Probability level.
\r\n\t
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Dr. Wei Wu has received awards from many national societies for the originality and quality of his projects. He has authored 70 peer-reviewed papers in international journals.",coeditorOneBiosketch:"A pioneering researcher in obstetrics and holder of three registered patents. Dr. Qiuqin Tang's research interests include genetic and epigenetic risk factors of reproductive and developmental health. She has authored over 20 papers in international journals.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"178661",title:"Dr.",name:"Wei",middleName:null,surname:"Wu",slug:"wei-wu",fullName:"Wei Wu",profilePictureURL:"https://mts.intechopen.com/storage/users/178661/images/system/178661.jpeg",biography:"Dr. Wei Wu is an associate professor and associate department\nchair in the Department of Toxicology, Nanjing Medical University, China, where he received his Ph.D. in Toxicology in 2012.\nHe was a guest researcher at the National Institute of Environmental Health Sciences (NIEHS) between 2017 and 2018. Dr.\nWu is a member of different national and international societies\nin the fields of human reproduction and toxicology and has\nreceived awards from many national societies for the originality and quality of his\nprojects. Dr. Wu has authored seventy-three peer-reviewed papers in international\njournals. He has edited four books and collaborated on ten others as well as seventeen patents and in the organization of three international conferences. He is a\nreviewer for ninety-eight journals.",institutionString:"Nanjing Medical University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Nanjing Medical University",institutionURL:null,country:{name:"China"}}}],coeditorOne:{id:"184798",title:"Ms.",name:"Qiuqin",middleName:null,surname:"Tang",slug:"qiuqin-tang",fullName:"Qiuqin Tang",profilePictureURL:"https://mts.intechopen.com/storage/users/184798/images/13334_n.jpg",biography:"Qiuqin Tang is an attending doctor of The Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital). Her research interests include genetic and epigenetic risk factors of reproductive and developmental health. She has authored over 20 papers in international journals such as EBioMedicine, Clinical Epigenetics, Molecular Human Reproduction, Scientific Reports, and European Journal of Endocrinology. She has collaborated in four books and three patents. She is the Editor-in-Chief of Journal of Woman\\'s Reproductive Health, and editor of many other journals including Journal of Gynecology and Obstetrics, and Journal of Gynecology and Obstetrics Forecast.",institutionString:"Nanjing Medical University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Nanjing Medical University",institutionURL:null,country:{name:"China"}}},coeditorTwo:{id:"48837",title:"Prof.",name:"Panagiotis",middleName:null,surname:"Tsikouras",slug:"panagiotis-tsikouras",fullName:"Panagiotis Tsikouras",profilePictureURL:"https://mts.intechopen.com/storage/users/48837/images/system/48837.jpg",biography:"Dr. Panagiotis Tsikouras is a specialist in obstetrics-gynecology,\nperinatal medicine, and contraception at the School of Medicine,\nDemocritus University of Thrace, Greece. He is also the headmaster of the Family Planning Centre and Gynecological Cytology\nLaboratory at the same university. Dr. Tsikouras is a fellow of the\nInternational Academy of Clinical and Applied Thrombosis/Hemostasis. His scientific activities focus on paediatric and adolescence medicine, gynecological oncology, high-risk pregnancies. He is a reviewer for several international journals and has numerous scientific publications to his credit, including papers and book chapters. He has also contributed to international and national guidelines on coagulation and thrombosis in obstetrics-gynecology.",institutionString:"Democritus University of Thrace, Komotini",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"11",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Democritus University of Thrace",institutionURL:null,country:{name:"Greece"}}},coeditorThree:{id:"290374",title:"Prof.",name:"Werner",middleName:null,surname:"Rath",slug:"werner-rath",fullName:"Werner Rath",profilePictureURL:"https://mts.intechopen.com/storage/users/290374/images/system/290374.jpg",biography:"Dr. Werner Rath is a specialist in obstetrics and gynecology, gynecologic oncology, perinatal medicine, and hemostaseology. He\nis currently a professor in the Gynecology and Obstetrics Faculty\nof Medicine, University of Kiel, Germany, and honorary doctor\nat the Democritus University of Thrace, Alexandroupoli University Hospital He previously served as chief of the Department\nof Gynecology and Obstetrics at University Hospital RWTH Aachen,\nGermany. Dr. Rath is a reviewer for numerous journals and chief editor of Geburtshilfe und Frauenheilkunde (GebFra). He has several publications, including thirteen\nbook chapters, to his credit.",institutionString:"Kiel University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Kiel University",institutionURL:null,country:{name:"Germany"}}},coeditorFour:{id:"299669",title:"Prof.",name:"Georg-Friedrich",middleName:null,surname:"Von Tempelhoff",slug:"georg-friedrich-von-tempelhoff",fullName:"Georg-Friedrich Von Tempelhoff",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:"St. Vinzenz Krankenhaus",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"79159",title:"Open Fetal Surgery and Fetoscopic Repair in Spina Bifida and Myelomeningocele in Romania",slug:"open-fetal-surgery-and-fetoscopic-repair-in-spina-bifida-and-myelomeningocele-in-romania",totalDownloads:61,totalCrossrefCites:0,authors:[null]},{id:"79947",title:"Endoscopic Approach to Ectopic Pregnancy",slug:"endoscopic-approach-to-ectopic-pregnancy",totalDownloads:59,totalCrossrefCites:0,authors:[null]},{id:"80212",title:"Diagnosis of Ectopic Pregnancy",slug:"diagnosis-of-ectopic-pregnancy",totalDownloads:74,totalCrossrefCites:0,authors:[null]},{id:"80756",title:"Medical Management of Ectopic Pregnancy",slug:"medical-management-of-ectopic-pregnancy",totalDownloads:32,totalCrossrefCites:0,authors:[null]},{id:"81269",title:"Fetal Craniospinal Malformations: Aetiology and Diagnosis",slug:"fetal-craniospinal-malformations-aetiology-and-diagnosis",totalDownloads:13,totalCrossrefCites:0,authors:[null]},{id:"81570",title:"Prenatal Diagnosis of Diaphragmatic Hernia",slug:"prenatal-diagnosis-of-diaphragmatic-hernia",totalDownloads:15,totalCrossrefCites:0,authors:[null]},{id:"81868",title:"Prenatal Diagnosis: The Main Advances in the Application of Identification of Biomarkers Based on Multi-Omics",slug:"prenatal-diagnosis-the-main-advances-in-the-application-of-identification-of-biomarkers-based-on-mul",totalDownloads:0,totalCrossrefCites:0,authors:[null]},{id:"81273",title:"Ectopic Pregnancy after Ipsilateral Salpingectomy",slug:"ectopic-pregnancy-after-ipsilateral-salpingectomy",totalDownloads:13,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"278926",firstName:"Ivana",lastName:"Barac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/278926/images/8058_n.jpg",email:"ivana.