Predictive Performance Model in Collaborative Supply Chain Using Decision Tree and Clustering

Technique

Ridha Derrouiche LSTI, ESC Saint-Etienne 51-53 cours Fauriel BP29

42000 Saint-Etienne – France. ridha_derrouiche@esc-saint-etienne.fr

Pongsak Holimchayachotikul and Komgrit Leksakul Chiang Mai University, Chiang Mai 50200,

Chiang Mai, 50200, Thailand holimchayachotikul@hotmail.com

komgrit@eng.cmu.ac.th

Abstract— This paper proposes an integrated framework between B2B supply chains (B2B-SC) and performance evaluation systems. This framework is based on data mining techniques, enabling the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. The results are deployment for collaborative performance guidelines, which were validated by the domain experts in terms of its real practical usage efficiency. This framework enables managers to develop systematic manners to predict future collaborative performance and recognize latent problems in their relationship. Its usages and difficulties were also discussed. Furthermore, the final predictive results and rules contain vital information relating to SC improvement in the long term.

Keywords-component; Business to Business (B2B), Supply chain (SC), Data Mining, Multi Attribute Decision Making, Performance Measurement.

I. INTRODUCTION Collaboration between Supply Chain partners have been

covered extensively in the strategic management literature [1-6]. As a fact, several research surveys have shown that the core of supply chain management is the process improvement at the inter-enterprises level [7] and [8]. Some researchers have examined the theoretical implications of supply chain collaboration through unilateral supply policies [9-11]. Others have employed theoretical models to examine bilateral information exchange rather than unilateral policy incentives [12] and [13]. Some recent studies [1] and [14] are interested in a better characterization of the collaborative supply chain.

In recent times, most competitiveness improvements have concentrated on performance measurement (PM) systems in many organizations. It has been recognized in terms of the interrelationship [15]. Back in the 2000s, one of the main problems in PM was able to connect the present strategy and the forward PM improvement directions while being inward looking [16]. Furthermore, PM has often pointed out the KPI improvement on the individual financial aspects without concerning the collaboration [17]. It is due time to change PM with the innovation in terms of long-term collaborative value [18]. The performance tendency forecasting was based on the learning system from the in-deep experiences of the supply

chain managers and experts rather than just only comparison of historical data. Some of the interested aspects were proposed to be added in PM system such as: trust degree between partners, degree of information system sharing, long-term orientation and involvement of the partners [18]. There are many approaches to construct the most suitable PM for one’s own supply chain; still, the small number of researches about future performance planning capacity can provide the right direction after measurement instead of how they evaluate the previous KPI results; do their results achieve or not?

In this paper, a novel alternative by integrated (B2B-SC) predictive performance system together with multiple decision-making and data mining technique, namely C&RT and K-Means, is introduced. The idea was to have systematic manners to predict future collaborative performance and recognize latent problems in their collaboration. Moreover, it can be constructed from the assessment aspect of agility, collaborative, flexibility, information, partnership and productivity between partners.

II. BACKGROUND AND LITERATURE REVIEW In this section, we remain some basics concepts and the

literature related to our work such as: data mining, performance measurement, etc.

A. Data Mining (DM) Data mining has been broadly utilized and accepted in

business and production during the 1990s [19]. Currently, data mining is made of use not only in businesses but also in many different areas in supply chain and logistics engineering to retrieve knowledge for application in each operation unit. A few examples are demand forecasting system modeling, SC improvement roadmap rule extraction, quality assurance, scheduling, and decision support systems [20]. The data mining techniques can normally be categorized into four sorts i.e., association rules, clustering, classification, and prediction [21]. At the turn of century, the decision makings were used in production management to choose the suitable and agile solutions in real production. Nowadays, the attempt of integration between statistics and data mining approaches carry out the quality issues in terms of SC.

