Introduction to Industrial Engineering

🧭 Overview

🧠 One-sentence thesis

Industrial engineering focuses on designing and improving systems that integrate people, machines, information, and money to achieve goals efficiently, safely, and with high quality.

📌 Key points (3–5)

• Core definition: IEs design or improve systems (not just physical objects) that combine people, machines, information, and money to achieve specific goals.
• Efficiency vs effectiveness distinction: efficient means no wasted time/resources; effective means producing the desired result—a process can be one without the other.
• Breadth of IE work: examples range from hospital operating room turnaround times to manufacturing facility layouts to safety analysis and supplier quality control.
• Common confusion: efficiency vs effectiveness—efficiently producing unused reports is not effective; reducing production time without losing customer satisfaction is both efficient and effective.
• People-centered engineering: among all engineering disciplines, IEs think most about human factors, safety, and how people interact with systems.

🏭 Real-world applications

🏥 Healthcare process improvement

• An IE reduced operating room turnaround time from 45 to 20 minutes by redesigning the cleaning and preparation process.
• Result: more operations can be scheduled per day in each operating room.
• This illustrates improvement of an existing process focused on time efficiency.

✈️ Manufacturing facility design

• An IE laid out a new facility for corporate jet tail section manufacturing.
• Decisions included: material delivery locations, machine placement, workflow patterns, and shipping points.
• This illustrates new system design with focus on physical layout and material flow.

🔧 Process redesign for strength and speed

• An IE redesigned a steel cylinder manufacturing process from two-piece to one-piece construction.
• Benefits: faster manufacturing time and stronger final product.
• This shows how design changes can improve both efficiency and quality simultaneously.

🔍 Quality control and supplier management

• A lawnmower assembly plant had misaligned bolt holes; an IE used data analysis to trace the problem to one supplier not meeting tolerances.
• The IE worked with the supplier to improve their production process.
• This illustrates the information and quality aspects of IE work, plus cross-organizational collaboration.

🛡️ Safety analysis and injury prevention

• An IE noticed increasing back injuries in an automobile assembly plant.
• Through safety report analysis, identified the problem in engine assembly area caused by awkward redesign.
• Solution: redesigned assembly task and purchased new hoist; monitored results showing injury rate decline.
• This demonstrates the safety focus and data-driven improvement approach.

🎯 Core definition breakdown

📐 Design vs improvement

• Design: creating new facilities, processes, or systems from scratch.
• Improvement: enhancing existing facilities, processes, or systems (most IE work).
• Example: the jet facility layout was design work; the hospital operating room turnaround was improvement work.

🔗 Systems thinking

System: components including physical objects, processes, rules, and people that must work together.

• Most engineers design physical objects; IEs design systems.
• Material and information flow between system components.
• Changes to one part may affect other parts—interconnectedness is key.
• Don't confuse: a system is not just a collection of parts; it's about how those parts interact and depend on each other.

👥 The human element

• Among all engineering types, IEs think most about people.
• Must consider: what people are good at, what tasks should not be assigned to people, how to design jobs for speed, safety, and quality.
• Example: the back injury case required understanding how workers interacted with the redesigned engine.

💻 Machines and technology

• IEs must select appropriate machines, including computers.
• Machine selection affects system efficiency and capability.

📊 Information and data

• Data serves two purposes: immediate decision-making and system improvement analysis.
• Example: the supplier quality issue was identified through data gathering and analysis; the safety problem through report analysis.

💰 Financial considerations

• IEs must weigh costs and savings across different time periods.
• Trade-offs between immediate costs and future benefits are common.

🎯 Goal orientation

Goal: the purpose for which a designed system exists.

• IEs must think about different ways to accomplish goals and select the best approach.
• Every system exists for a specific purpose that guides design decisions.

⚡ Efficiency focus

• Achieving goals quickly and with least resource use.
• The excerpt notes IEs are sometimes called "efficiency engineers."

✅ Quality assurance

• Organizations have customers who expect specific quality levels.
• IEs ensure systems consistently deliver goods and services meeting customer needs.

🦺 Safety priority

• IEs design systems so people can and will work safely.
• Includes preventing mistakes and protecting from workplace hazards.

⚖️ Efficiency versus effectiveness

📏 Defining the distinction

Concept	Definition	Focus
Efficient	Doesn't waste time or resources	How resources are used
Effective	Produces desired effect or contributes to goal	Whether the goal is achieved

🔄 Four possible combinations

• Effective but not efficient: the process achieves the goal but could use fewer resources or less time without losing results.
  • Example: producing a product that satisfies customers but taking longer than necessary.
• Efficient but not effective: the process uses minimal resources but doesn't achieve the desired goal.
  • Example: a department efficiently producing reports that no one uses.
• Both efficient and effective: the ideal state IEs aim for.
• Neither: the worst case requiring complete redesign.

🎯 Relationship to IE definition

• The definition includes both "efficiency" and "goal" to capture both aspects.
• Don't confuse: being fast or cheap (efficient) doesn't matter if you're not accomplishing what's needed (effective).
• The excerpt emphasizes that "effectiveness engineer" might be more accurate than "efficiency engineer" because achieving the right goal matters more than just minimizing resources.

🎓 IE knowledge domains

The bolded words in the definition indicate areas IEs must master, translated into key questions:

🏗️ Design and improvement questions

• Where should a facility be located?
• How should components be laid out physically?
• What operating procedures should be used?

🔗 System questions

• How should tasks be allocated among different system parts?
• How should material and information flow among components?

👤 People questions

• What are people good at?
• What task types should not be assigned to people?
• How can jobs be designed for speed, safety, and quality?

🤖 Machine questions

• What machine types are available for different tasks?
• How should material and information be moved and stored?

📈 Information questions

• How can data determine system performance?

💵 Money questions

• How to trade off costs and savings occurring at different times, possibly over years?

🎯 Goal questions

• What is the system's goal?
• What are different ways the system could achieve that goal?

⚡ Efficiency questions

• How to produce products and services with least time and resources?

✅ Quality questions

• How to ensure consistent production of goods and services meeting customer needs?

🛡️ Safety questions

• How to keep people from making mistakes?
• How to protect people from workplace hazards?

🌟 The IE profession

💼 What IEs accomplish

• Create efficient and safe workplaces.
• Enable workers to be proud of high-quality products and services.
• Bring prosperity through improved efficiency.
• Provide good products and services through improved quality.
• Protect people through improved safety.

🎯 The bumper sticker version

"IEs make things better."

• This simple phrase captures the improvement-focused nature of the profession.
• Encompasses efficiency, quality, and safety improvements.

🧭 Overview

🧠 One-sentence thesis

Teams are groups of people with complementary skills working interdependently toward specific common goals, and their effectiveness depends on factors like cohesiveness, member skills, and avoiding pitfalls such as groupthink and lack of cooperation.

📌 Key points (3–5)

• Team vs. group: A team works interdependently toward common goals with shared accountability, while a group simply shares information with members working independently.
• What makes teams effective: Members depend on each other, trust one another, perform better collectively than individually, and share leadership responsibility.
• Group cohesiveness: The attractiveness of a team to its members affects satisfaction and performance, influenced by size, similarity, success, exclusiveness, and competition.
• Common confusion: Too much cohesiveness or conformity can lead to groupthink—conforming to group pressure while failing to think critically—which can make teams ineffective.
• Why teams fail: Unwillingness to cooperate, lack of managerial support, failure to delegate authority, and failure of teams to cooperate with each other.

🎯 Defining teams and their characteristics

🎯 What distinguishes a team from a group

A team (or work team) is a group of people with complementary skills who work together to achieve a specific goal.

• A consultant's distinction: "A group is a bunch of people in an elevator. A team is also a bunch of people in an elevator, but the elevator is broken."
• Working groups meet primarily to share information; members work independently and focus on individual department goals.
• Teams are responsible for achieving specific common goals and are empowered to make decisions needed to complete their tasks.
• Example: Department-store managers meeting monthly to discuss cost-cutting are a group if each focuses only on their own department goals.

🔑 Five key characteristics of work teams

Characteristic	What it means
Accountable for common goals	Members are collectively responsible and rewarded for achieving team goals
Function interdependently	Members must rely on each other for information, input, and expertise; cannot achieve goals alone
Stable	Teams remain intact long enough to finish tasks; members stay long enough to know each other
Have authority	Teams possess decision-making power to pursue goals and manage activities
Operate in social context	Teams work for larger organizations and have access to organizational resources

💪 What makes teams effective

💪 Factors contributing to effective teamwork

The excerpt identifies six factors that contribute to team effectiveness:

• Interdependence: When team members rely on each other to get the job done, productivity and efficiency are high.
• Trust: Teamwork is more effective when members trust each other.
• Collective performance: When team members perform better as a group than alone, collective performance exceeds individual performance.
• Mutual encouragement: When each member is encouraged by others to do their best, collective results improve.
• Satisfaction: The more team members derive satisfaction from being on the team, the more committed they become.
• Shared leadership: Teams function effectively when leadership responsibility rotates and is shared over time.

Don't confuse: These factors are not always straightforward; the excerpt notes that "such issues are rarely as clear-cut as they may seem at first glance."

📊 Organizational benefits of teams

Organizations report significant productivity gains from team-based operations:

Organization	Result
Xerox	Team-based operations 30% more productive than conventional operations
General Mills	Team-organized factories 40% more productive than traditional factories
Shenandoah Life Insurance	Teams cut case-handling time from 27 to 2 days; virtually eliminated service complaints
FedEx	Teams reduced service errors (lost packages, incorrect bills) by 13% in first year

🧲 Group cohesiveness and its effects

🧲 What is group cohesiveness

Group cohesiveness: the attractiveness of a team to its members.

• High cohesiveness: Membership is quite satisfying; members like being part of the group.
• Low cohesiveness: Members are unhappy and may even try to leave.
• The principle is based on the idea that groups are most effective when their members like being members of the group.

🔢 Five factors that contribute to cohesiveness

1. Size: The bigger the team, the less satisfied members tend to be. Large teams make close interaction harder, a few members dominate, and conflict becomes more likely.

2. Similarity: People usually get along better with people like themselves; teams are more cohesive when members share attitudes and experience.

3. Success: When teams are successful, members are satisfied, and other people are more attracted to their teams.

4. Exclusiveness: The harder it is to get into a group, the happier the people already in it. Status and perks increase members' satisfaction.

