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The Goal
Discussion Guide
Craig Paxson
The Goal Discussion Guide Craig Paxson
The Goal Discussion Guide
© Craig Paxson 2015
This work is licensed under the Creat...
The Goal Discussion Guide Craig Paxson
Introduction
Early in 2015, I volunteered to lead a reading discussion group at wor...
The Goal Discussion Guide Craig Paxson
Leaders Guide
How to Lead a Discussion of “The Goal”
Written in a fast-paced thrill...
The Goal Discussion Guide Craig Paxson
As discussion facilitator, you will ask questions to review the previous week’s rea...
The Goal Discussion Guide Craig Paxson
Introduction – Chapter 4
1. Why does Alex think the robots are so successful when h...
The Goal Discussion Guide Craig Paxson
Chapter 5 – 8
Review of Intro – Chapter 4
1. What is The Goal?
- Make Money
2. What...
The Goal Discussion Guide Craig Paxson
8. Define the measurements in your process’ terms
- Throughput
- Inventory
- Operat...
The Goal Discussion Guide Craig Paxson
Chapter 9 – 11
Review – Definitions Quiz Cards
1. Express the "goal" in terms of th...
The Goal Discussion Guide Craig Paxson
Definitions
Productivity – any action that moves the organization closer to the goa...
The Goal Discussion Guide Craig Paxson
Chapters 12 - 19
Play the Dice Game (Appendix 2)
1. Why does the spread of the line...
The Goal Discussion Guide Craig Paxson
9. Why does Jonah say “balance flow not capacities”?
- Each resource should only pr...
The Goal Discussion Guide Craig Paxson
Chapters 20 – 25
1. Why does Jonah say a plant should have bottlenecks?
- Every pro...
The Goal Discussion Guide Craig Paxson
8. Which resources in the system should we seek to optimize? Which should we not?
a...
The Goal Discussion Guide Craig Paxson
Chapters 26 – 31
1. What is the function of the drum and rope if used on a hike?
- ...
The Goal Discussion Guide Craig Paxson
7. How can the time material spends in plant be classified into four types?
- Setup...
The Goal Discussion Guide Craig Paxson
Chapters 32 - 40
1. What are the 5 Focusing Steps?
1. Identify the system constrain...
The Goal Discussion Guide Craig Paxson
Author Bio
Eli Goldratt is an educator, author, scientist, philosopher, and busines...
The Goal Discussion Guide Craig Paxson
Appendix 1: Week 1 Quiz Cards
What is the definition of Productivity? Express The G...
The Goal Discussion Guide Craig Paxson
Appendix 2: The Dice Game
Adapted from the game played on the hike in Chapter 12
Pu...
The Goal Discussion Guide Craig Paxson
Statistically, a single die can only roll six values, one each of 1, 2, and 3,4,5,6...
The Goal Discussion Guide Craig Paxson
Appendix 3: The Dot Game
Adapted from the Lean Manufacturing Cup Game
Purpose
The D...
The Goal Discussion Guide Craig Paxson
3. The line is broken into between 4 and 6 stations. You can adjust the number of s...
The Goal Discussion Guide Craig Paxson
Appendix 4: Little’s Law
Little’s Law states that inventory (I) is equal to cycle t...
he many ways to improve operations include increasing output per peri-
od, decreasing average inventory, decreasing flow t...
Applying Little’s Law and the Theor y of Constraints
which is proportional to inventory, increases.
On the other hand, whe...
Applying Little’s Law and the Theor y of Constraints
With this deterministic system, a student enters
every two minutes, a...
Applying Little’s Law and the Theor y of Constraints
the average inventories in the system are 5.67 students
with timers, ...
Fortunately, the simulation model also can be used
to depict that approach. In fact, if you assume inven-
tory must be an ...
The Goal Discussion Guide Craig Paxson
Participants Guide
How to Read “The Goal”
Written in a fast-paced thriller style, T...
The Goal Discussion Guide Craig Paxson
Your discussion facilitator will ask questions to review the previous week’s readin...
The Goal Discussion Guide Craig Paxson
Intro – Chapter 4
1. Why does Alex think the robots are so successful when he first...
The Goal Discussion Guide Craig Paxson
Chapter 5 – 8
1. What is the goal?
2. What does your process manufacture?
3. What t...
The Goal Discussion Guide Craig Paxson
Chapter 9 – 11
1. Express the "goal" in terms of throughput, inventory, and operati...
The Goal Discussion Guide Craig Paxson
Chapters 12 – 19
1. Why does the spread of the line of boy scouts discussed on page...
The Goal Discussion Guide Craig Paxson
Chapters 20 – 25
1. Why does Jonah say a plant should have bottlenecks?
2. What doe...
The Goal Discussion Guide Craig Paxson
Chapters 26 – 31
1. What is the function of the drum and rope if used on a hike?
2....
The Goal Discussion Guide Craig Paxson
Chapters 32 – 40
1. What are the 5 Focusing Steps?
2. What is the Process of Change...
The Goal Discussion Guide Craig Paxson
Author Bio
Eli Goldratt is an educator, author, scientist, philosopher, and busines...
The Goal Discussion Guide Craig Paxson
Further Reading
Theory of Constraints
“It’s Not Luck” by Eli Goldratt
Applying TOC ...
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"The Goal" Discussion Guide

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This discussion guide will lead a group of readers on a seven-week journey through "The Goal." Filled with interactive questions and exercises, the guide will help groups read and understand Eli Goldratt's seminal work. Participant and Leader's guides are included.

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"The Goal" Discussion Guide

  1. 1. The Goal Discussion Guide Craig Paxson
  2. 2. The Goal Discussion Guide Craig Paxson The Goal Discussion Guide © Craig Paxson 2015 This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4. You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Non-Commercial — You may not use the material for commercial purposes.
