Dynamic MCDM for Partners/Supplier Selectionapolo.dps.uminho.pt/.../SemanaGI2016_Rita_Ribeiro.pdf · Semana da Gestão Industrial Universidade do Minho –Guimarães October 24-28

Semana da Gestão IndustrialUniversidade do Minho – Guimarães

October 24-28 Outubro, 2016

Dynamic MCDM for

Partners/Supplier Selection

Rita A. Ribeiro,

Campus FCT- UNL, Caparica, Portugal

[email protected],

Leonilde L.Varela

DPS - University of Minho

Guimarães, Portugal

[email protected]

1

mailto:[email protected]

mailto:[email protected]



1. UNINOVA

2. A Tomada de Decisão Dinâmica

3. Método de Resolução

4. Exemplos de Eng. Industrial

5. Conclusão

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October 24-28 Outubro, 2016 3

UNINOVA – Institute for Development of New Technologies

Research Institute, non-profit entity, major owned by New University of Lisbon, plus other Industrial organizations

CA3 - Research Group on Computational Intelligence:

• Since 2001 the group has been highly involved in Space related projects (Portugal only joined ESA end 2000)

• http://WWW.CA3-UNINOVA.ORG

Soft Computing

Decision Support Systems

Image Processing

Knowledge Discovery/Data MiningScientificDomains

Software Engineering

Project Management

AeroSpace

SupportingTechnologies

Prototypes

1. Applied research with theoretical scientific support

BiomedicineApplicationareas Environment



• MCDM models are commonly used in organizations to rationalize the process of decision.

• Classical model assumption to simplify this type of problems assumes both criteria and alternatives are fixed a priori and that along time decisions are independent i.e. no spatial or temporal considerations are included in the model.

The validity of the decision model is rather limited, specifically when the values change over time and the alternatives and criteria may change over time.

As a result:

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2. Introdução à Tomada de Decisão Dinâmica : Motivação



• Very few decisions are made with absolute certainty because complete knowledge about all alternatives and criteria is seldom possible….

• Decision making is usually a nonlinear, recursive process. Most decisions are made by moving back and forth between the choice of criteria (changeable criteria) and the set of alternatives to choose from (along time, alternatives may disappear and new ones could arise).

Dynamic decision making: decisions are made within a time frame, hence, as time passes, the decision environment may grow or retract, consequently, new information, criteria and new alternatives may appear or disappear.

2. Introdução à Tomada de Decisão Dinâmica : Definição



Classical MCDM (Multiple Criteria Decision Making):

• Assumption: Both criteria and alternatives are fixed a priori and decision occurs

only once.

• Limitation: does not handle decision problems where values change over time and

the decision matrix is not fixed and static (i.e. the final decision is a consequence

of intermediate ones) there is no method to deal with this problem.

Dynamic MCDM:

• Assumption: Both the number of criteria and alternatives can change over time

(spatial aspect) and values also may change over time (temporal aspect). At least 2

decision matrices are required (current and historic and/or future).

• Limitation: more complex to calculate and implies available spatial-temporal data

(historic and/or forecast).

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2. Introdução à Tomada de Decisão Dinâmica



The past

status is

based on

historical

data

Forecast of future

situation can be

calculated by using

either a

quantitative model

or experts

knowledge

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2. Introdução à Tomada de Decisão Dinâmica : estrutura



3. Método de Resolução DMCDM: lógica



Steps:

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1. Generate the 3 matrices (historic, current and forecast);

2. Determine/assign the weights for each criteria for each of the 3 matrices;

3. Calculate for the priority vector (rating) for each alternative in each matrix using a selected aggregation operator (e.g. weighted average, reinforcement operators etc);

4. Merge the three matrices employing a selected aggregation operator. If necessary use again weights to establish importance of past, current and future importance;

5. Ranking is the ordered list of the merged priority vectors, where the best supplier is the one with higher value;

6. Retention policy: select list of alternatives to be considered for next iteration (historic data) and check stopping criterion (number of iterations or periodicity ).

3. Método de Resolução DMCDM: steps



4. Exemplos de DMCDM

1. B2B – Supplier selection with past information

2. Supplier selection with past and forecasting



• Many businesses rely on external suppliers for some of their operations, and often establish relationships with “reliable” suppliers.

• For this we need to store information about their past behavior.

An example of parameterization for this problem is :

Alternatives suppliers under consideration.

Criteria estimated delivery time, cost, previous reliability, quality (defect free).

Retention policy keep all suppliers that have met quality standards and delivered on

time in the past six months.

G. Campanella, A. Pereira, R. A. Ribeiro, and L. R. Varela. Collaborative Dynamic Decision Making: a Case Study from B2B Supplier Selection. In Decision Support Systems – Collaborative Models and Approaches in Real Environments. Hernández, J.E., Zarate, P., Dargam, F. Delibašic, B., Liu, S. and Ribeiro, R. (Eds.), Lecture Notes in Business Information Processing (LNBIP), Springer Berlin Heidelberg, vol 121: 88-102 (2012). DOI: 10.1007/978-3-642-32191-7_

5. a. Exemplo B2B (só 2 matrizes)

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5. a. Exemplo B2B



5. b. Exemplo com “forecast” (3 matrizes)

First, a company assembles an historical dashboard for the last year, including all

suppliers for which it has information available and defines associated criteria:

Design

Service

Providers

Cost per

hour

(Average)

CPH

On time

delivery

performance

OTD

Delay

penalty

DP

Quality

rating

QR

Lack of

Quality

Penalty

LQP

Portfolio

Rating

PR

MR1 75.50 95% 10.00 100% 0.00 90%

MR2 79.00 90% 5.00 98% 2.00 80%

MR3 72.50 95% 15.00 95% 6.00 85%

MR4 37.50 80% 20.00 80% 10.00 75%

MR5 45.00 78% 25.00 85% 15.00 80%

1. A.Arrais-Castro, M. L. R. Varela, R.A. Ribeiro, G. D. Putnik (2015). Spatial-Temporal Business Partnership Selectionin Uncertain Environments. FME Transactions, Vol 43, 353-361 doi:10.5937/fmet1504353A



Second, current data is normalized (fuzzified) using membership functions:



Determine the weighted rating for each criterion:

• Manufacturing resource 1 is the best choice for new assignments, followed by

manufacturing resource 3.

• Since manufacturing resource 6 did not fulfil any previous orders, its past score is zero.

Historic decision vector result:Uses Weighting functions



The managers have a low confidence regarding values

resulting from prognostics, hence weights are low.

Third, we repeat the process for future information. Forecasting: expert judgment

or quantitative methods ( moving linear averages, quadratic averages, etc.

Forecast resulting vector



According to the present evaluation the best option will

be manufacturing resource 1.

It is interesting to note that resource 4 had quote with:

lowest price, lowest delivery and lead times, and also

has a great portfolio; But the weight assigned to

Strategic Rating has pushed it back to second place.

• Having calculated the historical and forecasting scores for each alternative, we

evaluate the present status:



Summary of rating values for past, current and future information and their ranking:

Final decision for period t=1 (dynamic decision making)

Although manufacturing resource 4 offered a

competitive quote, the lack of previous interactions

and its low strategic (portfolio) rating pushed it back

to third place.

Manufacturing resource 1 is recommended as best

decision.

Periodic Evaluation (dynamic feature might change this choice!!!!!



This work extended the dynamic decision making model proposed by Campanella and

Ribeiro (2011) by including forecasting data and a data fusion process.

The dynamic model includes iterations and feedback, hence is is rather suitable for

periodic decisions. Supplier selection is a periodic decision in every company!

The advantage of this dynamic model and its extension is to consider the impact of past

and future information, by dealing both with historical and future data, and thus

enabling more informed decisions with enriched information.

The applicability of the introduced model was demonstrated with a case study

(illustrated by the numerical example), therefore showing how important it is to have a

holistic view and wider perspective about suppliers ‘selection´.

V. Conclusion

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Author´s RELEVANT REFERENCES FOR THIS WORK

1. Campanella, G. and Ribeiro, RA. A Framework for dynamic multiple criteria decision making. Decision Support Systems, Volume 52, Issue 1, December 2011, Pages 52-60.

2. Campanella, G., Pereira, A, Ribeiro, RA, and Varela, LR. Collaborative Dynamic Decision Making: a Case Study from B2B Supplier Selection. In Decision Support Systems – Collaborative Models and Approaches in Real Environments. Hernández, J.E., Zarate, P., Dargam, F. Delibašic, B., Liu, S. and Ribeiro, R. (Eds.), Lecture Notes in Business Information Processing (LNBIP), Springer Berlin Heidelberg, vol 121: 88-102 (2012).

3. A.Arrais-Castro, M. L. R. Varela, R.A. Ribeiro, G. D. Putnik (2015). Spatial-Temporal Business Partnership Selection in Uncertain Environments. FME Transactions, Vol 43, 353-361 doi:10.5937/fmet1504353A

4. Arrais-Castro, M.L. R. Varela, G. D. Putnik, R. A. Ribeiro, F. C. C. Dargam Collaborative Negotiation Platform using a Dynamic Multi-Criteria Decision Model. International Journal of Decision Support System Technology, 7(1) :1-14 (2015) DOI: 10.4018/ijdsst.2015010101

5. J. J. Jassbi, R. A. Ribeiro and L. R. Varela Dynamic MCDM with future knowledge for supplier selection. Journal ofDecision Systems 23:3, 232-248, (2014) http://dx.doi.org/10.1080/12460125.2014.886850

6. R. A. Ribeiro, A. Falcão, A. Mora, J. M. Fonseca (2013) FIF: A Fuzzy information fusion algorithm based on

multi-criteria decision making, Knowledge-Based Systems Journal 58: 23–32 DOI: http://dx.doi.org/10.1016/j.knosys.2013.08.032.

7. M. L. R. Varela, G. D. Putnik, R. A. Ribeiro. A web-based platform for collaborative manufacturing scheduling in a virtual entreprise. International Journal Information and Communication Technologies for the Adanced Entreprise. Vol 2, nr 2, (2012) ISSN:1647-1707 pp 87-108, URL: http://www.ict4ae.org/downloads/ictae_2012_vol_2.pdf#page=88

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http://dx.doi.org/10.1080/12460125.2014.886850

Documents

Dynamic MCDM for Partners/Supplier Selectionapolo.dps.uminho.pt/.../SemanaGI2016_Rita_Ribeiro.pdf · Semana da Gestão Industrial Universidade do Minho –Guimarães October 24-28