5. Competition: Members value membership more highly when motivated to achieve common goals—especially when those goals mean outperforming other teams.

⚠️ The danger of too much cohesiveness

• A cohesive team with goals aligned with organizational goals is most likely to succeed.
• However: Too much cohesiveness can be problematic.
• When members get too wrapped up in immediate team goals, the whole team may lose sight of larger organizational goals.
• Too much conformity can make the group resist change and fresh ideas, or adopt its own dysfunctional tendencies.

Don't confuse: High cohesiveness is generally good, but excessive cohesiveness can harm both team and organizational effectiveness.

🚨 Groupthink and team failure

🚨 What is groupthink

Groupthink: the tendency to conform to group pressure in making decisions, while failing to think critically or to consider outside influences.

• Occurs when there's too much conformity to the team's rules and guidelines.
• The group may resist change and fresh ideas.
• Example: The space shuttle Challenger explosion in January 1986—engineers from a component supplier warned the launch might be risky because of weather but were persuaded to reverse their recommendation by NASA officials who wanted the launch to proceed as scheduled.

❌ Four common obstacles to team success

Obstacle	What it means	Example from excerpt
Unwillingness to cooperate	Members don't or won't commit to a common goal or set of activities	Half a product-development team wants to create a new product, half wants to improve an existing one—team gets stuck for weeks or months
Lack of managerial support	Management isn't willing to commit needed resources (funding, key personnel)	Team will probably fall short of goals without organizational resources
Failure to delegate authority	Team leaders (often former supervisors) give instructions and expect them carried out instead of building consensus	This approach doesn't work well in leading a team, where success depends on letting people make their own decisions
Failure of teams to cooperate	In team-based organizations, teams can't agree on mutual goals or duplicate efforts	Neither the teams nor the organization is likely to meet with much success

🛠️ Skills and roles team members need

🛠️ Three essential skill sets

Every team requires some mixture of three sets of skills:

1. Technical skills: Teams need people with skills to perform required tasks.

   • Example: If your project calls for a lot of math work, it's good to have someone with the necessary quantitative skills.

2. Decision-making and problem-solving skills: Teams need members skilled in identifying problems, evaluating alternative solutions, and deciding on the best options.

   • Every task is subject to problems, and handling every problem means deciding on the best solution.

3. Interpersonal skills: Teams benefit from members who know how to listen, provide feedback, and smooth ruffled feathers.

   • These people are usually good at communicating the team's goals and needs to outsiders.

Important note: The key to success is the right mix of these skills. No team needs to possess all these skills from day one—teams often gain skills when members volunteer for tasks and perfect their skills in the process. Effective teamwork develops over time.

🎭 Two categories of team roles

Every team faces two basic challenges:

1. Accomplishing its assigned task
2. Maintaining or improving group cohesiveness

Team roles divide into two categories based on which challenge they address:

• Task-facilitating roles: Help accomplish the team's assigned task
• Relationship-building roles: Help maintain or improve group cohesiveness

Whether you affect the team's work positively or negatively depends on the extent to which you help or hinder the team in meeting these two challenges.

Don't confuse: Team members can have as much impact on a team's success as its leaders—you'll be a team member more often than a leader, both as a student and in the workforce.

🎓 Practical advice for class team projects

🎓 Why teamwork matters in education and career

• More than two-thirds of all students report having participated in organized team work.
• A survey of Fortune 1000 companies reveals that 79% rely on self-managing teams and 91% on various forms of employee work groups.
• The skill most employers value in new employees is the ability to work in teams.
• A survey of 60+ top organizations found that 60% cited "inability to work in teams" as the reason high-potential leadership candidates derail (stop moving up), while only 9% attributed failure to "lack of technical ability."

One student's perspective: "In the real world, you have to work with people. You don't always know the people you work with, and you don't always get along with them. Your boss won't particularly care, and if you can't get the job done, your job may end up on the line."

📋 Eight strategies for successful class team projects

1. Draw up a team charter: At the beginning, create a charter that includes goals, ways to ensure each member's ideas are considered and respected, meeting times and places, consequences for skipping meetings or not doing fair share, and how conflicts will be resolved.

2. Contribute your ideas: Share your ideas with your group; they might be valuable. The worst that could happen is that they won't be used.

3. Never miss a meeting: Pick a weekly meeting time, write it into your schedule as if it were a class, never skip it, and make meetings productive.

4. Be considerate of each other: Be patient, listen to everyone, communicate frequently, involve everyone in decision making, don't think you're always right, be positive, avoid infighting, build trust.

5. Create a process for resolving conflict: Do this before conflict arises. Set up rules to help decide whether conflict is constructive, personal, or arises because someone won't pull their weight.

6. Use the strengths of each team member: Some students are good researchers, others are good writers, others have strong problem-solving or computer skills, while others are good at generating ideas. Match tasks to strengths.

7. Don't do all the work yourself: Work with your team to get the work done. The experience of working in a team is more important than the project output.

8. Set deadlines: Don't leave everything to the end; divide up tasks, hold team members accountable, and set intermediary deadlines for each team member to get their work done.

🧭 Overview

🧠 One-sentence thesis

Problem solving is a learnable process skill that uses structured strategies to identify problems, develop solutions, and take action—making it essential for personal success and a top employer priority in manufacturing and other industries.

📌 Key points (3–5)

• Core definition: Problem solving is the process of identifying a problem, developing possible solution paths, and taking appropriate action.
• Two strategy types: Algorithmic strategies (step-by-step for single-solution problems) vs. heuristic methods (general guides for multiple-solution problems).
• Root cause matters: The first step is always to identify the root cause, not just surface symptoms—ask "why" repeatedly and gather facts before jumping to solutions.
• Common confusion: Don't confuse the visible symptom with the underlying cause; similar problems can arise from different root events.
• Why it matters for careers: Employers rank problem solving as the number one skill deficiency, making it critical for efficiency, safety, communication, and business success.

🔍 What problem solving is and why it matters

🔍 The basic definition

Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.

• It's a basic life skill used daily at home, school, and work—often without conscious thought.
• Example: It's raining and you need to go to the store. Possible solutions include taking an umbrella and walking, driving, taking the bus, calling a friend, or going another day. No single "right" answer exists.
• Different people solve the same problem differently.

💼 Why employers care

• In the fast-changing global economy, employers identify everyday problem solving as crucial to organizational success.
• For employees, it demonstrates independence and initiative and enables practical and creative solutions.
• A 2011 manufacturing survey found problem solving was the number one skill deficiency among current employees, making it difficult for companies to adapt to industry changes.
• Professional skills like problem solving are considered critical to business success, affecting efficiency, profitability, safety, and cost reduction.

🛠️ Two types of problem solving strategies

🛠️ Algorithmic strategies

• What they are: Traditional step-by-step guides to solving problems.
• When to use them: Best when there is a single path to the correct solution.
• Examples from the excerpt:
  • Math problems: "multiply and divide, then add or subtract"
  • Mnemonics: "Spring Forward, Fall Back" (daylight saving time) or "Righty Tighty, Lefty Loosey" (turning bolts and screws)
• They provide a fixed order of steps to follow.

🧩 Heuristic methods

• What they are: General guides used to identify possible solutions when no single solution exists.
• When to use them: For problems with multiple possible solutions or complex situations.
• IDEAL framework (a popular heuristic):
  • Identify the problem
  • Define the context
  • Explore possible strategies
  • Act on a plan
  • Look back and learn
• Building a toolbox of strategies improves problem solving skills; with practice, you can recognize and use multiple strategies for complex problems.

🌱 The problem solving process

🌱 Step 1: Identify the root cause

• Why it's first: You must recognize there is a problem and identify the right cause, not just the symptom.
• Root cause: The underlying event or condition that started the problem.
• Don't confuse: Similar problems can arise from different events; the real issue may not be apparent at first.

Example from the excerpt:

• Symptom: Your student ID card is demagnetized after working in the workshop.
• First assumption: Wear and tear (the card is old).
• Root cause discovered: A strong magnet stored under a workbench demagnetized multiple students' cards that same week.

🔎 How to find the root cause

• Ask questions and gather information rather than guessing solutions.
• Be curious and make logical deductions rather than assumptions.
• Questions to ask:
  • What do you know about the problem? What don't you know?
  • When was the last time it worked correctly?
  • What has changed since then?
  • Can you diagram the process into separate steps?
  • Where in the process is the problem occurring?

🔧 Root Cause Analysis (RCA)

Root cause analysis (RCA): A method of problem solving that helps people answer the question of why the problem occurred.

• Uses specific tools like:
  • "5 Why Analysis": Ask "why" repeatedly to drill down to the underlying cause.
  • "Cause and Effect Diagram": Visual tool to map contributing factors.
• Goal: Identify the origin so you can address it properly and prevent recurrence.

🔄 Step 2: Explore possible solutions

• Once the root cause is identified and the scope defined, explore strategies to fix the problem.
• It's okay to ask for help: Problem solving is learned with practice; no one knows everything.
• Collaboration improves outcomes: Working together improves workplace communication and accelerates finding solutions.

💡 Brainstorming technique

Brainstorming: A technique designed to generate a large number of ideas for the solution to a problem.

• Goal: Come up with as many ideas as you can in a fixed amount of time.
• Best done in a group, but can be done individually.
• Quantity over quality initially—generate options before evaluating them.

🗣️ Communication in problem solving

🗣️ Why communication matters

• When issues arise, they must be addressed efficiently and in a timely manner.
• Effective communication can:
  • Prevent problems from recurring
  • Avoid injury to personnel
  • Reduce rework and scrap
  • Reduce cost and save money
• Especially crucial in manufacturing with heavy, costly, and sometimes dangerous equipment.

👥 The huddle strategy

Huddle: A short meeting with everyone standing in a circle (like football players).

• Purpose: Ensure team members are aware of how their work impacts one another.
• Daily huddles: Make team members aware of schedule changes, problems, or safety issues.
• Impromptu huddles: Gather information on a specific issue and get each team member's input.
• When done right, huddles create collaboration, communication, and accountability to results.

⚠️ Don't try to solve everything at once

• Quote from the excerpt: "Never try to solve all the problems at once—make them line up for you one-by-one." (Richard Sloma)
• Focus on one problem at a time for effective resolution.

🎯 Problem solving as a learnable skill

🎯 It improves with practice

• The ability to solve problems is a skill at which you can improve.
• Learning about different strategies and when to use them gives you a good start.
• Building a toolbox of problem solving strategies will improve your skills over time.

