supply chain risk mitigation - nus - tliap … · iii. supply chain risk management with demand...

TLI – Asia Pacific White Papers Series

Volume 13-Nov-RISK

Supply Chain Risk Mitigation

Supply Chain Risk Mitigation

TLI – Asia Pacific Whitepaper Series: Supply Chain Risk Mitigation

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CONTENTS

EEXXEECCUUTTIIVVEE SSUUMMMMAARRYY ............................................................................................................................. 3

II.. RRiisskk mmiittiiggaattiioonn ................................................................................................................................. 4

11.. PPrroobblleemm SSttaatteemmeenntt .......................................................................................................................... 4

22.. DDeevveellooppmmeenntt ooff ssuuppppllyy cchhaaiinn rriisskk mmiittiiggaattiioonn ppoolliicciieess ..................................................................... 4

33.. EEvvaalluuaattiinngg tthhee eeffffeeccttiivveenneessss ooff rriisskk mmiittiiggaattiioonn ppoolliicciieess ................................................................... 6

IIII.. AA nneeww ((rr,, QQ)) ppoolliiccyy uunnddeerr ssuuppppllyy cchhaaiinn ddiissrruuppttiioonnss ....................................................................... 7

11.. PPrroobblleemm ssttaatteemmeenntt .......................................................................................................................... 7

22.. AAss--iiss .................................................................................................................................................. 8

33.. OOuurr AApppprrooaacchh ................................................................................................................................... 9

44.. NNoovveellttyy ........................................................................................................................................... 10

55.. BBeenneeffiittss .......................................................................................................................................... 10

IIIIII.. SSuuppppllyy CChhaaiinn RRiisskk MMaannaaggeemmeenntt wwiitthh DDeemmaanndd UUnncceerrttaaiinnttyy:: BBiilleevveell MMuullttii--ccrriitteerriiaa GGaammee

MMooddeellss ................................................................................................................................................... 13

11.. PPrroobblleemm ssttaatteemmeenntt ........................................................................................................................ 13

22.. AAss--iiss ................................................................................................................................................ 13

33.. MMooddeell DDeessccrriippttiioonn .......................................................................................................................... 14

44.. NNoovveellttyy ........................................................................................................................................... 15

55.. BBeenneeffiittss .......................................................................................................................................... 15

66.. MMaannaaggeemmeenntt IInnssiigghhttss .................................................................................................................... 15


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IIVV.. CCoonncclluussiioonn ..................................................................................................................................... 16

RREEFFEERREENNCCEESS ........................................................................................................................................... 17


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EEXXEECCUUTTIIVVEE SSUUMMMMAARRYY

Risk mitigation is a series of actions that are undertaken to limit any negative consequences of a

particular event. After investigating and assessing the possible risks, responsibility would usually dictate

that managers take a proactive role in coordinated activities to manage and control supply chain risks.

In part I, risk mitigation approaches are identified which involve the development of mitigation policies

and the evaluation. Then two studies are followed in parts II and III to demonstrate how the supply

chain risks from uncertain supply and demand can be mitigated, respectively.


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II.. RRiisskk mmiittiiggaattiioonn

11.. PPrroobblleemm SSttaatteemmeenntt

Risk mitigation activities are recommended to be conducted after identifying and assessing risks within

risk management framework (ISO 31000:2009 Risk Management - Principles and guidelines). However,

risk mitigation strategies should be designed and applied even before the disruption happens.

Especially in a supply chain scenario, risks can be propagated and amplified in a supply chain network.

It could be too late if we just act after we detect a risk since high losses normally incurred in the early

stage of a disruption if no predictive plan is in place. So we need to apply risk mitigation policies not

only after but also before a disruption.

One important aspect to mitigate supply chain risk proactively is to build flexibility in the supply chain

(Tang & Tomlin, 2008). While it is clear that flexibility enhances supply chain resiliency, it remains

unclear how much flexibility is needed to mitigate supply chain risks. For example, maintaining extra

capacity and inventory at some critical nodes of a supply chain is a mitigation strategy. The issues are

how much and where we should allocate the redundancies. Therefore, we need to investigate risk

mitigation strategies (policies) on how a supply chain organization can obtain significant strategic value

by implementing a mitigation program.

