This document summarizes a presentation on cross-disciplinary operations management research. It discusses research at the interfaces of operations and economics, marketing, finance/risk management, and behavioral science. It provides examples of empirical research that take a broad view. The presentation emphasizes the opportunities for new insights through cross-disciplinary research, while also noting the difficulties and risks. It concludes that interface research can strengthen the empirical foundations of operations management.
3. Operations-Economics
• Game theory
• Information economics (mechanism design)
• Principal-agent theory
• Auction theory
3
4. Chen (MS, 2007)
potential suppliers • linear production costs
• private information
1 2 n
• independent draws from common cdf
• risk neutral
How much?
From whom?
• Revenue function R(Q), Q = input quantity
• R( ) concave, increasing
buyer • risk neutral
Chen, F. (2007), “Auctioning supply contracts,” Management Science 53(10), 1562-1576.
4
5. Supply Contract Auctions
• Method
– Buyer announces a supply contract P(Q)
– Suppliers submit bids for fees they are willing to pay
– Highest bid wins
– The wining supplier decides the quantity to deliver
• This is a private-value auction, and thus is insensitive to
auction forms (revenue equivalence theorem)
• Optimal supply contract is easy to obtain
* ~
Q (c) � arg maxQ [ R(Q) � H (c)Q], �c �[c, c]
P ** (.) : P ' (Q* (c)) � c, �c � [c, c]
Increasing, concave in Q
independent of number of bidders
5
6. Newsvendor example
• Model
– retail price p
– demand D, with cdf G(.)
– lost sales, zero salvage value
Q
R(Q) � pE min{Q, D} � pQ � p � G( y)dy
0
• Demand is uniform [0,1], costs are uniform [0,1], p=2
P
Q * (c ) � 1 � c
1
P** (Q ) � Q � Q 2
2
0 Q
1 6
7. Main Contributions
• A new and better optimal procurement auction
design
• The new design provides fresh explanations for two
prevalent industry practices in retail industry:
– Slotting allowances
• Up-front, lump-sum fee from a manufacturer to a retailer
• Prevalent in the grocery industry, $6 to $9 billion a year (U.S.)
• Controversial (antitrust investigations by FTC and the Justice Department)
– Vendor managed inventory (VMI)
• Delegation of inventory decision rights to suppliers
• Known rationale: vendor expertise, coordination, information technology
7
9. Chen and Xiao (2009)
• Channel rebates
– Manufacturer � retailer
– Based on realized sales to consumers
• Two common forms of rebates
– Linear rebate
– Target rebate
Chen, F. and W. Xiao, “Is using target rebates good for the manufacturer?” Working Paper,
Graduate School of Business, Columbia University. 9
10. • Chrysler’s case
– Chrysler’s dealer-incentive program in Jan, 2001
Rebate
$500 per unit
$250 per unit
$150 per unit
75% 100% 110% Sales
Target
10
11. – Backfired in April, 2001:
Chrysler’s U.S. sales 18%
Overall auto industry 10%
• Is using target rebate good for
manufacturers?
11
12. Model Setup
• Setting
– A risk-neutral manufacturer (M)
– A risk-neutral retailer (R)
– One selling season
– Production occurs before the selling season
– Demand in the selling season, D(� , e) :
• � : base demand (or market condition) � ~ F (�)
• e : retailer sales effort
12
13. • Contract
– A wholesale price: w
– Rebate: ��x if x � t
r ( x) � �
�(� � � ) x if x � t
Rebate
� ��
�
t Sales
Target
13
14. • Sequence of events
Manufacturer
selling season
contract produce
and deliver q
Retailer order q observe exert e sales x
�
x � min{D(� , e), q}
14
15. Main results
• Assumptions
– Cost of effort:
V (e) � �e 2
– Additive demand:
D(� , e) � � � e
– Multiplicative demand:
D(� , e) � �e
The manufacturer is better off by eliminating the use of
targets.
15
16. Operations-Finance
(Risk Management)
• Not a new topic
– E.g., demand uncertainty, supply uncertainty (yield, leadtime, price)
• Changing the objective function
– E.g., mean-variance trade off (Chen and Federgruen 2000 and many
others)
• Interface with Finance
– Material flow, information, and cash flow
– Bankruptcies and loans
• Swinney, R. and S. Netessine (2007), “Long-term contracts under the
threat of supplier default,” forthcoming M&SOM.
• Babich et al. (2007), “Risk, financing and the optimal number of suppliers,”
University of Michigan Working Paper.
16
17. Swinney and Netessine (2007)
• Contracting under the threat of supplier
default
• The possibility of losing a supplier to
bankruptcy affects the buyer’s decisions, such
as the procurement pricing and the length of
contract
• How?
17
18. Model Setup
• One buyer, two potential suppliers (ex ante identical)
• Two time periods
• Deterministic demands, uncertain production costs
– Demand in each period is normalized to 1
– Supplier i’s cost in period t = ct+di
• Buyer has all the bargaining power, makes contract offers
(contract with one supplier at a time, switching cost k)
• Suppliers are small firms at risk of bankruptcy, but accepts any
contract with nonnegative expected profits
• No private information
18
24. Main Contributions
• Modeling
– Considering supplier bankruptcy in supply-chain
contracting
• Without the possibility of supplier failure,
– Buyer always prefers short-term contracts
• With the possibility of supplier failure,
– When the switching cost exceeds a threshold level, buyer
prefers long-term contracts
– Long-term commitment creates an incentive for the buyer
to be generous in the first period, diminishing the
probability of default
24
25. Babich et al. (2007)
• Dual role by suppliers
– Providers of components
– Financiers through trade credit loans (delayed
payments for goods)
• Typical trade credit contracts in the U.S.
