set up workflows and layouts that maximize output and minimize waste.
Capacity Planning: Deciding how much to produce and when, balancing demand with resource constraints.
Quality Management: Ensuring products or services meet standards through techniques like Six Sigma and Total Quality Management (TQM).
Inventory & Supply Chain Management: Managing stock levels, coordinating suppliers, and smoothing the flow of goods.
Lean Operations: Eliminating waste and continuous improvement (Kaizen) to increase efficiency.
Forecasting & Demand Planning: Predicting customer demand to make informed production and purchasing decisions.
Project Management in Operations: Tools like PERT/CPM for scheduling and resource management on complex projects.

By the end of this guide, you’ll have an integrated view of how these elements fit together. We’ll provide practical examples (e.g. code for forecasting or optimization) and actionable advice at each step, so you can apply these concepts whether you’re managing a factory line, a healthcare clinic, or any service business.

Operations Strategy and Competitiveness

Operations strategy aligns the operations function with a company’s overall business strategy. In other words, it defines how operations will support the company's competitive priorities. Common competitive priorities include:

Cost Efficiency: Manufacturing at the lowest possible cost (e.g. Walmart’s low-price strategy).
Quality and Reliability: Superior product/service quality (e.g. Toyota’s reputation for reliability).
Flexibility & Speed: Quickly adapting to customer needs or changing volumes (e.g. Zara’s fast fashion model).
Innovation: Bringing new products or processes to market (e.g. a biotech firm’s R&D pipeline).

Operations managers set policies to deliver on these priorities. For example, a low-cost strategy might emphasize tight cost control, high asset utilization, and standardization. A high-quality strategy might focus on quality control programs, continuous improvement, and certifications (like ISO 9001).

Trade-offs are a big part of operations strategy. You often can’t optimize everything simultaneously. For instance:

Pursuing the lowest cost might sacrifice flexibility or customization.
Being highly flexible might increase costs or decrease efficiency.
Emphasizing speed (fast delivery) might require higher inventory or more expensive logistics.

An MBA student learns to identify these trade-offs and manage them. A helpful framework is the Four Vs of Operations:

Volume: High vs. low production volume (e.g. mass production vs. job shops).
Variety: Wide vs. narrow product/service variety.
Variation: Variation in demand over time (seasonality, trends).
Visibility: How much the customer sees the operations processes.

High-volume, low-variety operations (like a car assembly line) can be highly standardized and efficient. Low-volume, high-variety operations (like custom furniture workshops) need flexibility. Visibility affects service industries: a restaurant has high visibility (customers see the cooks), whereas a factory has low visibility.

Actionable Insight

Align operations with strategy: If your company’s competitive edge is speedy delivery, invest in fast logistics and flexible production. If quality is the goal, implement robust quality management systems.
Identify trade-offs: Use tools like the Product/Process Matrix to see if your operations fit your product complexity and volume. Reevaluate processes if they don’t align with priorities.
KPI Example: Establish key performance indicators (KPIs) aligned with strategy, e.g. Order Fulfillment Lead Time for a fast-delivery focus, or Defect Rate for a quality focus.

Process Design and Analysis

At its core, operations management is about processes – the steps that convert resources into products or services. Process design determines how work flows through a system. This includes facility layouts, workflow charts, and technology choices.

Types of Process Layouts

Job Shop (or Project-Based): Facilities organized around specific tasks (e.g. a hospital operating in departments like radiology, surgery). High flexibility, low volume.
Batch Flow: Groups of similar products move through the same process route (e.g. a bakery making batches of various breads). Moderate variety and volume.
Assembly Line (Flow Shop): Resources arranged in sequence, and products follow a fixed path (e.g. car manufacturing). High volume, low variety.
Continuous Flow: Highly automated, continuous production (e.g. chemicals, oil refining).

Process Analysis Tools

Process Flowcharting: Mapping out each step, decision, or material flow in the process. Visual tools help identify bottlenecks and waste.
Value Stream Mapping: Often used in lean to map both material and information flow across the entire process, highlighting value-added vs. non-value-added steps.
Bottleneck Analysis: Identify the capacity-constrained step (bottleneck) that limits the entire process throughput. The Theory of Constraints (TOC) says improving the bottleneck is the best way to boost flow.
Little’s Law: In steady state, Average Inventory = Throughput Rate × Flow Time. This simple relationship helps in capacity planning and understandings delays. For example, if a factory produces 10 cars/day and average car spends 5 days in production, the factory typically has 50 cars in the system.

