Manufacturing competency and strategic success in the automobile industry

Competency and Its Components

Competency

Competency is the integration of knowledge, skills, and behaviors that drive organizational success and achieve specific goals Management competency includes emotional intelligence, systems thinking, and negotiation skills, which enhance overall performance and impact staff functions These competencies are progressive, benefiting both the organization and its personnel by enriching employee achievements and fostering a proactive organizational culture to meet global competition and future challenges They assist in defining goals, particularly in human resources, and establish a framework for objective proficiency and consistent standards through a shared language around organizational requirements Additionally, technical competencies encompass the skills related to processes and functions within the organization, supporting the development and implementation of relevant procedures and policies.

Competency is essential for meeting HR needs within organizations and communities, defined as the ability to learn from past contexts and experiences In high-pressure situations, effective managers draw on successful solutions from their previous encounters Therefore, it is crucial for these managers to understand situations contextually and build a repository of potential solutions through ongoing training Ultimately, competency evolves over time, shaped by experience and knowledge beyond formal training.

Competencies play a crucial role in improving performance by developing essential skills, knowledge, and abilities within individuals and organizations They serve as a framework for differentiating between subpar and outstanding performance, encompassing attributes at the organizational, team, and individual levels that significantly contribute to enhanced manufacturing outcomes This multifaceted approach highlights the importance of leveraging competencies to maintain a competitive edge in the industry.

Figure 1.1 illustrates various aspects of competency, highlighting how the acquisition of skills and abilities by an organization's personnel leads to improved performance By clarifying job requirements, competencies create opportunities for enhancing existing job profiles.

Competencies can be developed through both individual and organizational efforts Effective top management plays a crucial role in identifying and managing these competencies to improve work processes and enhance human skills This development can be integrated with organizational learning, which includes on-the-job training, classroom instruction, and various training opportunities.

In today's competitive global market, businesses are shifting from economies of scale to prioritizing innovation, efficiency, and value addition To thrive, management must strategically focus on developing employee-centric competencies A well-defined strategy aligns the organization's direction and scope with its assets and evolving competencies, ensuring adaptability to changing market demands.

Effective evolution and management of HR competencies are crucial for an organization's excellence and survival Organizations must develop HR management competency systems that encompass a diverse range of personnel skills, promote various job attributes, and allow flexibility in incentive decisions to adapt to dynamic organizational needs The competency development domain is increasingly recognized in administrative management across global businesses, with competency models enabling organizations to make critical business decisions.

Levels in Competence Study (www.astd.org).

The following issues highlight the need for fostering competencies:

1 For apprehending performance, it is pertinent to observe the traits and pursuit of successful personnel, rather than following a set of assumptions pertaining to traits and intelligence

2 Competencies are the tools to evaluate personnel performance at the workplace

3 Competencies can be mastered and harnessed

4 Competencies should be highlighted and made accessible

5 Competencies should be correlated to meaningful process end results depicting personnel performance at the workplace

Core competencies are universal across various businesses and organizations, with teamwork, participative management, and customer focus being essential skills applicable in multiple domains While specific technical competencies may not fit all industrial contexts, departments can develop function-specific competencies to enhance their core skills tailored to their unique work needs It is crucial to maintain and practice these capabilities or skill sets in their respective fields of technology to ensure optimal performance.

To achieve organizational objectives, it is essential to deploy skilled personnel in targeted areas of the workplace, showcasing their capabilities through effective training and qualifications Teams should be strategically assigned to foster and demonstrate competence, while individuals must continuously develop and exhibit their skill levels Competence development encompasses both traditional education and practical on-the-job training (OJT) methods Ultimately, an organization’s market performance is assessed by comparing its success to that of the overall industry.

An organization’s competitiveness involves a set of distinct technologi- cal attributes, complementary assets, and organizational practices lead- ing the organization’s competitive capabilities in one or more businesses.

Personnel with greater experience and skill tend to exhibit superior improvements and encounter fewer surprises compared to their less experienced counterparts (Levinthal and March, 1993) Competency is often defined as possessing the necessary skill sets, potential, authority, and qualifications Key attributes that contribute to effective competencies in specific tasks include domain knowledge, aptitude, attitude, commitment, and motivation Furthermore, competency is closely linked to workplace performance and should be assessed based on productivity.

Collective application of personnel skills, expertise, and aptitude for delivering productivity objectives of an organization.

Competencies can be classified in different ways, and each type of compe- tency has different significance and relevance.

Core competencies are essential internal capabilities vital for an organization's success To achieve effective organizational performance and uphold core values, personnel must embody these competencies Key traits of personal core competencies include teamwork, motivation, flexibility, aptitude, and interpersonal skills Individuals must demonstrate the ability to perform effectively across various applications and maintain desired performance levels throughout the organization.

Core competencies differ based on the job nature and technology utilized within an organization For instance, an electronic equipment manufacturer may prioritize expertise in designing electronic components, while a software company emphasizes high-quality software coding skills To remain competitive, core competencies must continuously evolve in response to the rapidly changing organizational landscape, ensuring they are flexible and aligned with industry advancements that drive growth and opportunities.

Prahalad and Hamel (1994) define core competencies as the collective learning within a firm, emphasizing the integration of various technologies and coordination of diverse production skills In the short term, an organization’s competitiveness is influenced by the price or performance of its products, while long-term success relies on the ability to innovate and develop offerings more rapidly and cost-effectively than competitors They identify 'competitor differentiation, extendibility, and customer value' as essential conditions for establishing core competencies.

Core competency enables organizations to deliver customer benefits while facilitating market expansion, making it difficult for competitors to replicate This strategic mindset encourages the effective mobilization and focus of resources, allowing technology executives and R&D teams to articulate their company's unique strengths By leveraging core competencies, businesses can gain a competitive advantage without disrupting ongoing operations Additionally, core competencies play a dynamic role in enhancing project team potential, improving strategies by aligning with the external environment, minimizing path-dependent influences, and strategically organizing resources through guidance rather than control.

Core competencies play a crucial role in enhancing entrepreneurial performance and driving business success They inform strategic development aligned with a firm's overall performance and values In the context of Indian organizations, core competencies are particularly significant, as they enable SMEs to proactively adapt to market changes, embrace technology upgrades, and enhance human resources This proactive approach is essential for fostering competency development and ensuring long-term organizational effectiveness.

Competency Issues

Competence is essential for both individuals and organizations, encompassing various technological skills that are stable and differentiated Competencies can be categorized at both the organizational and employee levels, with more experienced and trained individuals typically outperforming their less experienced counterparts Enhanced learning contributes to greater reliability and fewer unexpected outcomes In the manufacturing sector, competency serves as a critical factor for enhancing competitiveness, positioning manufacturing as a key asset for organizations seeking competitive advantage The interplay of technological, marketing, and integrative competencies significantly impacts overall company performance.

Manufacturing competencies are crucial for a company's strategic success, enhancing competitiveness and influencing quality and firm performance Competitive strategies significantly impact these factors, as highlighted by Amoako-Gyampah and Acquaah (2008) In the global market, quality and business excellence awards serve as key motivators for improving the competencies of Indian firms, according to Dutta (2007) Furthermore, small and medium enterprises are recognized as vital engines for economic growth worldwide, as noted by Singh et al (2008).

Leadership competency models offer significant advantages for both organizations and individuals by providing a comprehensive framework for effectiveness These models challenge common assumptions and enhance understanding of the skills and behaviors essential for successful leadership.

In today's competitive landscape, companies face challenges in maintaining resources to adapt to new services and product offerings, particularly when development costs increase despite the need to reduce development time As competition grows fiercer, it is crucial for firms to strengthen and safeguard their competitive advantages while addressing their organizational weaknesses.

Drejer (2001) established a framework for competency development aligned with management practices in firms This model emphasizes the importance of viewing it as a stable foundation while also allowing for dynamic growth in competency development within management.

In today's fast-paced business landscape, companies are challenged by the rapid integration of new technologies, competitive pressures, and the need to comply with evolving environmental and safety regulations To sustain a competitive advantage, it is essential for organizations to develop unique competencies that set them apart in the market.

To enhance competitiveness, organizations must strengthen technology acquisition and integration, improving suppliers' capabilities in delivering integrated electronic and mechanical components Developing competencies strategically is essential, necessitating the creation of a robust strategic architecture A framework and competence model have been introduced to facilitate the formation and enhancement of these strategies.

Traditional methods for competency development in sustainable global manufacturing are often time-consuming and costly By identifying specific competencies and developing relevant scenarios, organizations can enhance their operational effectiveness Competencies reflect various emotions and social interactions encountered in daily work life, providing a nuanced understanding of employee dynamics Innovative competency frameworks offer valuable insights into new dimensions that can significantly boost a firm’s performance (Stokes and Oiry, 2012).

In modern organizations, performance is assessed using predefined measures that reflect the organization's ability to maintain essential competencies for successful project execution These competencies are complex and subjective, making them challenging to define and quantify A novel modeling approach that incorporates prioritized fuzzy aggregation, factor analysis, and fuzzy neural networks has been developed to explore the relationship between project competencies and key performance indicators (KPIs) Factor analysis is utilized to analyze various project competencies, leading to the establishment of a standardized framework and methodology for evaluating how these competencies impact project KPIs (Omar and Fayek, 2016).

Competency management focuses on the distribution and development of skills within an organization, enhancing business performance through reliable measurement techniques The architecture of design activities supports this management by emphasizing the qualitative aspects of work A pressing need exists for advanced measurement techniques to assess end-user computing capabilities Knowledge, a crucial component of human resources and assets, is often embedded in even the simplest tools In today's market, product pricing is increasingly influenced by quality rather than mere supply and demand, leading to the integration of production factors and industrial processes aimed at reducing end product costs while managing competencies to boost overall firm performance.

Competency management, when integrated with strategic management, assesses core competencies in relation to processes and products It plays a crucial role in managing interdependencies across project domains—organization, process, and product—through the use of matrix-based tools and the value-creating network concept Experts emphasize the responsibility of design organizations to develop products that meet customer requirements (Bonjour and Micaelli, 2010).

In recent years, the automotive industry has undergone significant transformations, particularly with the introduction of competency management This new framework assists automakers in evaluating whether to develop components internally or outsource production As a result, the decision-making process regarding supplier products has shifted to become independent and complementary to the assembler's products.

Hong and Stohle (2005) highlighted essential concepts in competency management, emphasizing the challenge of building a self-generative and self-renewable organization Recognized as a crucial strategy for enhancing organizational competitiveness, competency management plays a significant role in identifying opportunities and facilitating expansion in the global market (Lee).

Indian organizations have adopted various philosophies such as 5S, TPM, and TQM, yet substantial improvements remain elusive Competency management and development play a crucial role in effectively implementing these philosophies An analysis of the TQM index, competency index, and 5S index before and after competency-based training reveals that the manufacturing sector can enhance its competitiveness by prioritizing competency management and training Human Resource Development (HRD) interventions, including training, performance management, and career management, significantly influence the development of employee competencies, thereby boosting organizational effectiveness This study presents a model that integrates these factors, with its validity confirmed through structural equation modeling (SEM).

A resource-based perspective highlights how a firm can achieve a competitive advantage through its valuable, rare, and sustainable resources and capabilities This approach reveals the long-term implications of outsourcing while emphasizing the importance of organizational resources in integrating the diverse knowledge necessary for developing complex products.

Strategy and Its Aspects

Strategy

Strategy, derived from the Greek word for 'generalship,' focuses on developing leadership qualities for organizational initiatives It aligns with the overall goals of the organization, considering available resources and constraints Top management utilizes various strategies to achieve these goals, with objectives defining the aims measured by success metrics While objectives highlight what needs to be accomplished, strategy outlines the path to success, serving as the action plan for achieving these goals (Flint, 2000).

Strategy involves the intentional selection of diverse activities to create a unique value mix (Porter, 1996) Corporate strategy outlines a comprehensive plan for the organization, recognizing the distinction between production and the purchasing and consumption of goods (Jones and Parker, 2004) Manufacturing strategy (MS) has received significant focus in various models and frameworks, highlighting the connection between production capabilities and business competitiveness, ultimately fostering the development of enhanced strategies.

Strategic success involves the formulation and execution of key organizational objectives while considering available resources It encompasses strategic thinking, which focuses on creating and implementing unique business insights and opportunities to achieve sustainable competitive advantage Additionally, strategic planning can be conducted individually or collaboratively, leading to beneficial outcomes for the organization.

SA describes the aptitude of organizations to attain business competitive- ness by strategically evolving and imbibing innovativeness into design, manufacturing processes, services, and related business functions Agility

The concept of 21 is crucial for organizations seeking to adapt swiftly to change, ensuring sustained growth and competitiveness Strategic agility (SA) encompasses the organizational capabilities necessary to identify new market opportunities and implement effective mechanisms for rapid business process transformations This approach enables the creation of innovative products and services, ultimately enhancing business-related competencies.

Strategic agility (SA) includes multiple forms of agility such as operational, customer, partnering, portfolio, business process, and supply chain agility It reflects an organization's ability to respond effectively to both anticipated and unexpected changes by efficiently utilizing resources and knowledge to develop innovative solutions that enhance competitive advantage in both the short and long term Key dimensions of SA include strategic sensitivity, collective commitment, and resource fluidity.

