CHAPTER – 6 DEVELOPMENT OF …shodhganga.inflibnet.ac.in/bitstream/10603/35771/16/16_chapter_6.pdf141 CHAPTER – 6 DEVELOPMENT OF IMPLEMENTATION PLAN 6.1 Introduction This chapter

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CHAPTER – 6 DEVELOPMENT OF IMPLEMENTATION PLAN

6.1 Introduction

This chapter presents results of empirical study carried out in large and medium

scale manufacturing organizations of India, case studies conducted in two

manufacturing organizations, synthesis of results, learning from various phases of the

study, and use of learning issues in a structured manner within the boundaries of a

qualitative model, to develop workable and effective management process for achieving

strategic flexibility in Indian manufacturing organizations. The inferences from each of

the above phases have been compiled and listed. With a careful analysis, the

overlapping and alike inferences have been analyzed to develop a list of independent

learning issues. These learning issues have then been taken as options for a qualitative

modeling involving Option Field Methodology (OFM), Option Profile Methodology

(OPM), Analytic Hierarchy Process (AHP) and Fuzzy Set Theory (FST). Following this,

an implementation plan has been developed for managing different manufacturing

flexibility showing preferred strategies under various conditions of optimism,

pessimism, and realism.

6.2 Synthesis of Learning Issues

Learning’s from the empirical study and case studies has been synthesized and

presented in the form of issues enumerated below.

• Organizations need to achieve flexibility at strategic level in order to respond the

hypercompetitive market in an effective manner.

• In order to achieve flexibility at strategic level, organizations need to develop

dynamic capabilities.

Issues concerning dynamic capabilities

• Majority of the organizations have developed dynamic capabilities considerably

to achieve the strategic flexibility.

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• Adoption of soft technologies like MRP, MRP II and ERP etc. have been found

to be the key areas of interest in manufacturing organizations.

• Organizations are becoming more and more aware about their technological

priorities and optimal use of technology.

• Organizations have recognized the potential of technology and innovation that

have continuously harnessed the power of its knowledge capital.

• Government’s role in adaptation of new technology is very limited.

• Organizations have realized the importance of training and development of the

employees for proper utilization of technology and manufacturing resources.

• Formulation of a technology strategy is a key part of the overall business

strategy.

• Most of the organizations have fully developed R&D centre in terms of qualified

manpower and equipment.

• The R&D initiatives taken by organizations have enabled them to develop their

own technology, designs and patents.

• Impeccably future proofing capital investments assure evolutionary capabilities

of manufacturing infrastructure.

• Formation of strategic alliance with partners is a key to achieve flexibility.

• Market fluctuations and competitive excellence has emerged as prominent

factors, for offering new or radically improved products.

• Technologies like rapid prototyping and robotics etc. are not being used at a

large extent in most of the organizations.

• Emphasis on higher education and training of employees in other reputed

organizations is needed as well as there is a need of proper grievance handling

system of employees in most of the organizations.

• Implementing major changes in product design and features is not very common

for most of the organizations; rather they prefer to improve the existing products.

Issues concerning strategic flexibility

• The development of dynamic capabilities has helped the organizations to achieve

the strategic flexibility, in course leads to the introduction of new products with

minimal lead-time.

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• The responsiveness of the organizations to the customer demands, competitor’s

actions and operating efficiently at different levels of output has emerged as the

most important, amongst flexibility-oriented tasks.

• Multi skilling of employees have been found to be the key area of interest for

achieving manufacturing flexibility.

• Most of the organizations have a capability to handle changes in product

configurations quickly and efficiently during the manufacturing process to

accommodate customer preferences.

• Use of advanced technologies have resulted in strengthen the organization

capabilities to handle rapid increase in production volumes and adjust the level

of production quickly and profitably.

• Most of the organizations are capable of incorporating minor alterations in

product design to meet customization.

• The cost of the modifying or creating new product has been an area of concern

for the organizations.

• Organizations are very concerned about monitoring competitors' strategies and

tactics and reacting quickly to competitors' actions.

• Organizations give high weight age to changing customer needs and prefer to

react quickly to them.

• Organizations give emphasis to build both proactive as well as reactive strategies

to deal with market changes.

• Expansion by acquisition is not very common for most of the organizations.