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"55401",title:"Product Life Cycle Risk Management",doi:"10.5772/intechopen.68797",slug:"product-life-cycle-risk-management",body:'\nNowadays, risk management is an integral part of every state-of-the-art enterprise. This is because engaging in an enterprise always involves various kinds of risk. Thus, it is necessary to develop methods for risk management system integration into enterprise processes. Risks shall be controlled on all levels of an organization and exist in terms of costs and environmental and occupational safety. As far as manufacturing enterprises are concerned, it is also required to possess a strategy of the risk management related to the finished products. Every product has its product life cycle, and it is necessary to consider the various spectra of issues which may occur during the product life cycle and manage the risks related to them. The product life cycle management (PLM) has become a standard: widely recognized element of the information structure of modern enterprises. In order to promote comprehensibility and definiteness, it is seen as consisting of several phases. The aim of this is to make the risk management more tractable because it is, in fact, feasible to manage risks for each phase separately. Unfortunately, no comprehensive methodology managing the entire product life cycle has so far been developed. Some methodologies attempt to combine a number of known methods, but they are unsatisfactory in most cases. There is a need for a comprehensive, sophisticated method which would cover all possible risks throughout the entire product life cycle. This chapter deals with the primary life cycle phases—of conceiving, designing, realizing, and servicing a product. The risk management, especially at the beginning of the product life cycle, is a very important management task since this measure may be beneficial for following phases and save a considerable cost, the company’s reputation, and even human health.
\nThe product life cycle model is based on the idea of a biological cycle, i.e., the process from birth to death. The pattern holds good for a commercial product, and it can also be understood as a process embedded within all the other processes of an enterprise. In risk management, all participating subjects must understand the relationship between the project management processes and the other enterprise processes. The product life cycle is a natural framework for the investigation of relationships and processes in the product management. It can be described as a means to define the start and end of a product and all phases in between. The way that defines the life cycle varies from industry to industry, but it also varies within the same industry in relation to different organizations and businesses. In product life cycle management, the risk approach changes once different phases are reached. This change depends on how much information is available and project progress. A typical product life cycle description covering all phases is shown in \nFigure 1\n.
\nProduct life cycle phases.
The product life cycle or product life cycle management (PLM) is a control process maintained from conception through design and production to service and disposal. PLM includes people, data, processes, and business systems and represents the main information flow within companies. At the same time, PLM systems help organizations to cope with increased complexity and new engineering tasks related to new product developments for global competitive markets [1]. Low-quality data generated in the product conception phase may mean considerably higher costs in the subsequent phases [2]. The number of components involved with most of today’s products, along with their complexity, increases. This trend can evidently be seen in all industries. It is not exceptional for the number of product components to be in the hundreds of thousands or even in six figures (automotive, aerospace, and marine industry) [3]. Thus, it is necessary to take extreme care, to prevent the risk of failures, from the very beginning of the life cycle of each product. The potential existence and detection of nonconformities throughout the product life cycle are shown in \nFigure 2\n.
\nExistence and detection of nonconformities during the product.
The risk management aim is to add the highest permanent value to all company’s processes. It contributes to a better understanding of all the possible advantages and disadvantages of all the factors affecting the project or organization. It increases the probability of success and decreases the probability of failure and of uncertainty as regards achieving general objectives. The final output of the risk management process should be a decision about whether and how to treat risks. If there is an unacceptable level of risk, it may be necessary to stop the current process(es) and accept certain countermeasures which will abate the risk level. Residual risks that cannot effectively be abated by such countermeasures may be processed using crisis plans [4]. Risk priority assessment shall be performed whether the risk is acceptable or not. Risk management should be a continuous and ever-improving process integrated into the strategy of the organization and the enforcement of this strategy.
