Tattva Final[1]

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a t JOURNAL

A RESEARCH JOURNAL OF BIMTECH STUDENTS

Vol. I Issue 1 October 2010


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TATTVA is a forum for BIMTECH students to bring to light the outcomes of their journey of discovery into the issues on which they have been cogitating - whether the issues are inmarketing, finance, world business, retail management, insurance or sustainability orlivelihood or microfinance. It is aspirational in intent, striving for rigour and has something of importance or an insight to share. In short, it is a platform for full fledged researchers-in-the-

making. We have christened it TATTVA, meaning essence or the most vital ingredient.

Welcome to TATTVA - a Research Journal of BIMTECH Students!


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Management educators and planners may not reach a consensus on the issue of curriculum design and the mix of conceptual knowledge, applied experience andultimately assimilation of the knowledge, but there is no disagreement on the issue of integrating original and applied business research into management education.

At BIMTECH we consider summer projects not only as an opportunity to exposestudents to the knowledge, practice, discipline, and ethics followed among corporatebusiness community but also as a platform to encourage conducting original research,albeit on a limited scope and scale.

In our journey to continuously improve and increase the research content of projectsexecuted by students we have taken many concrete steps. Some of them being:

Align Research Methodology course to bring focus on design of experiments Integrate SPSS and Excel into various courses through projects and assignments Deliver Management Sciences, Supply Chain and Business Simulation & Modelling

courses using Excel Incorporate Multivariate Data Analysis tools as part of the course Encourage students to take up live projects requiring solution to real business

problems by collecting primary data and analysing with some statistical tools Continuously enriching the course contents with Modelling and Simulationexercises

This year, for the first time, BIMTECH has brought out TATTVA - a Research Journal of BIMTECH Students , for showcasing nine research projects. It was a tough task for theadvisory board to select only nine out of so many excellent research projects. We planto bring out second volume shortly.

Such a venture like TATTVA cannot come to fruition without the active participation of motivated students, help and cooperation of faculty guides at different centres of learning at BIMTECH and the research project guides in the corporate sector who havegiven valuable guidance and quality inputs to our students.

We hope you will appreciate the efforts put in by students under the guidance of industry and faculty guides. Your feedback on these projects will work as a motivatorfor all those who are involved in bringing out this issue.

We would in future be publishing the research efforts of students such as short term,special research reports etc. in TATTVA.

Dr. A. K. DeyProfessorSupply Chain & Operations ManagementCentre for Business Management

Preface

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Advisory Board

Prof. K. K. KrishnanProf Sangeeta Shukla

Dr. A. K. DeyProf. R. J. MasilamaniProf. Ashok Malhotra

Student Committee

Sweta Behera (CIBM)Upasana Singh (CIBP)Rahul Goyal (CRM)

R. Natraj (CBM)Parul Agarwal (CBM)Priyanka Raina (CBM)

Industry Guides

Ms. Madhulika Mishra

Deputy Manager (HRD)Corporate OfficeMMTC Limited

Mr. Chandrashekhar SinghBusiness Manager MIRC Electronics Ltd.

Mr. Jaspreet JollyStore Manager (Rohini)Infiniti Retail Limited

Mr. Sushil Gopinath

Store Manager (South Ext.)Wills LifestyleITC Limited

Mr. Sukrit Vijayakar

Head Product TradingInternational Supplies and Trading DivisionEssar Oil Limited, Mumbai

Mr. T. SrinivasHead International Supplies and Trading DivisionEssar Oil Limited, Mumbai

Mr. Ravi AroraSr Manager (Sales & Marketing)New Holland Fiat (India) Ltd.

Mr Deepak Bhandari

R K Feed EquipmentsHalol, Gujarat

Mr. N. C. DhalFormer SDM, Sambalpur DivisionPrincipal, Sales Training CentreLIC of India

Dr. Subhendu ChakrabartiScientist & Head Business Processes DivisionCentral Leather Research InstituteChennai

Faculty Guides

Prof. Pankaj PriyaProf. Ravi AgarwalProf. Manosi Choudhuri

Dr. Anupam VarmaDr. A. K. DeyProf. R. J. MasilamaniProf. K. K. Krishnan

Student Contributors

Parul AggarwalSweta BeheraUpasana SinghPallavi BanerjeeVijay Gaur

Mahima GuptaRahul GoyalK. NatrajSweta Agarwal


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R&D Intangibles Valuation - Integrating Monte Carlo and Real Options & 1Fund Allocation Process Through AHP: A Focus on Public Funded ResearchOrganizations in IndiaNataraj K and Ravi Agarwal

MMTC: A Case of Knowledge Management and Learning Organization 13Pallavi Banerjee and Manosi Chaudhuri

Factors of After-Sales Service Affecting Customer Satisfaction in Indian 23Consumer Electronics and Appliances MarketThe Case of MIRC Electronics Ltd. (ONIDA), Pune Region, IndiaParul Aggarwal and R. J. Masilamani

Introduction of Concierge Service in Vadodara 37Sweta Agarwal and A. K. Dey

Evaluating Potential Market for Exporting Gasoline in Perspective with Nigeria 45Upasana Singh and Anupam Varma

Factors Influencing the Purchase of Private Labels: A Case Study on Roma 57Mahima Gupta and Pankaj Priya

Competitive Analysis of New Holland Tractors in Western U.P. and Haryana 65Vijay Gaur and R.J. Masilamani

Underwriting of Female Lives vis--vis Underwriting Practices in General 73(A Case Study of the LIC of India, Sambalpur Divisional Office)Sweta Behera and K.K. Krishnan

Fast Fashion Retail Strategy based on Consumer Perception: 81An Empirical Research in Delhi NCRRahul Goyal and Pankaj Priya

contents

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Technological innovation involves extreme risk as the R&D activity is uncertain in terms of the cost involved in producing the output, timeduration involved in creating the output and also most importantly the output itself is not certain. This intangible nature of the R&D projects makes it relatively hard to value when compared to other tangible projects. Hence capturing the actual value of the intangibles is considered to be highly challenging by the researchers. In this paper a holistic approach is made with the objective of valuing the intangibility of the R&D projects. The project starts by creating a framework for selection of the most appealing projects by evaluating the alternatives using multifactor criteria based onhierarchical considerations which help in fund allocation process, followed by the projects income projection using tabula rasa method and life cycleanalysis method. After performing the basic discounted cash flow valuation, simulation analysis is done through Monte Carlo method to incorporate

uncertainties involved. As R&D activity will be carried out in phases there exist some options with the management which adds more value to the R&Doutput. This is captured here by incorporating binomial lattice real options approach. The mentioned models are empirically implemented in a live project which involves creation of collagens.

