Ch 6 Isoarchic and Multi-Criteria Control of Supply Chain NW

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Chapter 6

Isoarchic and Multi-criteria Control of Supply

Chain Network

F. Ounnar and P. Pujo

Abstract Supply chains and more particularly supply chain networks are more

and more subjected to extreme dynamic operations, where it is asked that each ac-

tor has more flexibility and reactivity on the one hand and a specialization bring-

ing more productivity on the other hand. Companies try to achieve the commongoal of satisfying customers needs through partnership. Negotiation between

partners is thus required involving each partner management and production or-

ganization. This situation makes it difficult to obtain the best response with re-

spect to the need of each customer. For that, a new approach is proposed for cus-

tomersupplier relationship control, in which the partnership is considered in the

context of an association of potential suppliers within a network: an isoarchic con-

trol model for a supply chain network based on a holonic architecture. The deci-

sion-making mechanism is produced thanks to the properties of a decision-making

center, called autonomous control entity, associated to each actor of the logisticnetwork, which makes it possible to quantify a multi-criteria evaluation. An im-

plementation of the simulation of such a system is done via a distributed simula-

tion environment high-level architecture). A case study is presented.

Keywords Holonic, multi-criteria, supply chain network, isoarchic control model

6.1 Introduction

Supply chains and more particularly supply chain networks are more and more

subjected to extreme dynamic operations, where it is asked that each actor has

more flexibility and reactivity on the one hand and a specialization bringing more

productivity on the other hand. This forced many companies to search for new

forms of organizations. Studies have been conducted on the durability of customer

supplier relationships (Alcouffe and Corrg, 1999), the dynamics of these

relationships and their influence on inter-company costs (Brandolese et al., 2000,

F. Ounnar () and P. Pujo

LSIS, UMR CNRS 6168, Aix Marseille University, France

e-mail: [email protected]


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F. Ounnar and P. Pujo162

Harri, 2002) with the aim to improve supply chain management productivity and

effectiveness. In addition to these, we can mention the work of Toolea and

Donaldson (2002) on customersupplier (C-S) relationship performance and of

Nesheim (2001), Smart and Harrison (2003) and Holmlund-Rytknen and Strand-

vik (2005) on the impact of bidding within C-S relationships. Other studies fo-

cused on the definition of concepts allowing improved cooperation between com-

panies (Telle et al., 2004, Lauras et al., 2003) or proposed an autonomous

decentralized optimization system based on material requirement planning, such

as the work of Nishi et al. (2005).

Today, the customer is placed at the centre of the organization. So, companies

try to achieve the common goal of satisfying customers needs through partner-

ship. Partnership control involves all the actions developed together in order to

achieve common objectives and to timely react to any failure of any partner. Ne-

gotiation between partners is thus required involving each partner management

and production organization. This situation makes it difficult to obtain the best re-

sponse with respect to the need of each customer.

For that, C-S relationship control at a tactical/operational level is proposed in

which the partnership1 is considered in the context of an association of potential

suppliers within a network and in which all C-S partners negotiate according to a

contract-net-type protocol (Smith, 1980) in order to meet customer requirements

as much as possible. The goal of this approach is to provide support for control-

ling a meshed logistics network through the dynamic behavior of C-S relation-

ships at network level. By meshed logistics network is meant a supply organiza-tion in which several supply solutions are offered at each step (i.e., at each mesh

of the chain). The difficult part of such an organization is to select, for each supply

step, the best solution among all the possible ones. The proposed approach is an

isoarchic and multi-criteria control model for supply chain networks based on a

holonic architecture. Indeed, the isoarchic approach can be implemented via the

holonic paradigm, given specific software developments. Each supplier organizes

and controls his own activities, obtained by proposing his best conditions for the

execution of calls for proposals (CFPs) launched by customers. The decision-

making mechanism is produced thanks to the properties of a decision-making cen-tre, called an autonomous control entity (ACE), associated to each actor of the lo-

gistic network, which makes it possible to quantify a multi-criteria evaluation.

After introducing the holonic paradigm and self-organized control in C-S rela-

tionships (Sectis. 6.2 and 6.3), a two-step supplier self-assessment approach, with

respect to received CFP, is described in Sect. 6.3. In order to validate the proposed

approach, it is necessary to validate first the global partnership network model.

For that, a distributed simulation platform using high-level architecture (HLA)

standard (IEEE P1516)) is developed in Java (Sect. 6.4). Validation is done with a

study case from the cosmetic industry by comparing operations under classical

1 The goal of the partnership is to collectively ensure the dispatching of orders from different

customers, while respecting each partners interest.


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sions are performed in real time without forecasting. This can be based only on a

strong reactivity of the logistics network actors who must put in common and

share knowledge. We define a dynamic logistics chain as a logistics chain pro-

gressively built within the logistics network.

