An online performance evaluation of business partners system: OLAP based approach

H.C.W. LAUDepartment of Industrial & Systems Engineering

The Hong Kong Polytechnic UniversityHung Hom

HONG KONGmfhenry@,dinet.polyu. edu.hk

Abstract -The core of manufacturing activities has shifted fromthe physical production of goods to the systematic processing ofknowledge to create value for the customers from shortenedproduct development cycle, integrated enterprise modelling,improved outbound logistics and fast response to market needsusing innovative information tools and digital technology in thenetwork environment. Manufacturing-related business servicesthat capitalize on information technology are expanding fast.The competitiveness of tomorrow's manufacturing enterpriselies in the development and adoption of a wide range of digitaltechnologies and tools and the creation of virtual andfast-response enterprise to meet the new industrial order of thenext century. The formation of digital manufacturing networksis taking up momentum to meet this challenge. In this newbusiness model of virtual enterprise, the partnership synergy isan important issue to be addressed. This paper attempts toprovide a model to analyze the issue of partner synergy in adigital manufacturing network. In particular, a model toevaluate the performance of partners in "real time" based on theOnLine Analytical Processing (OLAP) technology has beenproposed. In this paper, a case example has also been covered toevaluate the feasibility of this approach.

I. INTRODUCTION

In the past decade, the change in the global economy hasentirely redefined indus-trial competitiveness. The coreactivity of manufacturing is no longer confined to makingthings but lies in the systematic processing of knowledge tocreate value for customers. Manufacturing-related businessservices that capitalize on Internet and informationtechnologies are expanding fast. In fact, the availability ofdigital tools and equipment in the marketplace provide a majoropportunity for enterprises to build stronger relationships withcustomers and partners which is regarded as the key tobuilding a successful business.A model of collaborative manufacturing network known as

Factory On Demand (FOD) is proposed by the authors [1] tolink up the manufacturing resources of small & medium firmsto form dynamic virtual enterprise for specific tasks. Variousservices from industrial design, product modelling andanalysis, rapid prototyping and tooling can be outsourced andsought from the FOD network. Among the many technical &organizational problems to be solved, the selection of virtualpartners in such a dynamic network is of prime importance.

Partner selection is a major process of business operation[2,3]. With a good partner selection, the supplier may providea good price, quality and delivery status so the efficiency ofenterprise business can be improved. This paper presents amethod of the partner selection based on price, quality anddelivery with the 'Factory On Demand' [1] case. By

G.T.S. HODepartment of Industrial & Systems Engineering

The Hong Kong Polytechnic UniversityHung Hom.HONG KONG

039001OOr@,polyu.edu.hk

implementing Online Analytical Processing (OLAP) on'Factory On Demand', the data needed for automatic selectionof partners with minimum intervention can be achieved..

II. WHAT IS OLAP?

Data mining is a technology that provides sophisticatedanalysis for applications such as data warehouse [4,5,6,7].Data mining tools require different data formats in relationaland multi-dimensional database systems. The shared dataaccess interface of data mining tools will enable easierexchange of information among different sources. A typicalexample of data mining tool is On-line Analytical Processing(OLAP) which provides a service for accessing, viewing andanalyzing on large volumes of data with high flexibility andperformance. The essential characteristic of OLAP is that itenables a numerical and statistical analysis of data which isorganized in multi-dimension. OLAP provides quickresponses and accurate result to the user, thereby improve theapplication security and user interface [8].

III. FEATURES OF MULTI- DIMENSIONAL DATAMODEL

An OLAP data model consists of descriptive data(dimensions) and quantitative value (measures) whichgenerates the OLAP data cube [9] by building up twoele-ments: fact table and dimension (an example to illustratethe data cube is covered in the following section). In the facttable, the required data and user-defined method for analysisis specified clearly. In the dimension, the different dimensionlevels are clearly defined for using on different views ofOLAP data cube. Typical dimension includes location,company and time whereas typical measure includes price,sales and profit. In multi-dimensional data model, data isorganized in a hierarchy that represents different levels of thedata. This distinct feature can be used to compute a complexquery and analyze data on reports, thereby achieving theswitch of views of data in different dimensions more easily.