• Fluctuations in demand are fulfilled by temporary arrangements of workers/

overtime etc.

• Suppliers’ contribution to the new product development may be increased.

• Developing technological capabilities has a very high positive impact for

achieving flexibility at strategic level.

• Strategic alliance with the parteners improves the expansion flexibility and new

product flexibility.

6.3 Methodology for Modeling

In the present study, a

Field Methodology (OFM), Options Profile Methodology (OPM), Analytic Hierarchy

Process (AHP) and Fuzzy Set Theory (FST), has be

a flexible system methodology framework (Sushil, 1994). A brief description of the

model is given below.

The first step of the modeling

management of dynamic capabii

converted into a conceptual design. OFM/OPM (Warfiel

largely used as a basis for this purpose.

developed were ranked using AHP (Saaty,

Figure 6.1 Qualitative modeling

OFM

•Generating various options .•Putting options into categories.•Clustering the dimensions and sequencing

OPM•Deciding various profiles or courses of actions•Assigning options from option fields to profiles

AHP•Deciding the objectives of features of design •Deciding weight ages of features through paired comparison

FST

•Quantifying the contributions of each profile to each objective by making position matrices.

•Making weighted position matrices.•Ranking the profiles under various schemes by dominance

Methodology for Modeling

qualitative model using various techniques like Options


Process (AHP) and Fuzzy Set Theory (FST), has been evolved as shown in figure 6.1,


modeling was the listing of options as a solution to flexible

dynamic capabiities for achieving strategic flexibility. The list

converted into a conceptual design. OFM/OPM (Warfield 1979, 1982, 1990) was

used as a basis for this purpose. Finally, the alternative options profiles

ranked using AHP (Saaty, 1980) and FST (Zadeh 1965).

Figure 6.1 Qualitative modeling approaches

Generating various options .Putting options into categories.Clustering the dimensions and sequencing

Deciding various profiles or courses of actionsAssigning options from option fields to profiles

Deciding the objectives of features of design Deciding weight ages of features through paired comparison

Quantifying the contributions of each profile to each objective by making position matrices.Making weighted position matrices.Ranking the profiles under various schemes by dominance

Options


ure 6.1, in


n to flexible

. The list then

d 1979, 1982, 1990) was

options profiles

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6.3.1 Options Field / Options Profile Methodology (OFM/OPM)

In order to adopt the method of idea writing to design, Warfield (1979)

introduced a methodology for the conceptual design of systems which results into a

portrayal on one page of the products of a conceptual design a foot. This portrayal

shows not only what is accepted in the design but also what options are rejected. The

Options Field Methodology and the Options Profile Methodology provide means for

thorough development of design situation, descriptions and design target description.

They involve discovery and of the target with dimensionality of the design situation.

Various steps involved identification of dimensionality of the situation, and facilitate

matching dimensionality in these two methodologies are described below:

Options Field Methodology (OFM)

a) Construction of a polystructure: The completed options field is a polystructure.

Its construction begins with the generation and classification of a set of options.

This set may be generated using modified idea writing in response to a carefully

formulated triggering question. This question defines the context and must,

therefore, reflect substantial insight into the design situation. The question must

be neither too broad nor too narrow. It must stimulate creative and productive

responses that do not stray from the topic under consideration.

b) Initial structuring (placing options in categories: Once a set is developed, the

initial structuring begins. The initial structuring is for placing options into

categories. A relationship that may be used for this initial structuring is “in the

same category as”. Theory of dimensionality is used for placing the options into

categories.

The structural theory of dimensionality of situations and processes

introduces options field and options profile as byproducts of design activity.

Options field is a triply structured-quad, since it is a four levels structure, whose

levels are named as Target, Cluster, Dimension and Options reading from the top

to bottom (Figure 6.2).

It is triply structured because its structure incorporates three distinct

relationships (Warfield, 1990) described as membership in a dimension for

classifying options into dimensions interdependence for classifying dimensions

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into interdependent cluster and time preference relationship” for relating

dimensions to each other in clusters.

