Understanding Supply Chain Robustness

7/31/2019 Understanding Supply Chain Robustness

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Abstract —

This paper reviews important concepts behindsupply 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 or noises in not so regular

operating conditions. Those adopting robust-oriented

approaches (techniques) will have more chance to stay

successful in the market.

I. INTRODUCTION

NCERTAINTY 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. In the

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 to protect against unforeseen

events. Significant efforts have been used to expedite orders,

to check order status at frequent intervals, to deploy

inventory ―just-in-case‖ and to add safety margins to lead

times, among several 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 today,

competition is no longer between companies but between

supply chain networks with similar product offerings, serving

the same customer (Gaonkar and Viswanadham, 2004).

The objective of a robust supply chain is to ensure that

the supply chain’s network structure and its management

(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 user often assumes that the design

1 Corresponding author. Address: Industrial

Engineering, 2º Andar – Bloco 3 – Parque

Tecnologico, Imaculada Conceicao 1155, Pontifícia

Universidade Catolica do Parana, 80215-901,

Curitiba, Parana, BRAZIL. E-mail:

[email protected] – Telephone: +55 41 3271 1333,

Fax: +55 41 3271 1345.

is optimal based on a series of assumptions (expected

demand, lead time, etc) and will operate as efficiently and

effectively as possible. Perhaps even more important, if

performed properly, design for robustness will ensure that

the selected supply chain design will, under less than

expected 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 to produce 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 thebest forecasting system available, 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 unexpected

variation levels, the supply chain must be robust enough to

deal with this variation so that the undesirable outcomes can

be minimized (note that avoiding this unexpected

randomness is out of someone’s control).

Supply chain robustness is needed but how exactly can

one make a supply chain more robust? In the literature, 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 mathematical model, 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 and

Harrison, 2005; Gaonkar and Viswanadham, 2004; Yu and

Li, 2000; Bertsimas and Thiele, 2004; and Leung et al.,

2007). On the other hand, Tee and Rossetti (2002) use

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, and consequently, 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 these problems,

however, has its obvious negative financial (and operational)

Understanding supply chain robustness

Guilherme E. Vieira1

and Reynaldo Lemos

U

mailto:[email protected]





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 this research.) The reader isalso 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 and

Malpathak (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 to

another 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

events. The 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 haveonly 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 robustness depends heavily on the

cooperation (collaboration) of its participants. Imagine a

disaster event happening in one part of the country. The

population demand for bare necessities in that region would

vary (in this case, grow) much more than planned. Retailers

may want to re-allocate goods from other sites; their

transportation system 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 be mobilized, prepared and willing tocollaborate to minimize the effect on the service level that

the disaster would 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 paper works 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 a

supplier 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 CHAIN PROBLEMS AND

INSTABILITY

As mentioned previously, random factors in a supply chain

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

system’s performance are related to the following factors.

Supplier lead time.

Poor product quality received from supplier.

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 inthis research. (A more general review of problems for

instability is given by (Gaonkar and Viswanadham, 2004). A

classification of disruptions modes that occur in a supply

chain, causing instability and 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 orweather 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 chain may

manifest itself in three broad forms - deviation, disruption

and disaster (Gaonkar and Viswanadham, 2004). A

deviation is said to have occurred when one or more

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 the structure of the supply chain is

radically transformed, through the non-availability of certain

production, warehousing and distribution facilities or

transportation options due to unexpected events caused by

human or nature. A disaster is defined as a temporary

irrecoverable shutdown of the supply chain network due to

unforeseen catastrophic system-wide disruptions.

In general, it is possible to design supply chains that arerobust enough to profitably continue operations in the face of

expected deviations and unexpected disruptions. 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 the supply 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 and delayed 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 in

environmental conditions (Gaonkar and Viswanadham,

2004). In other words, supply chain robustness is related to

the ability of the SC to maintain it expected performance,

despite some unexpected deviations or disruptions.

Since robustness is the insensitivity of production

performances (Saitou and Malpathak, 1999), or the ability to

maintain a certain performance level, despite unpredicted

harmful uncertainty, an analysis to measure SC robustness

needs to consider appropriate performance metrics.

Performance measures that can be used must consider

indicators commonly used by industries, such as inventorylevels, 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 AND EXPERIMENTAL DESIGN

According to Park (1996), robust design is an

engineering 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 and manufacturing

costs. Taguchi extensively uses experimental designs

primarily as a tool to design products which are more robust

(less sensitive) to noise factors. Taguchi’s parameter design

is an important tool for robust design. His tolerance design

can be also classified as a robust design. In a narrow sense

robust design is identical to parameter design, but in a wider

sense, parameter design is a subset of robust design.

