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Spring OTM_300_Ch08_LeanToyota_Activity_Master

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Created on February 20, 2026

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Activity Bank

TPS and

Lean Systems

Dr. Amir Fard Bahreini

FOR ALL QUESTIONS WORK TOGETHER

  • You are encouraged to work with your neighbor or nearby classmates on all questions.
  • Many questions are designed to be discussed out loud and are not meant to be completed individually
  • The goal is to give you time to talk, think together, and learn from each other and yes, with a little room to catch up and chat before we refocus on the course material.

Cleveland Clinic - Waste Types

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Intel

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Boston Dyanmics

1. How would OEE help diagnose the real problem?2. What types of time should management eliminate first? 3. Why might adding more engineers worsen the problem?

Boston Dynamics engineers assemble robotic arms. Engineers are highly skilled and expensive. However, due to layout inefficiencies, they spend significant time searching for tools, waiting for calibration approvals, and fixing minor defects from upstream machining. Senior leadership is debating whether to hire more engineers to increase robot output. The operations manager argues that the real issue is low productive utilization rather than insufficient staffing.

Activity 1

End of Activity 1 Please wait for instructions before going to the next page.

© Amir Fard Bahreini. All rights reserved.Course materials may not be reproduced, distributed, or shared without permission.

Pfizer Manufacturing Plant — Value-Added Time Percentage

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Amazon Fulfillment Centers — Value-Added %

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Cleveland Clinic

1. What is the Value-Added % of the patient journey? 2. How does waiting drive hospital congestion? 3. Why does reducing flow time reduce inventory (patients in system)?

At Cleveland Clinic, a patient’s total treatment journey takes 12 hours in the hospital. However, actual medical procedures only total 1 hour. The remaining time is spent waiting for tests, bed availability, and physician review. Hospital leadership is concerned about long patient stays and overcrowding but is hesitant to reduce buffers between departments.

Activity 2

End of Activity 2 Please wait for instructions before going to the next page.

© Amir Fard Bahreini. All rights reserved.Course materials may not be reproduced, distributed, or shared without permission.

IKEA

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

BMW – “Too Much Buffer or Too Much Risk?”

BMW’s Munich plant produces electric vehicle battery modules. The welding department supplies the battery casing to final assembly. Historically, BMW operated with large buffers between welding and assembly — sometimes up to 3 days of inventory — because managers feared line stoppages. Recently, leadership introduced a Kanban system to limit work-in-process inventory. Engineers calculated the required number of containers using daily demand, replenishment time, and safety stock. However, once implemented, supervisors became nervous: when minor machine breakdowns occurred, the assembly line occasionally slowed down due to part shortages. Finance loves the reduced inventory and freed-up floor space. Operations managers are split — some argue inventory reduction is exposing weaknesses in the system, while others claim the new system is “too fragile.” Questions are on next page!

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

BMW – “Too Much Buffer or Too Much Risk?”

1. What operational problems were likely hidden by the old large buffers? (Think quality issues, downtime, long replenishment time, coordination failures.) 2. If BMW wants fewer Kanban containers, which variables in the Kanban formula must improve? (Be specific: replenishment time? safety stock? demand variability? reliability?) 3. Is the new system actually fragile — or is it revealing deeper process instability? Should BMW increase buffers again, or fix the root causes? 4. If BMW’s market demand suddenly becomes more volatile, which parameter increases? What does that imply for Kanban levels?

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Activity 3

End of Activity 3 Please wait for instructions before going to the next page.

© Amir Fard Bahreini. All rights reserved.Course materials may not be reproduced, distributed, or shared without permission.

BMW – Jidoka and Information Turnaround

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Intel

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Amazon Fulfillment Centers — Value-Added %

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Activity 4

End of Activity 4 Please wait for instructions before going to the next page.

© Amir Fard Bahreini. All rights reserved.Course materials may not be reproduced, distributed, or shared without permission.

BMW – Jidoka and Information Turnaround

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Amazon Fulfillment Centers — Value-Added %

© McGraw Hill | © 2025 UW–Madison | Dept. of OIM | AFB • All rights reserved

Cleveland Clinic

  1. Using the ITAT formula logic, explain why large buffers delay defect detection.
  2. If Cleveland Clinic doubles processing speed but keeps the same buffer size, what happens to ITAT? Is that enough to ensure quality?
  3. Why does Lean insist that inventory reduction improves quality — not just efficiency?
  4. Why might a system with lower inventory feel “riskier” in the short term?

Cleveland Clinic operates multiple diagnostic labs. Blood samples are collected throughout the hospital and sent to a central testing lab. Historically, the lab maintained large queues between processing and final review to ensure no technician ever ran out of work. Recently, hospital leadership noticed that when a calibration error occurs in a testing machine, incorrect results may be produced for hours before the issue is discovered. By the time the defect is caught, dozens of patients may need retesting, creating delays, stress, and reputational risk. Some managers argue that speeding up lab throughput will reduce delays. Others argue that reducing queue sizes between steps will improve quality detection. Finance is concerned that lowering buffers may create idle time and reduce staff utilization.