Keywords: Patent Valuation, Real Options, Binomial lattice, Monte Carlo simulation, AHP

JEL Classification: G11, C15, C52

R&D Intangibles Valuation - IntegratingMonte Carlo and Real Options & Fund

Allocation Process through AHP

Abstract

IntroductionPatent valuation problems currently hinder efficient transfer of technologyunlike the market for used cars,no equivalent of the used car lot has emerged in thecontext of patented technology.-

(Denton & Heald, 2003)

Most of the public funded organizations pore onmeliorating the small and medium scale industries. Alsoin transferring the technology these organizations donot have any rigid valuation framework and thetechnology is priced as per the buying power of theclients. But in order to have an internal evaluationmechanism they need a framework that can be appliedto value the knowledge base being produced by theirvarious departments. Also in order to carefully allocatethe limited sanctioned budget a decision making tool isalso needed in order to select the most valuable projectsfrom the alternatives. Thus these two requirements are

the prime focus of this research paper. Also it will beclear from the next section that all the research done tillnow in this area are focused only on various methods of valuation. i.e. they straight away start with the valuationissue. But for the research organization, where thescientists are not familiar with much of the financialconcepts it becomes difficult to understand andimplement the process described. Hence another focusof this paper is to provide a complete framework startingfrom project selection till the valuation in a step by stepfashion so that it might be easily implemented by theresearch organizations.

Literature Review

Myers (1984) was among the first to publish in theacademic literature the notion that financial optionpricing methods could be applied to strategic issuesconcerning real assets rather than just financial assets.

*Centre for Business Management 2009-11, Birla Institute of Management Technology Under the guidance of Prof. Ravi Agarwal, Centre for Business Management, Birla Institute of Management Technology

Nataraj K*


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(Myers S. , 1977) identified investments in real assets asmere options. (Dixit & Pindyck, 1994) argued that mostinvestment decisions have three importantcharacteristics in terms of real options. They are i)investment is partially or completely irreversible. ii)

there is always uncertainty over the future return fromthe investment and iii) the investors have choices toinvest at flexible time.

The dynamics of the expected cost to completion of theR&D project are described by the controlled diffusionprocess by (Pindyck, 1993) and given as:

.....1

Where dZ is an increment to a Gauss Wiener process,

the first term is the control of the diffusion process andthe second term corresponds to what Pindyck callstechnical uncertainty.

(Luehrman, 1998) presented a framework that bridgesthe gap between the practicalities of real world capitalprojects and the higher mathematics associated withformal option-pricing theory. A simulation approach tovalue patents and patent-protected R&D projectsbased on the Real Options approach was given by(Schwartz, 2002). Similar to it a real options model of

R&D valuation was given by (Hsu & Schwartz, 2003).

(Lev, 2003) has reviewed the various aspects of intangible assets by focusing on the question, How canwe accomplish the main objective of promotingimprovements to the reporting of intangible assets? Aunified approach to the valuation of an R&D projectthat integrates three analytical tools: Discounted CashFlow, Decision Trees, and Real Options was proposedby (Boer P. F., 2004). The traditional valuation methodsdrawback in valuing intangibles and other suggested

method for valuing them especially Brand names,flexibility and Patents were explained with examples by(Damodaran, Dealing with Intangibles: Valuing BrandNames, Flexibility and Patents, 2006). (Lerner & Farrar,2006) Provided a review of patent licensing approacheswhich has aimed to better understand currentpractices and the theoretical underpinnings of those

practices and discussed a licensing model that canaccommodate open source software.

(Carlsson, Fuller, Heikkila, & Majlender, 2007) in theirpaper presented a fuzzy mixed integer programming

model for the R&D optimal portfolio selectionproblem. (Sereno, 2009) finds that Pharmaceutical IPRinvestments have the property that much of the valueof the investment is associated with future cash flowsthat are contingent on intermediate decisions. (Mun,2002) in his book have explained completely aboutvarious real options model and the procedure to solvethem. Also (Razgatis, 2009) in his book have analyzedall the methods of valuation and have also discussedabout the deal making process.

Framework for R&D Portfolio selection/Resource Allocation:

Government funded research institutes like CLRIallocates the funds to various departments based onthe knowledge base created by each of the departmentin the previous fiscal. Few might argue that this wouldmotivate the departments to produce moreknowledge base, but this might hinder thedevelopment of a most valuable patent or technology.Also allocating funds should not be like an appraisal

mechanism.

Hence what is required is a standard mechanism forfund allocation. In other words a framework that wouldprioritize the activities or rank the activities basedupon the objectives of the firm. Any researchorganization will be having n number of crucialdecision problems regarding the selection or acceptingthe project proposals. The obvious aim will be to selectthe most compelling project in terms of many factorswhich will be discussed below.

The work from (Carlsson, Fuller, Heikkila, & Majlender,2007), (Bekkum, Pennings, & Smit, 2009) all focuses onthe R&D portfolio selection. (Suresh Kumar, 2004)provides a judgmental model for R&D projectevaluation using multifactor criteria based onhierarchic considerations.