Figure 6.2 Four level structure quad

c) Identifying the design dimensions: After the set of categories has been

achieved, it is reasonable to believe that learning has occurred. At this point, it is

appropriate to ask whether every category should be taken as a dimension of the

design. The criteria for making this decision is to ask whether some option(s) in

that category really must be specified in order to provide adequate definition of

the alternative represented by choosing one or more options from each

dimension, or whether any particular category is not essential to the definition of

the target.

d) Discovering Clusters of dependent dimensions: Once the group has settled on

the dimensions of the target, a second structuring occurs. Now the set of

dimensions is tructured. The relationship used is “independent of”. Two

dimensions are defined to be independent if a choice of one or more options in

one of the dimensions does not rule out any choices in the other dimension. If

two dimensions are interdependent, the choice of options in one may be

restricted by the choice of options in the other. Following this structuring, there

is a defined set of clusters, each cluster consisting of a set of dimensions, and

each dimension consisting of a set of similar options.

e) Establishing a choice-making sequence for clusters: Now the third structuring

begins. This structuring takes the clusters as elements to be structured. The

structuring relationship involves the sequence in which choices of options should

be made. A suitable relationship is “should be considered first in making choices

of options.”

Options Dimensions Cluster Target

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f) Sequencing dimensions within clusters: A fourth structuring is carried out now.

In this, structuring is carried out separately for each cluster and initial decision-

making sequence among dimensions in each cluster is defined.

g) Displaying the completed options field: It is then appropriate to organize the

options field by placing dimensions in the order determined with name of each

dimension heading a list of options therein and with the cluster clearly identified.

Options Profile Methodology (OPM)

Options Profile is the visual representation of an alternative consisting of a set of

chosen options with at least one option coming from each dimension in the options

field. Each option that has been selected is so designated by a line drawn from the bullet

in front of the selected option down to the tie line. In applications, it is common to

construct several options profiles for a given options field. Each options profile

represents one design alternative. In choosing options, choices are made in the sequence

determined in formulating the way options field is represented. Having made the

profiles, next task is to list various objectives of the design or targets.

Following this, contribution of each profile to each objective is determined by paired

comparison. Analytical Hierarchy process is employed for the purpose. A brief

description of the AHP and FST is given below.

6.3.2 Analytic Hierarchy Process (AHP)

Saaty (1980, 1982, 1986, 1990), Saaty and Vergas (1982), Saaty and Kearns

(1985) describe and elaborate on the process. The Analytic Hierarchy Process has been

discussed in detail in section 4.16.

6.3.3 Fuzzy Set Theory (FST)

a) Fuzzy set Theory (FST) was developed by Zadeh (1965). This theory is based on

the fact that certain sets have imprecise boundaries. Fuzzy sets and sub-sets are

those ill specified and non-distinct collection of objects with unsharp boundaries

in which transition from membership to non-membership is gradual rather than

abrupt. A fuzzy set is characterized by a membership function, defined as a real

number in the interval (0, 1). For example, a membership measure (X) = 0.5

suggests that X is a member of set A to a degree 0.5 on a scale where 0 is no

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membership at all, and 1 is complete membership. Thus, a fuzzy set can be

reduced to a crisp set by transforming memberships to extremes of the range

zero or one. FST has been successfully applied to automata theory, system

analysis, decision theory, man machine systems, modeling of industrial

processes etc. In this study, it has been used for the purpose of ranking of

options profiles in an integrated form with analytical hierarchy.

b) Ranking of alternatives using FST: The fuzzy set methodology for multi-criteria

decision making is used to analyze various options. The fuzzy set techniques are

designed such that quantitative and non-quantitative factors, and the view points

of the interest groups can be readily incorporated into the decision making

process. Ranks of the options in a group process are achieved through a

dominance matrix designed for the purpose.

In order to represent the views of each of the interest group, a position matrix is

prepared from the responses of all the experts in the group by giving numerical values to

the qualitative assessment. Average value of each element representing the group

response is worked out by multiplying membership function value of each alternative as

given by the respondents with assigned weight i.e. the eigen vector weight as

determined by AHP. This way some of the bias in the matrix can be eliminated. The

weighted matrices for each of the interest group are thus, prepared.

There are three ways to aggregate the weighted matrix viz. optimistic, average

and pessimistic aggregation. The highest value among various group responses

represents the optimistic value, the lowest value represents the pessimistic value and the

average of all the values represents the mean value.

An n x n matrix ‘D’ called dominance matrix is prepared to display the

dominance structure between all possible pairs of options. The element dij is the number

of features for which membership value of option j dominates or is greater than option i.