  3. 3. The Goal Discussion Guide Craig Paxson Introduction Early in 2015, I volunteered to lead a reading discussion group at work. The book I chose to read was “The Goal” by Eliyahu Goldratt. I scoured the internet for a reading and discussion guide appropriate for a weekly group session and could not discover any. I found plenty of synopses and some college syllabi, but not any discussion guides. So I decided to create one. This booklet is the discussion guide I created. Because “The Goal” uses the Socratic Method - “ask - tell - ask”, I decided to create the readings in that same method. Each week’s reading begins with Alex asking a question of Jonah, then Jonah’s response, Alex learning from that answer, and then the next question posed by Alex. The discussion guide is broken into 7 weeks of reading. Each week has questions to be answered by the participants. Some weeks have exercises (for instance, the dice game played on the hike) to further illustrate the concepts discussed in the book. It will be helpful if the leader can customize the discussion questions and exercises to the organization.
  4. 4. The Goal Discussion Guide Craig Paxson Leaders Guide How to Lead a Discussion of “The Goal” Written in a fast-paced thriller style, “The Goal”, a gripping novel, is transforming management thinking throughout the world. It is a book to recommend to your friends in industry - even to your bosses - but not to your competitors. Alex Rogo is a harried plant manager working ever more desperately to try improve performance. His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant - or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a professor from student days - Jonah - to help him break out of conventional ways of thinking to see what needs to be done. The story of Alex's fight to save his plant is more than compulsive reading. It contains a serious message for all managers in industry and explains the ideas, which underline the Theory of Constraints (TOC), developed by Eli Goldratt. This is a seven week discussion of “The Goal.” Each week, you will read a selected set of chapters. “The Goal” is written using the Socratic Method - Ask - Tell - Ask. The chapter are selected based on the question that Jonah poses to Alex, what Alex learns and Alex’s next questions. As you read each section, think about the following questions: 1. What is the current situation? What did Alex learn? 2. What questions does Alex currently have? 3. What hints does Jonah give Alex? 4. What answers will Alex will discover? 5. How does this apply to my organization?
  5. 5. The Goal Discussion Guide Craig Paxson As discussion facilitator, you will ask questions to review the previous week’s reading and may present exercises to enhance learning. Reading Schedule Intro – Chap 4 Chaps 5 – 8 Chaps 9 – 11 Chaps 12 – 19 Chaps 20 – 25 Chaps 26 – 31 Chaps 32 – 40
  6. 6. The Goal Discussion Guide Craig Paxson Introduction – Chapter 4 1. Why does Alex think the robots are so successful when he first talks to Jonah? - because their localized cost per part had gone down 2. How does Jonah indicate that the robots were not successful? - sales had not increased, labor had not decreased and inventory had gone up 3. How does Jonah define productivity? - the act of bringing an organization closer to its goal. Every action that brings an organization closer to its goal is productive. Every action that does not bring an organization closer to its goal is not productive. Next Meeting: Chapters 5 - 8
  7. 7. The Goal Discussion Guide Craig Paxson Chapter 5 – 8 Review of Intro – Chapter 4 1. What is The Goal? - Make Money 2. What does your process manufacture? 3. What three common financial measures express the goal to "make money"? - Net Profit, Return on Investment and Cash Flow 4. Express the “goal” in terms of those financial measures - Increase net profit while simultaneously increasing both ROI and cash flow 5. What three measures are useful at the operational level to express the goal? - Throughput, Inventory and Operational Expense 6. Define throughput, inventory, and operational expense. - Throughput - the rate at which the system generates money through sales - Inventory - all the money the system has invested in purchasing things which it intends to sell - Operational Expense - all the money the system spends in order to turn inventory into throughput 7. Jonah claims the common financial measures are related to the operational measures. How? - Net Profit = Throughput – Operating Expense - ROI = (Throughput – Operating Expense) / Inventory - Cash Flow = Throughput –– Operating Expense ± ΔInventory
  8. 8. The Goal Discussion Guide Craig Paxson 8. Define the measurements in your process’ terms - Throughput - Inventory - Operating Expense 9. What questions does Jonah leave Alex with? What do you think Alex will discover? - Local Optimums - Operational Rules Next Meeting: Chapters 9 - 11
  9. 9. The Goal Discussion Guide Craig Paxson Chapter 9 – 11 Review – Definitions Quiz Cards 1. Express the "goal" in terms of throughput, inventory, and operational expense. - Increase throughput while simultaneously decreasing inventory and cash flow 2. What is the result of high efficiencies on a non-constraint machine? - Excess inventory 3. Do high efficiencies necessarily imply higher profit? - No – high efficiencies lead to increased inventory, unless that inventory is turned into throughput 4. Why is it important that throughput be defined in terms of sales rather than production? - We don’t get paid for finished goods 5. What causes a balanced plant to fail? - Inventory, and the carrying cost of inventory goes up because of statistical fluctuations and dependent events 6. What are the type of operational operating expenses? - Variable and Fixed or - Controllable and Uncontrollable 7. What is the equation for Productivity? - Productivity = Throughput / Operating Expense 8. What questions does Jonah leave Alex with? What do you think Alex will discover? Next Meeting: Chapters 12 – 19
  10. 10. The Goal Discussion Guide Craig Paxson Definitions Productivity – any action that moves the organization closer to the goal The Goal in financial measures - Increase net profit while simultaneously increasing both ROI and cash flow Operational Measures Throughput - the rate at which the system generates money through sales Inventory - all the money the system has invested in purchasing things which it intends to sell Operational Expense - all the money the system spends in order to turn inventory into throughput The Goal in operational measures - Increase throughput while simultaneously decreasing inventory and cash flow Financial Measure Equations Net Profit = Throughput – Operating Expense ROI = (Throughput – Operating Expense) / Inventory Cash Flow = Throughput – Operating Expense ± ΔInventory Productivity = Throughput / Operating Expense