Strategic adaptation and learning encompass three key dimensions: process adaptation and experimentation, collaborative technology sourcing, and a proactive technology posture, which collectively enhance an organization's competitive advantage (Ahmad and Schroeder, 2011) To improve competitiveness, organizations must adopt a strategy-driven approach and establish a strategic architecture that fosters the development of essential core competencies Moreover, manufacturing systems play a crucial role in enhancing company-level competitiveness, as a well-functioning manufacturing system significantly impacts overall firm performance.

Alsudiri et al (2013) identified key factors linking strategic alignment (SA), project management, and business strategy Their framework illustrates the connection between business strategy and project management, enabling companies to effectively implement business strategies by integrating their projects within the overarching strategy.

Strategic alignment (SA) plays a crucial role in driving economic growth and enhancing the competitiveness of small and medium enterprises (SMEs) in the global market By influencing competitor behavior through government regulations, SA redefines strategic entry barriers Companies can leverage these rules to disrupt competitors' transactions and governance frameworks, ultimately shaping their own governance structures based on transaction attributes.

Regional strategies offer alternative and potentially more effective solutions compared to globally integrated and locally responsive methods Companies adopt varied paths for regionalization and globalization, focusing on clusters of nations with similar market conditions and uniform consumer needs, which helps reduce adaptation costs These strategies play a crucial role in shaping a firm’s overall global strategy (Schlie and Yip, 2000).

Financial performance is often used to measurefirm performance There is need for an integrated framework to assess manufacturingflexibility and

firm performance Moreover, SA and thinking is reckoned to be an integral parameter towards manufacturingflexibility and competitiveness (Mishra et al., 2014).

Corporate social responsibility (CSR) plays a crucial role for managers, yet it remains an informal aspect of corporate structure In the automotive industry, CSR significantly influences corporate reputation and customer satisfaction, although no direct correlation exists between these factors To enhance production efficiency, industries must adopt strategic agility, encouraging managers to embrace new perspectives while prioritizing fundamental responsibilities (Hanzaee and Sadeghian, 2014).

A CSR competency framework equips managers and practitioners with essential skills and competencies related to corporate social responsibility Large organizations leverage these attributes, encompassing attitudes, knowledge, and practical skills The framework outlines a strategic approach for integrating CSR into mainstream business practices, as highlighted by Shinnaranantana et al (2013).

Strategic thinking plays a crucial role in shaping strategy formulation and actions, ultimately influencing business performance It encompasses a range of skills, including conceptual ability, visionary thinking, analytical ability, synthesizing ability, objectivity, creativity, and learning ability, collectively referred to as 'strategic thinking competency.' This competency model serves as a framework for enhancing strategic thinking, leading to improved strategies and business outcomes Additionally, it is utilized in designing training programs aimed at cultivating proficient strategic thinkers (Nuntamanop et al., 2013).

Upgrading technology, reducing costs, and improving product quality are significant challenges faced by SMEs worldwide In India, SMEs often struggle with product design and development capabilities, yet government policies play a crucial role in shaping competitive strategies on a global scale A framework illustrates the connection between manufacturing processes and strategic thinking, highlighting various manufacturing strategy processes and their effects on business performance By fostering strategic thinking competencies, organizations can drive process innovation, ultimately enhancing their performance through the development and reinforcement of effective strategies.

Through the use of mid-segment cars, the industry’s concept, evolution,physical aspects, and contribution to the growing need of the segment have been highlighted.

Outsourcing, combined with strategic thinking, plays a crucial role in promoting sustainable growth within organizations Key technological competencies, including machining and composite technologies, along with product group expertise in structural and brake system parts, are vital for organizational success Core competencies significantly influence various criteria such as operational effectiveness, safety, technological features, cost efficiency, usage quantity, procurement sufficiency, and workforce capabilities, ultimately impacting strategic outsourcing decisions.

Management plays a crucial role in production alongside money, machines, and materials According to Peter Drucker, innovation and marketing are essential elements of effective management It involves a series of interconnected functions, including planning, controlling, organizing, corporate policy, and directing resources to achieve strategic objectives Management control is pivotal in ensuring these processes align with the firm's goals.

Manufacturing Competency and Strategic Success

Manufacturing Competency

Organizations that implement flexible production routines and focus on product innovation and timely responsiveness achieve significant business success by effectively coordinating and redeploying both internal and external competencies Short-term competitiveness is driven by the strategic success of various products, while long-term competitiveness hinges on the organization's ability to provide cost-effective, innovative products with a high level of flexibility.

Manufacturing strategy is closely linked to competitive strategy, significantly impacting firm performance within the manufacturing sector The relationship between manufacturing and competitive strategies—particularly concerning quality, flexibility, cost, and delivery—is vital Quality, in particular, plays a crucial role in enhancing firm performance, allowing organizations to better navigate the challenges of a competitive manufacturing landscape (Amoako-Gyampah et al., 2008).

Manufacturing competency and competence management involve the effective management, development, and deployment of operational strategies The relationship between these competencies is influenced by their integration into both individual and organizational resources Competence management and agile management are interconnected, highlighting their mutual importance Global and strategic manufacturing competencies significantly impact an organization's performance and technological advancement, ultimately supporting strategies that boost competitiveness in the global market.

Manufacturing competency encompasses three interconnected concepts: organizational capability, administrative heritage, and core competency These elements are influenced by economic conditions and play a crucial role in strategy formulation Additionally, integrating a resource-based view with economic analysis is essential for a comprehensive understanding of manufacturing effectiveness.

A manufacturing competencies framework encompasses essential areas such as research and development, production, information systems, human resources, and marketing These functional competencies significantly influence organizational performance, with marketing, research and development, and production competencies being critical factors that drive customer satisfaction and operational efficiency (Masoud, 2013).

Manufacturing competency and competence-based management have significantly transformed strategic management, highlighting the interconnection between design management and value-creating networks Design managers must integrate these fields to enhance their company's competitiveness Design organizations play a crucial role in strengthening core competencies, as established by Bonjour and Micaelli (2010) The relationship between in-house development, external acquisition, and an organization's strategic orientation is vital Furthermore, low- and high-performing firms exhibit distinct strategies and methods for competency development and acquisition, influenced by a combination of integrated and discrete factors.

The adoption of technology plays a crucial role in enhancing operational competitiveness within international manufacturing organizations Understanding the mechanisms of trust building is essential for improving this competitiveness Managers should prioritize their actions, as these directly contribute to enhancing their firm's competitive edge (Kristianto et al., 2012).

Knowledge and manufacturing competencies significantly influence international performance, particularly for small firms entering global markets By assessing the impact of absorptive capacity on the interplay between organizational networks and knowledge competencies, firms can drive performance and facilitate early expansion (Park and Rhee, 2012) In an environment marked by rapid changes in customer expectations, technology, and competition, manufacturers are focusing on enhancing competencies, especially manufacturing flexibility, throughout the value chain Manufacturing flexibility enables organizations to produce diverse products tailored to customer demands while ensuring high performance, making it a vital aspect of value chain adaptability This flexibility is crucial for improving competitiveness and fostering customer trust, as highlighted in existing literature that examines the connections between flexible capabilities, competencies, and customer satisfaction (Zhang et al., 2003).

Technical activities are vital factors in the globalization of multinatio- nal firms The intangible nature of technological assets suggests that

R, D and E (Research, Development and Engineering) activities should be managed strategically, sometimes favoring centralization and, at times, decentralization Strategic management of R, D and E activ- ities leads to improved competencies and hence enhanced business performance

In today's competitive market, companies must prioritize rationality in their order-winning criteria, competitive priorities, and improvement activities to address customer pressures Indian firms focus on enhancing process and product quality, as well as ensuring timely delivery To achieve these competitive priorities, they adopt manufacturing strategies such as Total Quality Management (TQM), Material Requirements Planning (MRP), Just-In-Time (JIT), and Statistical Process Control (SPC) Manufacturing efficiency and quality have become crucial for maintaining industry competency and improving business performance These challenges drive the development of advanced manufacturing strategies aimed at fostering economically sustainable organizations, ultimately leading to enhanced firm performance.

Competencies play a crucial role in shaping the performance of various service industries, which can be categorized based on supply and demand predictability, maintenance level, labor intensity, delivery methods, and customization levels These classifications result in distinct service types, including service factories, service stores, service shops, and service complexes Effective strategic management, combined with these competencies, enhances service quality and fosters greater competitiveness and overall firm performance.

Cloud manufacturing is a service-oriented, customer-centric, and demand-driven model that emphasizes strategic vision influenced by competencies Key commercial implementations, such as industrial control systems, automation, service composition, and flexible business models, are essential for its production Enhancing business performance can be achieved through increased competition and advancements in industrial control systems, business model innovation, flexibility enablement, and the application of cloud computing in manufacturing.

The relationship between export performance, competencies, and manufacturing strategy is crucial for manufacturing SMEs By implementing an effective manufacturing strategy, these SMEs can achieve a competitive edge, leading to increased export success Additionally, this strategic approach enables them to identify new opportunities and anticipate potential threats, facilitating further growth in global markets (Singh and Mahmood, 2014).

Strategic Success

Strategy defines an organization's direction, aligning its resources and adapting to evolving competencies It involves the intentional development of unique initiatives to create a distinct value mix (Porter, 1996) Achieving strategic success requires both the formulation and execution of key objectives, considering available resources and taking a comprehensive view of the competitive internal and external environments.

Strategic planning is essential for organizations to gather necessary data and execute management efficiently, ultimately unlocking business potential It empowers executives to make informed decisions based on current assumptions and practices Effective strategic thinking involves creating and executing unique business insights and opportunities to achieve a sustainable competitive advantage Initiatives in strategic planning can be undertaken individually or collaboratively, yielding significant organizational benefits Furthermore, strategic thinking emphasizes the development of innovative capabilities by evaluating available resources and challenging common knowledge to enhance decision-making Contemporary strategic thought shifts the focus from the traditional questions of ‘What?’ to deeper inquiries of ‘Why?’ and ‘How?’.

The interdependencies among process, organization, and product are effectively managed through matrix-based tools Design management, alongside strategic management, assesses design core competencies in relation to organizational, product, and process structures Small business owners must grasp the growth strategies necessary for their success, as these strategies can inadvertently lead to their firms' downfall Strategies that help larger organizations thrive may not yield the same results for smaller enterprises, highlighting specific boundary conditions that influence the effectiveness of strategies on firm performance based on size (Armstrong, 2013).

Competitiveness refers to a firm's ability and performance, which can be categorized into short-term and long-term perspectives Short-term competitiveness endures through a single business cycle, while long-term competitiveness spans multiple cycles The concept of 'unthreatened competitive advantage' highlights a significant edge that may persist well into the foreseeable future Manufacturing capabilities play a crucial role in shaping a firm's competencies, strategy formulation, and overall competitiveness Additionally, the notion of absorptive capacity illustrates that robust manufacturing capabilities are linked to enhanced operational performance, especially for firms prioritizing operational excellence Ultimately, strong manufacturing capabilities significantly contribute to firm performance and facilitate better customer integration.

In today's competitive business landscape, maintenance management systems are essential for enhancing productivity, equipment effectiveness, environmental sustainability, and workplace safety Manufacturing companies that prioritize effective maintenance practices are likely to see significant improvements in business performance The interplay between functional strategies, market orientation, and human resource management plays a crucial role in shaping competitive strategies, with performance outcomes varying between family-owned and non-family firms Notably, HR participation, flexibility, and delivery strategies significantly boost profitability for family firms, while cost manufacturing strategies and market orientation align with cost leadership approaches for both family and non-family enterprises.

Performance measurement systems significantly influence business performance and competitiveness by utilizing integrated measures from the balanced scorecard framework Organizations employing these systems tend to outperform their competitors, as flexible and integrated performance measurement approaches enhance administrative efficiency and formalization Furthermore, the implementation of quality management practices positively impacts customer satisfaction and productivity, fostering increased market competition and ultimately leading to improved firm performance.

Countries strive to enhance competitiveness between domestic and global firms, with product development strategies emphasizing variety and the introduction of product platforms The black box method has seen improvements in supplier relations, while product line management can be approached in either 'static' or 'dynamic' ways The alignment of design and production activities allows for the application of lean production system analysis to design processes Moreover, the evolution of product development models within organizations is closely linked to product variety strategies, with multiproject-level organization and strategy being particularly promising (Muffatto, 1998).

Industrial competitiveness is essential for nations pursuing export-oriented industrialization, and it can be analyzed from multiple perspectives The overall performance of various organizations serves as a reflection of their competitiveness Utilizing theories from operations and strategic management, an AHP-based model has been developed to assess the significance of different drivers and indicators of industrial competitiveness This model highlights the importance of organizational performance indicators in evaluating industrial competitiveness and aids in identifying key factors that contribute to improved company performance.

The automotive sector demonstrates flexibility in strategy development, with a strong correlation between strategies focused on competency enhancement, investment, quality, and cost, and overall competitiveness Key challenges include a growth-supportive environment, a shortage of technical manpower, and market funding constraints, while delivery time, quality, and cost exert significant pressure on the industry To navigate these challenges, organizations must invest in new competencies and prioritize quality improvement and cost reduction to enhance their competitive edge.

Corporate financial performance (CFP) is influenced by corporate social performance (CSP), which enhances productivity and reduces costs CSP elements like environmental performance, labor practices, and community development play a significant role in boosting customer satisfaction However, factors beyond CSP, such as product quality, service perception, and brand understanding, are also crucial for customers In the automobile industry, the indirect contribution of CSP to consumer satisfaction, along with its direct impact on financial performance through increased productivity and cost reduction, underscores its overall significance (Reijnders et al., 2012).