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B. Performance Measurement (PM) On the other research rivers, PM context [22] comprised of

the multi-criteria decision attribute (MCDA) are most commonly accepted for use. The classification are as follows: hierarchical techniques, deployment approaches, scoring method and objective programming. For example, performance improvement of the selection of freight logistics hub in Thailand was developed by coordinated simulation and AHP [23]. K. A. Associates [24] figured out that PM, among collaborative SC networks, is vital for management. There have been many certain attempts to deploy and explore AI and data mining techniques to make up for the typical techniques in optimizing PM in SCM with a better development roadmap [20]. M. F. Joanna et. al. [25] have illustrated that DM is a vital tool for efficiency of SC integration to analyze many quality views. An amalgamation of fuzzy theory has been broadly manipulated to optimize and handle SC configurations in many points. For incidence, Bevilacqua et al. [26] have used a fuzzy-QFD means to supplier selection. Jain et al. [27] have then made a contribution to supplier selection using fuzzy association rules mining method. Almejalli, K et al. [28] have applied fuzzy neural network and GA for real time identification of road traffic control measures. In addition, it seems like a more appropriate way for the academic field; nevertheless, it might not be suitable in practical use for SC domain managers and engineers from several fields.

At this point, we design this predictive collaboration measurement based on the flexibility in terms of usage and interpretation rather that the complexity from many equations. In the real world of PM on the SC domain, there are many KPIs in one PM system, so this proposed work also effectively groups and reduces KPIs as small units by MCDA before predictive modeling. It has resulted in providing vital fuzzy rules which can track back in the deep detail of analysis following the component of MCDA.

C. Simple Additive Weighting (SAW) This is also called the weighted sum method [22] and is the

simplest and still the widest used MADM method. Here, each attribute is given a weight, and the sum of all weights must be 1. Each alternative is assessed with regard to every attribute. The overall or composite performance score of an alternative is given in Equation 1

1 1( ) /

M M

i ij ij normal jj j

P w m w= =

⎡ ⎤= ⎢ ⎥⎣ ⎦∑ ∑

(1)

Where ijm normal represents the normalized value of ijm and iP is the overall or composite score of the alternative iA . The

alternative with the highest value of iP is considered as the best alternative. The attributes can be beneficial or non-beneficial.

D. Classification and Regression Tree (C&RT) Decision trees are constructed by algorithms indicated

many means of separating the interested datasets into branch-like sub-trees. These sub-trees are the child of a root node at the top of the tree. The standard format of this decision tree

modeling way is drawn in Fig. 1. Moreover, decision rules can foresee the values of unseen valuable observations including input values, but might not enclose values for those response values [29].

Figure 1. The example of Decision Tree. [29]

C&RT, abbreviated from Classification and Regression Tree, was initially portrayed in the academic book [30]. C&RT is a recursive procedure (the two obtained subsets are divided, and then the continuous mechanism replicates until some other stopping condition is reached). Two subsets are portioned by C&RT means, thus the records within each subset are more much harmonized than in the preceding subset. The same forecaster field may make use of numerous times at diverse stages in the tree. It applies suitable separating to create the best way of data with missing values.

E. K-Means Clustering K-Means algorithm is a clustering method in statistical and

machine learning [31]. The objective of the K-Means algorithm is to select the number and position of center points while minimizing the sum of distance between data and center points.

III. METHODOLOGY The methodology of integrated (B2B-SC) predictive

collaboration performance evaluation model, based on multiple decision making, named Simple Additive Weighting (SAW), data mining techniques, C&RT and K-means, is shown in Fig. 2. The data set of relationship between enterprise and its direct customers from R. Derrouiche et al., [21], and the PM model and questionnaire were used for demonstration. Before model construction, each sub-KPI weight definition was conducted by the aggregation from the result of attribute ranking algorithm using information gain based on ranker search. In this research, the collaborative performance scoring was developed from SA; the weight of macro criteria and sub-KPIs were assigned from the previous stage of this methodology.

Moreover, the weighting was also criticized by the domain experts. Last but not least, the C&RT and K-Means model for B2B-SC collaborative performance prediction was performed based on the two datasets from the main dataset. One was the training set and the other was the testing set. In this research, both worst and best relationship types were selected at the collaborative performance learning dataset to demonstrate this framework potential. Before C&RT model construction, Pearson feature selection was applied to identify the significant inputs which have an effect on collaborative performance

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score. Next, the model components using these datasets were defined by the general configuration from domain experts concerning their related SC context. Finally, the results were interpreted by the domain expert to forecast the overall collaborative performance and plan their collaborative performance improvement direction.