🎯 Process over perfection

• Problem solving is a process that uses steps to solve problems.
• Most strategies provide steps that help you identify the problem and choose the best solution.
• With practice, you'll recognize and use multiple strategies to solve complex problems.

🧭 Overview

🧠 One-sentence thesis

Industrial engineers think systematically by blaming systems rather than people when problems occur, and they continuously improve processes to help ordinary people do extraordinary work efficiently, safely, and consistently.

📌 Key points (3–5)

• Core IE mindset: When problems occur, IEs blame the system, not the people, and keep asking "why" until the root cause is identified.
• Design philosophy: Systems should be designed so people do tasks right the first time every time, working efficiently, well, and safely.
• Process over inspection: Quality comes from good processes, not from inspecting and fixing problems after they occur.
• Common confusion: IE ideas get repackaged periodically (TQM, CQI, lean manufacturing, Six Sigma) but the core principles remain the same.
• Universal application: IE concepts apply to any organization and even to improving yourself as an individual.

🔍 How Industrial Engineers Think

🔍 Blame the system, not the people

The IE mindset: when something goes wrong, an IE blames the system, not the people.

• This is the foundational thinking pattern that distinguishes IEs.
• Even when people make mistakes, the IE asks how the system can be improved to prevent those mistakes.
• Example: A customer receives the wrong shipment. Instead of blaming the worker who applied the label, the IE traces back through the system—asking why the wrong label was used, why shipments were moved, why orders changed—potentially discovering problems in order tracking, sales processes, or label-printing procedures.

❓ The "Five Whys" approach

• IEs keep asking "why" until they identify the root cause.
• Each answer leads to another "why" question, drilling deeper into the system.
• The solution often involves changing the physical system, information system, or procedures—not punishing individuals.
• Example solution: Don't print shipping labels until the order is actually being shipped, preventing premature labeling of items that might change.

🎯 Core Design Principles

🎯 Right the first time, every time

• Design systems so people perform tasks correctly on the first attempt, consistently.
• The goal is to eliminate the need for rework or correction.
• This principle connects to reducing variation—when tasks are done consistently, quality improves.

🛡️ Efficiency, quality, and safety together

• How a person does a job is important in achieving all three goals simultaneously.
• The process for doing a task makes a big difference in outcomes.
• Systems should help ordinary people do extraordinary work—the system elevates performance, not just exceptional individuals.

📊 Process over inspection

• Achieve quality by having good processes, not by inspecting goods and services to fix problems after they occur.
• Prevention is built into the system design rather than relying on detection and correction.
• Don't confuse: This doesn't mean no inspection ever happens, but inspection shouldn't be the primary quality mechanism.

🔧 Continuous Improvement Philosophy

🔧 Always room for improvement

• "If it ain't broke, it can still be improved"—IEs are always thinking "this could be done better."
• Small incremental improvements add up over time.
• Sometimes more radical reengineering may be needed instead of incremental changes.

🔄 Reduce variation

• Reducing variation in a system leads to tasks being done consistently.
• Consistency supports quality, efficiency, and safety goals.
• Variation makes it harder to predict outcomes and maintain standards.

🏭 IE ideas repackaged over time

Concept	What it means
Same core ideas	IE principles have been around for decades
Different names	TQM, CQI, re-engineering, Toyota system, lean manufacturing, Six Sigma
Why it matters	Don't be confused by new labels—recognize the underlying IE principles

🌐 Broader IE Principles

🌐 What IEs design

• While most engineers design physical objects, industrial engineers design systems.
• A system includes physical objects but also includes rules and procedures that aren't physical.
• IEs can work for any organization because they improve processes and systems universally.

👥 People and teams

• Happy employees are productive employees.
• A team using good team processes will produce better work than any individual could alone.
• The customer comes first (though not always right).
• All products and services involve both product and service elements.

📈 Decision-making and information

• Decisions should be based on facts, logic, and analysis, not hunches.
• People grasp information better, especially data, when displayed visually.
• Don't computerize an inefficient process—make the process efficient first, then apply technology.

🌍 Context and learning

• Every organization must scan the environment for change and consider its place in the global economy.
• Think globally, act locally—consider the larger context when making changes.
• IEs must engage in lifelong learning: keep up with new technologies, software, and ideas.

🪞 Applying IE to Yourself

🪞 IE is about you

• Industrial engineering concepts can be applied to your own life.
• As an IE improves organizational systems, you can improve the system that is you.
• Use good processes for doing your own work.

🪞 Seven Habits connection

The excerpt references Stephen Covey's Seven Habits as a way to apply IE ideas to personal effectiveness:

1. Be proactive
2. Begin with the end in mind
3. Put first things first
4. Think win/win
5. Seek first to understand, then to be understood
6. Synergize
7. Sharpen the saw

These habits help you become an effective person using IE thinking—working within your context (team, organization, community, world) while maintaining a systems approach to the big picture.

🧭 Overview

🧠 One-sentence thesis

Industrial engineers use models—especially mathematical ones—to experiment with and optimize real-world systems without disrupting actual operations, enabling them to find efficient solutions to complex problems like resource allocation and scheduling.

📌 Key points (3–5)

• Why models are needed: Real-world experimentation (moving equipment, changing procedures) is difficult and disruptive, so IEs create models to test changes safely.
• The model cycle: Models represent relevant parts of real systems, allow analysis to extract information, and require interpretation back to the real world with the reminder "it's only a model."
• Deterministic optimization models: These models have no probabilities and find the best (optimal) solution; linear programming (LP) is a common type used for efficiency problems.
• Common confusion: Models are never exactly correct—they simplify reality by including only relevant features and may need adjustments (e.g., different models for different time periods).
• Wide applicability: Deterministic models solve diverse IE problems including product mix, scheduling, blending, staffing, transportation, and routing.

🔄 How models work in IE practice

🌍 Real world system

• The first step is drawing boundaries: deciding what is inside your system and what is in the environment.
• Example: To study customer service at a bank, include tellers, drive-up windows, and ATMs; exclude roads and traffic systems that bring people to the bank.
• This boundary decision determines what the model must represent.

🧮 The model itself

Model: A representation of a real-world system, usually mathematical for IEs, expressed in equations or computer code.

• Physical models are used by some engineers (e.g., civil engineers use scale models on shaking tables for earthquake testing).
• IEs tend to use mathematical models because they work with processes, flows, and resource allocation.
• Example: An IE might use queuing theory mathematics to model the bank system.

➡️ Representation arrow

• The model represents the real world system but only the relevant parts.
• Must include: information that affects the outcome (e.g., time between customer arrivals, service time per customer—both vary).
• Can exclude: irrelevant details (e.g., color of customers' clothing).
• May conditionally include: features depending on purpose (e.g., customer disabilities if predicting wheelchair-accessible teller requirements).
• Example from queuing: The M/M/1 model describes time between arrivals as an exponential random variable with average 1 customer per λ (say 6 minutes) and service time with average 1 customer per μ (say 4 minutes).

🔁 Analysis loop

• Once the model exists, the IE extracts information through analysis.
• IEs use frequently-used models with proven mathematical results.
• Example: The M/M/1 queuing model can compute the average number of people in the queue using established formulas.

🔙 Interpretation arrow

• The IE interprets mathematical results back to the real world system.
• Critical reminder: "It's only a model"—predictions may not be perfect.
• Don't confuse: Model assumptions with reality. The M/M/1 model assumes constant average arrival rate, so results using λ = 10 customers per hour apply only to periods with that rate.
• Example: A separate model might be needed for the lunch hour, which is probably busier.

🎯 Deterministic optimization models

📐 What deterministic means

Deterministic model: A model with no probabilities.

Optimization: Finding the optimal (best) solution.

• IEs are responsible for efficiency, including efficient use of time and resources.
• While calculus can find maxima/minima of functions, IEs often need to maximize or minimize linear functions with many variables and constraints.
• This is not easy despite sounding simple.

🌾 Linear programming example: the feed problem

Scenario: A dairy farmer wants to mix feed for cows that meets nutrient requirements at minimum cost.

Given data:

Feed	Protein %	Potassium %	Cost ($/ton)
Alfalfa	28%	0.26%	$160
Hominy	Medium	Good	Cheaper than alfalfa
Corn cobs	Poor	Poor	Free (waste product)

Requirements: Feed must meet protein and potassium requirements (as percentages).

Decision variables: What fraction should be alfalfa (A), hominy (H), and corn cobs (C)?

Constraint: Because ingredients represent percentages of the whole, A + H + C = 1.

Linear programming formulation: Expressing the situation as minimizing a linear objective function (cost) subject to linear constraints (protein and potassium).

Intuition: The optimal mix will probably need alfalfa to meet protein requirement, hominy to meet potassium requirement, and corn cobs to keep cost down.

🔧 Solving LP models

• Can be solved mathematically.
• Computer programs help solve these problems.
• Excel is one such program, though better specialized tools exist for LP models.
• This promotes efficiency by helping use resources efficiently (e.g., keeping cattle healthy while minimizing feed cost).

🔢 Integer programming variation

• The feed example assumed any real amount of ingredients can be bought.
• Don't confuse: If only integer quantities can be purchased, LP is not appropriate.
• Integer programming model (IP): A model where decision variables must be integers.

🛠️ Common IE applications of deterministic models

📦 Production and resource problems

• Product mix: Determine how much of each product type to make subject to constraints on available resources.
• Production scheduling: Determine how much of each product type to make in different time periods to meet specified production amounts by certain times.
• Blending: Determine the best blend of inputs to minimize cost of producing a mixture (the feed example was a blending model).
• Cutting stock: Determine the best way to cut resource material to maximize profit (e.g., a log can be cut into lumber of various dimensions sold for different amounts).

👥 People and routing problems

• Staffing: Determine the best way to assign people to jobs to maximize their preference or productivity based on their abilities at different jobs.
• Assignment: Determine the best way to assign resources to tasks.
• Transportation: Determine the best way to route resources through a transportation network to minimize cost while delivering appropriate amounts to each location.
• Traveling salesman problem: Determine the best route among a number of points that visits each point at least once.

🎓 IE skills required

Industrial engineers must be able to:

1. Recognize situations where a deterministic model can be applied.
2. Create an appropriate model.
3. Solve the model using an appropriate tool.