Business Process Reengineering Complementary Competences (BPRCC), as defined by Kassahun and Molla (2013), includes both Business Process Reengineering Complementary Transformational Competences (BPRCTC) and Business Process Reengineering Managerial Competences (BPRCMC) The BPRCC significantly influences company performance by enhancing strategies and improving business competencies A measurement instrument for BPRCC has been developed specifically for the public sector in developing economies.

An advanced and authoritative organizational structure significantly enhances a company's performance and growth, directly influencing its managerial capabilities This structure not only fosters the achievement of organizational value but also plays a crucial role in improving overall execution (Verle et al., 2014).

Agile manufacturing is a crucial strategy for manufacturers seeking significant performance enhancements to excel in competitive markets and adapt to evolving customer demands It emphasizes the organization's ability to efficiently respond to changes in products and processes The increasing complexity of manufacturing requirements has highlighted the importance of agility as a key driver of competitive advantage, enabling manufacturers to achieve success without compromising quality or efficiency.

In today's competitive landscape, organizations are increasingly evaluated based on their effective and efficient use of capabilities that drive consistent industrial performance Contemporary strategic management focuses on enhancing methods for managing knowledge and intangible resources The interplay of core competencies—technological, marketing, and integrative—significantly influences strategic competency Both market and technological turbulence play a moderate but essential role in shaping the relationships between key constituents of organizational performance and core competencies, with market turbulence specifically regulating the link between integrative competencies and organizational performance.

Organizational learning capability significantly influences product innovation and the performance of SMEs, with design management capability playing a crucial facilitating role Understanding the interplay between product innovation, design management, and organizational learning is essential for enhancing innovation performance Design management, characterized by its dynamic nature, evolves from learning and enables companies to adapt to environmental changes effectively Additionally, industry-based efficiency evaluations offer management insights into best practices and identify less efficient sectors through comparative analysis The literature suggests that various factors, including service activities and financial strength, also contribute to the key competitive priorities of quality, flexibility, cost, and delivery.

Objectives

This book addresses the gap in literature regarding the impact of manufacturing competencies on the strategic success of firms, particularly in the automobile manufacturing industry It aims to investigate and propose specific manufacturing competencies that can enhance strategic outcomes The work focuses on key objectives to provide valuable insights into this critical area.

1 Synthesizing the concept of strategic success in automobile industry

2 Exploring manufacturing competencies in automobile industry

3 Analyzing the impact of manufacturing competencies on strategic success in automobile industry

Based on the above objectives following issues have been explored in this work:

1 Strategic success in the context of automobile industry has been defined.

2 Manufacturing competency and its components have been elaborated.

3 The various aspects of strategic success to manufacturing competency have been correlated.

4 The impact of manufacturing competency on strategic success has been analyzed and modeled using suitable qualitative and quantita- tive techniques.

For accomplishing the objectives, the following methodology has been followed:

1 A detailed literature review has been carried out to ascertain the significance of manufacturing competencies and strategic success.

2 A survey of several automobile manufacturing units have been com- pleted through a specially prepared questionnaire for understanding and assessing the current situation.

3 Suitable qualitative and quantitative techniques have been employed to correlate manufacturing competencies and strategic success.

4 To authenticate the findings of the survey, these findings have been followed by case studies in selected automobile manufacturing units of North India.

5 The results of survey and case studies have been synthesized to come out with a suitable model.

This study aims to identify key factors that enhance manufacturing competency and strategic success within the North Indian automobile industry Given the complexity of the topic, the research emphasizes a detailed analysis of the strategies employed by various organizations and their outcomes To achieve this, the study is conducted within the framework of flexible systems methodology (FSM).

The three basic components of FSM are actor, situation, and process The

A situation is managed by an actor through a well-developed management process that effectively recreates the situation The actor plays a crucial role in both the process and the situation itself This research encompasses several distinct phases.

A comprehensive literature review explores the competency and strategy factors utilized by manufacturing organizations globally, examining their historical evolution and associated challenges It analyzes the development of manufacturing processes and strategic practices, highlighting their relevance and limitations The review also showcases the tools and techniques used in implementing these factors, emphasizing the significant benefits gained by Western countries through successful competency-strategy implementation programs.

A comprehensive survey involving 118 manufacturing organizations was conducted to assess the manufacturing competencies of Indian entrepreneurs The research process included designing a detailed questionnaire focused on competency aspects and maintenance strategies, pretesting it on a representative sample, and collecting data through various communication methods The analysis aimed to evaluate traditional maintenance strategies and the proactive initiatives of Indian entrepreneurs, measuring the impact on manufacturing performance Statistical techniques were employed to analyze performance indicators resulting from competency and strategy implementations in North Indian automobile manufacturing The study also identified challenges in executing these strategies and highlighted key success factors for enhancing manufacturing competency in the region.

The survey, complemented by case studies of selected Indian manufacturing organizations, aims to evaluate the manufacturing performance achieved by Indian entrepreneurs These case studies highlight the step-by-step implementation processes that organizations follow to attain strategic success through manufacturing competencies They focus on the tools and techniques utilized by these organizations to enhance competency effectiveness and the outcomes of successful strategies Analyzing data on key performance indicators reveals their significant role in improving quality and maintenance functions The comprehensive case studies provide insights into manufacturing organizations, key competency factors, adopted strategies, and the resulting performance enhancements over specific time frames.

The insights gained from the literature review, survey analysis, and case studies have been successfully utilized to develop a 'competency-strategic model' tailored for Indian manufacturing organizations Additionally, the research has formulated strategic guidelines aimed at addressing the challenges faced during the implementation of these programs.

This research examines the impact of competency factors on business performance within automobile and auto parts manufacturing units in the northern region of the country The study aims to describe how these competency drivers influence performance parameters in automobile manufacturing A comprehensive survey was conducted across numerous automobile units using a specially designed questionnaire to establish the effects of these competency factors on strategic decision-making.

This study focuses on analyzing the impact of competency on strategic approaches to enhance firm performance To effectively conduct the survey, a comprehensive questionnaire was developed through an extensive literature review and validated by industry consultants and academics The questionnaire utilizes a four-point Likert scale, with each performance parameter and dimension comprising several related items.

An industrial database was established for automobile manufacturing organizations in the northern region to conduct a survey on manufacturing competency The organizations received questionnaires and were contacted through postal mail, email, telephone interviews, and personal visits to explain the research's purpose, relevance, and to address any questions, thereby facilitating their responses to the manufacturing competency questionnaires.

Finalized manufacturing competency questionnaires were sent to around 350 automobile and parts manufacturers, with 150 follow-up calls made to engage industry representatives Additionally, approximately 250 emails containing the questionnaires were distributed to various automobile units in the northern region of the country Interviews were conducted with relevant personnel to gather clarifications and insights.

Organizations with multiple products received individual responses for each item These responses were compiled and analyzed to assess the performance of the North Indian market.

Problem Formulation and Research Methodology

Gathering Required Information through Questionnaire

Validation of Empirical Data through Case Studies

Synthesising the Data Collected through Empirical Studies and Case Studies

To Evaluate the impact of Manufacturing Competency on Strategic Success

The methodology for the automobile manufacturing industry is illustrated through a block diagram Most responses to the manufacturing competency questionnaires were provided by senior management, including vice presidents, general managers, heads of operations, maintenance, process engineering, quality assurance, and quality managers.

A total of 118 completed questionnaires were collected, designed to be simple, comprehensive, and relevant to various competency and strategy factors These questionnaires provided essential data to achieve the research objectives For a detailed overview of the manufacturing competency questionnaire, please refer to Appendix I.

2.4 Information management a) Information strategy b) Information analysis c) System coordination

3.1 Leadership: a) Focus: i Impact ii Motivation b) Knowledge: i Conceptual ii Analytical iii Strategic iv Expertise c) Manage i Change ii Performance 3.2 Communication: a) Marketing b) Promotion c) Public relations d) Internal corporate

3.4 Knowledge: a) Knowledge transfer methodology b) Technology transfer c) International collaborations

5.6 Logistics: a) Import b) Export c) Warehousing d) Waste management e) Product standardization

Figure 3.2 depicts the proposed model showing the competency factors and performance attributes for evaluating the relations between compe- tency and strategy.

To understand the advantages of an effective manufacturing competency approach, it is crucial to analyze its impact on various strategic success factors and overall organizational performance.

Reliability Analysis of Competency and Strategy

Cronbach Alpha Reliability Analysis

Cronbach alpha analysis measures the internal consistency of grouped variables in a questionnaire, indicating its reliability A higher Cronbach alpha value signifies greater reliability, with Nunnaly (1978) suggesting a threshold of 0.7 as acceptable, although lower values may be considered in some cases The reliability index is assessed across various sections of the questionnaire, including manufacturing competencies, strategic success, output, and the overall questionnaire Additionally, Cronbach alpha indices are analyzed for all parameters within these categories to ensure comprehensive reliability evaluation.

Table 4.1a indicates that the manufacturing competency factors have indices exceeding 0.760, demonstrating strong internal consistency in the data responses Notably, the parameters for manufacturing competencies are particularly high, with process planning at 0.886, product design and development at 0.879, quality control at 0.828, product concept at 0.826, production planning and control at 0.778, and raw material and equipment at 0.769 Overall, the evaluated index for the manufacturing competencies section is 0.968, reflecting a robust level of internal consistency.

Table 4.1b presents the indices for strategic success factors, revealing that all indices exceed 0.730 This indicates a strong internal consistency in the available data responses.

The strategic success parameters exhibit high internal consistency, with indices indicating strong performance in key areas: strategic agility (0.818), management (0.901), teamwork (0.890), administration (0.738), and interpersonal skills (0.885) Overall, the strategic success section achieved an impressive index of 0.967.

The analysis reveals that all indices exceed 0.900, with the output section scoring 0.906 and the overall questionnaire achieving 0.985 This indicates a high level of internal consistency in the data responses, suggesting that the items within the study are reliably consistent.

Response Analysis

The respondents were evaluated on multiple statements regarding their manufacturing competencies, with data gathered using a four-point scale ranging from "not at all" to "to some extent."

Cronbach Alpha Reliability Index of the Questionnaire

(c) Output Factors and Overall Questionnaire

Overall Questionnaire 0.985 extent (B), reasonably well (C), and to a great extent (D) – regarding implementation.

Different factors in Manufacturing Competency are:

Table 4.2 presents data on product concept issues, highlighting that many organizations have developed a structured concept generation process (PPSp.0) that fosters innovation and marketing (PPSX.4), encourages interdepartmental collaboration (PPSQ.0), centralizes planning (PPSQ.0), and promotes creativity (PPSX.0) However, for factors with lower performance scores (PPS), there are opportunities for improvement, as illustrated in Figure 4.1, which depicts performance by issue.

The response analysis results showed that maximum weightage was given to the product concept attribute ‘well-planned concept generation processes.’It was followed by‘company policies towards innovation,’and

The marketing department's motivation for new concepts is influenced by preferences for a centralized planning structure, the development of interdepartmental relationships, and organizational flexibility to adapt to customer needs The analysis revealed that 23.7% of respondents have successfully implemented a well-planned structured concept generation process, while 33.9% and 37.3% reported achieving this to some extent or reasonably well, respectively.

In the organization, the concepts of 'innovation' and the 'marketing department's motivation for new ideas' were evaluated similarly It was found that 33.9% and 45.8% of respondents felt that company policies supported innovation to some extent or reasonably well Additionally, 48.3% and 31.4% indicated that the marketing department was sufficiently motivated to propose new concepts to a similar degree.

The analysis revealed that various product concepts, including 'centralized planning development,' 'organizational flexibility towards changes,' and 'interdepartmental relations for new ideas,' were present to varying degrees within the organizations, with implementation rates of 46.6%, 62.7%, and 46.6%, respectively However, a significant portion of respondents—27.1%, 19.5%, and 25.4%—indicated that these concepts were not effectively implemented in their organizations.

Table 4.3 highlights the performance concerning product design and development challenges The examination of issues within the manufacturing organization indicates that a substantial number of organizations face these challenges.

Response Analysis of Product Concept s.

Do you have a well-planned and structured concept generation process in your organization?

Do your company policies promote innovation?

Do you feel that the marketing department is adequately motivated to get an idea about the new product?

Does your organization encourage the deploy- ment of inter departmen- tal teams to identify and create new ideas?

Is your organization flex- ible enough for making changes during opera- tions and maintenance to satisfy customer needs?

Does your organization use a centralized plan- ning structure for idea generation?

(Total Point Scored ‘TPS’=A × 1 + B × 2 + C × 3 + D × 4) 56.9 2.28 effective design technology (PPSg.8), computer technology for analysis (PPST.0), product life cycle (PPS=5.2), aesthetics and ergonomics of products (PPSW.8), and simulation and modeling (PPSS.8).

The results showed that maximum emphasis was given to‘implementa- tion of design technology program’ in the organization, followed by

Modeling and simulating for product analysis, along with tracking design and development costs, are critical aspects of effective product design Equally important is the consideration of product life cycles, as well as ergonomics and aesthetics, which significantly influence design outcomes Conversely, the use of computers for analyses received less emphasis in comparison to these other factors Figure 4.2 illustrates the performance metrics of various organizations in relation to these priorities.