Example: Flowchart and Little’s Law

Imagine an online retailer receiving orders, picking items, packing, and shipping. A flowchart outlines each step from order receipt to delivery. If currently orders pile up at packing (the bottleneck), Little’s Law can help:

Throughput Rate (R): 20 orders per day.
Improvement needed to reduce flow time (W): check inventory (I = R × W).
If currently there are 100 orders awaiting packing on average, W = I/R = 100/20 = 5 days from order to shipping. To cut that to 3 days, the operations manager must increase R (pack faster) or reduce I (inventory in the system).

Actionable Steps to Improve Processes:

Map out current (as-is) processes using flowcharts or value-stream maps. Identify non-value steps (delays, rework).
Identify bottlenecks: Which step limits the throughput? Focus resources on that step (add manpower, streamline, etc.).
Apply metrics: Track cycle time, throughput, and work-in-progress. Little’s Law can verify if inventory levels match flow time.
Redesign process if needed: Maybe move from a job-shop to a cellular layout to reduce movement, or implement a pull system (Kanban) to better match production with demand.

Capacity Planning and Facility Layout

Capacity is the maximum output a process or facility can achieve. Capacity planning ensures a business has the right size and amount of resources to meet demand. Key concepts include:

Design Capacity vs Effective Capacity: The ideal maximum (design) vs what’s actually realistic after maintenance downtime, shifts, etc.
Utilization: (Actual output) / (Design capacity). High utilization (>85%) can mean little buffer for rush orders; low utilization wastes resources.

Steps in Capacity Planning

Forecast Demand: Short-term and long-term (addressed in later sections).
Compare to current capacity: If forecast > capacity, consider expanding (new machines, shifts, outsourcing). If forecast < capacity, consider reducing resources or finding new products/markets.
Plan timing: Capacity changes take time (planning, approvals, labor training). Use leading indicators to plan ahead.

Capacity-Related Tools

Queuing Theory Basics: Understand lines/waits. Example: Customers at a bank teller – as utilization goes up, wait times grow exponentially.
Spreadsheet Analysis: Simple models (e.g. break-even volume, capacity utilization over time).
Linear Programming: Optimize product mix subject to resource constraints. (We include a brief Python example below.)

Code Example: Linear Programming for Production Planning

Python’s PuLP library can solve production and capacity allocation problems. For example, suppose a factory makes two products (A and B). Each unit of A uses 2 machine-hours and each B uses 3 hours. Available machine-hours per week: 120. Profit per A is $20, per B is $30. We want to maximize weekly profit.

!pip install pulp from pulp import LpMaximize, LpProblem, LpVariable, LpStatus

Define the problem

prob = LpProblem("Production_Planning", LpMaximize)

Define variables: number of units to produce (>= 0)

A = LpVariable('Units_of_A', lowBound=0, cat='Integer') B = LpVariable('Units_of_B', lowBound=0, cat='Integer')

Objective: maximize profit (20 * A + 30 * B)

prob += 20A + 30B, "Total_Profit"

Constraint: 2 hours per A + 3 hours per B <= 120 machine hours

prob += 2A + 3B <= 120, "Machine_Hours"

Solve the problem

prob.solve() print("Status:", LpStatus[prob.status]) print("Produce {} units of A".format(int(A.value()))) print("Produce {} units of B".format(int(B.value()))) print("Maximum Profit = ${}".format(int(prob.objective.value())))

This code sets up and solves the problem. In practice, an operations manager might plug real data from production processes to get optimal production quantities. The solution tells you how to allocate production to maximize profit given capacity constraints.

Facility Layout

Physical arrangement of equipment and workspaces greatly affects flow and efficiency. Common layouts are:

Process Layout (Functional Layout): Group similar functions together. Best for varied workloads (e.g. hospital departments).
Product Layout (Line Layout): Equipment arranged in sequence of the steps (e.g. assembly line).
Fixed-Position Layout: Product (like a ship or plane) stays fixed, and workers/tools come to it.
Cellular Layout: Hybrid where machines are grouped into cells, each cell makes a family of parts (used in lean manufacturing).