  11. 11. The Goal Discussion Guide Craig Paxson Chapters 12 - 19 Play the Dice Game (Appendix 2) 1. Why does the spread of the line of boy scouts discussed on page 100 always become longer as time goes on? - the leaders are going faster than Herbie 2. What characteristics of the hiking troop relate to the production characteristics of throughput, inventory, and operational expense? - Throughput – the distance covered by the last scout in the troop - Inventory – the total length of the line - Operational Expense –– energy expended by the troop 3. Using the hike analogy on page 113, what happens in a plant if the fastest operations are put at the beginning of the production process, the slowest operations are put at the end, and all workers produce at a high efficiency? - inventory goes up 4. What is Herbie in terms of TOC? - the Bottleneck or Constraint 5. In terms of TOC what has been done when Herbie goes to the front of the line? - exploiting the constraint, letting the constraint dictate throughput 6. In terms of TOC what has been done when items are removed from Herbie's pack? - elevating the constraint – making it go faster 7. Why was Pete so happy even through the order was not delivered on time? - Pete produced the 100 parts needed even though the total throughput was less 8. Define a bottleneck - any resource where capacity is less than demand
  12. 12. The Goal Discussion Guide Craig Paxson 9. Why does Jonah say “balance flow not capacities”? - Each resource should only produce as much as the constraint Next Meeting: Chapters 20 - 25
  13. 13. The Goal Discussion Guide Craig Paxson Chapters 20 – 25 1. Why does Jonah say a plant should have bottlenecks? - Every production line has a bottleneck. It is impossible to not have one, so we should strive to utilize the bottleneck to meet operational goals 2. What does lost time at a bottleneck cost? - The throughput (revenue) or profit that could have been generated 3. What two things can be done to optimize a bottleneck? - Only work on things that contribute to throughput - Ensure there are always materials for the bottleneck to work on 4. What is the effect of the "efficient" operation of non-bottleneck machines? - Excess inventory 5. What determines the level of utilization of a non-bottleneck machine? - the level of utilization of a non-bottleneck is not determined by its own potential, but by some other constraint in the system 6. What are the combinations of production flow through a bottleneck and non-bottleneck? a. N -> B – non-bottleneck feeding bottleneck b. B -> N – bottleneck feeding non-bottleneck c. B and N -> Assembly – bottleneck and non-bottleneck feeding assembly d. B -> market and N -> market 7. What is the difference between activating a resource and utilizing a resource?
  14. 14. The Goal Discussion Guide Craig Paxson 8. Which resources in the system should we seek to optimize? Which should we not? a. Only optimize the bottlenecks – constrain every other resource to the throughput of the bottleneck 9. What does Jonah suggest is the actual constraint in the system? - Policy – he says “you created this monster by the decisions you made” and “ 10. What do you think is the solution Jonah is proposing? Next Meeting: Chapters 26 - 31
  15. 15. The Goal Discussion Guide Craig Paxson Chapters 26 – 31 1. What is the function of the drum and rope if used on a hike? - Keep everyone walking at the same pace, and keep everyone from getting spread out 2. What is the drum for the production facility? - The pace of the bottleneck 3. What is the rope for the production facility? - The total amount of work in process inventory 4. Why is a rope needed for assembly operations? - To ensure the correct amount of WIP and ensure bottlenecks do not run out of work. 5. What is the next logical step after establishing the drum and rope for the production process? - Cut batch sizes – cutting WIP improves throughput (see Little’s Law, Appendix 4) 6. What does cutting batch sizes in half for non-bottleneck operations accomplish? - Eases cash flow because less cash is tied in inventory - Improves throughput
  16. 16. The Goal Discussion Guide Craig Paxson 7. How can the time material spends in plant be classified into four types? - Setup – time spent waiting for a resource while the resource is being prepared - Process – time spent being worked on - Queue – waiting on a resource while that resource is busy on something else - Wait – for another part 8. What is time saved on a non-bottleneck machine? - A mirage This is a good time to bring up the concept of Little’s Law. See the Appendix on Little’s Law. Play the Dot Game (Appendix 3) to illustrate the effect Work In Process inventory has on throughput, cycle time and cost. Next Meeting: Chapters 32 - 40
  17. 17. The Goal Discussion Guide Craig Paxson Chapters 32 - 40 1. What are the 5 Focusing Steps? 1. Identify the system constraint 2. Exploit the constraint 3. Subordinate everything to the constraint 4. Elevate the constraint 5. If a constraint has been broken, go back to Step 1, but do not allow inertia to cause a constraint 2. What is the Process of Change? 1. What to Change 2. What to Change To 3. How to Cause the Change 4. Alex and his team have moved from the Cost world to the Throughput world. 5. In each world, what is the relative importance of Inventory (I), Operating Expense (OE) and Throughput (T) and why? Cost World Throughput World 1. Operating Expense 2. Throughput 3. Inventory 1. Throughput 2. Inventory 3. Operating Expense What are your most important learnings?
  18. 18. The Goal Discussion Guide Craig Paxson Author Bio Eli Goldratt is an educator, author, scientist, philosopher, and business leader. But he is, first and foremost, a thinker who provokes others to think. Often characterized as unconventional, stimulating, and "a slayer of sacred cows," Dr. Goldratt exhorts his audience to examine and reassess their business practices with a fresh, new vision. He obtained his Bachelor of Science degree from Tel Aviv University and his Masters of Science, and Doctorate of Philosophy from Bar-Ilan University. In addition to his pioneering work in Business Management and education, he holds patents in a number of areas ranging from medical devices to drip irrigation to temperature sensors.