22.. DDeevveellooppmmeenntt ooff ssuuppppllyy cchhaaiinn rriisskk mmiittiiggaattiioonn ppoolliicciieess While there are many tactics for mitigating risks, it is important to know that the goal is not always about

eliminating the risk, but to reduce the risk to a level that is acceptable to the firm and the focus of the

risk mitigation strategy should be on creating controls that monitor and handle the identified risk. Most

available techniques and strategies for supply chain risk mitigation generally fall under the following

three broad categories:

• Risk alleviation by implementing controls to prevent an event

• Risk limitation by implementing strategies and controls to limit the likelihood of effects of risks

• Risk relief by reducing the loss of the risk.

Most of the researchers suggest strategies to deal with supply chain disturbances focussing on the area

of risk limitation and relief and the strategies contributing to the reduction in loss, probability, speed,

frequency, and exposure to risk events. However, traditional risk management approaches should be

enhanced to increase levels of resilience in the organization through the management of multiple

variables.


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Tang (2006) presented some “robust” strategies that possess two properties. First, these strategies will

enable a supply chain to manage the inherent fluctuations efficiently regardless of the occurrence of

major disruptions. Second, these strategies will make a supply chain more resilient in the face of major

disruptions. A sample of different risks and possible mitigation strategies for handling supply chain risk

(SCR) in a retail supply chain is presented by Oke & Gopalakrishnan and is shown in Figure 1 (Oke &

Gopalakrishnan, 2009).

Figure 1: Mitigation strategies employed in a retail supply chain (Oke & Gopalakrishnan, 2009)

It is also important to note that a firm is never isolated and risk management becomes more effective

when it includes the firm’s strategic partners. This is seen in paper by Lavastre et al. (2011) where they

present a ranked list of risk mitigation methods from a survey on French companies. Of the top five

supply chain risk mitigation strategies that are viewed as effective and efficient, three involved working

together with partners.

Rank Methods to effectively and efficiently minimize risk 1 Communication and information exchange (forecasting, operational) 2 Accompanying providers/suppliers in improving their performance 3 Forecast accuracy 4 Long term continuity in relations with partners 5 Safety stocks (Vendor owned inventory (VOI) or in-house) 6 Establishment of emergency scenarios 7 Introduction of strict and formal procedures that are consistently respected 8 Activity planning using Advanced Planning System 9 External partner-owned safety stocks

10 Dual sourcing or manufacturing 11 Responsiveness due to Supply Chain Event Management 12 Introduction of sanctions and penalties for misconduct, faults, or mistakes 13 Centralization of operations (stocks, production and/or distribution) 14 Centralization of decision making


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15 Reduction of number of suppliers 16 Geographical proximity to partners 17 Introduction of rewards in absence of misconduct or faults 18 Personal, friendly relationships with partners 19 Appointment of risk manager who convenes an SCRM group 20 Cultural proximity with partners 21 Presence of focal firm which coordinates supply chain

Table 1: Ranking of risk mitigation methods (Lavastre, Gunasekaran, & Spalanzani, 2011) Based on the literature study, it can be seen that while there are costs for implementing these

strategies, they provide additional selling points for acquiring and retaining apprehensive customers.

However, the strategies are mostly just proposed and discussed in the papers, and there are not much

model testing and case studies to justify the statements made.

33.. EEvvaalluuaattiinngg tthhee eeffffeeccttiivveenneessss ooff rriisskk mmiittiiggaattiioonn ppoolliicciieess Evaluation of a mitigation strategy’s effectiveness should go beyond economic losses and include, but

not restricted to, speed of recovery, extent of recovery and reduction in losses. One of the simplest and

easily understandable methods is to consider the cost-benefit ratio of the risk mitigation strategy. Costs

for a risk mitigation strategy include the cost of accepting the risk, reduction in operational effectiveness,

implementation, and preparation in terms of employee education and training. Other methods that could

be employed include resource gaps in the wake of an event and comparisons between the actual and

estimated losses.

Ni, Chen, & Chen (2010) suggested approaches on evaluating the effectiveness of mitigation strategies

through plotting the assessment results on the same graph followed by selecting an appropriate

graphical edition based on the arithmetic operation of the evaluation test. The graphical extensions are

useful as they maintain the ease of both understanding and performing assessment from the risk matrix

approach.

Figure 2: Evaluation of risk mitigation policies (Ni, Chen, & Chen, 2010)

The above proposed matrix approach could be used together with Value at Risk (VaR) to evaluate

mitigation strategies. The approach would be to compare the difference in the reduction of potential loss


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after each implementation. A further step of including the cost of implementation between mitigation

strategies would also be helpful in deciding the mitigation strategy.

