Matching Supply with Demand: An Introduction to Operations Management Gérard Cachon ChristianTerwiesch All slides in this file are copyrighted by Gerard Cachon and Christian Terwiesch. Any instructor that adopts

Matching Supply with Demand: An Introduction to Operations Management

as a required text for their course is free to use and modify these slides as desired. All others must obtain explicit written permission from the authors to use these slides.

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Quality Rework Quality at the Source

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Process interruption due to rework

Scan Passenger Passenger Prep 6 Retrieve items 3.4

4 X-ray items 2.73

     Suppose 10% of bins need to be rescanned. The average activity time to X-ray is then 0.9 x 20 + 0.1 x 40 = 22 secs X-ray capacity is now 60 / 22 = 2.73 bins per minute.

The bottleneck ’s capacity is reduced due to rework!

Hence – avoid sending re-work through the bottleneck!

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Batching and quality

A B C

= Good unit = Bad unit  Suppose process A can start making defective units and once it starts to make defective units it does so until corrective action is taken,  Suppose quality inspection to discover defective units is only done at process step C.

 With two units allowed in the buffers, there will be four defective units made before the problem is discovered.

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Quality at the source

A B C

= Good unit = Bad unit  But with 6 units allowed in the buffer, there will be 12 defective units before the problem is discovered!

 Hence:    Large batches are problematic when quality is an issue.

Large batches can lead to lots of wasted capacity – imagine if step B were the bottleneck!

Firms should adopt “quality at the source” whenever possible:    Inspect for quality when an item is produced.

Inspect the 1 st item in a batch rather than inspecting only when the batch is completed. Inspecting for quality is most valuable in front of the bottleneck.

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Quality and Six Sigma

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M&M Exercise

Form groups around a scale and a sample of M&M bags A bag of M&M’s should be between 48 and 52g Measure the samples on your table: Compute x1, x2, x3, x4, x5 Compute x-bar and range (R) Number of defects All data will be compiled in master spread sheet Yield = %tage of units according to specifications How many defects will we have in 1MM bags?

Analysis of new sample in production environment

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Measure Process Capability: Quantifying the Common Cause Variation

Process capability measure

Lower Specification Limit (LSL) Upper Specification Limit (USL) C p



USL

 6  ˆ

LSL

Process A (with st. dev  A ) Process B (with st. dev  B ) X-3  A X-2  A X-1  A X X+1  A X+2  3  X+3  A x  1  2  3  4  5  6  C p 0.33

0.67

1.00

1.33

1.67

2.00

P{defect}ppm 0.317

0.0455

0.0027

0.0001

0.0000006

2x10 -9 X-6  B X X+6  B • Estimate standard deviation in excel • Look at standard deviation relative to specification limits • Don’t confuse control limits with specification limits: a process can be in control, yet be incapable / out of control, but still capable

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317,000 45,500 2,700 63 0,6 0,00

Two Types of Variation

Common Cause Variation (low level) Common Cause Variation (high level) Assignable Cause Variation

• Need to measure and reduce common cause variation • Identify assignable cause variation as soon as possible • What is common cause variation for one person might be assignable cause to the other

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Two (similar) Frameworks for Managing Quality

Six Sigma System Toyota Quality System

Jidoka Andon cord Detect, stop, alert Capability Analysis Conformance Analysis Avoid Root cause problem solving Eliminate Assignable Cause Poka Yoke Build-in quality Ishikawa Diagram Kaizen

Some commonalities:

Avoid defects by keeping variation out of the process If there is variation, create an alarm and trigger process improvement actions The process is never perfect – you keep on repeating these cycles

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Investigate for Assignable Cause

Detect Abnormal Variation in the Process: Detect – Stop - Alert

Jidoka

If equipment malfunctions / gets out of control, it shuts itself down automatically to prevent further damage Requires the following steps: Detect Alert Stop

Andon Board / Cord

A way to implement Jidoka in an assembly line Make defects visibly stand out Once worker observes a defect, he shuts down the line by pulling the andon / cord The station number appears on the andon board Source: www.riboparts.com

, www.NYtimes.com

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Detect Abnormal Variation in the Process: Identifying Assignable Causes

Process Parameter

Upper Control Limit (UCL) Center Line Lower Control Limit (LCL)

Time • Track process parameter over time - mean - percentage defects • Distinguish between - common cause variation (within control limits) - assignable cause variation (outside control limits) • Measure process performance: how much common cause variation is in the process while the process is “in control”?

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Root Cause Problem Solving

Ishikawa Diagram

A brainstorming technique of what might have contributed to a problem

Pareto Chart

Maps out the assignable causes of a problem in the categories of the Ishikawa diagram Shaped like a fish-bone Easy to use Order root causes in decreasing order of frequency of occurrence 80-20 logic

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Quality Management: Conclusion

Variation (once again) is the root cause for all operational problems Difference between common cause variation and assignable causes Quality and flow are tightly related Toyota Production System (TPS) provides an integrated framework of managing quality AND flow Strong similarities between Six Sigma and TPS Measure and reduce the common cause variation Build processes that are robust to variation / operators (poka-yoke) Detect assignable causes through control charts Detect - Stop – Alert (Jidoka, Andon Cord) Root cause problem solving (Ishikawa, Pareto)

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