A dash is entered for the diagonal dij element. If the Kth column is summed, the total

number of dominances of option K over all options is obtained. Similarly, if the Kth row

is summed, the number of times the Kth option is being dominated by all other options

is determined.

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Outcomes that are more favorable have higher column sums and lower row

sums. In cases where an option is very close to another option on the basis of aggregate

weighted position matrix, the dominance among the options exists only if the

membership value of the second option is outside the specified limit. The options can be

considered equivalent with respect to that feature. This range may be set for each

problem (for example 5 percent of the membership value) but should not be too large;

otherwise lot of information is likely to be lost. As in the case of weighted position

matrices, three dominance matrices namely optimistic dominance matrix, pessimistic

dominance matrix and mean dominance matrix are prepared.

The ranks of options are normally decided by examining ranks obtained from

extent of dominance and also extent of being dominated by other options. Although any

of the optimistic, pessimistic and average approaches can be used but there are

shortcomings in each. The best course of action for a decision maker in such a situation

may be to use a Hadley’s criteria of cautious optimism (Hadley, 1967). The decision

maker may choose different coefficients of optimism. If ‘A’ is the dominance weight of

the option as determined from optimistic matrix and B that of the pessimistic dominance

matrix, weight of the option according to Hadley criterion is determined by the

relationship: W = x A + (1-) x B.

Since the process of choosing the coefficient of optimism in the Hadley criterion

of ‘Cautious Optimism’ is a judgment based approach, ranks of the options from the

dominance matrix is considered on the basis of dominance and ignoring the

considerations of being dominated.

6.4 Qualitative Modeling using OFM, OPM, AHP, and FST

The learning issues as given in section 6.2 have been analyzed and restructured

to convert them into following options of the OFM:

1. Use of flexible procedure and practices.

2. Adapt to changing market environment quickly.

3. Reconfigure the human resources according to market conditions.

4. Design and develop new products.

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5. Making changes in the features of the existing products to meet customer

requirements.

6. Delivery of innovation in design of products.

7. Developing in house R&D.

8. Allocating sufficient funds for R&D every year.

9. Applying in house R&D for new product development and modifying the

existing products.

10. Quality planning and improvement

11. Adoption and adaptation of new technology.

12. Introduction of process improvements in the manufacturing system.

13. Redesigning the existing manufacturing system effectively within the available

facilities.

14. Handle varying manufacturing schedules

15. Developing capacity to handle varied output volumes for the different products.

16. Handle increasing production volumes rapidly.

17. Process improvements in the manufacturing system to improve flexibility.

18. Switching between varied production volume levels quickly and efficiently.

19. Transformation of new product design into production quickly.

20. Making minor alterations in product design and modifying the product features

quickly and efficiently.

21. Providing sufficient funds for addition of new and advanced machinery.

22. Reacting quickly to changing customers’ demand.

23. Closely monitor competitors' strategies and tactics.

24. Reacting quickly to competitors’ actions.

25. Building proactive and reactive strategies for effectively responding to the

changing market needs.

26. Create new market opportunities.

27. Developing new technologies by applying internal R&D.

28. Technology adoption and up gradation of existing technology through

acquisitions, collaboration and tie-ups.

29. Effective use of soft technologies like ERP, MRP, MRP II, office automation

etc.

30. Develop teams to make continuous changes and improvements in the system.

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31. Aligning human resources to cater market demands effectively.

32. Design new methods of performance and efficiency measurement.

33. Encourage multiskilling of employees.

34. Provide job security to employees.

35. Give weight age to Training and development of employees.

36. Preparing and effectively implementing human resource welfare policies.

37. Introducing new products quickly and whenever their need arises...

38. Periodic review of changing market demands.

39. Involving supplier(s) in design and new product development process.

40. Form strategic alliance with suppliers.

41. Obtain new resources and capabilities through acquisition.

42. Strengthen resources through alliances.

43. Conducting periodic reviews of the alliances to assess the performance.

44. Adopt performance based incentive policies

45. Find alternate supplier for each specific component or raw material.

46. Switch between different suppliers quickly.

47. Capacity enhancement through expansion.

48. Have different modes of transportation for delivering products to the customers.

49. Review the customer needs periodically.

50. Reduction in production costs.

51. High cost involved in achieving flexibility at strategic level as a major barrier.

52. Recruit manpower from outside and shift people internally.

6.4.1 Putting the Options into Categories

These options were then put into various categories and the categories were

named too. The categories are:

a) Setting objectives of achieving strategic flexibility.

b) Formulating manufacturing strategy.

c) Achieving and maintaining competitive advantage.

d) Bringing innovation in product design and features.

e) Manufacturing customized products.