  19. 19. The Goal Discussion Guide Craig Paxson Appendix 1: Week 1 Quiz Cards What is the definition of Productivity? Express The Goal in operational measures Express The Goal in financial measures What is the equation for Net Profit in operational terms What is the definition of Throughput? What is the equation for ROI in operational terms What is the definition of Inventory? What is the equation for Cash Flow in operational terms What is the definition of Operational Expense?
  20. 20. The Goal Discussion Guide Craig Paxson Appendix 2: The Dice Game Adapted from the game played on the hike in Chapter 12 Purpose: The dice game will demonstrate how variability and dependent events impact throughput. Materials Required: 1 6-sided die 4 - 6 cups or bowls representing stages in the production process Matches, pennies, poker chips or other items to move from bowl to bowl (minimum of 40). Procedure: Setup: 1. Set up a production line of 5 - 6 cups or bowls. 2. Place the tokens into the first bowl. Game Play: 1. Worker 1 will roll the die and move the resulting number to the second cup in the line. 2. Worker 2 will roll the die and move the resulting number of tokens to the next cup in the process. 3. Repeat the procedure for the remaining workers. The last worker moves the tokens to “finished goods.” 4. Each worker rolling the die and moving tokens counts as “1 day.” You will play the game for 10 days. 5. Each student will record their roll and the number of tokens they moved during each turn.
  21. 21. The Goal Discussion Guide Craig Paxson Statistically, a single die can only roll six values, one each of 1, 2, and 3,4,5,6. The average value for one roll is 3.5 (1+2+3+4+5+6=21/6=3.5). With 10 days of production, on average we would expect to move 3.5 tokens per day for a total of 35 tokens produced. Discussion Questions: 1. How many tokens did the line produce? Versus expected? 2. How many tokens did each station produce versus expected?
  22. 22. The Goal Discussion Guide Craig Paxson Appendix 3: The Dot Game Adapted from the Lean Manufacturing Cup Game Purpose The Dot game simulates a simple manufacturing system and demonstrates how work in process (WIP) inventory affects throughput, cycle time and cost. Materials Required 2.5” x 2.5” Post-It Notes 4 colors of 3/4” round stickers Pen and Paper or Flipboard Procedure 1. The game requires between 4 and 6 players. Larger groups can have multiple “lines” or observers. 2. The “line” will manufacture a Post-It that looks like the one below. Make an example product and post it for the team to see.
  23. 23. The Goal Discussion Guide Craig Paxson 3. The line is broken into between 4 and 6 stations. You can adjust the number of stations based on the number of players. 1. Station 1 - Raw Materials. This player will tear off the requisite number of Post- Its and pass to the next station. 2. Station 2 - Red. This player will put on the one red dot. 3. Station 3- Blue. This player will put on the two blue dots. 4. Station 4 - Green. This player will put on the two green dots. 5. Station 5 - Yellow. This player will put on the one yellow dot. 6. Station 6 - Inspection. This player will inspect the Post-It and make sure it meets standard. 4. The game is played in multiple rounds. Each round will be timed for 5 minutes. 1. Round 1 - Batches of 6. Each player will complete 6 Post Its prior to passing the entire batch of 6 to the next station. Record the number of Post-Its completed, the number remaining in WIP and the time for the first Post-It to be completed (Cycle Time). 2. Round 2 - Batches of 1. Each player will complete 1 Post-It and pass it to the next station. Record the number of Post-Its completed, the number remaining in WIP and the time for the first Post-It to be completed. Discussion: 1. What effect did batch size have on Throughput? 2. Cycle Time 3. Cost?
  24. 24. The Goal Discussion Guide Craig Paxson Appendix 4: Little’s Law Little’s Law states that inventory (I) is equal to cycle time (CT) multiplied by throughput (T). The equation looks like this: Inventory (I) = Cycle Time (CT) * Throughput(T). Alternatively, the law can represent Cycle Time: CT = I / T Or Throughput: T = I / CT The implications of Little’s Law are that Inventory management can control Cycle Time and Throughput. We also know that as Inventory rises, so does Operating Expense (because of carrying costs). We also know from chapters 5 - 11 that: Net Profit = Throughput – Operating Expense ROI = (Throughput – Operating Expense) / Inventory Cash Flow = Throughput –– Operating Expense ± ΔInventory Productivity = Throughput / Operating Expense Can you describe how Cycle Time fits into Jonah’s operation terms and how it might affect Productivity and Net Profit? See the following article for a more in-depth discussion.
