SUPPLIER SELECTION USING FUZZY INTERFACE...

SUPPLIER SELECTION USING FUZZYINTERFACE SYSTEM

1C. ElanchezhianResearch Scholar, Department of Production Technology, M.I.T. Campus,

Anna University, Chrompet, Chennai-600044, Tamilnadu, India,E-mail: [email protected].

2B. Vijaya Ramnath,Research Scholar, Department of Production Technology, M.I.T. Campus,

Anna University, Chrompet, Chennai-600044, Tamilnadu, India.

3R. KesavanAsst. Professor, Department of Production Technology, M.I.T. Campus,

Anna University, Chrompet, Chennai-600044, Tamilnadu, India.

Abstract: Traditionally, marketing, distribution, planning, manufac-turing, and the purchasing organizations along the supply chainoperated independently. These organizations have their own objectivesand these are often conflicting. Marketing’s objective of high customerservice and maximum sales dollars conflict with manufacturing anddistribution goals. Many manufacturing operations are designed tomaximize throughput and lower costs with little consideration for theimpact on inventory levels and distribution capabilities. Purchasingcontracts are often negotiated with very little information beyondhistorical buying patterns. The result of these factors is that there isnot a single, integrated plan for the organization there were as manyplans as businesses. Clearly, there is a need for a mechanism throughwhich these different functions can be integrated together. Supply chainmanagement is a strategy through which such integration can beachieved.

A supply chain consists of three types of entities: customers, a producer,and the producer’s suppliers. The extended supply chain includescustomers’ customers and suppliers’ suppliers. Supply chainmanagement oversees and optimizes the processes of acquiring inputsfrom suppliers (purchasing), converting those inputs into a finishedproduct and delivering those products – or outputs - to customer’sfulfillment.

International Journal of Marketing and FinanceVol. 1 No. 1 (January-June 2011) pp. 1-23

mailto:[email protected]

C. Elanchezhian B. Vijaya Ramnath & R. Kesavan

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In general, this paper intends to provide empirical evidence of thecriteria and the procedures for the supplier selection process used indifferent corporate environment. It plans also evaluate rigorousregulations as per ISO 9001 standards. Finally identify the suitabilityof the Fuzzy Inference system to assist in decision making to resolvethe supplier selection problem.

Keywords: Supply chain, supplier selection process, Fuzzy InferenceSystem.

1. INTRODUCTION

There has been an evolution in the role and structure of thepurchasing function through the nineties. The purchasing functionhas gained great importance in the supply chain management dueto globalization. Purchasing involves buying the raw materials,supplies and components for the organization. The activities alsoassociated with selecting a supplier. Supplier rating is the result ofa formal supplier evaluation system. Suppliers are given a titleaccording to their attainment of some level of performance, such asdelivery, lead time, quality, price, or some combination of variables.The motivation for the establishment of such a rating system is partof the effort of manufacturers and service firms to ensure that thedesired characteristics of a purchased product or service is built inand not determined later by some after-the-fact indicator. Thesupplier rating may take the form of a hierarchical ranking frompoor to excellent and whatever rankings the firm chooses to insertin between the two. For some firms, the supplier rating may comein the form of some sort of award system or as some variation ofcertification. Much of this attention to vender rating is a direct resultof the widespread implementation of the just-in-time concept in theUnited States and its focus on the critical role of the buyer-supplierrelationship.

Most firms want vendors that will produce all of the productsand services defect-free and deliver them just in time (or as close tothis ideal as reasonably possible). Some type of vehicle is needed todetermine which supplying firms are capable of coming satisfac-torily close to this and thus to be retained as current suppliers. Onesuch vehicle is the vendor rating.

Supplier Selection using Fuzzy Interface System

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Some authors have identified several criteria for supplierselection such as net price, quality, delivery, capacity, previousexperience, communication system, service, geographical location,financial capability, cost reduction programme, capacity to train thebuyer’s engineers, ability to provide maintenance contract, creditavailability.

In general, this paper intends to provide empirical evidence ofthe criteria and the procedures for the supplier selection processused in different corporate environment. It plans also evaluaterigorous regulations as per ISO 9001 standards. Finally identify thesuitability of the Fuzzy Inference system to assist in decision makingto resolve the supplier selection problem.