Actionable Advice: Evaluate if your layout matches your product variety and volume. If workers walk too much or materials shuttle randomly, consider reorganizing. Tools like the Systematic Layout Planning (SLP) aid in designing layouts to minimize movement.

Forecasting and Demand Planning

Accurate forecasts are foundation for planning in operations. Whether you’re planning production runs, staffing, or inventory, knowing expected demand prevents costly over/under-stocking.

Forecasting Methods

Qualitative Methods: Useful for new products or when data is scarce. Includes Delphi method (experts’ consensus), market research, panel consensus.
Time Series Analysis (Quantitative): Applies when historical data exist. Common techniques:
Moving Averages: Smooth out fluctuations to spot trends. - Exponential Smoothing: Weighted average, giving more weight to recent observations. Simple to implement. - ARIMA Models: Autoregressive integrated moving average for more complex patterns (requires statistical software).
Causal Methods: Regression models that use leading indicators (e.g. trend = years, promotions, economic factors).

Forecasting Example with Exponential Smoothing (Python)

Consider monthly sales data:

import pandas as pd from statsmodels.tsa.holtwinters import ExponentialSmoothing

Example monthly sales (in units)

sales = pd.Series([120, 130, 125, 145, 155, 150, 160, 170, 165, 175, 180, 190]) model = ExponentialSmoothing(sales, trend='add', seasonal=None) fit = model.fit() forecast = fit.forecast(3) print("Next 3-month forecast (units):", forecast.values.round(0))

This script fits an additive trend model and forecasts future sales. In operations, you would update such a forecast periodically and use it to set production plans or inventory orders.

Dealing with Uncertainty

Real-world forecasts are never perfect. MBA courses teach forecast accuracy metrics (e.g., Mean Absolute Percentage Error - MAPE). They also cover safety stock calculations: keeping extra inventory to buffer against demand variability or supply delays.

Safety Stock Formula Example (for a simple case when demand is uncertain): Safety Stock = Z * σ_demand * sqrt(lead_time) where Z is the Z-score for desired service level (e.g., Z≈1.64 for 95% service level), and σ_demand is the standard deviation of demand per period.

Actionable Forecasting Tips

Use a rolling forecast: Continuously update forecasts with the latest data.
Combine methods: For example, weigh a statistical forecast and expert opinion.
Collaborate across functions: Sales, marketing, and finance input can improve forecasts (Sales & Operations Planning, S&OP).
Monitor forecast accuracy and adjust model/parameters as needed.

Inventory Management and Supply Chain

Inventory ties up capital, but stockouts risk lost sales. Managing inventory balances these. In an MBA program, you learn various inventory models and how to design supply chains for efficiency and resilience.

Key Inventory Concepts

Economic Order Quantity (EOQ): The optimal order size that minimizes the sum of ordering costs and holding costs.

[ EOQ = \sqrt{\frac{2DS}{H}} ] where D is annual demand, S is ordering cost per order, and H is holding cost per unit per year.

Reorder Point (ROP): The inventory level at which you place a new order.

[ ROP = \text{(demand per period × lead time)} + \text{safety stock} ]

ABC Analysis: Segment inventory into categories (A = most valuable 10-20% of items, B = moderate, C = least) to focus control efforts.
Just-In-Time (JIT): A pull-based system where parts are produced or ordered only as needed, minimizing inventory. Originated with Toyota’s lean manufacturing.

Inventory Management Example: EOQ Calculation

Suppose an electronics company sells 10,000 headphones per year. Each order costs $50 to place, and holding cost is $5 per headphone per year.

D = 10,000 units/year
S = $50/order
H = $5/unit-year

EOQ = √(2 * 10000 * 50 / 5) = √(500,000) ≈ 707 units.

So order ~707 units each time. If lead time is 2 weeks (0.038 years) and weekly demand is 192, then ROP (without safety stock) ≈ 192 * 2 = 384 units.

Supply Chain Management

Operations also encompasses the supply chain: sourcing materials, managing suppliers, and distribution networks.