The analysis assessed that 30.5% respondents reported that the ‘effective design technology program’ was implemented to a great extent in the organization, whereas 21.2% and 31.4% reported it either ‘not at all’ or

The organization reported a satisfactory level of product design and development ideas, including the use of product life cycles and computerized technology for analyses Notably, 63.6% and 66.1% of respondents indicated successful implementation of these strategies.

A significant portion of respondents indicated varying levels of implementation in their organizations, with 19.5% and 10.2% reporting ‘no implementation,’ while 13.6% and 21.2% felt it was done ‘reasonably well.’ Similarly, in the context of utilizing modeling and simulation for design analysis, 50.0% of participants reported it was done ‘to some extent,’ whereas 23.7% rated it as ‘reasonably well’ and 20.3% stated it was ‘not at all’ implemented in their organizations.

‘tracking design and development costs’ and ‘inclusion of ergonomics and aesthetics in product designing’ in their organization was at a reasonable level, while 39.0% and 32.2% respectively reported‘to some extent.’

P er for mance R a ti ng

Performance Chart for Product Concept.

A recent analysis of computer usage in design processes revealed varying levels of dependency among organizations Specifically, 16.3% of respondents utilized computers for over 75% of their design activities, while 26.3% reported usage between 50% and 75% Additionally, 19.5% of organizations engaged computers for 25% to 50% of their processes, and a significant 38.1% relied on computers for less than 25% of their design work.

Table 4.4 portrays the data regarding process planning issues An analysis of process planning issues shows that most organizations have generally

Response Analysis of Product Design and Development

Does your organization have an effective design technology program

Does your organization use computerized tech- nology for analysis purposes?

Does the design program include procedures like product life cycle?

Does the design program include aes- thetics and ergonomics of the product?

Does your organization use simulation and modeling for analyzing designs?

Does your organization track design and devel- opment program costs?

What percentage of the designing is done with the aid of computer?

The Total Point Scored (TPS) for manufacturing organizations indicates a low rating of 57.6, with specific areas of process planning performance assessed The effective process planning program scored 67.16, while tracking process planning costs achieved a score of 70.70 However, material and machine selection scored lower at 56.10, and group technology received a notably low score of 5.80 In contrast, the finishing and assembly of the product scored 63.70, highlighting areas for improvement in the manufacturing process.

The implementation of a design technology program received the highest priority, followed closely by the importance of simulating the assembly and finishing processes of products and tracking process planning costs Equal emphasis was placed on integrating departmental preferences and utilizing mechanisms for selecting machines and materials Conversely, the least emphasis was placed on software-based planning and the regular updates of software systems.

A recent analysis revealed varying levels of computerized process planning among organizations, with 7.6% utilizing it for over 75%, 18.6% for 50-75%, 38.1% for 25-50%, and 35.6% using it for less than 25% Additionally, 27.1% of respondents indicated that their organization had an effective process planning program to a great extent, while 24.7% rated it as reasonably effective and 38.1% reported it to be effective to some extent.

‘usage of mechanism for machine and material selection,’ as ‘reasonably well’ while 71.2% reported ‘to some extent’ Figure 4.3 represents the performance of various organizations.

In addressing process planning challenges, organizations reported varying preferences: 20.3% emphasized the integration of departmental preferences, 24.7% highlighted the importance of group technology, and 23.7% noted the necessity for regularly updated planning software to align with technological advancements.

Performance Chart for Product Design and Development. was no implementation at all; whereas 43.2%, 50.0%, and 47.5% reported their implementation was there ‘to some extent’ On the issues of

‘process planning costs,’ and ‘considering assembling and finishing of products,’ 50.0% and 39.0% of the organizations reported reasonably well implementation, while 32.2% and 48.3% reported the implementation ‘to some extent’.

Response Analysis of Process Planning

No of Companies Scoring Points

Percent points Scored (PPS) TPS 100/4*N

Does your organization have an effective process planning program?

Does your organization apply group technology?

Does your organization possess a mechanism for material and machine selection?

Is the planning software is updated and reviewed periodically in accordance with technological changes?

Does your organization track process planning costs?

Does your organization prefer the integration of different departments?

Does your organization take into account the

finishing and assembly of the product?

What percentage of the process planning is done with the aid of technology?

Table 4.5 illustrates performance regarding the raw material and equip- ment issues.

An analysis of manufacturing organizations reveals that while many utilize ERP software for record keeping, critical factors such as ownership of transportation, inventory storage, and departmental involvement in machine selection require urgent improvement due to their underperformance Figure 4.4 highlights these performance issues.

Correlation Analysis

The purpose of correlation analysis is to identify the relationship within various parameters Moreover, perception was measured by correlation

Comparative Result of All Factors

The lead time was measured at 57.60 with a standard deviation of 2.31 across 12 evaluations, all assessed on a uniform scale The correlation analysis employed was the Karl Pearson correlation, utilizing a significance level of 0.05, as detailed in Table 4.15.

The correlation matrix aimed to identify the relationships and directionality among manufacturing competencies and strategic success in organizational output processes A hypothesis was formulated to examine these relationships at a significance level of 0.05.

H01: There was no relationship between the product concept and output process.

The correlation matrix analysis revealed that the null hypothesis was not valid, as significant correlations were found between the product concept and various output processes, positively impacting the organization Notably, the correlations with key parameters were strong, including reliability (r = 0.727), competitiveness (r = 0.684), quality (r = 0.675), production time (r = 0.613), production capacity (r = 0.606), growth and expansion (r = 0.527), and productivity (r = 0.500), indicating a notably positive relationship.

To enhance production capacity and time, businesses must focus on improving lead time and ensuring high-quality outputs Reliability and productivity are crucial for fostering growth and expansion, which in turn boosts competitiveness in the market Increasing sales and profit margins can be achieved by expanding market share and enhancing the customer base Effective product concept, design, and development, alongside meticulous process planning and resource management, are essential for successful production planning and control Implementing robust quality control measures and embracing strategic agility, management, and teamwork will further strengthen administration and interpersonal relations within the organization.

H02: There was no relationship between the product design and devel- opment and output process.

The analysis of the correlation matrix revealed that the null hypothesis was not valid, as significant correlations were found between product design and development and various organizational output processes Specifically, strong positive correlations were identified with reliability (r = 0.758), competitiveness (r = 0.696), quality (r = 0.692), production time (r = 0.635), production capacity (r = 0.606), growth and expansion (r = 0.556), productivity (r = 0.554), lead time (r = 0.527), and profit (r = 0.505).

H03: There was no relationship between the process planning and output process.

The correlation matrix analysis revealed that the null hypothesis was not acceptable, indicating significant positive correlations between process planning and various output processes Specifically, strong correlations were found with reliability (r = 0.746), competitiveness (r = 0.675), quality (r = 0.595), and production capacity (r = 0.550), highlighting the positive impact of process planning on organizational performance.

H04: There was no relationship between the raw material and equipment and output process.

The correlation matrix analysis revealed that the null hypothesis was not valid, indicating significant positive correlations between raw materials, equipment, and various output processes Specifically, the correlations were strong for reliability (r = 0.770), competitiveness (r = 0.704), quality (r = 0.690), production capacity (r = 0.672), production time (r = 0.631), productivity (r = 0.553), growth and expansion (r = 0.552), market share (r = 0.526), and profit (r = 0.511).

H05: There was no relationship between the production planning and output process.

The analysis of the correlation matrix revealed that the null hypothesis was not valid, as significant correlations were found between production planning and various output processes, positively impacting the organization Specifically, the correlations with key parameters were notably strong: reliability (r = 0.688), competitiveness (r = 0.638), production capacity (r = 0.614), quality (r = 0.590), growth and expansion (r = 0.553), production time (r = 0.549), and market share (r = 0.508), indicating a significant positive relationship.

H06: There was no relationship between quality control and output process.

The analysis of the correlation matrix indicated that the null hypothesis was not valid, as significant positive correlations were found between quality control and various output processes within the organization Specifically, quality control showed strong correlations with production capacity (r = 0.653), reliability (r = 0.652), production time (r = 0.628), quality (r = 0.586), productivity (r = 0.564), growth and expansion (r = 0.531), and competitiveness (r = 0.525).

H07: There was no relationship between the strategic agility and output process.

The analysis of the correlation matrix revealed that the null hypothesis was not acceptable, as significant positive correlations were found between strategic agility and various organizational outputs Specifically, strategic agility demonstrated strong correlations with quality (r = 0.709), production capacity (r = 0.679), production time (r = 0.677), reliability (r = 0.643), productivity (r = 0.612), growth and expansion (r = 0.590), competitiveness (r = 0.569), profit (r = 0.554), sales (r = 0.547), and lead time (r = 0.506).

H08: There was no relationship between the management and output process.

The correlation matrix analysis revealed that the null hypothesis was not valid, as significant positive correlations were found between management and various output processes Key correlations included reliability (r = 0.700), profit (r = 0.655), production time (r = 0.640), and competitiveness (r = 0.639) Additional noteworthy correlations were observed with growth and expansion (r = 0.606), lead time (r = 0.597), productivity (r = 0.581), quality (r = 0.547), sales (r = 0.529), market share (r = 0.521), production capacity (r = 0.516), and customer base (r = 0.515), all indicating a significantly positive relationship with management.

H 09 : There was no relationship between the team work and output process.

The analysis of the correlation matrix revealed that the null hypothesis was not acceptable, as significant positive correlations were found between teamwork and various organizational output processes Specifically, the correlation coefficients indicated strong relationships: reliability (r = 0.806), competitiveness (r = 0.708), production time (r = 0.652), quality (r = 0.647), production capacity (r = 0.607), productivity (r = 0.596), profit (r = 0.584), growth and expansion (r = 0.555), sales (r = 0.522), and market share (r = 0.508).

H10: There was no relationship between the administration and output process.

The correlation matrix analysis revealed that the null hypothesis was not valid, indicating significant positive correlations between administration and various output processes Specifically, strong correlations were identified with reliability (r = 0.685), competitiveness (r = 0.631), quality (r = 0.624), productivity (r = 0.575), production time (r = 0.566), production capacity (r = 0.527), and profit (r = 0.507).

H11: There was no relationship between the interpersonal and output process.

The analysis of the correlation matrix revealed that the null hypothesis was not acceptable, as significant correlations were found between interpersonal factors and various output processes, positively impacting the organization Specifically, the correlations with key parameters were notably strong: reliability (r = 0.738), competitiveness (r = 0.610), production time (r = 0.554), quality (r = 0.553), and production capacity (r = 0.528), indicating a significant positive relationship.

Case Studies in Manufacturing Industries

Case Study at the Two-Wheeler Manufacturing Unit

2.4 Information management a) Information strategy b) Information analysis c) System coordination

3.1 Leadership: a) Focus: i Impact ii Motivation b) Knowledge: i Conceptual ii Analytical iii Strategic iv Expertise c) Manage i Change ii Performance 3.2 Communication: a) Marketing b) Promotion c) Public relations d) Internal corporate

3.4 Knowledge: a) Knowledge transfer methodology b) Technology transfer c) International collaborations

5.6 Logistics: a) Import b) Export c) Warehousing d) Waste management e) Product standardization

Figure 3.2 depicts the proposed model showing the competency factors and performance attributes for evaluating the relations between compe- tency and strategy.

To understand the benefits of an effective manufacturing competency approach, it is crucial to analyze its impact on various strategic success factors and overall organizational performance.

This study employs a range of statistical tools, including Cronbach’s alpha, ANOVA, and multiple regression analysis, to assess the impact of competency initiatives on firm performance in manufacturing organizations Additionally, qualitative techniques such as AHP, TOPSIS, and fuzzy logic have been applied, with findings further validated through structural equation modeling (SEM) using AMOS 21.0 software The research utilizes confirmatory factor analysis (CFA) to explore the relationships between competency and strategic success variables, drawing data from manufacturing competency questionnaires distributed across various North Indian automobile manufacturing industries.

The research employs a multiple-descriptive case study method to evaluate the actor's capability, focusing on selected manufacturing organizations This approach is favored for several reasons: it allows for an in-depth exploration of the organization beyond the limited data points obtained through surveys, necessitates the use of multiple evidence sources to ensure comprehensive research, and requires unique strategies for effective research design and analysis.

When choosing organizations for in-depth case studies, several key factors were taken into account Firstly, the selected organizations must represent various facets of the manufacturing sector, including competition and complexity Secondly, it is essential to include organizations from diverse manufacturing sectors Lastly, preference was given to those organizations that participated in the survey by responding to the manufacturing competency questionnaire.

Dimensions and Performance parameters for tracking impact of competency on strategic success

Prof it (Annually) Competitiveness Growth and Expansion Production Capacity Production Time Lead Time Productivity Market Share Quality Reliability Customer Base

The feasibility of obtaining authentic data on competency factors through personal interactions, observations, and published sources is evident, although many organizations have been hesitant to publicly share their performance achievements despite significant questionnaire responses from prominent Indian entrepreneurs Descriptive case studies highlight the step-by-step implementation procedures adopted by various manufacturing organizations, with strong support from the industry, including Honda, Suzuki, Mahindra and Mahindra, SML Isuzu, and others These case studies provide detailed insights into organizational information, the necessity for implementation, adopted strategies, and their respective timelines, showcasing the impact of these strategies on enhancing each firm's performance.

In Chapter 9, the authors present a competency-strategy model tailored for the North Indian automobile manufacturing sector, developed through a comprehensive literature review, questionnaire surveys, and both quantitative and qualitative analyses, alongside case studies The chapter also summarizes the key research achievements, discusses the limitations of the study, and offers recommendations for future research avenues.