2. LITERATURE SURVEY

This chapter provides the literature that has been studied tounderstand the basic concepts of supply chain management. Oneof the most important process performed in organization today isthe evaluation, selection and continuous improvement of suppliers.It is understood from different literature that there is no standardprocedure to evaluate and select a right vendor. There are severalcriteria to evaluate and select the vendor. This review first includesthe general frame work used in supplier selection process.Next, some of the methods currently available are attacheddiscussed.

Supply chain management (SCM) is the management of anetwork of interconnected businesses involved in the ultimateprovision of product and service packages required by endcustomers (Harland, 1996).

Supply Chain Management spans all movement and storage ofraw materials, work-in-process inventory, and finished goods frompoint-of-origin to point-of-consumption (supply chain).

Barla (2003) did a case study of vendor selection and evaluationfor a manufacturing company under lean philosophy. In order toreduce the vendor base, the vendor selection and evaluation studyis conducted using the multi-attribute selection model.


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According to Chiphon (1996) supply chain management is theprocess of strategically managing the movement and storage ofmaterial, parts and finished goods inventory from suppliers throughthe firm and to the customer.

Choy and Lee (2002) proposed a case-based supplier manage-ment tool (CBSMT) using the case-based reasoning (CBR) techniquein the areas of intelligent supplier selection and management thatwill enhance performance, compared with the traditional approach.

Dickson (1966) in his seminal paper identified 23 supplierselection criteria, which deeply influenced later researches in thisarea reviewed, annotated, and classified 74 related articles whichhad appeared since 1966.

3. OBJECTIVE OF THIS WORK

The major objective of this paper is to evaluate suppliers in acorporate environment using Fuzzy Inference system and to developa software model using MatLab software which will accept the inputvalues as input parameters such as primary criteria like source ofmaterial, manufacturing facility, turnover, income tax clearance andfinal criteria like price, performance, quality, delivery schedule andreturns the output values through output parameter, overall rankingof different supplier and the maximum overall ranking value canbe picked, which will enable us to identify the most effectivesupplier.

4. FUZZY INFERENCE SYSTEM

Fuzzy inference is the process of formulating the mapping from agiven input to an output using fuzzy logic. The mapping thenprovides a basis from which decisions can be made, or patternsdiscerned. The process of fuzzy inference involves all of the piecesthat are described in the previous sections: Membership Functions,Logical Operations, and If-Then Rules. IT can implement two typesof fuzzy inference systems in the toolbox:

FIS have been successfully applied in fields such as automaticcontrol, data classification, decision analysis, expert systems, and


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computer vision. Because of its multidisciplinary nature, FIS areassociated with a number of names, such as fuzzy-rule-basedsystems, fuzzy expert systems, fuzzy modeling, fuzzy associativememory, fuzzy logic controllers, and simply fuzzy systems.

4.1. The Fuzzy Inference Diagram

The fuzzy inference system consists of main five steps. They areFuzzifier, Rule base, Fuzzy inference engine, Defizzifier and outputquantity. The fuzzy inference system in shown by a simple diagramin Figure 1.

Figure 1: Block Diagram of FIS

In this figure, the flow proceeds up from the inputs in the lowerleft, then across each row, or rule, and then down the rule outputsto finish in the lower right. This compact flow shows everything atonce, from linguistic variable fuzzification all the way throughdefuzzification of the aggregate output.

The Figure 2 shows the actual full-size fuzzy inference diagram.There is a lot to see in a fuzzy inference diagram For instance, fromthis diagram with these particular inputs, it is understand that theimplication method is truncation with the min function. The maxfunction is being used for the fuzzy OR operation. Rule 3 (the bottom-


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most row in the diagram shown previously) is having the strongestinfluence on the output. and so on. The Rule Viewer described inThe Rule Viewer is a MATLAB implementation of the fuzzyinference diagram is shown in Figure 2.

Figure 2: Block Diagram of Rule Based FIS

In Figure 2 it is stated that fuzzy inference system consisting offive steps. The input value (crips value) to be fired in the systemand that will be converted in the form of output by the followingmentioned steps.

Step 1. Fuzzify Inputs

The first step is to take the inputs and determine the degree to whichthey belong to each of the appropriate fuzzy sets via membershipfunctions. In Fuzzy Logic Toolbox software, the input is alwaysa crisp numerical value limited to the universe of discourse ofthe input variable (in this case the interval between 0 and 10) andthe output is a fuzzy degree of membership in the qualifyinglinguistic set (always the interval between 0 and 1). Fuzzificationof the input amounts to either a table lookup or a functionevaluation.