Push vs Pull: Push systems forecast demand and schedule production in advance; pull systems react to actual demand (Kanban is a pull tool).
Bullwhip Effect: Variability in orders amplifies upstream in the supply chain due to batching, price fluctuations, and demand estimation errors. Managing this requires information sharing and smoothing order policies.
Supplier Management: Evaluate supplier quality, lead times, and reliability. Key in methods like Supplier Scorecards or strategic partnerships.

Actionable Supply Chain Advice:

Diversify and qualify suppliers to minimize risk of stockouts.
Implement Inventory Visibility: Use ERP/WMS systems for real-time tracking.
Coordinate with partners: Adopt Vendor-Managed Inventory (VMI) or Collaborative Planning Forecasting Replenishment (CPFR) where feasible.
Optimize transportation and warehousing: For instance, locate distribution centers near major markets (analyzing geographically to reduce costs).

Code Snippet: Simple Inventory Reorder Simulation

To illustrate inventory reordering, consider the following Python example. It simulates daily demand and triggers a reorder when inventory drops below a threshold.

import random

daily_demand = [random.randint(5, 15) for _ in range(20)] # Simulate 20 days of demand inventory = 100 reorder_point = 30 order_amount = 70

for day, demand in enumerate(daily_demand, start=1): inventory -= demand print(f"Day {day}: Demand={demand}, Inventory={inventory}") if inventory <= reorder_point: inventory += order_amount print(f" Reorder placed. New inventory = {inventory}\n")

This script shows how inventory declines and when a reorder of 70 units is triggered once the level hits 30. In practice, an operations manager uses such insights to set policies (EOQ, safety stock, reorder points) and inform an inventory system or ERP.

Quality Management and Improvement

Quality in operations means doing things right: minimizing defects, errors, or customer complaints. MBA curricula emphasize structured quality frameworks:

Total Quality Management (TQM): A philosophy of continuous improvement involving everyone in the organization. Focus on customer satisfaction, prevention not inspection, and data-driven problem-solving.
Six Sigma: A data-driven approach aiming for near-perfection (3.4 defects per million opportunities). Follows the DMAIC cycle: Define, Measure, Analyze, Improve, Control.
ISO Standards: International standards like ISO 9001 establish quality management systems for consistent output.
Quality Tools: Fishbone (Ishikawa) diagrams for root causes, Pareto charts (80/20 rule), and Control Charts for monitoring process stability.

Control Charts Example

Control charts help detect if a process is stable or has special-cause variation. For instance, plotting daily part dimensions, you set centerline=mean, UCL/LCL = mean ± 3σ. Points outside these limits signal a problem.

Although heavy data analysis is not always part of your day-to-day, knowledge of these tools lets an operations manager work with quality engineers effectively.

Actionable Steps for Quality

Collect data on defects and cost of poor quality (scrap, rework, returns).
Implement a quality control plan: define acceptable quality levels (AQLs) and inspection points.
Adopt continuous improvement (Kaizen): Every employee suggests small improvements. Lean methodologies (see next section) align closely with this.
Use Six Sigma projects: For example, redesign a process step to reduce variance. Statistical software (e.g. Minitab, or Python/R libraries) can analyze process capability.

Lean Operations and Waste Elimination

Lean management is about maximizing value by eliminating waste (“muda”). Typical wastes in lean (the acronym TIMWOOD):

Transportation (unnecessary movement of goods)
Inventory (excess stock)
Motion (unnecessary movement by people)
Waiting (idle time)
Overproduction
Overprocessing
Defects

Lean tools commonly taught include:

5S: Sort, Set in order, Shine, Standardize, Sustain. Organize the workplace for efficiency and safety.
Value Stream Mapping (VSM): Visualize processes, identify waste in flow.
Kanban: A pull system using cards/signals to trigger production or replenishment orders. Often used with JIT.
Kaizen Events: Short, focused improvement blitz where cross-functional teams fix a problem area in days.
Cellular Manufacturing: Group machines into cells that complete a sequence of operations for a family of products, reducing material movement.

