esd faculty lunch research talk mustafa Ça ğ rı gürbüz april 14, 2009 ctl @ mit
TRANSCRIPT
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 GürbüzMIT-Zaragoza International Logistics Program, Zaragoza, Spain
Co-authors: Kamran Moinzadeh, Yong-Pin ZhouUniversity 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)
Outbound Shipments: Each costs “$K” and takes “L” time units
DistributionCenter
Supplier 1
Retailer 1 Retailer 2 Retailer N
Supplier 2
Inbound Shipment 2: Costs “$K02” Takes “L02” time units
Inbound Shipment 1: Costs “$K01” Takes “L01” time units
Savings from fixed outboundordering costs
Savings from fixed inboundordering costs
Order trigger at all retailers, combined
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
Inbound quantitydistribution
Expected cycle time
Outbound quantitydistribution
Inventory positiondistribution
Inventory leveldistribution
Inbound penaltycost
Outbound penaltycost
Orderingcost
Holding/Shortagecost
Cost Rate
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 Qj.
• 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
0 t t+o2 t+L01
1) Trigger for Item 1 (or Item 2)2) Raise the inventory positionfor Item 1 and Item 23) Item 1 is shipped out fromSupplier 1
Item 2 is shipped out from Supplier 2
1) Both items arrive at thewarehouse at the same time2) They are shipped to theretailers
0 t t+l12
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 Qj.
• 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
0 t t+o2 t+L01
1) Trigger for Item 1 (or Item 2) 2) Raise the inventory positionfor Item 1 3) Item 1 is shipped out fromSupplier 1
1) Raise the inventory positionfor Item 2 2) Item 2 is shipped out from Supplier 2
1) Both items arrive at thewarehouse at the same time2) They are shipped to theretailers
0 t t+l12
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:
0 t t+o2 t+L01
1) Trigger for Item 1 OR 22) Raise the inventory positionfor items 1 and 23) Item 1 is shipped out Supplier 1
1) Item 2 is shipped out from Supplier 2
1) Both items arrive at thewarehouse at the same time2) They are shipped to theretailers
0 t t+l12
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 Qj.
– 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:
0 t t+o2 t+L01
1) Trigger for Item 1 OR 22) Raise the inventory positionfor Item 13) Item 1 is shipped out Supplier 1
1) Raise the inventory position for Item 22) Item 2 is shipped out from Supplier 2
1) Both items arrive at thewarehouse at the same time2) They are shipped to theretailers
0 t t+l12
Summary of Coordinated (across items) continuous review policies
MISO-1:
1. Monitor IP for one item only
2. External delay
MISO-2:
1. Monitor IP for one item only
2. Internal delay
MISO-3:
1. Monitor IP for all items
2. External delay
MISO-4:
1. Monitor IP for all items
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 segmentso Protecting seats for each segment based on demand
(capacity allocation)
• Why should passenger rail companies use it?o Unfilled train seats = Lost Revenueo Full trains = Lost Revenue
• Why should YOU care? Understanding it can help you save money
Borrowed from S. Joiner
A Simple Example
Seat Utilization: 93%
Total Revenue: 1590€
Revenue per Seat: 28,90€
Departure Day-14 Days -7 Days
50€ 50€ 50€
Departure Day-14 Days -7 Days
30€ 30€ 30€
Departure Day-14 Days -7 Days
20€ 30€ 50€
No Revenue Management• Pricing Scheme:
Seat Utilization: 100% Total Revenue: 2470€ Revenue per Seat: 45,33€
Seat Utilization: 65% Total Revenue: 1850€ Revenue per Seat: 32,50€
DiscountingRevenue Management
Total Revenue Summary
No RM: 1850€Discount: 1590€
(Max 4) (Max 14)
Why is this so difficult?• Data Limitations:
o Limited historical data is availableo 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 tripo A seat protected for the Zaragoza-Barcelona leg means
one less seat is available for the Madrid-Barcelona leg
BarcelonaZaragozaMadridBorrowed 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: – [email protected]– [email protected]
• Address: – Avda. Gomez Laguna, 25, Planta 1,50009 Zaragoza, Spain
• Phone: – +34 619 44 62 66