IV. DIFFERENCE BETWEEN TRADITIONALAPPROACH AND OLAP

In the traditional approach, when a user needs to retrieveinformation across multi-tables, he/she must clearly define thenecessary tables for finding the specific information, e.g.Microsoft Access. When a user needs to know how much salesis taken for a certain year & city and uses Internet as

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0-7803-9701-0/06/$20.00 (C2006 IEEE

promotion media, the tables and their relationships must beclearly determined

Then, the Structured Query Language (SQL) is constructedto retrieve information from the tables as below.

" Select sum(a.store_sales) from sales_fact_1997 a,promotion b, region c, store d

where a.store id = d.store_id and c.region_id = d.region_idand b.promotion id = a.promotion_id and c.sales_countryUSA and b.media_type = Internet"

By using the OLAP approach, the table used for the queryand the data used to perform the calculation are defined in thedimension and measures separately. Then, the user builds up a

complex calculation on calculated member to meet thespecific requirements as shown in Figure 2.

r 1gU1i.,

Because of the calculation and analyzes have beenpre-computed in OLAP server previously, only a simplemulti-dimension expression (MDX) is necessary to constructfor retrieving identical result as shown in the following.

" Select [Measures].[Store Sales] on columns,[Store]. [Sales_country] on rows from sales where([Promotion].[Media Type]. [Internet],[Region]. [Sales-country]. [USA])"

In the above expression, it can be shown that MDX is moresimple and clear than SQL statement. When the user

requirement is changed, only minor part of the OLAP datacube and simple multi-dimension expression are needed to bemodified to fulfil the user requirement. In traditional approach,the SQL statement may be rewritten to fulfil a new

requirement. So, OLAP approach is more efficient and easierto implement than the traditional method.

In general, MDX is suitable for decision support system.The typical example of decision support system as follows.

IF([Measures].[Unit Sales] > 1000,[Measures].[Store_Sales] * 0.9, [Meas-ures].[Store_Sales])".

V. THE IMPLEMENTATION OF MULTI-DIMENSIONFEATURES ON PARTNER SELECTION

Before the implementation of OLAP, the calculatedmember that used in partner selection is constructed by themeasures. The method of how to find suitable partners isdescribed in Figure 3.

Figure 3: Partner scoring method

In the above diagram, the scores of delivery level, qualitylevel and price level of each job is defined by the measures.Then, the average score of a job is calculated by averagescores ofthe delivery level, price level and quality level oftheprevious jobs that had been done by service provider. Finally,the overall average of the service provider is determined byaccumulating the different weighting of the latest 4 jobs inmaximum. Table 1 shows the weight of average score of thejob

Job Weighting MethodAvailable1 Average Scores of Job x 100%2 Average Scores of Jobl x 75% + Average

Scores of Job2 x 25%3 Average Scores of Jobl x 50% + Average

Scores of Job2 x 30% +Average Scores of Job3 x 20%

4 or above Average Scores of Jobl x 40% + AverageScores of Job2 x 30% +Average Scores of Job3 x 20% + AverageScores of Job4 x 10%

Deviation Percentage =Quoted Price - Required Pricex 100%

Required PriceTable 1: Job weighting method

5.1 Score for Price

In the above formula, the "Deviation Percentage" isdetermined by the customer required price and serviceprovider quoted price. When "Deviation Percentage" gets a

negative result, it means that the service providers can providea lower price level for their job. In Table 2, the score of pricelevel is determined by "Deviation Percent-age" of the pricelevel.

2006 IEEE International Conference on Industrial Informatics

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Deviation Level ScorePercentageBelow -25% Extremely Low 7 marksBetween -25% Very Low 6 marksand -15%Between -15% Fairly Low 5 marks

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Between -5% and Average 4 marks

Between1% and Fairly High 3 marks

Between 15% and Very High 2 marks

Above 25% Extremely High 1 marks

Table 2: Price level score mapping5.2 Score for Delivery

Deviation Level ScorePercentageBelow -25% Extremely 7 marks

LowBetween -25% Very Low 6 marksand -15%Between -15% Fairly Low 5 marksand -5%Between -5% Average 4 marksand 5%Between 5% Fairly High 3 marksand 15%Between 15% Very High 2 marksand 25%Above 25% Extremely 1 marks

HighTable 3: Delivery level score mapping table

In the above formula, the "Deviation Percentage" isdetermined by the actual delivery date, the proposed deliverydate and the days needed for the job. When "DeviationPercentage" gets a negative result, it means that the serviceproviders deliver the product/service on or before theproposed date. Similarly, a positive result signifies an on timedelivery. In Table 3, the score of delivery level is determinedby "Deviation Percentage" of the delivery level.