  25. 25. he many ways to improve operations include increasing output per peri- od, decreasing average inventory, decreasing flow time and reducing defects. Little’s Law states inventory is equal to flow time multiplied by flow rate. It is deceptively simple and has application in the areas of reduction of flow times, safety inventory and safety capacity. The theory of constraints (TOC) has appli- cation for improving production processes when sales are limited by plant capac- ity. It focuses on identifying the bottleneck and then exploiting and elevating it. TOC and Little’s Law are important tools for both Six Sigma and lean. In the operations management major at the University of Wisconsin Oshkosh, we want our graduates to be apply Six Sigma, lean, TOC and Little’s Law to improve opera- tions at their companies. For our students to learn how to apply these techniques effectively, it often is necessary for us to start with simple processes and then move to more complex and realistic processes after they have grasped the basic concepts. This approach can be particularly useful for understanding Little’s Law and TOC. Little’s Law generally is best understood when it is used to reduce cycle times (flow times), while TOC leads quickly to being able to identify and elevate a physical constraint (bottleneck) to increase throughput (flow rates). In a recent article, Robert Gerst warned, “Little’s Law is everywhere.”1 TOC is also everywhere, particularly when an improvement project focuses directly on increasing flow rates. We have used two models to provide a framework for moving from the basics to a more complete understanding of both Little’s Law and TOC. The first model is simple and deterministic. The second model is also simple, but it is stochastic, involving random elements for both arrivals and processing times. Little’s Law While decreasing cycle or flow time can be instrumental in improving customer service, you must be careful not to ignore the throughput (flow rate) of the system. Both measures are directly related to average inventory as defined by Little’s Law.2, 3 Little’s Law defines the relationship among the three variables of flow rate (throughput), inventory and flow time (cycle time). A notation in a recently pub- lished book provides the following equation:4 I = R x T where: I = average inventory; R = average flow rate; T = average flow time. In any system, when one of these variables changes value, a second variable (and possibly a third) also must change value. The issue of which variables change value depends on the structure of the system. The capacity of the system determines the average flow rate for the system. For a system operating at capacity, an increase in inventory will result in a propor- tional increase in average flow time, with average flow rate remaining relatively constant. Obviously this is an undesirable result because the revenue from the system, which is proportional to flow rate, remains constant while carrying cost, C Y C L E T I M E R E D U C T I O N Applying Little’s Law And the Theory of Constraints THEY CAN BE USED IN SIX SIGMA AND LEAN PROJECTS TO IMPROVE OPERATIONS. T By Michael R. Godfrey and D. Brent Bandy, University of Wisconsin Oshkosh S I X S I G M A F O R U M M A G A Z I N E I F E B R U A R Y 2 0 0 5 I 37
  26. 26. Applying Little’s Law and the Theor y of Constraints which is proportional to inventory, increases. On the other hand, when a system has either excess (or variable) capacity, it is possible for an increase in inventory in the system to result in a proportional increase in flow rate, with flow time remaining constant. This is a positive outcome because the flow rate and inventory—and thus the revenue and holding cost— will increase by the same percentage. This being a pos- itive outcome is based on assuming the revenue per unit exceeds the holding cost per unit. If this is not the case, however, then the system will operate at a loss and should be shut down. Finally, given a third scenario in which the system has either excess (or variable) capacity, it is possible an increase in inventory will result in changes in both flow rate and flow time. Of course, the values of the three variables will still satisfy Little’s Law. But whether an increase in inventory will lead to a desir- able result depends on the relative changes of the three values. Theory of Constraints TOC is a system improvement methodology that focuses on the system constraint. Applying TOC entails answering the following questions:5 • What should be changed? • What should it be changed to? • How can the change be brought about? Eli Goldratt created five focusing steps to drive improvement efforts at the constraint: 1. Identify the constraint (physical or policy). 2. Decide how to exploit the constraint (get the most out of it). 3. Subordinate everything else (adjust the rest of the system to enable the constraint to operate effectively). 4. Elevate the constraint (invest time, energy and money to eliminate the constraint). 5. Go back to step one, but beware of inertia. These five focusing steps help answer the three ear- lier questions.6 A physical constraint within a process, also called a bottleneck, determines the flow rate or capacity of that process. Flow Rate and Flow Time Measures Flow time and the flow rate measures are related but different. When discussing physical constraints within an organization’s operations, you must exer- cise care when distinguishing between activities that determine flow times and resources that are physical constraints. Some systems use a simple linear flow, in which each sequential activity is performed by a different resource. In such systems, the longest activity is, by definition, performed by the physical constraint (assuming all re- sources are available whenever the system is operating). In a three-station line, for example, with each sta- tion staffed by a different person, the workstation with the longest processing time would determine the flow rate of the line. There would be a single path on the line; therefore the flow time would be the summation of the individual workstation processing times on this critical path. In this situation, increasing flow rates by applying TOC principles would decrease flow times. The fol- lowing actions would lead to increased flow rate at the constraint: • Decreasing downtimes at the constraint. • Improving job scheduling at the constraint. • Eliminating processing steps at the constraint. • Shifting processing steps from the constraint to other resources. • Adding more units of the constraint resource. Likewise, you could apply Little’s Law to reduce flow time, for example, by reducing the processing times of tasks on the single path, including those per- formed by the constraint. If you reduce flow time on the path, then you also would increase flow rate. You also could reduce flow time by moving tasks off this single path. In more complex operations—for example, those in which parallel workstations exist or a process branches to different paths—the physical constraint may not per- form an activity on the critical path. If this is the case, increasing flow rate at the constraint will not decrease flow time, nor will attempts to reduce flow time increase flow rate. The following two examples help highlight the differences between flow rate and flow time. Example One Consider a simple deterministic system to demon- strate three scenarios: 1. System operating at capacity. 2. System operating at less than capacity (flow rate changes). 3. System operating at less than capacity (both flow rate and flow time change). 38 I F E B R U A R Y 2 0 0 5 I W W W . A S Q . O R G