Reliability Analysis of Competency and Strategy

This chapter examines how manufacturing competencies influence the strategic success of the automobile manufacturing industry through data analysis and interpretation It details the analytical methods employed to achieve the study's objectives, utilizing the Statistical Package for the Social Sciences (SPSS) 21.0, now known as PASW – Predictive Analytics Software Key statistical techniques applied include multiple regression, ANOVA, two-tailed t-tests, Cronbach's alpha, and correlation analysis.

Cronbach alpha analysis measures the internal consistency of grouped variables in a questionnaire, indicating its reliability A higher coefficient value signifies greater reliability, with Nunnally (1978) suggesting 0.7 as an acceptable threshold, although lower values may also be considered The reliability index is assessed for various sections of the questionnaire, including manufacturing competencies, strategic success, output, and the overall questionnaire Additionally, Cronbach alpha indices are evaluated for all parameters related to these sections.

Table 4.1a indicates that the indices for manufacturing competency factors exceed 0.760, demonstrating strong internal consistency in the data responses Notably, the parameters for manufacturing competencies show high values, with process planning at 0.886, product design and development at 0.879, quality control at 0.828, product concept at 0.826, production planning and control at 0.778, and raw material and equipment at 0.769 Overall, the evaluated index for the manufacturing competencies section is 0.968, reflecting a robust internal consistency among the items.

Table 4.1b presents the indices for strategic success factors, indicating that all parameters exceed a value of 0.730 This demonstrates a high level of internal consistency in the data responses collected.

The strategic success parameters exhibit high internal consistency, with indices such as strategic agility at 0.818, management at 0.901, teamwork at 0.890, administration at 0.738, and interpersonal skills at 0.885 Overall, the strategic success section achieves an impressive index of 0.967, highlighting the effectiveness of these factors in contributing to organizational success.

The analysis reveals that all indices exceed 0.900, indicating strong internal consistency in the data responses Specifically, the output section scored 0.906, while the overall questionnaire achieved a remarkable score of 0.985, suggesting that the items within the study demonstrate a high level of reliability.

The surveyed respondents evaluated various statements related to manufacturing competencies using a four-point scale, ranging from "not at all" to "to some extent."

Cronbach Alpha Reliability Index of the Questionnaire

(c) Output Factors and Overall Questionnaire

Overall Questionnaire 0.985 extent (B), reasonably well (C), and to a great extent (D) – regarding implementation.

Different factors in Manufacturing Competency are:

Table 4.2 presents data on product concept issues, highlighting that many organizations have established a structured concept generation process (PPSp.0) that fosters innovation and marketing (PPSX.4), encourages interdepartmental collaboration (PPSQ.0), centralizes planning (PPSQ.0), and promotes creativity (PPSX.0) However, for factors with lower performance scores, improvements are recommended Figure 4.1 illustrates the performance of these issues.

The response analysis results showed that maximum weightage was given to the product concept attribute ‘well-planned concept generation processes.’It was followed by‘company policies towards innovation,’and

The marketing department's motivation for new concepts is influenced by a preference for a centralized planning structure, the development of interdepartmental relationships, and organizational flexibility to adapt to customer needs The analysis revealed that 23.7% of respondents have implemented a well-planned structured concept generation process, while 33.9% and 37.3% reported having done so to some extent or reasonably well, respectively.

Case Study at the Four-Wheeler Manufacturing Unit

Established in 1981, the four-wheeler manufacturing unit aimed to modernize the Indian automobile industry by producing fuel-efficient and indigenous utility vehicles It has become a leader in the car sector, excelling in both revenue and sales volume The production of cars began in 1983 with an initial output of 800 vehicles, and by 2004, the unit had surpassed 5 million vehicles produced The manufacturing facilities, located in Manesar and Gurgaon, boast an impressive annual production capacity of over 700,000 units.

The four-wheeler manufacturing unit implemented a uniform policy with the same fabric and color for all employees, fostering a strong sense of identity To minimize downtime between shifts, employees arrive early for their work schedules The facility features an open office layout and embraces continuous improvement practices such as kaizen, job rotation, teamwork, quality circles, and on-the-job training.

Triple Zero and Coexistence with Local Communities.

In response to the demand for a dependable, high-quality, and affordable vehicle, MSIL was tasked with modernizing and expanding the automobile sector to meet key policy objectives.

• Modernization of Indian automobile industry

• For economic growth, a large volume of vehicles had to be produced

• For conservation of scarce resources, fuel-efficient vehicles were the most pressing need

5.2.3 Company Strategy and Business Initiatives

For thirty years, the leading manufacturer of mini and compact cars has demonstrated technical excellence by delivering powerful and efficient lightweight engines This commitment to performance and fuel efficiency has established the company as the preferred employer for aspiring managers and automotive engineers nationwide.

The four-wheeler manufacturing unit has consistently prioritized customer satisfaction, earning the top ranking among Indian car manufacturers for nine consecutive years, as recognized by J.D Power Asia Pacific This achievement reflects the unit's commitment to excellence in service and quality.

5.2.4 Technology Initiatives Taken by the Four-Wheeler Manufacturing Unit

Indian consumers prioritize fuel efficiency when selecting automobiles, as maximizing energy from fuel is crucial for economic and environmental sustainability Simultaneously, the demand for rapid acceleration and improved performance is driven by a young, speed-conscious population in a rapidly growing market Additionally, space efficiency is essential to navigate crowded parking areas and congested roads.

The K-series engines represent a significant advancement in technology, aimed at serving humanity by utilizing minimal resources to reach a broader customer base This approach enhances long-term safety, happiness, well-being, and health while addressing societal needs By fostering better technologies, innovative thinking, and improved processes, the unit is committed to creating superior vehicles that ultimately enhance the quality of life for customers The collaborative efforts of the research and development team, alongside the organization's team, have led to numerous achievements in this mission.

Cor p ora te S er vic es & Chief R isk Of fic er

Cor p ora te & G over nmen t Af fairs Cor p ora te Comm unic ations C o rpo ra te Soc ia l Re sp onsibility & Sust aina bility

B O A RD OF DIRE C T OR S Pro duc tion Eng ine er ing Supply C hai n Q u a lit y A ssu rance A d mi ni str a tion Marketi ng & S a le s

Manag ing Dir ec tor Join t Manag ing Dir ec to r Man uf ac tur ing F acilitie s P lanning En vir onmen t Managemen t

Research and development are crucial for enhancing product quality and innovation Effective component sourcing ensures high standards in manufacturing, while robust quality assurance practices maintain reliability Human resources play a vital role in fostering a skilled workforce Domestic sales strategies must be complemented by international marketing efforts to expand market reach Additionally, promoting spare parts and accessories is essential for customer retention A strong marketing and strategy development approach is necessary for the growth of pre-owned car services, and prioritizing road safety is imperative for protecting consumers and enhancing brand reputation.

F inanc e Inf or ma tion T echnolog y Cor p ora te S ecr et ar ial & Lega l Sk ill D eve lopmen t: ITI Pr oj ec ts

Supplier Q u ality A ssuranc e Man uf ac tur ing Q u ality Ne w Pr o duc t Q u ality

V endor de ve lopmen t Consuma ble s Gr een Supply Chain

V ehicle D esig n & T esting Pr o duc tion P lanning Co st Managemen t Alt er na te f ue l and f ron tier te chnolog ie s

ORG A N ISA TI ON STR U C T U RE FIGURE 5.19 Organization Structure of the Four-Wheeler Manufacturing Unit.

• The unit launched many new models in India in the last few years

• In India, some of the most fuel-efficient petrol cars come with the organizations badge

The introduction of factory-fitted CNG (compressed natural gas) variants marks a significant advancement in eco-friendly vehicle technology These vehicles are equipped with cutting-edge i-GPI (Intelligent Gas Port Injection) technology, ensuring optimal performance and efficiency The integration of state-of-the-art i-GPI technology enhances the driving experience while promoting sustainable fuel usage in the automotive industry.

The innovative single minute exchange of dies (SMED) concept has been implemented, enabling die setups to be completed in under ten minutes This advancement significantly enhances operational efficiency and maximizes machine utilization.

• Almost all of its cars obey ELV (End of Life Vehicles) norms, which means they can be fully recycled and are free from any hazardous material.

The K-series features innovative lightweight construction technologies, including plastic intake components, lightweight pistons, nut-less connecting rods, and an optimized cylinder block Additionally, it incorporates a high-pressure semi-return fuel system and Smart Distributor Less Ignition (SDLI) with advanced injectors and plug top coils, all designed to enhance performance.

The Wagon R Green combines performance, spaciousness, and comfort in an innovative design, now featuring CNG technology This model prioritizes fuel efficiency, safety, and reliability while delivering enhanced power, making it a cost-effective choice for customers seeking low ownership expenses.

The Electronic Stability Program (ESP) utilizes an onboard microcomputer to monitor vehicle stability in real-time through various sensors When the vehicle experiences instability, such as during lane changes or high-speed cornering, ESP automatically engages differential brakes on all four wheels, ensuring the vehicle remains stable and on its intended path without requiring any action from the driver.

• Sequential injection has been introduced in LPG (liquefied petroleum gas) vehicles to ensure reduced emissions, better fuel economy, and improved performance.

• Variable geometry turbocharger (VGT) has been introduced in diesel engines for improving performance and fuel efficiency.

5.2.5 Management Initiatives Taken by the Four-Wheeler

The organization fosters a culture of accountability, transparency, and ethical conduct across all business interactions, embraced by management, employees, and the board of directors It has implemented robust procedures and systems to ensure that the board is well-informed and equipped to fulfill its responsibilities, while also providing management with the strategic direction necessary to generate long-term shareholder value.

To meet the organizational responsibilities of safe working environment, the company has established an OHSMS (occupational health and safety management system) for:

Effective risk management involves identifying potential hazards through thorough assessments and both internal and external audits By implementing necessary preventive measures and control actions, organizations can mitigate the risks of injury, loss, damage, or ill health.

• Complying with legal and other obligations: They ensure that busi- ness here is managed in accordance with occupational health stan- dards and safety legislations.

Case Study at the Heavy Vehicle Manufacturing Unit

Established in 1983, the heavy vehicle manufacturing unit focused on producing light commercial vehicles (LCVs) while pioneering advanced production technologies This initiative sought to create a new organizational culture and value system, empowering the company to confidently navigate the challenges of competitive markets.

Growth for the Last Five Years.

Profit for the Last Five Years.

The Indian commercial vehicle sector is actively addressing challenges posed by the economic slowdown while also focusing on new product development A notable player in this field is a heavy vehicle manufacturing unit based in Ropar, Punjab, which is enhancing its presence in the medium and heavy commercial vehicle (M and HCV) segment, particularly in trucks and buses, following a partnership with a Japanese commercial vehicle manufacturer in 2006 Despite a 19.13% decline in the M and HCV segment during the April–December 2012 period, the future outlook remains optimistic.

5.3.1 Technological Initiatives at the Heavy Vehicle Manufacturing Unit

Recognizing the growth potential in the Indian market, the company is actively investing in its development by appointing a dedicated director of research and development, who also serves as a full-time board director The division is currently conducting in-depth research on the Indian market to identify opportunities for customized products.

In the past two years, the unit has invested Rs 14 crores in research and development, infrastructure, and engine testing This fiscal year, it aims to expand its workforce from 30 to 100 to meet new project demands involving Japanese designs Additionally, the new management has initiated its entry into the M and HCV market by launching two buses and a cargo truck based on its heavy vehicle manufacturing chassis.

The company is actively developing new cargo trucks exceeding 16 tons for the heavy commercial vehicle market Currently, their product lineup includes vehicles ranging from 5.5 to 12 tons in cargo capacity and buses up to 16 tons Their long-term strategy aims to establish a presence in the 5.5 to 49-ton segment, with the heavy vehicle manufacturing unit contributing technical expertise for vehicles in the 12-ton and above category.

Diesel engines offer numerous advantages like a longer cruising range, low

CO2 emissions, and a superior fuel economy The unit is focusing on enhancing these advantages and reducing emissions to produce the best diesel engines in the world.

5.3.1.2 Low Pollution Alternative Fuel Vehicles

The unit is focused on creating hybrid-electric trucks and vehicles that utilize alternative energy sources like dimethyl ether, liquefied petroleum gas, and compressed natural gas These low-emission alternative-fuel vehicles enhance the efficient use of limited resources while promoting cleaner emissions.

5.3.1.4 Upgrading the Bread-And-Butter Models

Current research at Ropar focuses on enhancing branded Mazda products, known as 'bread-and-butter models,' which significantly contributed to the sale of 13,646 units in FY'12 Among these, passenger carriers accounted for 6,611 units, while cargo vehicles made up 7,035 units, with the brand itself contributing 154 units From April to December 2012, total sales reached 8,915 units, split between 4,456 passenger carriers and 4,459 cargo units, with the brand contributing 117 units Despite a projected growth target of 10%, the company anticipates similar sales figures for FY'13, reflecting the ongoing slowdown in the commercial vehicle market.

5.3.1.4 Engine Localization to Trigger Growth

Since 1984, the 3.5-liter Mazda engine has undergone numerous upgrades at the in-house research and development center to comply with BS-IV emission norms, while existing LCVs are powered by both BS-III and BS-IV engines Thanks to localized Mazda products, the company has achieved a 13% market share in the 100,000-unit LCV market Over the years, several variants of the 3.5-liter engine have been developed, and Mazda is now enhancing its research and development capabilities with a planned investment of INR 200 crores.