Figure 2. The methodology of B2B-SC predictive collaborative performance evaluation model

IV. RESULTS AND DISCUSSION

A. Data Preparation After the data set of relationship between enterprise and its

direct customer questionnaire gathering following R. Derrouiche et al., [32] Framework’s in Fig. 3. to analyze a dyadic relation and to evaluate its performance, the attribute ranking algorithm using information gain based on ranker search was calculated for the two types of relationships.

Figure 3. Framework to analysis a dyadic relation and to evaluate its performance [32]

These results are shown in Fig. 4. In addition, the questionnaire from R. Derrouiche et al., [32] was able to characterize collaborative relation between two or more partners in a supply chain, evaluating their related performances accordingly. The former level is the common perspective as follows: relation climate, relation structure, IT-used and relation lifecycle and the later level consists of the perceived satisfaction of the relation and its perceived effectiveness.

Figure 4. The sub-KPI impact results from the attribute ranking algorithm using information gain, based on ranker search

These represent the macro view of model. For example, the macro view of relation climate has six micro views, and each micro view has also two sub-micro views.

Next, the data cleaning and input-output format following C&RT and K-Means structure was conducted to prepare the learning data. Primary impact of each sub-KPI (i) from each relationship type (j) was calculated from equation 2. Then weight definition was performed according to equation 2.

414

1 1

(2)m n

ij ij ijj i

weight impact of KPI impact of KPI= =

= ∑∑

B. C&RT Decision Tree of predictive collaborative performance construction The simple additive weight of each sub macro view was

constructed using the weight from the results of primary impact from Fig.4. At this stage, the collaborative performance was also calculated as the future response of C&RT model with 7 as the maximum tree depth as shown in Fig.5.

Figure 5. C&RT and Clustering model for predictive collaborative performance evaluation model

Then, Pearson feature selection was applied to identify the significant inputs which have an effect on collaborative performance score. The significant inputs are the inputs which pass the 95 percentage of confidence interval (red line).

C. C&RT Decision Tree model performance analysis The learning dataset of C&RT Decision Tree comes from

the prepared dataset; the training set makes up 80 percentage,

while the testing set is 20 percentage and the assigned data for predictive performance testing is from experts. In addition, the C&RT Decision Tree model for B2B-SC collaborative performance prediction capacity of this model was expressed in terms of the error of the predictive output. The mean absolute error of and testing set is 0.027; it can be regarded as the error acceptance from domain experts assumptions.

D. C&RT Decision Tree model deployment On the practical deployment, the domain users tried on it as

follows:

• Prepare their collaborative performance data according the input-output model format and then feed it into the model.

• Put performance improvement scenarios and analyze the result in terms of real usage feasibility and how to take advantage from the KPI sensitivity analysis, related to their expected collaborative performance.

• Analyze the impact of sub-KPIs to their expected collaborative performance.

• Form the sub-KPI improvement planning based on the model result and their long term strategy.

For instance, one of domain experts exercised his performance improvement road map, in which he profound his research on the case study of B2B-SC. His assumption stated most of the best relation types merely started in the maturity phase of the product life cycle. Between the two partners, they take effort to develop the cooperation climate in the long-term orientation with a very high participation. These come from the high degree of engagement and commitment, compatibility and solidarity, power exerted and confidence. Besides, it can improve satisfaction with their partner. To prove his assumption, the statement of this assumption was converted to the estimated value of sub-KPIs and then put all of its values to the C&RT decision tree model.

As a result, we found that the predictive collaborative performance value is a very high value, which is greater than 75 percent of collaborative performance from the main improvement condition (engagement and commitment > 3.425, compatibility and solidarity > 3.084, power exerted > 4.253 and confidence > 4.245); it is corresponding to his assumption.

E. Performance Clustering based on K-Means Construction Before performing the K-Means algorithm, the number of

clusters was set to three. The percentage membership volume in each cluster was 42,14 and 55 percent. The Fig. 6 to 8 show the important variables to the clustering process.

