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Pareto analysis-simplified
J.Skorkovský, KPH
What is it ?
• tool to specify priorities
• which job have to be done earlier than the
others
• which rejects must be solved firstly
• which product gives us the biggest
revenues
• 80|20 rule
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%
Pareto chart
Lorenz curve
High priorities
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.
Statements II.
• The ABC analysis suggests, that
inventories of an organization are not of
equal value
• The inventory is grouped into three
categories (A, B, and C) in order of their
estimated importance.
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)
ABC Distribution
ABC class Number of items Total amount required
A 10% 70%
B 20% 20%
C 70% 10%
Total 100% 100%
Minor difference from distribution mentioned before !!
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)
13
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:
Preferential treatment
Item
code
Annual
consumption
(value)
Number of
orders
Value per
order
Average
inventory
1 60000 8 7500 3750
2 4000 3 1333 666
3 1000 1 1000 500
TOTAL INVENTORY (PT) = 4916
TOTAL INVENTORY (EQT)= 8125
Determination of the Reorder Point (ROP)
• ROP=expected demand during lead time + safety stock
20
50=ROP
50-20=30
Determination of the Reorder Point (ROP)
(home study)
• ROP = expected demand during lead time + z* σdLT
where z = number of standard deviations and
σdLT = the standard deviation of lead time demand and z* σdLT =Safety Stock
aan
Example
(home study)
• The manager of a construction supply house
determined knows that demand for sand during
lead time averages is 50 tons.
• The manager knows, that demand during lead
time could be described by a normal distribution
that has a mean of 50 tons and a standard
deviation of 5 tons
• The manager is willing to accept a stock out risk
of no more than 3 percent
Example-data
(home study)
• Expected lead time averages = 50 tons.
• σdLT = 5 tons
• Risk = 3 % max
• Questions :
– What value of z (number of standard deviation)is appropriate?
– How much safety stock should be held?
– What reorder point should be used?
Example-solution
(home study)
• Service level =1,00-0,03 (risk) =0,97 and from
probability tables you will get z= +1,88
See next slide with probability table
Probability table
Example-solution
(home study)
• Service level =1,00-0,03 =0,97 and from
probability tables we have got : z= +1,88
• Safety stock = z * σdLT = 1,88 * 5 =9,40 tons
• ROP = expected lead time demand + safety
stock = 50 + 9.40 = 59.40 tons
• For z=1 service level =84,13 %
• For z=2 service level= 97,72 %
• For z=3 service level = 99,87% (see six sigma)
ABC and VED and service levels
A items should have low level of service level (0,8 or so )
B items should have low level of service level (0,95 or so)
C items should have low level of service level (0,95 to 0,98 or so)
D items should have low level of service level (0,8 or so )
E items should have low level of service level (0,95 or so)
V items should have low level of service level (0,95 to 0,98 or so)
Matrix
Resource : https://www.youtube.com/watch?v=tO5MmOBdkxk
Prof. Arun Kanda (IIT), 2003

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Pareto_analysis-inventory_ABC_simplified_20160511.ppt

  • 2. What is it ? • tool to specify priorities • which job have to be done earlier than the others • which rejects must be solved firstly • which product gives us the biggest revenues • 80|20 rule
  • 3. 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%
  • 5. Use of PA in Inventory Management
  • 6. 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.
  • 7. Statements II. • The ABC analysis suggests, that inventories of an organization are not of equal value • The inventory is grouped into three categories (A, B, and C) in order of their estimated importance.
  • 8. 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 !!
  • 9. Example of possible categories allocation-graphical representation (4051 items in the stock)
  • 10. ABC Distribution ABC class Number of items Total amount required A 10% 70% B 20% 20% C 70% 10% Total 100% 100% Minor difference from distribution mentioned before !!
  • 11. Objective of ABC analysis • Rationalization of ordering policies – Equal treatment OR – Preferential treatment See next slide
  • 12. 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
  • 13. Carrying cost (will be presented next slide) 13 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:
  • 14. Preferential treatment Item code Annual consumption (value) Number of orders Value per order Average inventory 1 60000 8 7500 3750 2 4000 3 1333 666 3 1000 1 1000 500 TOTAL INVENTORY (PT) = 4916 TOTAL INVENTORY (EQT)= 8125
  • 15. Determination of the Reorder Point (ROP) • ROP=expected demand during lead time + safety stock 20 50=ROP 50-20=30
  • 16. Determination of the Reorder Point (ROP) (home study) • ROP = expected demand during lead time + z* σdLT where z = number of standard deviations and σdLT = the standard deviation of lead time demand and z* σdLT =Safety Stock aan
  • 17. Example (home study) • The manager of a construction supply house determined knows that demand for sand during lead time averages is 50 tons. • The manager knows, that demand during lead time could be described by a normal distribution that has a mean of 50 tons and a standard deviation of 5 tons • The manager is willing to accept a stock out risk of no more than 3 percent
  • 18. Example-data (home study) • Expected lead time averages = 50 tons. • σdLT = 5 tons • Risk = 3 % max • Questions : – What value of z (number of standard deviation)is appropriate? – How much safety stock should be held? – What reorder point should be used?
  • 19. Example-solution (home study) • Service level =1,00-0,03 (risk) =0,97 and from probability tables you will get z= +1,88 See next slide with probability table
  • 21. Example-solution (home study) • Service level =1,00-0,03 =0,97 and from probability tables we have got : z= +1,88 • Safety stock = z * σdLT = 1,88 * 5 =9,40 tons • ROP = expected lead time demand + safety stock = 50 + 9.40 = 59.40 tons • For z=1 service level =84,13 % • For z=2 service level= 97,72 % • For z=3 service level = 99,87% (see six sigma)
  • 22. ABC and VED and service levels A items should have low level of service level (0,8 or so ) B items should have low level of service level (0,95 or so) C items should have low level of service level (0,95 to 0,98 or so) D items should have low level of service level (0,8 or so ) E items should have low level of service level (0,95 or so) V items should have low level of service level (0,95 to 0,98 or so)