Understanding_supply_chain_robustness Versão 2

8/8/2019 Understanding_supply_chain_robustness Verso 2

1/6

1

Understanding supply chain robustness

Guilherme E. Vieira1

[email protected] / [email protected] ProfessorPontifical Catholic University

of ParanaPRBrazil

Reynaldo Lemos

[email protected]

MS StudentPontifical Catholic University ofParanaPRBrazil

Abstract:This paper reviews important concepts behind supply

chain (SC) problems and instability, which lead to the

need to design and operate more robust supply chains.

Based on several works reviewed, a supply chain is

considered robust when it is insensitive to variations ornoises in not so regular operating conditions. Those

adopting robust-oriented approaches (techniques) will

have more chance to stay successful in the market.

Key-words: Supply chain,robustness, performance.

I. INTRODUCTIONUncertainty is one of the most challenging and

important problems in supply chain (SC) management

(SCM) (Mo and Harrison, 2005). Indeed, it is a primary

difficulty in the practical analysis of SC performance. Inthe absence of randomness, the problems of material and

product supply are eliminated; all demands, production,

and transportation behavior would be completely fixed,and therefore, perfectly predictable (Sabria and Beamon,

2000). And because supply chain performance is

inherently unpredictable and chaotic, supply chain

practitioners often must seek safety mechanisms toprotect against unforeseen events. Significant efforts

have been used to expedite orders, to check order status

at frequent intervals, to deploy inventory just-in-caseand to add safety margins to lead times, among several

1 Corresponding author. Address:

Industrial Engineering, 2 Andar Bloco

3 Parque Tecnologico, Imaculada

Conceicao 1155, Pontifcia Universidade

Catolica do Parana, 80215-901, Curitiba,

Parana, BRAZIL. E-mail:

[email protected] Telephone: +55 41

3271 1333, Fax: +55 41 3271 1345.

other creative ways to counter the occurrence of

unforeseen events (Gaonkar and Viswanadham, 2004).Existing ERP (Enterprise Resources Planning), SCM

and other B2B (Business-to-Business) solutions are

designed to improve efficiency of supply chains but not

to enhance their reliability or robustness under

uncertainty. However, a supply chain should be

designed for robustness and therefore robustly controlled

in order to be and to stay competitive. In fact, under the

intense competitive scenario prevalent todaycompetition is no longer between companies bu

between supply chain networks with similar productofferings, serving the same customer (Gaonkar and

Viswanadham, 2004).

Supply chain robustness is needed but how exactly

can one make a supply chain more robust? In theliterature, one can find two main approaches to analyze

robustness: Analytical models (mainly based on linear

and non-linear mathematical models) and simulation-based approaches. From these, the first one is by far the

most commonly used. Many researchers have modeled

supply chain robustness as an optimization mathematicamodel, often considering probability of occurrence of

different scenarios. Then, the optimal values for

parameters like quantity shipped from site to site or

quantity to be produced are determined (Mo andHarrison, 2005; Gaonkar and Viswanadham, 2004; Yu

and Li, 2000; Bertsimas and Thiele, 2004; and Leung e

al., 2007). On the other hand, Tee and Rossetti (2002use simulation to assess system robustness while many

have used discrete computer simulation to evaluate

supply chain performance only (Reiner, 2005; Tee and

Rossetti, 2001; Vieira and Cesar, 2005; Vieira, 2004

and Vieira and Cesar, 2004).In practice, a company often carries (safety)

inventory (from raw-materials to finished goods) to

protect itself from running out of stock owing to

uncertainty (i.e., long lead times, demand fluctuations

etc.) and eventually not meeting demand, andconsequently, not selling as much product as it could

(besides having to pay penalty fees for late deliveries

and/or having its image deteriorated). However, carrying

too much inventory to minimize the risk for theseproblems, however, has its obvious negative financia

(and operational) implications. A robust supply chain

can better deal with random events that cause theseproblems at a lower cost.

The paper reviews important concepts about supply

chain and SC robustness, explains some of the causes of

SC problems and instability and describes ideas behind

the design for robustness and design of experiments

(DOE) that can be used to analyze SC robustness. Fina

considerations and suggestions for future research aregiven at the end.
mailto:[email protected]:[email protected]


2/6

2

II. SUPPLY CHAIN ROBUSTNESSThe objective of a robust supply chain is to ensure

that the supply chains network structure and itsmanagement (and control) policies will operate well

under a wide variety of situations which leads towards

risk minimization of undesirable outcomes. During the

initial steps of a supply chain structure design, the useroften assumes that the design is optimal based on a

series of assumptions (expected demand, lead time, etc)and will operate as efficiently and effectively aspossible. Perhaps even more important, if performed

properly, design for robustness will ensure that the

selected supply chain design will, under less thanexpected or unusual circumstances, not perform

unacceptably poor (Hicks, 1999). This should not be

confused with simple variance. Random variance may

have been introduced in the first design phases toproduce more realistic approaches. So, design for

robustness is not centered on randomness and its effects;

rather, it is the evaluation of the results of changing

some of the external given data assumptions (Hicks,

1999). It is known that demand is not a hundred percent

predictable and despite the best forecasting systemavailable, a forecast will always have errors. So, the

randomness of the demand is to be taken into account at

the earlier stages of the supply chain design and

operation. However, if the demand reaches unexpectedvariation levels, the supply chain must be robust enough

to deal with this variation so that the undesirable

outcomes can be minimized (note that avoiding thisunexpected randomness is out of someones control).

According to Mo and Harrison (2005), the concept of

robust design was first introduced by Genuchi Taguchi

in the 1960s, and was subsequently accepted in the fieldof experimental design and quality control. The basic

idea of robust design is to make a manufacturing process

insensitive to noise factors. Taguchi divided variablesinto two categories: design factors and noise factors.

Design factors are controllable decisions affecting a

process while noise factors are those variablesrepresenting field sources of variation. The goal is to

design a product or process to be robust to noise. One

way to determine a robust design is to find a set of

design variables that provides the minimum deviationfrom a target value of the response when noise variables

are considered at different levels.

Gaonkar and Viswanadham (2004) state that in aperfect world, the plans generated by the channel (a

dominant organization in a supply chain) master would

allow all the partners to synchronize their activities andbusiness processes leading to greater efficiencies and

profits for everyone. For example, components would

arrive at the assembler site on time for production to

start, adequate inventory of all components would be

available before production and demand would be

deterministically predictable. However, in the practicaworld, uncertainty rules. Consequentially, sales routinely

deviate from forecasts; components are damaged in

transit; production yields fail to meet plan; and

shipments are held up in customs. In truth, schedule

execution as per plans generated by supply chain

planning is just a myth.

Supply chains need to be robust at three levels,

strategic, tactical, and operational, and they need to beable to handle minor regular operating deviations and

major disruptions at each of these levels as seen in Table1. For example, at the operational level, companies

require decision support systems that can act on

information from various partners regarding various

deviations and disruptions to reschedule activities so thathe business processes are synchronized and deliveries

are undertaken within customer delivery windows and

cost limitations. At the tactical level, plans need to haveredundancies in terms of human and machine resources

and also logistics and supply organizations. At the

strategic level, more reliable partners with intrinsiccapabilities in deviation and disruption handling, and the

skills and ability to adapt to changing market conditions

will be preferred and selected (Gaonkar and

Viswanadham, 2004).Table 1. Types of deviations (Gaonkar and Viswanadham, 2004)

Planning Level Type of Event Example

Strategic Deviation Logistics/Manufacturing

Capacity addition

Disruption Supplier bankruptcy

Tactical Deviation Order forecast

Disruption Port strike

Operational Deviation Lead-time variation

Disruption Machine/Truckbreakdown

The strategic level relates mainly to the design of the

physical supply chain, in term of, for instance, numberof participants in each SC echelon (level) and their

locationit should, therefore, be addressed more closelyduring the SC design phases. Supply chain designmodels determine strategic decisions, such as the most

cost-effective location of facilities (including plants and

distribution centers), flow of goods, services

information and funds throughout the supply chain, and

assignment of customers to distribution centers (Mo andHarrison, 2005; Sabria and Beamon, 2000). These types

of models do not seek to determine required inventorylevels, and customer service levels (Sabria and Beamon

2000). Gaonkar and Viswanadham (2004) also focused

on the design of robust supply chains at the strategic

level through the selection of suppliers that minimize thevariability of supply chain performance in terms of cost

and output.


3/6

3

Tactical and operational planning deals mainly with

policies and methods adopted by the SC members tocreate and maintain robustness. The main aspects that

can be considered in these classes are probably related to

production planning and scheduling, sourcing

variability, demand uncertainty and forecasting, and

selection and use of transportation operators.

Hicks (1999) and Mo and Harrison (2005), however,

focus on design supply chain robustness whereas this

research focuses on the operational side of SCrobustness, which could also be called supply chain

operations robustness. According to Sabria and Beamon(2000), the main purpose of the optimization at the

operational level is to determine the safety stock for each

product at each location, the size and frequency of the

product batches that are replenished or assembled, thereplenishment transport and production lead times, and

the customer service levels. The research concentrates

on the sourcing, planning, scheduling and demandaspects of a supply chain operation from the robustness

point-of-view. Within this category, supplier and

production lead-time variances, collaboration aspects,risk management and scheduling robustness are of

particular interest in the overall aspects of this study.

In order to quickly adapt to fluctuating market

demands, the resource allocation and the scheduling(configuration) of a flexible manufacturing system

(FMS) should not simply be optimized for the current

production plan, rather, it should ideally be optimizedfor robustness against the variation in production plans,

so that the system can deal with the variation with

minimal reconfiguration while achieving consistently

efficient production under all production plans of

interest (Saitou et al., 2002; Saitou and Malpathak,1999). Saitou and Malpathak (1999) specifically define

the FMS robustness as the insensitivity of production

performance against variations in the production plan.

(Analyzing robustness from this point-of-view is also a

very interesting research area, but is not part of thisresearch.) The reader is also encouraged to look at

[Saitou and Qvam, 1998; Bulgak et al., 1999; and

Shang, 1995) for more information on FMS robustness.

The ideas from Saitu et al. (2002) and Saitou andMalpathak (1999) can certainly be extrapolated to supply

chain systems, where minimal reconfiguration of

resources (suppliers, transporters, distribution centers,manufacturing plants), production, and transportation

schedules in face of unexpected events make a robust

supply chain. In this case, decision variables are

resource allocation and production schedules (plans).

The less changes that are applied to these variables, the

more robust the system is. In the case of a SC, there are

other decision variables, such as supplier re-allocation,new supplier contracts, re-allocation of transportation

routes, re-assignment of production from a plant to

another, re-assignment of product from one DC toanother or even from on retailer to another. It is a

combination of factors: One will try to minimize

changes to production schedules/plans, collection and

distribution plans, suppliers. Reallocating material from

one site to another (plant to plant, or DC to DC, for

instance) can help the chain better deal with the

unexpected event(s) and consequently, minimize

disruptions and negative consequences of these eventsThe better and faster the SC can do this, the more robust

it will be.For a SC to be considered robust, it is not enough to

have only one link of the chain be robust (if this is

possible) but all the main participants of the supply

chain must strive for robustness as a unit. SC robustnesdepends heavily on the cooperation (collaboration) of its

participants. Imagine a disaster event happening in one

part of the country. The population demand for barenecessities in that region would vary (in this case, grow)

much more than planned. Retailers may want to re

allocate goods from other sites; their transportationsystem must be able to cope with these new

transportation needs. Industries would need to focus

more on the needs of that part of the country, and

consequently, the respective suppliers of these

industries. The whole chain would have to bemobilized, prepared and willing to collaborate to

minimize the effect on the service level that the disasterwould cause.

Another (simpler) situation would be when it takes

much longer to receive a shipment from a supplier then

expected (especially when it involves international

suppliers, since the distance, bureaucracy, and paperworks can really make it more difficult to manage)

Stages downstream will have to deal with this

unexpected delay. For some industry sectors, even

upstream stages are affected as well. This is usually the

case with car assembly plants. A late shipment from asupplier can make the car maker re-schedule its

production and as a consequence, other suppliers will

have to deal with the new MRP and deliver the

unexpected goods to the car maker.

III. MAIN CAUSES OF SUPPLY CHAINPROBLEMS AND INSTABILITY

As mentioned previously, random factors in a supplychain can reach undesirable levels and combinations

asserting the need for a SC designed and operated for

robustness. Some of the problems that cause instability

and perturb the systems performance are related to thefollowing factors.

Supplier lead time.Poor product quality received from supplier.


4/6

4

Demand variations.Production stoppages due to random machine

failures, workers strike, severe weather conditions.

From these, supplier lead time and demand variations

are probably the most important aspects for most

companies, and for this reason, these variables are given

more attention in this research. (A more general reviewof problems for instability is given by (Gaonkar and

Viswanadham, 2004). A classification of disruptions

modes that occur in a supply chain, causing instabilityand performance degeneration are shown in Table 2.Table 2. Modes of disruptions (Gaonkar and Viswanadham, 2004)

Modes of Disruptions Description

Supply side Delay or unavailability of materials

from suppliers, leading to a shortage of

inputs that could paralyze the production.

Transportation Delay or unavailability of either inbound

or outbound transportation to move goods

due to carrier breakdown or weather

problems.

Facilities Breakdown of machine, power or water

failure leading to delay or unavailability of

plants, warehouses and office buildings.

Breaches in freight or

partnerships

Violation of the integrity of cargoes,

products (can be due either to theft or

tempering with criminal purpose, e.g.

smuggling weapon inside containers) or

company proprietary information.

Failed

Communications

Failure of information and

communication infrastructure due to line,

computer hardware or software failures or

virus attacks, leading to the inability to

coordinate operations and execute

transactions.

Wild demand

fluctuations

Sudden loss of demand due to economic

downturn, company bankruptcies, war, etc.

Based on its nature, uncertainty in the supply chainmay manifest itself in three broad forms - deviation,

disruption and disaster (Gaonkar and Viswanadham,

2004). A deviation is said to have occurred when one ormore parameters, such as cost, demand, or lead-time

within the supply chain system stray from their expected

or mean value, without any changes to the underlying

supply chain structure. A disruption occurs when thestructure of the supply chain is radically transformed,

through the non-availability of certain production,

warehousing and distribution facilities or transportationoptions due to unexpected events caused by human or

nature. A disaster is defined as a temporary irrecoverableshutdown of the supply chain network due to unforeseen

catastrophic system-wide disruptions.In general, it is possible to design supply chains that

are robust enough to profitably continue operations in

the face of expected deviations and unexpecteddisruptions. However, it is impossible to design a supply

chain network that is robust enough to react to disasters.

However, to better manage the uncertainties in thesupply chain it is necessary to identify the exceptions

that can occur in the chain, estimate the probabilities of

their occurrence map out the chain of immediate anddelayed consequential events that propagate through the

chain and quantify their impact. In this context, it

becomes important to identify the possible exceptions in

a supply chain and their consequences before proceeding

to the development of analytical models (Gaonkar and

Viswanadham, 2004).

Robust optimization generates supply chain solutions

that maintain their optimality under minor deviations inenvironmental conditions (Gaonkar and Viswanadham

2004). In other words, supply chain robustness is relatedto the ability of the SC to maintain it expected

performance, despite some unexpected deviations or

disruptions.