🧭 Overview

🧠 One-sentence thesis

Deming's 14 Points emphasize that lasting quality and productivity improvements come from transforming management systems and empowering people, rather than from inspecting finished products or pressuring workers with quotas and slogans.

📌 Key points (3–5)

• Deming's impact: After US manufacturers ignored him post-WWII, Deming helped Japan rebuild its production systems using statistical process control, and Japan credits him for major quality and efficiency gains.
• System over inspection: The 14 Points call for building quality into the process from the start, improving systems constantly, and eliminating mass inspection as the primary quality tool.
• People-centered philosophy: Deming insisted on driving out fear, removing barriers to pride in workmanship, and rejecting numerical quotas or merit ratings that judge workers instead of improving systems.
• Common confusion: Deming is known for "measure, measure, measure," but he stressed using measurements to improve processes, not to judge or incentivize individual employees—using scores to judge people leads to manipulation, not improvement.
• Leadership and transformation: The 14 Points require leadership that helps workers do better jobs, cross-department teamwork, and a company-wide commitment to transformation.

🏭 Deming's journey and influence

🇺🇸 Post-WWII rejection in the US

• W. Edwards Deming (1900–1993) applied statistical process control during World War II to help US war production.
• After the war, US manufacturers faced soaring consumer demand and felt little need to focus on efficiency and quality, so they largely ignored Deming's ideas.

🇯🇵 Success in Japan

• In 1950, the Union of Japanese Scientists and Engineers (JUSE) invited Deming to Japan to help rebuild Japanese production.
• Japan credits Deming for playing a major role in the success of Japanese manufacturing, especially improvements in quality and efficiency.
• The most prestigious quality award in Japan (awarded by JUSE) is the Deming Prize.
• Deming donated the royalties from his first Japanese lectures (transcribed into a book) back to JUSE.

📺 Rediscovery in America

• In 1980, NBC aired a documentary titled "If Japan Can … Why Can't We?" explaining Japanese progress and crediting Deming.
• After the documentary, Deming said "his phone rang off the hook"—US companies finally sought his help.
• Deming published Out of Crisis in 1986, summarizing his teaching in 14 Points.

💭 Deming's character

• He composed an easily sung version of the Star Spangled Banner.
• When asked how he wanted to be remembered, he said "I probably won't even be remembered," but added "Well, maybe … as someone who tried to keep America from committing suicide."

🔧 The 14 Points: system transformation

🎯 Points 1–2: Purpose and philosophy

1. Create constancy of purpose towards improvement of product and service, with the aim to become competitive, stay in business, and provide jobs.
2. Adopt the new philosophy: We are in a new economic age; Western management must awaken to the challenge, learn their responsibilities, and take on leadership for change.

🔍 Points 3–5: Quality in the process, not inspection

3. Cease dependence on inspection to achieve quality. Eliminate the need for mass inspection by building quality into the product in the first place.
4. End awarding business on price tag alone. Instead, minimize total cost. Move toward a single supplier for any one item, on a long-term relationship of loyalty and trust.
5. Improve constantly and forever the system of production and service, to improve quality and productivity, and thus constantly decrease costs.

• Don't confuse: Deming is not against measurement—he is against relying on inspection after the fact instead of preventing defects during production.

👥 Points 6–9: Training, leadership, and teamwork

6. Institute training on the job.
7. Institute leadership. The aim of supervision should be to help people and machines do a better job. Supervision of management and production workers both need overhaul.
8. Drive out fear, so that everyone may work effectively for the company.
9. Break down barriers between departments. People in research, design, sales, and production must work as a team to foresee problems.

🚫 Points 10–12: Eliminate quotas, slogans, and merit ratings

10. Eliminate slogans, exhortations, and targets for the workforce asking for zero defects and new productivity levels. Such exhortations only create adversarial relationships, as the bulk of the causes of low quality and low productivity belong to the system and lie beyond the power of the workforce.
11. (a) Eliminate work standards (quotas) on the factory floor; substitute leadership. (b) Eliminate management by objective and management by numbers; substitute leadership.
12. (a) Remove barriers that rob the hourly worker of the right to pride of workmanship; the responsibility of supervisors must change from sheer numbers to quality. (b) Remove barriers that rob people in management and engineering of their right to pride of workmanship. This means abolishing the annual or merit rating and management by objective.

🌱 Points 13–14: Education and company-wide transformation

13. Institute a vigorous program of education and self-improvement.
14. Put everybody in the company to work to accomplish the transformation. The transformation is everybody's job.

🧑‍🤝‍🧑 Deming's focus on people

🛡️ Drive out fear (Point 8 elaborated)

"No one can put his best performance unless he feels secure. Se comes from the Latin, meaning without, cure means fear or care. Secure means without fear, not afraid to express ideas, not afraid to ask questions."

• Fear takes many faces; a common denominator is loss from impaired performance and padded figures.
• Example: If workers are afraid to report problems or ask questions, they cannot contribute to improvement.

🎨 Workers need good materials, not slogans (Point 10 elaborated)

"Eliminate targets, slogans, exhortations, posters for the work force that urge them to increase productivity. 'Your work is your self-portrait. Would you sign it?' No – not when you give me defective canvas to work with, paint not suited to the job, brushes worn out, so that I can not call it my work."

• Posters and slogans never helped anyone do a better job.
• The primary cause of poor work is not lack of effort by workers—it is the system that provides defective materials and inadequate tools.

📊 Goals without a roadmap harm performance

"Goals are necessary for you and for me, but numerical goals set for other people, without a road map to reach the goal, have effects opposite to the effects sought."

• Deming was famous for insisting on measurements, but he thought numbers should not be used to judge workers.
• Don't confuse: Measuring to improve the process ≠ measuring to judge or incentivize individual employees.

🤝 Remove barriers and trust workers

"Give the work force a chance to work with pride, and the 3 per cent that apparently don't care will erode itself by peer pressure."

• Deming emphasized repeatedly the need to remove barriers that prevent good work.
• Fundamentally, Deming believed in people: "People require in their careers, more than money, ever-broadening opportunities to add something to society, materially and otherwise."

⚠️ Misusing customer satisfaction scores

• Denove and Power (from J.D. Power and Associates) describe customer satisfaction surveys that help companies listen to the voice of the customer and become more profitable.
• However, some companies use these surveys to judge particular stores or make managers' salaries dependent on satisfaction scores.
• Natural effect: employees manipulate the ratings, even begging customers for good reviews.
• The lesson: no single quantitative measure, or even a group of measures, can replace good judgment.
• This echoes Deming's warning: use feedback to improve the process, not to judge people.

Deming's approach	Common misuse	Result of misuse
Measure to improve the process	Measure to judge/incentivize employees	Manipulation, fear, padded figures
Remove barriers, provide good tools	Set numerical targets without a roadmap	Adversarial relationships, opposite effects
Drive out fear, encourage questions	Use merit ratings and quotas	Workers hide problems, cannot contribute

🔑 Core philosophy summary

🔄 System thinking

• The bulk of quality and productivity problems belong to the system, not to individual workers.
• Management must take responsibility for improving the system constantly and forever.
• Building quality into the process from the start is more effective than inspecting finished products.

🧑‍💼 Leadership, not pressure

• Supervision should help people and machines do better jobs, not enforce quotas or slogans.
• Eliminate management by numbers and management by objective; substitute leadership.
• Workers need training, good tools, and the removal of barriers—not exhortations or fear.

🌍 Transformation is everybody's job

• The 14 Points require a company-wide commitment.
• Cross-department teamwork (research, design, sales, production) is essential to foresee and solve problems.
• Education and self-improvement must be vigorous and ongoing.

🧭 Overview

🧠 One-sentence thesis

Industrial engineering uniquely focuses on designing production systems where people and machines work together efficiently, safely, and with high quality by adapting the workplace to human capabilities and limitations.

📌 Key points (3–5)

• IE's human-centered approach: Among all engineering specialties, industrial engineering focuses most on people, designing systems that match what people and machines each do best.
• Physical ergonomics: Adapts workstations and tools to individual workers' bodies to prevent injury and reduce harm, using knowledge of anatomy, physiology, and physics.
• Safety as system design: IEs design systems so safety occurs naturally; when accidents happen, the instinct is to blame and fix the system, not the worker.
• Cognitive engineering: Builds on psychology to design tasks humans can perform well, balancing stimulation (avoiding both boredom and stress) and keeping humans "in the loop."
• Common confusion: Safety is not just about training workers to follow rules—it's fundamentally about designing the system to eliminate hazards and make safe behavior the natural choice.

🤝 People vs. Machines in Production

🤝 Complementary strengths

• People and machines have different capabilities: some tasks favor people (e.g., helping customers), others favor machines (e.g., lifting very heavy objects).
• Many production tasks require a combination of both.
• The goal is to design a system that achieves efficiency, quality, and safety by leveraging the strengths of each.

🎯 The design principle

• IEs must think about what people and machines "can and can't do quickly, well, and safely."
• This requires understanding both human limitations and machine capabilities.
• Example: A production line might use machines for repetitive heavy lifting while people handle quality inspection and problem-solving.

🦴 Physical Ergonomics

🦴 What physical ergonomics does

Physical ergonomics: the study and design of work to prevent harm to workers' bodies, using knowledge from physics, anatomy, and physiology.

• Researchers monitor physiological conditions (heart rate, oxygen uptake) during tasks to determine exact effects of different work on humans.
• The focus is on understanding how work methods can cause harm and preventing those situations.

🔧 Workplace adaptations

The IE may redesign jobs in several ways:

• Reduce the need to stand
• Provide better chairs for workers
• Provide better hand tools
• Reduce the need to lift heavy objects

Core principle: Ergonomics stresses adapting the workplace to the worker, not forcing workers to adapt to poorly designed workplaces.

👤 Individual differences matter

• Adaptations must be individual—one size does not fit all.
• Adjustable workstations help accommodate different workers:
  • Tables and chairs that can be raised or lowered
  • Stations that accommodate left-handed and right-handed workers
• Example: Two workers of different heights need different desk heights to maintain good posture and avoid strain.

💰 Why organizations should care

Besides being the right thing to do:

• Prevention can save the organization money
• Reduces liability exposure of the organization
• OSHA provides case studies showing how job redesign has reduced ergonomic issues

🛡️ Safety and Work Environment

🛡️ The scope of workplace danger

• In 2013, 4,585 workers died from work-related causes in the US (Bureau of Labor Statistics data).
• The workplace can be dangerous, but hazards can be reduced through design.
• The IE designs the workplace to reduce danger from tools, machines, and materials.