TATTVA - Vol. 1 Issue 1 October 2010

dK = _ Idt + IK dz


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Model Formulation and Application:

The four perspectives of the Balance Score Cardmethod are included in the AHP model. Several factorsare identified under each perspective through which

the proposals will be pair wise compared and rated.The proposed model is shown in the figure 1.Application of the AHP starts by disaggregating theproblem structure into multilevel hierarchy. Thedecision authority might determine weights orpriorities of a set of criteria in one level of the problemto the next level just above. By repetition of thisprocess level by level the matrices summarizing the

priorities of the alternatives at the lowest levelaccording to their influence on the overall goal or focusof the hierarchy. The proposals from differentdepartments are analyzed using this model and thebest proposal is selected whichever is having the

highest score. The various steps involved are Fixing theAHP Judgment scale; Pair wise comparison of thefactors; Normalization of the comparison matrix;Finding Normalized matrix for all the criteria involved;Calculation of composite hierarchical priorities;Grading Scale for project proposal; Proposal rating;Scoring the Proposal

R&D Intangibles Valuation - Integrating Monte Carlo and Real Options & Fund Allocation Process through AHP

Figure 1: AHP Model

In te l lec tua l Proper ty IncomeProjections:

Analyzing the income producing capability of theintellectual property is the primary step in thevaluation process.

The common methods that are used in practice areExtrapolation, Tabula rasa, Life cycle analyses,Sensitivity/scenario analyses, Simulation analysesMonte Carlo, Judgmental methods.

In case of projects involving R&D where the entire lifeof the project is imbibed with uncertainties and usuallywith no close comparables it is clear that the analyst

should start from scratch. Hence Tabula Rasa method ispreferred here.

Tabula Rasa method:

Tabula rasa in Latin means clean slate. Initially theprocess begins with a clean slate. The primary concernis the uncertainty involved in deciding from where to

Best Portfolioof Projects

InternalBusinessProcess

Learningand

Growth

Customer Financial

Human

Expertise

Infrastructure

Availability of

Technology

Inhouse

Availablity of

material

resources

Probability of

technical

success

Anticipated

completion time

Extent of

innovation

Technological

relevance of the

project

Relevance of

projects

objectives in the

social context

Reputation of

project Leader

Anticipated

change of

commercial

success

Utility of reginol

resources

Financial

Feasibility

of the

project

Commercial

Sponsorship

Aids or

collaboration for

the project from

outside source

Expected

economic benifit

from the output


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begin. Without any available past data the best way toproceed is to conduct extensive interviews with theinventor. The analyst should begin with detailed set of questions that help in filling the clean slate with data.Once the slate is filled with data the next step will be to

prepare an income projection. Here Project life cycleanalyses is used where the intellectual property isassociated with the product or service.

Project life cycle analyses:

The stages involved are Development phase,Introduction phase, Growth phase, Maturity phase,Decline Phase. The product development stage beginswhen the company finds and develops a newproduct/service idea. During the product development

stage, revenues are typically zero and profits aretypically negative. During the introduction phase, asthe product is new to the market the revenue will below. In the market growth stage the company will beginto make significant profit also by the end of this phasethe profit begins to decline. In the maturity phase theprofit will see neither growth nor decline and will beleveled off. It is in this phase the competition will be atits peak. Due to emergence of the new products orservices the decline phase starts for the currentproduct or service.

Input for Income based approach (DCF):

The output at this point will be the required input forbasic Discounted Cash Flow method. i.e. from theassumptions about the future prospects of theunderlying project the estimates are made for theincome producing ability throughout its useful life. Thisestimate will be used to find the present value of theproject or patent or technology.

Which model to apply?

The elegance of the closed form Black Scholes equationfor option value comes at the price of a set of assumptions that simply do not apply to technologyvaluations solutions. Also the important aspect forchoosing real options methodology is its ability to

include multiple flexibilities available with the decisionmaker. When closed form methodology is used, all theoptions cannot be applied simultaneously. i.e. optionto choose cannot be valued using closed formmethodology. Hence this project will focus on binomial

lattice trees. Empirical application of the Valuation Model:Collagens from bio waste

C L R I being operating in leather industry produces biowaste from its slaughtering house in bulk. The wastefrom slaughtering house includes, Blood, tissue, Lungsetc. Disposal of this waste was of a big problem faced.According to industry experts 90% of the waste whentreated properly at source can be recycled. Scientists at

CLRI have decided not only to handle the waste todispose them but also to innovate a way in such a waythat some valuable products can be produced from thebio waste produced. They have invented that from thesoft tissues of the animal a medical product can beproduced which is rich in fibrous protein calledcollagens.

Now in order to find the value of the patent thefollowing assumption is made, i.e. considering CLRI as apart of a company and not as a separate research

institute. Hence now C L R I is a R&D department of thecompany. As this particular invention is in the medicalside it is assumed that the company is operating inpharmaceutical company.

Now let us apply the whole work of the project into thisproblem. Assume that the proposal is made and hasbeen formally approved and fund is allocated to theproject. The next step will be the income projectionthrough tabula rasa and the life cycle analyses.

Being in the pharmaceutical sector, the product isexpected to face severe competition. First step is tofind the useful life of the patent protected product.After expiry of the patent the products useful life canbe calculated as the multiple of the cash flows receivedtill that period.



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The patent will be granted for the period of 20 years,consider that it takes 2 years to complete the inventionand 2 years to set up the production facility andsimultaneously for initial trial and drug approval theoverall useful life of the patent is reduced to 16 years.

But considering the fact that the pharmaceuticalindustry is highly competitive a better product isintroduced in the market by the competitors in another8-10 years, the useful life of the patent protectedproduct is further reduced to 10 years, and theremaining years will view a decline in the revenue.

Application of Income Method/DCFMethod:

Now for this 10 year period the income projectionneeds to be made along with the growth rateconsidering the life cycle analysis.

The income generation for the next 16 years will beginafter the initiation of commercialization phase. Thefirst 4 years is meant for the development phase.

The calculation of the value of the patent is given in theFigure 2. As it is very clear from the calculation that thenormal DCF model is a static one and does not allowany variations. None of the business will be performingas per the basic initial assumption. Due to highuncertainty few factors will be varying which will affectthe predicted NPV value and hence it is necessary toincorporate the variable nature of few factors to arriveat a close to actual value of the NPV.

Hence in order to find the value close to the actual theapproach is now towards Monte Carlo method.

A p p l i c a t i o n o f M o n t e C a r l o

simulation:For performing the Monte Carlo simulation, Crystal Ballsoftware from Oracle is used. This software haspredefined distribution which can be applied to thevarious parameters involved in the valuation. To beselective in nature let us see only those distributionused in the project.

Uniform Distribution:

A uniform distribution, or uniform prescribedrandomness, is one that assigns equal probability toany value between prescribed upper and lower

bounds. Here the COGS are assumed to follow theuniform distribution.

Triangular Distribution:

A Triangular Distribution is similar to the UniformDistribution in that it also assumes zero probability of values below the specified lower bound and above theupper bound. It differs in that the TriangularDistribution uses a most likely value and constructs atriangle of probability that varies linearly from the

maximum probability at the most likely value to zero atthe upper and lower bounds. Here the WACC/discountrate is assumed to follow the triangular distribution.

Beta Distribution:

Beta key attributes are (effectively) a combination of the Triangle and Normal Distributions, with a smoothdecrease in probability away from the peak value,similar to the Normal Distribution, but with exactupper and lower bounds like the Triangular

Distribution. Here the S.G.A costs are assumed tofollow the beta distribution. As the revenues arealready adjusted according to the life cycle, nodistribution is assumed for it. Else normal distributioncan be assumed for the same.

Once the required distributions are assigned to thecorresponding parameters then the forecast


Figure 3: Distribution of NPV through Simulation


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l a t i o n



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parameter (NPV) is defined for initiating the simulationafter prescribing the required number of simulations.

After the simulation is complete the distribution isobtained for the forecast parameter as shown in the

figure 3. It can be inferred that the value of the patent isimproved when compared to normal DCF valuation. Inspite of this improvement in the value still this methodvaluation faces a strong drawback, i.e. the decisionmaking capability of the management with respect tothis patent which will add value is not captured. For thisreason now the focus is shifted to Real Optionsapproach.

Application of Real Options Method:

Real Options tend to value the flexibility in terms of making business decisions. Due to confidentialityissues only the basic data were shared and in order tocover the full application of the real options methodseveral assumptions will be made which will help invaluing any kind of projects in future by C L R I. Themanagement can expand its operation by 30% whichwill incur an addition cost of 10 Lakhs at any pointduring the life of the project. Also they can contract itsoperation by 10% which will result in saving of 12 Lakhs.It has been assumed that the project can be abandoned

at any point which will fetch 30 Lakhs.

Initially each of the options will be valued separatelyfollowed by the valuation of the options consideringsimultaneously.

Option to Expand:

The inputs required for solving the real options are:Calculated from Monte Carlo valuationCalculated by logarithmic cash flow returns appro-

ach by forecasting the free cash flow value whileperforming the Monte Carlo simulation method.

Number of years/number of steps in lattice

Remaining parameters are either found directly or byusing the formula mentioned in section 6.

After finding all the parameters involved both thelattice, lattice of the underlying and the optionvaluation lattice is derived.

(from Monte Carlo simulation results)

Lattice Evolution of the Underlying:The calculation of the lattice of the underlying is shownin the below figure 4. The highlighted upper node is

calculated by which yields the value as Rs 49.83L.The highlighted lower node is calculated by whichyields the value as Rs 28.19L. In similar way the latticeobtained for all 15 time steps.

Option Valuation Lattice:Additional input required for this step, Expansionfactor: 1.3, with additional cost of Rs 10 Laks.Calculation of the option valuation lattice starts withthe terminal node of the previous lattice. i.e. the valueof that particular node (Rs2685.99 L) is compared with

the value after including the expansion factor. Then themaximum value is chosen as the value of the node inthe option lattice.Value of that node = Max (Rs2685.99, {(2685.99 * 1.3)

10} = Rs 3481.79 Lakhs

For highlighted intermediate node too comparison ismade between the value of keeping the option as openand the value of expansion option. The value of keeping the option open is obtained by the process of backward integration. i.e. by applying the formula:

.....5The calculation is shown in the below figure 5.

On comparing both the lattice the expansion option isfound to have the value of Rs 7.434 Lakhs (differencebetween the starting nodes of both the lattices).


t

s = 28.48% ; T = 15 years ; dt = 1

2 => u = 1.329

3 => d = 0.752

4 => p = 0.518

S0

= 37.48 Lacks

s dt u = es dt d = e

-(rf - b) (dt)s

p =u-d

d-

(S u)*0(S d)*0

[( p)up + (1 - p)down ]e [- rf )( )]dt


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Option to Contract:

As we are dealing with the same project the lattice of the underlying will be the same. Hence only the optionvaluation lattice will be calculated keeping the lattice of

the underlying from previous calculation. Additionalinput required for this step, Contraction factor: 0.9,with additional savings of Rs 12 Laks. Calculation of theoption valuation lattice starts with the terminal node of the previous lattice. i.e. the value of that particularnode (Rs2685.99 L) is compared with the value afterincluding the expansion factor. Then the maximumvalue is chosen as the value of the node in the optionlattice. Value of that node = Max (Rs2685.99, {(2685.99* 0.9) + 12}

= Rs 2685.99 Lakhs

On comparing both the lattice the contraction option isfound to have the value of Rs 8.252 Lakhs (differencebetween the starting nodes of both the lattices).

Option to Exit/Abandon:Similar to the calculation of the above 2 options, thecalculation is performed and on comparing both thelattice the expansion option is found to have the valueof Rs 6.258 Lakhs (difference between the startingnodes of both the lattices).

Option to Choose:

In the real world business situations, all the options willbe available simultaneously and the option thatprovides maximum value needs to be chosen. Realoptions provide the method for finding this value toothrough Option to choose method.