Case Strategy Impleme

ntation

Cost

New

Probabili

ty (L)

New

Impact

(I)

New VaR

(L*I)

Original

VaR

ROI

1 Increase safety stock amount in

warehouse by 2 extra weeks

3000 10% 600000 6000 10000 1000

2 Source for alternate suppliers in region 1000 5% 100000 5000 10000 4000

3 Purchase futures to hedge against raw

material cost increases

2000 100% 1000 1000 10000 7000

Table 2: Sample comparison of mitigation strategies using VaR

Supply chain with different structures and mitigation strategies will present various performance

behaviors with the impacts from disruptions. So the modeling of supply chain disruptions is important

for testing mitigation strategies. Combining network modeling and agent-based simulation, the

effectiveness of the mitigation strategies and the related disruption scenarios can be evaluated

extensively.

IIII.. AA nneeww ((rr,, QQ)) ppoolliiccyy uunnddeerr ssuuppppllyy cchhaaiinn ddiissrruuppttiioonnss

11.. PPrroobblleemm ssttaatteemmeenntt We found at a global MNC server manufacturer that it is struggling to fulfill its demand commitments

when any disruption happens at the suppliers’ end for the want of raw material or parts. Whenever a

disruption like earthquake or flood happens at supplier’s site, production gets hampered and supplier

fails to deliver the items in time. As a result, the manufacturer’s production schedule gets affected and it

fails to satisfy the customer demand in time. These risks can result in a loss of business in tune of

billions of dollars. Events like economic downturn, political unrest etc. also have a negative impact on

customer demand and the manufacturer can lose business or incur more inventory cost if it does not

modify its inventory control policies to incorporate these risks. For example, when Triple Disasters

(earthquake, tsunami and nuclear) hit Japan in 2011, Apple faced shortage as its sub-supplier,

ELectrotechno (Mitsubishi Gas Chemical Sub), was unable to provide BT resin. Before the disaster,

ELectrotechno used to hold 50% of market share for global BT resin supply and like Apple, other

manufacturers were also severely affected. Similarly, the Bangkok flood in 2011 affected the

production capabilities of several hard disk drive (HDD) manufacturers and led to global shortage of

HDDs and subsequently, a steep price rise for the HDDs. Therefore, impact of these disruptions can be


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quite huge and effective techniques should be employed to handle these risks efficiently. An efficient

inventory control policy ensures timely arrivals of items to the production facility and thus makes sure

timely delivery of products to the customers. These, in turn, results in lower inventory at the

manufacturer’s site and also lesser backorders. The inventory control policy should be such that it can

adjusts its parameters based on the changing supply chain environment so that losses can be averted.

The existing inventory policy of the manufacturer does not take into account these risks and cannot

adjusts its parameters based on the changing environment so that losses can be averted. The existing

inventory policy places the order based on the inventory on hand at the time of order placement and

demand forecasts during the lead time only.

22.. AAss--iiss The manufacturer needs thousands of parts or components for its assembly process; some of them are

very specialized and can only be supplied by a few specific suppliers. It was found that the majority of

the items required are supplied by a single supplier and no alternative supplier is readily available. As a

result, this supplier becomes very critical for the firm and proper inventory policies should be in place to

timely arrival of parts and satisfy customer demand in time and reduce number of backlogs. The

inventory policy is very susceptible to any supply delay risk or customer demand risk and results in

more backorders or high inventory. It was also found that the manufacturer does not follow any

traditional ordering policies like (r, Q) or (s, S) polices for ordering. The HQ generates demand forecast

for each product at different locations for future periods. These forecasts include input from sales

department for any swing in demand. Based on these demand forecasts and expected delivery time to

the customers at different locations, the requirement for different items at next periods are estimated.

Orders for items required at a future period are placed to the suppliers according to these estimates, in

advance. When actual demand is realized at a period, the forecast for the next periods are adjusted

and updated incorporating this new information. The updated demand forecasts are also sent to the

suppliers and order quantities are adjusted accordingly. Items arrive at the warehouse (Hub) after a

known lead time. These items are then assembled into the final product and is shipped to the

customers at different locations. The final product reaches the customer after the transportation delay

(depending on the location of the customer). The schematic diagram for the manufacturer’s supply

chain is shown in Figure 3.