🔒 Design for safety

Key principle: An IE's instinct should be to design the system so that safety occurs naturally.

Example of safety-by-design:

Operation of a punch press often requires that two buttons, away from the punch location itself, be pressed simultaneously with the worker's left and right hands. If the worker's hands are pressing those buttons, the hands cannot be under the press, so cannot be injured.

🔍 Systematic safety analysis

Tools that help IEs think systematically about what can go wrong:

• FMEA (Failure Mode and Effects Analysis)
• Fault tree analysis: helps trace how errors or faults can lead to accidents
• Accident analysis: Any accident should be carefully analyzed to determine the cause, then the system should be changed to eliminate or reduce the chance of recurrence

🎯 Blame the system, not the worker

• If an injury occurs, an IE's first thought should be to blame the system.
• Example: Lockout and tagout procedures protect maintenance workers from accidental equipment startup—but the system must make these procedures easy and natural to follow.

📚 Safety training and enforcement

• The IE may be in charge of safety training programs, which should include the reasons for certain rules (not just the rules themselves).
• Many organizations have a one-strike policy: any violation of a safety rule leads to immediate dismissal.
• While this may seem extreme, it conveys clearly the organization's dedication to safety.

🌡️ Environmental factors

Apart from injury prevention, the IE must consider effects on worker comfort:

• Vibration
• Heat and cold
• Humidity
• Noise
• Air quality
• Lighting

🏥 From safety to health

• The field has expanded from injury-causing conditions to include disease-causing conditions.
• The "safety manager" is now the "safety and health manager."
• Example: Worker stress is both a health concern and a potential safety concern if the stressed worker is less safety-conscious.

⏰ Shift work and long hours

According to 2001 Bureau of Labor Statistics data:

• Almost 15 million Americans work evening shift, night shift, rotating shifts, or other irregular schedules
• The International Labour Office (2003) reports that working hours in the US exceed Japan and most of western Europe
• Both shift work and long work hours have been associated with health and safety risks

💪 Health promotion programs

Some companies have introduced programs to promote good health:

• Smoking cessation programs
• Safe driving programs
• Exercise programs

Motivation: At least partly to reduce health insurance premiums the company pays for workers.

Controversy: Some companies have forbidden workers to smoke off the job, but such programs have been controversial.

🧠 Cognitive Engineering

🧠 What cognitive engineering does

Cognitive engineering: builds on knowledge from psychology about human abilities in memory, perception, reasoning, and attention to design tasks that a human can do with efficiency, quality, and safety.

Again, the focus is on adapting the workplace to the human.

🧩 Supporting human cognitive abilities

Human ability	How to support it	Example
Memory	Provide checklists	A human who must remember tasks in a specific order can be given a checklist
Perception	Computer alerts for changes	Cockpit alarms for loss of altitude detect changes and alert the human
Reasoning	Computer decision support	An immunohematologist interpreting blood tests to identify antibodies is supported by a computer system
Attention	Share the task	A person monitoring several information sources can share the task with computers and other humans

⚖️ The stimulation balance

A critical balance must be achieved:

• Under-stimulation → leads to boredom
• Over-stimulation → leads to stress
• Both can lead to losses in efficiency, quality, and safety

Key finding: Generally, the human performs better when the worker clearly has control of the environment, including work pace.

Don't confuse: Automation is not always better—shifting control to the computer can lead to:

• Boredom
• Stress
• Inattentiveness
• Over-reliance on the computer

🎮 The "out of the loop" problem

Example from the Three Mile Island accident:

• Workers' normal job largely consists of monitoring a smoothly running reactor (boring, leads to lack of vigilance)
• When a problem occurs, the person is "out of the loop" because computer controls have been running the plant
• Workers must spend time figuring out what has happened
• A second problem: the control room design did not convey crucial information (coolant level in the reactor); workers had to infer it from other indicators

🖥️ Usability design

The design of controls (including computer hardware and software) to support human tasks requires careful analysis of usability, which is affected by:

• Screen layout
• Task sequence
• Many other factors

NASA's Human Systems Integration Division advances human-centered design through:

• Analysis, experimentation, and modeling of human performance
• Human-automation interaction studies
• Goal: dramatic improvements in safety, efficiency, and mission success

✈️ Real-world case: Flight 583 (1993)

An FAA analysis of an airliner accident shows the interplay of design, training, and behavior:

What happened:

• Flight 583 was level at 33,000 feet when leading edge slats deployed inadvertently
• Autopilot disconnected; captain manually controlled the airplane
• The airplane went through several violent pitch oscillations and lost 5,000 feet
• 2 people were killed

Probable cause (National Transportation Safety Board):

• Inadequate design of the flap/slat actuation handle by Douglas Aircraft Company
• The handle could be easily and inadvertently dislodged from the retracted position, causing slat extension during cruise flight

Contributing factors:

• Captain's attempt to recover, given the MD-11's reduced longitudinal stability and light control force characteristics in cruise flight
• Lack of specific MD-11 pilot training in recovery from high-altitude upsets
• Influence of the stall warning system on the captain's control responses
• Lack of seat restraint usage by occupants (contributed to injury severity)

Root cause: Poor design of a handle.

Key lesson: Even with trained pilots, poor design can lead to accidents—the system must be designed to prevent errors, not rely solely on human vigilance and training.

🧭 Overview

🧠 One-sentence thesis

Systems thinking emphasizes understanding how parts of a system work together as a whole, recognizing that changes to one part can have surprising effects on others and that many problems require improving the entire system rather than isolated components.

📌 Key points (3–5)

• What a system is: a group of interacting parts (human and machine activities) that work together to achieve a common purpose, with inputs, outputs, and information flows between parts.
• Analysis vs synthesis: analysis takes a system apart to understand individual parts; synthesis (systems thinking) focuses on how parts work together and how the system functions as a whole.
• Feedback loops and unintended consequences: systems with feedback can produce surprising effects; improving one part may have good or bad consequences on another part.
• Common confusion: cause and effect are not closely related in time and space—today's problems often come from yesterday's "solutions," and short-term improvements may cause long-term disasters.
• Why it matters for IEs: industrial engineers design and improve production systems, requiring them to consider the larger context, interactions with the environment, and emergent properties of the whole system.

🧩 Core definition and scope

🧩 What constitutes a system

System: "A group or work pattern of interacting human and machine activities, directed by information, which operate on and/or direct material, information, energy, and/or humans to achieve a common specific purpose or objective." (Feigenbaum)

• Parts interact through cause and effect or through exchange of information or material.
• Each part can be thought of as a process with its own inputs and outputs.
• Example: The US educational system includes preschool through higher education; students graduating from middle school (output) become high school inputs.

🔲 Drawing system boundaries

• Defining a system means drawing a line to include some parts and exclude others.
• The educational system includes schools but not roads students travel on or organizations that employ graduates.
• Trade-off: Looking at a larger system is more accurate but harder.
• Systems approach for IEs: Consider the environment surrounding the system and move boundaries outward as much as possible to examine problems in their larger context.
• Don't confuse: You can study a system without including everything, but you must remember to include interactions with the environment (e.g., transportation system, employment system).

🔄 System analysis and feedback

🔄 Feedback loops

Feedback: when outputs are fed back in as inputs for future system action (also called a feedback loop).

• Not all systems have strong feedback; the educational system has little feedback, which may hamper improvement.
• Example: High schools rarely ask graduates for feedback on how well their education prepared them for college or work.

🔍 Analysis vs synthesis

Approach	Focus	Purpose
Analysis	Take a system apart	Understand how individual parts work
Synthesis (systems thinking)	Put parts together	Understand how parts work together and how the system works as a whole

• Understanding each part of the educational system alone is not enough for good understanding or recommendations.
• Better recommendations come from understanding how parts work together.

⚠️ Unintended consequences

• A change to one part can have surprising effects on other parts.
• Example: A state requires students entering universities to meet certain standards (e.g., foreign language knowledge). Good for universities, but high schools must provide more language classes and hire more teachers.
• Example: Using antibiotics cures diseases (good for individual patient-doctor system) but creates antibiotic-resistant bacteria (bad for the larger system).
• Key insight: Improving one part may have good or bad consequences on another part.

🏗️ System properties and classifications

🌟 Emergent properties

Emergent properties: properties of the whole that are not the property of any part.

• Example: Living systems are alive, but you can't isolate that property in any single part—it's a property of the entire system.

📊 System classifications

Systems can be classified in these ways:

Dimension	Type A	Type B	Example A	Example B
Origin	Natural	Man-made	A river	A bridge
Change	Static	Dynamic	A bridge	The U.S. economy
Nature	Physical	Abstract	A factory	Architect's drawing of the factory
Interaction	Open	Closed	Interacting with environment	Interacting very little with environment

🔁 System archetypes

• Certain types of systems with feedback occur frequently in organizations and society.
• William Braun describes 10 system archetypes (common patterns):
  • Limits to Growth (aka Limits to Success)
  • Shifting the Burden
  • Eroding Goals
  • Escalation
  • Success to the Successful
  • Tragedy of the Commons
  • Fixes that Fail
  • Growth and Underinvestment
  • Accidental Adversaries
  • Attractiveness Principle
• Learning to recognize these patterns helps you know what actions to take.

📚 Laws of complex systems

📖 Peter Senge's Fifth Discipline

• Peter Senge argues organizations must become learning organizations by building knowledge of four disciplines: personal mastery, mental models, shared vision, and team learning.
• The "fifth discipline" is systems thinking.

⚖️ Eleven laws of complex systems

These laws describe how complex systems behave:

⚖️ Law 1: Yesterday's solutions → Today's problems

• Solutions can have unintended and undesired effects.

⚖️ Law 2: The harder you push, the harder the system pushes back

• "Compensating feedback" may keep a system in the state it started.

⚖️ Law 3: Behavior grows better before it grows worse

• Actions that make short-term improvement may cause long-term disaster.
• Don't confuse: Short-term success with long-term sustainability.

⚖️ Law 4: The easy way out usually leads back in

• Easy and obvious solutions would have been done already if they would have worked.
• Hard work is needed to find the real solution.

⚖️ Law 5: The cure can be worse than the disease

• Some easy solutions become addictive.

⚖️ Law 6: Faster is slower

• Any organization has an optimal rate of growth.

⚖️ Law 7: Cause and effect are not closely related in time and space

• This is a key source of confusion in complex systems.