Here too the option valuation lattice alone is done asthe lattice of the underlying is the same. Themethodology for calculating the option lattice is alsosimilar i.e. the value of the nodes will be decided aftercomparing all the options value simultaneously andchoose the option that gives the higher value. Theoption to choose has the value of Rs 13.283 Lakhs.

from the results of various options value:Option to Expand Rs 7.434 Lakhs

Option to Contract Rs 8.252 LakhsOption to Exit Rs 6.258 LakhsOption to Choose Rs 13.283 Lakhs

This implies that when the decision making is imbibed

with multiple options the value also increase. Section 6:

Conclusion and Scope for furtherresearch

In this project a framework for stream lining the fundallocation process carried out in CLRI is proposed usingAHP method and advanced valuation methods such asReal Options and Monte Carlo simulation are used tovalue the intangibles. It is also evidenced that thesemethods provide far better value than the traditionalvaluation methods. Also it is observed that thesemethods are best suitable in valuation of intangibleswhen the economic benefits of the underlying areknown. It has been remarked from the empiricalapplication of the advanced models to value theCollagens project that even the advanced methodsbetrays to capture the complete value of the patent ortechnology. i.e. when the option involves otherbenefits apart from economic benefits like socialbenefits, there are no strong framework or models tocaptivate the value of those benefits.

Hence in public funded organizations like CLRI wherethe prime focus is on societal benefit and not economicbenefit, there is a lack of valuation framework thatproves the efficiency of their knowledge base.

A basic approach has been put forward that would helpin ascertaining the value of the intangibles whichdemands any kind of benefits valuation. It is also aknown fact that this field valuation of intangiblesthrough real options is much younger when comparedto other financial concepts, the scope of furtherresearch is very wide open that in future a singleframework might be applied to fetch the actual value of the intangibles involving any type of benefits. Theproposed basic approach when further enhanced willdo the needful and the valuation of intangibles might



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L a t t i c e E v o

l u t i o n o

f t h e U n

d e r

l y i n g

P r e s e n t V a l u e o

f t h e p r o j e c t

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F i g u r e 4 : L a t t i c e E v o

l u t i o n o

f t h e U n

d e r

l y i n g



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F i g u r e 5 : O p t i o n v a

l u a t i o n L a t t i c e

O p t i o n v a

l u a t i o n L a t t i c e

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I N T E R M E D I A T E N O D E

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O p e n

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( 1 - p

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M a x

( O p e n

, E x p a n

d )



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*Centre for Business Management 2009-11, Birla Institute of Management Technology Under the guidance of Prof. Manosi Chaudhuri, Centre for Business Management, Birla Institute of Management Technology

PURPOSE: The vast subject of Knowledge Management has recently started gaining significance in India. However, despite the investment of time, resources and money, only few companies can claim to have successfully implemented an organization-wide Knowledge Management System.These past experiences reiterate technology infrastructure to be only an enabler of knowledge management and the employees to be the driving force.Thus, the purpose of the research is to explore the perception of employees towards knowledge creation, transfer and sharing practices in an IndianPublic Sector Enterprise, MMTC. This case seeks to recognize and recommend characteristics that enhance knowledge sharing attitudes in employeesand work culture in organizations.

The project also explores the need of developing knowledge management strategy and linking it with corporate goals and human resourcesstrategy and processes. It also identifies the existing knowledge initiatives and resources in the organization as a first step towards knowledgemanagement in the firm.

In a knowledge economy where knowledge is a source of competitive advantage for organizations, continuous learning can enableorganizations to adapt to changes faster than their competitors. Thus, companies aim to emerge as Learning Organizations which is defined by Professor Garvin as an organization skilled at creating and transferring knowledge, and at modifying its behaviour to reflect the new knowledge. Thisdefinition emphasises the inter-relatedness of the two concepts of Knowledge Management and Learning Organization.

The research describes the traits that define a learning organization and provides actionable steps which can enable this particular organization to emerge as one.

APPROACH:The research uses primary data collected using two questionnaires. The first questionnaire attempts to capture the perception of employees towards knowledge management initiatives and their linkage to Human Resource processes. The second questionnaire is a learningOrganization assessment survey developed by Garvin, Edmondson and Gino (2008) and published in Harvard Business Review (2008). The case

contains an analysis of the primary data using factor and discriminant analysis and perception mapping.

FINDINGS: This case provides a roadmap for knowledge management implementation based on two broad categories of knowledgeManagement strategy, namely Codification (which is Explicit knowledge oriented) and Personalisation (which is Tacit Knowledge Oriented). The paper emphasises on enhancing social interaction and collaboration within the organization to successfully share and transfer knowledge.

To emerge as a Learning Organization, the organization would need to improve on processes of information collection and transfer as well asinitiate openness to new ideas and experimentation as a part of their organization culture.

Key words: Knowledge management, Learning organization, Explicit knowledge, Collective knowledge, Factor Analysis

MMTC : A Case of Knowledge Management and

Learning Organization

Abstract

IntroductionThe post industrial society today is on the brink of change and is increasingly expressed as a knowledgeeconomy. Scholars all over the world are definingknowledge as a powerful economic resource and asource of sustained and unique competitive advantage.

The importance of knowledge is not unknown to India

and its government. The erstwhile President of India, Dr.A. P. J. Abdul Kalams work India 2020: A Vision for theNew Millennium stressed on the importance of knowledge and ways to facilitate Indias transition toemerge as a knowledge economy. With this view, Prime

Pallavi Banerjee*


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Minister Dr. Manmohan Singh set up a knowledgecommission in 2005 with the vision to develop Indiainto a knowledge-leveraging 21st century society.

There is vast amount of literature defining Knowledge

differently. For the purpose of the case, it is defined asa whole set of intuition, reasoning, insights,experiences related to products, technology,processes, customers, markets, competition, etc. so asto enable effective action.

In an organization, there is a continuous flow of knowledge through processes of creating,disseminating, applying, renewing and updating theknowledge. Bain and Company emphasises on thegrowing need to tap and manage the knowledge

processes within an organization. Knowledgemanagement is based on the premise of successfulbusinesses being a collection of distinctive knowledgebases (Bain and Company, 2009). This case seeks tounderstand the broad concepts developed withinknowledge management and the steps which canenable an organization to emerge as a LearningOrganization. There is an attempt to develop aconceptual framework for knowledge managementarchitecture as a part of the case.

Literature Review

In one of his path breaking works, Peter Druckeracknowledges knowledge to be emerging as a criticalresource. Thus, managements most significantresponsibility is to ensure productivity of knowledgewhich is going to emerge as the competitive advantageof a nation, industry, a firm.