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f) Monitoring closely the customers’ needs, competitors’ actions and changing

market conditions.

g) Developing in-house R&D capabilities

h) Formulating proactive and reactive strategies to cope with market fluctuations.

i) Allocating funds for acquiring various dynamic capabilities.

j) Developing human resource development through formal education and

trainings.

k) Giving emphasis on customer focus and commitment.

l) Acquiring new technology and upgrading existing technology.

m) Market leadership through alliance and acquisition.

n) Having agile supply chain.

6.4.2 Dimensions of the Design

The above categories were scrutinized to include them or exclude any of them

for the design. All of these have been included and considered as the dimensions of the

design.

6.4.3 Clustering

The dimensions were put into broader categories called clusters. The principles

have already been explained. These are shown in the next section.

6.4.4 Sequencing of Clusters and Dimensions within Clusters

Following the clustering of the dimensions, the clusters were put into sequence

as per the importance of an area. The sequencing of dimensions within clusters was then

carried out. The resultant clusters with sequenced dimensions are given below:

I. Innovation and R&D

a) Bringing innovation in product design and features.

b) Manufacturing customized products.

c) Developing in-house R&D capabilities.

d) Acquiring new technology and upgrading existing technology.

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II. Business performance and strategic planning

a) Formulating manufacturing strategy.

b) Setting objectives of achieving strategic flexibility.

c) Achieving and maintaining competitive advantage.

d) Allocating funds for acquiring various dynamic capabilities.

e) Developing human resource development through formal education and

trainings.

III. Forming strategic alliance with partners

a) Market leadership through alliance and acquisition.

b) Developing and maintaining agile supply chain.

IV. Responsiveness to market conditions

a) Monitoring closely the customers’ needs, competitors’ actions and changing

market conditions.

b) Formulating proactive and reactive strategies to cope with market fluctuations.

c) Giving emphasis on customer focus and commitment.

6.4.5 Options Profile Methodology

Various profiles or courses of action planned to achieve different dimensions of

flexibility at strategic level, for the purpose of this study are delineated as follows:

I. Technology based approach (TCB), i.e. achieving Strategic flexibility by

investing in technology. Following this approach, an organization may make

investments in advanced technology and upgrade their technology in order to

gain competitive edge. Adopting new technology and upgrading the existing

technology is required for manufacturing new products with latest features

according to the customer demand.

II. Innovation based approach (INB), i.e. achieving Strategic flexibility by

incorporating and improving the innovative capabilities and making investments

to strengthen the R&D of the organization. It also includes building capabilities

to create new product designs and modifying the existing products efficiently.

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III. Human resource based approach (HRB), i.e. achieving Strategic flexibility by

improving the competences of human resources at various levels within the

organization. It includes human resource development through formal education

and trainings of employees, formulating human resource welfare policies,

encouraging multimillion of employees and involving employees in formatting

strategies.

IV. Strategic alliance based approach (SAB), i.e. achieving Strategic flexibility by

entering into strategic alliance with partners. This type of approach helps in

exploiting the competences of the partners along with the core competences of

the organization in order to achieve flexibility at strategic level.

After deciding upon various profiles, the next task was to find out the

options from each cluster contributing to each profile. For this purpose,

completed option fields have been displayed. A tie line has been drawn on the

bottom. Each option contributing to a profile has been joined to the tie line

through its bullet (Figure 6.3)

6.4.6 Analytic Hierarchy Process Modeling

Analytic Hierarchy Process (AHP) has been discussed in detail in section 4.16.

The dimensions of strategic flexibility have been identified from literature review in

chapter II of the study. For the purpose of paired comparison method of AHP, three

respondents compared each objective with each other, independently. These were:

technology manager of SML, Ropar, production manager of HUL, Rajpura and the

researcher himself. Matrices of these values as filled by the respondents are given in

Appendix – II and III. These matrices also show the calculation of Eigen vector and the

weights of the objectives. The weightings given by the respondents were quite

consistent and the consistency ratio was found to be well within the limit of 10%.