Moreover, the mean and standard deviation of each cluster are also shown in Fig. 8. From the B2B-SCM domain experts’ brainstorming results, we can imply that the B2B-SC in cluster I has the highest performance with 67.4944 of collaborative performance score. On the other hand, the B2B-SC in cluster II has the lowest performance with a collaborative performance score of 32.9239. What’s more is that these interpretations actually correspond with the relationship. Such members of cluster II are a bad relationship

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Figure 6. Performance clustering model for predictive collaborative perfermance

Figure 7. Mean of each Performance cluster

Figure 8. Stand deviation of each Performance cluster

F. Integrated Deployment between Performance Clustering model and Predictive Collaborative Performance Evaluation Model This is the main contribution of this research work. To

construct the strategies and KPIs of B2B-SCM improvement with this framework, we can start to collect the data from the following questionnaire of the framework of R. Derrouiche et al., [32]. Next, data format preparation to two of the models has to be conducted and pushed to the performance clustering model. At this point, we can locate the interested company performance to the right cluster based on the business context. This leads to understanding of the nature of its B2B-SCM performance management to answer the question of “Where are we?”, Who are our benchmark ?, How can we improve our self to move to the better position in business with B2B-SCM attribute improvement.

Moreover, we can refer to the mean and standard cluster deviation: to investigate, to extract and to diagnose the strength (increase and promote) and weakness (eradicate and diminish). This process should be conducted from the top management in each company and domain experts to define the the strengths and weaknesses. It can reply the question “Who are you?”.

After we realize the present position, the users can utilize the predictive collaborative performance evaluation model by feeding the KPI scenarios which are translated from the strategies before real implementation. It can answer the question “How can we move best?”.

At this stage, the result illustrate how we can link the clustering, modeling and classification function of data mining to construct the predictive collaborative performance model. It can investigate and extract the secret of B2B-SCM.

V. CONCLUSION This paper has described the integrated application between

B2B supply chains (B2B-SC) performance evaluation systems, data mining and multi-criteria decision attribute techniques, developing predictive collaborative performance evaluation model and performance clustering model. After the methodology implementation and deployment, the results prove the model advantage in terms of long term planning based on expected performance. Moreover, this proposed a

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model that allows users to combine human perception and judgment with C&RT decision tree of predictive collaborative performance related with their SC context. It results from the innovation decision making, concerning the changing in terms of computational result and human instinct. There are some certain limitations such as the users have to know a some data mining and multi-criteria decision attribute techniques; it is vital in the initial stage of practical deployment. This work is focused on a quantitative approach on how managers consider the role of each sub-KPI and its impact on the long term collaborative performance. To illustrate it on dashboard or simplified graphics is another way to communicate among the invokers from the different units. The next step of this research aims to compare the present model with neuro-fuzzy based on innovative algorithms or swarm intelligence to find the appropriate components such as the sharpness of membership function. Moreover, some of uncertainty factors from the SC environment or even management should be covered using Fuzzy MCDA to be handled in the first step before predictive collaborative performance evaluation model construction. On the other hand, the PM framework might be added of some measurement metrics from the SCOR model, BSC.

REFERENCES [1] Simatupang Togar M. and Sridharan Ramaswami, An integrative

framework for supply chain collaboration, The International Journal of Logistics Management, 2005, 16(2), 257 – 274.

[2] Bowersox, D.J., Closs, D.J. and Keller, S.B., How supply chain competency leads to business success, Supply Chain Management Review, 2000, 4(4), 70-8.

[3] Crowston K, (1994) « A taxonomy of organizational dependencies and coordination mechanism»,(http://ccs.mit.edu/papers/CCSWP174.html , the date of last access: February 2011)

[4] Lapide, L., Are we moving from buyers and sellers to collaborators. 2002, AMR Research Report

[5] Cefrio, «Collaboration et outils collaboratifs pour la PME manufacturière», Québec, 2004 (www.cefrio.qc.ca, the date of last access: February 2011)

[6] Venkatraman N. and Bensaou M., (1996), « Inter-organizational relationships and information technology: a conceptual synthesis and a research framework », European Journal of Information Systems, Vol. 5, n°2, p 84-91.

[7] Boyson, S., Corsi, T.M., Dresner, M.E., Harrington, L.H., Logistics and the Extended Enterprise: Benchmarks and Best Practices for the Manufacturing Professional, Wiley, New York, NY, 1999.

[8] Stank, T.P., Crum, M.R. and Arango, M., Benefits of interfirm coordination in food industry supply chain, Journal of Business Logistics, 1999, 20(2), 21-41.