Dinner for Twoa 2 input, 1 output, 3 rule system

Rule 1 If service is poor orfood as rancid, then 6pis cheap.

OutputTip (5-25%)

Rule 2 If service is good, then6p is average.

Rule 3 If service is excellent orfood is delicious. then6p is generous.

Input 1Service(0-10)

Input 2Food(0-10)

The inputs are crisp(non-fuzzy) numberslimited to a specificrange.

All rules are evaluatedin parallel using fuzzyreasoning.

The results of the rulesare combined and distilled(defuzzified).

The result is a crisp (non-fuzzy) number.


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Step 2. Apply Fuzzy Operator

After the inputs are fuzzified, you know the degree to which eachpart of the antecedent is satisfied for each rule. If the antecedent ofa given rule has more than one part, the fuzzy operator is applied toobtain one number that represents the result of the antecedent forthat rule. This number is then applied to the output function.The input to the fuzzy operator is two or more membershipvalues from fuzzified input variables. The output is a single truthvalue.

Step 3. Apply Implication Method

Before applying the implication method, you must determine therule’s weight. Every rule has a weight (a number between 0 and 1),which is applied to the number given by the antecedent. Generally,this weight is 1 (as it is for this example) and thus has no effect at allon the implication process. From time to time you may want toweight one rule relative to the others by changing its weight valueto something other than 1.

Step 4. Aggregate All Outputs

Because decisions are based on the testing of all of the rules in a FIS,the rules must be combined in some manner in order to make adecision. Aggregation is the process by which the fuzzy sets thatrepresent the outputs of each rule are combined into a single fuzzyset. Aggregation only occurs once for each output variable, just priorto the fifth and final step, defuzzification. The input of the aggregationprocess is the list of truncated output functions returned by theimplication process for each rule. The output of the aggregationprocess is one fuzzy set for each output variable.

Step 5. Defuzzify

The input for the defuzzification process is a fuzzy set (the aggregateoutput fuzzy set) and the output is a single number. As much asfuzziness helps the rule evaluation during the intermediate steps,the final desired output for each variable is generally a singlenumber. However, the aggregate of a fuzzy set encompasses a range


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of output values, and so must be defuzzified in order to resolvea single output value from the set.

4.2. Application of Fuzzy Inference System

The fuzzy inference system is a flexible decision making tool whichis applicable in the following system.

Control Systems

The fuzzy inference system is applicable for concept of map, trafficcontrol system, Self adjusting washing machines (size of washadjusts amount of detergent, water temperature, wash cycle, TVadjusts sharpness, brightness, color, contrast Elevators forcontrolling elevators for peak and slack times etc.

Decision Systems

For the case of decision making it is applicable in Bank decisionsupport system for securities trading, New product pricing, Creditgranting, How to allocate resources to an enterprise’s paper pool,What constitutes safety and suitability in an investment portfolioand Share market.

4.3. Fuzzy Set Theory

The premise of fuzzy modeling is that an uncertainty approach ismore consistent with human information processing, becausehumans have an ability to analyze imprecise concepts. This impreciseknowledge is essential to human-cognitive processes and it iseffectively modeled through the use of linguistic values and degreesof membership in fuzzy set theory.

To understand the mathematical definition of fuzzy sets,consider a finite set of objects X. The finite set is described as: X =x1, x2, x3,..., xn.

where xi are the elements in the set X. Each element xi has amembership value (u), which represents its grade of membershipin a fuzzy set. The set of membership values associated with thefuzzy set occur along the continuous [0, 1]; the interval over a fuzzy


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set applies. A fuzzy set A can be represented as a linear combinationof the following form:

A = u1(x1), u2(x2), u3(x3),..., un(xn)

A fuzzy set can be expressed as a vector, a table, or a standardfunction whose parameters can be adjusted to fit a given system.The nature of fuzzy set theory makes it useful for handling a varietyof imprecise or inexact The Fuzzy Rule Base consists of fuzzy“if–then” (also called “Antecedent-Consequence”) rules which aregenerated based on the concept of the dominant rule of a datasample. Exemplifying, assume we have a rule. Conditions. “if x1 isA and x2 is B, then y is C”.

where x1 and x2 are variables, y is a solution variable, and A, B,and C are fuzzy terms. For a data sample, if its belief value forvariable x1 in fuzzy term A is larger than the belief value for x1 inany other fuzzy terms, and its belief for value variable x2 in fuzzyterm B is larger than its belief value for x2 in any other fuzzy terms,then the fuzzy rule is called the dominant rule of the data sample.