\nThis method of risk analysis has been developed uniquely for the assessment of risks identified by utilizing a combination of different tools within the product life cycle. The use of this unique assessment method guarantees a unified system for the entire product life cycle. There is a priority attached to each identified risk, according to its risk value. Risk priority may change during the life cycle.
\nFor priority risk assessment, the basic principle of the method from Dr. Hsia [5], as customized for this particular situation, was used. The diagram is applied once all risks associated with a given product life cycle phase have been identified. Risk value R is divided into five priority areas, from A to E. Risk value R combines the probability index RP and the impact index RI. With one index as the horizontal axis and the other as the vertical axis, a diagram (which is reminiscent of a sector of a baseball field) can be drawn. The bottom left corner represents the lowest coordinates (0,0), and the upper right corner represents the largest coordinates (1,1), as shown in \nFigure 3\n.
\nBaseball field diagram.
There are five priority areas outlined in the diagram. Priority area A represents risks of the highest priority for which it is necessary to carry out immediate countermeasures. The area designated as B represents risks of the next highest priority. Thus, the risks have the next highest priority to call on resources for management, and so on, down to the E priority area where risks may be neglected.
\nThe probability value is categorized into five levels: very likely, likely, possible, unlikely, and very unlikely. The relative descriptions of these probabilities are shown in \nTable 1\n. Furthermore, the impact event level is also categorized into five levels: very serious, serious, moderate, minor, and negligible. The exact criterion description depends on the particular product, as seen in \nTable 2\n. The higher level of either, the more serious the issue.
\nLevel | \nDescription | \nProbability (%) | \n
---|---|---|
5 | \nVery likely | \n1–0.1 | \n
4 | \nLikely | \n0.1–0.01 | \n
3 | \nPossible | \n0.01–0.001 | \n
2 | \nUnlikely | \n0.001–0.0001 | \n
1 | \nVery unlikely | \nLess than 0.0001 | \n
Probability level.
Level | \nDescription | \nCriterion | \n
---|---|---|
5 | \nVery serious | \n\n |
4 | \nSerious | \n\n |
3 | \nModerate | \nDepends on the event | \n
2 | \nMinor | \n\n |
1 | \nNegligible | \n\n |
Impact level.
When assessing or calculating a probability level, these three approaches may be used, individually or in a combination.
\nThe use of appropriate historical data, for example, data from previous, similar, projects (the Lessons Learned database), provides the ability to extrapolate an approximate probability of occurrence of certain effects or failures. The historical data must be in regard to the same system, equipment, or operational concept. If there is only a low frequency of occurrence (of a particular situation), then any estimate based on historical data is very uncertain.
\nFor probability prediction, a number of “predictive techniques” can be used, for example, fault tree analysis, event tree analysis, or Markov chains. When historical data are unavailable or unsuitable, it is then necessary to determine probabilities from the system or equipment operation analysis in terms of fault or failure conditions. Numerical data for equipment, people, and systems come from experience or from other data sources, and it is used in order to estimate the probability of the top event. Simulation techniques that generate probabilities of component, equipment, and system failures caused by degradation or aging may also be used here.
\nIn systematic and structured processes, it is possible to use expert estimates in order to assess probabilities. There are many formal methods designed to obtain relevant expert estimates; these help in forming proper, pertinent questions. Available methods are Delphi, What If?, category classification, and absolute probability estimates.
\nWhen determining or calculating the impact level of a risk, the criteria (character and type) must be stated. The impact of risk factors can be determined from previous, similar, projects, or they can be estimated by an expert. The analytic hierarchy process (AHP) method facilitates determination of the priority of risk factors when multiple criteria in relation to specific impacts are available. AHP has attracted the attention of many researchers because of its logical and mathematical approach and because the input data for the method are easily attainable. The method is a decision-making tool that can be used for the solution of complex problems. More details can be found in Thomas L. Saaty’s work [6].
\nAfter the identification of all the possible risk events, it is necessary to determine the probability and impact index of each given event and its consequent risk value
where
\nFrom Eq. (2), μ\n
Item | \nEvent | \nProbability level | \nImpact level | \nProbability index | \nImpact index | \nRisk value | \n
---|---|---|---|---|---|---|
1. | \nRisk | \n1–5 | \n1–5 | \n0–1 | \n0–1 | \n0–1.41 | \n
Example of a table with assessed risks.
The first phase of the product life cycle, called conception, has a number of risk factors and influences associated with it. These risks must be treated immediately once identified, if possible. However, this is not always feasible, and so it becomes necessary to transfer the risk to the next product life cycle phase (again, where this is possible). All risks which are identified must be recorded in order not to be forgotten in the later product phases. Some risks may not be possible to eliminate but can only be mitigated to a certain level. These cases must also be transferred to the next product phase. Risks present at the conception phase which were not totally mitigated there, so that there is still a residual risk or a risk which was not possible to treat at that phase, are then inputs to the design phase. At the design phase, identified design risks are added to these inputs. Together, these risks make up the risk input to the design phase. All risks discovered at or before the design phase which have not been totally mitigated, so there is still a residual risk or risks which were not possible to treat in that phase, become inputs to the third phase: the phase of realization. The procedure is then the same as for previous phases. Risks originating at the realization phase are added to these input risks and treated together here. Risks relating to the service phase consist of risks identified in previous product phases, but not completely mitigated, plus risks particular to the service phase. It should be noted that most of these identified risks, associated with these phases, are predicted risks only. Thus, it is not possible to determine their precise probability; it is only possible to make a qualified estimate.