[9] Taylor, A.T, 2002, Supply chain coordination under channel rebates with sales effort effects. Management Science, 48(8) 992-1007.

[10] Chen, F., Market segmentation, advanced demand information and supply chain performance. Manufacturing & Service Operations Management, 2001, 3(1) 54-67.

[11] Klastorin T.D., Kamran Moinzadeh and Joong Son, Coordinating orders in supply chains through price discounts. IIE Transactions, 2002, 34(8) 679-689. (http://faculty.washington.edu/kamran/TimingDiscPaper.pdf , the date of last access: February 2011)

[12] Governing, S., Information flows in capacitated supply chains in fixed ordering costs. Management Science 2002, 48(5), 644-651.

[13] : Li, L., Information sharing in a supply chain with horizontal competition. Management Science, 2002, 48(9) 1196-1212.

[14] Lambert, D.M., Knemeyer, A.M. and Gardner, J.T., Supply chain partnerships: model validation and implementation, Journal of Business Logistics, 2004, 25(2), 21-42.

[15] G. J. C. Da Silveira, "Improving trade-offs in manufacturing: Method and illustration," International Journal of Production Economics, vol. 95, pp. 27-38, 2005.

[16] A. Gunasekaran and B. Kobu, "Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications," International Journal of Production Research, vol. 45, pp. 2819-2840, 2007.

[17] A. Gunasekaran and E. W. T. Ngai, "Information systems in supply chain integration and management," European Journal of Operational Research, vol. 159, pp. 269-295, 2004.

[18] K. McCormack, M. B. Ladeira, and M. P. Valadares De Oliveira, "Supply chain maturity and performance in Brazil," Supply Chain Management, vol. 13, pp. 272-282, 2008.

[19] J. Han and M. Kamber, Data mining: concepts and techniques.Morgan Kaufmann Publishers, 2001.

[20] P. Holimchayachotikul, R. Derrouiche, K. Leksakul and G. Guizzi "B2B Supply Chain Performance Enhancement Road Map Using Data Mining Techniques," in The 9th International Conference on SYSTEM SCIENCE and SIMULATION in ENGINEERING (ICOSSSE'10) Iwate, Japan, 2010.

[21] Derrouiche R., Neubert G. and Bouras A., SAVINO M., B2B Relationship Management: A Framework to Explore Impact of Collaboration, International Journal of Production Planning & Control (IJPPC), Volume 21, Issue 6, pages 528 - 546, 2010.

[22] P.C. Fishburn, Method for estimating addtive utilities, Management Science, vol.13-17, pp.435-453, 1997.

[23] J. Wanitwattanakosol, P. Holimchayachotikul, . Nimsrikul and A. Sopadang, Performance Improvement of Selection the Freight Logistics Hub in Thailand by Coordinated Simulation and AHP," Industrial engineering and management system (IEMS) vol. 9, pp. 88-96 2010.

[24] K. A. Associates, A Guidebook for Developing a Transit Performance-measurement System. Washington, DC., 2003.

[25] M. F. Joanna Oleskow, Paulina Golinska and Katarzyna Maruszewska, Data Mining as a Suitable Tool for Efficient Supply Chain Integration Springer Berlin Heidelberg, 2007.

[26] M. Bevilacquaa, A fuzzy-QFD approach to supplier selection, Journal of Purchasing and Supply Management vol. 12, pp. 14-27, 2006.

[27] V. Jain, S. Wadhwa, and S. G. Deshmukh Supplier selection using fuzzy association rules mining approach, International Journal of Production Research, vol. 45, pp. 1323 – 1353, 2007.

[28] K. Almejalli, Dahal, K., Hossain, M.A. , Real time identification of road traffic control measures .Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. vol. 144: Springer Berlin / Heidelberg, 2008.

[29] SAS Publishing", "Decision Trees— What Are They?," 2007. [30] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification

and Regression [31] A. Jain, R. Dubes, Algorithms for Clustering Data, Prentice-Hall,

Englewood Cli!s, NJ, 1988. [32] Derrouiche, R. Neubert G. and Bouras A., Supply chain management: a

framework to characterize the collaborative strategies International Journal of Computer Integrated Manufacturing(IJCIM), Vol. 21, Issue 4, June 2008 , pp. 426-439.

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