The research and development division is expanding with new staff, including three Japanese engineers, to enhance future product offerings The CV manufacturer plans to utilize various series of 4- and 6-cylinder engines in its M and HCVs, along with transmissions that are currently sourced from Japan.

With modifications in fuel and electronic adjustments, engines exhibit varying power outputs, such as 4-cylinder engines producing 150–175 horsepower and 6-cylinder engines generating 230–300 horsepower, suitable for trucks and buses Currently, these commercial vehicles are undergoing testing to assess their viability in Indian market conditions, utilizing imported engines.

The next phase involves establishing an agreement for the localization of the engines Following the introduction of the 27-seater bus equipped with a 4-cylinder, 5.2-liter engine, a 45-seater super-luxury bus (LT) has also been launched.

134) with a 6-cylinder, 7.7-liter engine But the company pictures more market potential for the mid-segment 41-seater bus as competitors have done well in this category.

In 2011, the unit introduced the IS12T, a 12.5-ton cargo truck featuring a 4-cylinder, 5.2-liter engine with 150 hp Despite positive customer feedback, the truck's pricing, driven by the use of an imported engine, has been a concern As a result, the company is currently selling the IS12T on a non-profit basis to enhance accessibility for customers.

The buses equipped with advanced engines can rival offerings from Mercedes and Volvo; however, they struggle against local competitors like Tata Motors and Ashok Leyland unless they adapt imported engines and transmissions for their models Their product lineup features various branded buses, including mini buses, ambulances, school buses, executive buses, and city buses Additionally, the truck range encompasses crew cab trucks, tippers, and cargo carriers, such as the Sartaj and Cosmo models.

Since 2001, CNG power has been a prominent feature on the platform, with the company asserting a 70% market share in Delhi's passenger carrier segment Plans are underway to introduce more variants on the Mazda platform to capture untapped market areas Additionally, the light commercial vehicle (LCV) market has outperformed other commercial vehicle segments, with domestic sales experiencing a 15% growth from 327,406 units in April-December 2011 to 378,509 units in the same period in 2012.

In 2012, M and HCV domestic sales fell by 19%, totaling 198,079 units compared to 244,921 units the previous year The company has established a robust vendor base of 476 units to support the localization of heavy vehicle manufacturing Although exports remain minimal, with only 1,000 units shipped annually to countries like Bangladesh, Sri Lanka, Nepal, and various African nations, there are promising prospects for expanding its product range The company prioritizes transparency, corporate governance, and ethical practices, viewing itself as a trustee for its stakeholders By focusing on quality services and vehicles, it aims to achieve profitability and corporate excellence.

Case Study at the Agricultural Manufacturing Unit

In the 1960s, the green revolution led to a significant rise in tractor usage, highlighting the urgent need for the country to develop sufficient indigenous manufacturing capabilities to satisfy the growing demand for these agricultural machines.

In 1965, the Central Mechanical Engineering Research Institute (CMERI) in Durgapur began designing tractors that embodied wisdom and knowledge The product's name was chosen to reflect India's identity, power, and elegance while ensuring ease of pronunciation By 1970, the Punjab government had established this innovative unit.

Growth for the Last Five Years.

This company endeavors to create India’s largest network for distribution of automobiles, automobile-related products, and services.

The founders of Mahindra held a strong belief in the exceptional capabilities of Indians, inspiring the team to demonstrate this conviction By fostering self-belief, Mahindra aims to establish itself as a globally recognized brand renowned for the quality of its products and services.

The Mahindra Group's core values are essential to all its companies, particularly within the agricultural manufacturing unit These values are shaped by historical influences, current practices, and future aspirations They serve as a guiding compass for both corporate actions and personal conduct.

The agricultural manufacturing unit is dedicated to achieving long-term success while prioritizing the needs of the country and upholding ethical business standards The core values that guide this unit are illustrated in Figure 5.40.

In pursuit of customers’ trust

Easy on people and the plant

PROFESSIONALISM GOOD CORPORATE CITIZENSHIP CUSTOMER FIRST QUALITY FOCUS DIGNITY OF THE INDIVIDUAL

We aim to disrupt traditional perspectives and leverage our resources creatively to foster positive transformation for our stakeholders and communities worldwide, empowering them to rise.

They sought the best people and give them freedom and opportunity to grow They support well-reasoned risk-taking and innovation, but demand performance.

The success of the agricultural manufacturing unit relies on its customers, as it strives to meet their expectations and needs promptly and courteously An integrated development strategy is illustrated in Figure 5.41.

Quality is the basis for delivering value for money to the customers The unit makes quality a driving force in their products, work and while interacting with others.

The unit values an individual’s dignity, respect the time and efforts of others, and sustains the right to express disparity Through their actions, they nurture trust, transparency, and fairness.

5.4.3.6 Continuously Improving Systems and Processes

They promote the plan-do-check-act (PDCA) method for analysis and improvement Emphasis is laid on education and training so that every- body can do their jobs better.

5.4.3.7 Improving Productivity, Safety, Effectiveness, and Reducing Waste by Using Kaizen

The company trains for consistency in reducing variation, building a foundation for common knowledge and allows workers for easy under- stand their roles.

5.4.3.8 Encouraging Staff to Learn from One Another and Offer an

Environment and Culture for Effective Teamwork

The unit is dedicated to fostering effective leadership by encouraging managers and supervisors to thoroughly understand their processes and team members It emphasizes the importance of not just overseeing tasks, but also providing essential resources and support to empower each staff member to achieve their best performance.

5.4.3.9 Emphasis the Importance of Transformational Leadership and Participative Management

The unit encourages employees not just focus on meeting quotas and targets, but to reach their full potential It also emphases on eliminating fear.

5.4.3.10 Allowing People to Perform at Their Best by Ensuring, They are Not Afraid to Express Concerns or Ideas

There is an emphasis on doing the right thing versus blaming others when mistakes occur Workers must be encouraged tofind better ways of doing things.

5.4.3.11 Ensuring that Leaders Work with Teams and Act in the Company’s Best Interests

Honest and open communication should be employed to allay fear and break down barriers among departments.

5.4.3.12 Building the‘Internal Customer’ Concept–Recognize that Each Department Serves Other Departments that Use Their Output

The goal is to build a shared vision and use cross-functional teamwork for creating understanding and reduce adverse relationships.

5.4.3.13 Rather than Measuring the People Behind a Process, Measure the Process

Encourage individuals to take pride in their unique contributions without making comparisons By treating everyone equally and fostering an environment free from competition, the overall quality of work will naturally improve over time, elevating everyone's performance to new heights.

5.4.3.14 Enable Self-Improvement by Implementing Education

The goal is to enhance employee skills and promote continuous learning, equipping them to face future challenges This approach fosters a more adaptable workforce, leading to significant improvements and resilience in the face of change.

1 Consistency of purpose to plan services and products, that will have a market and keep the organisation in competition and provide jobs

2 Emphasize short-term profits: short-term thinking and a push from bankers and owners for dividends

3 Mobility of management: job hopping

Throughout the study period, the farm division prioritized maintaining fuel efficiency while adhering to new engine emission standards Table 5.6 highlights the technologies imported by this agricultural manufacturing unit.

This was done on the engines with improvement focused on technology and overall tractor optimization Efforts were focused on developing a range of mechanization solutions:

• Global vision with specific focus on exports backed by long-term strategy

• Capability displayed in developing new products

• Low costs for reengineering efforts and improved productivity

• Capability to develop service and sales network

Technologies Imported by This Unit

1 New generation engine management system 2009 Technology absorbed

2 Electronic programs for safety, stability, and steering control 2009 Technology absorbed

3 Controller Area Network (CAN)-based networking 2009 Technology absorbed

4 Advanced material technology 2009 Technology absorbed

5 Development of components using alternate material and advanced manufacturing processes 2010 In process of absorption

6 Engine upgrades and emission improvement technologies 2010 In process of absorption

7 Technology for Noise Vibration Harshness (NVH)

8 Electrical and electronic technology for safety, infotain- ment, and convenience feature addition 2010 Technology absorbed

9 New suspension system for improved comfort 2010 Technology absorbed

10 Agri implements technology transfer 2010 In process of absorption

11 Advanced engine technologies 2011 In process of absorption

12 Advanced propulsion technologies 2011 In process of absorption

13 Technology for NVH improvement 2012 In process of absorption

14 Hybrid vehicle technology 2012 In process of absorption

• Rapidly increasing exports: world-class management and quality systems certifiedfirst through ISO 9000

Total Quality Management (TQM) is built on key pillars including quality assurance in manufacturing, new product development, customer operations, sales, and supplier management Central to this model is the goal of satisfying employees, suppliers, customers, stakeholders, and society as a whole Enhanced collaboration among these three major stakeholders has led to significant improvements in overall performance.

Continuous improvement is a fundamental principle of Total Quality Management (TQM), essential for maintaining market share in a competitive environment Companies that fail to innovate risk losing ground to rivals, as evidenced by European electronics firms and American automakers facing challenges from Japanese competitors Effective process management and employee feedback are crucial for identifying improvement opportunities in processes, products, and services A key tool for this is the PDCA cycle, also known as the Deming cycle, named after the American quality expert Edward Deming, which emphasizes a systematic approach to continuous improvement.

1 Select the theme or project

3 Analyze the cause and determine corrective action

Swaraj prioritizes employee training to expose them to innovative ideas and concepts, fostering personal and professional growth The company emphasizes the development of its members through various training programs and workshops focused on managerial, behavioral, and technological skills Their training and development strategy is closely aligned with the firm's strategic plan, ensuring that the business objectives guide the necessary competencies, knowledge, and skills required for organizational success.

5.4.8 Impact of Competencies on Strategic Success of the Agricultural Manufacturing Unit

The strategy involves starting with imported technologies and gradually localizing them over time Additionally, significant research and development efforts are essential for gaining a foothold in the tractor segment.

Multi-Criteria Decision-Making Techniques

Analytical Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) is a systematic decision-making method that organizes and evaluates multiple criteria by creating a hierarchical structure and assessing their relative significance It facilitates the comparison of alternatives for each criterion and ranks them accordingly, as noted by Decision Support System (DSS) Resources AHP incorporates both objective and subjective evaluation metrics, serving as an effective tool for ensuring consistency in decision-making and minimizing bias, as highlighted by the team.

The Analytic Hierarchy Process (AHP) helps organizations reduce decision-making distractions, such as planning issues and lack of participation, which can hinder effective choices This method is particularly useful when decisions involve multiple criteria and value ratings AHP breaks down complex problems into smaller evaluations while maintaining their relevance to the overall decision As a multi-attribute decision-making (MADM) technique, AHP utilizes matrices and eigenvectors to derive approximate values, as established by Saaty in 1980.

The Analytic Hierarchy Process (AHP) is a widely recognized prescriptive and descriptive decision-making model, applicable across numerous fields, with its effectiveness validated by various organizations (Saaty, 1994) As the most popular multiple-criteria decision-making (MCDM) technique today, AHP facilitates the comparison of criteria or alternatives through a pairwise approach, utilizing an absolute numbers scale supported by theoretical and empirical research (Singh and Ahuja, 2012).

AHP can be applied to the following situations (De Steigur et al., 2003):

• Choice: Choosing one alternative from a given set of alternatives, when there are multiple criteria involved

• Ranking: Putting alternatives in an order from most to least desirable or vice-versa

• Prioritization: Determining the relative importance of alternatives, as opposed to selecting only one or simply ranking them

• Resource allocation: Allocating resources between set of alternatives

• Benchmarking: Comparing the processes in own organization with those of other top-performing organizations

• Quality management: Concerning multidimensional aspects of qual- ity improvement

• Conflict resolution: Resolving disputes among parties with incompa- tible goals or positions.

The Analytic Hierarchy Process (AHP) is widely applied in complex situations, addressing thousands of problems related to selection, priority setting, resource allocation, and planning Its applications extend to total quality management, balanced scorecard development, quality function deployment, business process reengineering, and forecasting, showcasing its versatility in enhancing decision-making across various fields.

6.1.1 Comparison Scale for Pairwise Comparison

Pairwise comparison is an important stage in AHP for determining priority values of attributes and providing a relative rating for alternatives.

A measurement scale quantifies the relative significance of various factors through numerical values that correspond to verbal assessments This discrete scale ranges from 1 to 9, with 9 indicating the highest importance of one factor over another, while 1 represents equal importance between two factors, as illustrated in Table 6.1 (Singh and Ahuja, 2012).

In this study, the significance of the jth sub-objective is assessed in relation to the ith sub-objective To facilitate this comparison, an 11 × 11 matrix was constructed, reflecting the number of variables involved in the analysis This matrix serves as a systematic approach for evaluating the interrelationships among the sub-objectives.

1 In the matrix, the diagonal elements are kept 1.

2 Values in the upper triangular matrix are filled by using the data compiled through responses from various organizations.

3 For the lower triangular matrix, the upper diagonal values are reci- procated as a ji ẳ 1=aij Thus, the comparison matrix for different attributes is shown in Table 6.2.

In the Analytic Hierarchy Process (AHP), the analysis involves squaring the pairwise comparison matrix iteratively to derive the eigenvectors The initial matrix squaring process is illustrated in Table 6.2, while the resulting matrix after calculations is presented in Table 6.3.