The Fuzzy Inference Engine takes all possible combinations ofthe previously determined fuzzy sets, compares them with the fuzzyrule base, and assigns to each combination the corresponding fuzzyregion of influence.

The Defuzzifier uses all information about the input and outputfuzzy sets and determines, using the information in terms of fuzzysets, a numerical output value. Several defuzzification strategiesexist, but the methodology use in this paper is mom (Mean ofMaximum – the average of the maximum.

4.4. Membership Function

A membership function (MF) is a curve that defines how each pointin the input space is mapped to a membership value (or degree ofmembership) between 0 and 1. The input space is sometimes referredto as the universe of discourse, a fancy name for a simple concept.

The only condition a MF must really satisfy is that it must varybetween 0 and 1. The function itself can be an arbitrary curve whose


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shape we can define as a function that suits us from the point ofview of simplicity, convenience, speed, and efficiency. A classicalset might be expressed as A = {x| x > 6}. A fuzzy set is an extensionof a classical set. If X is the universe of discourse and its elementsare denoted by x, then a fuzzy set A in X is defined as a set of orderedpairs. A = {x, µA(x)| x X}. The general form of a membership functionis in Figure 3.

Figure 3: General form of Membership Function

The simplest membership functions are formed using straightlines. Of these, the simplest is the triangular membership function,and it has the function name trim. It is nothing more than a collectionof three points forming a triangle.

The trapezoidal membership function has a flat top and reallyis just a truncated triangle curve. These straight line membershipfunctions have the advantage of simplicity.

The two type used membership function are shown in Fig 4 andFig 5.

Figure 4: Triangular MF


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Figure 5: Trapezoidal MF

5. MATLAB SOFTWARE

MATLAB is a numerical computing environment and programminglanguage. Created by The MathWorks, MATLAB allows easy matrixmanipulation, plotting of functions and data, implementation ofalgorithms, creation of user interfaces, and interfacing with prog-rams in other languages. Although it is numeric only, an optionaltoolbox uses the MuPAD symbolic engine, allowing access tocomputer algebra capabilities.

6. METHODOLOGY

To maximize performance of supply chains, companies must ensurethat they have an actualized picture of suppliers and demand issues.They must ensure the efficient, timely, and cost-effective procurementof goods, starting at the product integration stage. We also noted inthe “Factors and models for supplier selection” section that therewere too many variables to cope with, most of which not suited forquantifcation. Therefore, fuzzy logic concepts could better representsthe cognitive processes of the specialists.

6.1. Supplier Selection Process

The supplier selection is a complex process which is required tofollow the following steps:


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Formalize the Process is one of the techniques in supplierselection process. The product and vendor review process must beformalized. To be thorough and fair, the requirements need to beidentified for the tooling and the vendor. There should be a formalrequest for proposal or at least a checklist by which evaluations areperformed so the various prospective solutions can be understood,documented and reviewed.

Identify the Stakeholders is necessary in supplier selectionprocess. The correct stakeholders need to be identified and involvedin the tool selection process. All too often purchases are done in avacuum without the involvement of all stakeholders and thus onlya partial set of requirements are identified.

6.2. Supplier Selection Criteria

The block diagram of supplier selection criteria are shown in Fig 6.

Figure 6: Block Diagram of Supplier Selection Criteria


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6.3. Supplier Selection Preliminary Criteria

The preliminary criteria for supplier selection process are listedbelow.

General Information

• Financial Status

• Sales Tax & Income Tax Clearance

• Organization structure

• Approval from Consultants

• Past Records

• Available product quality

• Product Development Activity

Manufacturing Facilities

• Experience, knowledge and capability of production

• Technical collaboration

• Adequacy of plant & machineries

• Raw Material Source

• Storage of material

• Material handling Facilities

Quality Control Management

• Experience, knowledge and capability of QC manager

• Availability of suitable measurement tool

• Calibration of measuring tools

• System of written down work/QC instruction

• Raw material identification and issue procedure

• Acceptance and rejection record

• System for control of non conforming product.