The matrix containing weights of all the objectives as decided by various

respondents is given in Table 6.1.

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156

Innovation and R&D Business performance and strategic planning

Forming alliance with partners

Responsiveness to market conditions

Design new methods of performance and efficiency measurement. Reduction in production costs. Process improvements in the manufacturing system to improve flexibility.

Provide job security to employees.

• Give weight age to Training and development of employees.

• Preparing and effectively implementing Human resource welfare policies.

• Have different modes of transportation for delivering products to the customers.

• Recruit manpower from outside and shift people internally.

• Adopt performance based incentive policies

• Technology adoption and up gradation of existing technology through acquisitions, collaboration and tie-ups.

• High cost involved in achieving flexibility at strategic level as a major barrier.

Legend

Technology Based Approach

Innovation Based Approach

Strategic Alliance Based Approach

Human Resource Based Approach

Figure 6.3 Options Profile methodology Tie Line

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Table 6.1 Weights of different strategic flexibility dimensions

Participant Objective

Researcher Technology

Manager Production Manager

SCF 0.45 0.34 0.48 MFF 0.21 0.34 0.19

MKTF 0.08 0.08 0.08 NPF 0.14 0.16 0.18

EXPF 0.12 0.08 0.07

The Role of supply chain flexibility has been found to be the largely important,

followed by manufacturing flexibility, new product flexibility, market flexibility and

expansion flexibility. This could be attributed to the fact that there has been a

tremendous pressure on manufacturers on account of customer expectations, customer’s

requirement in product customization, quality improvement and growing global

competition. Customers are demanding more variety, better quality and service

including both reliability and faster delivery. Producing good quality and delivering the

right quantity of a product at right location and that is sold in the market only during a

limited period of time also pose a series of challenges for manufacturing industry.

6.4.7 Fuzzy set theory

Fuzzy set Theory (FST) developed by Zadeh (1965) is based on recognition that

certain sets have imprecise boundaries. Fuzzy sets and sub-sets are those ill specified

and non-distinct collection of objects with unsharp boundaries in which transition from

membership to non-membership is gradual rather than abrupt. A fuzzy set is

characterized by a membership function, defined as a real number in the interval (0, 1).

Thus, a fuzzy set can be reduced to a crisp set by transforming memberships to extremes

of the range zero or one. In this study, FST has been used for the purpose of ranking of

options profiles in an integrated form with AHP.

After determining the weights of the objectives, the position matrices are

determined. In these matrices, the qualitative values of contribution of each profile to

each objective have been decided. The three respondents as discussed earlier have used

their expertise to determine this matrix. The position matrices along with the weights

determined earlier are given in Appendix - III. From the position matrices, weighted

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position matrices have been determined by multiplying each value with the

corresponding weight calculated.

Weighted position matrices have been shown in Appendix-IV. From these

weighted position matrices, optimistic, average and pessimistic weighted position

matrices have been formed using Fuzzy Set Theory. For optimistic matrix, the highest

value of each position has been selected, for pessimistic the lowest values and for

average matrix, the average values have been selected. These values are depicted in

tables 6.2 to 6.4.

Table 6.2 Optimistic weighted position matrix

Innovation

Based Approach

Human Resource

Based Approach

Strategic Alliance Based

Approach

Technology based approach

SCF 0.240 0.240 0.144 0.405

MFF 0.170 0.238 0.170 0.238

MKTF 0.040 0.056 0.072 0.040

NPF 0.162 0.098 0.162 0.144

EXPF 0.084 0.060 0.060 0.084

Table 6.3 Pessimistic weighted position matrix

Innovation Based

Approach

Human Resource

Based Approach


Approach


SCF 0.135 0.225 0.102 0.238

MFF 0.105 0.171 0.057 0.105

MKTF 0.024 0.024 0.056 0.024

NPF 0.112 0.054 0.126 0.098

EXPF 0.049 0.021 0.021 0.035

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Table 6.4 Average weighted position matrix