Since robustness is the insensitivity of productionperformances (Saitou and Malpathak, 1999), or the

ability to maintain a certain performance level, despite

unpredicted harmful uncertainty, an analysis to measureSC robustness needs to consider appropriate

performance metrics. Performance measures that can be

used must consider indicators commonly used byindustries, such as inventory levels, customer order lead

times, and customer service level. Besides these, an

indicator of the bullwhip effect is also an interesting

measure.

IV. DESIGN FOR ROBUSTNESS ANDEXPERIMENTAL DESIGN

According to Park (1996), robust design is anengineering methodology for optimizing the product and

process conditions so that they are minimally sensitive to

different causes of variations, and which produce high-

quality products with low development andmanufacturing costs. Taguchi extensively uses

experimental designs primarily as a tool to design

products which are more robust (less sensitive) to noisefactors. Taguchis parameter design is an important too

for robust design. His tolerance design can be also

classified as a robust design. In a narrow sense robus

design is identical to parameter design, but in a widersense, parameter design is a subset of robust design.

Two major tools used in robust design are:

Signal-to-noise ratio, which measures qualitywith emphasis on variation;

Orthogonal arrays, which accommodate manydesign factors (parameters) simultaneously.

The overall quality system should be designed toproduce a product (process, or, in this case, a supply

chain) that is robust with respect to all noise factors (i.e.

undesirable and uncontrollable sources that cause

deviation from target values in products (or process)functional characteristics). In order to achieve


5/6

5

robustness, the product and the process should be

designed so that they are minimally sensitive to noisefactors (Park, 1996).

Experiments are carried out by researchers or

engineers in all fields of study to compare the effects of

several conditions or to discover something new. If an

experiment is to be performed most efficiently, then a

scientific approach to planning it must be considered.

The statistical design of experiments is the process of

planning experiments so that appropriate data will becollected, the minimum number of experiments will be

performed to acquire the necessary technicalinformation, and suitable statistical methods will be used

to analyze the collected data. The statistical approach to

experimental design is necessary if one wishes to draw

meaningful conclusions from the data. There are twoaspects to any experimental design: the design of the

experiment and the statistical analysis of the collected

data (Park, 1996).There are five different types of designs which differ

according to the experimental objective they meet as

follows (NIST/SEMATECH, 2007).Comparative objective: If you have one or

several factors under investigation, but the primary

goal of your experiment is to make a conclusion

about one a-priori important factor, (in the presenceof, and/or in spite of the existence of the other

factors), and the question of interest is whether or not

that factor is "significant", (i.e., whether or not thereis a significant change in the response for different

levels of that factor), then you have a comparative

problem and you need a comparative design solution.

Screening objective: The primary purpose ofthe experiment is to select or screen out the fewimportant main effects from the many less important

ones. These screening designs are also termed main

effects designs.

Response Surface (method) objective: Theexperiment is designed to allow one to estimate theinteraction between factors including even quadratic

effects and to develop the (local) shape of the

response surface. For this reason, they are termed

response surface method (RSM) designs. RSMdesigns are used to (a) find improved or optimal

process settings, (b) troubleshoot process problems

and weak points, and (c) Make a product or processmore robust against external and non-controllable

influences. "Robust" means relatively insensitive to

these influences.

Optimizing responses when factors areproportions of a mixture objective: If you have

factors that are proportions of a mixture and you want

to know what the "best" proportions of the factors are

so as to maximize (or minimize) a response, then you

need a mixture design.

Optimal fitting of a regression modeobjective: If you want to model a response as a

mathematical function (either known or empirical) of

a few continuous factors and you desire "good"

model parameter estimates (i.e., unbiased and

minimum variance), then you need a regression

design.