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Figure 3: Manufacturer’s supply chain

33.. OOuurr AApppprrooaacchh To overcome the drawbacks of the existing policy, we propose a modified (r, Q) policy when demand

for the items is stochastic but time-varying; Q and r, respectively, represent ordering quantity and

reorder point. It works as follows: r and Q values are computed at the beginning of each time period

based on the forecasted demand and various cost parameters. The algorithm by Federgruen and

Zheng (1992) is used to determine the values of r and Q, for that period and replenishment order is

placed based on these values. When demand for that period is realized, the demand forecasts for the

next planning periods are adjusted, by incorporating the realized demand. The r and Q values for the

next period are recalculated based on the new demand forecasts and cost parameters for the new

period (if different from the last period) and order is placed based on the adjusted r and Q values. We

call this new proposed policy time-adjusted (r, Q) policy. Unlike traditional (r, Q) policies, r and Q values

are not fixed for this policy, but varies from period to period. The new policy adjusts the parameter

values at every time period, taking into consideration the adjusted demand forecast and any changes in

the cost parameters. Mathematically, the policy is given by

0

≤= >

( ) ( ) ( ) ( )

( ) ( )j j j

jj j

Q t if IP t r tq t

if IP t r t

where IPj(t) and qj(t) are the inventory position (inventory on hand + inventory on order – backorders)

and quantity ordered for item j at the beginning of time period t.


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The policy is depicted in Figure 4.

Figure 4: Time-adjusted (r, Q) ordering policy

44.. NNoovveellttyy The proposed time-adjusted (r, Q) policy removes the drawbacks of the existing policy by incorporating

the elements of uncertainties into it. The policy is more dynamic in nature as it changes its parameters

when customer demands changes or lead time of replenishment changes. Since the ordering quantity,

Q and reorder point, r, determine the number of items the system will be carrying or number of

backorders it will face and the number of orders to be placed, determination of Q and r is of utmost

importance. The proposed policy also takes into account any changes in the cost parameters like

holding cost or backorder cost and adjusts its parameters accordingly. Thus, the time-adjusted (r, Q)

policy is more capable of adjusting itself when any disruptions occurs and can order more anticipating

the disruption or order less anticipating less customer demand. The novelty of this proposed policy is its

dynamism under uncertain environment.

55.. BBeenneeffiittss We conducted some numerical experiments assuming demand to be normally distributed to show the

benefits of implementing the time-adjusted (r, Q) policy over the existing policy. Random demands are

generated and cost for both the policies are computed. Summary of finding for 100 samples are shown

is Table 3. It can be seen that the average cost difference is 11. 98 % with a maximum of 29.35% and a

minimum of 1.34%. The time-adjusted (r, Q) policy performs better than the existing policy for all the

samples.

r2

Time

r3

r1

Q1

Q3

t1 t2 t3

Inventory Position

TR


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Average cost difference 11.98

Maximum difference 29.35

Minimum difference 1.34

Time- adjusted (r, Q) policy is better (%) 100

Existing policy is better (%) 0

Table 3: Comparison for two policies (normal demand)

We also investigate how the benefit is sensitive to the changes in holding cost and ordering cost. Figure

5 graphically shows the behavior of cost improvement for various holding cost and ordering cost.

Generally, the benefit increases with increasing ordering cost, but decreases with increasing holding

cost.

Figure 5: Sensitivity analysis for ordering cost and holding cost

We then study the performance of the policies when some disruptions happen. It can be seen from

Figure 6 that time-adjusted (r, Q) policy always provides lower cost than the existing policy for all values

of Lead time, though the difference starts getting lesser with increasing lead time. The time-adjusted (r,

Q) policy places the replenishment order based on the lead time and due to its dynamic nature, it

modifies the order quantity and reorder point and thus incurs lesser cost. When lead time starts getting

higher, the proposed policy keeps more inventory than the existing policy (but places less orders) and

inventory carrying cost increases, and thus, cost difference decreases.

0

5

10

15

20

25

30

35

40

10 20 30 40

% Im

prov

emen

t

Holding cost rate

100

200

300

400

500


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Figure 6: Impact of lead time uncertainty on cost difference

When mean demand increases, time-adjusted (r, Q), cost difference increases steadily as evident in

Figure 7; from 12.01 % for mean =10 to 45.02% for mean = 50. The existing policy starts keeping more

inventory when mean demand gets higher (sometimes more than twice compared to time-adjusted (r, Q)

policy) and incurs more cost; whereas time-adjusted (r, Q) policy places more orders and keep less

inventory to tackle this high demand.