PC PDD PP RME PPC QC SA MGT ADM TW INT

1 Equal Importance Two factors contribute equally to the objective

Experience and judgment favor one factor over another

Experience and judgement strongly favour one factor over another

A factor is strongly favored and its dominance is demonstrated in practice

The evidence of favouring one factor over another is of the highest possible order of affirmation

2, 4, 6, 8: Intermediate value when compromise is needed

Second iteration: squaring the matrix is shown in Table 6.3 Table 6.4 shows the resultant matrix.

PC PDD PP RME PPC QC SA MGT ADM TW INT

TABLE6.4 MatrixAfterSecondIteration PC PDD PP RME PPC QC SA MGT ADM TW INT PC 2872.45 4438.69 5340.62 6974.14 1714.72 2798.22 6494.77 2351.15 10288.61 11912.35 10984.37 PDD 1680.65 2632.94 3168.98 4071.42 989.09 1634.29 3808.05 1397.90 5948.68 6848.58 6318.28 PP 1508.00 2375.16 2872.43 3716.26 881.76 1471.69 3455.14 1260.45 5367.07 6120.42 5609.11 RME 1028.55 1623.39 1954.30 2604.66 607.40 1007.11 2412.34 863.62 3818.11 4320.70 3948.24 PPC 4343.57 6701.10 8116.23 10695.58 2641.43 4223.94 9953.24 3624.27 15759.88 18305.18 16941.94 QC 2712.22 4268.77 5146.2 6750.21 1657.92 2704.39 6287.39 2311.34 9927.79 11542.13 10630.92 SA 956.18 1503.35 1812.14 2405.70 564.55 934.523 2229.17 798.33 3523.71 3996.73 3651.96 MGT 3064.23 4736.07 5724.76 7508.78 1839.67 2955.26 7000.54 2571.01 10964.38 12703.14 11797.83 ADM 769.24 1218.97 1471.95 1972.00 461.75 763.16 1823.53 645.84 2925.15 3311.16 3006.41 TW 564.71 891.247 1074.57 1428.22 344.49 561.79 1326.85 479.83 2117.73 2437.29 2232.94 INT 666.47 1034.23 1251.44 1644.45 406.91 649.80 1534.55 561.04 2434.65 2828.97 2620.24

Third iteration: Table 6.5 provides the values for the results obtained after squaring the matrix in Table 6.4.

After comparing the eigenvector matrices from the second and third iterations, we observe that they are nearly identical, indicating that additional squaring and iterations will yield the same solution Next, we will evaluate the consistency ratio (CR) to assess the reliability of our results.

To determine the next priority vector, the eigenvector of the matrix must be normalized This normalization process involves dividing each entry in every column by the sum of the entries in that column The normalized matrix is presented in Table 6.6.

The normalized value (rij) is calculated as: r ij ẳa ij =Pa ij Eq:6:1

Table 6.7 shows the normalized matrix with priority weights Further, the approximate value of priority weights (W1, W2, W3, , Wj) is obtained as:

The Consistency Ratio (CR) is calculated by comparing the Random Index (RI) to the Consistency Index (CI) A CR value of less than 0.1, or 10%, indicates that the judgments made are consistent and acceptable.

TABLE6.5 MatrixAfterThirdIteration PC PDD PP RME PPC QC SA MGT ADM TW INT PC 81352435.95 127296994.5 153615161.5 201990640.90 48745995.7 79685605.92 187857263.9 68148885.93 296784604.5 341123264.6 313657961.9 PDD 47527203.03 74368274.08 89743117.14 117993152.1 28474291.79 46550595.83 109738494.5 39811945.71 173353374.5 199249324.7 183209503.6 PP 42788446.83 66959040.81 80797895.35 106232652.3 25632889.36 41909208.92 98799570.22 35842297 156069602.6 179372882.1 164928548.1 RME 29640285.38 46384612.92 55973864.47 73605795.61 17757663.19 29033307.65 68453433.43 24830172.52 108145267.7 124283656.5 114268993.5 PPC 124283423.80 194470861.5 234679518.8 308601518.7 74473267.02 121735016.7 287006245.5 104114569.1 453436384 521175039.6 479214880.20 QC 78539412.27 122896272.2 148306025.7 195015339.9 47061839.56 76929740.97 181369729.5 65796551.1 286532847.4 329339086.2 302825255.7 SA 27446498.46 42950976.53 51830392.41 68156005.41 16443440.77 26884338.49 63385373.95 22992168.9 100137918.4 115083235 105810778.5 MGT 87166795.1 136390651.1 164589915.7 216422776.3 52228317.9 85375463.81 201279677.2 73018540.72 317982681.3 365485832.1 336065142.6 ADM 22428232.47 35099693.47 42356195.84 55702378.32 13437782.94 21970229.29 51802527.45 18789499.02 81844050.01 64055660.44 86474405.91 TW 16462205.66 25761079.77 31087290.95 40881455.87 9864227.248 16125472.65 38020054.09 13791516.79 60068162.85 69037038.82 6376048.97 INT 19164349.99 29987204.14 36187446 47586304.54 11483780.65 18771343.85 44256363.66 16054526.54 69919910.27 80365256.07 73895148.11

PC PDD PP RME PPC QC SA MGT ADM TW INT

Normalized Matrix With Priority Weights

The consistency test values are shown in Table 6.9.

The resulting weights for the criteria based on the above pairwise compar- isons are given in Table 6.10.

Table 6.10 showcases the AHP results, confirming the validation of previous work through this analytical tool The key factors identified—production planning and control, product concept, quality control, and management—were prioritized based on both questionnaire analysis and case studies.

Maximum Eigenvalue CI RI CR

Raw Material and Equipment 5.53% 7 Production Planning and Control 20.57% 1

Technique for Order of Preference by Similarity to Ideal

TOPSIS is a method designed for tackling Multi-Criteria Decision-Making (MCDM) problems, focusing on selecting alternatives that are closest to the Positive Ideal Solution (PIS) while being farthest from the Negative Ideal Solution (NIS) This approach offers a more realistic modeling perspective compared to non-compensatory methods, which strictly include or exclude alternatives based on rigid cut-offs In TOPSIS, the NIS aims to maximize costs and minimize benefits, while the PIS seeks to maximize benefits and minimize costs, under the assumption that each criterion needs to be either maximized or minimized.

In this method, options are graded according to ideal solution similarity. The option has a higher grade, if the option is closer to an ideal solution.

An ideal solution represents the optimal choice under any circumstances, determined by an algorithm that evaluates perceived positive and negative ideal solutions based on available attribute values for various alternatives The most favorable solution is identified by the shortest distance to the positive ideal solution and the longest distance from the negative ideal solution, with distances calculated using Euclidean metrics This approach involves assessing m alternatives across n attributes, with scores assigned to each option relative to each criterion.

NIS is a ranking technique that evaluates alternatives based on their proximity to an ideal solution This method operates on the straightforward principle that the optimal choice maximizes benefits while being distinctly superior to the least favorable option, which offers minimal benefits In this framework, the ideal solution is assigned a rank of 1, while the worst alternative approaches a rank of 0 The NIS method incorporates three types of attributes or criteria for assessment.

In this method, two artificial alternatives are hypothesized:

Ideal alternative: The alternative which has the best level for all attri- butes considered.

Negative ideal alternative: The alternative which has the worst attribute values.

Let Xijscore of option i with respect to criterion j

We have a matrix X = (Xij) m×n matrix.

In decision-making, let J represent the set of beneficial attributes where an increase is preferred, while J’ denotes the set of negative attributes where a decrease is desirable The decision matrix for the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is illustrated in Table 6.11.

Step 1: Construct the normalized decision matrix This step transforms various attribute dimensions into non-dimensional attributes, which allows comparisons across criteria The decision problem can be concisely expressed in the normalized decision matrix Table 6.12 provides the normalized decision matrix.

Normalize scores or data as follows: r ij ẳ X ij = X 2 ij for iẳ1; ; m; jẳ1; ; n Eq:6:4

PC PDD PP RME PPC QC SA MGT ADM TW INT

PC PDD PP RME PPC QC SA MGT ADM TW INT Weights

Step 2: Construct the weighted normalized decision matrix Not all of the selection criteria may be of equal importance and hence weighting was introduced from AHP technique to quantify the relative importance of the different selection criteria The weighting decision matrix is simply con- structed by multiply each element of each column of the normalized decision matrix by the random weights Multiply each column of the normalized decision matrix by its associated weight The weighted normalized decision matrix is shown in Table 6.13 An element of the new matrix is: v ij ẳ w j r ij Step 3: Determine the ideal and negative ideal solutions Tables 6.14 and 6.15 shows the weighted normalized decision matrix for ideal and non- ideal solutions, respectively.

Ideal solution, A ẳfv1 ; ; vn g, where v j ẳmax v ij if j2J; min v ij if j2J 0

Eq:6:5 Negative ideal solution, A 0 ẳfv1 0 ;vn 0g, where v 0 ẳmin v ij if j2J; max v ij if j2J 0

Step 4: Calculate the separation measures for each alternative. Tables 6.16a and 6.16b shows separation from ideal and non-ideal solu- tions respectively The separation from the ideal alternative is:

PC PDD PP RME PPC QC SA MGT ADM TW INT

Similarly, the separation from the negative ideal alternative is:

Step 5: Calculate the relative closeness to the ideal solution Ci *

Weighted Normalized Decision Matrix for the Ideal Solution

PC PDD PP RME PPC QC SA MGT ADM TW INT Max

Weighted Normalized Decision Matrix for the Negative Ideal Solution

PC PDD PP RME PPC QC SA MGT ADM TW INT Min

Select the option with Ci *closest to 1.

Table 6.17 displays the TOPSIS results, confirming that the factors identified through questionnaire analysis, case studies, and AHP align with previous findings The key factors selected for further validation include production planning and control, product concept, quality control, and management.

VIKOR Method

The compromise solution, known as VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje), is a multi-criteria optimization method used in multi-attribute decision-making (MADM) It aims to identify the most feasible solution that closely aligns with the ideal solution through mutual concessions By focusing on ranking and selecting alternatives amidst conflicting criteria, the VIKOR method establishes a compromise ranking list and solution using a multi-criteria ranking index that measures the "closeness" to the ideal outcome.

The procedure of VIKOR for ranking alternatives is as follows:

Step 1: Determine that best XJ * and the worst Xj - values of all criterion functions, where j = 1, 2, , n Table 6.18 shows the decision matrix

Separation from the Ideal Solution

PC PDD PP RME PPC QC SA MGT ADM TW INT

TABLE6.16B SeparationfromtheNegativeIdealSolution PC PDD PP RME PPC QC SA MGT ADM TW INT PC 0.000071 0.000762 0.000301 0.000036 0.000000 0.001209 0.000027 0.000000 0.000267 0.000168 0.000003 PDD 0.000000 0.000037 0.000564 0.000087 0.000004 0.000002 0.000009 0.000003 0.000771 0.000049 0.000016 PP 0.000005 0.000000 0.000042 0.001095 0.000010 0.000029 0.000278 0.000008 0.000381 0.000020 0.000001 RME 0.000088 0.000022 0.000000 0.000051 0.000041 0.000028 0.000040 0.000015 0.000421 0.000066 0.000441 PPC 0.001702 0.000501 0.000386 0.000146 0.000114 0.000847 0.000111 0.000348 0.000000 0.000010 0.000001 QC 0.000000 0.000507 0.000138 0.000174 0.000006 0.000100 0.000301 0.001864 0.000021 0.000071 0.000093 SA 0.000069 0.000010 0.000000 0.000034 0.000026 0.000003 0.000025 0.000005 0.000144 0.000352 0.000065 MGT 0.001304 0.000421 0.000334 0.000306 0.000033 0.000000 0.000250 0.000102 0.000166 0.000021 0.000077 ADM 0.000011 0.000000 0.000002 0.000001 0.000522 0.000194 0.000008 0.000034 0.000057 0.000399 0.000183 TW 0.000018 0.000037 0.000107 0.000037 0.000248 0.000076 0.000000 0.000212 0.000000 0.000053 0.000483 INT 0.000830 0.000223 0.001101 0.000000 0.000825 0.000117 0.000018 0.000148 0.000018 0.000001 0.000108

Step 2: Range Standardized Decision Matrix

X 0 ij ẳ ẵðX ij Xj ị=ðXJ Xj ị Eq:6:10

Following Table 6.19 shows the range standardized decision matrix for the VIKOR method.

Step 3: Compute the S i (the maximum utility) and R i (the minimum regret) values, i=1, 2, ., m by the relations:

PC PDD PP RME PPC QC SA MGT ADM TW INT

Attributes Si* Si’ Si* + Si’ Ci=Si’/Si* + Si’ Rank

Raw Material and Equipment 0.0852 0.0502 0.1354 0.3706 7 Production Planning and Control 0.0724 0.064 0.1364 0.4694 1

Eq:6:12 where Wjis the weight of the j th criterion which expresses the relative importance of criteria.

In multi-criteria decision-making (MCDM), the significance of each criterion is represented by its weight To facilitate the comparison of these weights across diverse criteria, range standardization is employed, converting various scales and units into a uniform measurable format.

The matrix D' represents the data after applying range standardization, where max Xij and min Xij denote the maximum and minimum values of criterion j, respectively All values within D' are normalized to the range of 0 to 1, resulting in D' = ð0x 0 ị mxn.

The standard deviation (σj) calculated for every criterion indepen- dently as:

PC PDD PP RME PPC QC SA MGT ADM TW INT

Where X¯ ’ j is the mean of the values of the jth criterion after normal- isation and j= 1,2, n.