⚖️ Law 8: Small changes can produce big results

• But the areas of highest leverage are often the least obvious.

⚖️ Law 9: You can have your cake and eat it too – but not at once

• Example: An improvement in quality pays off eventually in improved profits (not immediately).

⚖️ Law 10: Dividing an elephant in half does not produce two small elephants

• Some problems must be solved by improving the whole system, not by breaking it into parts.

⚖️ Law 11: There is no blame

• "You and the cause of your problems are part of a single system."

🏭 Systems thinking for industrial engineers

🏭 Production systems

Production system: a system that produces goods or services for customers.

• While most engineers design physical objects (cars, bridges), IEs design and improve production systems.
• IEs must think about how a production system works as a system using systems thinking concepts.

🔧 Why systems thinking matters for IEs

• The systems approach reminds IEs to:
  • Consider the environment surrounding the system being studied.
  • Move boundaries outward as much as possible.
  • Examine problems in their larger context.
• This is why some industrial engineering departments are called "Industrial and Systems Engineering" or "Industrial, Manufacturing, and Systems Engineering" (e.g., UTA's department).
• Don't confuse: "Systems engineering" sometimes has a more limited meaning, referring to designing computer and information systems.

🧭 Overview

🧠 One-sentence thesis

Lean operations eliminate all forms of waste by focusing on customer-defined value, continuous flow, pull production, and relentless pursuit of perfection rather than merely competing against rivals.

📌 Key points (3–5)

• Eight types of waste (TIM WOODS): Transport, Inventory, Motion, Waiting, Over-production, Over-processing, Defects, and Skills underutilization—all must be eliminated.
• Value is customer-defined: Activities are classified as Value Added (VA), Non-Value Added (NVA, eliminate immediately), or Essential Non-Value Added (ENVA, regulatory/policy requirements).
• Continuous flow eliminates batches and queues: Products should never wait; bottlenecks are identified where Work in Progress (WIP) piles up and must be addressed.
• Pull vs push: No product is made until a customer asks for it; reducing lead time allows less inventory and more variety.
• Common confusion: Optimizing individual steps vs improving overall flow—lean focuses on system-wide flow, not isolated efficiency gains.

🗑️ The eight wastes (TIM WOODS)

🗑️ What "lean" means

Lean operations have no waste.

• The word "lean" means skinny or having no fat—applied to operations, it means eliminating all forms of waste.
• Waste is also called muda in lean terminology.

📋 The TIM WOODS framework

Lean identifies eight categories of waste:

Letter	Waste type	What it includes
T	Transport	Moving people, products, and information
I	Inventory	Storing parts, pieces, documentation ahead of requirements
M	Motion	Bending, turning, reaching, lifting
W	Waiting	For parts, information, instructions, equipment
O	Over-production	Making more than is IMMEDIATELY required
O	Over-processing	Tighter tolerances or higher grade materials than necessary
D	Defects	Rework, scrap, incorrect documentation
S	Skills	Under-utilizing capabilities, delegating tasks with inadequate training

• The framework of lean operations focuses on value, value stream, flow, pull, and perfection to reduce all these waste types.

💎 Customer-defined value

💎 What value means in lean

Value is defined by the customer.

• An organization must have a clear definition of value as perceived by the customer.
• Time and money spent on features that the customer does not perceive as valuable are wasted.
• Knowing what the customer values requires becoming close to the customer and constantly soliciting feedback.
• Don't confuse: "value" is not what the company thinks is important—it is only what the customer perceives as valuable.

🗺️ Value stream mapping

In lean operations, a process flow diagram is called a value stream map.

The diagram identifies all activities and places them into three categories:

Category	Abbreviation	Definition	Action
Value Added	VA	Activities that create value as perceived by the customer	Keep
Non-Value Added	NVA	Activities which don't create value as perceived by the customer	Eliminate immediately
Essential Non-Value Added	ENVA	Activities that create no value but are company or regulatory policies	Can't be eliminated just yet

⏱️ Time analysis in value streams

• Value stream mapping identifies the time actually spent in adding value.
• It also identifies the time the product spends in storage or transport.
• Time in storage or transport is waste and should be eliminated.
• Example: An organization might discover that a product spends 95% of its time waiting or moving, with only 5% in value-adding activities—this reveals opportunities for waste reduction.

🌊 Continuous flow principles

🌊 What continuous flow means

In continuous flow, a product never waits but flows continuously through the manufacturing system, thus eliminating time in storage and in transport.

• Batches and queues should be eliminated.
• The principle can be applied in the production of services also.

🚫 Why batches create waste

• If products move through production systems in batches, the first item in a batch must wait until the last item is completed before it moves to the next processing step.
• Batches mean product spends time waiting, and that time is waste.
• The presence of queues means a product was completed at a previous step before the next step was ready for more input.

🎯 Matching production to demand

• Since the final step is shipping the product to customers, product should be produced at the rate that meets the market demand.
• Don't confuse: producing faster than demand creates inventory waste; producing slower creates waiting waste.

🔧 Setup reduction

One barrier to flow and one reason for using batches is setup time—the time necessary to switch the production facility from producing one kind of product to another.

Example: Richards Industries reduced setup times from an average of 50 minutes to 27 minutes, enabling them to reduce typical batch size from 200 to about 20 to 30.

• Shorter setup times allow smaller batches, which improves flow and reduces inventory waste.

🔍 Identifying and eliminating bottlenecks

🔍 What a bottleneck is

A bottleneck is the narrowest part of a bottle and limits the flow in or out of the bottle.

• In production, a bottleneck is the place in the process with the least capacity.
• Bottlenecks can be identified by looking for places where WIP (Work in Progress) piles up and creates queues.

🧮 Bottleneck example: mess kit washing

The excerpt describes a World War II example:

• Original setup: Four tubs—two for washing, two for rinsing.
• Problem: Washing took three times as long as rinsing (3 minutes vs 1 minute).
• Capacity analysis:
  • Two wash tubs: 40 soldiers per hour
  • Two rinse tubs: 120 soldiers per hour
  • Washing was the bottleneck (long lines formed at wash tubs)
• Solution: Change to three wash tubs and one rinse tub.
• New capacity:
  • Three wash tubs: 60 soldiers per hour
  • One rinse tub: 60 soldiers per hour
  • Balanced capacity eliminated waiting lines

🔄 Improving bottleneck capacity

The processing rate at a bottleneck can be increased by:

1. Reducing the time to process one item
2. Adding more processing capability

• As a bottleneck's rate is improved, WIP in front of it will disappear.
• Warning: Another bottleneck may now appear elsewhere in the system.

🎯 System-wide vs local optimization

The excerpt emphasizes that waste-reduction initiatives should "improve the overall flow rather than merely optimize individual steps."

• Don't confuse: making one step faster in isolation may not improve overall system throughput if that step wasn't the bottleneck.

🎣 Pull systems and continuous improvement

🎣 Pull vs push production

In a lean system, no product or service is produced until a customer asks for it—that is, product is pulled not pushed through the system.

• Some product must be maintained in sales places to meet immediate demand.
• Reduction in lead time through improvements (such as daily deliveries) reduces the amount of stock kept on hand and allows more variety in stock.

🔄 Continuous improvement mindset

Lean systems constantly seek perfection by working to continuously improve.

Key principle from Womack and Jones' Lean Thinking:

"To hell with your competitors; compete against perfection by identifying all activities that are muda and eliminating them."

• An organization should not compete against its competitors.
• If benchmarking shows the company is doing better than competitors, it should not relax.
• The focus is on eliminating all waste, not on being marginally better than rivals.

🛠️ Tools for continuous improvement

In addition to the tools described (value stream mapping, bottleneck analysis, setup reduction), industrial engineers use many other tools to continuously reduce wastes in production or service systems throughout the IE curriculum.

🧭 Overview

🧠 One-sentence thesis

Industrial engineers continuously improve production and service systems by systematically applying structured problem-solving methods—PDCA or DMAIC—that rely on teams, data, and experimentation to identify and eliminate waste.

📌 Key points (3–5)

• Core IE philosophy: IEs focus on continuous improvement rather than one-time design; the goal is to compete against perfection, not just competitors.
• Two main frameworks: PDCA (Plan-Do-Check-Act) and DMAIC (Define-Measure-Analyze-Improve-Control) guide the improvement process with similar but distinct steps.
• Process-focused approach: Instead of trying to improve an entire system at once, IEs target specific processes—any activity that takes inputs, adds value, and produces outputs.
• Common confusion: PDCA vs. DMAIC—both are cyclical and data-driven, but DMAIC explicitly adds a "Control" step and emphasizes rigorous root-cause analysis, while PDCA (also called the Shewhart Cycle) more clearly emphasizes the never-ending cycle.
• Essential tools: Both methods use teams, documentation, flowcharts, data collection (check sheets, histograms, Pareto charts), root cause analysis (fishbone diagrams, five whys), experiments, and control charts.

🔄 The continuous improvement mindset

🎯 Competing against perfection

• The excerpt quotes Lean Thinking: "To hell with your competitors; compete against perfection by identifying all activities that are muda and eliminating them."
• Even if benchmarking shows a company outperforms competitors, it should not relax.
• From Good to Great: visionary companies focus on "beating themselves" by asking daily "How can we improve ourselves to do better tomorrow than we did today?"—sometimes for over 150 years.

🔧 Process definition

A process can be described as any activity or group of activities that takes an input, adds value to it, and provides an output to an internal or external customer.

• Improving the whole system at once is hard, so IEs focus on one process at a time.
• Example: instead of redesigning an entire hospital, focus on the process of moving patients from the Emergency Department to a hospital room.

🔁 PDCA: Plan-Do-Check-Act

📋 The four steps

The Shewhart Cycle (popularized by Deming) consists of:

Step	What it involves
Plan	Ask: What data do we have? Where are the biggest problems? What improvements could we make? What experiments should we run? How will we analyze the data?
Do	Carry out the planned experiments to test proposed improvements.
Check	Observe effects, analyze data, decide which improvements (if any) should be implemented.
Act	Reflect on what was learned; implement effective improvements or repeat the cycle to refine promising ideas.

🔄 The cycle never ends

• When you finish PDCA, you do it again.
• "You are never done because you must practice continuous quality improvements."
• The name "Shewhart Cycle" emphasizes the need to repeat the steps.