Prahalad (1998) also reiterates this in his work as hequotes a recent survey where Japanese managers haverated knowledge creation and innovation and notquality as first source of competitive advantage.

Knowledge gains its value when applied for social orcommercial benefit. Thus, Knowledge Managementencompasses creation, sharing and utilisation of knowledge whether at individual, group (or team) andorganizational level. Knowledge has primarily been

distinguished in two forms, namely: Tacit /Explicit Knowledge Individual /Collective knowledge

Hislop (2002) defines tacit knowledge is highly

personal and difficult to formalise and even share withothers. Subjective insights, individuals action andexperience as well as the ideals and values are someexamples of tacit form of knowledge. On the otherhand, explicit knowledge is codified, impersonal andeasy to communicate in an organization. Rules,documents and manuals are certain manifestations of explicit knowledge.

These classifications form the framework of theKnowledge spiral suggested by Nonaka and Takeuchi,

popularly denoted as the SECI model. SECI modelsuggests four modes of knowledge conversion, namelySocialisation (sharing of tacit knowledge fromindividual to organisation level), Externalisation(conversion of tacit to explicit knowledge),Combination (explicit knowledge sharing betweenorganisations such as documents) and Internalisation(conversion of explicit into individual tacit knowledge).This process is closely linked to the concept of organisational learning, in which individual learning isconverted into the organisational learning and

memory.

Mukherji (2005) involves one of the most popularframeworks to differentiate between two broad typesof knowledge strategies namely, Codification andPersonalisation. Codification strategy is primarilycentred around creation of repositories for storage andeasy use and retrieval of codified knowledge.Personalisation strategy on the other hand, isconcerned with improving face-to-face knowledgesharing with its focus on tacit knowledge possessed by

different workers.

There is very little literature available in the context of Knowledge Management and Organizational Learningin India. World Bank (2004) emphasises that it is theopportune time for India to emerge as a knowledgeeconomy. To enable the transition into a knowledgeeconomy, India needs to focus on more skilled and



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educated workers and identify and implement efficientinnovation systems.

Pillania (2006) finds that knowledge managementcontinues to be considered a technology oriented

strategy, instead of a human resources strategy inIndia. Knowledge Management is still considered to bea task of few designated ones and no requirement of knowledge sharing is felt. Communities of Practices arefound rarely in Indian companies except in a fewsoftware companies.

The literature available on the subject of LearningOrganizations is extensive as well as largely subdividedinto two camps- one which advocates the ideal of Learning Organization and the other camp which

argues sceptically about its merits.

Interest in the area of Organizational Learningpredated that of Knowledge Management; howeverthere is a large amount of overlapping between thetwo vast subjects. It is difficult to separate the twosubjects as knowledge begins at the point wherelearning ends and learning begins with knowledgeprocesses.

Mohan Thite focuses on the need of learning

organization in a knowledge economy and considers itto be a daunting yet pertinent ideal for organizations.To become learning organizations, firms would requirefundamental changes in the mindset and remain open-minded to constant change.

Although there is vast literature available onOrganizational Learning and becoming a learningorganization, there are few assessment tools availableto quantitatively measure and compare organizationson their Learning practices.

Due to its comprehensive format and in-depth analysisof traits of learning Organizations, an assessmentsurvey developed by Professors Garvin, Edmondsonand Gino and published in Harvard Business Review(2008) was taken up to conduct a pilot study of learningpractices in MMTC.

The assessment survey is based on three primarybuilding blocks which Garvin et al. consider to bethree broad factors identified as essential fororganizational learning. These building blocks includeseveral characteristics under each block.

1. A Supportive Learning EnvironmentA work environment which is supportive of learning involves four characteristics which are: Psychological Safety Appreciation of differences Openness to new ideas Time for Reflection

2. Concrete Learning Processes and PracticesA learning organization requires a series of stepsand implementation of learning practices. An

organization should develop learning processeswhich involve: Experimentation Information Collection and Transfer Analysis Education and Training

3. Leadership that Reinforces LearningLearning processes need to be strongly driven bytop management. The leaders of the organizationshould: Demonstrate willingness to listen to divergent

viewpoints. Advocate the importance of devoting timetowards problem identification, experimentation,knowledge transfer, analysis and reflection. Initiate active discussion and questioning onmajor organizational issues.

Rationale for The Study

MMTC today faces the challenge of a major loss of itssupervisory and managerial level employees due toemployees reaching the age of superannuation in thenext five-ten years. The challenge lies in not justacquiring and developing new talent, but in losing thewealth of knowledge existing within these employees.There is an immense need to harness the existingknowledge so as to prevent new employees fromcontinuously reinventing the wheel.

MMTC : A Case of Knowledge Management and Learning Organization


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Knowledge from its primary definition is seen to residewithin individuals. While intellectual assets in theorganisation can exist in the form of documents andmanuals, knowledge gained through experience andperformance of the tasks is embedded in individuals.

Thus, it is essential to adopt processes and practiceswhich generate new knowledge as well as organise andshare existing intellectual assets within anorganisation. There is a need to assess the impact of adopting these practices on organisation culture. SinceKnowledge Management processes require hugeinvestment in form of time, money and effort, it isimportant to determine the motivation of employeestowards such practices.

The Project explores the need of Knowledge

Management (KM) practices in MMTC. It attempts toidentify the current knowledge assets in theorganisation and evaluate the factors which can gainemployee and leadership support towards it, therebyensuring its success.

Knowledge generation and sharing cannot beconsidered nor implemented without ensuringindividual and team learning. A pilot study has beenundertaken in MMTC to determine the characteristicsrequired by the organisation to emerge as a learning

organisation

Methodology

To capture the perception of employees regardingknowledge management initiatives, a questionnairewas designed based on an extensive literature review.The questionnaire also seeks to identify existingknowledge resources and their usage in theorganisation. A five point Likert scale was utilised in thequestionnaire so as to maintain simplicity yet providingample scope to agree/disagree. The design of the studyundertaken is exploratory and descriptive in nature. Aneffective sample size of 40 employees was utilised forthe research on Knowledge Management. Based onconvenience sampling, employees of diversedepartments and designations were included in thesample.