The term `Screening Design' refers to anexperimental plan that is intended to find the few

significant factors from a list of many potential ones

Alternatively, one may refer to a design as a screeningdesign if its primary purpose is to identify significant

main effects, rather than interaction effects, the latter

being assumed an order of magnitude less important

(Tee and Rossetti, 2001).The basic purpose of a fractional factorial design is to

economically investigate cause-and-effect relationships

of significance in a given experimental setting. For

example, an experiment might be designed to determinehow to make a product better or a process more robus

against the influence of external and non-controllablinfluences (NIST/SEMATECH, 2007). Park (1996also mentions that fractional factorial designs that use

orthogonal arrays are often employed in order to screen

the important factors that impact product (or process)performance.

Experiments might be designed to troubleshoot a

process, to determine bottlenecks, or to specify whichcomponent(s) of a product are most in need of

improvement. Experiments might also be designed to

optimize yield, or to minimize defect levels, or to movea process away from an unstable operating zone. Allthese aims and purposes can be achieved using fractiona

factorial designs and their appropriate design

enhancements (NIST/SEMATECH, 2007). Park (1996)states that fractional factorial designs are often inevitable

in industrial experiments since there are many factors

concerned, and the number of possible experiments islimited owing to cost and time.

Existing fractional factorial layouts are quite limited

and difficult to use. Robust design adds a new

dimension to conventional experimental design

Taguchi developed the foundations of robust design andthe concept of robust design has many aspects, among

them are (Park, 1996):1. Finding set of conditions for design variableswhich are robust to noise;

2. Achieving the smallest variation in a productsfunction about a desired target value;


6/6

6

3. Minimizing the number of experiments usingorthogonal arrays (OA) and testing for confirmation.In fact, use of OA is indispensable for robust design.

V. FINAL CONSIDERATIONSSupply chain robustness is still a concept not

perfectly defined in the literature, at least, not in the

logistics or supply chain field. However, it is something

enterprises should pursuit in order to stay alive in thethough competitive world of nowadays. This paper

brought together some of the concepts and ideas that canbe used to design and operate a robust supply chain.

Future studies can consider, for instance: used of

Taguchis robust parameter design approach in order tobetter estimate the appropriate level for the design

parameters to deal with the noise factors; Response

surface design and analysis to model and evaluate SC

robustness; analysis of the relationship between supplychain robustness and its bullwhip effect.

VI. ACKNOWLEDGEMENTSThe corresponding author would like to thank the

Pontifical Catholic University of Parana (PUCPR) andthe Coordenao de Aperfeioamento de Pessoal de

Nvel Superior(CAPES, grant number BEX3466/06-0)

for the financial support given to this research project.

VII. REFERENCESBertsimas, D., and Thiele, A., 2004. A Robust Optimization

Approach to Supply Chain Management (book chapter). LectureNotes in Computer Science, Springer Berlin / Heidelberg.

Bulgak, A. A., Tarakci, Y., and Veter, V., 1999. Robust design ofasynchronous flexible assembly systems. International Journal of

Production Research, 37(14), 3169-3184.Chan, F. T. S., Qi, H.J, Chan, H. Q., Lau, H. C. W., Ip, R. W. L.,

2003. A conceptual model of performance measurement forsupply chains. Management Decision. 41/7, 635-642.

Gaonkar, R., and Viswanadham, N., 2004. A Conceptual and

Analytical Framework for the Management of Risk in SupplyChains. Proceedings of the 2004 IEEE International Conferenceon Robotics & Automation. New Orleans, LA.

Gunasekaran, A., Patel, C., and McGaughey, R. E., 2004. Aframework for supply chain performance measurement.International Journal of Production Economics, 87, 333347.

Hayya, J., Kim, J. G., Disney, S. M., Harrison, T. P., and Chatfield,

D., 2006. Estimation in supply chain inventory management.International Journal of Production Research, 44(7), 13131330.