🎯 DMAIC: Define-Measure-Analyze-Improve-Control

📐 The five phases

Define

• Select a process for improvement.
• The project champion assigns a team and gives them a project charter.
• Develop a preliminary process map.
• Use Voice of the Customer to determine real requirements.

Measure

• Determine current status and performance measures.
• Identify the gap between current and desired status.
• Identify critical process inputs (the Xs) and outputs (the Ys).
• Develop a detailed process map.
• Determine possible root causes.

Analyze

• Evaluate contributions of various possible root causes.
• Emphasis is on rigorous analysis of data.

Improve

• Test possible improvements through designed experiments.
• Develop an implementation plan for improvements that best meet project objectives.

Control

• The project champion carries out the implementation plan.
• Sustain improvement by training workers and implementing control charts.
• As with PDCA, when done with DMAIC, you do it again.

🔍 PDCA vs. DMAIC comparison

Aspect	PDCA	DMAIC
Emphasis	The cyclical nature (Shewhart Cycle)	Adds explicit Control step
Structure	Four steps	Five steps
Root cause	Included but less explicit	Emphasized in Analyze phase
Control	Implicit in Act	Explicit separate step

Common features both share:

• Make sure you're solving an important problem
• Use teams to generate more ideas than individuals can alone
• Use facts, experiments, and data for decision making
• Continuously improve quality

🛠️ Essential tools for both methods

👥 Teams and documentation

Teams

• Continuous improvement requires involvement of everyone who works on the process.
• Teams should include people from upstream (suppliers) and downstream (customers) processes.
• Example: a team improving patient transfers should include movers, Emergency Department staff, and hospital ward staff.
• Team members may need training in tools and support for data analysis.

Documentation

• Quote from Robitaille (page 65): "If the documents aren't correct, the system will always have problems."
• First step: determine if the process is actually implemented as documented.
• Teams should document their work: data collection, analysis, conclusions.
• Final step: ensure recommended changes are reflected in process documentation and training materials.
• Documents form the long-lasting memory for the organization.

📊 Visual and data collection tools

Flowchart (Process flow diagram)

• A visual representation of steps in the process being studied.
• For manufacturing: shows operations done by different workers on the product.
• For services: shows steps performed by different workers for the customer.
• Should follow the product or customer.
• SIPOC acronym: for each process, include Suppliers, Inputs, Process, Outputs, and Customers.
• Example: The excerpt shows a flowchart from Parkview Hospital mapping patient flow from Emergency Department to Hospital, including average time at each step.

Check sheet

• A simple chart allowing workers to put a check mark next to the type of problem that occurred.
• Records all exceptions and problems routinely.
• Each instance may seem isolated, but analysis may reveal patterns that should be studied and fixed.

Histogram

• Displays categorical data (such as from check sheets) visually.
• Makes relative frequency of different problem types easier to see.

📈 Pareto chart and the 80-20 principle

Pareto chart

• A special histogram with categories listed from most frequent to least frequent.
• Example: Parkview Hospital chart showed causes for delay in moving patients from Emergency Department to hospital bed.
• The biggest cause is listed first and should usually be focused on first.
• Fixing the biggest causes eliminates a large proportion of defects.

The Pareto principle (80-20 rule)

• Named after economist Vilfredo Pareto, generalized by J. M. Juran.
• Juran's examples:
  • 20% of customers account for over 80% of sales
  • A few percent of purchase orders account for bulk of purchase dollars
  • A few percent of employees account for most absenteeism
  • Roughly 20% of parts contain 80% of factory costs
• Important caveat: The rule doesn't always hold exactly (the Parkview example showed the largest 2 causes accounted for only 56.7% of problems), but it's often a useful guideline.

Defect concentration chart

• Records or displays problems according to location.
• Example: breakdowns of machines displayed on a factory map to see if concentrated in a particular area.
• Example: defects in welds displayed on a product diagram to see if concentrated in a particular part.

🧠 Root cause and analysis tools

💡 Brainstorming and Nominal Group Technique

Five-step process:

1. Clear statement of the problem or issue (e.g., "generate possible causes why customers sometimes receive shipments missing items")
2. Silent generation of ideas by each individual, writing on paper
3. Round robin collection of ideas, recorded visibly; each person gives one idea per round, can "pass"; no evaluation during this step; more and more different ideas are better
4. Clarification and combination of similar ideas; don't over-combine—defer to the person who volunteered an idea
5. Prioritization (not always appropriate):
   • If brainstorming causes: use data, not voting
   • If brainstorming next steps: prioritization is needed
   • Methods: rank ordering (10 for highest to 1 for lowest) or voting with colored dots/pens

🐟 Cause and effect diagram (Fishbone diagram)

• Causes grouped into overall categories such as people, equipment, methods, and materials.
• These labels go on the "major bones" of the fishbone.
• More specific ideas are categorized under those labels.
• Smaller lines can be added as needed.

❓ The five whys and root cause analysis

Root cause analysis (RCA): an in-depth investigation into the cause or causes of an identified problem, customer complaint, nonconformance, nonfulfillment of a requirement, or undesirable condition.

Goals:

1. Determine why the situation occurred, tracing back in time through previous steps
2. Prevent the situation from occurring again

Key principle: The goal is not to blame a person, but to fix the system.

The five whys approach: Continue asking "why" at least five times until the root cause is identified.

Example from the excerpt:

• Why did the customer receive the wrong shipment?
  • Because the wrong shipping label was put on the customer's shipment.
• Why was the wrong shipping label put on the customer's shipment?
  • Because some shipments were removed from the shipping department.
• Why were the shipments removed?
  • Because the customer had made some last-minute changes to the order.
• Why did the customer make some last-minute changes?
  • And so forth...

Don't confuse: Root cause analysis is about tracing back through the system, not stopping at the first "why."

📐 Advanced analytical tools

Regression analysis

• A scatter diagram shows the effect of only one variable.
• Regression allows for more independent or explanatory variables.
• With more variables, plots cannot be used, but mathematical techniques can indicate which variables are most important in explaining variation in the dependent variable (the variable being studied).

Design of experiments and analysis of variance (ANOVA)

• After careful data analysis, a team may have good ideas about why the problem occurs and how to fix it.
• A carefully designed experiment can test these ideas.
• ANOVA is a mathematical technique (like regression) for determining which variables have the most effect on the variable being studied.

Control charts

• Key measurements of a process should be monitored to ensure the process functions within required limits.
• Design and use requires mathematical analysis to distinguish natural variation in the system from clear indications that the process has changed.

🧭 Overview

🧠 One-sentence thesis

Organizations define their purpose and direction through mission statements (why they exist), vision statements (what they aspire to become), and values statements (how they operate), which together provide essential guidance for industrial engineers working within those organizations.

📌 Key points (3–5)

• Mission statement: a clear, succinct statement of why an organization exists—its fundamental purpose.
• Vision statement: describes what the organization wants to be in the future; paints a picture of the ideal future state.
• Values statement: sets priorities and describes how members interact with each other and outsiders; guides trade-offs.
• Common confusion: mission vs. vision—mission is about current purpose ("why we exist now"), vision is about future aspiration ("what we want to become").
• Why it matters for IEs: IEs need clear organizational mission/vision/values to understand what "effectiveness" means for that system and to design improvements aligned with organizational goals.

🎯 Mission statements

🎯 What a mission statement is

Mission statement: a clear, succinct statement of why an organization exists.

• Stephen Covey's principle: "Begin with the end in mind"—organizations should define their purpose clearly.
• Alternative framing: "Why not just shut this organization down?" The answer should be valid now and one hundred years into the future.
• Example: "Google's mission is to organize the world's information and make it universally accessible and useful."

✅ Attributes of a good mission statement

A well-crafted mission statement should:

1. State the purpose for which the organization exists
2. Have a narrow focus
3. Be clear and get to the point
4. Be realistic, feasible, and achievable
5. Be a succinct one sentence with few adjectives and adverbs
6. Provide guidance for leadership and employees
7. Let prospective employees know what the company is like
8. Be unique to that organization

🔍 How mission guides decisions

• The mission helps organizations decide what activities to undertake and what to decline.
• Example: If a prospective client approached LDM (which sells copper alloy rods and billets) to ask for lead-free copper billets, the company would respond "we can." But if asked for copper pipes, they would say "we don't do that."
• Companies often refer clients to other companies based on clear understanding of each other's missions.

❌ Poor mission statement examples

Some mission statements fail to clarify what the organization actually does:

• Southwest Airlines: "dedication to the highest quality of Customer Service delivered with a sense of warmth, friendliness, individual pride, and company spirit"—doesn't say they're an airline.
• Henderson Manufacturing: focuses on quality and service but doesn't specify what they manufacture.
• Don't confuse: A mission statement is not a list of values or aspirations; it must clearly state the organization's purpose.

🔮 Vision statements

🔮 What a vision statement is

Vision statement: a statement of how the organization would like to be perceived by its customers; describes what the organization wants to be.

• Mission gives the reason the organization exists; vision describes what the organization wants to become.
• Vision is the destination for the organization.
• Example: "SpaceX's vision statement is to advance the future."

✨ Attributes of a good vision statement

According to the Alliance for NonProfit Management, a vision statement should be:

• Realistic and credible
• Well articulated and easily understood
• Appropriate, ambitious, and responsive to change
• Should orient the group's energies and serve as a guide to action
• Consistent with the organization's values
• Should challenge and inspire the group to achieve its mission

Additional characteristics:

1. State what the organization aims to be in the future
2. Allow for growth and development
3. Be inspiring to employees (this is where adjectives and adverbs belong, not in the mission statement)
4. Be clear

🎨 Vision paints the future picture

• Ron Robinson (ABARIS Consulting): a vision statement should paint "a picture of the ideal organization in the future."
• Should not look only a few years into the future—should be longer-term.
• Example: "Clemson [University] will be one of the nation's top-20 public universities."

💎 Values statements

💎 What a values statement is

Values statement: represents the core priorities in the organization's culture, including what drives members' priorities and how they truly act in the organization.

• Values govern how people in the organization will get to their vision.
• Example: Toastmasters International values: Integrity, Respect, Service, Excellence.