While all organisations aim to emerge as a LearningOrganisation, there are few standardised tools toassess the individual and organisational learning of firms. With this view, a standardised assessmentsurvey developed by Professors Garvin et al. in 2008

has been applied as a pilot study on a smaller sampleaudience of 30 employees in the organisation.

Data Analysis and Results

Analysis of the primary data collected for KnowledgeManagement was undertaken through application of factor and discriminant analysis. The assessmentsurvey of learning Organisation was primarily studiedusing the benchmark data given as a part of assessment tool. Factor and discriminant analysis was

also applied to confirm the results acquired throughthe comparison with benchmark data. The primarydata was utilised for analysis after conductingreliability analysis.

Knowledge Management

Factor analysis was applied on all twenty questions inthe knowledge management questionnaire. However,to refine the results acquired through factor analysis,the anti-image matrix and communalities table wereanalysed. Factors with lower values in anti-imagematrix and communalities table were dropped untilthe KMO value was found suitable for further analysis.

Finally nine items of the questionnaire was consideredsuitable for further. These extracted factors include:

Key Success Factors Organising available knowledge resources in

MMTC can reduce duplication of work Innovation in daily routine tasks is not encouraged

/practiced in your job Knowledge Management practices should be

initiated by each department head Knowledge creation groups such as Learning

teams(Informal self organizing groups that shareknowledge and use it to solve problems together)is encouraged in MMTC



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A formal Mentoring program so that juniors cangain knowledge from their senior employeesshould be implemented in MMTC

MMTC possesses a participative work culturewhere employees of different departments

interact and formally share their expertise andknowledge. New practices related to the field of knowledge

management cannot be implemented withoutsupport of top/senior management

Organising and sharing Knowledge should form aformal part of your daily work

Sharing of expertise and knowledge with peers isrecognized and rewarded in MMTC

KMO and Bartletts Test of Sphericity

With only the nine factors considered, KMO was foundto be above 0.7 and the null hypothesis as given byBartletts test stood rejected. This indicated that thecorrelation matrix was not an identity matrix at 0.05level of significance and there was a degree of correlation amongst the factors considered. Thus,further analysis using factor and discriminant analysiscould be further carried out.

Reliability Analysis

T-tests applied to each of the nine factors determinedthat the questions allowed the respondents to clearlyagree or disagree. Each factor was rejected at 0.05significance level.

Reliability analysis was also carried out by testing theadequacy of sample size using Cronbach analysis. Theanalysis yielded acceptable result of 0 .794.

Validity of The Factors Included

To determine the primary components that canstrongly impact knowledge initiatives in MMTC, Factoranalysis was applied on all extracted nine factors. Onapplication of factor analysis on nine factors, threecomponents were extracted which explained 70% of the variance and the alignment of the nine factorswithin the three dimensions was noted.

The rotated component matrix (included in annexureA) was examined to identify the loadings anddetermine the factors falling in each component. Theestablished three dimensions included: Leadership and Social Interaction, which reflects

the people aspect of Knowledge Managementsystem. Organization of Knowledge assets and initiatives

in the organization, which reflects the processaspect of Knowledge Management system.

Work culture and innovation, which reflects theorganizational culture of MMTC.

Discriminant analysis was then applied to decipher anysignificant differences between the opinions given byemployees at different designations. Discriminant

Analysis is significant for a study which takes intoaccount peoples perceptions. It can be considered tobe a confirmatory factor analysis since the factorsconsidered in factor analysis should be able toreclassify the responses correctly according to thedependant variable.

Discriminant analysis was applied on the basis of fourdesignations - deputy manager, manager, seniormanager and deputy general manager. 82.5% of thedata was reclassified correctly on the basis of the

independent variables in the analysis. This reflectedthat the factors considered for factor analysis weresuccessfully able to map perceptions across thegroups/designations.

The results achieved for Knowledge Management(three extracted components) were found to be similarto the three building blocks identified by Garvin et al.and utilised in the Learning Organisation assessmentsurvey. The three critical factors for KnowledgeManagement architecture determined through

application of factor analysis included leadership,Knowledge initiatives and processes, and conduciveorganization culture. The building blocks identified byGarvin for continuous learning in organizations alsosimilarly involves leadership that initiates learning,learning practices and processes and supportivelearning culture and environment. To further explorethe long-term goal of MMTC emerging as a learning



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organisation, data was collected and analysed throughuse of the assessment tool developed by Garvin et al.

Learning Organisation Analysis

The primary data collected in MMTC was organisedaccording to each building block and theirsubcomponents. The data collected was furthersubdivided into two sets, one of which measured theperception of employees on the subcomponents of building blocks, while the other reflects theexpectations of the employees from the organisation.For further data analysis, nine factors under perceptionand expectation each were considered.

The building Block of Leadership in the assessment tool

does not have sub components and cannot be easilyscaled. It is therefore excluded from factor analysis anddiscriminant analysis. However, its overall scoring isincluded in perception mapping and assessment of thegap between Perception and Expectation.

Reliability Analysis

Using SPSS, Cronbach Alpha was calculated separatelyfor the two groups of data, namely Perception of Employees and Expectation of Employees.

Cronbach Alpha measures slightly less than 0.7 in boththe cases but this can be attributed to lower number of respondents.

T-Test conducted on Perception and Expectation of Employees on the same parameters, was rejected at0.05 level of significance and reflected significantdifferences between perception and expectation of employees.

Factor and Discriminant Analysis

On application of factor analysis, the perception andexpectation of each sub-component loaded ontodifferent components in the rotated componentmatrix. The purpose of factor analysis for this primarydata is not data reduction; instead it validates the

significant difference between the expectation andperception of employees in MMTC. The perceptionabout each subcomponent lies under a different factoras compared to the expectation of that subcomponent.Discriminant analysis was applied as a confirmatory

factor analysis taking designation as dependantvariable. 81.5% of the responses were correctlyreclassified with few errors at the first two levels. Thesatisfactory results reflected that the perception atsenior managerial positions could be clearlydistinguished from that of the lower managerialpositions through the components considered in thequestionnaire.