An approach based on the holonic paradigm and isoarchic architecture for self-

organized C-S relationships is proposed. In this approach, all the partner entities

(customers, suppliers) of a self-organized logistics network exchange through the

same communication media and negotiate to answer at best customers expecta-

tions and to exploit at best suppliers capabilities. In the proposed self-organized

network, customers launch CFPs, potential suppliers enter negotiations from

which the best network answer emerges for each CFP and allows identifying the

next mesh of the logistics chain.

The logistics network is thus built on honest and transparent partnership. An

important condition for the good operation of this type of relationship is the exis-

tence of mutual trust among partners. It is necessary to sensitize and engage logis-

tics people into a policy of permanent progress, made of continuous improvement

with the aim to maximize economic potential. Beyond internal optimization of

their production, and in connection with it, customers must optimize their relation-

ships with their suppliers. Each supplier positions itself with respect to the various

customers and shows its capacity to provide the needed support while letting each

partner use its own assets.

A durable C-S relationship in a dynamic partnership requires the use of an ap-

propriate set of tools supporting items such as contractual relationships, trust de-velopment between partners and assessment of relevant, coherent and motivated

suppliers. Trust, reciprocity and shared goals are the principal components of a

strong C-S relationship.

6.3 Control of a Dynamic Logistic Network: Isoarchic and

Multi-criteria Control

A self-organized control model is proposed in which the decision system manages

the operations of a set of entities belonging to a partnership. The self-organization

concept is conditioned on one hand to the use of a decentralized decision structure

and, on the other hand, to the consideration of the real behavior of each entity.

With this approach, there is no forecast-based organization since self-organization

implies a real-time decision-making mode. For that, and also to allow each sup-

plier to take part in negotiations, an ACE has been associated to each partner. This

entity allows dialog with the other network members and self-assessment with re-

spect to received CFPs.The main objective of our approach is to ensure the dispatching of customers

orders through negotiations between the suppliers potentially able to answer a

CFPs issued by a customer. Because of the limited capacity of each supplier, it is


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product being processed onto the resource, i.e. which resource will be in charge of

the next stage or which product will be processed by a given resource (Pujo et al.,

2009).

6.3.2 Definition of Self-organized Control

Self-organized control implies decision-making in real time without forecasting

(Ounnar and Pujo, 2001). In order to organize, one must define a common objec-

tive for all the decision centres involved in the organization. This can be done with

different ways according to the properties and characteristics of the entities to be

organized, and expressed in terms of synchronization, coordination, sharing, co-

operation, negotiation and/or operation design. The solution retained to make all

the entities work is obtained by emergence. Indeed, in absence of hierarchy, each

entity participates in the solution proposal definition and also in the solution

evaluation. The proposal that appears the best performing with respect to pre-

established criteria is selected.

Many ways exist to locally handle decision-making for each partner. The use of

scheduling heuristics could be envisaged at this level. A different approach is cho-

sen aiming to maximize the consideration of, sometimes diverging, customers

and suppliers interests. As already mentioned the objective of our approach is to

make emerge the supplier answering best a CFP launched by a customer into thepartnership network, while respecting the interest and exploiting at best the capac-

ity of each partner. In a first step, a supplier sorts out all the received CFPs with

the objective to define the most advantageous CFP for him. In a second step, a lo-

cal assessment is performed for the CFP selected in step 1. This individual evalua-

tion allows the supplier to position himself with respect to the best response circu-

lating on the network for the selected CFP. This way, the selection of a supplier

proposing the best performance for a given CFP will emerge and this supplier will

be in an optimal situation. If a supplier is not retained for his selected CFP, he can

then bid on other CFPs that he did not rank as high.Both steps can be implemented with the use of qualitative and quantitative cri-

teria. Three classes of multi-criteria methods can be distinguished (see Figure 6.1):

decision aid methods, elementary methods and mathematical optimization meth-

ods. The choice of one class of methods may depend on the data available to treat

the multi-criteria problem or on the way the decision-maker models his prefer-

ences.


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6 Isoarchic and Multi-criteria Control of Supply Chain Network 167

Fig.

6.1

Multi-criteriamethod

usedforeachstep


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The AHP (Saaty, 1980) was chosen. It has several advantages over other deci-

sion-making approaches (Vargas, 1990, Wedley, 1990), which include ability (i)

to handle qualitative and quantitative attributes, (ii) to hierarchically structure

problems to gain insights into the decision-making process and (iii) to monitor the

judgment consistency of a decision-maker. Furthermore, AHP has the capability to

quantify and rank the alternatives (CFPs) using pairwise comparison of criteria

(Harker, 1989). All these characteristics make the strong points of the AHP

method (Ounnar, 1999). AHP has demonstrated robustness across a range of ap-

plication domains.

