ESD Faculty Lunch Research
Talk
Mustafa Çağrı Gürbüz
April 14, 2009
CTL @ MIT
Introduction
• BS in Industrial Engineering, Bilkent University,
Ankara Turkey, 1999
• MS in Industrial Engineering, Bilkent University,
Ankara Turkey, 2001
• PhD in Operations Management, Michael G. Foster
School of Business, University of Washington, 2006
• Faculty member at the Zaragoza Logistics Center
since 09/2006
– Visiting faculty at CTL until 09/2009
Agenda
• Academic Research
– Inventory/Transportation Management in Distribution
Systems
• “Coordinated Replenishment Strategies in
Inventory/Distribution Systems”, with K. Moinzadeh and Y.
Zhou, Management Science, Vol. 53 (2), 2007, 293-307.
– Inventory Management under random supply
• “Supplier Diversification Under Binomial Yield”, with M.
Fadıloğlu and E. Berk, Operations Research Letters, Vol. 36 (5),
2008, 539-542.
– Contracting retailer/manufacturer efforts in a newsboy
setting
– Impact of random deal offerings for perishable products
under continuous review
– The impact of accountability on the bullwhip factor
•
• Projects at ZLC
– Revenue Management for the passenger rail industry
– Measuring carbon footprint due to transportation for the
European distribution of Print Green
– Investigating Spain’s potential in distributing goods in
Europe
Coordinated Replenishment
Across Retailers & Suppliers
Mustafa Cağrı Gürbüz
MIT-Zaragoza International Logistics Program, Zaragoza,
Spain
Co-authors: Kamran Moinzadeh, Yong-Pin Zhou
University of Washington, Michael G. Foster School of Business
Distribution Costs!
• Distribution costs are cited as 10% of GDP for
developed countries, and 20% or more for
developing countries (a World Bank research paper
by Bagai and Wilson, 2006)
• Distribution costs represent on average 15% of the
selling price (Van Damme 2000) in European
companies
– 32%: transportation costs
– 31%: inventory costs
– 28%: facility costs
• Industry Week Value Chain Survey conducted in
2005 (www.industryweek.com) 
– The percentage of respondents stating more than
10% increase in distribution costs of sales has
more than doubled since 2003
Borrowed from Dr. Emre Berk
Consolidation/Coordination
• Majority of the companies use some form of
shipment consolidation meaning:
– Combining multiple shipments into a
single group (across time, locations,
products) to achieve lower costs
•Time based consolidation
•Quantity based consolidation
•Time and quantity based
Coordinated replenishment (two-items)
Savings from
fixed inbound
ordering costs
Supplier 1
Supplier 2
Inbound Shipment 1:
Costs “$K01”
Takes “L01” time units
Inbound Shipment 2:
Costs “$K02”
Takes “L02” time units
Distribution
Center
Savings from
fixed outbound
ordering costs
Order trigger at all
retailers, combined
Retailer 1
Retailer 2
Retailer N
Outbound Shipments:
Each costs “$K” and
takes “L” time units
Challenges
– Use of information to decide;
• How to coordinate shipments?
– When to order?
– How much to order?
– GOAL: To minimize the overall cost,
which is the sum of:
• Fixed Ordering/Setup,
• Holding/Backorder,
• Transportation.
– The optimal solution to this problem?
Analysis
Cost
Rate
Inbound quantity
distribution
Inbound penalty
cost
Inventory level
distribution
Holding/Shortage
cost
Ordering
cost
Outbound penalty
cost
Inventory position
distribution
Expected cycle time
Outbound quantity
distribution
Coordination across retailers
alone
• Each item is ordered independently
– but retailers are replenished simultaneously
• Policy MII0: The warehouse orders to raise
all the retailers’ inventory position to Sj for
item j whenever
– any retailer’s inventory position for item j drops
to sj
OR
– the total demand at all the retailers for item j
reaches Qj (for j=1,…M).
Coordination across retailers &
items (suppliers)
Policy MISO-1:
•
•
•
Consider Sub-policy j for all j=1,2,..,M :
– Monitor IP for item j only,
• Trigger Mechanism: Replenishment happens whenever:
– any retailer’s inventory position for item j drops to sj or
– the total demand at all the retailers for item j reaches
Q j.
• Dispatch Mechanism:
– Raise all the retailers’ inventory position to Si for item
i when the replenishment is triggered,
– Ask the supplier to ship item i exactly l1i (L01-L0i) time
units after replenishment is triggered (assume L01≥L0i
for all i).
Evaluate the cost rate for Sub-policy j
Pick the sub-policy with the minimum cost rate.
Illustration of Policy MISO-1
Item 2 is shipped
out from Supplier 2
0
0
tt
t+
t+l12
o
t+L01
2
1) Trigger for Item 1 (or Item 2)
2) Raise the inventory position
for Item 1 and Item 2
3) Item 1 is shipped out from
Supplier 1
1) Both items arrive at the
warehouse at the same
time
2) They are shipped to the
retailers
Coordination across retailers &
items (suppliers)
Policy MISO-2:
•
•
•
Consider Sub-policy j for all j=1,2,..,M :
– Monitor IP for item j only,
• Trigger Mechanism: Replenishment happens whenever:
– any retailer’s inventory position for item j drops to sj or
– the total demand at all the retailers for item j reaches
Q j.
• Dispatch Mechanism:
– Raise all the retailers’ inventory position to Si for item
i and ask the supplier to ship item i exactly l1i (L01-L0i)
time units for all i after replenishment is triggered
(assume L01≥L0i for all i).
Evaluate the cost rate for Sub-policy j
Pick the sub-policy with the minimum cost rate.