Figure 7: Impact of demand variability on cost

0

20

40

60

80

100

120

0.00 2.00 4.00 6.00 8.00

10.00 12.00 14.00 16.00 18.00

1 2 3 4 5 6 7 8 9 10

% O

F TI

ME

AVE

RAG

E

LEAD TIME

Average % (r,Q) is better % Existing is better

0

20

40

60

80

100

120

0.00 5.00

10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

10 20 30 40 50

% o

f tim

e

Ave

rage

Impr

ovem

ent

(%)

Mean demand

Average % (r,Q) policy is better % Existing policy is better


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IIIIII.. SSuuppppllyy CChhaaiinn RRiisskk MMaannaaggeemmeenntt wwiitthh DDeemmaanndd UUnncceerrttaaiinnttyy:: BBiilleevveell MMuullttii--

ccrriitteerriiaa GGaammee MMooddeellss

11.. PPrroobblleemm ssttaatteemmeenntt Demand uncertainty means that it is difficult to accurately predict customer demand in the future. This

poses a significant challenge because it makes inventory hard to control and manage. In the multiple

tiers supply chain, the demand uncertainty can even cause more serious results and leads to Bullwhip

effect. Further, each decision-maker faces the competition from others. In the multiple tiers supply chain,

leaders behave as Cournot firms with respect to other leaders, but as Stackelberg firms with respect to

followers. So, for leaders, how to make decision about pricing and supplies with demand uncertainty is

becoming very difficult. Followers need to make orders with both price given by leaders and demand

uncertainty. We utilize a bilevel multi-criteria game model to address these problems. With this model,

retailers can make their orders by considering the competition from other retailers, the uncertain

demand and the price given by manufacturers. While manufacturers can make their decisions about

theprice and supply by considering both the competition with other manufacturers and the orders.

22.. AAss--iiss The multiple tiers supply chain has been extensively studied by utilizing game theory under stochastic

demands. For example, Lau and Lau (2005) consider a two-echelon supply chain with one

manufacturer and one retailer by game theory. They assume that a manufacturer wholesales a product

to a retailer, who in turn retails it to the consumer under the stochastic demand. Corbett and Karmarkar

(2001) first consider simultaneous quantity competition at multiple tiers in the supply chain with

deterministic demands, then Adida and DeMiguel (2011) generalize this idea by presenting bilevel

game models to study a two-echelon supply chain competition where multiple manufacturers who

compete in quantities to supply a set of products to multiple risk-averse retailers who compete in

quantities to satisfy the uncertain consumer demand.

Recently, some researchers study supply chain competition with demand uncertainty by utilizing robust

game theory. Harks and Miller (2011) study the resource allocation games by utilizing the worst-case

cost sharing methods in terms of the ratio of the minimum guaranteed surplus of a Nash equilibrium

and the maximal surplus. They show that an upper bound on the efficiency can be found. Jiang,

Netessine and Savin (2011) study the robust Newsvendor competition under asymmetric information.

They show the existence and derive closed-form expressions for the robust optimization Nash

equilibrium solution for a game with an arbitrary number of players. Wadecki, Babich and Wu (2012)

study the optimal subsidy decisions of manufacturers in four supply chain structures and show that

competing manufacturers face an important tradeoff. Our models extend the above supply chain

competition models to simultaneous competition among manufacturers and retailers, product and


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retailer differentiation, and retailer risk aversion. These methods mainly consider that there is one

objective to be optimized for each player (manufacturer, retailer or consumer) in the supply chain.

However in the real-world supply chain, decision-making processes always have several social

concerns and thus often have more than one conflicting objectives to be optimized simultaneously. So it

is interesting to study supply chain competition by multi-criteria game theory.

33.. MMooddeell DDeessccrriippttiioonn We consider to model multi-criteria competition in a supply chain which contains M manufacturers who

compete in quantities to supply products to N risk-averse retailers who compete in quantities to

satisfy the uncertain consumer demand (see Figure 8). In this model each manufacturer has two

objectives (i.e., maximize profit and minimize cost for the supply products) by choosing supply

quantities for each of the products under the anticipation of the order quantities of the retailers and

also the anticipation of the wholesale prices resulting from the market clearing conditions. While each

retailer has also two objectives (i.e., maximize expected utility from retail sales and minimize the risk

expressed as the standard deviation of profit) by deciding wholesale market order quantities for each of

the P products.

