Fuzzy numbers to approximate linguistic variable values.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"960",leadTitle:null,fullTitle:"Cancer of the Uterine Endometrium - Advances and Controversies",title:"Cancer of the Uterine Endometrium",subtitle:"Advances and Controversies",reviewType:"peer-reviewed",abstract:"The book Cancer of the Uterine Endometrium - Advances and Controversies brings together an international collaboration of authors who share their contributions for the management of endometrial carcinoma. 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\r\n\tThis book will focus on the wide specter of mucosal inflammation intending to provide the reader with a comprehensive overview of the current knowledge on the topic. More and more studies are focused on finding different aspects of mucosal inflammation since it is involved in the pathogenesis of many diseases - from gut to lungs, urogenital system, etc. To better understand the causes of its occurrence as well as the search for therapeutic strategies, many resources are invested in scientific developments in this field.
\r\n\r\n\tFacts about the role of calprotectin and other biomarkers were accumulated. Participation of neutrophils appears an attractive way to explain the involvement of different subpopulations of immunocompetent cells and cytokines in mucosal inflammation. Over the last five years, scientific developments in the field have discovered more genes involved in the pathogenesis of mucosal inflammation elucidating the interaction of innate immune mechanisms with the microorganisms in the gut and their role in maintaining intestinal homeostasis. Besides, the science aims at identifying and characterizing immune and non-immune cells involved in the emergence and maintenance of chronic inflammation.
\r\n\r\n\tThe book aims to cover the developing diagnostic methods for identifying the mucosal inflammation, towards a better analysis of the inflammation, understanding of the relationship between genetic and proteomic markers and response to therapy; and improving therapeutic options for patients who have mucosal inflammation.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"2e7bde3621cdf64518356b76e3132542",bookSignature:"Dr. Tsvetelina Velikova",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8843.jpg",keywords:"Inflammation, Neutrophils, Cytokines, Mucosal Biomarkers, Gut Tolerance, Antigen Tolerance, Mayo Score, Lesions, Acute Inflammation, Chronic Inflammation, Gut Permeability, Asthma",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 16th 2020",dateEndSecondStepPublish:"July 7th 2020",dateEndThirdStepPublish:"September 5th 2020",dateEndFourthStepPublish:"November 24th 2020",dateEndFifthStepPublish:"January 23rd 2021",remainingDaysToSecondStep:"8 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Velikova research focuses on autoimmune disorders, such as celiac disease, IBD, diabetes, asthma, as well as on the delicate autoimmunity mechanisms involving Th17 and Treg cells, cytokines, biomarkers, novel biologic therapies. She has been engaged in fifteen projects in the field of immunology and internal medicine. She is an editorial board member and reviewer for several medical journals and has publications in eminent journals and book chapters in the field of gastrointestinal immunology.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"180979",title:"Dr.",name:"Tsvetelina",middleName:null,surname:"Velikova",slug:"tsvetelina-velikova",fullName:"Tsvetelina Velikova",profilePictureURL:"https://mts.intechopen.com/storage/users/180979/images/system/180979.jpg",biography:"Dr. Tsvetelina Velikova received her MD and Ph.D. degrees, both with honors, from the Medical University of Sofia, Bulgaria. Subsequently, she became involved in active immunology research and teaching. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"742",title:"Align Agile Drivers, Capabilities and Providers to Achieve Agility: a Fuzzy-Logic QFD Approach",doi:"10.5772/5344",slug:"align_agile_drivers__capabilities_and_providers_to_achieve_agility__a_fuzzy-logic_qfd_approach",body:'\n\t\tAt the beginning of the twenty-first century, the world faces profound changes in many aspects, especially marketing competition, technological innovations and customer demands. A world-wide dispersion of education and technology has led to intense and increasingly global competition and an accelerated rate of change in the marketplace and innovation. There is a continuing fragmentation of mass markets into niche markets, as customers become more demanding with their increasing expectations. This critical situation has led to major revisions in business priorities, strategic vision, and the viability of conventional and even relatively contemporary models and methods developed thus far [1]. To cope with these changing competitive markets, as well as the ability to meet customer demands for increasingly shorter delivery times, and to ensure that the supply can be synchronized to meet the peaks and troughs of the demand are obviously of critical importance [2, 3]. Hence, companies now require a high level of maneuverability encompassing the entire spectrum of activities within an organization. Consequently, agility in addressing new ways to manage enterprises for quick and effective reaction to changing markets, driven by customer-designed products and services, has become the dominant vehicle for competition [4].
\n\t\t\tGenerally, agility benefits can mass customization, increase market share, satisfy customer requirements, facilitate rapid introduction of new products, eliminate non-value-added activities, reduce product costs and increase the competitiveness of enterprises. Accordingly, agility has been advocated as the business paradigm of the 21st century, being considered the winning strategy for becoming a global leader in an increasingly competitive market of quickly changing customer requirements [5-7]. However, the ability to build agility has not developed as rapidly as anticipated, because the development of technology to manage an agile enterprise is still in progress [4, 6, 8]. Thus, in embracing agility, many important questions must be asked, such as: Precisely what is agility, and how can it be measured? How will companies know when they possess this attribute since no simple metrics or indices are available? How and to what degree do the attributes of an enterprise affect its business performance? How does one compare agility with a competitive enterprise? To improve entrepreneurial agility, how does one identify the principal unfavorable factors? How can one assist in more effectively achieving agility [8-10]? Answers to such questions are critical to practitioners and the theory of agile entrepreneurial design. Therefore, the purpose of this research is to seek solutions to some of these problems, with a particular focus on agile strategic planning and measurement, as well as identifying the principal obstacles to improvement of agility.
\n\t\t\tActually, the purpose of agile strategic planning is to unite the resources of an enterprise and to create business value. Agile enterprises are concerned with change, uncertainty and unpredictability within their business environment and making an appropriate response; therefore, these enterprises require a number of distinguishing attributes to promptly deal with the changes within their environment. Such attributes consist of four principal elements [7, 8]: responsiveness, competency, flexibility/adaptability and quickness/speed. Furthermore, the foundation for agility is comprised of the integration of information technologies, personnel, business process organization, innovation and facilities into strategic competitive attributes. To be truly agile, an enterprise must logically integrate and deploy a number of distinguishing providers with drivers and good capabilities, being finally transformed into strategic competitive edges [11].
\n\t\t\tMany theoretical models have been proposed for agile enterprise planning [1, 12-15]; however, only a few provide integrated methodologies suitable for adoption to enhance by identifying providers, beginning with the competitive bases of the enterprise. The relationship matrix in the quality function deployment (QFD) method provides an excellent tool for aligning important concepts and linking processes. Moreover, fuzzy logic is a useful tool for capturing the ambiguity and multiplicity of meanings of the linguistic judgments required to express both relationships and rates of agility attributes.To assist managers in more efficiently achieving agility, a systematic methodology, based on fuzzy logic and the relationship matrix in the QFD is devised to provide a means for linking the perspectives from agility drivers with their corresponding capabilities and providers, thereby measuring the agility of an enterprise as well as identifying the principal obstacles to improvement.
\n\t\t\tThe remainder of this report is organized as follows. In Section II the related research is reviewed. In section III a conceptual model of an agile enterprise is described in detail for the development of a systematic evaluative methodology in Section IV. The development of a practical case is presented illustrated in Section V. Finally, Section VI a concluding discussion.
\n\t\t\n\t\t\t\tA. Methodology\n\t\t\t
\n\t\t\tNumerous studies for developing methodologies have been proposed to assist managers in the implementation of strategic planning for achieving agility. For example, to promote a new understanding of cooperation as a vital means of survival and prosperity in the new business era, Preiss et al. [12] proffered a generic model for approaching agility. This model consists of certain steps that can assist an enterprise in understanding its business environment and the changes occurring there, the attributes enabling the infrastructure, and the business processes that should be recognized in the subsequent actions of the organization to sustain its competitive advantage. The first integrated framework to achieve agility was proposed by Gunasekaran [15]. The framework explains how the major capabilities of agile manufacturing should be supported and integrated with appropriate providers to develop an adaptable organization. Seeking to exploit the concept and practices of agility, two research teams [1, 10] have developed a three-step methodology for achieving agility. This methodology provides manufacturing companies with a tool for understanding the total concept of agility, assessing their current positions, determining their need for agility and the capabilities required for achievement, as well as adopting relevant practices which can induce these capabilities. A three-step model was also suggested by Jackson and Johansson [14] to analyze the agility of production systems. Their methodology begins with an assessment of the degree of market turbulence, to determine the relevance of agility in a specific context. Then, the strategic view of the company is examined, with a particular focus on potentials to enhance flexibility and change competencies as viable strategies to achieve a competitive advantage.
\n\t\t\tAlthough structured frameworks to formulate agility have been identified, most of them for strategic formulation are structural in nature. Thus, to assure that the providers can satisfy the strategic direction of an enterprise, an integrated methodology suitable for adoption to enhance agility by identifying its providers, beginning with competitive bases of the enterprise, is critical to both practitioners and the theory of agile enterprise design.
\n\t\t\t\n\t\t\t\tB. Measurement\n\t\t\t
\n\t\t\tMany approaches to the measurement of agility have been proposed to assist managers in assessment; however, most of these methods assess only the capabilities of agility. Some authors [10, 16, 17] have defined an agility index as a combination of measurement of the intensity levels of enabling attributes; whereas, other measuring methods [18,19] have been developed on the basis of the logical concept of an analytical hierarchical process (AHP). An evaluation index for a mass-customization product manufacturing agility was devised by Yang and Li [20]. Furthermore, to overcome the vagueness of agility assessment, Tsourveloudis and Valavanis [21] designed some IF-THEN rules based on fuzzy logic; moreover, Lin et al. [6] developed a fuzzy agility index (FAI) based on providers using fuzzy logic. Each of these techniques, however, with the exception of the agility providers, seems to address only a limited aspect of a very complicated problem. Although each technique contributes to an understanding of the problem, each - functioning alone - is insufficient for handling the problem in its entirety because the selection of the provider and the assessment should be linked with the drivers and the capabilities [22]. It is therefore necessary to examine the problem from a broader perspective.