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6.4. Supplier Selection Method

A large number of methods using quality, quantity, price, anddelivery schedule and service factor has been developed for thepurpose of rating the supplier. The weightages vary from item toitem depending upon cost criticality, availability of the item andmarket conditions of demand of supply. In this context, the IndianInstitute of Technology at Madras has complied a supplier ratingindex using 300 parameters to assess the supplier performance.Several supplier selection methods are available as follows.

Weighted Point Plan is quantified to the above method pointsallocated for quality, delivery and price. The nos of factors and pointscan be altered to suit individual products. An addition of weightedaverage of the performance parameters is known as weighted pointplan.

Categorical Method depends primarily on the experience andjudgment of the buyer and hence this is my subjective. The buyermakes out a list of all relevant parameters and annual performance.

Cost Ratio method system involves computing the actual costincurred in procurement, cashing transportation, packaging etc.costs relating to quality may include factory visit, approval ofsamples inspection.

Key Questions Approach involves many questions, what, where,how, why, when, what for and who are asked about the item in thismethod. When these questions are answered, subsidiary questionarise. This question should include issues on management, labouretc.

Table 1Various Data About Suppliers

Supplier-A Supplier-B Supplier-C Supplier-DPrice (Rs) 1786 1850 1374 2400Quality Average Standard Below Above

Average AverageService Good Normal Good ExcellentDelivery 5 months 6 months 4 months 7 months


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From the above data the best supplier shall be selected as percustomer requirement. The proposed inference system appliestriangular and trapezoidal membership functions to define the shapeof both input and output variables. The values of the are drawn foreach value of the inputs from x1 to x4. Analyzing vector a, we havefour tangible input parameters (x1, x2, x3, x4) and an output, definedas follows:

Price (x1): This aspect of the evaluation refers to the cost of theproduct which can be supplied to manufacturer. It is evaluated as“low”, “medium”, or “high” than the mean industry level in thesame commodity.

Quality Level (x2): This is the actual rejection rate of defectiveincoming material detected by the customer’s Incoming Quality andProduction Departments. The characteristic is defined in comparisonwith the average in the industry as “below average”, “average”,“above average”, and “standard” in class” depending on customer’sperception.

Service (x3): it is important for the customer to have servicingfacility of the product. After purchasing the product, if the customerfaces any difficulties, the supplier should rectify the problem. Servicecan be divided into three categories depending on its ownperformance: “poor”, “average”, and “good” when customerexpectations.

Delivery Rate (x4): it is the supplier’s compliance with thepredetermined order quantity, mix, and delivery dates to supportcustomer’s production requirements. MF can be divided into threecategories depending on its own performance: “poor”, “average”, and“good” when customer expectations have been completely achieved.

6.5. Fuzzy Inference System Model

It shows that input variables Price (x1), Quality (x2), Delivery schedule(x3) and Service (x4) which shall be used as input in inference systemand the out put shall be a numerical value of all the input as supplierselection rating. The fuzzy inference model for the above exampleis shown in Figure 7.


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Figure 7: Fuzzy Inference System Model

Price (x1): This aspect of the evaluation refers to the cost of theproduct which can be supplied to manufacturer. It is evaluated as“low”, “medium”, or “high” than the mean industry level in thesame commodity. The category has been created as follows andmembership function is shown in Figure 8.

Low = Less than Rs 1500 Trapezoidal membership function

Medium = Between Rs 1300 & 1700 Triangular membership function

High = Rs 1500 and above Trapezoidal membership function

Figure 8: Price Membership Function

Quality Level (x2): This is the actual rejection rate of defectiveincoming material detected by the customer’s Incoming Quality andProduction Departments. The classification are in comparison withthe average in the industry as “below average”, “average”, “aboveaverage”, and “standard” in class” depending on customer’sperception and membership function is shown in Figure 9.


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Below average = Less than 40 point Trapezoidal membership function

Average = between 30 to 50 Triangular membership function

Above Average = Between 45 to 65 Triangular membership function

Standard = Above 55 point Trapezoidal membership function

Figure 9: Quality Membership Function

Delivery Rate (x4): it is the supplier’s compliance with thepredetermined order quantity, mix, and delivery dates to supportcustomer’s production requirements. Figure 6.4 shows that theevaluation of the company can be divided into three categoriesdepending on its own performance: “poor”, “average”, and “good”when customer expectations have been completely achieved as inFigure 10.

