Vladimir Krasojevic managed several supply chain optimization projects, three of which are summarized here: 1. An assembly lead time reduction project at a watch factory reduced lead time from 35 to 12 days by optimizing thermal testing, batch release processes, and activities. 2. A material pull and production smoothing project at a factory reduced average inventory from 25 to 10 days by implementing weekly material pulls and a flat pricing model with suppliers. 3. A process flow optimization project reduced repacking lead time at two social work companies from 45 to 20 days by standardizing their process to continuous small-cell production.

1. Examples of Supply Chain Optimization projects Initiated and Managed by
Vladimir Krasojevic
Due to confidentiality only projects older than 5 years included

2. Assembly Lead Time reduction
Context: High WIP inventory & assemblage time
• 15 steps=Total
activity time 4
hours+36 hours
thermical test
• Lead time = 35 days
• Batch
management
• High
quality/craftmans
hip/activity
• Final Quality
check after
thermical test
• Full batch release
© vladimir krasojevic

3. Assembly Lead Time reduction
Results: 65% improvement of WIP/LT
• CHANGES:
– Thermical test multiple
changes of temperature in 12
hours instead of stable high
temperature for 36 h;
Bottleneck: QA
– Release of good watches from
the batch: specific pieces
retained: Technology solution
for batch management
improved
– Process group activities
– Cash-flow (WIP) & lead time
reduced from 35 to 12 days
© vladimir krasojevic

4. Material PULL and Heijunka
• High volume factory
• Supplier with European DC
• Distance: 60 km
• Order lead time according to standard ERP
configuration: Make to order, based on production
plan, safety stock, price according to EOQ
• Expensive material
• Simple portfolio : 3-5 different item materials SKUs
• Average stock in factory 25 days
Context: High value stock and ERP ordering
© vladimir krasojevic

5. Material PULL and Heijunka
Results: 60% improvement with weekly Pull
• CHANGES:
– Change in ERP – weekly requirements and
stock by AT item ; Bottleneck: Purchasing,
QA, IS
– Weekly requirements submitted: Full truck
load sent during the week.
– Price list adapted to reflect continuous
flow: flat price per item independently on
quantity in delivery
– Cash-flow (factory AT stock) reduced from
25 to ~ 10 days
© vladimir krasojevic

6. Supplier driven VMI
Context: No warehouse in factory: VMI
• New factory built in residential area
• No on-site direct material warehouse (max 1 day
stock)
• Supplier of blanks is «next door»
• New VMI process is established to «help-out» factory
• Daily exchange of scheduling, stock and production
plan of cigarette factory
• 1 truck used to replenish by pallete according to
scheduling
• Average stock in factory 1 day. Supplier: 17 days
© vladimir krasojevic

7. Supplier driven VMI
Results: 97% (60%) less vs. other factories
• CHANGES:
– Daily exchange of stock and
scheduling in EXCEL; Bottleneck:
IS integration
– Expert (informal) rules for
printing at supplier(slow movers
25 days, fast movers 3 days).
– Commitment & price through
daily orders: no remuneration for
Supplier stock (recompensated by
lower paper stock)
– Cash-flow (factory blanks stock)
1 instead of 45 days (18 with
supplier)© vladimir krasojevic

8. Process flow optimization
Context: Different lead time & WIP inventory
• Repacking process is highly manual
• 2 different social-work companies have
been used for the same task
• Bottleneck identified with time needed
for repacking (lack of resources and
long lead-time)
• Field visit and analysis of the process
identified major difference in process:
– Small cell, continuous repacking in teams of
two vs.
• Batch repacking, team work by activity
• Repacking time 45 days in
bigger of two companies
Company 1
Company 2
© vladimir krasojevic,

9. Process flow optimization
Results: 50% time reduction with same teams
• CHANGES:
– Use of the same process in 2
companies: 50 % reduction of lead
time and work in progress
– Initial questions whether specific social
work company process could be
transferred to another place
– Higher satisfaction of operators (they
see results of their work, every day)
– Elimination of unnecessary control
steps (quantity & quality)
– New lead time for market: 20 days and
reduced WIP stock
Company 1
Company 2
Company 2
© vladimir krasojevic

10. Cost-to-serve optimization
Context: Cost, stock and availability
• Activity Based Costing from manufacturer to
Retailer – simulation and estimates
• Assumption might be wrong (ie: delivery
frequency and minimum stock in POS)
• OoS in POS – holly grail of FMCG stock
management service level 98%
• Manufacturer push to retailers
• Plannograms: retailers paid to expose all
products in portfolio
• Manufacturers stock 30 DOI, Retailers
distribution system 12 DOI, POS estimate
20-30 DOI (previous assumptions 3-7 DOI)
© vladimir krasojevic

11. Cost-to-serve optimization
Results: Improvement initiatives started
• CHANGES:
– Retailer to eliminate back-office stock and
store all in shelves: 25 % reduction of OoS
potential (implemented in other categories)
– Introduction of fiscal warehouse status in
Switzerland 1 day of retail stock value = 20
days of manufacture value (bonded
warehouse status): potential to reduce retail
distribution stock from 12 to 3 days (75%)
– Higher interest for collaboration,
consumption driven supply : demand sensing,
daily deliveries, portfolio optimization (retail
stock potential from 20-30 to 3 DOI (80%)
© vladimir krasojevic

12. Replenishment and Stock Optimization
Context: Replenishment of Vending Machines
• Vending machines visited once in 1-3
weeks to be replenished
• Machines are usually filled up to their
capacity – «column size»
• Operator use the rule defined by back
office
• If he decides to change the rule, handheld
computer has to be reprogrammed
• Most of operators are not changing
quantity in order to be more efficient
• Stock in vending machines is up to 50
days of inventory
© vladimir krasojevic

13. Replenishment and Stock Optimization
Results: 60% potential of stock reduction
69%
1315%
0
100
200
300
400
500
600
700
800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0%
200%
400%
600%
800%
1000%
1200%
1400%
Stock loaded Sales Remaining Safety factor
• CHANGES:
– Detailed analysis of sales, stock,
replenishment not in line with consumption:
60% reduction potential identified
– New rules defined: load standard quantity in
multiple of 5 packs (slow movers), 10 packs
fast movers
– New order preparation proposed on
automatic line, based on historic sales
– Higher frequency of delivery proposed to
reduce time for cash collection
– Total cash-flow improvement potential up to
75%. Project not implemented.
© vladimir krasojevic

14. Direct Store Deliveries Optimization
Context: Van Sales - Portfolio Planning Complexity
• Gemba walk
• Complex population with different SKUs by nationality
• Delivery specific portfolio picking and preparation
• Complex Order Cheking process (Manual and without
system support)
• Van sales deliveries supported by complex delivery
planning and forecasting process
• 2.5 hours (total) spend in the morning and at the end of
the day on order control, checking and reconciliation
• 60% returned from delivery (not sold)
© vladimir krasojevic
• 2 hours considered as «lost for sales» daily from deliverymen

15. Direct Store Deliveries Optimization
Results: 2 hours more for sales, better FA, low return
• CHANGES:
– Standard portfolio for every
delivery
– Every load identical: Heijunka
– Forecast on returned vs loaded to
understand optimal portfolio
– 2 hours eliminated as non-value
added and used for extra sales
(25% productivity improvement)
– Returned goods non delivered
50% reduction
– Extra sales (not claimed)
© vladimir krasojevic