\n\t\t\t\n\t\t\t\tC. QFD Relationship Matrix\n\t\t\t
\n\t\t\tThe QFD method was designed to emphasize detailed pre-planning to meet customer needs and requirements for new product development. It employs several charts, called house of quality (HOQ), to translate the desires of the customer into the design or engineering characteristics of the product and subsequently into the characteristics of the parts, process plan and production requirements related to its manufacture. Phase I translates the voice of the customer into corresponding engineering characteristics; phase II moves one step backward in the design process by translating the engineering characteristics into characteristics of the parts; phase III identifies the critical process parameters and operations; and finally, phase IV identifies the detailed production requirements. The basic format of the HOQ consists of seven different major components: (1) customer requirements (CRs), (2) importance of customers’ requirements, (3) design requirements (DRs), (4) relationship matrix for CRs and DRs, (5) correlation among DRs (6) competitive analysis of competitors, and (7) prioritization of design requirements, as shown in Figure 1.
\n\t\t\tA basic house-of-quality (HOQ) matrix.
Although QFD has been proposed for customer-driven product development and delivery methodology, an enterprise can achieve various corporate strategic goals such as a reduction in customer complaints, improvement in design reliability and customer satisfaction, easier design change, a reduction in product-development-cycle time, and organizational efficiency by using this method [23, 24]. Similarly, QFD can be extended for aligning drivers with providers to achieve agility and make priority decisions concerning the specific provider improvements that should be made for enhancing the agility level of an enterprise.
\n\t\t\tA simplified form of the HOQ matrix, in which the importance of customers’ requirements, correlation analyses among DRs are removed, is utilized in this study. This simplified form is called a relationship matrix, wherein CRs are represented on the left side. Identifying the relative importance of the various CRs is an important step in discerning those that are critical and also helps in prioritizing the design effort. DRs are represented on the upper portion of the relationship matrix. The relative importance of the DRs can be calculated by using the relative importance of the CRs and the level assigned to the relationships between CRs and DRs, presented in the main body of the matrix, which can be represented in symbolic or numerical form. The level of the relationships is typically assessed by an evaluation team in a subjective manner.
\n\t\t\t\n\t\t\t\tD. Fuzzy Logic\n\t\t\t
\n\t\t\tA fuzzy set can be defined mathematically by assigning a value to each possible member in a universe representing its grade of membership. Membership in the fuzzy set, to a greater or lesser degree, is indicated by a larger or smaller membership grade. Fuzzy-set methods allow uncertain and imprecise systems of the real world to be captured through the use of linguistic terms so that computers can emulate human thought processes. Thus, fuzzy logic is a very powerful tool capable of dealing with decisions involving complex, ambiguous and vague phenomena that can be assessed only by linguistic values rather than by numerical terms. Fuzzy logic enables one to effectively and efficiently quantify imprecise information, perform reasoning processes and make decisions based on vague and incomplete data [25]. On the basis of previous study [26], the experts can make a significant measurement of the possibility of an event when it is known; however, in uncertain situations characterized by either a lack of evidence or the inability of the experts to make a significant measurement when available information is scarce, managers often react very incompetently. Fuzzy logic, by making no global assumptions about the independence, exhaustiveness, or exclusiveness of the underlying evidence, tolerates a blurred boundary in definitions [25]. Thus, fuzzy logic brings the hope of incorporating qualitative factors into decision-making.
\n\t\t\tFuzzy logic is currently being used extensively in many industrial applications as well as in managerial decision making. For example, it has been used in multi-attribute decision-making situations to select R&D project evaluation [27]. Ben Ghalia et al. [28] used fuzzy-logic inference for estimating hotel-room demand by eliciting knowledge from hotel managers and building fuzzy IF-THEN rules. Lin and Chen [29] devised a fuzzy-possible-success-rating for evaluating go/no-go decisions for new-product screening based on the product-marketing competitive advantages, superiority, technological suitability and risk. Chen and Chiou [30] devised a fuzzy credit rating for commercial loans. Hui et al. [31] obtained data from experienced supervisors to create a fuzzy-rule-based system for balance control of assembly lines in apparel manufacturing. Organizational transformations have been widely adopted by firms to improve competitive advantage. Chu et al. [32] uses a nonadditive fuzzy integral to develop a framework to assess performance of organization transformation.
\n\t\tThe goal of an agile enterprise is to enrich/satisfy customers and employees. An enterprise essentially possesses a set of capabilities for making appropriate responses to changes occurring in its business environment. However, the business conditions in which many companies find themselves are characterized by volatile and unpredictable demand; thus, there is an increasing urgency for pursuing agility. Agility might, therefore, be defined as the capability of an enterprise to respond rapidly to changes in the market and customers’ demands. To be truly agile, an enterprise should possess a number of distinguishing agility-providers. From a review of the relevant literature [1, 4, 6, 12, 14], the author has developed a conceptual model of an agile enterprise, as shown in Figure 2.
\n\t\t\tA conceptual framework of an agile enterprise.
The main driving force behind agility is change. There is nothing new about change; however, change is currently occurring at a much faster rate than ever before. Turbulence and uncertainty in the business environment have become the main causes of failures in enterprises. The number of changes and their type, specification or characteristics cannot be easily determined and probably is indefinite. Different enterprises with dissimilar characteristics and circumstances experience various changes that are specific and perhaps unique to themselves. However, there are some common characteristics in changes that occur, which can produce a general consequence for all enterprises. By summarizing previous studies [1, 4, 7, 8], the general areas of change in a business environment can be categorized as (1) market volatility caused by growth of the market niche, increasing introduction of new product and shrinkage of product life; (2) intense competition caused by rapidly changing markets, pressure from increasing costs, international competitiveness, Internet usage and a short development time for new products; (3) changes in customer requirements caused by demands for customization, increased expectations for quality and quicker delivery time; (4) accelerating technological changes caused by the introduction of new and efficient production facilities and system integration; and (5) changes in social factors caused by environmental protection, workforce/workplace expectations and legal pressure.
\n\t\t\tAgile enterprises are concerned with change, uncertainty and unpredictability within their business environment and making appropriate responses. Therefore, such enterprises require a number of distinguishing capabilities, or “fitness,” to deal with these concerns. These capabilities consist of four principal elements [7, 8]: (1) responsiveness, the ability to see/identify changes, to respond quickly, reactively or proactively, and to recover; (2) competency, the efficiency and effectiveness of an enterprise in reaching its goals; (3) flexibility/adaptability, the ability to implement different processes and achieve different goals with the same facilities; and (4) quickness/speed, the ability to culminate an activity in the shortest possible time.
\n\t\t\tAchieving agility requires responsiveness in strategies, technologies, personnel, business processes and facilities. Agility-providers should exhibit agile characteristics as well as make available and determine the agility capabilities and behavior of an enterprise. Numerous studies dedicated to identifying agility-providers from which organization leaders can select items appropriate to their own strategies, organizational business processes and information systems have been conducted. For example, Kumar and Motwani [33] identified twenty-three factors that influence a firm’s agility. Goldman et al. [34] suggested that agility has four underlying components: (1) delivering value to customers, (2) being ready for change, (3) valuing human knowledge and skills, and (4) forming virtual partnerships. The “next generation manufacturing” project identified six attributes for agility: (1) customers, (2) physical plant and equipment, (3) human resources, (4) global markets, (5) core competency, and (6) practices and cultures [35]. Moreover, Yusuf et al. [36] proffered a set of thirty-two agile attributes grouped into four dimensions: (1) core competency management, (2) virtual enterprise, (3) capability for reconfiguration, and (4) knowledge-driven enterprises. These attributes, representing most aspects of agility, determine the entire behavior of an enterprise. Most recently, Ren et al. [37], following the work of Yusuf et al. [36] based on a survey circulated among UK enterprises, conducted principal component analysis to confirm the correlations between the thirty-two attributes. Finally, six principal components encompassing fifteen attributes were identified as critical agility-enabling-attributes: (1) human knowledge and skills, (2) customization, (3) partnership and change, (4) technology, (5) integration and competence, and (6) team-building. From this review we can see that different researchers provide certain insights into different aspects of agility providers. It is highly probable that there is no single set of agility providers reflecting all aspects.
\n\t\t\tAlthough several researchers [1, 12-15] have accepted a conceptual model for achieve agility, the purpose of agile strategic planning is to unite the resources of an enterprise to compete with the change in environment and to create business value, which according to some studies [4, 22] can be maximized and the competitive threat minimized only by selecting agile providers for investments aligned to the company\'s business strategy and competitive bases in the market. Thus, the first priority should be to understand the relationships among the specific market field requirement, as well as the agility capabilities and providers, to deploy and integrate both capabilities and providers, and to transform them into a competitive edge.
\n\t\t\tTo assist managers in more efficiently achieving agility, on the basis of the conceptual model of an agile enterprise, and by using the relationship matrix in the QFD approach, a systematic model for linking and integrating agility drivers, capabilities and providers, can be constructed as shown in Figure 3.
\n\t\t\tA systematic agility-linking model.
Specifically, this model can be described as follows:
\n\t\t\tAnalysis of agile strategy: to identify the degree of the agile abilities that can provide the required strength for responding to changes and searching for competitive advantage by maintaining alignment between agility drivers and agile abilities.
Identification of agile providers: to find agility providers constituting the means by which the so-called needs of an enterprise relation to capabilities can be achieved by linking between abilities and providers.
As mentioned in the previous section, the deployment and integration of agility drivers, capabilities and providers, and their transformation into a competitive edge is critical for achieving agility. Due to an either “imprecise” or “vague” definition of agile attributes and relationships, the deploying and integrating evaluation process is associated with uncertainty and complexity. Managers must make a decision by considering agile attributes and relationships which might have non-numerical values. All attributes must be integrated within the evaluation decision although none of them may exactly satisfy the ideals of the enterprises. Conventional "crisp" evaluation approaches cannot handle such decisions suitably or effectively. Since humans have the capability of understanding and analyzing obscure or imprecise events which are not easily incorporated into existing analytical methods, the corporate strategic planning decision is made primarily on the basis of the opinions of experts. On the basis of previous research [38], in situations where evaluators are unable to make a significant assessment, linguistic expressions are used to estimate ambiguous events. Linguistic terms usually have vague meanings. One way to capture the meanings of linguistic terms is to use the fuzzy-logic approach to associate each term with a possibility distribution [39].
\n\t\t\tTo assist managers in more efficiently achieving agility by using the relationship matrix in the QFD approach and fuzzy logic, an evaluation algorithm composed of four major parts (as shown in Figure 4) was devised for development and evaluation.
\n\t\t\tA method for evaluating and achieving agility.
First, identify the agility drivers on the basis of a survey of the business operation environment, determine the agility-level needs and identify the requirements for measuring the capabilities, and select the required providers for assessment. Second, apply the relationship matrix to link and analyze the fuzzy average relation-weight of the capabilities and providers. Third, synthesize the fuzzy ratings and average relation-weights of the capabilities to obtain the fuzzy-agility-index (FAI) of the enterprise and match the FAI with an appropriate linguistic term to label the agility level. Fourth, synthesize the fuzzy ratings and average relation-weights of the providers to obtain the fuzzy merit-relation-value index for each and rank them to identify the major barriers to enable managerial proactive implementation of appropriate ameliorating measures, a stepwise procedure for which follows.
\n\t\t\tForm a self-assessment committee.
Collect and survey data or information to identify the agility drivers, determine the needed capabilities and select the required providers for assessment.
Select the preference scale for measurement.
Apply the relationship matrix and use linguistic measurement to evaluate the agility attributes, relationship-levels and prepare a translation.
Analyze the fuzzy average relation-weights of the capabilities and providers.
Aggregate the fuzzy ratings and average relation-weights of capabilities into an FAI.
Match the FAI with an appropriate linguistic agility level.
Analyze the agility and offer suggestions.
\n\t\t\t\tA. Self-Assessment Committee\n\t\t\t
\n\t\t\tThe essentials of an agile enterprise consist of integration of strategies, personnel, processes, networks and information systems. For knowledge acquisition to be successful, it is important that a variety of experts from different functions be chosen. Such a selection ensures that not only the complete domain is covered, but also that no single aspect of the business receives a greater emphasis within the final system.
\n\t\t\t\n\t\t\t\tB. Preparation for Assessment\n\t\t\t
\n\t\t\tBefore assessing, the committee must survey the changes in the business operation environment and examine the organization’s capability. On the basis of the external environmental survey and internal capability assessment, the committee can identify the main drivers, determine the level of agility needed and the capabilities of the enterprise in response to unpredictable changes, and select the agility-enabled attributes that are the means by which the so-called capabilities can be achieved.
\n\t\t\t\n\t\t\t\tC. Preference Scale System\n\t\t\t
\n\t\t\tDue to impreciseness and ambiguity in the criteria, which exist in the evaluation of agility, a precision-based evaluation may not be practical. Thus, the ratings of the attributes and the relationship-level assessment are frequently measured in linguistic terms rather than numerical ones.
\n\t\t\tThe ad hoc usage of linguistic terms and corresponding membership functions is characteristic of fuzzy logic. It is notable that many popular linguistic terms and corresponding membership functions have been proposed for assessment [38, 40]. For the sake of convenience and in lieu of elicitation from the assessors, linguistic terms and corresponding membership functions were obtained directly from previous studies, or, on the basis of the needs of cognitive perspectives and available data characteristics, data from previous studies were used as the foundation for modification to meet individual situations and requirements, the results for which more satisfactorily fit users’ needs. Furthermore, it is generally suggested that linguistic levels not exceed nine levels representing the limits of absolute human discrimination [41].
\n\t\t\t\n\t\t\t\tD. Relationship-Matrix Application, Linguistic Measurement, and Translation\n\t\t\t
\n\t\t\tIn preparation for evaluating agility, the assessors must survey and study the related data or information concerning implementation to gain an understanding of what will be considered in the evaluation.
\n\t\t\tAfter studying the data, on the basis of the experts\' experience and knowledge, the assessors can directly use the aforementioned linguistic terms to assess the rating which characterizes the merit level of the various factors. Furthermore, the linguistic terms can be used to assess interrelationship level located in the central portion of the relationship matrix, indicating the experts’ perceptions regarding relationships between drivers, capabilities and providers, implemented by direct assignment or indirect pair comparisons.
\n\t\t\tAfter the factors are rated and the interrelationship-level evaluated, the fuzzy numbers such as those listed in Table I are used to approximate the linguistic values.
\n\t\t\tLevels of change | \n\t\t\t\t\t\tMerit ratings | \n\t\t\t\t\t\tRelationship-levels | \n\t\t\t\t\t|||
Linguistic variable | \n\t\t\t\t\t\tFuzzy number | \n\t\t\t\t\t\tLinguistic variable | \n\t\t\t\t\t\tFuzzy number | \n\t\t\t\t\t\tLinguistic variable | \n\t\t\t\t\t\tFuzzy number | \n\t\t\t\t\t
Extremely Low (EL) | \n\t\t\t\t\t\t(0, 0.05, 0.15) | \n\t\t\t\t\t\tWorst (W) | \n\t\t\t\t\t\t(0, 0.05, 0.15) | \n\t\t\t\t\t\tVery Low (VL) | \n\t\t\t\t\t\t(0, 0 .1 , 0.2) | \n\t\t\t\t\t
Very Low (VL) | \n\t\t\t\t\t\t(0.1, 0.2, 0.3) | \n\t\t\t\t\t\tVery Poor (VP) | \n\t\t\t\t\t\t(0.1, 0.2, 0.3) | \n\t\t\t\t\t\tLow (L) | \n\t\t\t\t\t\t(0.1, 0.2 5 , 0. 4 ) | \n\t\t\t\t\t
Low (L) | \n\t\t\t\t\t\t(0. 2 , 0.35, 0.5) | \n\t\t\t\t\t\tPoor (P) | \n\t\t\t\t\t\t(0. 2 , 0.35, 0.5) | \n\t\t\t\t\t\tFair (F) | \n\t\t\t\t\t\t(0. 3 , 0.5, 0. 7 ) | \n\t\t\t\t\t
Fair (F) | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\tFair (F) | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\tHigh (H) | \n\t\t\t\t\t\t(0. 6 , 0. 7 5, 0. 9 ) | \n\t\t\t\t\t
High (H) | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\tGood (G) | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\tVery High (VH) | \n\t\t\t\t\t\t(0. 8 , 0. 9 , 1 . 0 ) | \n\t\t\t\t\t
Very High (VH) | \n\t\t\t\t\t\t(0.7, 0.8, 0.9) | \n\t\t\t\t\t\tVery Good (VG) | \n\t\t\t\t\t\t(0.7, 0.8, 0.9) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
Extremely High (EH) | \n\t\t\t\t\t\t(0.85, 0.95, 1.0) | \n\t\t\t\t\t\tE xcellent (E) | \n\t\t\t\t\t\t(0.85, 0.95, 1.0) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t |
Fuzzy numbers to approximate linguistic variable values.
\n\t\t\t\tE. Analysis of Fuzzy Average Relation-Weights\n\t\t\t
\n\t\t\tAggregation of the different experts\' opinions in group decision-making is important, wherein many methods such as the arithmetical mean, median, and mode can be used. Since the median operation is more robust in a small sample, this method is recommended for aggregating these assessments.
\n\t\t\tOn the basis of the traditional QFD methodology [42] and the definition of the fuzzy weighted average [43], the fuzzy average relation-weight representing the total relationship-levels between a particular column item and the entire list of row items can then be calculated as
\n\t\t\twhere FARWAC\n\t\t\t\t\n\t\t\t\t\tj\n\t\t\t\t denotes the fuzzy average relation-weight of the j\n\t\t\t\tth agility capability to all the agility drivers; FLCAD\n\t\t\t\t\n\t\t\t\t\ti\n\t\t\t\t denotes the fuzzy level in change of the i\n\t\t\t\tth drivers; FRLADAC\n\t\t\t\t\n\t\t\t\t\tij\n\t\t\t\t denotes the fuzzy relationship-level between driver i and capability j.
\n\t\t\twhere FARWAP\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t denotes the fuzzy average relation-weight of k\n\t\t\t\tth providers to all the agility capabilities; FARWAC\n\t\t\t\t\n\t\t\t\t\tj\n\t\t\t\t denotes the fuzzy average relation-weight of the j\n\t\t\t\tth capability derived from Eq (1); FRLACAP\n\t\t\t\t\n\t\t\t\t\tjk\n\t\t\t\t denotes the fuzzy relation-level between capability j and provider k.
\n\t\t\tThe calculation of the membership function of a fuzzy weighted average is tedious, as indicated in [44, 45].
\n\t\t\t\n\t\t\t\tF. Aggregation of Fuzzy Ratings and Average Relation-Weights into Fuzzy-Agility Index\n\t\t\t
\n\t\t\tRepresenting the composite agility level of an enterprise, the fuzzy-agility index (FAI) constitutes a fusion of information, i.e., a consolidation of the fuzzy merit of agility capabilities with the fuzzy average relation-weight of the drivers. The higher the FAI of an enterprise is, the higher its agility.
\n\t\t\tAccording to the fuzzy weighted average operation [43], the FAI is defined as
\n\t\t\twhere FMACj denotes the fuzzy merit of the j\n\t\t\t\tth agility capability and FARWAC\n\t\t\t\t\n\t\t\t\t\tj\n\t\t\t\t denotes the fuzzy average relation-weight of the j\n\t\t\t\tth capability derived from Eq (1).
\n\t\t\t\n\t\t\t\tG. Matching FAI with an Appropriate Linguistic Level\n\t\t\t
\n\t\t\tOnce the FAI has been compiled, one can further approximate a linguistic label whose meaning is the same as (or closest to) the meaning of the FAI from the natural-language expression set of an agility label (AL).
\n\t\t\tSeveral methods for matching the membership function with linguistic terms have been proposed. Three basic techniques include (1) Euclidean distance, (2) successive approximation, and (3) piecewise decomposition. The Euclidean distance method is most frequently utilized because it is the most intuitive form of human perception of proximity [46].
\n\t\t\tThe Euclidean method consists of calculating the Euclidean distance from the given membership function to each functions representing the natural-language agility level expression set. Suppose that the natural-language agility level expression set is AL, U\n\t\t\t\t\tFAI\n\t\t\t\t and U\n\t\t\t\t\tALi\n\t\t\t\t are the membership functions of FAI and the natural-language agility level expression, respectively. Then, the distance between the fuzzy number FAI and each fuzzy-number AL\n\t\t\t\ti AL can be calculated as
\n\t\t\twhere p = {x\n\t\t\t\t\n\t\t\t\t\t0\n\t\t\t\t\n\t\t\t\t, x\n\t\t\t\t\n\t\t\t\t\t1\n\t\t\t\t\n\t\t\t\t, …, x\n\t\t\t\t\n\t\t\t\t\tm\n\t\t\t\t} [0, 1] so that 0 = x\n\t\t\t\t\n\t\t\t\t\t0\n\t\t\t\t\n\t\t\t\tx\n\t\t\t\t\n\t\t\t\t\t1\n\t\t\t\t\n\t\t\t\t…\n\t\t\t\tx\n\t\t\t\t\n\t\t\t\t\tm\n\t\t\t\t\n\t\t\t\t= 1.0. To simplify, let p = {0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1}. Then, the distance from the FAI to each of the members in the set AL can be calculated and the closest natural expression with the minimum distance identified.
\n\t\t\t\n\t\t\t\tH. Analysis and Suggestions\n\t\t\t
\n\t\t\tAs mentioned in the previous section, an evaluation of agility not only determines the agility of an enterprise but also, most importantly, helps managers identify the principal adverse factors for implementing an appropriate plan to enhance the agility level.
\n\t\t\tAgility-enabling attributes are supposed to provide and determine the entire agile behavior of an enterprise. To identify the principal obstacles to enhancing the agility level, a fuzzy agility-provider merit-relation-value index (FAPMRVI) combining the merit ratings and the average relation-weights of providers derived from Eq (2) is defined. The lower the FAPMRVI of a factor is, the lower the degree of contribution for the factor.
\n\t\t\tIf the fuzzy average relation-weight is used to calculate FAPMRVI\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t directly, the high value obtained neutralizes the low merit ratings in the calculation of FAPMRVI; therefore, the actual principal obstacles (low merit rating and high average relation-weight) cannot be identified. If a high value is given to FARWAP\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t, then [(1, 1, 1) FARWAP\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t] becomes a low value. Hence, to elicit the factor with the lowest merit rating and the highest average-relation-weight for each agility provider k, the fuzzy index for FAPMRVI\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t is defined as
\n\t\t\twhere FARVAP\n\t\t\t\t\n\t\t\t\t\t’\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t = [(1, 1, 1) FARWAP\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t]; FMAP\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t denotes the fuzzy merit of the k\n\t\t\t\tth agility provider.
\n\t\t\tSince fuzzy numbers do not always yield a totally ordered set as real numbers do, all the FAPMRVI\n\t\t\t\t\n\t\t\t\t\tk\n\t\t\t\t must be ranked. Many methods have been developed to rank fuzzy numbers [40, 47]. Here, the ranking of the fuzzy numbers is based on Chen and Hwang’s left-and-right fuzzy-ranking method [40] since it not only preserves the ranking order but also considers the absolute location of each fuzzy number. The shortcoming of this method is that the ranking score depends on the definition of their fuzzy maximizing and minimizing sets.
\n\t\t\tIn the left-and-right fuzzy-ranking method, the fuzzy maximizing and minimizing sets are, respectively, defined as
\n\t\t\tWhen a triangular fuzzy number is given, the FAPMRVI defined as U\n\t\t\t\t\n\t\t\t\t\tFAPMRVI\n\t\t\t\t\n\t\t\t\tR [0, 1] with a triangular membership function. Thu, the right-and-left scores of the FAPMRVI can be obtained, respectively, as
\n\t\t\tFinally, the total score of the FAPMRVI can be obtained by combining the left and right scores, being defined as
\n\t\t\tIn this section, an agility development project of an international IT products-and-services enterprise in Taiwan is cited to demonstrate the evaluation procedure for this approach.
\n\t\t\t\n\t\t\t\tA. Subject of Case Study\n\t\t\t
\n\t\t\t“Enterprise A” is an internationally recognized IT products-and-services company, particularly noted for PCs and notebooks, earning an annual revenue of about US $6.2 billion in 2005. This enterprise employs marketing and service operations across the Asia-Pacific Rim, Europe, the Middle East, and the Americas, supporting dealers and distributors in more than 100 nations. In the 1990’s, the markets for IT products matured; moreover, low-cost production in developing nations grew, thus prompting large multinational firms to simultaneously provide local responsiveness and global integration to in reaction to an uncertain business environment. Such changes profoundly challenged the enterprise. To achieve and sustain global success and satisfy new small-niche markets, this enterprise strived to become a major global supplier to enrich its customers, reduce to-market time, reduce the total cost of ownership, and enhance overall competitiveness.
\n\t\t\tSince an enterprise has been advocated as the 21st-century operation paradigm, and being perceived as a winning strategy for becoming national and international leader, the corporate management team (executive team) concluded that it wished to achieve an extremely agile enterprise through continuous improvement processes. Thus, an assessment team led by the executive vice president was organized. This team was selected from the most knowledgeable personnel who had mastered the principles of an agile enterprise and whose job it was to investigate and correct problems. The team membership encompassed the vice president of marketing, the general auditor, the global manufacturing manager, the director of human resources, a senior project manager and two consultants for business strategy. Each member brought particular concerns and desires into the decision, which had to be reconciled by consensus, a necessary procedure since all parties would contribute to the success or failure of the project.
\n\t\t\t\n\t\t\t\tB. Commitments of Project\n\t\t\t
\n\t\t\tThe aim of agility evaluation is to produce a good set of results, from which an agility index is determined for perceptions of the current situation, and another index for the goals toward increasing the agility of the enterprise. Since top-level commitment is essential, specific objectives for the development project were agreed on by the CEO:
\n\t\t\tTo implement an enterprise-wide self-assessment for establishing a baseline;
To identify the strengths of the enterprise and areas needing improvement for feedback to the management team;
To feed opportunities for improvement into the business planning cycle, including corporate objectives; and
To develop the process of self-assessment by using the agile enterprise model as an annual component of the business cycle.
\n\t\t\t\tC. Evaluation by Fuzzy QFD-Based Algorithm\n\t\t\t
\n\t\t\tWhen enterprise A sets the goal to implement an agile enterprise, the committee had several questions, such as: Precisely what is agility, and how can it be measured? How can both analytical and intuitive understandings of agility be developed in a particular business environment? How can the agility of enterprise A be improved? Answering these questions requires knowledge of what to measure, how to measure it and how to evaluate the results. Moreover, how to integrate drivers, capabilities and providers into alignment must be taken into account if the enterprise is to implement agility. Although important concepts and steps for development formulation have previously been identified, there is still no systematic tool to integrate these concepts. Furthermore, due to the existing ill-defined and ambiguous elements concerning agility factors and their interrelationships, experts can easily differentiate between high, medium, and low; however, it is difficult to judge whether a value (e.g., 0.2) is low or another value (e.g., 0.3) is also low. Therefore, it is easier to use linguistic terms to measure ambiguous events. Since linguistic variables contain ambiguity and a multiplicity of meanings and the information obtained can be expressed as a range in a fuzzy set instead of a single value as in traditional methods, fuzzy logic may be applied in this evaluation context. On the basis of the procedures of the fuzzy QFD-based algorithm, the agility development evaluation was implemented and the goal achieved. The deliberations concerning how to initiate agility development are summarized below:
\n\t\t\t1) Identify agility drivers, determine capabilities and select providers for assessment. To accurately elicit assessment criteria reflecting the entire set of features of an agile enterprise within a period of ten days, the committee made a series of business-environment changes, as well as trend surveying and analysis, the major content of which included changes in the marketplace, competitive circumstances and criteria; technological innovations and applications; changes in customer requirements; and changes in social factors. Moreover, to facilitate the experts’ holistic understanding of the current situation, two review meetings were held to discuss a series of activities, the major content of which included
\n\t\t\tEnterprise characteristics: enterprise priorities (quality, cost, time, customers satisfaction, etc.), perceived quickness, responsiveness, core business and competencies, as well as specific enterprise problems;
Policy and strategy: the key factors prompting the enterprise to change and the strategies adopted;
Business structure: organization, process, personnel, information technology and innovative structures providing the capability for achieving agility;
Practices: those performed in response to change
On the basis of discussion results, the committee further referred to the factors proposed in previous studies [1, 4, 7, 8, 10, 16-18]. The agility drivers were identified and the capabilities and providers for assessment selected, as shown in Table II. (This Table presents merely what the author assessed to be the most prevalent and meaningful factors for this case study).
\n\t\t\tDrivers | \n\t\t\t\t\t\tCapabilities | \n\t\t\t\t\t\tProviders | \n\t\t\t\t\t
Growth of niche market (A D 1 ) | \n\t\t\t\t\t\tSensing /Identifying changes and fast response (A C 1 ) | \n\t\t\t\t\t\tMulti-skilled and flexible pe rsonnel (A P 1 ) | \n\t\t\t\t\t
Increasing rate of change in product models (A D 2 ) | \n\t\t\t\t\t\tStrategic vision (A C 2 ) | \n\t\t\t\t\t\tWorkforce skill upgrade (A P 2 ) | \n\t\t\t\t\t
Product lifetime shrinkage (A D 3 ) | \n\t\t\t\t\t\tT echnological ability and appropriate product introduction (A C 3 ) . | \n\t\t\t\t\t\tQ uick new product introduction (A P 3 ) | \n\t\t\t\t\t
Rapidly changing market (A D 4 ) | \n\t\t\t\t\t\tCost - effectiveness (A C 4 ) | \n\t\t\t\t\t\tResponse to changing market requirements (A P 4 ) | \n\t\t\t\t\t
Increasing pressure on cost (A D 5 ) | \n\t\t\t\t\t\tCooperation and operation efficiency and effectiveness (A C 5 ) | \n\t\t\t\t\t\tProducts with substantial value-addition (A P 5 ) | \n\t\t\t\t\t
Increasing pressure of global market competition (A D 6 ) | \n\t\t\t\t\t\tProduct volume/model flexibility (A C 6 ) | \n\t\t\t\t\t\tFirst-time right design (A P 6 ) | \n\t\t\t\t\t
Decreasing new products time to market (A D 7 ) | \n\t\t\t\t\t\tOrganization/pe rsonnel flexibility (A C 7 ) | \n\t\t\t\t\t\tTrust-based relation s with customers/suppliers (A P 7 ) | \n\t\t\t\t\t
Quicker delivery time and time to market (A D 8 ) | \n\t\t\t\t\t\tProduct/service design , delivery alacrity and timeliness (A C 8 ) | \n\t\t\t\t\t\tTechnology awareness (A P 8 ) | \n\t\t\t\t\t
I ncreasing q uality expectation (A D 9 ) | \n\t\t\t\t\t\tFast operation time (A C 9 ) | \n\t\t\t\t\t\tSkill and knowledge enhanc ement (A P 9 ) | \n\t\t\t\t\t
Introduction of new soft technologies (software and methods) (A D 10 ) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | Concurrent execution of activities (A P 10 ) | \n\t\t\t\t\t
Environmental pressures (A D 11 ) | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | Information technology and communication (A P 11 ) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Empowerment and decentralized decision - making (A P 12 ) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Cross - functional team (A P 13 ) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Culture of change (A P 14 ) | \n\t\t\t\t\t
Agility-related factors.
2) Determine the preference scale for measurement. This is based on the needs for cognitive perspectives and available data characteristics and also considers the linguistic terms used in previous studies and modified to incorporate enterprise A situations. Furthermore, after two days of discussion based on a long-standing recognition of the meaning of linguistic values, ultimately the committee selected for assessment the linguistic terms and associated fuzzy numbers listed in Table I.
\n\t\t\t3) Apply the relationship matrix and use linguistic terms to assess agility attributes and relationship-levels, and translate the linguistic terms into fuzzy numbers. Within a period of six days, a series of brainstorming sessions was held to identify the relationships among the variables. For this, the experts were asked about the mutual relationships among variables (e.g., how a particular variable helps to achieve the others). By using the conclusions in the review meetings and brainstorming session, and on the basis of their experience, knowledge and judgment, the committee members applied the relationship matrix (as shown in Tables III and IV) and used the level scale W= {Extremely Low [EL], Very Low [VL], Low [L], Fair [F], High [H], Very High [VH], Extremely High [EH]} to measure the degree of change in the agility drivers. They used the value scale RS = {Very Low [VL], Low [L], Fair [F], High [H], Very High [VH]} to evaluate extent of the relationships between agility drivers and capabilities, as well as the relationship-levels between capabilities and providers; moreover, they used the rating scale R = {Worst [W], Very Poor [VP], Poor [P], Fair [F], Good [G], Very Good [VG], Excellent [E]} to assess the merit ratings of the capabilities and providers. A sample of the linguistic assignment is shown in Tables III and IV. Furthermore, on the basis of the associated relations shown in Table I, fuzzy numbers approximating the linguistic terms and linguistic assignments were translated into fuzzy numbers.
\n\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Agility capabilities | \n\t\t\t\t\t|||||||||
\n\t\t\t\t\t\t | AC 1 | \n\t\t\t\t\t\tAC 2 | \n\t\t\t\t\t\tAC 3 | \n\t\t\t\t\t\tAC 4 | \n\t\t\t\t\t\tAC 5 | \n\t\t\t\t\t\tAC 6 | \n\t\t\t\t\t\tAC 7 | \n\t\t\t\t\t\tAC 8 | \n\t\t\t\t\t\tAC 9 | \n\t\t\t\t\t||
Merits of agility capabilities Level of change | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tVG | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tP | \n\t\t\t\t\t\tF | \n\t\t\t\t\t||
Agility drivers | \n\t\t\t\t\t\tAD 1 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t
AD 2 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 3 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 4 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 5 | \n\t\t\t\t\t\tEH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
AD 6 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 7 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 8 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AD 9 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t|
AD 10 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
AD 11 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tL | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tL | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tL | \n\t\t\t\t\t
Agility capability related to drivers: agile strategies analysis matrix (assigned by general auditor).
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Agility providers | \n\t\t\t\t\t|||||||||||||
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | AP 1 | \n\t\t\t\t\t\tA P 2 | \n\t\t\t\t\t\tA P 3 | \n\t\t\t\t\t\tA P 4 | \n\t\t\t\t\t\tA P 5 | \n\t\t\t\t\t\tA P 6 | \n\t\t\t\t\t\tA P 7 | \n\t\t\t\t\t\tA P 8 | \n\t\t\t\t\t\tA P 9 | \n\t\t\t\t\t\tAP 10 | \n\t\t\t\t\t\tA P 11 | \n\t\t\t\t\t\tA P 12 | \n\t\t\t\t\t\tA P 13 | \n\t\t\t\t\t\tA P 14 | \n\t\t\t\t\t
Merits of agility providers | \n\t\t\t\t\t\tVG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tVG | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tVG | \n\t\t\t\t\t\tVG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tG | \n\t\t\t\t\t\tE | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tG | \n\t\t\t\t\t|
Agility capabilities | \n\t\t\t\t\t\tAC 1 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tL | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t
AC 2 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t|
AC 3 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
A C 4 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tF | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
A C 5 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
A C 6 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tF | \n\t\t\t\t\t|
A C 7 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
A C 8 | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t|
A C 9 | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t\tVH | \n\t\t\t\t\t\tH | \n\t\t\t\t\t
Agility providers related to capabilities: principle obstacle identification matrix (assigned by general auditor).
4) Analyze the fuzzy average relation-weight in the relationship matrix. Before this analysis, the committee used the median operation to integrate the different assignments under the same factors given by different experts. Furthermore, by applying Eqs. (1)-(2), the fuzzy average relation-weights of the agility capabilities and providers can be calculated, respectively. The results are listed in Table V.
\n\t\t\tA gility capabilit y | \n\t\t\t\t\t\tF uzzy average relation- weights | \n\t\t\t\t\t\tA gility providers | \n\t\t\t\t\t\tF uzzy average relation- weights | \n\t\t\t\t\t
Sensing /Identifying changes and respon ding (A C 1 ) . | \n\t\t\t\t\t\t(0.61, 0.76, 0.92) | \n\t\t\t\t\t\tMulti-skilled and flexible pe rsonnel (A P 1 ) | \n\t\t\t\t\t\t(0.60, 0.74, 0.90) | \n\t\t\t\t\t
Strategic vision (A C 2 ) . | \n\t\t\t\t\t\t(0.60, 0.76, 0.91) | \n\t\t\t\t\t\tWorkforce skill upgrade (A P 2 ) | \n\t\t\t\t\t\t(0.55, 0.72, 0.88) | \n\t\t\t\t\t
T echnological ability and appropriate product introduction (A C ) . | \n\t\t\t\t\t\t(0.65, 0.79, 0.93) | \n\t\t\t\t\t\tQ uick new product introduction (A P 3 ) | \n\t\t\t\t\t\t(0.60, 0.76, 0.91) | \n\t\t\t\t\t
Cost - effectiveness (A C 4 ) . | \n\t\t\t\t\t\t(0.61, 0.77, 0.92) | \n\t\t\t\t\t\tResponse to changing market requirements (A P 4 ) | \n\t\t\t\t\t\t(0.63, 0.78, 0.93) | \n\t\t\t\t\t
Cooperation and operations efficiency and effectiveness (A C 5 ) . | \n\t\t\t\t\t\t(0.58, 0.75, 0.91) | \n\t\t\t\t\t\tProducts with substantial value-addition (A P 5 ) | \n\t\t\t\t\t\t(0.52 0.70, 0.87) | \n\t\t\t\t\t
Product volume/model flexibility (A C 6 ) | \n\t\t\t\t\t\t(0.54, 0.73, 0.89) | \n\t\t\t\t\t\tFirst-time right design (A P 6 ) | \n\t\t\t\t\t\t(0.62, 0.77, 0.93) | \n\t\t\t\t\t
Organization/pe rsonnel flexibility (A C 7 ) | \n\t\t\t\t\t\t(0.46, 0.63, 0.76) | \n\t\t\t\t\t\tTrust-based relation s with customers/suppliers (A P 7 ) | \n\t\t\t\t\t\t(0.55, 0.73, 0.89) | \n\t\t\t\t\t
Product/service design , delivery alacrity and timeliness (A C 8 ) | \n\t\t\t\t\t\t(0.67, 0.82, 0.96) | \n\t\t\t\t\t\tTechnology awareness (A P 8 ) | \n\t\t\t\t\t\t(0.54, 0.72, 0.88) | \n\t\t\t\t\t
Fast operation time (A C 9 ) | \n\t\t\t\t\t\t(0.65, 0.81, 0.95) | \n\t\t\t\t\t\tSkill and knowledge enhanc ement (A P 9 ) | \n\t\t\t\t\t\t(0.60, 0.75, 0.9) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Concurrent execution of activities (A P 10 ) | \n\t\t\t\t\t\t(0.60, 0.76, 0.91) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Information technology and communication (A P 11 ) | \n\t\t\t\t\t\t(0.60, 0.75, 0.9) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Empowerment and decentralized decision - making (A P 12 ) | \n\t\t\t\t\t\t(0.52, 0.71, 0.88) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Cross - functional team (A P 13 ) | \n\t\t\t\t\t\t(0.55, 0.73, 0.89) | \n\t\t\t\t\t
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | Culture of change (A P 14 ) | \n\t\t\t\t\t\t(0.37, 0.58, 0.78) | \n\t\t\t\t\t
Fuzzy average relation-weights of agility capabilities and providers.
5) Aggregate the fuzzy ratings and fuzzy average relation-weights into an FAI. By applying Eq (3), the FAI for enterprise A was obtained as
\n\t\t\t\n\t\t\t\t\tFAI = (0.37, 0.56, 0.75)
\n\t\t\t6) Match the FAI with an appropriate linguistic level. Once the FAI was obtained, to identify the agility level, the committee further approximated a linguistic label whose meaning is the same as (or closest to) the meaning of the FAI from the natural-language agility-level (AL) expression set. In this case, the set AL = {Definitely Agile [DA], Extremely Agile [EA], Very Agile [VA], Highly Agile [HA], Agile [A], Slightly Agile [SA], Fairly [F], Slightly Slow [SS], Slowly [S]} was selected for labeling, the linguistics and corresponding membership functions of which are shown in Figure 5.
\n\t\t\tMatching fuzzy agility index with linguistic terms.
Then, by using Eq (4), the Euclidean distance D from the FAI to each member in set AL was calculated:
\n\t\t\tD(FAI, DA ) =2.0094, D(FAI, EA) = 2.0094, D(FAI, VA) = 1.7277,
\n\t\t\tD(FAI, HA) = 0.9924, D(FAI, A) = 1.1405, D(FAI, SA) =1.8168,
\n\t\t\tD(FAI, F) =2.0094, D(FAI, SS) =2.0094, D(FAI, S) = 2.0094
\n\t\t\tThus, by matching a linguistic label with the minimum D, the agility level of enterprise A can be labeled as “Highly Agile”, as shown in Figure 5.
\n\t\t\t7) Analyze and suggest. Since the agility index of enterprise A is “Highly Agile” (according to the evaluation), far from the “Extremely Agile” objective, obstacles within the organization can stop or impact the achievement of the company. Agility providers are supposed to enable and determine the entire agile behavior of an enterprise. By applying Eq (5), the fourteen fuzzy agility-provider merit-relation-value indexes (FAPMRVIs) listed in Table VI were obtained.
\n\t\t\tAgility providers | \n\t\t\t\t\t\tMerit s of agility provider | \n\t\t\t\t\t\t(1.0, 1.0, 1.0) (-) F AR W A P i | \n\t\t\t\t\t\tFuzzy relation-value index es | \n\t\t\t\t\t\tRanking score s | \n\t\t\t\t\t
A P 1 | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\t(0. 1 , 0. 26 , 0. 4 ) | \n\t\t\t\t\t\t(0.0 3 , 0. 13 , 0. 28 ) | \n\t\t\t\t\t\t0.1808 | \n\t\t\t\t\t
A P 2 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 12 , 0. 28 , 0. 4 5) | \n\t\t\t\t\t\t(0. 06 , 0. 182 , 0. 36 ) | \n\t\t\t\t\t\t0.2339 | \n\t\t\t\t\t
A P 3 | \n\t\t\t\t\t\t(0.7, 0.8, 0.9) | \n\t\t\t\t\t\t(0. 09 , 0. 24 , 0. 4 ) | \n\t\t\t\t\t\t(0.0 63 , 0. 192 , 0. 36 ) | \n\t\t\t\t\t\t0.2391 | \n\t\t\t\t\t
A P 4 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 07 , 0. 22 , 0. 37 ) | \n\t\t\t\t\t\t(0.0 35 , 0. 143 , 0.2 96 ) | \n\t\t\t\t\t\t0.1929 | \n\t\t\t\t\t
A P 5 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 13 , 0. 3 , 0. 48 ) | \n\t\t\t\t\t\t(0.0 65 , 0. 195 , 0. 384 ) | \n\t\t\t\t\t\t0.2478 | \n\t\t\t\t\t
A P 6 | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\t(0. 07 , 0. 23 , 0. 38 ) | \n\t\t\t\t\t\t(0.0 21 , 0. 115 , 0. 266 ) | \n\t\t\t\t\t\t0.1681 | \n\t\t\t\t\t
A P 7 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 11 , 0. 27 , 0. 45 ) | \n\t\t\t\t\t\t(0. 055 , 0 .176 , 0.3 6 ) | \n\t\t\t\t\t\t0.2305 | \n\t\t\t\t\t
A P 8 | \n\t\t\t\t\t\t(0.7, 0.8, 0.9) | \n\t\t\t\t\t\t(0. 1 2, 0. 28 , 0. 46 ) | \n\t\t\t\t\t\t(0. 084 , 0. 224 , 0. 414 ) | \n\t\t\t\t\t\t0.2722 | \n\t\t\t\t\t
A P 9 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 1 , 0. 25 , 0. 4 ) | \n\t\t\t\t\t\t(0. 05 , 0. 163 , 0. 32 ) | \n\t\t\t\t\t\t0.2115 | \n\t\t\t\t\t
A P 10 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 09 , 0. 24 , 0. 4 ) | \n\t\t\t\t\t\t(0.0 45 , 0. 156 , 0. 32 ) | \n\t\t\t\t\t\t0.2077 | \n\t\t\t\t\t
A P 11 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 1 , 0. 2 5, 0. 4 ) | \n\t\t\t\t\t\t(0.0 5 , 0. 163 , 0. 32 ) | \n\t\t\t\t\t\t0.2115 | \n\t\t\t\t\t
A P 12 | \n\t\t\t\t\t\t(0.5, 0.65, 0.8) | \n\t\t\t\t\t\t(0. 12 , 0. 29 , 0. 48 ) | \n\t\t\t\t\t\t(0.0 6 , 0. 189 , 0. 384) | \n\t\t\t\t\t\t0.2444 | \n\t\t\t\t\t
A P 13 | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\t(0. 11 , 0. 27 , 0. 45 ) | \n\t\t\t\t\t\t(0.0 33 , 0.1 35 , 0. 315 ) | \n\t\t\t\t\t\t0.1947 | \n\t\t\t\t\t
A P 14 | \n\t\t\t\t\t\t(0.3, 0.5, 0.7) | \n\t\t\t\t\t\t(0. 22 , 0. 42 , 0. 63 ) | \n\t\t\t\t\t\t(0.0 66 , 0. 21 , 0. 441 ) | \n\t\t\t\t\t\t0.2709 | \n\t\t\t\t\t
Fuzzy merit-relation-value indexes of agility providers.
Moreover, by applying Eqs (6)-(10), the FAPMRVIs were defuzzified, as listed in Table V. These indices represent the effect of each provider contributing to the agility level of enterprise A. On the basis of the Pareto principle, the committee decided to focus their resources on a few critical factors and sets a scale of 0.2 as the management’s threshold for identifying the factors for improvement. Subsequently, as shown in Table VI, four providers performed lower than the threshold, namely (1) first-time right design, (2) multi-skilled and flexible personnel, (3) response to changing market requirements, and (4) cross-functional teams. These providers represent the most significant contributions for enhancing the agility of the enterprise. In connection with the weakest providers within the organization, the committee suggested that an action plan be implemented to improve the adverse providers and to enhance the agility level of the company.
\n\t\t\tAfter five years and ten cycles of continuous implemented improvement, the agility index of enterprise A has risen close to the “Extremely Agile” level; moreover, the managers are able to capture information on demand immediately from all over the world to make rapid and appropriate decisions to respond more efficiently and effectively to customers. The tangible benefits are the mean lead-time for responding to customers’ demands reduced by approximately 37% under the same inventory level; sales-average increased by 11%, 23%, 27%, 17% and 19% during the five years; an ascent from ninth of fourth position in the world market, especially boosted by becoming the leading brand of PCs and notebooks in the European market.
\n\t\tThe agility of an enterprise is perceived as the dominant competitive vehicle. This report has highlighted the following questions: How close is the enterprise to becoming agile? How can the enterprise effectively improve its agility? Deploying and integrating agility providers, capabilities and drivers and transforming them into strategic competitive edges are critical for an enterprise to achieve agility. Although important concepts and steps for achieving agility have been identified, there is still no systematic tool for integrating these steps. Most of the existing approaches for agility development are structural in nature. Also, conventional (crisp) evaluation approaches which are unsuitable and ineffective for handling situations which by nature lead to complexity and vagueness have been evaluated. To compensate for these limitations, a QFD-based framework to logically integrate the agility provider, capability and driver has been proposed. The methodology provides a systematic structure for translating the agility drivers in the business environment into capabilities needed and subsequently for determining the requirements of agility-enabled attributes. In addition a fuzzy agility index (FAI) composed of agility capability ratings and its relation-weights with drivers has been developed for agility measurement in an enterprise. This report has also described how the proposed approach was applied to develop agility in a Taiwanese PC enterprise. Through development and evaluation, it has been shown that the proposed framework and procedures can enhance the agility of an enterprise, as well as ensure a competitive edge.
\n\t\t\tThis method has been developed from the QFD concept and adapted for a PC enterprise which served as an initial case study for validating the model and approach. The enterprise and managers involved in the case study were generally pleased with the approach. This work provides potential value to practitioners by offering a rational structure to logically integrate different elements at various stages of strategic planning. The uncertainty and vagueness of assessment of each attribute and relationship have been addressed to assure relatively realistic information. An unprecedented application of the QFD and fuzzy logic has been demonstrated to researchers.
\n\t\t\tSince the case study has demonstrated the usefulness of the model for business strategic planning, it is hoped that more managers will be encouraged to adopt this method. However, neither a single case study nor several necessarily provide a true measure of the relative performance and success of this model. Further research should be done to bring this method to maturity and to compare the efficiency of the method in different types of planning (such as information-strategy, marketing, product-roadmap, knowledge-management, etc.). Moreover, this approach does not focus on finding an optimal deployment but merely addresses prioritizing agility providers. For further research, a goal-programming model can be developed to select in greater detail the combination of agility capabilities and providers which results in optimal levels of agility, subject to cost and other enterprise constraints.
\n\t\t\tIt is acknowledged that the evaluation levels and members involved in any particular implementation will be different, depending on the firm involved. The agility drivers and entrepreneurial objectives and strategies vary from firm to firm. For example, enterprises in high-tech industries, stressing competitive advantage through innovation, may have decided on agility capabilities and providers differently from firms in traditional industries seeking to compete in flexibility, global sourcing and low-cost providers.
\n\t\t\tFurthermore, according to the comments from the previous case, this approach resolves some of the problems in traditional methods of strategic business planning, having several advantages when compared to previous methods:
\n\t\t\tThis method provides a structured procedure for identifying the agility drivers in a business environment, thereby deploying capabilities needed to finally determine the providers that will support or enhance the agility of the enterprise. Furthermore, the case study demonstrated that having providers align with strategy and drivers ensures that the providers can cope with strategic direction and provide a competitive edge for the enterprise.
This method gives the analyst more convincing and reliable results. The FAI was expressed in a range of values, providing an overall description of the agility of an enterprise and ensuring that the decision made in the evaluation is not biased. As an example, an agility index having a fuzzy value (0.37, 0.56, 0.75) indicates that the agility level is closer to “Highly Agile,” but also not far away from “Agile.”
This method provides a guiding, dynamic document linking the business strategy of a firm with its environment and outlines details for implementation through continuous process improvement and total quality management.
This method provides a first step in preventing a majority of inappropriate assessments and also expedites the eventual financial analysis by highlighting the most important benefits and drawbacks for formulating a comprehensive plan for improvement.
Finally, there are some limitations to the fuzzy-logic approach. The membership function of natural language expression depends on the managerial perspective of the experts, who must be at a strategic level in the enterprise to evaluate the importance of all aspects such as strategy, marketing and technology. Furthermore, competitive situations and requirements vary from one enterprise or industry to another; hence, a company must establish its unique membership function appropriate to its own specific environment and considerations. Moreover, the computation of a fuzzy weighted average is still complicated and not easily appreciated by managers. Fortunately, this calculation has been computerized to increase accuracy while reducing both computation time and the possibility of errors.
\n\t\tThe mankind has relied on different sources of energy during its economic development throughout the centuries. Whereas coal has been the main energy source in the nineteenth century, oil was in twentieth one. The possible scenarios for remediation of greenhouse effect due to carbon dioxide released by energy production and industry are rendered to minimization of emissions and its recycling. The latter is accomplished by the production of energy sources and chemicals of practical importance from carbon dioxide.
The emission minimization consists in two approaches: replacement of the fossil fuels by renewable ones (solar, wind energies, biomass, etc.) or improvement of energy efficiency in all human activities in different ways. The distribution of energy sources for the European Union for the year 2016 is shown in Figure 1. One can see that the share of renewables is bigger than the powerful nuclear energy with a leading role in energy production. The biggest part (more than 60%) of the renewable energy sources is assigned to the biomass and waste utilization.
Production of primary energy, EU-28, 2016 (% of total, based on tons of oil equivalent). Source: Eurostat (nrg_100a) and (nrg_107a) [1].
One of the ways to cope with the problem of carbon dioxide emissions is to close the carbon cycle using renewable fuels from presently grown biomass, by recycling the released carbon dioxide by the present vegetation by photosynthesis. This is the philosophy of biomass utilization as energy source. The most spread biofuels in the present period are biogas, produced by anaerobic digestion of organic waste, bioethanol, produced from cereals and/or lignocellulosic residues and biodiesel, produced by trans-esterification of lipids with methanol or ethanol.
In this review, we shall concentrate ourselves to the application of biogas as renewable energy source and also as a feedstock for the production of chemicals and other fuels.
Biogas is produced by anaerobic digestion of organic matter of natural origin [2, 3, 4]. The main advantage of this process consists in the combined environmental and energy effect.
Biogas consists mainly of methane, carbon dioxide, and traces of hydrogen sulfide and mercaptanes, as well as residual amounts of oxygen and nitrogen. Small amounts of ethane and hydrogen are possible too. Biogas is obtained by anaerobic digestion of organic waste of biologic origin. The most exploited ones are of agricultural origin (manure, poultry litter, hay, and straw) [5], from food industry, stillage from ethanol production [6], landfill gas, activated sludge from wastewater treatment plants, etc. One of the simplest and the mostly spread flow sheets for biogas production and utilization is shown in Figure 2 [7].
Illustration of biogas cycle, formation, and applications. Scheme taken from [7].
The main fuel in the scheme, shown in Figure 2, is biogas, utilized for energy (thermal one and electricity) or fuel for transport. The carbon dioxide released after combustion is absorbed by the vegetation by photosynthesis, thus closing the carbon cycle. The residual sludge from the digester is rich of organic nitrogen, and therefore, it is suitable for fertilizing the soil.
In the past, biogas has been widely spread as an energy source in the households in the countries of Africa and Asia. Although quite primitive as design, the anaerobic digesters have solved the problems with autonomous energy supply for many households in India, Pakistan, Indo-China, etc.
Later, biogas became very important and essential share as energy source for the countries in Western Europe and Northern America. Besides heating, biogas is now more frequently used for the production of electricity and transport fuel in many municipalities. It is already added to the pipelines for natural gas distribution of household purposes.
A new trend in biogas production and utilization is the so-called biorefinery concept. This concept not only presumes the use of renewable biomass as energy source but also combines it with the production of chemicals, such as plastics, solvents, and synthetic fuels [8]. An example for this is the Danish Bioethanol Concept presented by Zafar [9]. It comprises the ethanol production from lignocellulosic biomass with biogas production of the stillage and cellulose waste. The residual cellulose waste is additionally recycled after wet-oxidation for additional conversion into biogas. A detailed review on biogas applications is published recently by Sawyerr et al. [10].
The variety of anaerobic digesters for biogas production is very broad: from the very primitive pits to most sophisticated bioreactors, such as the floating drum reactor, the upflow anaerobic sludge blanket (UASB) reactor [11, 12, 13], and multistage bioreactor with separated compartments [14, 15]. The choice for anaerobic digester depends on the origin of substrate, and the intermediates are converted during the consecutive steps of hydrolysis, acidification, acetogenesis, and final methanation. In case an accumulation of fatty acids takes place, the reactor with separated compartments is preferable. The most exploited digester for biogas production from domestic waste, activated sludge, and manure is the UASB reactor.
The mostly used substrates for biogas production are the manure from cattle, pigs, and poultry litter. This application competes with the traditional use of manure for soil fertilization. When the amounts of manure prevail the demand for fertilization, biogas production is welcome because double problem is solved: on the one hand, the waste is destroyed and removed, and on the other hand, renewable energy is produced saving money and contributing for carbon cycle closing. That is why attention is paid to the utilization of cattle dung, lignocellulose waste, waste from food and beverage processing, activated sludge from wastewater treatment plants, and household solid waste with landfill gas use. The waste treatment is associated with energy production and reduction of the energy demand of the main enterprise.
Crude glycerol is the main residue from biodiesel production. The amount of this waste product is about 10% from the produced fuel. The poor quality of this glycerol, containing water, potassium hydroxide, and some methanol makes it non-suitable for market purposes even after purification. One alternative utilization of this residual glycerol is in its direct conversion into biogas, thus supplying the biodiesel plant with energy simultaneously. However, as a very simple and digestible substrate, glycerol yields large amounts of organic acids as intermediates, leading to strong inhibition of methanogenic bacteria [16, 17, 18]. That is why glycerol must be used as substrate for biogas production very cautiously with the addition of small amounts, thus making this process with little practical use. It is reported, however, that small additions of glycerol to other basic substrates, i.e. manure, can boost biogas production, as reported by Robra et al. [19] and Astals et al. [20].
Food industry is also a good source for biogas production.
Traditional biogas contains approximately 60% (vol.) methane, almost 40% carbon dioxide, small amounts of ethane and hydrogen (less than 0.5% together), hydrogen sulfide and mercaptanes (some ppm), humidity, and traces of oxygen. Its net energy capacity is ca. 24 MJ/nm3 at methane content of 60% (vol.). The first and most direct use of biogas is for heating purposes for maintenance of the equipment and the farm, where the animal dung is treated. The same applies for its use for domestic purposes, besides heating, e.g., cooking and lighting, as firstly used in Asian and African countries.
Another more sophisticated use of the biogas heating capacity is its utilization as heat energy in beverage and ethanol production. There the stillage remaining after distillation is recycled for biogas production. The resulting biogas is combusted for boiler heating and for energy for operation of distillation columns. Thus, the problems with the treatment of the residual stillage are solved by conversion into biogas, thus mitigating the problems with energy supply and spending. Calculations show that in some cases, stillage utilization as biogas can cover almost the whole energy demand for heating the distillation process. Besides these straightforward applications, biogas is also injected into the grid for natural gas supply for domestic use [21, 22]. For this purpose, a preliminary scrubbing of the carbon dioxide and sulfur compounds is necessary.
Biogas is suitable for generation of electric power in combination with heat recovery. Usually the gas is combusted in engines with internal combustion coupled to turbine. The released heat (being around 60% of the utilized energy) is used for heating purposes for maintenance of the anaerobic digester or for household needs. This method is widely applied for the treatment of activated sludge, a residue from municipal wastewater treatment plants [23, 24].
Electricity production by gas turbines can be applied by biogas as a fuel, thus replacing the natural gas for small-scale applications (or power within 25–100 kW).
The use of biogas as a fuel for civil transport and road vehicles instead of natural gas is already spread in Western Europe and the United States [25]. There are many vehicles in Sweden operating on biogas in the urban public transport [26].
Another very attractive application of biogas for electricity production is its use in fuel cells. The specialized cells for these purposes are described briefly by O’Hayre et al. [27]. Prior to biogas feed, carbon dioxide and sulfur compounds must be removed by scrubbing to avoid corrosion and catalyst poisoning and to rise the gas energy capacity. A sketch of such a fuel cell is shown in Figure 3, cf. [28].
Principal sketch of methane-driven fuel cell, from [28].
The classic process for methane-driven fuel cells is to convert catalytically by steam reforming methane into a mixture of carbon monoxide and hydrogen and to use the latter in a traditional hydrogen/oxygen fuel cell to generate electricity. The advantages of fuel cell applications with methane as a fuel compared to the traditional heat power stations consist in their higher efficiency, clean waste gases (containing almost only carbon dioxide), and higher efficiency at low loads than the gas turbine equipment [29]. Moreover, the released heat can be utilized for different purposes; the main one is to maintain the temperature regime in the fuel cell. There are many practical applications of these methods. It is already widely commercialized. A disadvantage of this method is the necessity of consequent reactions of steam reforming and carbon monoxide removal as well as the operation at high temperatures (about 750°C), being harmful for the metal parts of the equipment [30, 31]. Higher temperatures are preferred to avoid coke deposition on the catalyst [31].
There are new efforts to lower the operation temperature to 500°C in order to keep the equipment durability [32, 33]. Another improvement of the technology is to use the mixture of carbon monoxide and hydrogen as a fuel simultaneously, thus simplifying the whole process, but applying new catalytic process.
The most attractive option is to convert methane (biogas, respectively) into electricity in one step, thus avoiding the steam reforming and carbon dioxide removal. There are some new studies showing direct catalytic oxidation of methane in the anodic space of solid oxide fuel cells (SOFCs), with direct activation of the C-H bonds in the methane molecule [28, 34, 35, 36]. A platinum catalyst was used for this purpose at low temperatures, e.g., 80°C. However, the catalyst deactivates, and the process is limited by methane diffusion in the anodic space. As a result, the power density is still low for practical use.
Besides as a fuel, biogas could be used as a feedstock for synthetic organic fuel production. There are studies claiming for biogas recovery as fuels applying catalytic auto-reforming. Another approach is the dry reforming consisting in converting the equimolar mixture of methane and carbon dioxide into synthesis gas (an equimolar mixture of carbon monoxide and hydrogen).
Afterward, this synthesis gas is converted into a mixture of light hydrocarbons by the catalytic Fischer-Tropsch process. The resulting Fischer-Tropsch process yields liquid hydrocarbon fuels (methanol and dimethyl ether). The intrinsically high-energy density of these fuels and their transportability make them highly desirable. Such synthetic fuels do not contain any sulfur. In addition, methanol (arguably the “simplest” synthetic carbonaceous fuel) is a candidate both as a hydrogen source for a fuel cell vehicle and indeed as a transport fuel, and dimethyl ether is viewed as a “superclean” diesel fuel [36]. It is well known that methanol is a starting material in chemical industry. It is a liquid at room temperature and has much easier storage and transport capabilities than alternatives such as methane and hydrogen. Methanol is used as solvent, gasoline additive, and a chemical feedstock for production of biodiesel and other chemicals of high value. Therefore, the wide application of methanol motivates its large-scale production, which is ever increasing.
However, presently, the dominant technology of methanol is a two-step catalytic process, which is too expensive. A large number of industrial-scale chemical manufacturing processes are currently operated worldwide on the basis of strongly endothermic chemical reactions. The steam reforming of hydrocarbons to yield syngas and hydrogen is a classic example:
The above, highly endothermic reaction is used worldwide for the high-volume production of “merchant hydrogen” in the gas, food, and fertilizer industries, i.e., other portions of energy have to be spent with the consequent air pollution by carbon dioxide.
At present, a relevant technology for methanol production resides in the transformation of CO2 and CH4 to molecules having industrial added values. Among such technologies, a great attention is focused on the production of synthesis gas (gaseous mixture of CO and H2) that constitutes a versatile building block for subsequent production of methanol or chemical intermediates in petrochemical industries. Methanol is still produced on a world scale from synthesis gas, which is combination of varying amounts of H2, CO, and CO2 (at 200–300°C, 50–100 bar), which is itself product of steam reforming of methane (SRM; at ca. 800°C over Ni-based catalyst), followed by further conversion processes such as Fischer-Tropsch (FT) synthesis. This two-step process incurs high energy and capital demands. Additionally, this process gives many other light and heavy weight co-products along with the methanol product. Therefore, additional energy and cost in the conventional methanol plants are directed to the separation of these coproducts from methanol prior to the final deposition of product.
The direct synthesis of methanol from syngas requires a H2/CO ratio of about 2 [37, 38]. Since the syngas produced by dry reforming of methane (DRM) is too poor of H2 (H2/CO ≤ 1) to be fed to a FT synthesis unit, the bi-reforming of methane (BRM), combining DRM with steam reforming of methane (SRM) (H2/CO = 3) and the utilization of the most important two greenhouse gases CH4 and CO2 with water, may yield a syngas with ratio close to 2, the so-called “metgas”:
To date, only one plant with the combination of steam and dry reforming has been recently demonstrated by the Japan Oil, Gas, and Metals National Cooperation. No other industrial technology for DRM has been developed because the selection and design of suitable reforming catalyst remain an important challenge. Ni-based catalysts are the most attractive candidates for large-scale industrial applications due to their high activity in DRM and SRM [39, 40, 41, 42, 43], low cost, and wide availability compared to noble metals. However, they are sensitive to deactivation caused by the metal particles sintering and carbon formation at high reaction temperature of reforming processes. Development of selective and coke-resistance modified Ni-based reforming catalysts is a key challenge for successful application of bi-reforming for methanol production. Modifying Ni catalysts with suitable promoters and supported on reducible metal oxide carriers will give the opportunity to develop active and stable catalysts for bi-reforming of methane.
A “super-dry” CH4 reforming reaction for enhanced CO production from CH4 and CO2 was developed [44]. Ni/MgAl2O4 was used as a CH4 reforming catalyst, Fe2O3/MgAl2O4 was used as a solid oxygen carrier, and CaO/Al2O3 was used as a CO2 sorbent. The isothermal coupling of these three different processes resulted in a higher CO production than conventional dry reforming by avoiding back reactions with water. Equation (3) shows the global reaction of this two-step process, in which CO and H2O are inherently separated because of the two-step process configuration:
It is important to note that despite the apparently higher endothermic effect of the super-dry reforming process than conventional DRM (Eq. 1), the required heat input per mole CO2 converted is much lower (110 kJ/mol CO2 compared to 247 kJ/mol CO2). Finally, given the availability of a renewable source of H2, applications are possible where CO and H2 can be combined in different ratios for the formation of chemicals or fuels [45, 46]. Indeed, an efficient and separate production of high purity CO and H2 would further establish the role of syngas as a versatile and flexible platform mixture.
All these methods and techniques are applicable when biogas is available. Some other applications are described briefly below.
First of all, biogas must be purified for sulfur compounds prior to its use [47]. Afterward, methane and carbon dioxide have to be separated by membrane processes using gas-liquid systems [48] or swing pressure adsorption [49]. Once methane and carbon dioxide are separated, each of them has its own route for further application. Besides the already mentioned applications as a fuel for transport and energy purposes, dry reforming and steam reforming to obtain synthesis gas, the purified methane can be converted into light hydrocarbons, e.g., ethane and ethylene by advanced methods, like the so-called VYJ process [50, 51, 52, 53]. By this method, methane is converted in one step into ethylene by catalytic or electrocatalytic reaction [54, 55, 56].
High yields up to 88% in total are attained [50]. The rest of nonreacted methane is trapped in molecular sieves and recycled to the reactor [50, 53, 54]. In this way, the use of methane reaches 97% with an ethylene yield of 85% [50].
As ethylene is a basic feedstock for the mostly spread polymerizations and many value-added chemicals, it is clear that this way of biogas utilization is quite promising one.
The usual criteria for the feasibility of an anaerobic digestion technology are the type of digester, the operation temperature, the necessary retention time of the substrate in the reactor, the substrate acidity (the initial pH value), and the presence of certain chemicals in the inlet slurry.
However, the most important one is energy demand for the biogas formation and the energy potential of the produced biogas.
There are two typical temperature ranges for biogas production: mesophilic one (at 30–35°C) and thermophilic one (at 55–60°C). Different genera of methanogenic microorganisms are capable to accomplish the processes in those two cases. The advantages of the thermophilic regime are in the higher production rate and the lack of pathogens in the outlet slurry. However, the energy input for maintenance of this regime is higher than for the mesophilic one.
The question of the energy demand for any industrial process is of crucial importance for its economic reliability. The same applies to biogas production.
There are some methodologies for the estimation of the feasibility of biogas production [57, 58]. They all involve the demand of heat for temperature maintenance and electricity for mechanical operations (stirring, pumping, and transport) and comparison to the energy yield after anaerobic digestion.
Generally, the operations for a certain flowsheet are separated into production processes and support ones. The production processes in the considered case are the reception of the substrate and its storage, pre-treatment of feed (dilution, pH adjustment, acid hydrolysis, etc.), and anaerobic digestion with biogas production. The removal of the digestate and its storage and processing are also included. This set of processes is called as Level 1 [57].
Once biogas is produced, it could be used for direct heat and/or electricity production and supplied to customers or for own use (Level 2). More sophisticated operations, such as gas cleaning, upgrading (i.e., removal of carbon dioxide), and compressing the upgraded gas, are required if the gas will be distributed by the gas distribution grid or for some chemical applications.
The methodologies for energy demand evaluation consist in the inventory of all such processes and auxiliary ones with their energy demand per unit production (i.e., amount of produced biogas with certain energy potential). Then, the overall energy demand is compared to the biogas yield with its energy potential, and the percentage of the energy input to the overall yield is a measure for feasibility of the studied technology.
The structures of the energy demand for different flow sheets and the weight of different subprocesses depend on the substrate properties (particles size, chemical structure and content, moisture, and total solid content) and the amount to be treated, the digester construction and design.
Berglund and Borjesson [58] proposed a methodology based on the life-cycle perspective including the energy required for the production of the substrates (including crop growth, harvesting, transport, etc.). The energy efficiency is defined by the ratio of the energy input to the energy yield of the produced biogas. It was found that the energy input corresponds mainly to 15–40% of the energy content of the produced biogas. The subprocesses of extensive handling of raw materials may lead to considerably increase the energy input and thus to undermine the feasibility of the entire technology.
In case the gas will be used as a feedstock for other chemical applications (e.g., dry reforming and steam reforming), the operational costs of the processes at Levels 1 and 2 have to be compared to the operational costs for the chemical processes and the prices of the produced chemicals or other final products.
The main disadvantage of biomass produced fuels is the inevitable release of CO2 in the atmosphere after combustion. Therefore, big efforts are made in the recent years for remediation of this adverse effect of greenhouse gas. The best way to cope with this problem is the natural assimilation by the vegetation by photosynthesis, but it is not sufficient due to the very large emissions from industrial sources, energy production, transport, and household. That is why many other methods are proposed and studied in the recent years.
One of them is the direct use of pure carbon dioxide as a solvent in supercritical extraction in the pharmaceutical industry. However, this application is limited and cannot be a substantial solution of the problem. There are many efforts to recycle carbon dioxide to produce different organic chemicals: formic acid, methanol, dimethyl-ether, poly-carbonates, acrylic acid, etc. [59, 60]. All of these methods are applicable for the residual carbon dioxide after separation from biogas. Therefore, not only methane but also carbon dioxide in biogas is valuable source of energy and value-added product.
The data presented here illustrate one of the very important biorefinery approaches to produce simultaneous energy and value-added chemicals from biomass, thus reducing the demand of fossil fuels and resulting in overloading of atmosphere by greenhouse gases. The same applies to the water and soil pollution, since those resulting from biomass processing are nature compatible and facilitate the formation of close energy and material cycle. One of the ways to do it is biogas production from such waste.
At the end, we can say that biogas extends its area of application leading simultaneously to protect the environment by waste treatment, natural gas, and fossil fuel saving, as well as to replace, at least partially, the oil as a feedstock for organic value-added products.
This work was supported by the Bulgarian Ministry of Education and Science under the National Research Program Eplus: Low Carbon Energy for the Transport and Households, grant agreement D01-214/2018.
The authors declare no conflict of interest.
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\n\nMetadata for all publications is also automatically deposited in IntechOpen's OAI repository, making them available through the Open Access Infrastructure for Research in Europe's (OpenAIRE) search interface further establishing our compliance.
\n\nIn other words, publishing with IntechOpen guarantees compliance.
\n\nRead more about Open Access in Horizon 2020 here.
\n\nWhich scientific publication to choose?
\n\nWhen choosing a publication, Horizon 2020 grant recipients are encouraged to provide open access to various types of scientific publications including monographs, edited books and conference proceedings.
\n\nIntechOpen publishes all of the aforementioned formats in compliance with the requirements and criteria established by the European Commission for the Horizon 2020 Program.
\n\nAuthors requiring additional information are welcome to send their inquiries to funders@intechopen.com
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