Hicks, D. A., 1999. A four step methodology for using simulation

and optimization technologies in strategic supply chain planning.Proceedings of the I999 Winter Simulation Conference, P. A.Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans,eds.

Leung, S. C. H., Tsang, S. O. S., Ng, W.L., and Wu, Y., 2007. A

robust optimization model for multi-site production planningproblem in an uncertain environment. European Journal ofOperational Research 181, 224238.

Mo, Y., and Harrison, T. P., 2005. A conceptual framework forrobust supply chain design under demand uncertainty. Supply

Chain Optimization. Springer. Panos M. Pardalos and JosephGeunes, eds.

NIST/SEMATECH e-Handbook of Statistical Methodshttp://www.itl.nist.gov/div898/handbook/, April 2007.

Park, S. H., 1996. Robust Design and Analysis for Quality

Engineering. Chapman & Hall.Sabria, E. H., and Beamon, B. M., 2000. A multi-objective approach

to simultaneous strategic and operational planning in supplychain design. Omega - The International Journal of ManagemenScience. Elsevier. 28, 581598.

Saitou, K., and Malpathak, S., 1999. Robustness optimization of FMS

under production plan variations: the case of cyclic productionProceedings of the 1999 ASME Computers in Engineering

Conference, Las Vegas, Nevada.Saitou, K., Malpathak, S., and Qvam, H., 2002. Robust design of

flexible manufacturing systems using colored Petri net andgenetic algorithm. Journal of Intelligent Manufacturing, 13, 339351.

Saitou, K., and Qvam, H., 1998. Robustness optimization of FMSunder production plan variations: preliminary resultsProceedings of the 1998 ASME Design Engineering Technica

Conference. Atlanta, Georgia.Shang, J. S., 1995. Robust design and optimization of material

handling in an FMS. International Journal of ProductionResearch, 33(9), 2437-2454.

Shang, J. S., Liz, S., and Tadikamalla, P. 2004. Operational design oa supply chain system using the Taguchi method, responsesurface methodology, simulation, and optimization. InternationaJournal of Production Research, 42, 18, 38233849.

Reiner, G., 2005. Customer-oriented improvement and evaluation osupply chain processes supported by simulation modelsInternational Journal of Production Economics, 96, 381395.

Tee, Yeu-San, and Rossetti, M. D., 2002. A robustness study of amulti-echelon inventory model via Simulation. InternationaJournal of Production Economics, 80, 265277.

Tee, Yeu-San., Rossetti, M.D., 2001. Using simulation to evaluate acontinuous review (R, Q) two-echelon inventory model

Proceedings of the Sixth Annual International Conference on

Industrial EngineeringTheory, Application, and Practice, SanFrancisco, CA, November 1820.

Vieira, G. E., and Cesar Jr, O., 2005. A conceptual model for thecreation of supply chain simulation models. Proceedings of the2005 Winter Simulation Conference. M. E. Kuhl, N. M. SteigerF. B. Armstrong, and J. A. Joines, eds.

Vieira, G. E., 2004. Ideas for modeling and simulation of supplychains with Arena. Proceedings of the 2004 Winter Simulation

Conference, R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. APeters, eds.

Vieira, G. E., and Cesar Jr, O., 2004. Analysis of collaborativeplanning in supply chains based on computer simulation(Original in Portuguese: Anlise do Planejamento Colaborativoem Cadeias de Suprimentos Baseada em SimulaoComputacional). XI symposium on Industrial Engineering (XSIMPEP)Bauru, SP, Brazil, December 0810.

Yu, C., and Li, H., 2000. A robust optimization model for stochastic

logistic problems. International Journal of Production Economics64, 385-397.
http://www.springerlink.com/content/105633/http://www.springerlink.com/content/105633/http://www.springerlink.com/content/105633/http://www.springerlink.com/content/105633/http://www.springerlink.com/content/105633/

Understanding_supply_chain_robustness Versão 2

Documents

Transcript of Understanding_supply_chain_robustness Versão 2