Innovation Based

Approach

Human Resource Based

Approach


Approach


SCF 0.182 0.234 0.127 0.326

MFF 0.136 0.199 0.097 0.159

MKTF 0.029 0.040 0.061 0.035

NPF 0.133 0.077 0.144 0.123

EXPF 0.063 0.040 0.035 0.064

Based on above optimistic, pessimistic and average weighted position matrices,

other matrices have been computed at various degrees of optimism (80%, 60%, 40%

and 20%) and tabulated in Appendix – IV and V. The outcome of weighted position

matrices for optimism, pessimistic, average and different cautious approaches have been

compiled in the Table 6.5, which depicts the comparative association between different

flexibility dimensions and various profiles. It also shows the sequence of different

strategies and approaches to be followed under different market conditions.

Table 6.5 Preferred strategies for building various strategic flexibility dimensions

Optimistic 80% Optimistic

60% Optimistic

40% Optimistic

20% Optimistic

100% Pessimistic

Average

SCF IV-II-I-III IV-II-I-III IV-II-I-III IV-II-I-III IV-II-I-III IV-II-I-III IV-II-I-III

MFF II-IV-III-I II-IV-I-III II-IV-I-III II-IV-I-III II-IV-I-III II-IV-I-III II-IV-I-III

MKTF III-II-I-IV III-II-IV-I III-II-IV-I III-II-IV-I III-II-IV-I III-IV-II-I III-II-IV-I

NPF I-III-IV-II I-III-IV-II III-I-IV-II III-I-IV-II III-I-IV-II III-I-IV-II III-I-IV-II

EXPF IV-I-III-II IV-I-III-II I-IV-II-III I-IV-II-III I-IV-II-III I-IV-II-III IV-I-II-III

I – Innovation based approach (INB). II – Human resource based approach (HRB).

III – Strategic alliance based approach (SAB). IV – Technology based approach (TCB).

Following this, dominance matrices have been prepared. In these matrices, the

dominance of each course of action over the others has been tabulated. The cell value

denotes that a course of action dominates other courses of action in how many criteria

and it is dominated by another course of action in how many criteria. In the matrix,

profile written on the top, dominates the profile written on the left. Thus, row sum

depicts the number by which a criterion is dominated and the column sum depicts the

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number by which the profile dominates all other profiles. The matrices are presented in

Table 6.6 to Table 6.9.

Table 6.6 Dominance Matrix Optimistic

Innovation

Based Approach

Human Resource

Based Approach


Approach

Technology based

approach


- 3 2 3


2 - 3 3


3 2 - 3


2 2 2 -

Column Sum 7 7 7 9

Rank II II II I

Table 6.7 Dominance Matrix 100% Pessimistic

Innovation

Based Approach

Human Resource

Based Approach


Approach

Technology based

approach


- 3 2 3


2 - 2 4


3 3 - 3


2 1 2 -

Column Sum 7 7 6 10

Rank II II IV I

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Table 6.8 Dominance Matrix Average

Innovation

Based Approach

Human Resource

Based Approach


Approach

Technology based

approach


- 3 2 4


2 - 2 3


3 3 - 3


1 2 2 -

Column Sum 6 8 6 10

Rank III II III I The results of the dominance matrices indicate that technology based

approach has emerged as the preferred strategy for achieving flexibility at strategic

level, followed by human resource based approach.

The similar dominance matrices for various degrees of optimism (80%, 60%, 40% and

20%) have been compiled in Appendix – IV. The results of Hadley’s dominance matrix

of cautious optimism are also in line with the optimistic and the average matrix.

The results of all the dominance matrices have been summarized in Table 6.9.

Table 6.9 Hadley’s matrix of cautious optimism

Rank Profile

100% Optimistic

80% Optimistic

60% Optimistic

40% Optimistic

20% Optimistic

100% Pessimistic

Average

Innovation Based

Approach II II III III III II III

Human Resource

Based Approach

II II II II II II II


Approach

II IV IV IV IV IV III

Technology based

approach I I I I I I I

162

6.5 Discussion and development of an implementation plan

The results of qualitative modeling depicted that all the approaches employed

like optimistic, average, pessimistic and Hadley’s cautious optimism have brought out

the technology based approach as the most preferred strategy for achieving flexibility at

strategic level in Indian manufacturing organizations followed by human resource based

aapproach

Following observations have also been depicted from the results of qualitative analysis:

i. For achieving supply chain flexibility, technology based approach has emerged

as the most proffered approach followed by human resource based approach.

ii. For attaining manufacturing flexibility, human resource based approach is the

most proffered approach.

iii. In case of market flexibility, strategic alliance based approach is most important.

iv. Innovation based approach has been found out to be most significant approach

for achieving new product flexibility followed by strategic alliance based

approach.

v. Finally, for achieving expansion flexibility, innovation and technology based

approaches have been found to be equally important.