\nAll risks identified in the fourth phase become the feedback for the life cycle of the new generation product—if it is impossible to treat them. The same applies to all risks which weren’t predicted but which were subsequently identified when investigating an incident—but also could not be treated. Generally, it may be said that the method leads to a closed, unceasing cycle of the risk management tasks and forms the continuous process of improvement. The entire process is displayed in \nFigure 4\n. The detailed description of the risk management method, as it pertains to each individual phase, follows.
\nEntire process of risk management for the entire product life cycle.
At the beginning of each product’s life cycle, there is always a customer who expresses his needs; these needs must be heard. There is no universal voice of the customer (VOC), each is unique, and they are very diverse. Customers have many different requirements. Even within a single purchasing unit, various different requirements may appear [7]. All these voices must be considered and balanced in order to develop a successful product. For a better understanding of the customer’s needs, a discussion with them should be held; it is important at this point to identify the basic customer’s needs. First, it is needed to define requirements, answer the questions raised by developers, and then advise and criticize the process of the actual product development or the evaluation of the prototype design. General requirements should be split into more specific detailed requirements—the customer should be urged in order to clarify and express thoroughly his demands until they make perfect sense from the supplier’s perspective.
\nVoice of customer is usually the input for critical to customer (CTC). Critical to customer (CTC) are measurable standards of product performance which has been determined essential to its customers. CTC is generally defined in the process of the voice of customer assessment by methods ranging from survey or interview to focus groups. CTC provides a straightforward method for the prioritization and selection of appropriate input requirements for the whole process. CTC items are internally reflected in critical-to-quality (CTQ) criteria as shown in \nFigure 5\n.
\nExample of critical to quality criteria.
Further, CTC and CTQ are used as inputs for risk analysis techniques like the Delphi method. Another input for the preliminary risk analysis of the entire product life cycle is the Lessons Learned database, as it is termed: recommendations based on experience, from which others can learn in order to improve their performance. This may be supplied in the form of knowledge from data product management (DPM), enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (SCM). It is necessary to consider whether a similar product has already been developed in the past and what risks occurred and how they were treated. Hence, the same countermeasures may be applied to the current product (possibly with adjustments or improvements). As was mentioned above, the inputs to the very first risk analyses should be CTC, CTQ, and the Lessons Learned database, as shown in \nFigure 6\n.
\nRisk process management at the conceptual stage.
It is always difficult to assess risks, especially in the initial product life cycle phases, when no or only very minimal data is provided. Despite this, the risk analysis is important and an integral part of any new product development. Every risk analyst starts with risk identification. The means by which proper risk identification can be accomplished may differ. Delphi, What If?, preliminary hazard analysis, and just a simple brainstorming are all recommended tools. Risk estimation and measurement for the early phases of a product’s life cycle are complicated. Since no real-time data are available, methods like the analytic hierarchical process, for impact, and Markov chains, for probability estimation, should be utilized. Also, specific countermeasures or treatments from the Lessons Learned database can be accessed. Subsequently, risk values are calculated in order to determine the priorities of the various risks. If a priority dictates that a risk be treated, the risk must, in most cases, be reduced, avoided, or transferred. In exceptional cases, the risk is accepted. After this treatment, all countermeasures must be verified, and all risks must be measured again in order to discover whether there is any residual risk. Subsequent risk monitoring is essential. Sometimes, it is not possible to treat certain risks or residual risks in this phase. Consequently, these risks are transferred to the next product life cycle phase along with all the accepted risks identified due to proper risk monitoring. The whole process is shown in \nFigure 7\n. This process is identical for the conception, design, and realization phases.
\nProcess of risk treating at early product life cycle phases.
In the (conception) phase, it is relatively easy to make changes. In the subsequent product phases, the ease, and indeed possibility, of any change decreases, and the costs of changes rapidly grow over the time since the product is committed to a certain technology, configuration, and performance. Therefore, it is highly desirable to identify all risks during the first two phases—while it is still feasible to make changes and take countermeasures with certain ease. Unidentified risks in the realization and service phases may endanger the overall financial viability of the product.
\nThis product life cycle phase is also very likely to produce a large number of nonconformities which may cause trouble in subsequent phases. A design risk assessment is the act of determining the potential risks in the design process, either in the detailed design, in subsequent analysis or simulation, or in validation or possible tool design [8]. It provides a broader evaluation of the design—beyond just CTQs—and enables the elimination of possible failures and reduces the impact of potential failures. Thus, it is possible to categorize these risk factors into groups and manage risks for each group separately according to their severity (see \nTable 4\n).
\n\n | Identification | \nAnalysis | \nDecision | \n||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
No. | \nRisk | \nDescription | \nCategory | \nTreating in this phase? | \nProbability level | \nImpact level | \nProbability index | \nImpact index | \nRisk value | \nPriority | \nAccepted | \n
1. | \n\n | \n | \n | Yes/no | \n1–5 | \n1–5 | \n0–1 | \n0–1 | \n0–1.41 | \nA–E | \nYes/no | \n
Table for the risk analysis.
The process of risk management for the design phase is shown in \nFigure 8\n. Accepted risks and residual risks with accepted risk values from the conception phase are transferred and evaluated again at the design phase, in order to determine possible increases in risk.
\nThe process of risk management for the design phase.
The identification of new risks in the design phase usually starts with the bill of materials and its list of components. These can assist in constructing a block reliability diagram, whereby weaknesses and risks can be identified. Building of a prototype and simulations can also help considerably in the identification of new product risk areas. Then, a simulation relating to the treatment of the product and its placement into the working environment are important. For the better understanding of customer’s requirements, it is appropriate to set an appointment with the customer, introduce the prototype, and afterward possibly customize it according to the customer’s stated considerations. It is likely that other possible risks will be observed from the way the customer deals with the prototype. A simple brainstorming session with customers is often essential. Further, it is still necessary to follow CTQ, CTC, and VOC as inputs for the Delphi method. At this phase, knowledge management is easier to apply, and the knowledge from DPM, ERP, CRM or SCM, and the Lessons Learned database should be used to deliberate about risks from previous similar projects or from the past generally. It is necessary to start planning the preventative maintenance of a product.
\nThere are specific parts and systems determined in a bill of materials at the design phase: the systems, subsystems, and components which form the final product are defined here. This also forms an input to the reliability analysis: from analyses of single components to the reliability analysis of the entire system. For the correct understanding of the entire system reliability, it is necessary to make a detailed reliability diagram of the product. This may help to uncover product weaknesses. The reliability block diagram is a diagrammatic method to scheme how the reliability or otherwise of individual components contributes to the failure of the complete, complex system. This method is also known as the dependency diagram.
\nThe reliability block diagram is usually produced as a group of blocks connected in a parallel and serial configuration. The example is shown in \nFigure 9\n. Each block represents a system component with a certain failure rate. There are often parallel paths, meaning that all of them must fail for system breakdown to happen. They are in contrast to the serial paths where a failure of any component leads to the system breakdown.
\nThe reliability block diagram.
At the design phase, it is necessary to plan the maintenance focused on reliability. Identified risks from this phase will serve as inputs for the maintenance plan. The successful application of the reliability program demands good product understanding as well as a good knowledge of operational conditions, context, associated systems all together with possible failures, and their consequences. Maintenance is a common way of treating less severe risks. The initial maintenance program should be formed in cooperation between supplier and user. A possible reliability program application is shown in \nFigure 10\n.
\nReliability program application example.
During product development and production, it is essential to think of product disposal and related risks. Some of the customer’s requirements might be concerned with various regulations regarding the usage of restricted and hazardous substances. Directives dealing with the use and handling of hazardous substances, waste, and its collection are as follows:
\nWaste electrical and electronic equipment (WEEE)—concerning the collection, recovery, and processing of electrical waste
\nRestriction of the use of certain hazardous substances (RoHS)—concerning the restriction of the use of hazardous substance in products
\nCTC and CTQ requirements are the basis of the risk management at the phase of realization. Accepted risks and residual risks with accepted risk values from the design phase are transferred and evaluated at the phase of realization again in order to verify their possible increase. Prior the production start, there is a need for a thorough simulation, tests, and planning. The process of the risk management at the phase of realization is shown in \nFigure 11\n. Once all the tests and simulations [computer-aided production engineering (CAPE), computer-aided production planning (CAPP)] are done, risks are identified and the design is validated; it should be made sure that already specified CTQs define strict quality standards in the production. These can contain tolerances, procedures, performance, and safety tests which should be done prior the product delivery to customers. All these instructions should be stated in so-called control plans and refer to prior quality planning and tests. All production processes shall be tracked and monitored in a process map.
\nThe process of the risk management at the phase of realization.
It is also recommended to use quality management tools for risk identification at the phase of realization. Most of the seven quality basic tools are quantitative methods that contribute to better process control, process monitoring, process understanding, including diagnostics, troubleshooting, and generally better process operation [9].
\nThe entire production process must be mapped in order to identify weak and risky spots. The example of a process map at the realization phase is shown in \nFigure 12\n.
\nExample of a process map.
For identification of factors and elements entering the process, the SIPOC method is highly recommended. The general SIPOC process map is a chronological representation of the most significant steps (up to 6), events, or operations in a process. It provides the basis for identification of the process inputs and outputs, and hence it shows possible risks affecting the process. It also gives a simplified view of the entire process. Inputs, outputs, and also suppliers and customers (internal or external) are identified in this diagram. The SIPOC example is shown in \nFigure 13\n.
\nThe SIPOC diagram example.
\n\nFigure 14\n shows a production process. It is possible to see a certain pattern in the graph. Based on observation, it is possible to find out root causes or define a certain period when an event occurred. The run chart shows a production process during a specific part of the year. Here, a steep value which increases in the month of July can be observed and a subsequent sharp fall in the month of August. A period of interest is defined by this observation: it is necessary to find out what happened or what changed at that time. There are a few possibilities of the usage, and the method application is mostly user-friendly.
\nExample of a production process.
By this phase, there should already be a minimal amount of risks, and all significant risks should have been identified at prior phases as no design changes are possible at this phase. CTC and CTQ are also very important for the final phase of the product life cycle and need to be considered. Accepted risks and residual risks with accepted risk values from the realization phase are transferred to the service phase and evaluated again in order to determine whether they might increase. The entire process is shown in \nFigure 15\n.
\nProcess of risk treating at the final product life cycle phase.
Most of these risks can only be treated by preventive maintenance, customer support, or product manual. Risks involved at this phase are usually related to stocking, transportation, or disposal. Detailed feedbacks from customers, service experience, claims, and reports are recorded in the Lessons Learned database and used for the development of the next-generation product. The process of the risk management at the service phase is shown in \nFigure 16\n.
\nThe process of the risk management at the phase of realization.
There are various methods of interpretation in relation to life test data or operational data. Many of these interpretation methods use theoretical statistical distributions which model the lifetime of monitored components (time before failure). Hence, quantification of reliability uses methods involving mathematical statistics, and the theory of probability for which the proper theoretical distribution usage is essential.
\nWhen analyzing the reliability of electronic and other components, Weibull distribution is often used. Weibull analysis is applied when addressing the following kinds of questions: How many failures should be expected in certain conditions? How reliable is the current construction or technology in comparison with the innovated technology? How to quantify the product reliability? The advantage of Weibull distribution is its ability to approximate other distributions (e.g., exponential, normal, or log-normal). On the basis of a small sample of data, it is also capable of determining the distribution shape suitable for modeling the time to failure [10].
\nWhen analyzing reliability, complications may arise from the occurrence of censored data (i.e., if failure does not occur in all monitored components in the monitored time interval) and also when performing rapid tests. The analytical procedures of modern software tools allow to perform reliability analyses with respect to these complications and thus the prediction of failures on the qualitatively higher level [11].
\nWhen monitoring risks, failed countermeasures or even new risks may be identified. When this happens, it is necessary to take action against the newly uncovered risk immediately. First, it is essential to find out whether this problem has already been observed in the past and, if so, what kind of countermeasure was used there. In this case, the countermeasure clearly must be reviewed and then improved or replaced. If the situation appears to be entirely new, however, the process of incident investigation must be initiated. Finally, not all risks can be predicted. The investigation process is based on the PDCA (Plan-Do-Check-Act—Deming cycle) cycle as shown in \nFigure 17\n.
\nThe investigation process.
A list of the best incident investigation methods is set out in \nTable 5\n. In addition, this table indicates whether the method is qualitative, quantitative, or combined. It also indicates the phase of the life cycle for which each method is useful and its character. Subsequently, it describes whether it is a combination of methods and option of applicability only by one analyst.
\nAnalysis | \nQuantitative | \nQualitative | \nDesign | \nRealize | \nService | \nMethod’s character | \nCombination of several methods? | \nCan it be applied by a single analyst? | \n
---|---|---|---|---|---|---|---|---|
Fault tree analysis | \nX | \nX | \nX | \nX | \nX | \nSupporting | \n\n | X | \n
Event tree analysis | \nX | \nX | \nX | \nX | \nX | \nSupporting | \n\n | X | \n
Causes and consequences analysis | \nX | \nX | \nX | \nX | \nX | \nSupporting | \nX | \nX | \n
Current reality tree | \n\n | X | \n\n | X | \nX | \nSupporting | \nX | \n\n |
Multiple event sequencing | \n\n | X | \n\n | X | \nX | \nSupporting | \n\n | \n |
Sequentially timed events plotting procedure | \n\n | X | \n\n | X | \nX | \nSupporting | \nx | \n\n |
Schematic report analysis diagram | \n\n | X | \n\n | X | \nX | \nSupporting | \n\n | X | \n
Tripod beta analysis | \n\n | X | \n\n | X | \nX | \nInvestigating | \n\n | \n |
Root cause analysis | \n\n | X | \nX | \nX | \nX | \nSupporting | \nX | \nX | \n
Root cause failure analysis | \n\n | X | \n\n | X | \nX | \nSupporting | \nX | \nX | \n
Events and causal factor charting | \n\n | X | \n\n | X | \nx | \nSupporting | \nX | \nX | \n
Savannah river plant root cause analysis | \n\n | X | \n\n | X | \nX | \nSupporting | \nX | \nX | \n
Tap root | \n\n | X | \n\n | X | \nX | \nInvestigating | \nX | \nX | \n
Event root cause analysis procedure | \nX | \nX | \n\n | X | \nX | \nInvestigating | \nX | \nX | \n
HSYS | \nX | \nX | \n\n | X | \nX | \nSupporting | \nX | \n\n |
Assessment of safety significant teams | \n\n | X | \nX | \nX | \nX | \nSupporting | \nX | \nX | \n
Safety through organizational learning | \n\n | X | \nX | \nX | \nX | \nSupporting | \n\n | \n |
Causal tree method | \nX | \nX | \n\n | X | \nX | \nInvestigating | \nX | \n\n |
Systematic accident cause analysis | \nX | \nX | \nX | \nX | \nX | \nSupporting | \nX | \nX | \n
Systematic cause analysis technique | \n\n | X | \nX | \nX | \nX | \nSupporting | \nX | \nX | \n
Six sigma | \nX | \nX | \nX | \nX | \nX | \nInvestigating | \nX | \n\n |
Management oversight and risk tree | \nX | \nX | \n\n | X | \nX | \nInvestigating | \nX | \n\n |
Technique of operation | \n\n | X | \n\n | X | \nX | \nInvestigating | \nX | \n\n |
Change analysis | \n\n | X | \nX | \nX | \nX | \nSupporting | \n\n | X | \n
The list of best incident investigation methods.
The methodology comprises the risk analysis that combines with methods for the incident investigation where the results serve as the input risks for the new-generation products. Further, the methodology suggests to exploit the knowledge database which comes to light when managing incidents or already exists, and it is used for the purpose of the risk prevention. By recording of information of the origination, progress, and the way of previous incident solutions, the solution of a new or similar incident can be accelerated. The purpose of the knowledge base creation is also the objectification of probabilities and impacts of recorded risks. These records must be, in most cases, estimated, especially for completely new products, but thanks to the knowledge base, it is possible to refine the estimates. The methodology brings a new methodological approach that endeavors not only to prevent failures but also to remove the root cause as fast as possible and minimize its consequences. The use of the methodology can be customized for all kinds of industry.
\nThis research has been supported by the Ministry of Education, Youth and Sports of the Czech Republic under the RICE—New Technologies and Concepts for Smart Industrial Systems, project No. LO1607, by the European Commission under Marie Curie action FP7, project Risk Management Software System for SMEs in the Construction Industry (RiMaCon), project No. FP7-2012-IAPP-324387 and by the Student Grant Agency of the University of West Bohemia in Pilsen, Grant No. SGS-2015-020 “Technology and Materials Systems in Electrical Engineering.
\nCurrent agricultural and agro-industrial systems apply the linear mode of production and, therefore, the majority of today agricultural and agro-industrial production and consumption systems are unsustainable. In other words, current agricultural and agro-industrial systems are economically, environmentally, and socially not sustainable. Precisely, the problems associated with nowadays agricultural, and agro-industries are (1) inefficient use of resources, (2) inefficient use of energy, (3) high production costs, (4) high environmental risks, and (5) massive wealth gap between the poor and the rich. Therefore, sustainability is a key issue in this context, where sustainable development encompasses the integration of social and environmental issues with economic development to convene the pressing needs of the population at present without undercutting the requirements of future generations. One key issue is to mimic the sustainable models provided by natural ecosystems. Precisely, turning the linear mode of production (linear economy) into the cyclic mode of production (circular economy). The current farming and agro-industrial processes have two main problems, which are the inefficient use of energy and wastes are not utilized within the production processes, which leads to the degradation of the surrounding environment. In contrast, natural ecosystem -which should be mimicked- allows the efficient use of energy, and all wastes are bioremediated and utilized by the system. Hence, the current farming and agro-industrial processes (linear) should be amended to mimic the natural ecosystem (circular), where this leads to the concept of industrial ecology, which fills the gap between the farming and agro-industrial processes on the one hand, and the ecologically sustainable natural system on the other hand.
According to the EU, “the bioeconomy encompasses the production of renewable biological resources and the conversion of these resources and waste streams into value-added products, such as food, feed, bio-based products, and bioenergy” [1]. Furthermore, “the transition to a more circular economy, where the value of products, materials, and resources is maintained in the economy for as long as possible, and the generation of waste minimized, is an essential contribution to the EU’s efforts to develop a sustainable, low carbon, resource-efficient, and competitive economy. Such transition is the opportunity to transform the economy and generate new and sustainable competitive advantages” [2]. Consequently, the bioeconomy is broader and deeper than a circular economy. On the other hand, biomass is defined as “the biodegradable fraction of products, waste, and residues from biological origin from agriculture (including vegetal and animal substances), forestry and related industries including fisheries and aquaculture, as well as the biodegradable fraction of industrial and municipal waste” [3]. In other words, biomass types are agricultural biomass (crops residues and animal wastes), fisheries biomass, algae biomass, and forest biomass.
Circular bio-based economy aims at reaching a net zero-carbon community by creating sustainable technologies and efficient resource use approaches to substitute the fossil-based economy. The circular bioeconomy primarily depends on biomass as a building block, while social, economic, and environmental are the principal factors. The technologies that are projected to be industrialized under circular bioeconomy must guarantee that the value of product carbon is preserved to decrease the wastewater production, greenhouse gas (GHG) emissions, and impairment to the ecosystems. In the context of circular bioeconomy growth, the biomass production, process advancements, and reuse approaches ought to be well defined to meet the global supply chain and demand. This urges conducting techno-economic assessment (TEA) and life cycle analysis (LCA) of every product and process.
Bioproducts or bio-based products are biomaterials, biochemicals, and bioenergy derived from renewable biological resources. The biological resources include agriculture, forestry, and biologically derived waste. One of the renewable bioresources is lignocellulose. Cellulose-based materials and lignocellulosic tissues are biologically derived natural resources.