After calculating (σj) for all criteria, the (CV) of the criterion (j) will be as shown

The weight (Wj) of the criterion (j) can be defined as

Based on these, Table 6.20 provides the weights assigned to criteria. Step 4: Compute the value Q i ;i ẳ1;2; .;m, by the relation

Q i ẳf ðSi S ị=S S ịg ỵ ð1f ịðRi R ị=R R ịg Eq:6:16

Where S * = min Si, S - = max Si, R * = min Ri, R - = max Ri, and ν is the introduced weight of the strategy of Siand Ri.

Step 5: Rank the alternatives, sorting by the S, R, and Q values in decreasing order The results are shown in Table 6.21.

VIKOR has gained significant traction in addressing multi-criteria decision-making (MCDM) challenges across diverse sectors, including environmental policy, data envelopment analysis, and personnel training selection Key factors identified for further validation in this context include production planning and control, product concept development, quality control, and effective management practices.

Fuzzy Logic Using MATLAB

Fuzzy logic (FL) is grounded in the idea of fuzzy sets, which lack precise boundaries and include elements with varying degrees of membership A membership function (MF) maps each input point to a value between 0 and 1, allowing for flexible and efficient representation of relationships The shape of the MF can be tailored to be simple and effective, enhancing the connection between input and output variables This framework supports process optimization through fuzzy if-then rules, expressed as "if x is A then y is B," where A and B are defined by fuzzy sets.

In rule-based reasoning, the "if" segment is known as the premise, while the "then" segment is referred to as the conclusion For instance, if the product concept is low and the quality is high, the outcome is deemed acceptable These rules are established based on expert insights and are continuously refined through practical application and evaluation, which may validate or necessitate adjustments to them (Singh and Ahuja, 2012).

The function itself can be arbitrary curves whose shape can be define as a function that suits from the point of view of simplicity, convenience,

Attributes Si Ri Qi Rank

Interpersonal 0.58174 0.125705 0.981099 10 speed, and efficiency A function is a mathematical representation of the relationship between the input and output of a system or a process It facilitates the optimization of process output by defining the true relation- ship between input and the output variables Basically, it has been applied to validate the input factors determined from earlier tools Therefore, only the identified factors are tested and results obtained justify the earlier obtained results The fuzzy logic toolbox graphical user interface (GUI) tool to build a FIS is shown in Figure 6.1a.

Fuzzy inference involves creating a mapping from input to output using fuzzy logic (FL), which serves as a foundation for decision-making and pattern recognition.

Fuzzy Inference Systems (FISs) have proven effective in various domains, including automatic control, data classification, decision analysis, expert systems, and computer vision Due to their multidisciplinary characteristics, FISs are referred to by several names, including fuzzy-rule-based systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory, and fuzzy logic controllers The procedure of FIS relevant to this study is illustrated in Figure 6.1b.

The first step is to take the inputs and to determine the degree to which they belong to each of the appropriate fuzzy sets via MFs In fuzzy logic

FIS Editor: To handle high level issues for the system Membership Function

Editor: To define the shapes of all the functions

Surface Viewer: To view the dependency of one of the outputs Output

Rule Viewer: To view the fuzzy inference diagram

Rule Editor: To edit the list of rules

In the FL Toolbox, the software processes numerical inputs that are constrained to specific input variables, producing outputs that represent a fuzzy degree of membership within a defined linguistic set, always ranging between 0 and 1.

The Fuzzy Inference System (FIS) formulates rules that guide decision-making, primarily grounded in fuzzy set theory, fuzzy "IF-THEN" rules, and fuzzy reasoning.

Fuzzy inference systems (FIS) utilize "IF THEN " statements, employing connectors like "OR" and "AND" to formulate critical decision rules These systems can process both fuzzy and crisp inputs, yet they consistently produce outputs in the form of fuzzy sets.

Obtain the factors for validation from various MADM techniques i.e AHP, TOPSIS, VIKOR

Determining membership function for competency factors by consulting experts in industries

Setting Limits for membership function and Formation of

Employing Fuzzy Logic (Multi criteria Decision Making

FIGURE 6.1 b: FIS procedure for present study.

The defuzzification process takes an aggregate output fuzzy set as input and produces a single numerical value as output While fuzziness aids in evaluating rules throughout the process, the ultimate goal is to generate a precise numerical output for each variable.

Figure 6.2 depicts the empirical transfer function as a FL system with inputs and output being fuzzified using appropriate MFs.

The system evaluates inputs such as PC, PPC, QC, and MGT to determine the output, which indicates whether a selection should be accepted, considered, or rejected The subsequent sections will detail each component of this evaluation process.

The Product Concept (PC) is evaluated based on innovation and idea generation within a research and development environment By engaging with industry experts, fuzzy set rules have been established to classify PC: values deviating more than 3% from the actual are deemed low or high, while those within this range are considered optimal This classification system is illustrated in Table 6.22, with the corresponding fuzzy transfer function depicted in Figure 6.3.

Production planning and control (PPC) measurement is essential for effective production management Industry experts have established fuzzy set rules for PPC, stating that control levels deviating more than 2% from the actual measurement are classified as low or high, while those within this range are deemed optimum This classification is illustrated in Table 6.23, with the corresponding transfer function depicted in Figure 6.4.

Quality control (QC) is evaluated by the defect rate in delivered orders Industry experts have established fuzzy set rules for QC, stating that if the actual quality deviates by more than 4% from the standard, it is classified as low or high quality; otherwise, it is deemed optimum This is illustrated in Table 6.24, with the corresponding fuzzy transfer function depicted in Figure 6.5.

Management (MGT) is measured based on the support and cooperation from top management By consulting various experts from the industries,

TABLE 6.22 Range for PC measurement

The transfer function in fuzzy format for PC is defined by specific fuzzy set rules for MGT, which categorize the support level as low, high, or optimum based on whether it is less than or greater than 3% of the required amount This classification is illustrated in Table 6.25, with the corresponding transfer function depicted in Figure 6.6.

TABLE 6.23 Range for PPC Measurement

Transfer function in fuzzy format of PPC.

TABLE 6.24 Range for QC Measurement

6.4.3 Fuzzy Evaluation Rules and Solution

The article discusses key inputs in product development, including product concept, production planning, control, quality management, and overall management It highlights the establishment of 81 fuzzy rules, presented in Table 6.27, which follow the structure “if (condition a) and (condition b) and (condition c) and (condition d) then (result c),” reflecting various combinations of these input conditions.

Structural Equation Modeling

Validation of Qualitative Results through Structural Equation

This study utilized empirical data from a questionnaire survey, case studies, and Analytical Hierarchy Process (AHP) to validate findings through Structural Equation Modeling (SEM) Key input factors identified include production, planning and control, product concept, quality control, and management The model highlights various output factors such as sales, profit, growth and expansion, production capacity, production time, lead time, productivity, quality, competitiveness, and reliability.

In this study, Structural Equation Modeling (SEM) was conducted using Analysis of Moment Structures (AMOS) 21.0, which integrates various statistical techniques such as causal modeling, confirmatory factor analysis (CFA), and path analysis SEM is utilized to evaluate and specify models that illustrate the relationships between observed and unobserved variables, analyzing the interrelations through equations that define independent and dependent variables Observed variables, represented graphically by rectangles or squares, are measured on a Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), while unobserved variables, referred to as latent factors, are depicted by ovals or circles.

In AMOS, path diagrams are utilized to represent models, as illustrated in Figure 7.1, which depicts an outcome variable estimated by three observed variables A crucial skill in this process is translating data and theoretical hypotheses into a coherent path diagram, where observed variables, such as Ob1, Ob2, and Ob3, are represented within rectangles, derived from questionnaire responses.

The ellipse describes the latent variables (LVs) estimated from observed variables.

The error (e) shown in small circle, is an outcome in predicting a variable. The single-headed arrow describes predictive relations.

The double-headed arrow shows covariance.

Structural Equation Modeling (SEM) consists of two key components: the outer model, which connects observed variables to their indicators, and the inner model, illustrating the linear relationships between endogenous and exogenous unobserved variables SEM is often characterized as a blend of exploratory factor analysis (EFA) and multiple regression analysis (Ullman, 2001).

fit indexes are shown in Table 7.1.

CFA, or Confirmatory Factor Analysis, is employed when there is some understanding of the structure of the estimated variables In contrast, Exploratory Factor Analysis (EFA) is utilized in scenarios where the relationships between the unobserved and observed variables are ambiguous or not clearly defined.

This research employs Confirmatory Factor Analysis (CFA) within Structural Equation Modeling (SEM) to statistically test the proposed relationships between variables, grounded in empirical research and knowledge Unlike Exploratory Factor Analysis (EFA), CFA is utilized for a more precise data analysis, ensuring robust validation of the hypotheses.

SEM analysis focuses on assessing how well the proposed model aligns with observed data, particularly through the evaluation of the coefficients of hypothesized relationships A strong fit among multiple factors suggests consistency, which is crucial for enhancing overall performance (Barrett, 2007; Byrne, 1989; Schumacker and Lomax).

2004) Several goodness-of-fit indicators have been used for assessing a model.

7.1.1 Variables Involved in the Study

The variables chosen for this study were meticulously selected to reflect their impact on manufacturing competency and strategic success.

Cut-off Criteria for Several Fit Indexes

General for acceptable fit if data are continuous

Absolute/predictive fit chi-square

X 2 Ratio of x 2 to df ≤ 2 or 3, useful for nested models/model trimming Akaike information criterion

AIC The smaller the better; good for model comparison (nonnested), not a single model

BCC The smaller the better; good for model comparison, not a single model Bayes information criterion

BIC The smaller the better; good for model comparison (nonnested), not a single model

Consistent AIC CAIC The smaller the better; good for model comparison (nonnested), not a single model

ECVI The smaller the better; good for model comparison (nonnested), not a single model

Comparative fit Comparison to a baseline (independence) or other model Normed fit index NFI ≥ 0.95 for acceptance

Incremental fit index IFI ≥ 0.95 for acceptance

Incremental fit index TLI ≥ 0.95 can be 0 > TLI > 1 for acceptance 0.96

Tucker-Lewis index CFI ≥ 0.95 for acceptance 0.95

fit index RNI ≥ 0.95 similar to CFI but can be negative, therefore CFI better choice Parsimonious fi t

PNFI Very sensitive to model size

PCFI Sensitive to model size

PGFI Closer to 1 the better, thought typically lower than other indexes and sensitive to model size

Other goodness-of-fit index GFI ≥ 0.95 Not generally recommended

Adjusted GFI AGFI ≥ 0.95 Performance poor in simulation studies Hoelter 05 index Critical N largest samples size for accepting that model is correct Hoelter 01 index Hoelter suggestion, N = 200, better for satisfactory fit

In this study, the dependent variables or the performance parameters have not been separated into different variables, but only into one that is,

Output (OUTP) serves as a crucial metric for assessing the performance of automobile manufacturing units, encompassing aspects such as growth, expansion, quality, reliability, and cost This factor is instrumental in guiding companies through challenging and competitive landscapes, ensuring their success in the industry.

Considering the variables, the manufacturing competency questionnaire was designed and sent to small, medium, and large-scale industries for data collection.

The variables are selected based on correlation, regression, and AHP analysis, as shown in Table 7.2.

SEM of the Manufacturing Competency Model

The questionnaire was structured into four sections, with the first section focused on collecting demographic information about the respondents and their organizations This included details such as the organization name, job title, position, and years of experience, ensuring that participants had relevant backgrounds for the study.

The questionnaire effectively identified errors and discrepancies in the responses Additionally, its second, third, and fourth sections assessed the effectiveness of manufacturing competencies, strategic success, and output Each statement was specifically crafted to gather respondents' opinions on these critical areas.

General for acceptable fi t if data are continuous

RMR The smaller, the better; 0 indicate perfect fit

Root mean square error of approximation

RMSEA < 0.06 to 0.08 with confidence interval < 0.06 in the context of organization’s performance measurement using a four- point Likert scale Figure 7.2 shows the framework of SEM_MC model.

7.2.1 Data Screening for Preliminary Analysis

Following the collection of data through the questionnaire, a variety of analysis techniques were utilized to validate and enhance confidence in the findings This data facilitated the development of a Structural Equation Model (SEM) using AMOS, allowing for the exploration of the interrelationships among the study's variables The SEM analysis provided insightful results that contributed to a deeper understanding of the data.

TABLE 7.2 Independent Variables Taken for the Study Manufacturing Competency Independent Variables

Product Concept Production Planning & Control Quality Control

Production Capacity Production Time Lead Time Quality

Growth and Expansion Competitiveness Productivity Sales

The SEM_MC model framework identifies key variables that effectively illustrate the relationships within the model A crucial step in this analysis is the screening of these variables, ensuring that the data adheres to the requirement of multivariate normal distribution.

In this study, a univariate normality test was utilized due to the absence of a method for assessing multivariate normality in SPSS or AMOS Key metrics for data evaluation include skewness and kurtosis, illustrated in Figure 7.3 Additionally, Tables 7.3(a) and (b) present the skewness and kurtosis measurements for both independent and dependent variables.

Skewness indicates the asymmetry of a distribution, where a skewed variable has a mean that is not centrally located Conversely, kurtosis assesses the data's spread in relation to a normal distribution and reflects the distribution's peakedness.

According to Currie et al (1999), acceptable values for kurtosis are within the range of