A multi-criteria decision-making algorithm applying AHP is established in

each ACE associated to an RH and defines a CFP classification, taking into ac-

count RH, PH and OH constraints. The performance for the CFP ranked in first

position will be taken into account. We propose to implement AHP through two

main phases: configuration and exploitation. In order to be able to use the AHP

algorithm for ranking the received CFPs, it is first necessary to define the relative

importance of the criteria and their indicators: this is the setup (configuration)

phase. The dynamic exploitation phase of the AHP algorithm makes it possible to

rank the CFPs and obtain the priority vector of the considered CFPs.

In our study, five criteria are proposed (Ounnaret al., 2007):

1. Cost criterion C1. The objective of this criterion is to ensure delivery at the best

price. This criterion is a quantitative criterion that takes into account the vari-

ous costs in the acquisition of goods. The cost criterion can be reduced to twoindicators: cost of order I11 and cost of order delivery I12.

2. Lead time criterion C2. The objective of this criterion is to ensure that the cus-

tomer receives delivery as quickly as possible. The delay is the time between

the expression of a need by the customer and the actual satisfaction of this

need. This criterion can be reduced to two indicators: production time I21 and

delivery time I22.

3. Quality criterion C3. The objective of this criterion is to guarantee that the de-

livered products are of good quality and in accordance with specifications, that

is, the criterion aims to minimize poor unquality. The indicators for this crite-

rion can be quantitative or qualitative, and aim at describing continuity of ser-

vice, compliance with the rules and compliance with expectations concerning

the product. This criterion can be reduced to three indicators: rate of conformity

I31, respect of a referential I32 and rate of customer satisfaction I33.

4. Reliability criterion C4. Reliability is the ability of any device to carry out a re-

quired function, under given conditions, for a given duration. The objective of

this criterion is to guarantee that the delivered products are reliable. This crite-

rion can also be used to evaluate the capacity of the company to meet dead-

lines. This criterion can be reduced to two indicators: conformity in quantity of

the orders I41 and respect for delivery times I42.

5. Strategy criterion C5. In the evaluation of each suppliers performance, qualita-

tive criteria are taken into account. For example, privileged relationships link


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the customer and supplier. This criterion can be reduced to two indicators:

ailowance of differed payment I51 and degree of privilege I52.

This section defined a system of indicators for the application of the multi-criteria

decision method (AHP) to the evaluation of each potential supplier. The applica-tion of the proposed multi-criteria method provides the alternative (the CFP) for

which the supplier is the best.

6.3.2.2 Second Step: Performance Assessment for the First-ranked CFP

As indicated above we have focused on the class of elementary methods. These

methods are often implemented in practice (Vincke, 1989). In general, the deci-

sion-maker assigns a weightPc to each criterion representing the criterion relative

importance. Then, the decision-maker associates a markPacto each action with re-spect to each criterion. The global markFaof each action with respect to n criteria

is calculated as ( )( )ppF acn

cca =

=1

. Among elementary methods, we can mention

(Timmerman, 1986): categorical method, linear averaging method or weighted

point method; (Akbari Jokar, 2001): lexicographic method, conjunctive method

and disjunctive method.

The selected method should authorize consideration of qualitative and quantita-

tive criteria, rely on all (without exclusion) the chosen criteria and send only one

(not several) action as result. The categorical method and linear averaging methodmeet these three constraints. The unique difference between these methods is re-

lated to criteria weighting. The linear averaging method is complementary to the

categorical method, which is used to assess suppliers performance for a selected

CFP. Indeed, the linear weighting method cannot be directly applied for three

main reasons:

The considered indicators are not necessarily of the same nature. The linear weighting method, as it is defined, presents a monotony problem. The objective is not only to weight the various criteria but also to penalize or

neutralize indicators according to customer wish.

The five selected criteria used for assessment are considered as the coordinates of

an element in 5. We thus work in an -vector space of dimension 5. With theaim to eliminate the monotony problem and to be able to penalize a supplier, a

mark is given to the indicators in order to discriminate well enough, at the end, be-

tween the performances of the various suppliers on similarly chosen criteria. Per-

formance evaluation is thus based on three main points:

1. Respect of the scales between indicators.

2. Coherence of the scale between criteria.3. Implementation of coefficients k1 and k2 to be able to eventually penalize a sup-

plier.


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A supplier performance for a selected CFP (in step 1) is calculated by:

[ ] [ ]

2

32

31 332 21 1 2 2 3

locale 2

51 522

41 424 5

93

9 9

2 2

I

I Ip k p k p

P

I II I

p p

+ +

+ +

=

+ + + +

(6.1)

wherep1,p2,p3,p4 andp5 are constant coefficients providing customers with thepossibility to privilege one criterion above the others by giving weights with total

sum equal to 1.

The coefficient k1 is defined by: (desired cost)/(possible cost) = k1; Desired

cost corresponds to the cost that a customer is able to accept and Possible cost

reflects the cost proposed by the supplier to customer (I11+I12).

Coefficient k1 is equal to 1 if the possible cost is lower than the cost desired by

the customer. The following algorithm describes the calculation: if desired cost

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Ch 6 Isoarchic and Multi-Criteria Control of Supply Chain NW