Illustration of Policy MISO-2
1) Raise the inventory position
for Item 2
2) Item 2 is shipped
out from Supplier 2
0
0
tt
t+
t+l12
o
t+L01
2
1) Trigger for Item 1 (or Item 2)
2) Raise the inventory position
for Item 1
3) Item 1 is shipped out from
Supplier 1
1) Both items arrive at the
warehouse at the same
time
2) They are shipped to the
retailers
Coordination across retailers &
items (suppliers)
Policy MISO-3:
• Monitor IP for all items,
– Trigger Mechanism: Replenishment happens whenever:
• any retailer’s inventory position for any item j drops
to sj or
• the total demand at all the retailers for any item j
reaches Qj.
– Dispatch Mechanism:
• Raise all the retailers’ inventory position to Sj for
item j (all items j=1,2,..,M) when the replenishment
is triggered,
• Ask the supplier to ship item j exactly l1j (L01-L0j) time
units after replenishment has been triggered
(assume L01≥L0j for all j).
Illustration of Policy MISO-3:
1) Item 2 is shipped
out from Supplier 2
00
tt
t+
t+l12
o
t+L01
2
1) Trigger for Item 1 OR 2
2) Raise the inventory position
for items 1 and 2
3) Item 1 is shipped out
Supplier 1
1) Both items arrive at the
warehouse at the same
time
2) They are shipped to the
retailers
Coordination across retailers &
items
Policy MISO-4:
• Monitor IP for all items,
– Trigger Mechanism: Replenishment happens whenever:
• any retailer’s inventory position for any item j drops to sj
or
• the total demand at all the retailers for any item j reaches
Q j.
– Dispatch Mechanism:
• Raise all the retailers’ inventory position to Sj for item j
exactly l1j (L01-L0j) time units after replenishment has been
triggered,
• Ask the supplier to ship item j exactly l1j (L01-L0j) time units
after replenishment has been triggered (assume L01≥L0j for
all j).
• Assume no trigger will happen for item j for the next ljM
time units after the inventory position is raised to Sj for
j=1,..,M-1.
Illustration of Policy MISO-4:
1) Raise the inventory position
for Item 2
2) Item 2 is shipped out from
Supplier 2
00
tt
t+
t+l12
o
t+L01
2
1) Trigger for Item 1 OR 2
2) Raise the inventory position
for Item 1
3) Item 1 is shipped out
Supplier 1
1) Both items arrive at the
warehouse at the same
time
2) They are shipped to the
retailers
Summary of Coordinated
(across items) continuous review
policies
MISO-1:
MISO-2:
1. Monitor IP for one
item only
1. Monitor IP for one
item only
2. External delay
2. Internal delay
MISO-3:
MISO-4:
1. Monitor IP for all
items
1. Monitor IP for all
items
2. External delay
2. Internal delay
Numerical Results
• No significant difference between coordination
through internal or external delay:
– Policies MISO_3 and MISO_4 perform very closely
• Policies MISO_1 and MISO_2 are good heuristics:
– Their performance are pretty close to that of Policies
MISO_3 and MISO_4 in many cases
– Easier to analyze and compute
• Rankings (best-worst) of the policies are as follows
(the % improvement over the MIIO is given in
parentheses):
– MISO_3 (2.12%), MISO_4 (1.82%), MISO_2 (0.59%), and
MISO_1 (0.42%)
• Monitoring inventory positions for both items help
Policies MISO_3 and MISO_4 for higher .
Numerical Results
• Benefits from coordination across items increase for:
– More retailers (higher N)
– Larger fixed inbound/outbound ordering costs (higher
K0 and K/ K0)
– Larger outbound truck capacities (higher C)
– Smaller unit outbound transportation penalty cost
inbound (smaller )
– Smaller difference in transit times from supplier to
warehouse (larger L02/ L01)
Revenue Management?
• What is it?
o Pricing train seats for specific market segments
o Protecting seats for each segment based on
demand (capacity allocation)
• Why should passenger rail companies use it?
o Unfilled train seats = Lost Revenue
o Full trains = Lost Revenue
• Why should YOU care?
 Understanding it can help you save money
Borrowed from S. Joiner
A Simple Example
No Revenue
Discounting
Management
Management
• Pricing Scheme: Revenue
-14 Days
30€
20€
50€ (Max 4)
-7 Days
30€
50€ (Max 14)
Seat
100%
SeatUtilization:
Utilization:65%
93%
Total
2470€
1850€
TotalRevenue:
Revenue:
1590€
Revenue
45,33€
32,50€
Revenueper
perSeat:
Seat:
Departure Day
30€
50€
Total Revenue Summary
No RM:
1850€
Discount: 1590€
Why is this so difficult?
• Data Limitations:
o Limited historical data is available
o Historical data does not help understand how
customers will respond to price changes
• The Rail Network:
o Unlike in the previous example, passengers can
enter and exit the train at various locations
during a trip
o A seat protected for the Zaragoza-Barcelona leg
means one less seat is available for the MadridBarcelona leg
Madrid
Zaragoza
Barcelona
Borrowed from S. Joiner
The Research
• Question:
o What general guidelines can be established for
applying revenue management in the passenger
rail industry?
• Approach:
o Using historical passenger data and customer
surveys from RENFE to understand and predict
consumer behavior
• Simulation:
o Developed a simulation model to see how different
seat protection and pricing schemes affect revenue
Borrowed from S. Joiner
Contact information
• Email:
– mgurbuz@zlc.edu.es
– mgurbuz@mit.edu
• Address:
– Avda. Gomez Laguna, 25, Planta 1,
50009 Zaragoza, Spain
• Phone:
– +34 619 44 62 66