🧭 Attributes of a good values statement

A well-crafted values statement should:

1. Set priorities for the organization by stating what is important
2. Describe how members of the organization interact with each other and with others outside the organization
3. Provide guidance about trade-offs

📋 Values statement examples

IBM values:

• Dedication to every client's success
• Innovation that matters, for our company and for the world
• Trust and personal responsibility in all relationships

A2Z Computing Services has a comprehensive values statement covering responsibilities to:

• Communities they represent
• Residents of communities
• Businesses that advertise
• Nonprofit organizations
• Employees (safe work environment, fair wages, equal opportunity)
• Subcontractors, suppliers, banks, creditors, and investors

🏢 Why mission, vision, and values matter

🏆 Evidence from visionary companies

Collins and Porras research finding:

"A detailed pair-by-pair analysis showed that the visionary companies have generally been more ideologically driven and less purely profit-driven than the comparison companies in seventeen out of eighteen pairs."

• This is one of the clearest differences between visionary and comparison companies.
• Ideology (mission, vision, values) matters more than pure profit focus for long-term success.

🔧 Three reasons IEs should care

Reason	Explanation
Strategic guidance	"Begin with the end in mind"—visionary companies focus on ideology more than less successful companies
Operational clarity	IEs need to know what the organization does and with what values to understand what "effectiveness" means for that system
Career fit	You will be happier working for an organization compatible with your own mission, vision, and especially values

⚠️ When statements aren't good

• Some mission statements are poorly written and unclear.
• Some organizations make creating these statements into a ponderous exercise without much purpose.
• Despite these problems, the concept remains important for organizational direction and IE work.

🛠️ The IE's role in organizations

👥 Four groups in every organization

1. Founder/Directors/CEO/Entrepreneur: Determine the mission and broadly define processes and values
2. Managers: Set up and monitor processes to achieve the mission
3. Workers (line workers): Actually do the work—make products and deliver services to customers
4. Support (staff workers): Provide goods and services workers need that are not part of the mission (IT, accounting, cafeteria)

🔩 Where IEs fit

An IE can be in any of the four groups:

• Group 1: IE as CEO or director (not doing much IE work but using IE skills)
• Group 2: IE as manager or supervisor (common role)
• Group 3: IE as worker (rare—only if the organization's mission is to do industrial engineering, e.g., consulting companies like Accenture)
• Group 4: IE as support staff (common role)

Most IEs are managers or support staff.

🎨 What IEs design

IEs design systems (you can't really see a system).

• All engineers design, but most engineers design physical products or structures (objects you can see).
• IEs always think about systems, even though all engineers should consider systems.

DfX—Design for X:

• Manufacturability
• Usability
• Maintainability
• Reliability
• Repairability
• Recyclability

⚙️ What IEs actually do

Recall the definition of industrial engineering:

The design or improvement of a system of people, machines, information, and money to achieve some goal with efficiency, quality, and safety.

Key principle: An IE designs and works continually to improve a production system (a system that produces a product or service).

• When an IE solves a problem, the IE also makes a change to the system so that problem never occurs again.
• If an IE is solving problems all the time (e.g., expediting late orders), something is wrong.
• The IE should be working on the system, not putting out fires.

🎯 System focus vs. firefighting

• Don't confuse: Problem-solving vs. system improvement.
• Engineers solve problems, but IEs should solve problems by changing the system to prevent recurrence.
• Constant firefighting indicates the IE is not doing their job properly—they should be improving the system itself.

🧭 Overview

🧠 One-sentence thesis

Industrial engineers, like physicians and attorneys, belong to a profession that requires ongoing education and can pursue licensure through examinations and state approval, though licensure is optional for most IE practice.

📌 Key points (3–5)

• Licensure is optional but valuable: Unlike medicine and law, engineering practice doesn't require a license, but becoming a licensed PE is necessary to open your own firm or approve plans.
• The path to PE licensure: Pass the FE exam as a senior, graduate from an ABET-accredited program, gain 4 years of experience, then pass the PE and Ethics exams.
• The FE exam structure: A 6-hour computer-based exam with 110 multiple-choice questions covering 13 topic areas from mathematics to systems engineering.
• Professional societies support lifelong learning: Organizations like IISE, SME, ASQ, and NSPE offer memberships, publications, and conferences to help engineers stay current.
• Common confusion: Licensure importance varies by engineering type—most important for civil engineers, least important for industrial engineers.

🎓 Engineering as a profession

🏥 Comparison with other professions

The excerpt compares engineering to medicine and law:

Profession	Degree requirements	Licensing exam	State license	Continuing education
Physician	Undergraduate + medical degree	USMLE	Required	Required in most states
Attorney	Undergraduate + law degree	Bar exam	Required	Required in most states
Engineer	Undergraduate degree	FE + PE (optional)	Optional*	Required in 30 states if licensed

*Required only to be a principal in a firm or approve engineering plans and drawings.

🔧 When licensure matters

To be a principal in an engineering firm (for example, if you want to open your own firm as an engineer) or to approve engineering plans and drawings, you must be a licensed professional engineer (PE).

• Among engineering types, licensure is most important for civil engineers and least important for industrial engineers.
• If licensed in one state, most other states have a process for obtaining licensure there as well.

📝 The path to becoming a PE

🎯 Four steps in Texas

The Texas Board of Professional Engineers controls licensure. The steps are:

1. Pass the FE exam as a senior in an ABET-accredited program → become an Engineer in Training (EIT)
2. Graduate from an ABET-accredited program
3. Gain 4 years of "active practice in engineering work"
4. Pass the Principles and Practice exam and the Ethics exam

🏛️ Administering body

The National Council of Examiners for Engineering and Surveying (NCEES) administers the FE, PE, and Ethics exams.

💼 Career benefit of EIT status

• Some BSIE graduates obtain jobs by listing "Engineer in Training" on their resumes.
• Many employers respect this accomplishment and want to hire people with the knowledge, drive, and concentration required to pass the FE.

📋 The FE exam details

⏱️ Format and timing

• Computer-based exam administered year-round at NCEES-approved Pearson VUE test centers
• 110 multiple-choice questions
• 6-hour appointment includes:
  • Nondisclosure agreement
  • Tutorial (8 minutes)
  • Exam (5 hours and 20 minutes)
  • Scheduled break (25 minutes)

📚 Allowed resources

• Closed book but you can use the Supplied Reference Handbook
• A searchable online version of the Handbook is available during the test
• Only calculators from a limited list are allowed
• Recommendation: Become familiar with the Handbook before the exam because it contains formulas that can answer many questions

🎯 Passing strategy

If you can answer more than half the questions correctly, you have a good chance of passing.

The exam is hard because of the breadth of material and limited time. Recommended approach:

1. Focus first on questions you know you can answer
2. Then tackle questions you think you can answer
3. If time remains, try questions you don't think you can answer

Don't confuse: The Handbook is useful not just for the exam—it's a good summary to use while taking many engineering courses.

📖 The 13 topic areas

🔢 Mathematics and foundational sciences

The Industrial Engineering FE exam covers:

Topic area	Number of questions	Key subtopics
Mathematics	6–9	Analytic geometry, calculus, matrix operations, vector analysis, linear algebra
Engineering Sciences	5–8	Work/energy/power, material properties, electrical concepts
Ethics and Professional Practice	5–8	Codes of ethics, licensure, contracts, legal responsibility

💰 Engineering Economics

10–15 questions covering:

• Discounted cash flows (PW, EAC, FW, IRR, amortization)
• Cost types and breakdowns (fixed, variable, direct/indirect labor)
• Cost analyses (benefit-cost, breakeven, minimum cost, overhead)
• Accounting, cost estimation, depreciation, taxes, capital budgeting

📊 Probability and Statistics

10–15 questions covering:

• Combinatorics, probability distributions, conditional probabilities
• Sampling distributions and statistics
• Estimation and hypothesis testing
• Regression and system reliability
• Design of experiments (ANOVA, factorial designs)

💻 Modeling and Computations

8–12 questions covering:

• Algorithm and logic development
• Databases and decision theory
• Optimization modeling and linear programming
• Mathematical programming (network, integer, dynamic, transportation, assignment)
• Stochastic models (queuing, Markov, reliability) and simulation

🏭 Core IE topics

The exam covers five major IE application areas:

Area	Questions	Key concepts
Industrial Management	8–12	Planning, organizing, motivational theory, MBO, project management (PERT, CPM), productivity
Manufacturing/Production/Service	8–12	Manufacturing processes and systems, process design, inventory (EOQ), forecasting, scheduling, JIT, MRP, lean, automation, sustainable manufacturing
Facilities and Logistics	8–12	Flow analysis, layouts, location analysis, capacity analysis, material handling, supply chain
Human Factors/Ergonomics/Safety	8–12	Hazard identification, environmental stress, industrial hygiene, usability design, anthropometry, biomechanics, systems safety, cognitive engineering
Work Design	8–12	Methods analysis, time study, predetermined time standards (MOST, MTM), work sampling, learning curves

🎯 Quality and Systems Engineering

Quality (8–12 questions): Six sigma, management tools (fishbone, Pareto, QFD, TQM), control charts, process capability, sampling plans, design of experiments, reliability engineering

Systems Engineering (8–12 questions): Requirements analysis, system design, human systems integration, functional analysis, configuration management, risk management, verification, life-cycle engineering

🤝 Professional societies for lifelong learning

🏢 IE-focused organizations

Joining and participating in professional organizations can help you stay current in industrial engineering.

Student memberships are available at reduced rates:

Organization	Student dues	Professional dues	Publication
Institute of Industrial and Systems Engineers (IISE)	$37	$77 (first year), then $154	ISE Magazine (monthly)
Society of Manufacturing Engineers (SME)	$20	$138	Manufacturing Engineering (monthly)
American Society for Quality (ASQ)	$29	$159	Quality Progress (monthly, online)
National Society of Professional Engineers (NSPE)	Free*	$220	—

*Free student membership for full-time students in ABET-accredited programs; includes scholarship eligibility.

🌟 Student-oriented diversity organizations

These organizations are open to all students and very student-oriented:

• National Society of Black Engineers (NSBE): Founded in 1975 to increase the number of culturally responsible Black engineers who excel academically, succeed professionally, and positively impact the community.

• Society of Hispanic Professional Engineers (SHPE): Mission to change lives by empowering the Hispanic community to realize its fullest potential and impact the world through STEM awareness, access, support, and development.

• Society of Women Engineers (SWE): Seeks to stimulate women to achieve full potential in careers as engineers and leaders, expand the image of engineering, and demonstrate the value of diversity.

📱 What these organizations offer

Each organization provides:

• Useful website
• Magazine or other publications
• Annual conference
• Interest-based or geography-based groups for member interaction