  27. 27. Applying Little’s Law and the Theor y of Constraints With this deterministic system, a student enters every two minutes, and a student leaves every two min- utes. Five students are inside the system, with each spending 10 minutes there. Note that Little’s Law is satisfied by the system, as it must be. The inventory (five students) is equal to flow rate (0.5 students per minute) times flow time (10 minutes). Scenario one—system operating at capacity. In the first scenario, the capacity of the system is fixed at 0.5 students per minute. For this scenario, a machine pro- duces a ticket every two minutes. One student is at the machine and four students stand in line waiting. The waiting line is a buffer in front of the ticket machine. As soon as a student has a ticket, he or she is allowed to exit, and at exactly the same time, another student enters. What happens if you double the inventory by put- ting five more students into the system? The change inside the system is that nine students are in the buffer, and the flow time doubles from 10 to 20 min- utes. For this system, the optimal inventory is one, which results in a flow time of two minutes. Scenario two—system operating at less than capacity (flow rate changes). The second scenario is the one in which the capacity of the system is underutilized. For this scenario, there are 20 10-minute timers inside the system. Each student picks up an unused timer upon entering the system. When the timer goes off, the stu- dent exits the system, leaving the timer in the system. At any point in time, five students are holding timers inside the system, and there are 15 unused timers. What happens if you double the inventory by put- ting five more students into the system? The change inside the system is that there are now 10 students holding timers and 10 unused timers. Thus, in this case, the flow time remains at 10 minutes and the flow rate doubles from 0.5 students per minute to 1.0 stu- dent per minute. Note also you can double the inventory again (up to 20) and have the flow rate double again. However, at 20, you reach the capacity of the system, which is determined by the constraint (timers), and then revert to a point at which the flow rate will remain con- stant if inventory is increased beyond 20. Thus, for this system, the optimal inventory is 20, which results in a flow rate of two students per minute. Scenario three—system operating at less than capac- ity (both flow rate and flow time change). The third scenario is the one for which both flow rate and flow time change as inventory in the system increases. For this scenario, the students must go through two phases inside the system. In the first phase, students use 8.5-minute timers. There are 20 timers in the sys- tem. As soon as a student’s timer beeps, he or she pro- ceeds to the second phase and uses the timer to start a machine that requires 1.5 minutes to produce the ticket to leave the system. There is one machine for producing tickets. The capacity of the timer resource is 2.35 (20/8.5) stu- dents per minute. The capacity of the ticket machine is 0.67 (1/1.5) students per minute. Therefore, the ticket machine determines capacity, but because it is not fully utilized, flow rate is less than capacity. We can use Little’s Law to determine the average inventory in each phase of the system. The average inventory of students using the ticket machine is 0.75 (the flow rate of 0.5 per minute times the flow time of 1.5 minutes). Because there are five students in the system, the average number of students holding timers is 4.25 (5 – 0.75). After we double the invento- ry in the system to 10 students, the status of the system will change, but determining the new status is not as straightforward as for the first two scenarios. Because the system is not at capacity with five stu- dents in it, let’s assume as a first attempt the flow time in the system will not change and the inventory for each phase of the system will double. However, we can immediately see this is not possible for the second phase of the system because the machine can handle only one student at a time. Therefore, our first assumption was wrong, and we assume the system is now at capacity. So there will always be one student using the ticket machine, and the other nine either will be holding a timer or wait- ing in the buffer for the ticket machine. The system flow rate will be the capacity of the tick- et machine—0.67 students per minute. The flow time for the system will be 15 minutes (inventory of 10 divided by flow rate of 0.67), and each student will spend five minutes waiting in the buffer. Therefore, S I X S I G M A F O R U M M A G A Z I N E I F E B R U A R Y 2 0 0 5 I 39 YOU MUST EXERCISE CARE WHEN DISTINGUISHING BETWEEN ACTIVITIES THAT DETERMINE FLOW TIMES AND RESOURCES THAT ARE PHYSICAL CONSTRAINTS.
  28. 28. Applying Little’s Law and the Theor y of Constraints the average inventories in the system are 5.67 students with timers, 3.33 students waiting in the buffer and one student at the ticket machine. To summarize, doubling the inventory in the system from five to 10 increases the flow rate from 0.50 to 0.67 students per minute and increases the flow time from 10 to 15 min- utes. The optimal approach for this scenario can be determined by eliminating the wait in the buffer, which results in an optimal inventory of 6.67 students. In essence, the system has excess capacity, similar to scenario two in which there are fewer than 6.67 stu- dents in the system, and is at capacity, similar to sce- nario one in which there are 6.67 or more students in the system. Example Two In reality, almost all systems have variability and are much more complex than the system in example one. That example, with its three scenarios, demonstrated the moderate difficulty of applying Little’s Law and TOC to relatively simple systems. For the second example, we made the system from the third scenario of the previous example more com- plex by adding variability. Simulation is especially helpful in the analysis of systems with variability. For example, a recent article reported the use of simulation modeling of call center operations in launching a new fee based technical support program that guaranteed paying customers would wait less than one minute on hold.7 We used ProModel8 to develop a simulation model for this system with the ticket machine and the 20 timers. We then introduced variability by using a nor- mal probability distribution for the times associated with both the timer and the ticket machine. To avoid making the example too complex, we used the same value for the coefficient of variation for both the timer and the ticket machine. Thus, using c to rep- resent the coefficient of variation, the times in the sim- ulation for the timer were sampled from the normal probability distribution with a mean of 8.5 (minutes) and a standard deviation of 8.5c. For the ticket machine, the times were sampled from the normal probability distribution with a mean of 1.5 (minutes) and a standard deviation of 1.5c. We then ran the simulation for values of c from 0.0 up to 0.5 in increments of 0.1 and for system invento- ries from 5 to 10. The simulation used a 10-hour warmup period and a run time of 100 hours. The results for the average values over the 100 hours for average flow rate per minute are shown in Figure 1. The impact of variability on flow rate and thus on flow time (from Little’s Law) can be seen clearly. At a given