Two major tools used in robust design are:

Signal-to-noise ratio, which measures quality with

emphasis on variation;

Orthogonal arrays, which accommodate many

design factors (parameters) simultaneously.

The overall quality system should be designed to produce

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 product’s (or process’) functional characteristics).

In order to achieve robustness, the product and the process

should be designed so that they are minimally sensitive to

noise factors (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 be collected, the minimum number of

experiments will be performed to acquire the necessary

technical information, 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 two aspects

to any experimental design: the design of the experiment andthe 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 presence of, 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 there is a significant change in the

response for different levels of that factor), then you have

a comparative problem and you need a comparativedesign solution.

Screening objective: The primary purpose of the

experiment is to select or screen out the few important

main effects from the many less important ones. These

screening designs are also termed main effects designs.

Response Surface (method) objective: The

experiment is designed to allow one to estimate the

interaction 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. RSM designs are used to (a) find

improved or optimal process settings, (b) troubleshoot

process problems and weak points, and (c) Make a

product or process more robust against external and non-

controllable influences. "Robust" means relatively

insensitive to these influences.

Optimizing responses when factors are

proportions 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 model objective:

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 an experimental

plan that is intended to find the few significant factors from alist of many potential ones. Alternatively, one may refer to a

design as a screening design 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 determine how to make a

product better or a process more robust against the influence

of external and non-controllable influences

(NIST/SEMATECH, 2007). Park (1996) also mentions that

fractional factorial designs that use orthogonal arrays areoften 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 which component(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 move a process away from an unstable

operating zone. All these aims and purposes can be achieved

using fractional 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 is limitedowing 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 and the concept of robust

design has many aspects, among them are (Park, 1996):

1. Finding set of conditions for design variables which

are robust to noise;

2. Achieving the smallest variation in a product’s

function about a desired target value;

3. Minimizing the number of experiments using

orthogonal arrays (OA) and testing for confirmation. In

fact, use of OA is indispensable for robust design.

In this context, a methodology has been proposed for the

estimation of SC robustness based on Taguchi’s robust

parameter design (signal-to-noise and orthogonal arrays

DOEs); a robustness index formulae; discrete computer

simulation; and use of spreadsheets calculation an analysis

(Vieira et al., 2009). This methodology follows nine basic

steps (Figure 1). Some of these steps are not strictly

sequential; the designer should go back and forth as

necessary.

The uncontrollable parameters in the supply chain can be

parameters related to the distributions that model the

consumer (market) demand, processing and setup times,

transportation times, and failure and repair times. The

controllable parameters are those regarding the configuration

of the operation policy adopted, for instance, inventory

control or forecasting policies, number of trucks in a

distribution network, etc.

Figure 1. The proposed methodology for the design of robustness-oriented

supply chains.

V. FINAL CONSIDERATIONS

Supply 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 shouldpursuit in order to stay alive in the though competitive world

of nowadays. This paper brought together some of the

concepts and ideas that can be used to design and operate a

robust supply chain.

Future studies can consider, for instance: used of

Taguchi’s robust parameter design approach in order to

better 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;

1. Define key performance indicator (KPI)

5. Quantifying supply chain robustness using the robustness

index model

6. Define significance level (for F-test, confidence intervals and

number of replications)

9. Development and validation of the simulation model for the

current SC scenario – use of discrete simulation software

8. Analysis of input data – use of statistical data (e.g. INPUT

ANALYZER – ARENA, MiniTab, R)

10. Development of alternative scenarios

11. Run simulation experiments and put results back at the F-

test tables

12. Calculate robustness (RI) for the scenarios and perform

analysis and comparisons

2. Define uncontrollable parameters

3. Define design (controllable) parameters and scenarios

4. Model noise: the two levels for the uncontrollable factors

7. Create the orthogonal arrays and confounding factors (F-

tests) – use of spreadsheets



analysis of the relationship between supply chain robustness

and its bullwhip effect.

ACKNOWLEDGEMENTS

The corresponding author would like to thank the

Pontifical Catholic University of Parana (PUCPR), the

Coordenação de Aperfeiçoamento de Pessoal de Nível

Superior (CAPES), and the Conselho Nacional de

Desenvolvimento Científico e Tecnológico (CNPq) for the

financial support.

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Understanding Supply Chain Robustness

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