Figure 8: The structure of the supply chain

Then a multi-criteria bilevel game (MBG) model for describing supply chain competition can be defined

as follows: the th manufacturer faces the following decision problems,


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where is a Pareto Nash equilibrium to the retailers' multi-criteria game,

where is a stochastic vector inverse demand function of retailer j which

depends on the stochastic variable , is the standard deviation of . Then we define

that a mixed strategy is a weighted manufacturer-retailer equilibrium of the above MBG with

the given weight if is a Pareto Nash equilibrium to the retailers' multi-criteria game and

is a weighted Nash equilibrium to the manufacturers' multi-criteria game.

44.. NNoovveellttyy A multi-criteria bilevel game model is proposed for analysing the competition in a supply chain which

contains multiple suppliers who compete in quantities to supply a set of products to multiple risk-averse

retailers who compete in quantities to satisfy the uncertain consumer demand. This model contains the

information about uncertain demands and the competition not only between manufacturer and retailer,

but also among manufacturers and retailers. Each player in this model has two objectives to optimize.

55.. BBeenneeffiittss This model can be used to coordinate the supply chain, helping manufacturers make decisions about

pricing and supplies. To solve this model, a robust weighted approach can be proposed and the closed-

form solution to the robust weighted manufacturer-retailer equilibrium can be derived.

66.. MMaannaaggeemmeenntt IInnssiigghhttss 1. The efficiency of the decentralized supply chain may drop rapidly when manufacturers are

asymmetric (i.e. they have different costs) or retailers are asymmetric (i.e. they have different

risk aversion).

2. The asymmetry of product assortments at different retailers leads to a decrease in the degree

of competition among retailers.


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3. With different choice of weights, there are different weighted Nash equilibria. However a robust

weighted approach can lead to a unique robust weighted Nash equilibira.

4. The efficiency of the decentralized supply chain may drop sharply with the asymmetry of the

manufacturers because whereas in the centralized supply chain only the cheapest

manufacturers produce, in the decentralized chain all manufacturers may produce.

IIVV.. CCoonncclluussiioonn

In this article, we first describe the approach for supply chain risk mitigation involves 1) development of

mitigation policies and evaluating the effectiveness of policies. We also highlight that there are two

important aspects in the supply chain risk mitigation: building in robustness and resiliency. In the

second part, a novel time-adjusted inventory policy is proposed to solve the risk caused by supply

uncertainty. The new policy performs better than the existing policy for all the samples. In the third part,

a bilevel multi-criteria game model is used to address demand uncertainty considering multiple

performance criterions.


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RREEFFEERREENNCCEESS

Federgruen, A. &Y.-S. Zheng (1992). An Efficient Algorithm for Computing an Optimal (r, Q) Policy in

Continuous Review Stochastic Inventory Systems.Operations Research 40(4): 808-813.

Lavastre, O., Gunasekaran, A., & Spalanzani, A. (2011). Supply chain risk management in French

companies. Decision Support Systems.

Ni, H., Chen, A., & Chen, N. (2010). Some extensions on risk matrix approach. Safety Science 48,

1269-1278.

Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail

supply chain. International Journal of Production Economics, 168-174.

Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production

Economics 103, 451-488.

Tang, C. S., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International

Journal of Production Economics 116, 12-27.

The Logistics Institute – Asia Pacific National University of Singapore 21 Heng Mui Keng Terrace, #04-01, Singapore 119613 Tel: (65) 6516 4842 Fax: (65) 6775 3391 Email: [email protected] URL: www.tliap.nus.edu.sg

Singapore Institute of Manufacturing Technology 71 Nanyang Drive, Singapore 638075 Tel: (65) 6793 8388 Fax: (65) 6790 6377 Email: [email protected] URL: http://wwwsimtech.a-star.edu.sg

Institute of High Performance Computing

Fusionopolis 1 Fusionopolis Way,#16-16 Connexis , Singapore 138632 Tel: (65) 6419 1111 Fax: (65) 6463 0200 Email: [email protected] URL: www.tliap.nus.edu.sg

supply chain risk mitigation - nus - tliap … · iii. supply chain risk management with demand...

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