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How to construct Lorenz Curve and Pareto chart
• list of causes (type of rejects) in %
• table where the most frequent cause is
always on the left side of the graph
Reject Type Importance Importance (%) Accumulative (%)
1 Bad size 10 71% 71 %=71%
2 Bad material 3 21 % 92%=71%+21%
3 Rust 1 8% 100 %=92%+8%
Use of PA in Inventory Management
•
Statements I.
• ABC analysis divides an inventory into
three categories :
– "A items" with very tight control and accurate
records
– "B items" with less tightly controlled and good
records
– "C items" with the simplest controls possible
and minimal records.
Example of possible allocation into categories
• A’ items – 20% of the items accounts for 70% of the
annual consumption value of the items.
• ‘B’ items - 30% of the items accounts for 25% of the
annual consumption value of the items.
• ‘C’ items - 50% of the items accounts for 5% of the
annual consumption value of the items
Beware that 20+30+50=100 and 70+25+5=100 !!
Example of possible categories allocation-graphical
representation (4051 items in the stock)
Objective of ABC analysis
• Rationalization of ordering policies
– Equal treatment
OR
– Preferential treatment
See next slide
Equal treatment
Item
code
Annual
consumption
(value)
Number of
orders
Value per
order
Average
inventory
1 60000 4 15000 7500
2 4000 4 1000 500
3 1000 4 250 125
TOTAL INVENTORY (EQT) 8125
1. Value per order= Annual consumption/Numer of orders
2. Average inventory = Value per order/2 see next slide which is
taken from EOQ simplified presentation
Carrying cost (will be presented next slide)
8
Resource- Taylor- Wikipedia
To verify this relationship, we can specify any number of points
values of Q over the entire time period, t , and divide by the
number of points. For example, if Q = 5,000, the six points
designated from 5,000 to 0, as shown in shown figure, are
summed and divided by 6:

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Quality Analysis Tool PA .pptx

  • 1. How to construct Lorenz Curve and Pareto chart • list of causes (type of rejects) in % • table where the most frequent cause is always on the left side of the graph Reject Type Importance Importance (%) Accumulative (%) 1 Bad size 10 71% 71 %=71% 2 Bad material 3 21 % 92%=71%+21% 3 Rust 1 8% 100 %=92%+8%
  • 2. Use of PA in Inventory Management •
  • 3. Statements I. • ABC analysis divides an inventory into three categories : – "A items" with very tight control and accurate records – "B items" with less tightly controlled and good records – "C items" with the simplest controls possible and minimal records.
  • 4. Example of possible allocation into categories • A’ items – 20% of the items accounts for 70% of the annual consumption value of the items. • ‘B’ items - 30% of the items accounts for 25% of the annual consumption value of the items. • ‘C’ items - 50% of the items accounts for 5% of the annual consumption value of the items Beware that 20+30+50=100 and 70+25+5=100 !!
  • 5. Example of possible categories allocation-graphical representation (4051 items in the stock)
  • 6. Objective of ABC analysis • Rationalization of ordering policies – Equal treatment OR – Preferential treatment See next slide
  • 7. Equal treatment Item code Annual consumption (value) Number of orders Value per order Average inventory 1 60000 4 15000 7500 2 4000 4 1000 500 3 1000 4 250 125 TOTAL INVENTORY (EQT) 8125 1. Value per order= Annual consumption/Numer of orders 2. Average inventory = Value per order/2 see next slide which is taken from EOQ simplified presentation
  • 8. Carrying cost (will be presented next slide) 8 Resource- Taylor- Wikipedia To verify this relationship, we can specify any number of points values of Q over the entire time period, t , and divide by the number of points. For example, if Q = 5,000, the six points designated from 5,000 to 0, as shown in shown figure, are summed and divided by 6: