Definition of aviation safety and risk terms.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2309",leadTitle:null,fullTitle:"Principal Component Analysis - Engineering Applications",title:"Principal Component Analysis",subtitle:"Engineering Applications",reviewType:"peer-reviewed",abstract:"This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. 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\r\n\tDementia has become the leading neurological problem for human beings associated not only with the old population but newer generations as well. Various environmental and genetic factors are reported to be involved in the disease. This book hopes to comprise a detailed discussion on the relationship between different brain areas and cognition. Pathophysiology of dementia containing molecular mechanisms will be explained as well as the memory function that depends on the neuronal circuits among the different brain areas. Other neuronal circuits involved in memory, learning, and dementia will also be discussed. Mechanism of neuronal circuits involved in memory consolidation, the main neurological disorders associated with dementia, dementia screening, and its validation, along with neurophysiological tests, will be covered. Another aspect that this book hopes to cover is the modern therapeutic trends for the management of dementia. Biologics will be changing the therapeutic world of dementia in the near future. We aim to have a project that consists of various cutting-edge technologies that have been adopted for the treatment of dementia.
",isbn:"978-1-80356-783-9",printIsbn:"978-1-80356-782-2",pdfIsbn:"978-1-80356-784-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"d40f707b9ef020bb202be89404f77a1e",bookSignature:"Dr. Devendra Kumar, Prof. Sushil Kumar Singh and Dr. Ankit Ganeshpurkar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11637.jpg",keywords:"Cognition, Neuronal Circuits, Learning and Memories, Memory Consolidation in Dementia, Neuropsychological Tests, Treatments, New Therapeutic Tools, Biologics, Brain Areas and Cognition, Neuropsychology, Neurological Disorders, Modern Therapeutic Trends",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 1st 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"11 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Devendra Kumar's research interest includes the Design, Devolvement, and Biological screening of Small molecules, Metal complexes, Peptides for the management of Alzheimer's disease, Fragile X Syndrome, and Tuberculosis. Dr. Kumar worked on Alzheimer's disease and developed CNS active small molecules such as Acetylcholine, Butyl choline, Beta-secretase 1, Matrix Metalloprotein-2 and 9 inhibitors, and NMDA receptor antagonist.",coeditorOneBiosketch:"Dr. Singh is an eminent scientist and teacher in the field of neurodegenerative disorders. He was the Principal Investigator in the Development of bioactive molecules as therapeutic agents for Alzheimer’s disease and screening of their toxicity; IIT (BHU), Varanasi, as well as the Principal Investigator in Design and synthesis, is of Matrix Metallo Proteinase (MMP -2 & 9) inhibitors as therapeutic agents for Alzheimer’s disease; DBT, New Delhi.",coeditorTwoBiosketch:"Dr. Ganeshpurkar's objective is to create a niche in the field of medicinal chemistry and drug design research with an emphasis on the use of computational tools and artificial intelligence in lead identification and optimization. His research interest is in silico drug designing, lead identification, and optimization as well as design, synthesis and biological evaluation of Novel leads for various pathophysiological conditions such as Alzheimer’s and other neurodegenerative diseases.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"454030",title:"Dr.",name:"Devendra",middleName:null,surname:"Kumar",slug:"devendra-kumar",fullName:"Devendra Kumar",profilePictureURL:"https://mts.intechopen.com/storage/users/454030/images/system/454030.jpg",biography:"Dr. Devendra Kumar is an Assistant Professor in the Department of Pharmacy. Dr. Kumar did his Ph.D. in Pharmaceutical Sciences from the Indian Institute of Technology (Banaras Hindu University) and completed his postdoctoral research at the University of Texas, USA (2019-2021). His research interest includes Design, Devolvement, and Biological screening of Small molecules, Metal complexes, Peptides for the management of Alzheimer\\'s disease, Fragile X Syndrome, and Tuberculosis. Dr. Kumar worked on Alzheimer\\'s disease and developed CNS active small molecules such as Acetylcholine, Butyl choline, Beta-secretase 1, Matrix Metalloprotein-2 and 9 inhibitors, and NMDA receptor antagonist.\nAlong with the Drug Discovery, he is also working on the Pathophysiology of Fragile X Syndrome. His work on the Fragile X Syndrome includes identification of spine abnormality and the role of Microglia. The study of Microglia-Neuron communication in genetically modified animals is his thrust area. He is also working on the gene-editing tools using CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology and the development of Blood-Brain Barrier penetrating Polymers as a delivery vehicle for CRISPR molecules.",institutionString:"Dehradun Institute of Technology University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Dehradun Institute of Technology University",institutionURL:null,country:{name:"India"}}}],coeditorOne:{id:"182874",title:"Prof.",name:"Sushil Kumar",middleName:null,surname:"Singh",slug:"sushil-kumar-singh",fullName:"Sushil Kumar Singh",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSAm4QAG/Profile_Picture_2022-04-07T11:17:21.JPG",biography:"Principal Investigator, Development of bioactive molecules as therapeutic agent for Alzheimer’s disease and screening their toxicity; IIT (BHU), Varanasi.\r\nPrincipal Investigator, Design and synthesis is of Matrix Metallo Proteinase (MMP -2 & 9) inhibitors as therapeutic agents for Alzheimer’s disease; DBT, New Delhi.\r\nCo- Principal Investigator, Cestocidal activity of glands and hairs of fruits of Mallotus phillippinensis (Kampillaka Plant); ICMR, New Delhi.\r\nPrincipal Investigator, Ethno-medicinal plants as a source of new therapeutic agents against psoriasis; National medicinal Plant Board, AYUSH, New Delhi.\r\nPrincipal Investigator, Isolation of marker compounds from Withania somnifera; Natreon Inc., Kolkata.\r\nPrincipal Investigator, Isolation of marker Compounds from natural Sources; Drug Research and Development Center, Kolkata.\r\nOne of the Investigators of the Centre, Establishment of facilities for identification, chemical characterization, standardization and quality control of medicinal plants found in tribal area in central India; DST, New Delhi.",institutionString:"Banaras Hindu University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Banaras Hindu University",institutionURL:null,country:{name:"India"}}},coeditorTwo:{id:"465935",title:"Dr.",name:"Ankit",middleName:null,surname:"Ganeshpurkar",slug:"ankit-ganeshpurkar",fullName:"Ankit Ganeshpurkar",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RKF6EQAX/Profile_Picture_2022-04-07T11:30:06.jpg",biography:null,institutionString:"Bharati Vidyapeeth Deemed University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Bharati Vidyapeeth Deemed University",institutionURL:null,country:{name:"India"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"18",title:"Neuroscience",slug:"life-sciences-neuroscience"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6628",title:"Circadian Rhythm",subtitle:"Cellular and Molecular Mechanisms",isOpenForSubmission:!1,hash:"628bbcbfaf54a56710498540efe51b87",slug:"circadian-rhythm-cellular-and-molecular-mechanisms",bookSignature:"Mohamed Ahmed El-Esawi",coverURL:"https://cdn.intechopen.com/books/images_new/6628.jpg",editedByType:"Edited by",editors:[{id:"191770",title:"Dr.",name:"Mohamed A.",surname:"El-Esawi",slug:"mohamed-a.-el-esawi",fullName:"Mohamed A. 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Along with this ongoing change, safe aviation operations are crucial. Absolute safety does not exist. However, eliminating accidents and serious incidents is unachievable. Failures always occur, despite the most accomplished safety efforts, since all risks cannot be totally eliminated. No human-made system/innovation can be risk free. However, risk and error are acceptable in an inherently safe system. So, how to ensure that aircraft operations are safe if all risks cannot be eradicated? What is safety? What is risk? Safety is the condition in which the danger of harm to persons or property damage is minimized to and retained at or below an appropriate level by an ongoing process of hazard detection and risk management. In civil aviation, risk has been assessed as the probability of the occurrence of an air accident in terms of two aggregate indicators, the accident rate and the fatality rate. Thus, if new operations are to be undertaken, equipment is required to ensure that an acceptable level of safety is guaranteed and the upcoming risks are taken into consideration [1].
\nSafety and risk assessment are the two fundamental terms utilized in avionics to address the degree of safety of aircraft tasks [2]. The reason for this assessment is to distinguish the degree of safety related with a particular activity/activity by recognizing the normal risk(s) by giving direction in dynamic jobs to either acknowledge or not acknowledge the risk(s) to which the activity is required to be uncovered. Through this assessment, in light of a predecided adequate degree of risk, moderation procedures/remedial activities can and ought to be actualized dependent on explicit safety risks so as to decrease their potential impact(s). The phrasing of safety assessment and risk assessment has now converged into each other so that it has gotten hard to get whether they speak to two distinct techniques. In the event that it is unmistakable, in which circumstances we should utilize every one of them; or in the event that they supplement one another and the presentation of one command includes the accomplishment of the other. By the by, given these complexities, it is usually perceived that their definitive target is basic decide when and where estimates should be taken to guarantee the normal proper safety level.
\nAviation regulators and industry experts have continually developed and updated strategies and resources over the years for the evaluation of ongoing developments in the aviation industry in order to ensure appropriate standards of safety while enhancing flight operating capabilities, increasing the utilization of airspace, and reducing operational costs.
\nThe concept of aviation safety and risk can have different perceptions: zero deaths or serious incidents, free from hazards, aviation employee attitudes toward unsafe acts and conditions, avoidance of error, and regulatory conformity.
\nSafety is defined as the “State where the potential for harm to persons or property damage is minimized to and preserved at or below an appropriate level by an ongoing process of hazard recognition and safety risk management. For technological systems, risk is related to the possibility of part failure or the whole system causing hazard exposure and related consequences. In economic structures, risk may be exposed to the threat of losing market prospects and/or resources due to unpredictable circumstances. In social systems, risk is the chance of being exposed to injury hazard and/or life loss. Therefore, risk could be viewed as a combination of the probability (or frequency of occurrence) and the magnitude (or severity) of a hazardous event.”
\nWhen new equipment is developed, or for instance a new flight operational procedure is designed, the requirement for a safety assessment or risk assessment is very common in the aviation industry before the new technology or procedure is put into place. Very often, this is triggered by the operator/manufacturer through recommendations made by aviation associations/working groups or regulatory requirements.. An assessment usually includes an overall evaluation of something called a framework that may include a thorough review of particular subsystems. The assessment aims to identify the level of safety associated with a certain action/operation by identifying the expected risk(s) through guidance on the decision-making roles to either accept or not accept the risk(s) to which the operation will be exposed. This assessment, based on a predetermined acceptable level of risk, can and should implement mitigation strategies and corrective actions based on specific safety risks to reduce the potential effects of mitigation. Safety and risk assessments are the terms most commonly used for this assessment. Table 1 gives the definite of aviation safety and risk terms.
\nDefinitions term | \nMeaning | \n
---|---|
Consequence | \nAn event’s performance. A consequence may be definite or uncertain with positive or negative effects on goals | \n
Control (also can be called mitigation) | \nRisk-modifying measure | \n
Establishing the context | \nDefining external and internal requirements for risk management and defining the scope and risk standards for risk management policy | \n
Event | \nDefining the external and internal parameters to be taken into account when managing risk and setting the scope and risk criteria for the risk management policy | \n
Level of risk | \nRisk magnitude or mixture of risks expressed in terms of balancing consequences and probability | \n
Likelihood | \nChance of something happening | \n
Monitoring | \nRepetitive screening, tracking, vital observation, or status determination to identify deviations from the necessary or planned output | \n
Residual risk | \nRemaining risk after therapy | \n
Risk | \nThe impact of uncertainty on events’ potential goals and their consequences, or a combination thereof | \n
Risk analysis | \nSystem to grasp risk nature and assess risk level | \n
Risk assessment | \nIn this context, the overall process of risk identification, risk analysis, risk evaluation, and identification of controls (mitigation) | \n
Risk criteria | \nReference terms against which risk significance was evaluated | \n
Risk identification | \nProcess of finding, recognizing, and describing risks | \n
Risk management | \nCoordinated efforts to guide and monitor risk-related tasks | \n
Risk management plan | \nThe scheme within the department’s risk management system defining the strategy, management elements, and tools to contribute to risk management | \n
Risk management process | \nSystematic application of management policies, procedures, and practices to communicating, consulting, and context-setting, identifying, analyzing, evaluating, monitoring, and risk review activities | \n
Risk owner | \nPerson or entity with the accountability and authority to manager risk | \n
Risk profile | \nDescription of any set of risks. | \n
Risk source | \nElement, which alone or in combination, has the intrinsic potential to give rise to risk | \n
Definition of aviation safety and risk terms.
Level | \nEvent assessment priority criteria | \n|
---|---|---|
Hazard level | \nSeverity | \nHow bad is the situation? | \n
Risk level | \nSeverity + probability | \nHow bad are the situation and the possibility for it to happen? | \n
Fleet risk level | \nSeverity + probability + exposure | \nHow bad is the situation, the possibility for it to happen, and what is the size and utilization of the affected fleet | \n
Event assessment criteria.
A safety assessment mainly aims to identify which risks are expected to be exposed to a new operation/system or to be acceptable or not on the basis of the safety criteria normally established by aviation regulators.
\nThe ongoing safety assessment process continues the initiative that has begun during the design phase and ends with the launch of the new model of aircraft and continues until the aircraft is removed from service.
\nThree priorities are set in the ongoing safety assessment process: 1. Maintain airworthiness (certification) of the aircraft: in-service incidents are evaluated based on the safety standard of the certification process. 2. Maintain aircraft safety: in-service incidents are measured against the company’s internal health objectives. 3. Improve airplane safety: in-service incidents are analyzed to find potential for minimizing their number or exceeding the company’s protection objectives. It is intended that the safety assessment process will be continuous, iterative, and closed. When an incident is identified, assessed, and actions are taken, the surveillance continues to validate the action’s effectiveness. The safety of the aircraft depends on a variety of factors, including original design, development, aircraft crew and maintenance behavior, operational effects, parts quality, modifications, the atmosphere, and aging of the aircraft. The safety assessment consists of five steps: 1. Establish Monitor Parameters, 2. Monitor for Events, 3. Assess Event and Risk, 4. Develop Action Plan, and 5. Disposition Action Plan. Figure 1 shows the steps involved.
\nSafety assessment steps.
“
“
“
“
“
Now let us begin an in depth research on the safety assessment process. Figure 2 shows an in detail the flowchart of the ongoing safety assessment process.
\nSafety assessment process.
“Establish Expectations” includes establishing the company’s safety philosophy, assessing the role of safety within the company’s structure, and defining acceptable levels of risk and performance. It may vary from a structured formal security organization to an informal structure. The safety organization must be adequately autonomous to ensure it can affect the safety philosophy.
\nThere are at least two expectations levels. The first is standards levied by regulatory body, aimed at establishing minimum operational health. The second is the user’s own standards, which can surpass regulatory agency requirements. These expectations imposed internally may include parameters or requirements not covered by regulations or lower-than-required risk levels. Requirements and expectations can be dependent on many factors, including the following:
Basic aircraft requirements
Safety analysis
Requirements for regulatory reporting
Operational features (e.g., cargo vs. passenger carriers)
Aircraft maintenance programs
Operating environment conditions (e.g., operations, tropical vs. arctic operations)
Experiences identified by earlier process use (i.e., continuous improvement)
Industry-related accidents and incidents (where available)
Lessons learned
Fleet-specific expectations are passed on to the phase of the process “Establish Monitor Parameters.”
\nOnce actions are undertaken to address a problem, a follow-up testing process should be developed to track implementation and assess action effectiveness. This follow-up method can be achieved by comparing pre- and post-conditions of correction-affected systems. Comparison frequency should be based on probability and severity factors. During this post-implementation review, initial findings (e.g., teardown reports) may be reexamined. This verifies the action implemented eliminates the problem, and the assumptions used in the analysis are valid.
\nAfter the expectations and monitoring parameters are defined, they are then used during the “Monitor for Events” phase.
\nEvent monitoring includes two related but distinct elements. Collect and analyze data concerned with “new” events of concern and monitoring the effectiveness of previous actions that monitor events already evaluated through the process.
\nWhere no issue or pattern is identified, the process continues in the phase “Collect and Analyze Data.” Whenever the analysis identifies a possible issue or trend, the data will be summarized for process phase “Asses and Event Risk.”
\nThe “Assess Event and Risk” phase shown in Figure 2 is divided in to “Assess Event and Risk, “Determine Internal or External Issue Resolution” and “Notify Responsible Party.”
\nThe “Develop Action Plan” phase involves more analysis on event triggers and the creation of one or even more potential actions. This will include future behavior of risk assessment. Developing actions for all problems simultaneously due to limited resources may not be practical. Hence, the company must assign priorities based on its internal issue tolerance and potential regulatory oversight. Both priorities require resource allocation first to concentrate on safety-critical items and later to identify and monitor operator-sensitive issues. When developing an action, understanding the root cause is important.
\nIf the decision is to approve the proposed action(s), the process proceeds into the “Disposition Action Plan” phase and begins the “Prioritize and Schedule” and “Implement” steps. If the company does not accept the suggested action, they must decide how to proceed. If more research is necessary, then either the “Assess Event and Risk” step to reevaluate the significance of the recommended action or the “Develop Actions Plan” step to refine potential action may be returned. If the recommended action is not accepted and no further review is deemed appropriate, the process moves to “Register and Close” stage. The decision is then reported and sent for future reference.
\nWhen it is the decision is to implement, the “Disposition Action Plan” phase and the “Prioritize and Schedule” and “Implement” steps begin. When the decision is not to enforce the action, the process shifts to “Document and Close.” The decision is then documented and stored for future reference.
\nThe process includes determining relative priorities (e.g., risk reduction, cost, and implementation ease) of this and other actions and scheduling implementation. And once an implementing plan is developed and approved organizationally, implementation process is initiated.
\nFlight Operations Manual
Engineering Orders
Maintenance Alerts
Maintenance Manuals
Flight Operations Bulletins
For a manufacturer, the document issued may:
be focused toward the operator in the form of a Service Bulletin, Service Letter, All Operator Telex, Maintenance Tips, etc.
be directed toward its own organization which may include new process instructions, production guidelines, new drawings, etc.
If the sequence of documents, procedures, and changes have been completed and monitoring indicates that the problem has been successfully solved, the company will then go back to a mode of tracking criteria and perhaps assess lessons learned from the resolution process.
\nIf no action has been taken, the decision and the reasoning are maintained here.
\nSafety, reliability, quality, and cost-effectiveness.
Product quality and business processes efficiency.
Amount and cost of product modifications.
Human/machine-interface compatibility.
User satisfaction needs.
The steps mentioned above are the detail explanation of what happens in a safety assessment process during civil aircraft operation. Now a detail explanation of what happens in risk assessment process will be mentioned below.
\nBefore assessing a risk, different procedures are to be performed to identify an event. Aircraft safety depends on various factors including the original design, flight crew, manufacturing and maintenance activities, operational results, parts quality, modifications, surroundings, and aircraft being old. Departmental aviation risk assessments will follow a standard model. The model identifies the task and context, risks and possibility, present and future mitigation approaches, and the resulting amount of risk. The evaluator considers all possible realistic risk controls, determines those that already exist, assesses the current level of risk, and then selects additional risk measures to reduce the level of risk to one that would usually be appropriate to the organization.
\nSafety and risk assessment have merged into one another in such a way that it is impossible to explain both of them distinctively. There, in order to perform a preliminary risk assessment, we need the help of ongoing safety assessment. In this assessment, the primary focus is risk and the steps that are defined will be similar to safety assessment.
\n\nFigure 3 describes a suggested high-level method for ongoing safety assessment, part of the initial risk assessment phase. This standardized method involves five high-level steps:
Preliminary safety assessment.
Now let us begin an in depth research on the risk assessment process. Figure 4 shows an in-detail flowchart of the risk assessment process.
\nRisk management hierarchy.
When its extent, triggers, and magnitude are identified and the event is detected, risk assessment will begin. As investigation progresses, the next phase is generally to determine the problem’s likelihood. Risk assessment is conducted to identify the risk scale and determine if steps are required to manage it within separate boundaries. Risk assessment is not an end in itself but could control risks to a reasonable or bearable level. It is also the way of evaluating potential losses from a hazard using a combination of known circumstance information, knowledge of the primary process, and judgment of unknown or well-understood information. For understanding the risk management process, the definition used in the aviation industry must be clearly understood. Table 1 describes such terminology used in aircraft industries.
\nA work profile and risk assessment is included in every aviation activity. Where a task can relate to a defined task profile and risk assessment, the task preparer will refer to the profile of the task and follow it. If a task profile and risk assessment is covered and no deviations are reported, the task may proceed without further permission, although the pilot and any crew member should provide continuous operational risk assessments during a flight. The corresponding risk evaluation must be reviewed if any job profile changes. Where the planning and task personnel determine that the proposed activity does not fit within a current task profile, a new task profile is created in coordination with the correct designated individual. Danger and regulation awareness is essential to departmental aviation management. Risk management does not end with a risk assessment but is a continuous process for all people who are interested in air use and security. Aviation operators will provide risk evaluation and reduction services. The aviation risk management system of the department follows a qualitative rather than quantitative approach, while historical data are suitable for determining the probability of an incident and provide some indication of the potential effects. The risk assessment process involves looking for hazards, assessing their implications, probability, and identifying risk mitigation plans. This technique is used to assist in developing aviation risk assessments.
\nRisk assessment is an important part of the processes for concentrating and profiling tasks. We are tightly related and should be twice reviewed to ensure that there are no irregularities or contradictions. Suitable aspects of a job profile can be used to determine the risk management context, and the creation of a role profile can be used for further consideration. Current or expected risk controls may be part of the history, but these must also be assessed in the risk assessment. Both people engaged in aviation services planning and administration must use this aviation risk assessment process during the training and organization. Models of accidents and accidents show the importance of managing risks at all rates. The risk evaluation and judgment development shall include individuals within organizations and the individual aircraft operator responsible for flight and mission actions. Danger is calculated in order to assess the total risk ranking. The Local Control Center can establish evaluated levels of low risk negligence as a general policy, low to high, for acceptance by the Policy Coordinating Center or government. Only the Director General or delegate and aircraft operator can approve extreme risks, therefore, controls must be implemented to reduce risk or the task is not performed.
\nThe consequence is a loss or disadvantage incident demonstrated in qualitative instead of quantitative terms. As a result, the possible adverse effects of task-related incidents are evaluated in the sense of security, economic, organizational and public opinion is shown to the subcontractor and the agency. Selection of magnitude depends on risk parameters and design of risk. Subcontractor, aircraft, or equipment expert advice or background information can be used to determine an event’s implications, including information on actual accidents, incidents, or events. For opportunity, probability or possibility the agency uses conceptual concepts. That is the measurement of the likelihood of an incident with a certain outcome, along with a total of the exposure to the incident during the mission. The exposure can be defined as the frequency of the event and the time of the incidence during the behavior.
\nFactors like crashworthy seats and PPE such as helmets and fire resistant clothes can handle the consequences. Likelihood may be classified according to planning, architecture, expectations or functions. The system of controls will also be considered when determining on controls for determining efficacy. The least efficient risk management tool (PPE) is at the bottom of the map displaying more controls available to reduce risk, preferably using a number of controls to make the risk mitigation more efficient. The findings should be checked in an evaluation and debate process to ensure that no additional risks and hazards have been identified or properly managed. Section 4 shows the hierarchy of risk control.
\n\nFigure 5 represents the process risk assessment cycle. This helps visualize the process although, while the process may seem complex, the actual way should be relatively simple. Using standard format should support the risk process. Standard format reproduces typical aviation considerations. These are included to center the total aviation task picture. Without normal aviation operations, no activity can be considered. The process shows how to treat risk assessment step by step and how to arrange it so that errors can be understood clearly but more importantly.
\nRisk assessment cycle.
\nFigure 6 explains how the risk assessment process works and how interventions and activities are considered. The method requires skillful expertise and a detailed analysis to avoid making mistakes and prevent further risks.
\nRisk assessment process.
\n
Establishing the task context allows consideration of risk reduction. For example, if the task is to be performed in summer, icing is unlikely to be a significant risk in low-level operations. However, if the task under consideration is performed throughout the year, icing becomes consideration in certain areas of the state. The assessor should establish both external and internal perspectives. The external background can be political, social, environmental, financial, and human. Internal context considerations may contain specific project objectives and their importance to the department that is the department’s internal policies, standards, and guidelines. Department must identify risk factors including impact and probability measures.
Risk sources, their impact zones, causes, and possible impacts need to be identified. The traditional format or template provides certain criteria, but in a particular way, the evaluator must look past the obvious while considering a new task or common task. For starters, spray operations typically occur far away from built-up or blocked areas. The spray area may be situated in a low-jet route from which military aircraft fly at a very low altitude. The risks of a mid-air crash can be high if the pilot does not search (Airmen Notice) to warn may routes and where are involved. Significant causes and effects should be taken into consideration. It should also be pointed out that the “race” in aviation starts at the scheduling and planning stage, so issues of fatigue and adequate access to information should be addressed, as well as the calculation of power margins and the availability of landing areas. Risk assessment factors may include modalities for failure, failure classification, distribution of probability and conditional probability, probability for inspection detection, operational/maintenance restrictions, and candidate actions.
Understanding the idea of implementing an action plan is important, because it helps explain the root cause of an issue. It can help identify frequently occurring failures, and with time these problems have led to more serious causes. When the resolution is recognized, it should be developed in detail, tested to verify and validate the action. Records should include the rationale and benefits of recommended actions.
If the current checks found do not reduce the risk to appropriate standards or where there is consensus that further checks are needed in order to accomplish the job safely, further checks should then be incorporated into the evaluation. Additional checks cannot be applied without the mission priorities being taken into account. This will also be a delicate balancing act, so returning to the project can often help to keep risk management in mind.
\nFurther steps and evaluation are required where risk is already deemed intolerable or whether the function will benefit from further risk management.
\nUltimately, the appraisal and analysis process continues until one of the two consequences is reached, the task risk remains too high and then the assignment is denied or input from senior departmental management is required, the assignment risk is lowered to the acceptable level, and the work profile and risk assessment is accepted.
\nThis chapter describes step by step, or in detail, how risk assessment and safety assessment is initiated and processed in aviation. The initial assessment and major assessment are divided into two different aspects. It describes how to detect and process an event. If an event is not too serious, use basic process to eliminate the threat. But if the event is complicated, a major risk and safety assessment is considered and implemented. In the next chapter, we will discuss about the methods or tools required to carry out these assessment.
\nAny analysis is as valid as its conclusions, data, and analytical techniques. Therefore, the underlying assumptions, data, and analytical techniques should be identified and justified to ensure validity of analysis conclusions. Variability can be inherent in elements such as failure modes, failure results, failure levels, failure probability distribution functions, failure exposure times, failure detection techniques, failure independence, human interfaces (e.g., crew behavior and procedures), and limitation of analytical approaches, processes, and assumptions. The rationale of the conclusions on the above things should be an important part of the analysis.
\nAssumptions can be checked using experience with identical or similar systems or components with due allowance for design, duty cycle, and climate variations. Where the adequacy of the analysis cannot be entirely explained and where evidence or conclusions are crucial to the conclusion’s acceptability, extra conservatism should be incorporated into either the research or intervention. Additionally, all data and conclusions ambiguity should be analyzed to the degree required to show that the research results are indifferent to this ambiguity. Any assumptions and other uncertainties related to a safety analysis must be identified and documented in order to judge their effect on the conclusions of such an analysis and to conduct sensitivity analysis. Ongoing field experience should be tracked to continue validating conclusions and reducing risks, or to collect the data required to minimize the effects of the extra conservatism built into the initial study. Finding any flaws in the assumptions requires reviewing the safety analysis.
\nHowever, the need to calibrate the safety analysis with past experience helps ensure that the future forecast is realistic, ensuring that operational parameters (deadlines, etc.) remain constant. If the analysis does not calibrate, further evaluation is required to determine which safety analysis assumptions may be in error. The safety analysis will not predict accurately unless it can calibrate to actual experience.
\nIn the airline industry, understanding and defining what a hazard is has changed over the years and continues to be the subject of discussion and discussion in the world of aviation. Throughout the early 1950s, safety enhancements were attributed to resolution of technical issues, with an event/accident often defined as human error in the late 1960s as the underlying causal factor(s). Organizational factors started to be identified as possible threats in the 80s, adding to or triggering a safety standard in an activity. It is widely recognized that their natural root is a combination of various areas when determining threats or contributing factors.
\nHazard can be any factor within the following four main aspects:
Technical
Human
Organizationa
Environmental
Consequence definition | \n|
---|---|
Catastrophic | \nDepartment and/or contractor’s capability significantly affected through circumstances completely within the relevant organizations control Objectives mostly not achieved Litigation actions may occur | \n
Major | \nissues. Significant ongoing well-being issues | \n
Moderate | \n
Other definitions can be found in the aviation industry (Table 4).
\nSource | \nHazard definition | \n
---|---|
CAA UK, and Euro control | \nAny condition, occasion, or situation which could incite a mishap | \n
CAA UK | \nA physical circumstance, frequently following from some starting occasion that can prompt a mishap | \n
FAA | \nAny current or potential condition that can prompt injury, disease, or passing to individuals; harm to or loss of a framework, hardware or property; or harm to the earth. A peril is a condition that is an essential to a mishap or occurrence | \n
CAA Canada | \nA wellspring of possible mischief, or a circumstance with a potential for causing hurt as far as human injury; harm to wellbeing, property, nature, and different things of significant worth; or a mix of these Condition, article, or movement with the capability of making injury work force, harm to gear or structures, loss of material, or decrease of capacity to play out an endorsed work | \n
The definition of hazard.
Risk detection is historically a subjective task and therefore its effectiveness relies on individual or team knowledge to determine it. In the industry, various analytical tools and information sources are available for the risk detection process, such as organizational observations or process analysis.
\nInterviews with organizational experts and key informants: This method is very selective and limited because it is based exclusively on individual information and restrictions.
\nBrainstorming hazard sessions: Specialists in all operating fields found it to be helpful and successful in recognizing as many hazards as possible. This approach is largely based on knowledge and experience. Guidance on how these sessions and techniques can be used easily in the public domain on the Internet.
\nHazard and operability tool (HAZOP): brainstorming technique used to identify hazards and operability problems when the process design or scheduled changes are completed during brainstorming sessions. This strategy depends on the expertise and experience of the team and must be as interdisciplinary as possible to identify any deviations from the planned process, plan, or activity. It is considered very useful in new operations, when other approaches that focus on experienced personnel are less effective, as the team uses a range of standard questions to construct a list of possible deviations by integrating word (Table 5) with a variable parameter or process terms.
\n\n | \nThis is the complete negotiation of the design intention. No part of the intention is achieved and nothing else happens | \n
\n | \nThis is a quantitative increase | \n
\n | \nThis is a quantitative decrease | \n
\n | \nAll the design intention is achieved together with additions | \n
\n | \nOnly some of the design intention is achieved | \n
\n | \nThe logical opposite of the intention is achieved | \n
\n | \nComplete substitution, where no part of the original intention is achieved but something quite different happens | \n
\n | \nSomething happens earlier than expected relative to clock time | \n
\n | \nSomething happens later than expected relative to clock time | \n
\n | \nSomething happens before it is expected, relating to order of sequence | \n
\n | \nSomething happens after it is expected, relating to order of sequence | \n
HAZOP guide words.
Fault hazard analysis (FHA): This is a standardized and detailed approach used for the analysis of roles to identify and describe the potential nature of failures. If desired, it can only be used as a qualitative or quantitative analysis. A comprehensive top-down configuration analysis is required in order to evaluate computer danger modes, danger causes and potential system/operational performance. I would like to respond to the following questions:
What’s wrong with this?
How is it possible to fail?
How many times is it going to fail?
What will happen if it fails?
How important are the safety effects?
External intelligence sources of the company: Efficient to track and review current activities in order to recognize potential threats, analyze identified risks and recognize patterns. Examples are the following: dispatch logs, maintenance reports, manufacturing reports, and security reporting database of aircraft flight data (flight data extracted from equipment such as FDR or QAR).
\nExternal public information sources: Useful for showing operators temporarily or permanently recognized dangerous conditions. NOTAMs, AIPs, and rules for aviation.
\nFACS: Method of detecting human fault in accidents, major injuries, injuries, and other safety-related activities (based on the concept of Professor James Reason) and their inquiry and study. It also helps to determine where corrective measures and mitigation measures are required to eliminate the risk.
\nMethods/tools for risk analysis provide means for the analysis of formal or informal risk information as a result of a proposed action or the risk involved in failure to take a certain action. Support in determining the severity of risks posed by incidents which are or may be subjected to an aircraft operator; they help also to determine what events are most susceptible to a serious incident or accident.
\nRisk assessment techniques were originally designed for the nuclear sector and a range of uses, from chemistry to aeronautical, have undergone many approaches and tools over the years.
\nThere are currently a wide range of different risk assessment models in all types of business industries and the methodology used around the world is inconsistent.
\nQuantitative and qualitative evaluations coexist and organizational risk mitigation approaches must all be considered. Much effective risk management never perform systematic risk analyses and continue their use only for certain risks that need analytical reasoning or the acceptance of a contingency strategy. A qualitative risk analysis (designation of high, medium, or low probability or impacts) is deemed sufficient for the selection of the most important risks.
\nRegardless of the specific description, regulatory inclination is to break the risks down in two components of the hazard, but discrepancies are again noted on the designation of the two components: probability (or probability) of occurrence if the risk is caused by a risk, intensity (or magnitude) of the hazard-caused adverse effect. Likelihood is based on exposure to quantify the possibilities in stages, periods, men, etc. Thus, exposure may or may not be integrated depending on how the probability is calculated. Raised risk of adverse effects is raised by exposure to unhealthy conditions. Therefore, danger is described as follows:
\nEquation 1 – ICAO Risk equation.
\nThe ARMS working presents risk as a breakdown of each one of the four components:
\nEquation 2 - ARMS Risk Equation.
\nThe stability of an action without taking into account risk exposures, the effectiveness of barriers to the materialization of the risk, and the efficacy of barriers to recovery and without enabling the accomplishment of a worse-case situation, as seen in the ARMS bow-tie diagram, cannot be adequately assessed. However, as such factors require a high degree of subjectivity; they do not necessarily need to be included in the risk formula.
\nThe protection of an action cannot be adequately measured without taking into account hazard detection, the productivity of the barriers preventing the danger of materializing, or the efficacy of the barriers to recovery and inability to accomplish the worst case scenario (the worst scenario), as seen in the diagram, Weapons bow-tie. But as these factors require a high degree of subjectivity, they do not necessarily have to be included in the dangerous formula.
\nSimilar methodologies are described in a risk matrix for both elements, each of which has different acceptance rates (Figure 7). Authorities recommend that each operator develop its own matrix and criteria that best reflect its operating environment.
\nRisk assessment sample matrix.
If both the severity of the consequences and their likelihood of occurrence are expressed qualitatively (e.g., by words like high, medium, or low), the risk assessment is called a qualitative risk assessment. Table 6 provides an example of an aircraft operator’s qualitative criteria.
\nSeverity of consequences | \nLikelihood of occurrence | \n||||
---|---|---|---|---|---|
Severity Level | \nDefinition | \nValue | \nLikelihood level | \nDefinition | \nValue | \n
Catastrophic | \nHardware annihilated; different passings | \n5 | \nFrequent | \nLikely to occur many times | \n5 | \n
Hazardous | \nEnormous decrease in safety edges, physical trouble or a remaining burden to such an extent that administrators cannot be depended upon to play out their undertakings precisely or totally. Genuine injury or demise to various individuals. Significant gear harm | \n4 | \nOccasional | \nLikely to occur sometimes | \n4 | \n
Major | \nHuge decrease in safety edge, decrease in the capacity of administrators to adapt to unfriendly working conditions hindering their effectiveness. Genuine occurrence. Injury to people | \n3 | \nRemote | \nUnlikely but possible to occur | \n3 | \n
Minor | \nNuisance. Operating limitations. Use of emergency procedures. Minor incident | \n2 | \nImprobable | \nVery unlikely to occur | \n2 | \n
Negligible | \nLittle consequence | \n1 | \nExtremely improbable | \nAlmost inconceivable that the event will occur | \n1 | \n
Sample of severity and likelihood criteria [4].
Follow-ups are numerically described in a quantitative risk assessment or probabilistic risk assessment (e.g., number of persons who may have been hurt or killed) and are expressed as probabilities or frequencies (e.g., number of occurrences, probability of occurrence per unit time), as shown in Figure 8 and Table 7.
\nProbability and severity relationship for failure condition effects.
Failure condition | \nDefinition | \nQualitative probability | \nQuantitative probability—Average probability per flight hour | \n
---|---|---|---|
No safety effect | \nFailure conditions that would have no effect on safety; that would not affect the operational capability of the airplane or increased workload | \nNo probability requirement | \nNo probability requirement | \n
Minor | \nFailure conditions which would not significantly reduce airplane safety, and which involve crew actions that is well within their capabilities. May include, for example, a slight reduction in safety margins or functional capabilities, a slight increase in crew workload, such as routine flight plan changes, or some physical discomfort to passengers or cabin crew | \nProbable—that can be anticipated to occur one or more times during the entire operational life of each airplane | \nProbability >1 × 10−5\n | \n
Major | \nDisappointment conditions which would lessen the capacity of the plane or the capacity of the group to adapt to antagonistic working conditions to the degree that there would be, for instance, a critical decrease in safety edges or useful abilities, a huge increment in team outstanding task at hand or in conditions debilitating group effectiveness, or inconvenience to the flight team, of physical trouble to travelers or lodge group, conceivably including wounds | \nRemote—unlikely to occur to each airplane during its total life, but which may occur several times when considering the total operational life of a number of airplanes of the type | \n1 × 10−7 < Prob. < 1 × 10−5\n | \n
Hazardous | \nDisappointment conditions, which would decrease the capacity of the plane or the capacity of the group to adapt to unfriendly working, conditions to the degree that would be: \n
| \nExtremely remote—not anticipated to occur to each airplane during its total life but which may occur a few times when considering the total operational life of all airplanes of the type | \n1 × 10−9 < Prob. < 1 × 10−7\n | \n
Catastrophic | \nFailure conditions which would result in multiple fatalities, usually with the loss of the airplane | \nExtremely improbable—unlikely that they are not anticipated occurring during the entire operational life of all airplanes of one type | \nProbability <1 × 10−9\n | \n
Failure condition definition and relationship with probability [5].
Quantitative criteria are determined by the historical architecture and assessment of systems engineering. Aircraft regulators have long established quantitatively acceptable levels of quality for all aircraft construction equipment or systems in the certification specifications. Failure to comply with these criteria does not authorize unique certification of equipment.
\nRisk evaluation and risk control for each hazard or category are standardized, comprehensive hazard detection and risk assessment. The acceptability of risk is assessed by matching the measured risk level with defined requirements or safety objectives.
\nThis chapter includes the concept of safety and risks in civil aviation during operation and focuses only on the actual safety and risk assessment process that is carried out by different organizations in order to maximize the safety while trying to avoid possible risks. The steps and analysis that are carried out are actually based on facts and data. It is hard to determine whether these results are actually “safe and risk free” to be carried out. Lack of real-time data and real environment fact-findings make it difficult for this section of civil aviation to prosper. But day by day, results are getting better with the inclusion of new technologies and methods. This study shows that these assessments might not have too much impact on civil aviation but serve as a probability to avoid minimum risks and increase the safety concerns.
\nThe chapter also provided a review of some methods/models for civil aviation risk and safety assessment. The key findings provided insight into the efforts already made to improve such methods/models; their inherent difficulty and lack of sufficient versatility; lack of available data for calibration and testing; and lack of sufficient predictive capabilities to encourage the implementation of new technical, procedural, and operational concentrations to assess risk and safety. On the one hand, they aimed at increasing the system capacity and, on the other hand, at reducing the acceptable risk and safety thresholds. The need to develop “specialized” or “dedicated” methods/models for particular system parts has been discovered in many cases. Moreover, difficulties such as lack of real-life data were overcome by including expert judgment despite awareness of its uncertainty and biases. Also noted was the systematic need for balance and compromise between sophistication of methods/models, development time and expense, and consistency of performance. Prospective research needs to be considered to further improve the existing models in line with recommendations that generally implied risk and safety assessment capabilities during development and after implementation of new technologies, with generality on the one hand and dedication on the other hand, predictive capabilities, flexibility and easier understanding, and handling of modular system structures.
\nThe established financial reporting system within an entity is the basic source of information on its financial position and results. The economic and financial globalization of the world market has emphasized the importance of high quality financial reporting. For the business decision-making process, financial and audit reports are the main source of information, as they contain information on financial position, business results, changes in equity, cash-flows and other reliable information [1]. Development of the capital market and the increase in the number of interested parties (investors) created even higher demand of reliable, on time and fair financial statements as the main results of financial reporting. The regulation of the relationship between the state and society, owners of capital and management, various stakeholders and society, and others; has been further improved by a quality financial reporting and audit process. However, in order to fulfill their main purpose for all interested parties, financial statements must provide information that is true, objective, comprehensible, comparable and uniform [2]. In the first place, financial statements have to be publicly available, which is usually regulated by law. For example, Law on Accounting of the Republic of Serbia prescribes that all business entities have to submit their financial reports to the competent institution which later publishes them on the official internet site [3]. Information contained in financial statements can be used for numerous purposes. For example, other business entities can use them in the process of making business, financial, investment and other decisions. Likewise, banks and financial institutions can use them in order to approve loans or assess investment risks related to the certain business entity. However, financial information contained in financial statements are not processed and represent a raw data that should be analyzed in order to assess the performance of a certain business entity. Aside Notes to financial statements, as one of the qualitative statements that business entities prepare and report, all other statements are quantitative in nature and offer hundreds of pieces of data. Therefore, it is of great importance to perform certain type of analysis on the collected data in order to gain a solid basis for business decision making process. Analysis of financial statements is one of the most common methods of assessing business performance. The main goal of conducting the analysis of financial statements is to obtain information on the performance of the observed company, i.e. liquidity, profitability and solvency. Measuring financial performance using compiled and disclosed financial statements is a quantitative analysis of the position of the observed company, including the way in which the company uses the capital invested in business. High quality analysis of the performance of the observed entity provides a comprehensive image of the business, including meeting the information needs of stakeholders. The authors [4] point out in their paper that the analysis of financial performance is crucial in determining the efficiency in terms of the use of available resources. Likewise, an entity owners will be able to assess management skills and decisions that have been made in previous, as well as in current reporting period, so that they could analyze entities strengths, weaknesses and therefore improve their overall performance [5, 6, 7].
Some pieces of data disclosed in financial statements have informational power to be used on their own, such as Total assets, Sales revenue, or Net result. However, informational power of data increases when they are put into relation with other pieces of data. Therefore, financial statements analysis using ratios has been one of the most commonly used methods of assessing business performance. Financial ratio is a relative magnitude of two (or more) selected numerical values taken from financial statements. For example, relation between Net result and Equity will provide information on how much dollars of profit an entity earns for each dollar invested in equity. Results of financial statements analysis can be used to compare performance of a certain entity over a period of time, or for comparison with other entities within the industry. However, since financial statements analysis takes time and there are numerous financial ratios that analysts could use (and the fact that most of these ratios are correlated), the number of ratios that are being calculated and assessed should be reduced so that an analyst could focus on several of them without losing data that could be relevant for the analysis [8]. One of the methods that can be used is Principal Component Analysis (PCA), which reduces number of observed variables for any further, regression, or any other type of analysis [9]. PCA analysis has found its numerous purposes in different industries, for example, in image compressing [9, 10, 11], as well as in biometrics or “bioimaging” where physical characteristics of a person are used for its identification with application on communication devices and security systems.
The significance of PCA results is reflected in the fact that they can be used for more effective and efficient analysis of performance of certain entity, or for all business entities within a certain industry, or if analyzed financial data is related to whole economy, than results could be used for the analysis of all entities within it. The main advantages of PCA are precision of results; reduction of time needed for the analysis and evaluation of results; as well as reduction of related costs and efforts of the analyst.
With the development of technology, we have gained the ability to generate massive amounts of data. The use of correct methodologies for data analysis has become essential when dealing with complex financial challenges. In this paper, we discuss the theory underlying PCA. This type of analysis is one of the most used statistical tools in the field of financial data analysis. To ensure that the proper method is used for the analysis, theoretical knowledge and an comprehension of statistical methods are essential.
PCA is primarily designed as a statistical technique that selectively reduces the dimensionality of data in complex data sets while preserving maximum variance. Since research in the financial sector involves both a large amount of data and a large number of variables simultaneously, it is difficult for us to perform analysis for this type of data.
Visualization techniques are only useful in two or three dimensional spaces, and single-variable analysis does not provide precise results due to overlapping variance. To achieve dimensionality reduction, it is necessary to generate principal components, i.e., a new set of variables containing a linear combination of the original variables. PCA can be used for a variety of tasks. A very small number of components are sufficient to cope with the variability of a data set. Since the number of components is reduced by using principal components, the complexity of the analysis itself is also reduced by avoiding analyzing a large number of output variables.
The standard PCA procedure takes as its starting point a data set in which
This equation is valid even when the eigenvectors are multiplied by −1. Here,
The
For the final outcome of the PCA assessment to be successful and significant, numerous conditions must be met. Initially, it is crucial that the data entered are uninterrupted and that variables should be measured on an interval or ratio scale. This condition must be met because PCA tests important correlation patterns for these variables.
Another crucial requirement is that the relationships between the individual pairs of variables are linear. If there are nonlinear relationships between the individual pairs of variables, appropriate data transformation techniques, such as logarithmic transformations, should be considered. Presumptions for PCA are filling missing values with not null values, outliers handling, and normalization scaling. All outliers should be filtered out prior to analysis, as they can bias the results by affecting the magnitude of the correlation.
To obtain more accurate estimates for the correlation population parameters, a large sample size is required. The data sets must be linear in order to be formed. The basic principle of PCA is that high variance must be taken into account, while variables with lower variance can be considered noise and are not taken into account. All variables must be processed at the same level of measurement.
Eq. (2) associates the eigenvalue decomposition of the covariance matrix
Where
Here
Here
The variability associated with the set of retained principal components can be used to ensure the quality of any
The trace of
It is a common approach to use a pre-specified percentage of the total variance to determine how many principal components to keep, but graphical constraints often lead to keeping only the first two or three principal components. The percentage of total variance is a basic tool for measuring the quality of these low-dimensional graphical representations of the data set.
The biggest problem is the number of components needed to obtain a sufficient number of variances while achieving a reduction in dimensionality. There are several ways to determine the components, and one of them is to set a threshold.
The next very popular approach is the “Scree Plot” [14], where the components are arranged on the
The most popular method is parallel analysis [15], where PCA is performed with as many variables as the original data set includes. The average eigenvalues between the original data set and the simulated data set are measured. Any values from the original data that are lower than the data in the simulated set are discarded.
PCA has many advantages. In terms of maximizing variance in
Factor analysis is a method that is often combined with PCA and it inspires the concept of rotating principal components [16]. Assume that
Another method of simplifying the principal components is to limit the charges of the new variables. This is called adding a constraint. There are several variants of this strategy, one of which uses LASSO linear regression [17], that represents least absolute shrinkage and selection operator. In this approach, SCoTLASS components are discovered, solving the same optimization problem as PCA, but with the additional constraint
PCA is inherently sensitive to the occurrence of outliers and thus to large errors in data sets [19]. As a result, efforts have been made to define robust variants of PCA, and the terminology RPCA has been used to refer to several approaches to this problem. Huber’s early work focused on robust alternatives to covariance or correlation matrices and how they could be used to generate robust principal components [20]. The demand for methods to process very large data sets sparked renewed interest in robust PCA variants. This led to PCA research lines, especially in areas such as machine learning, image processing, web data analysis, and many others.
Wright et al. [21] defined RPCA as the sum of two
where
PCA was first introduced into mechanics by [22], as an analogue of the axis theorem. It was later named “PCA” by [23]. The range of applications in finance and economics is extensive. Take as an example [24], who used PCA to document three factor structures. Stock and Watson [25] used PCA to monitor economic development and activity, as well as the inflation index. Egloff et al. [26] used PCA as a way to analyze the dimensions of inconsistent dynamics. Volatility is a statistical measure that can be used to determine these inconsistencies using a two-factor volatility model. This includes long-term and short-term fluctuations in the volatility structure. Baker and Wurgler [27] used PCA to measure investors sentiment, i.e., their positive or negative view. This was done according to the principle of the number of sentiment proxies before Baker, [28] created the policy uncertainty index. This index represents potential risks in the near future.
The most important item in the construction of PCA is the estimation of the eigenvalues of the covariance matrix sample. Anderson and Weeks [29] and Anderson [30] showed that sample eigenvalues were consistent when dealing with asymptomatic sentiment proxy results. Waternaux [31] proved that similar results are obtained with simple eigenvalues as long as there is a fourth moment in the data. In addition to the discussions in the [32] book, [33] was able to establish the asymptotic distribution of eigenvectors using generalized assumptions.
However, this PCA approach to eigenvalues has some downsides. The first problem is certainly dimensionality, which can be noticed when the cross sectional dimension grows simultaneously with the sample in the same period. Then inconsistencies occur. Another problem arises from linear data types that do not include nonlinear patterns. A third problem [34] arises from the dependence of the asymptotic theory on fixed assumptions for the analysis. For these reasons, we have a problem when we use PCA for reimbursement data. Most of the time, we need years of data to make an assumption, which in turn leads to other problems, such as permanence and consistency of non-fixed parameters. This type of data has backlogs and volatility times often vary.
These problems stimulate the improvement in this field and motivate the development of tools for PCA methods. The approach to the problem, where the number of occurances grows in fixed time periods, touches all the listed downsides. Theoretically, it is known that as the frequency of the sample increases, the estimated variance and covariance increase. This is true until the microstructure of the market begins to take effect. Incidentally, this is not a serious problem if we choose a sampling frequency of minutes, which we use as opposed to the below one second time interval most often used for liquid stocks. A high frequency asymptotic analysis with the cross-sectional dimension is expected as the time interval increases sharply. This high frequency asymptotic framework allows us to perform non-parametric analysis as well as independent, non-static and analysis without underlying parameters as is the case with low frequency processes.
Asymptotic theory is very common in many contexts. Jacod et al. [13] and Jacod and Podolskij [35] also dealt with one problem that we deal with in this paper, where the cross sectional dimensions are invariant and the process is continuous. Mykland and Zhang [36] designed an alternative theory to the one put forward by [37], that discuss inference for volatility function dependence. It is based on the aggregation of local estimates and uses a finite number of blocks. Saha et al. [38] considered the expected values of the integrated covariance matrix under conditions where there is an error measure and the matrix is large containing high frequency data. Tao et al. [39] addressed work on the convergence rate. Jacod and Rosenbaum [40] analyzed estimators, composed of aggregating functions of estimates. They did so using integrated quarticity estimation. Heinrich and Podolskij [41] discussed empirical covariate matrices of Brownian integrals. Here is discussed the measurement of the leverage effect and its evaluation by the integrated correlation method [42].
PCA analysis can be used in analysis of financial data for different purposes. For example [43] used it to identify the type of impact on grouped impact factors, such as assessing the quality of accounting information and facilitating the process of financial analysis conducted by different users. On the other hand, [44] used PCA to assess the impact of the evolution of Finnish standards on IFRS (International Financial Reporting Standards). Finally [45] used PCA analysis to determine the macroeconomic impact on the profitability of Romanian listed companies, using data from 1997 to 2007, and identified following indicators: liquidity, solvency, and firm’s dimension.
When it comes to the use of PCA analysis in financial statements analysis, four papers that focus on Romanian listed companies will be reviewed first. All papers emphasize the importance of using PCA analysis in the analysis of key financial ratios. In the first paper author [46] analyzed the data of 16 initial variables which he grouped into 3 new variables (general efficiency indicator, indicator in correlation with historical debts of companies and development indicator (given long-term debt and deferred income). Those three variables where able to explain 96.72% of initial variability. In the second paper, [47] analyzed data for 2010 including initially seven indicators of standard financial analysis and they reduced them to only two (which explain 94% of initial variability). In third paper, [48] used data from the stock exchange in the period 2006–2011 to identify the main components of financial statements which explain 79.08% of initial variability. The same group of indicators has been used by [43] on research sample that consisted of 111 companies from Madrid stock exchange and 32 companies from Eurostoxx50 for reporting periods 2005–2007. Research results showed that those six indicators explained 87% of total variance, with the first two indicators at app 44% of total variance.
In order to provide an answer on defined research question, 3.013 medium and large business entities were selected by random and used as a research sample. Financial statements for 2019 reporting period have been downloaded manually from the official website of the Business Registers Agency (BRA). BRA is a state administrative body that collects financial statements and corresponding audit reports of business entities that operate within the territory of the Republic of Serbia. Information published by BRA is used for financial analysis of business entities and as a basis of decision-making process. Afterwards, data from the pdf files containing financial statements have been copied and recorded in pre-set up tables in Excel files. Namely, medium and large business entities in the Republic of Serbia have an obligation to prepare and disclose full set of financial statements, consisting of balance sheet, income statement, cash-flow statement, statement of changes in equity and notes to financial statements. Since all previously mentioned statement, except notes to financial statements, are quantitative in nature, they were used for this research. Values originally disclosed in RSD, as the reporting currency, were converted into euros by using the average exchange rate of euros on the balance sheet date (31st December). Values of each financial statement line is presented in thousands, and therefore they are presented as such in this research [49].
Financial statement item lines in official financial statements are marked by corresponding automatic data processing number (in Serbian: Automatska obrada podataka—AOP), that belongs to the national nomenclature system. These markings are used in order to perform control of mathematical calculations before each financial statement is accepted for publishing by BRA. They also serve as an instrument of connecting data and information regarding the same financial statement item presented in financial statements. Balance sheet items cover automatic data processing numbers from 0001 to 0465; income statement from 1001 to 1071; statement of cash-flows from 3001 to 3047; and statement of changes in equity from 4001 to 4252. Table 1 shows the formulas used for the calculation of the selected financial indicators that will be used in this research. Having in mind that these variables will be used in order to differentiate business entities to three major types of business activities, these variables have been selected by a common sense.
Variables | Derived from |
---|---|
Fixed assets in total assets | AOP2/AOP71 |
Percent sales of merchandise in total operating revenue | AOP1002/AOP1001 |
Percent sales of products and services in total operating revenue | AOP1009/AOP1018 |
Percent cost of merchandise sold in total operating expenses | AOP1019/AOP1018 |
Percent cost of material in total operating expenses | AOP1023/AOP1018 |
Percent fuel and energy cost in total operating expenses | AOP1024/AOP1018 |
Percent wage cost in total operating expenses | AOP1025/AOP1018 |
Percent productive service cost in total operating expenses | AOP1026/AOP1018 |
Percent depreciation cost in total operating expenses | AOP1027/AOP1018 |
Percent raw material in total assets | AOP45/AOP71 |
Percent WIP in total assets | AOP46/AOP71 |
Percent finished products in total assets | AOP47/AOP71 |
Percent WIP and finished products in total assets | (AOP46 + AOP47)/AOP71 |
Percent merchandise in total assets | AOP48/AOP71 |
Calculation of selected financial indicators.
Data preparation is a key process in data analysis. The basic preparation and cleaning procedures are:
Preparing a copy of the table
Adding new attributes
Conversion of column types
General data cleaning and adjustment
Specifically, the cleaning includes the following items:
Editing date variables—the most common formatting problems
Recoding of zeros/missing values
Decoding categorical variables using labels and hot encoding
Arranging outliers
Application of normalization/standardization/ log transformation
Calculating descriptive statistics—mean, median, mode, standard deviation, variance, rank, etc.
Calculating inferential statistics - distributions, t-value, p-value, frequencies, cross-tabulations, correlation, covariance, etc.
More advanced techniques include:
Coding:
Categorical variables are labeled as character variables and must be converted to a factor type for modeling purposes. Queues perform this task.
Outliers:
For numeric variables, we can identify deviations numerically by the value of the bias.
Normalization/logarithmic transformation:
One of the techniques to normalize the biased distribution is logarithmic transformation. First, a new variable is created, while later the value of the bias of this new variable is calculated and printed.
Standardization:
One of the standardization techniques is that all characteristics are centered around zero and have approximately the variance of one unit. Scaling is used so that the variable is converted. The result is that these variables are standardized with a mean of zero.
As part of the preparation for PCA, firstly missing values from the dataset were filled with zeros. After that, the data was scaled by using a standard scaler, which standardizes features by removing the mean and scaling to unit variance. The preprocessed dataset, was then used for:
PCA
Sparse PCA
Robust PCA
All three of the PCA methods were instanciated with the number of components set to 7. After PCA, the now transformed data went through several clustering methods for the purpose of comparing results. The clustering methods that were used for each PCA are:
K-means clustering
Agglomerative clustering
BIRCH clustering
Gaussian Mixture
Spectral clustering
Furthermore, each of the clustering methods were executed with just the preprocessed data, without PCA, also for the purpose of comparing results.
Data preparation:
Compute dot product matrix:
Eigenanalysis:
Compute eigenvectors:
Keep first 7 components:
Compute 7 features:
end procedure.
This chapter discusses the outcomes of PCA and cluster analysis. The initial variables that load on the principal components are studied. Correlations or covariances between the original variables and the principal components correlate with the loadings. The variable loadings are contained in a loading matrix, which is created by multiplying the eigenvector matrix by a diagonal matrix containing the square root of each eigenvalue. The entries are determined by the component extraction method used. Non-standardized loadings show the covariance between mean-centered variables and standardized component values, regardless of whether the extraction is based on the singular value decomposition of the matrix or the eigenvalue decomposition of the covariance matrix.
The eigenvalue decomposition of the correlation matrix results in the standardized charges. The correlations between the original variables and the component scores are represented by these loadings. Because they always vary between −1 and 1 and are independent of the scale used, standardized charges are easy to read. In most cases, a threshold is set and only variables with loadings above this threshold are examined.
The total variance presents sum of variances of principal components. The ratio between the variance of principal component and the total variance is the fraction of variance explained by a principal component.
Figure 1 shows total variance explained by using three methods of PCA. The steepest increase belongs to the PCA line, which cumulative explained variance is app. 87%. This line is almost parallel to the line from Sparse PCA which cumulative explained variance is 83%. However, when it comes to Robust PCA line it has been noticed that cumulative explained variance is only app. 26% and the increase of values is minimal.
Total variance explained.
PCA: The highest fraction of explained variance among these variables is 32%, and the lowest one is 5%. Cumulative explained variance is 86% (see Table 2).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 4.491515 | 32.082248 | 32.082248 |
Factor 1 | 2.540717 | 18.147978 | 50.230226 |
Factor 2 | 1.269778 | 9.069843 | 59.300069 |
Factor 3 | 1.243867 | 8.884762 | 68.184831 |
Factor 4 | 0.961330 | 6.866641 | 75.051473 |
Factor 5 | 0.867145 | 6.193891 | 81.245364 |
Factor 6 | 0.760536 | 5.432398 | 86.677761 |
PCA total variance explained.
Sparse PCA: The highest fraction of explained variance among these variables is 21%, and the lowest one is 5%. For instance, variables together explain 83% of the total variance (see Table 3).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 3.078591 | 21.989939 | 21.989939 |
Factor 1 | 2.186255 | 15.616108 | 37.606047 |
Factor 2 | 1.698036 | 12.128828 | 49.734874 |
Factor 3 | 1.757003 | 12.550022 | 62.284897 |
Factor 4 | 1.047037 | 7.478832 | 69.763729 |
Factor 5 | 1.062211 | 7.587224 | 77.350953 |
Factor 6 | 0.809469 | 5.781923 | 83.132875 |
Sparse PCA total variance explained.
Robust PCA: The highest fraction of explained variance among these variables is 21%, and the lowest one is 0%. For instance, variables together explain 25% of the total variance (see Table 4).
Factors | Total | % of variance | Cumulative % |
---|---|---|---|
Factor 0 | 3.035926 | 21.685184 | 21.685184 |
Factor 1 | 0.454951 | 3.249650 | 24.934834 |
Factor 2 | 0.108168 | 0.772628 | 25.707462 |
Factor 3 | 0.020284 | 0.144884 | 25.852346 |
Factor 4 | 0.006630 | 0.047355 | 25.899701 |
Factor 5 | 0.000018 | 0.000128 | 25.899829 |
Factor 6 | 0.000000 | 0.000000 | 25.899829 |
Robust PCA total variance explained.
PCA is the best approach for this kind of data, regarding number of features.
The amount of variance in each variable considered is represented by the communalities. The variance in each variable explained by all components or factors is estimated using the initial communalities.
The percent fuel and energy cost in total operating expenses is given here with 88% variance. The percent productive service cost in total operating expenses is given here with 75% variance. The percent finished products in total assets here is 75% of the estimated variance (see Table 5).
Columns | Communality |
---|---|
Percent merchandise in total assets | 0.159427 |
Percent sales of merchandise in total operating revenue | 0.222216 |
Percent cost of merchandise sold in total operating expenses | 0.224299 |
Percent sales of products and services in total operating revenue | 0.236318 |
Fixed assets in total assets | 0.347415 |
Percent cost of material in total operating expenses | 0.411423 |
Percent raw material in total assets | 0.426201 |
Percent WIP and finished products in total assets | 0.449704 |
Percent depreciation cost in total operating expenses | 0.683213 |
Percent wage cost in total operating expenses | 0.729997 |
Percent WIP in total assets | 0.731771 |
Percent finished products in total assets | 0.745349 |
Percent productive service cost in total operating expenses | 0.752027 |
Percent fuel and energy cost in total operating expenses | 0.880639 |
PCA communalities.
The percent fuel and energy cost in total operating expenses here is 91% variance. The percent finished products in total assets here is 80% of the estimated variance. The percent productive service cost in total operating expenses here is 74% variance (see Table 6).
Columns | Communality |
---|---|
Percent merchandise in total assets | 0.191833 |
Percent sales of products and services in total operating revenue | 0.227810 |
Percent sales of merchandise in total operating revenue | 0.260545 |
Percent cost of merchandise sold in total operating expenses | 0.263888 |
Fixed assets in total assets | 0.354743 |
Percent cost of material in total operating expenses | 0.407825 |
Percent raw material in total assets | 0.417451 |
Percent WIP and finished products in total assets | 0.451553 |
Percent depreciation cost in total operating expenses | 0.555661 |
Percent wage cost in total operating expenses | 0.695148 |
Percent WIP in total assets | 0.719447 |
Percent productive service cost in total operating expenses | 0.742714 |
Percent finished products in total assets | 0.800108 |
Percent fuel and energy cost in total operating expenses | 0.911274 |
Sparse PCA communalities.
The percent wage cost in total operating expenses here is 82% variance. The percent sales of merchandise in total operating revenue here is 79% of the estimated variance. The percent cost of merchandise sold in total operating expenses here is 74% variance (see Table 7).
Columns | Communality |
---|---|
Percent WIP in total assets | 0.200472 |
Percent merchandise in total assets | 0.317793 |
Percent finished products in total assets | 0.333984 |
Percent depreciation cost in total operating expenses | 0.345393 |
Percent fuel and energy cost in total operating expenses | 0.349862 |
Percent sales of products and services in total operating revenue | 0.365996 |
Percent raw material in total assets | 0.433737 |
Percent WIP and finished products in total assets | 0.444081 |
Percent cost of material in total operating expenses | 0.519423 |
Fixed assets in total assets | 0.651365 |
Percent productive service cost in total operating expenses | 0.680299 |
Percent cost of merchandise sold in total operating expenses | 0.745842 |
Percent sales of merchandise in total operating revenue | 0.789024 |
Percent wage cost in total operating expenses | 0.822730 |
Robust PCA communalities.
Figure 2 presents the amount of variance for each considered variable represented by the communalities. From the aspect of PCA and Sparse PCA it can be noticed that variable Percent fuel and energy cost in total operating expenses and variable Percent finished products in total assets have significant estimated variance. When it comes to Robust PCA, variance of 82% refers to the variable Percent wage cost in total operating expenses. From the economic point of view first two variables could be used to distinguish type of three major business activities. Mainly, the amount of fuel and energy cost will differ between business activities. It is expected that production entities will have higher values of fuel and energy costs because plant, machinery and equipment will require energy to operate. Also, merchandise entities will probably have higher values of fuel and energy costs compared to other services having in mind fuel spent for transportation of merchandise and energy needed for operation of their facilities. Second variable Percent finished products in total assets is also expected to be used for differentiation since only production entities will have this balance sheet line in their financial statements. Main surprise might be third variable Percent wage cost in total operating expenses, since most entities have very similar share of total wage costs in total operating expenses. Namely, although official state records showed that average wages differ across industries, management of companies usually plan operating expenses and their structure.
Amount of variance represented by the communalities.
The best approach for the PCA/Clustering combination regarding high level of Silhouette Index and Cluster Sizes are: K-means/Robust PCA and Spectral/Robust PCA. The Davies Bouldin Index implies that a smaller value gives better clustering. This produces the idea that no cluster has to be similar to another, and that object inside clusters are very uniformly distributed (see Table 8).
Clustering/PCA method | Cluster sizes | Silhouette index | Davies bouldin index |
---|---|---|---|
K-means/No PCA | (1345, 932, 733) | 0.30208710358306756 | 1.5444364169813884 |
K-means/PCA | (1353, 934, 723) | 0.3637346841903855 | 1.3405097768944103 |
K-means/Sparse PCA | (1356, 939, 715) | 0.36307616530243575 | 1.3418713066940657 |
K-means/Robust PCA | (1209, 944, 857) | 0.5193200382282146 | 0.7834359567299072 |
Agglomerative/no PCA | (1151, 935, 924) | 0.27839422485839554 | 1.7150687814273013 |
Agglomerative/ PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Agglomerative/sparse PCA | (1888, 893, 229) | 0.31642069773357084 | 1.4995739243069988 |
Agglomerative/robust PCA | (1311, 878, 821) | 0.4593880561940543 | 0.9274868826361716 |
Birch/no PCA | (1151, 935, 924) | 0.27839422485839554 | 1.7150687814273013 |
Birch/ PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Birch/sparse PCA | (1225, 962, 823) | 0.31642069773357084 | 1.4995739243069988 |
Birch/robust PCA | (1317, 867, 826) | 0.45631070311567473 | 0.9348852316431389 |
Gaussian mixture/no PCA | (1336, 992, 682) | 0.17495781525891207 | 2.1078218204567496 |
Gaussian mixture/ PCA | (1161, 1155, 694) | 0.2539355374019169 | 1.6227017939395394 |
Gaussian mixture/sparse PCA | (1161, 1155, 694) | 0.2539355374019169 | 1.6227017939395394 |
Gaussian mixture/robust PCA | (1467,784, 759) | 0.28455634384131373 | 1.1919962215015028 |
Spectral/no PCA | (2994, 8, 8) | 0.460433642421337 | 0.9718901349784725 |
Spectral/PCA | (3001, 7, 2) | 0.5399338738262545 | 0.6856986473871954 |
Spectral/sparse PCA | (3001, 7, 2) | 0.5399338738262545 | 0.6856986473871954 |
Spectral/robust PCA | (1346, 920, 744) | 0.5146721760042233 | 0.7917964357887189 |
PCA with different clustering methods.
This chapter was focused on the use of Principle component analysis in financial data science. Research has been conducted that included 3013 medium and large business entities and their financial statements from 2019 reporting period. PCA has been used in order to differentiate between the three major types of business activities - merchandising, manufacturing, and service. Therefore, 14 financial ratios have been selected by common sense and further analyzed according to their significance in dimensionality reduction. Results of clustering gave 7 new variables: 1. cost of merchandise sold in total operating expenses, and cost of material in total operating expenses; 2. fuel and energy cost in total operating expenses, and sales of product and services in total operating revenue; 3. wage costs in total operating expenses, and sales on merchandise in total operating revenue; 4. productive service cost in total operating expanses, and fixed assets in total assets; 5. depreciation cost in total operating expenses, and merchandise in total assets; 6. raw material in total assets, and WIP and finished products in total assets; 7. finished products in total assets, and WIP in total assets. These groups of variables were able to explain 86.7% of initial variability. Compared to the results of authors previously mentioned in literature review, it can be concluded that percentage is within the range of reached results. When it comes to initial communalities which estimated the variance in each variable, three financial ratios that had the highest percentage were: fuel and energy cost in total operating expenses (original PCA—88%, sparse PCA—91%); productive service cost in total operating expenses (original PCA—75%, sparse PCA—74%); and finished products in total assets (original PCA 75%, sparse PCA—80%). Although these ratios showed the best results, it has to be mentioned that there is a correlation between all of financial ratios used in analysis and therefore results would be different when ratios are used.
We would like to express our gratitude to Prof. Nemanja Stanišić, Ph.D. from the Singidunum University for supporting this research through valuable suggestions, and assignment of a research database.
Authors declare no conflict of interest.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | 0.178413 | −0.326641 | 0.415221 | −0.102354 | −0.025277 | −0.072754 | −0.141675 |
Percent sales of merchandise in total operating revenue | −0.436002 | 0.152729 | 0.080029 | −0.025182 | 0.028728 | 0.016652 | −0.025519 |
Percent sales of products and services in total operating revenue | 0.398117 | 0.022315 | −0.270570 | −0.046006 | 0.031509 | 0.012930 | −0.028959 |
Percent cost of merchandise sold in total operating expenses | −0.432559 | 0.162995 | 0.080296 | −0.035352 | 0.022542 | 0.026778 | −0.041260 |
Percent cost of material in total operating expenses | 0.269688 | 0.303749 | −0.078000 | −0.386050 | −0.243323 | −0.066637 | −0.166323 |
Percent fuel and energy cost in total operating expenses | 0.150958 | −0.217356 | 0.283494 | −0.008988 | 0.096350 | 0.822637 | −0.210100 |
Percent wage cost in total operating expenses | 0.210317 | −0.224488 | −0.145697 | 0.058149 | 0.719422 | −0.304476 | −0.022063 |
Percent productive service cost in total operating expenses | 0.137457 | −0.048360 | −0.397081 | 0.585499 | −0.374661 | 0.172006 | 0.245674 |
Percent depreciation cost in total operating expenses | 0.095815 | −0.269993 | 0.484683 | 0.000289 | −0.400868 | −0.359862 | 0.275725 |
Percent raw material in total assets | 0.190490 | 0.245296 | −0.165694 | −0.526444 | −0.137683 | 0.071830 | 0.032101 |
Percent WIP in total assets | 0.158335 | 0.359273 | 0.200936 | 0.383609 | −0.059087 | −0.175372 | −0.596528 |
Percent finished products in total assets | 0.174390 | 0.375283 | 0.278149 | 0.015943 | 0.252221 | 0.151402 | 0.640266 |
Percent WIP and finished products in total assets | 0.214830 | 0.474174 | 0.309621 | 0.255892 | 0.126328 | −0.013709 | 0.034896 |
Percent merchandise in total assets | −0.355975 | 0.151166 | −0.014079 | −0.013559 | 0.088827 | 0.039431 | 0.005508 |
PCA component matrix.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | 0.000000 | −0.020910 | −0.504987 | 0.000000 | −0.254639 | −0.185601 | −0.002365 |
Percent sales of merchandise in total operating revenue | 0.435472 | 0.000000 | 0.246576 | −0.085566 | 0.000000 | 0.052803 | 0.000000 |
Percent sales of products and services in total operating revenue | −0.433624 | 0.008108 | 0.000000 | 0.199284 | 0.000000 | 0.000000 | 0.000000 |
Percent cost of merchandise sold in total operating expenses | 0.438993 | 0.000000 | 0.254341 | −0.067395 | 0.000000 | 0.044065 | 0.000000 |
Percent cost of material in total operating expenses | −0.027509 | 0.085834 | 0.000000 | 0.630267 | 0.000000 | 0.045436 | −0.019978 |
Percent fuel and energy cost in total operating expenses | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | −0.954607 | 0.000000 |
Percent wage cost in total operating expenses | −0.453726 | 0.000000 | 0.000000 | −0.325539 | −0.594326 | 0.173439 | 0.000000 |
Percent productive service cost in total operating expenses | −0.333679 | 0.000000 | 0.000000 | −0.222344 | 0.762847 | 0.000000 | 0.000000 |
Percent depreciation cost in total operating expenses | 0.108694 | 0.000000 | −0.726249 | 0.000000 | 0.000000 | 0.128099 | 0.000000 |
Percent raw material in total assets | 0.000000 | −0.007293 | 0.083372 | 0.624407 | −0.000201 | 0.000000 | 0.143399 |
Percent WIP in total assets | 0.000000 | 0.460374 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | −0.712393 |
Percent finished products in total assets | 0.000000 | 0.573218 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.686680 |
Percent WIP and finished products in total assets | 0.000000 | 0.671977 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
Percent merchandise in total assets | 0.315974 | 0.000000 | 0.291738 | −0.076742 | 0.000000 | 0.031515 | 0.000000 |
Sparse PCA component matrix.
Columns/factors | Factor 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
---|---|---|---|---|---|---|---|
Fixed assets in total assets | −0.173467 | 0.275000 | 0.507855 | −0.499215 | 0.156525 | −0.088965 | 0.078114 |
Percent sales of merchandise in total operating revenue | 0.525938 | −0.128407 | 0.122190 | −0.093645 | −0.109748 | −0.078270 | 0.673835 |
Percent sales of products and services in total operating revenue | −0.444479 | −0.119035 | −0.307493 | −0.162452 | −0.114015 | −0.009416 | −0.142249 |
Percent cost of merchandise sold in total operating expenses | 0.510523 | −0.126489 | 0.118665 | −0.111772 | −0.087492 | 0.088045 | −0.653626 |
Percent cost of material in total operating expenses | −0.204519 | −0.558377 | 0.030438 | −0.282440 | −0.265091 | 0.084341 | 0.087885 |
Percent fuel and energy cost in total operating expenses | −0.119620 | 0.103472 | 0.453140 | 0.056594 | −0.245465 | 0.200560 | −0.125820 |
Percent wage cost in total operating expenses | −0.204794 | 0.179552 | 0.159958 | 0.368300 | −0.273464 | −0.715834 | −0.010912 |
Percent productive service cost in total operating expenses | −0.131802 | 0.032733 | 0.012123 | 0.533088 | −0.234838 | 0.537258 | 0.183658 |
Percent depreciation cost in total operating expenses | −0.098392 | 0.135731 | 0.478665 | 0.038015 | −0.137017 | 0.259630 | −0.023300 |
Percent raw material in total assets | −0.120240 | −0.430495 | 0.139923 | −0.141176 | −0.424059 | −0.117921 | 0.026698 |
Percent WIP in total assets | −0.048543 | −0.251299 | 0.164302 | 0.160983 | 0.282957 | −0.044589 | −0.000473 |
Percent finished products in total assets | −0.062609 | −0.324119 | 0.211913 | 0.207631 | 0.364950 | −0.057519 | 0.022279 |
Percent WIP and finished products in total assets | −0.071814 | −0.371772 | 0.243069 | 0.238158 | 0.418607 | −0.065970 | −0.072968 |
Percent merchandise in total assets | 0.289968 | −0.108389 | 0.082710 | 0.230665 | −0.308640 | −0.196863 | −0.167039 |
Robust PCA component matrix.
Author Stefana Janićijević contributed to the design and implementation of the research and analysis of the results. Authors Vule Mizdraković and Maja Kljajić prepared sections of the chapter that refers to the financial data science and financial reporting: introduction, related work, research methodology and analysis of discussion and result. All authors provided critical feedback and helped shape the research, analysis, and manuscript.
number of numerical variables
individuals
vector
data matrix
number of columns
linear combinations
vector of constants
covariance matrix
lagrange multiplier
matrix with orthonormal colums—eigenvectors
matrix with singular vectors
diagonal elements of the matrix
diagnal matrix with one square of the singular values
rank of the matrix
dimensional subspace
trace of matrix
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However, the field application of engineered nanomaterials (ENMs) has not been properly investigated yet, and many aspects have only been considered theoretically or with models, which make it difficult to properly assess the usefulness of ENMs for plant fertilization and protection.",book:{id:"6763",slug:"new-visions-in-plant-science",title:"New Visions in Plant Science",fullTitle:"New Visions in Plant Science"},signatures:"Luca Marchiol",authors:[{id:"163884",title:"Prof.",name:"Luca",middleName:null,surname:"Marchiol",slug:"luca-marchiol",fullName:"Luca Marchiol"}]},{id:"67311",doi:"10.5772/intechopen.86341",title:"Wheat Production in India: Trends and Prospects",slug:"wheat-production-in-india-trends-and-prospects",totalDownloads:2364,totalCrossrefCites:27,totalDimensionsCites:39,abstract:"Trends in Indian wheat production before and after the inception of the All India Coordinated Research Project (AICRP) on wheat have been analyzed to show its significant progress over the years. 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Mostafa",authors:[{id:"68104",title:"Prof.",name:"Soha",middleName:"Sayed Mohammad",surname:"Mostafa",slug:"soha-mostafa",fullName:"Soha Mostafa"}]},{id:"68218",doi:"10.5772/intechopen.87069",title:"Neglected and Underutilized Legume Crops: Improvement and Future Prospects",slug:"neglected-and-underutilized-legume-crops-improvement-and-future-prospects",totalDownloads:1828,totalCrossrefCites:10,totalDimensionsCites:22,abstract:"Sustainable agricultural productivity is hampered by over-dependency on major staple crops, neglect and underutilization of others, climate change, as well as land deterioration. Challenges posed by these limiting factors are undoubtedly contributing to global food insecurity, increased rural poverty, and malnutrition in the less developed countries. Miscellaneous neglected and underutilized grain legumes (MNUGLs) are crops primarily characterized by inherent features and capabilities to withstand the effects of abiotic stress and climate change, significantly replenish the soil, as well as boost food and protein security. This chapter provides insight into the benefits of MNUGLs as food and nutritional security climate smart crops, capable of growing on marginal lands. Exploring and improving MNUGLs depend on a number of factors among which are concerted research efforts, cultivation and production, as well as utilization awareness across global populace geared toward reawakening the interest on the abandoned legumes. The emergence of the clustered regularly interspaced short palindromic repeat (CRISPR/cas9) technology combined with marker-assisted selection (MAS) offers great opportunities to improve MNUGLs for sustainable utilization. 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As such, crops which are the main food source must be produced at a higher pace in order to cater in tandem with the food demand. In the past, traditional plant breeders practice classical breeding techniques to propagate plants with desirable traits. However, traditional breeding technique lies in that only individuals of the same or closely related species can be crossbred. Moreover, traditional breeders will not be able to obtain traits which are not inherent within the gene pool of their target plants through classical breeding. With recent advancements in the field of genetic engineering, it is now possible to insert beneficial genes from a completely different species or even kingdom into a target plant, yielding transgenic plants with multiple ideal traits. To develop a transgenic plant, parameters such as vector constructions, transformation methods, transgene integration, and inheritance of transgene need to be carefully considered to ensure the success of the transformation event. 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Therefore, a full understanding of plant-NP interaction and phytotoxicological mechanism is required for accurate risk assessment to ensure the safe use of nanoparticle. A range of analytical techniques have been developed to detect and characterize the uptake, translocation, cellular internalization and intracellular biotransformation of nanoparticles in plants. Imaging methodologies, including various electron microscopy, spectrometry-based techniques, together with ICP-based techniques such as ICP-OES, ICP-MS and SP-ICP-MS, have been widely used to obtain information about NPs size, morphology, size distribution, cellular localization, elemental speciation, mass concentration and so on. Due to the complexity of biological samples to be analyzed, these techniques are often combined accordingly to provide complementary information regarding plant-NP interaction. This review provides an introduction to the most widely used techniques in the study of interactions between plants and nanoparticles. 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