5.3 Score for Quality

In the quality level scores calculation; the questionnaire isused to collect the quality level from the customer throughInternet. Therefore, the quality level scores will be determinedby sum up each positive answer from the questions below andget the quality level scores from Table 4.Does the service provider have a proper customer support

procedures for their product?Does the service provider deliver a product in your

expected quality?Does the service provider afford a good communication

within the processing procedure?Does the service provider give sufficient information for

you to monitor the job?

Question Number Calculation ScoreMethod

Question 1 7marks/4 1.75marksQuestion 2 7marks/4 1.75marks

Question 3 7marks/4 1 .75marksQuestion 4 7marks/4 1 .75marks

Table 4: Quality level score mapping table

VI. REAL-LIFE IMPLEMENTATION OF OLAPTFCHNCT.C)GV

Figure 8: Visual Basic application using OLAP technology

MDX is not just used retrieve analytical result bymulti-dimensional expression (MDX) query. It can beembedded in some Windows based development tools such as

Visual Basic, Visual Fox Pro, Power Builder and etc. In theFigure 8, it is a simple Visual Basic program that uses

embedded MDX to find a suitable partner based on specificrequirements.

Because OLAP technology can be used on differentdevelopment tools, it can be transferred the above simple VBprogram on the partner selection of FOD. In FOD, after thecustomer submits the requirement to FOD server, the OLAPtechnology can be used to find available service providers forthe customer based on the core competence specify. In theFigure 9, the system lists out available service providers forthe customer to decide which company is more suitable forthem.The authors gratefully acknowledge the contributions ofIan X. Austan, Ann Burgmeyer, C.J. Essel, and S. H. Gold.

,AP technology on partner selection ofFOD

2006 IEEE International Conference on Industrial Informatics

and -5%

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[4] Berson, A., Data Warehousing, Data Mining and OLAP,|_ ~McGraw-Hall, 1997.

Sbi'Alnr ProvMde: Gu3ngzhouj Erat.rpre[5] Michael, L. G. and Bel, G. R., Data mining - a powerful

information creating tool, OCLC Systems & Services,m AM 3 | 1 illVol.15:2, 1999, pp8 -90.

[6] Robert S. C., Joseph A. V. and David B., Microsoft Data4.0 | >,''Warehousing, John Wiley & Sons, 1999.

ffis gl'' "*|Sl'f|t(l[7]Timothy, P., Jim, P. and Russell D., Microsoft OLAP211!191 |qB ii|,l Unleashed SAMS Publishing 1999.

............

[8] Margarent, A. H. and Rod, H., Facilitating corporate2# I.. knowledge: building the data warehouse, Information &

,_____________X_____________________ Computer Security, Vol.5:5, 1997, ppl70-174.Figure 10: History Jobs Information

[9] Erik, T., George, S. and Dick, C., Microsoft OLAPAnd then, the detailed history job information may be Solutions, John Wiley & Sons, 1999.

displayed by clicking the particular company name as shownin figure 10. Otherwise, it can be submitted E-mail to theseservice providers automatically by clicking the "E-mailchecked items" button.

VII. CONCLUSION AND FUTURE WORKS

Sum up the above opinions, OLAP approach may be moresuitable to be used on partner selection of FOD thantraditional approach such as MS Access because OLAPapproach can provide multi-dimension views of data and canbe used to produce a intermediate result to perform calculation.When the OLAP approach is ap-plied on FOD, the table withanalyzed data will help the customer on decision makingprocess. FOD will use the ActiveX control called "AgentOCX" to assist OLAP technology to perform predication andforecast the future behaviour of service providers andmonitoring the work in progress. Finally, FOD will use XMLto assist the original HTML and ASP on the Web developmentand uses its features on electronic data interchange.

VIII. REFERENCES

[1] Lee, W.B. and Lau, H.C.W., Factory on Demand: theshaping of an agile production network, InternationalJournal of Agile Management Systems, Vol.1:2, 1999,pp83-87.

[2] William, J. B., Ron, M. and Robert, R., Choosingsuccessful technology development partners: abest-practice model, Vol. 15:1/2, 1998, ppl24-138.

[3] Jaideep, M., Mohamed, Y., Yunus, K. and Elizabeth, F.,Supplier selection in developing countries: a modeldevelopment, Integrated Manufacturing Systems,Vol.10:3, 1999, ppl54-162.

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