– “net 30”
• Buyer does not have to pay for 30 days
– “2/10 net 30”
• 2% discount if buyer pays within 10 days, buyer has up
to 30 days to pay for the goods
25
26. Research Questions
• Optimal number of suppliers?
– Random yields
• => higher no. of suppliers
– Fixed cost of doing business with a supplier
• => lower no. of suppliers
– Financier role of a supplier
• => higher no. of suppliers
26
27. Model Setup
• One time period
• One buyer, infinite number of potential suppliers (identical)
• Random demand (D)
• Random yield
– Order y from supplier i, get y*Xi
– Suppliers have iid yields
• Decision variables:
– Number of suppliers (N)
– Total order quantity (z), to be equally divided among the N suppliers
– Trade credit loan from each supplier (S)
27
28. Optimization Model
• Cash position at the beginning of the period
– Internal capital (I)
ˆ
– Trade credit loan (NS): S � min S , wy � �
• Spending at the beginning of period
– Fixed costs (NC)
– Procurement costs (wz)
• Cash flow constraint: I + NS >= NC + wz
• Cash position at the end of period
p min �D, Q ( N , z )�� (1 � rI )[( I � NS ) � ( NC � wz )] � (1 � rS ) NS
28
29. Main Contributions
• Modeling contribution
– Trade credits
• Various comparative analysis
– E.g., optimal number of suppliers as a function of
• Demand standard deviation
• Mean and standard deviation of supplier yield
• Supplier loan limit
• Fixed costs
• Wholesale price
• Internal capital
29
30. Behavioral Operations
（行为运营学）
• Observations
– Worldwide Financial Tsunami
• Importance of human behavior
– American System, Japanese System
• Research follows practice
– Validation of theories
• Natural process of development, new theories
– Interface research
• opportunities, difficulties, risks
30
31. An Example
• Bullwhip Effect in a Supply Chain
• Theoretical results
– Chen, F. (1999), “Decentralized Supply Chains Subject to
Information delays,” Management Science 45 (8), 1076-
1090.
• Behavioral results
– Croson, R. and K. Donohue (2006), “Behavioral Causes
of the Bullwhip Effect and the Observed Value of
Inventory Information,” Management Science 52(3),
323-336.
• Lessons
31
32. Chen (MS, 1999)
N 1 Demand
• Decision structure
– local replenishment decisions
• Information structure
– local inventory status
• Cost structure
– holding and backorder costs
• Organizational structure
– team
– cost centers
32
33. Assumptions
Li
i 1 cdf F
li hi p
• Linear holding and backorder costs
• Constant leadtimes
• I.I.D. demands
• Common knowledge: costs, leadtimes, cdf
• Planning horizon: infinite
33
35. Optimal Decision Rules
• Installation, base-stock policy
( s1 , s2 , � , s * )
* *
N
• Computation
N i 1 cdf F
Li � Li � li
Clark-Scarf model ( S1* , S 2 ,�, S N )
* *
si* � Si* � Si*�1
35
37. Experimental Method
• Web-based computer game
• Human subjects
– undergraduate business students
• Monetary incentives
• Number of periods = 48, unknown to
participants
• Participants not to communicate with anyone
during experiment
37
40. Sharing of inventory information helps reduce order variance.
But the bullwhip effect still exists.
Subjects still underweight supply line.
40
41. Lessons
• Actual behavior differs from theoretical
predictions, due to cognitive limitations (e.g.,
the recency effect) and the difficulties in
managing a complex, dynamic system.
• Automated replenishment systems can be an
effective way to avoid human errors and thus
to improve system performance.
• New theory is needed, where decision makers
are “boundedly rational.”
41
42. Journal Publications
• Between 1985 and 2005
• Focusing on papers using human experiments
• Six journals
– Management Science, Manufacturing and Service Operations
Management, Production and Operations Management, Journal of
Operations Management, Decision Sciences, Journal of Applied
Psychology
• Findings
– 52 papers
– SSCI citations: 1108 (excluding self citations)
• Average 2.4 per year per article
– Behavioral issues arise in many OM settings
– Mostly published in interdisciplinary journals
– Rate of publications is relatively stable
42
43. Empirical Research, Broadly Defined
• Fisher, M. (2007), “Strengthening the
empirical base of operations management,”
M&SOM 9(4), 368-382.
43
44. Terwiesch et al. (MS, 2005)
• Background
– Information sharing holds great promise for
improving supply chain efficiency
– Collaborative Planning, Forecasting, and
Replenishment (CPFR)
– Substantial benefits have been reported (Wal-
mart, Best Buy, Procter & Gamble, Kimberly-Clark,
etc.)
– Reality of supply chain information sharing?
Terwiesch, C., Z. J. Ren, T. H. Ho, and M. A. Cohen, “An empirical analysis of forecast sharing in the
Semiconductor equipment supply chain,” Management Science 51(2), 208-220. 44
45. Methodology
• Semiconductor equipment supply chain
• Information sharing practices between a
buyer (a major chip manufacturer) and 78
suppliers (of chip-making tools)
• 2 years
• More than 3000 orders
45
49. Main Findings
• Order volatility (frequent changes to the delivery
date)
• Order inflation (and later order cancellation)
• => Suppliers wait and see => delays
• Conversely, buyer inflates more to those suppliers
who have not achieved on-time delivery in the past
• Vicious cycle!
• Information sharing has not lived up to its potential!
49
50. Economics Finance
Operations
Empirical
research
Marketing Behavioral
science
50