Good = Less than 5 months Trapezoidal membership function

Average = Between 4 to 7 month Triangular membership function

Poor = Above 6 month Trapezoidal membership function

Figure 10: Delivery Membership Function


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“If-Then” Rules for Above criteria”

“If-Then” Rules are created for the above criteria and thehas been written in fuzzy toolbox as shown in Figure 10.

(a) If Price low, quality below average, delivery poor andservice poor then rejected.

(b) If Price low, quality below average, delivery poor andservice average then rejected.

(c) If Price low, quality below average, delivery poor andservice good then rejected.

(d) If Price low, quality average, delivery poor and service goodthen rejected and So on….

7. RESULT AND DISCUSSION

Fuzzy Inference Systems are employed in domains where decisionmakers must repeatedly have to make decisions, especially incomplex situations. These systems must be judged on the basis ofits ability to infer, from a set of given inputs, the same output a specialistwould provide in any situation. Following this concept we havetested the reliability of the constructed FIS system, through a seriesof simulations,varying the value of one or several inputs simultaneously.

Using the FIS Editor software from Matlab (Graphical UserInterface) it constructed the commands to easily test the fuzzy systemdeveloped, observe its performance, and validate the model. As itcan be seen in output graph that each variable allows the user toindividually select from a range of values. The default value in eachcase is zero and the input changes when the scroll bar arrows areclicked. With the FIS model, it defined, the final stage was toimplement these functions and made several evaluations.

The input values for individual vendor’s are fired in theconstructed fuzzy inference system and the out of the individualvendors are shown in the form of graph. At the same time thecomposite rating of the individual supplier are also coming out atoutput. Finally the output rating of the supplier to be comparedeach other and the supplier shall be ranked from maximum ratingto minimum rating.


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The input values and composite score of Supplier-A is shownFigure 11.

Input Variables of Supplier-A: Price: Rs 1286

Quality: 55 points

Service: 50

Delivery: 5 months

Output points of Supplier-A: Point 88.6

Result: Supplier-A is Selected

Figure 11: Overall Rating of Supplier-A

The input values and composite score of Supplier-B is shownFigure 12.

Input Variables of Supplier-B: Price: Rs 1374

Quality: 65 points

Service: 60

Delivery: 6 months

Output points of Supplier-B: Point 87.7


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Result: Supplier-B is Choice-1

Figure 12: Overall Rating of Supplier-B

The input values and composite score of Supplier-C is shownFigure 13.

Input Variables of Supplier-C: Price: Rs 1375

Quality: 30 points

Service: 65

Delivery: 4 months

Output points of Supplier-C: Point 70

Result: Supplier-C is Rejected

Finally, from the above example it is concluded that the overall ratingof the suppliers-A, B & C are 88.6, 87.7 and 70 respectively. Hencesupplier-A is selected, Supplier-B is under consideration andSupplier-C is rejected.


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Figure 13: Overall Rating of Supplier-C

8. CONCLUSION

This paper has illustrated a modular Fuzzy Inference System forsupplier selection. It has been also observed that fuzzy set theorycan be applied to most of the traditional areas of production researchusing a language to model problems, which contain fuzzy relation-ships. Fuzzy set theory provides a proper language by which inde-finite and imprecise factors can be handled and it is able to integratequalitative and quantitative analysis with any type of numericalvariable or input. The structures of fuzzy systems are often simpleand more realistic than non-fuzzy models.

In this report specific application of the fuzzy inference theoryand modular approach that can help companies to make decisionsabout supplier selection. The methodology demonstrates that it canbe easily translated to model the decision-making process of facilitiesand service purchases within the company. Additionally, it can beused for supplier benchmark studies, negotiation process, supply


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chain improvement, supplier performance feedback, metricsdevelopment, supplier rankings development, allocation of orderquantities, and so on.

Purchasing managers and buyers can have a better idea of theirsupply base characteristics, and also can compare any supplier toany other supplier during quotation. A strategic commitment fromsuppliers is a vital determinant of business success which helpsdirectly and indirectly to improve companies’ performances andthis model is beneficial as it incorporates strategic and operationalmeasurements at the same time.

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