Lean Example: 5S Implementation

A manufacturing floor implementing 5S might:

Sort: Remove non-essential tools and parts.
Set in order: Label and arrange tools so every item has a designated place.
Shine: Clean the workspace and equipment daily.
Standardize: Create checklists and signals for how work is done (floor markings, instructions).
Sustain: Conduct regular audits and training to maintain the system.

This seemingly simple methodology has huge impact: reduced search times, lower inventory, safer work areas.

Actionable Lean Advice:

Start small: Run a pilot Kaizen in one department (e.g. the packing area) to demonstrate results.
Engage employees: Lean works best when workers are empowered to suggest and test improvements.
Track improvements: Use metrics like lead time or defect rate before and after a lean event to show benefits.
Integrate with quality: Many lean tools come hand-in-hand with quality tools (like mistake-proofing or Poka-Yoke).

Project Management in Operations

MBA courses often include project management since many operational changes are managed as projects. Key concepts:

Work Breakdown Structure (WBS): Decompose a project (like implementing a new production line) into tasks.
PERT/CPM: Techniques for scheduling tasks with dependencies. You identify the critical path (longest path of tasks) to determine the project duration.
Resource Leveling: Adjusting schedules to avoid resource conflicts.
Critical Chain Project Management (CCPM): Focuses on resource constraints and buffers.

Example: Suppose an operations manager needs to set up a new distribution center. There will be tasks like obtaining permits, construction, hiring, equipment installation. Using PERT, each task is mapped, durations estimated, and interdependencies charted. The critical path might be construction (3 months) -> equipment installation (1 month) -> staff training (2 weeks). Any delays on this chain delay the whole project.

Actionable Tips:

Use Gantt charts for quick visuals of project schedules (tools like Microsoft Project or even Excel).
Always build in buffer time for uncertainty, especially for longer projects.
Communicate progress regularly to stakeholders to ensure alignment and manage expectations.

Technology and Trends in Operations Management

Operations management continually evolves with technology. Current MBA courses cover topics like:

Enterprise Resource Planning (ERP): Integrated systems (like SAP, Oracle) that connect operations, finance, and HR between departments.
Automation & Robotics: Automated guided vehicles (AGVs), industrial robots, and AI/robots on production lines or warehouses.
Internet of Things (IoT): Smart sensors on machines provide real-time data (predictive maintenance, yield tracking).
Supply Chain Visibility Tools: Cloud-based platforms that show inventory and shipments across the globe.
Data Analytics & Machine Learning: Advanced demand forecasting, dynamic pricing, fraud detection in logistics, etc.

For example, a factory might use machine learning to predict equipment failure (predictive maintenance) based on vibration and temperature sensors. Or retailers use analytics to restock items just fast enough based on sales patterns.

Emerging Concepts:

Industry 4.0 / Smart Manufacturing: A highly digitized, connected industrial environment enabling mass customization and real-time optimization.
Sustainability in Ops: Lean and quality tools can also drive waste reduction (e.g., energy use, scrap reduction). Regulations and ethics in environmental management are increasingly part of OM courses.
Service Operations Analytics: Using data for call centers, healthcare (patient flow optimization), hospitality (yield management).

Actionable Approach:

Stay informed about new technologies and evaluate ROI: e.g. a small pilot of IoT sensors in one machine before scaling up.
Integrate continuous learning: Encourage cross-training and certification (Six Sigma belts, project management certifications, data analytics courses).
Consider Agile operations: Traditional OM is often plan-driven; however, the idea of agile (quickly iterating improvements) is catching on in areas like new product introduction or process design.

Practical Examples & Case Illustrations

To cement understanding, here are some illustrative examples that tie together these concepts:

Manufacturing Case: A car manufacturer implements a pull system for parts. They notice too much inventory of headlight assemblies. After analyzing demand (forecasting) and using EOQ formulas, they switch to smaller, more frequent orders from the supplier. They map out the process flow, identify a bottleneck at paint ovens (capacity issue), and use linear programming to allocate overtime shifts (code snippet could apply here). Lean teams apply 5S in the engine assembly area, reducing cycle time. Over time, defect rates drop (quality) and customer delivery times improve.
Service Case (Healthcare): A hospital runs outpatients needing lab tests. They use queueing theory to decide how many technicians to schedule per shift to keep patient wait times under 15 minutes. They implement a Kanban system for replenishing critical lab supplies, cutting waste. By mapping patient flow (value stream mapping), they reduce bottlenecks between reception and examination rooms. Forecasting algorithms using past patient counts help schedule staff for busy vs slow days.
Retail Example: A clothing retailer uses point-of-sale data to forecast demand of each style. An inventory management system (ERP) auto-generates orders when stock hits the reorder point. They segment products into A/B/C categories, focusing marketing on “A” items. In peak season, they plan capacity by building pop-up stores and using extra warehouse space to handle expected volume. After a promotional campaign, actual sales were 20% higher than forecast; by analyzing this error, they refine their forecasting model (maybe by including marketing spend as a variable).

These scenarios demonstrate how operations concepts interlink: forecasting informs inventory, which affects capacity and flow, which requires quality oversight and continuous improvement.

Actionable Advice for Operations Managers

Finally, to make this guide practical for MBA students and business professionals, here’s a checklist of actions and best practices:

Measure and Monitor: Implement a dashboard of operations metrics (leading and lagging indicators). Key metrics: throughput time, defect rate, unit cost, inventory turnover, on-time delivery rate.
Continuous Improvement Culture: Encourage ideas from all staff. Hold regular review meetings (like daily stand-ups or Cabinet-Level reviews of KPIs) to spot issues early.
Cross-Functional Collaboration: Work closely with marketing (for demand signals), finance (for cost and investment analysis), and HR (for staffing and skills development).
Iterative Decision-Making: Operations management isn’t one-and-done. Use PDCA (Plan-Do-Check-Act) across everything: test a change on a small scale, check results, then roll out.
Strategic Alignment: Regularly revisit how operations supports corporate strategy. If the company shifts strategy (e.g. moves from cost leadership to innovation), operations processes must adapt.
Risk Management: Identify operational risks (supply disruptions, equipment breakdowns, safety incidents) and plan mitigation (e.g. safety stock, maintenance schedules, redundant suppliers).
Invest in People and Tech: The best processes still need skilled operators and managers. Additionally, evaluate technology (AI, automation) for productivity gains, but balance with costs and workforce impacts.

Example Action Plan

Audit Current Process: Pick a high-priority process (like order fulfillment). Map it end-to-end.
Set Goals: For example, "Reduce lead time by 20%" or "Cut defect rate by half".
Identify Improvements: Use tools (5 Whys, Fishbone diagram) to find root causes of issues.
Develop Solutions: Brainstorm fixes (e.g. more staff at bottleneck, new reorder system).
Implement & Track: Roll out changes, and measure outcomes against goals.
Standardize Success: If an improvement works, codify it in standard operating procedures (SOPs).
Review and Repeat: Conduct regular reviews. The environment changes, so should processes.

Conclusion and Future Outlook

Operations Management in an MBA context is about marrying theory with the practical demands of running real-world systems. We’ve covered the foundational topics: from designing efficient processes and planning capacity, to managing inventory, ensuring quality, and embracing continuous improvement. Throughout, actionable insights and examples have demonstrated how these concepts drive competitiveness and add value.

Emerging trends (automation, data analytics, sustainable operations) add new tools to the operations manager’s toolkit. Staying adaptable and data-driven is key. Operations isn’t static – it evolves as markets and technologies do.

As an MBA graduate or professional leveraging this knowledge, remember that operations management is dynamic. Applying these principles will often require tailoring to your industry’s specifics. But the core ideas of maximizing efficiency, minimizing waste, and aligning operations with strategy remain universal.

Key takeaways:

Operations strategy must suit your market position (cost leader vs innovator vs service leader).
Analyze processes and bottlenecks; use models (Little’s Law, queuing theory) to understand flows.
Forecast intelligently and keep safety stock to buffer uncertainty.
Lean and Six Sigma provide frameworks to cut waste and improve quality continuously.
Use data and software tools (from ERP to optimization algorithms) to make informed decisions.
Embrace cross-functional teamwork and always measure impact.

By integrating these operations management principles into your business decisions, you’ll drive better results: lower costs, faster delivery, higher quality, and ultimately superior customer satisfaction.