In this study a combined AHP and FST methodology has been applied to

determine the different approaches to be used to achieve strategic flexibility in

manufacturing organizations, under different market conditions. The following

conclusions have been outlined from the Hadley’s matrix of cautious optimism as

detailed in Table 6.9.

Technology based approach and human resource based approach have

significantly influenced the achievement of strategic flexibility in most of the

conditions. This can be attributed to the fact that under hypercompetitive market

conditions, it is preferable to adopt new technology to keep pace with the dynamic

environment. The organizations must invest in advanced technologies.

163

Technology based approach has emerged as a very significant factor towards

achieving supply chain flexibility and expansion flexibility. The use of information

technology and soft technologies like MRP, MRP II and ERP has enabled the

organizations to keep the control of materials and resources in a better way, thus

providing flexibility in supply chain. Similarly technology plays a vital role in the

expansion process of any organization. Thus, organizations must continuously upgrade

and make sufficient investments in technology in order to have high expansion

flexibility.

Innovation based approach has come out to be a very important aspect in

achieving new product flexibility. There are many instances where a product is

completely outdated by the launch of new innovative product. For example, the use of

compact discs has outdated the use of cassettes. Thus, organizations must adopt

innovation based approach in order to achieve flexibility in launching a new product.

Innovation based approach includes the strengthening the R & D and improving the

innovative capabilities. It can be observed that, as the environment changes from

optimistic to pessimistic conditions; the strategic alliance based becomes more

significant for new product flexibility. This can be attributed to the fact that under

pessimistic environment conditions, organizations instead of investing in R & D, may

exploit the competences and capabilities of their alliance partners, thus sharing the risks

equally.

Human resource based approach has significant effect in achieving

manufacturing flexibility. It indicates that an organization must encourage multi skilling

of employees and must provide proper training, development and education of their

employees. On the job training facilities must be strengthened and all the employees

must be encouraged to train the fellow employees. Te employees and managers must be

involved in routine decision making processes relating to manufacturing strategies.

Market flexibility can be achieved by employing strategic alliance based approach. As

discussed earlier, the capabilities and competences of the alliance partners must be

combined with the organizational capabilities in order to explore the new market

opportunities and creating new markets.

Based upon the results and outcomes of the qualitative study, an implementation

plan for dynamic capabilities-

showmen in figure 6.4.

Figure 6.4 implementation plan

6.6 Concluding remarks

This study has established the fact that

concept. The dimensions of strategic flexibility, in context with manufacturing

organizations have been identified. Further, for achieving these flexibility dimensions,

different approaches and strategies have b

combined AHP and FST methodologies, applied to the inputs provided by experts. This

study will help the managers to choose the best strategy for achieving a strategic

flexibility dimension under different environment

0

0.5

1

1.5

2

2.5

3

3.5

4

SCF MFF

upon the results and outcomes of the qualitative study, an implementation

-strategic flexibility relationships has been evolved as

implementation plan for dynamic capabilities-strategic flexibility relationships

This study has established the fact that strategic flexibility is a multi dimensional



different approaches and strategies have been evolved. The analysis made by using



flexibility dimension under different environment conditions.

MKTF NPF EXPF

INB

HRB

SAB

TCB

upon the results and outcomes of the qualitative study, an implementation

strategic flexibility relationships has been evolved as

exibility

strategic flexibility is a multi dimensional



een evolved. The analysis made by using



INB

HRB

SAB

TCB

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CHAPTER – 6 DEVELOPMENT OF …shodhganga.inflibnet.ac.in/bitstream/10603/35771/16/16_chapter_6.pdf141 CHAPTER – 6 DEVELOPMENT OF IMPLEMENTATION PLAN 6.1 Introduction This chapter