value for inventory, increasing variability decreases flow rate and increases flow time. Interestingly, this simple example can be used to shed light on the impact of increasing variability on systems that are and are not operating at capacity. When the inventory is five, the system is not at capaci- ty, which corresponds to systems in which the just-in- time manufacturing approach is effective.9 When there is no variability, there is no waiting time for the entities as they flow through the system. As the level of variability increases, entities start encounter- ing waiting time in the buffer because the previous entity is still using the ticket machine. As a result, aver- age flow time increases and flow rate decreases. Of course, real just-in-time systems do not behave like this because they operate in such a way that flow rate is maintained. However, the model demonstrates that for a system with no wait time, when variability increases to the point there is waiting, both average flow time and average inventory will increase. When the inventory is eight, the system is at capaci- ty, and TOC is appropriate. As variability increases, the buffer in front of the ticket machine (the constraint) will at times be empty when it finishes working on the prior entity. Based on TOC concepts, we know the sys- tem’s average flow rate will decrease, and from Little’s Law, we know the average flow time will increase. The system being considered does not correspond to what happens in real life TOC systems. Such sys- tems have a given level of variability, and essentially, what needs to be done is to determine the optimal inventory for operating the system. 40 I F E B R U A R Y 2 0 0 5 I W W W . A S Q . O R G 0.70 0.65 0.0 0.1 0.2 0.3 0.4 0.5 0.60 0.55 0.50 0.45 0.40 5 6 7 8 9 10 Inventory Throughputperminute Figure 1. Throughput vs. Inventory as a Function of Coefficient of Variation
  29. 29. Fortunately, the simulation model also can be used to depict that approach. In fact, if you assume inven- tory must be an integer value, it is not necessary to make more runs with the model. All you need to do is to look at the results from the standpoint of a given level of variability rather than from a given level of inventory as was done in Figure 1. To do this, you need to assign economic values for inventory, flow time and flow rate. Again, let’s take a simplified approach and assign economic values of +50 for each unit per minute increase in flow rate, -1 for each additional unit of inventory and -1 for each addi- tional minute of flow time. Now let’s use these values to determine the optimal inventory for a value of 0.2 for the coefficient of variation. The pertinent values are shown in Table 1, where you can see the optimal level of inventory is eight units. We carried out similar analy- sis for each of the other values for the coefficient of variation. The results are shown in Table 2. If there is very little variability, the optimal invento- ry is therefore seven. For fairly small levels of variabil- ity (values of c from 0.1 to 0.3), the optimal inventory is eight. Finally, for larger levels of variability (values of c from 0.4 to 0.5), the optimal inventory is nine. Manufacturing and Nonmanufacturing Both Little’s Law and TOC are relevant tools as part of broader Six Sigma or lean projects and can be applied to these projects in any type of operations sys- tem. Our students have applied Little’s Law to reduce flow times in a variety of systems, including a hospital, an order processing system at a manufacturer and an operation for processing insurance applications. Some of our students also have applied TOC con- cepts in the same projects in which they reduced flow times. When the critical path of activities includes an activity performed by the constraint resource, increas- ing flow rate at the constraint will then reduce flow time. When the critical path does not include an activity performed by the constraint, separate but related projects have to be undertaken—one to increase the flow rate of the constraint and a second to reduce flow time on the critical path. These students and most practitioners must learn to apply Little’s Law and TOC appropriately. Further- more, the use of process modeling, simulation and optimization can aid in applying these techniques to systems, which in turn can lead to significant improve- ments in operations. REFERENCES AND NOTES 1. Robert Gerst, “The Little Known Law,” Six Sigma Forum Magazine, Vol. 3, No. 2, pp. 18-23. 2. John D.C. Little, “A Proof for the Queuing Formula: L = lW,” Operations Research, Vol. 9, No. 3, pp. 383-387. 3. Ravi Anupindi, Sunil Chopra, Sudhakar D. Desmukh, Jan A. Van Mieghem and Eitan Zemel, Managing Business Process Flows, Prentice Hall, 1999, p. 42. 4. Ibid. 5. H. William Dettmer, Goldratt’s Theory of Constraints: A Systems Approach to Continuous Improvement, ASQ Quality Press, 1997, p. 11. 6. Ibid., pp. 13-15. 7. Robert M. Saltzman and Vijay Mehrotra, “A Call Center Uses Simulation To Drive Strategic Change,” Interfaces, Vol. 31, No. 3, pp. 87-101. 8. Information on ProModel, a discrete even simulation software, can be found at www.promodel.com/products/promodel. 9. Just-in-time manufacturing is an optimal material requirement planning system for a manufacturing process in which there is little or no manufac- turing material inventory on hand at the manufacturing site and little or no incoming inspection. Applying Little’s Law and the Theor y of Constraints S I X S I G M A F O R U M M A G A Z I N E I F E B R U A R Y 2 0 0 5 I 41 WHAT DO YOU THINK OF THIS ARTICLE? Please share your comments and thoughts with the editor by e-mailing godfrey@asq.org. Table 1. Economic Analysis For a Coefficient of Variation of 0.2 Average Average throughput in cycle time Economic Inventory units per minute in minutes value 5 0.4720 10.59 31.61 6 0.5507 10.89 38.18 7 0.6172 11.34 43.38 8 0.6553 12.20 45.33 9 0.6652 13.53 43.99 10 0.6662 15.01 41.61 Table 2. Optimal Inventory as a Function Of Coefficient of Variation Coefficient of variation 0.0 0.1 0.2 0.3 0.4 0.5 Optimal inventory 7 8 8 8 9 9
  30. 30. The Goal Discussion Guide Craig Paxson Participants Guide How to Read “The Goal” Written in a fast-paced thriller style, The Goal, a gripping novel, is transforming management thinking throughout the world. It is a book to recommend to your friends in industry - even to your bosses - but not to your competitors. Alex Rogo is a harried plant manager working ever more desperately to try improve performance. His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant - or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a professor from student days - Jonah - to help him break out of conventional ways of thinking to see what needs to be done. The story of Alex's fight to save his plant is more than compulsive reading. It contains a serious message for all managers in industry and explains the ideas, which underline the Theory of Constraints (TOC), developed by Eli Goldratt. This is a seven week discussion of “The Goal.” Each week, you will read a selected set of chapters. The Goal is written using the Socratic Method - Ask - Tell - Ask. The chapter are selected based on the question that Jonah poses to Alex, what Alex learns and Alex’s next questions. As you read, each section, think about the following questions: 1. What is the current situation? What did Alex learn? 2. What questions does Alex currently have? 3. What hints does Jonah give Alex? 4. What do you think the answers Alex will discover are? 5. How does this apply to my organization?