Perception Matching

Primary data collected using the assessment surveydeveloped by Garvin et al. was essentially analysedthrough the benchmarking scores provided as a part of the diagnostic tool.

Comparison could also be drawn between theexpectations of employees as compared to the ratingsgiven by them for each questionnaire item whichtogether form the perception of the employees.

By assessing performance on each building block, theareas which require improvement can be identified.Garvin et al. believe the assessment tool can pinpointareas where companies need to foster knowledgesharing, idea development, learning from mistakes andholistic thinking.

On comparison of perception score with expectationscore given by employees as well as comparison of perception score with benchmark score, the areasnoted for improvement were Appreciation of differences(Supportive learning environment),Analysis and Information transfer(Concrete learningpractices).

Discussions and Recommendations

Before investment in knowledge managementarchitecture, the organisation would need to establish



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a roadmap which would be followed to developknowledge initiatives suitable for Indian PSU, MMTC.(Roadmap in Annexure C).

The first step in the roadmap involves selection of a

suitable knowledge strategy (Personalization orCodification). Based on the selected strategy, suitableknowledge program and practices could be developed.In case of MMTC, initiating personalization strategywould enable them to garner support of employees.The strategy would include knowledge sharinginitiatives such as: Developing experience sharing sessions Sharing and recording best practices and lessons

learnt from errors within the organizationamongst the regions or departments. This could

be done via email/newsletter Mapping employee competency and preparing a

list of experts in different domains. The expertisecan be channelized through the intranet as allemployees have ready access to contact details of the experts.

Implementing Mentoring and Peer Assistprograms initiated by prospective mentees.

Although MMTC assigns mentors to all new recruits, itis essential to devote resources to ensure that the

mentor-mentee relationship is established andcontinuously worked upon.

In India, Public Sector Enterprises (PSE) such as NTPCgenerally undertake codification strategy throughdevelopment of a knowledge portal ,dedicated tosharing of knowledge through articles and documentson one hand and collaborative tools of online chat anddiscussion rooms on the other.

The knowledge sharing practices and knowledge portal

developed would have to be aligned closely to aKnowledge Management strategy which would beestablished on the basis of the needs of the employees.The knowledge management strategy would in turn beclosely linked to the corporate strategy of theorganization.

The final aim of the organisation should be to utilise theknowledge to ensure continuous learning throughoutthe organisation. Based on the results deduced fromthe assessment survey of Garvin et. al., a need was feltto not only establish knowledge sharing initiatives, but

the significance of refurbishing the organizationculture was also realised. Thus, there is a need toemphasise innovation, team building, and openness todivergent viewpoints, and leadership that believes inopen-door policy as a part of the organization culture.The HRD department (training cell) should renew focuson leadership, team building and entrepreneurshipskills which could be included in the training anddevelopment programmes designed by MMTC for bothits corporate and regional offices.

Conclusion

The three focus areas to consider while tappingknowledge creation, generation, sharing anddissemination processes so as to emerge as acontinually developing and learning organisation are organisation culture and the work environment , theknowledge sharing and Learning processes andpractices and top management support and leadershipin the organisation.

Limitations

The research was conducted on a small samplesize due to restriction of time and access. It wasalso limited to the corporate office of MMTC andcould not be extended to the various regionaloffices located in other cities of India.

Knowledge Management was an unfamiliar termfor many of the employees thereby securingfewer uptakes in terms of responses to interviewsand questionnaires.

Due to paucity of time and interest, research wasrestricted only to the people issues involved inimplementation of Knowledge Management.Issues related to technology infrastructure relatedto KM were not given equal consideration in theprimary research.



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ANNEXURES

Annexure A : Rotated Component Matrix for Knowledge Management

Rotated Component Matrixa

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 4 iterations.

Component1 2 3

Practiced by department head .790 .170 .006

Organising knowledge reduces duplication .718 .188 -.167

Learning Teams .707 .178 .442

Knowledge sharing part of daily work .117 .895 .162

Top management support .150 .881 -.035

Formal mentoring program .437 .651 -.078

Knowledge sharing recognized .104 .033 .809

Innovation encouraged -.387 -.113 .622Knowledge sharing work culture .587 .265 .620

Annexure B

BUILDING BLOCK 1 : SUPPORTIVE LEARNING ENVIRONMENT EXPECTATION

PERCEPTION

BENCHMARKING

SCOREPsychological Safety

TOP QUARTILE (90)

TOP QUARTILE(100)

TOP : 87-100

Appreciation Of Differences

TOP QUARTILE (80)

THIRD QUARTILE(68)

THIRD : 65-79

Openness to NewIdeas

BOTTOM QUARTILE(56) BOTTOM QUARTILE(62) BOTTOM: 38-80

Time For Reflection TOP QUARTILE (100) TOP QUARTILE (97) TOP: 65-100

BUILDING BLOCK 2 : CONCRETE LEARNING PROCESSES AND PRACTICES

Experimentation

THIRD QUARTILE (78)

BOTTOM QUARTILE(40)

THIRD:72-82BOTTOM: 18-53

Information Collection SECOND QUARTILE (78) SECOND QUARTILE (76) SECOND: 71-79

Analysis

THIRD QUARTILE(77)

SECOND QUARTILE (61) THIRD: 72-86

SECOND: 57-70

Information Transfer THIRD QUARTILE(79) TOP QUARTILE (100) THIRD: 72-84TOP : 85-100

Education AndTraining

TOP QUARTILE (100) TOP QUARTILE (98) TOP : 90-100

BUILDING BLOCK 3 : LEADERSHIP THAT REINFORCES LEARNING

Leadership TOP QUARTILE(88) TOP QUARTILE(90) TOP: 83-100



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References

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Garvin David A., Amy C. Edmondson, and Francesca Gino. (2008) Is

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Nonaka I. and Konno N. (1998) The Concept of Ba: Building aFoundation for Knowledge Creation. California Management Review , Vol. 40, No. 3

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Publishing.Rigby, K.Darrell (2009), Management Tools 2009: An Executives Guide,

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