  31. 31. The Goal Discussion Guide Craig Paxson Your discussion facilitator will ask questions to review the previous week’s reading and may present exercises to enhance your learning. Reading Schedule Intro – Chap 4 Chaps 5 – 8 Chaps 9 – 11 Chaps 12 – 19 Chaps 20 – 25 Chaps 26 – 31 Chaps 32 – 40
  32. 32. The Goal Discussion Guide Craig Paxson Intro – Chapter 4 1. Why does Alex think the robots are so successful when he first talks to Jonah? 2. How does Jonah indicate that the robots were not successful? 3. How does Jonah define productivity? Next Meeting: Chapters 5 - 8
  33. 33. The Goal Discussion Guide Craig Paxson Chapter 5 – 8 1. What is the goal? 2. What does your process manufacture? 3. What three common financial measures express the goal to "make money"? 4. Express the “goal” in terms of those financial measures 5. What three measures are useful at the operational level to express the goal? 6. Define throughput, inventory, and operational expense. 7. Jonah claims the common financial measures are related to the operational measures. How? 8. Define Throughput, Inventory and Operating Expense in your process’ terms 9. What questions does Jonah leave Alex with? What do you think Alex will discover? Next Meeting: Chapters 9 - 11
  34. 34. The Goal Discussion Guide Craig Paxson Chapter 9 – 11 1. Express the "goal" in terms of throughput, inventory, and operational expense. 2. What is the result of high efficiencies on a non-constraint machine? 3. Do high efficiencies necessarily imply higher profit? 4. Why is it important that throughput be defined in terms of sales rather than production? 5. What causes a balanced plant to fail? 6. What are the type of operational operating expenses? 7. What is the equation for Productivity? 8. What questions does Jonah leave Alex with? What do you think Alex will discover? Next Meeting: Chapters 12 - 19
  35. 35. The Goal Discussion Guide Craig Paxson Chapters 12 – 19 1. Why does the spread of the line of boy scouts discussed on page 100 always become longer as time goes on? 2. What characteristics of the hiking troop relate to the production characteristics of Throughput, Inventory, and Operational Expense? 3. Using the hike analogy on page 113, what happens in a plant if the fastest operations are put at the beginning of the production process, the slowest operations are put at the end, and all workers produce at a high efficiency? 4. What is Herbie in terms of TOC? 5. In terms of TOC what has been done when Herbie goes to the front of the line? 6. In terms of TOC what has been done when items are removed from Herbie's pack? 7. Why was Pete so happy even through the order was not delivered on time? 8. Define a bottleneck 9. Why does Jonah say “balance flow not capacities”? Next Meeting: Chapters 20 – 25
  36. 36. The Goal Discussion Guide Craig Paxson Chapters 20 – 25 1. Why does Jonah say a plant should have bottlenecks? 2. What does lost time at a bottleneck cost? 3. What two things can be done to optimize a bottleneck? 4. What is the effect of the "efficient" operation of non-bottleneck machines? 5. What determines the level of utilization of a non-bottleneck machine? 6. What are the combinations of production flow through a bottleneck and non-bottleneck? 7. What is the difference between activating a resource and utilizing a resource? 8. Which resources in the system should we seek to optimize? 9. What does Jonah suggest is the actual constraint in the system? 10. What do you think is the solution Jonah is proposing? Next Meeting: Chapters 26 – 31
  37. 37. The Goal Discussion Guide Craig Paxson Chapters 26 – 31 1. What is the function of the drum and rope if used on a hike? 2. What is the drum for the production facility? 3. What is the rope for the production facility? 4. Why is a rope needed for assembly operations? 5. What is the next logical step after establishing the drum and rope for the production process? 6. What does cutting batch sizes in half for non-bottleneck operations accomplish? 7. How can the time material spends in plant be classified into four types? 8. What is time saved on a non-bottleneck machine? Next Meeting: Chapters 32 – 40
  38. 38. The Goal Discussion Guide Craig Paxson Chapters 32 – 40 1. What are the 5 Focusing Steps? 2. What is the Process of Change? 3. Alex and his team have moved from the _______________ world to the _____________ world. 4. In each world, what is the relative importance of Inventory (I), Operating Expense (OE) and Throughput (T) and why? ____________ World _______________ World 1. 1. 2. 2. 3. 3. 5. What are your most important learnings?
  39. 39. The Goal Discussion Guide Craig Paxson Author Bio Eli Goldratt is an educator, author, scientist, philosopher, and business leader. But he is, first and foremost, a thinker who provokes others to think. Often characterized as unconventional, stimulating, and "a slayer of sacred cows," Dr. Goldratt exhorts his audience to examine and reassess their business practices with a fresh, new vision. He obtained his Bachelor of Science degree from Tel Aviv University and his Masters of Science, and Doctorate of Philosophy from Bar-Ilan University. In addition to his pioneering work in Business Management and education, he holds patents in a number of areas ranging from medical devices to drip irrigation to temperature sensors
  40. 40. The Goal Discussion Guide Craig Paxson Further Reading Theory of Constraints “It’s Not Luck” by Eli Goldratt Applying TOC to sales and marketing “Critical Chain” by Eli Goldratt Applying TOC to project management “What is this thing called Theory of Constraints” by Eli Goldratt Further explanation of the Five Focusing Steps, the Process of Change and implementing TOC “Breaking the Constraints to World-Class Performance” by H. William Dettmer Very in-depth discussion of the Theory of Constraints Lean “Gemba Kaizen” by Masaaki Imai Applying the principles of lean production and continuous improvement “Office Kaizen” by William Lareau Applying the principles of lean production and continuous improvement to office and administrative functions

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