Conventional bioproducts and emerging bioproducts are two broad categories used to categorize bioproducts. Examples of conventional bioproducts include building materials, pulp and paper, and forest products. Examples of emerging bioproducts include biofuels, bioenergy, starch-based, and cellulose-based ethanol or bioethanol, bio-based adhesives, biochemicals, bioplastics, etc. Bioproducts derived from bioresources can replace much of the fuels, chemicals, plastics, etc. that are currently derived from petroleum. As a result, the emerging bioproducts are environmentally friendly products and independent of fossil sources.
Bioprocessing and bioproducts production include the use of engineered microbiological systems for generating biofuels, bioelectricity, and new high-value bioproducts. Additionally, scientists are investigating the utilization of forestry products in untraditional applications, including industrial foams and flame-retardant materials. This needs to combine a conglomerate of mathematics, biology, and industrial design, and consists of numerous varieties of biotechnological processes, which pertain to the design, development, and implementation of processes, technologies for the sustainable manufacture of biomaterials, biochemicals, and bioenergy from renewable bioresources. Bioprocessing deals with the design and development of equipment and processes for making bioproducts such as food, feed, pharmaceuticals, nutraceuticals, biochemicals, biopolymers, and paper from biological materials (i.e., biomaterials). Practically, bioprocessing takes place in devices called bioreactors.
Bioreactors are categorized, based on the mode of operation, as a batch, semi-continuous or continuous bioreactors. Microorganisms growing in bioreactors may be submerged in a liquid medium or may be attached to the surface of a solid medium. The bioenvironmental conditions inside the bioreactor, such as temperature, nutrient concentrations, pH, and dissolved gases (especially oxygen for aerobic processes) affect the growth and productivity of the microorganisms.
Biological wastes i.e., biowastes, generated from agriculture, wastewater treatment, or industry are a largely untapped source for the production of value-added bioproducts or bioenergy. Their recovery utilizes biological and chemical processes that provide alternative sources for chemical feedstocks to produce different products e.g., bioplastics or other biopolymers, high-value biochemicals, protein for animal feed, and enzymes. For example, nutrients, cellulose, volatile fatty acids, extracellular polymeric substances, or proteins can be recovered from biowastes. Similarly, many opportunities exist for alternative energy products, e.g., bioethanol, biobutanol, biogas, biohydrogen, or bioelectricity. Resource biorecovery thus supports sustainability goals by reinjecting products into the circular economy.
For instance, the value-added bioprocessing of fish waste produces numerous bioproducts, which are: (1) pharmaceuticals such as proteins, jadomycin, collagen, lactic acid, glycerol, proteases, lipases, and collagenases; (2) nutraceuticals such as omega-3, amino acids, fish oil, fatty acids, carotenoids, isoflavones, and lutein; (3) chemicals such as 1,2-propanediol and 1,3-propanediol, dihydroxy-acetone, and methanol; (4) biofuels such as biodiesel, bioethanol, and biohydrogen; and (5) further products such as fish meal and fish silage. On the other hand, the value-added bioprocessing of slaughtering waste produces the same above-mentioned products except that the intermediate product, in this case, is the tallow compared to fish oil as an intermediate product in the bioprocessing of fish waste.
Furthermore, there are several potential uses of fish waste in the production of further pharmaceuticals such as chymotrypsin, pepsin, enzyme inhibitors, anticoagulants, insulin, nucleic acid, nucleotides, protamine, and proteolytic enzymes. Besides, several biochemicals can be produced such as bile salts, glue, gelatin, leather, and pearl essence.
The industrial ecology is the design of industrial infrastructures as a series of interlocking manufactured ecosystems in order to maximize the energy use efficiency, reduce the costs, reduce the environmental problems, identify new value-added products, and maximize the resources use efficiency, which leads to the sustainability. An important key issue is the interpretation of the insinuation of employing the ecological models to restore the agro-industrial systems, through applying the concepts of eco-design and eco-efficiency leading to a cleaner production allowing to reach a zero-waste and zero-emission system. This requires inevitably conducting environmental impact assessment and life cycle analysis of the agro-industrial processes and products. On the other hand, the aim of eco-efficiency is to boost the values of products by optimizing the utilization of resources and minimizing the negative environmental impacts by incorporating both efficiency and innovation into the industrial process without expensive pollution control techniques.
The cleaner production procedure is the first step in the implementation of the concept of industrial ecology. The procedure includes: (1) the examination of production systems in terms of the efficient use of natural resources and the efficient use of energy, and (2) the utilization of life cycle analyses method to evaluate the products and the agro-industrial processes in order to minimize waste and pollution as well as reduce costs and identify new prospects such as new products and employment opportunities.
Regarding the products, the procedure aims at reducing the negative impacts throughout the entire life cycle of the product from cradle to grave, i.e., from design to final disposal. Regarding the agro-industrial processes, cleaner production aims at (1) efficient use of raw materials, (2) efficient use of energy, and (3) reduction of emissions and wastes. An important key issue is to incorporate environmental concerns into designing processes and delivering the products.
The concept of integrated bioindustrial systems aims at (a) designing circular production and consumption systems leading to maximize the efficiencies of resources and energy uses and to allow the required energy and resources for forthcoming development, (2) forming a further ecologically sound and healthy environment through less waste is generated at each level of production and the conversion of waste into value-added products, and (3) emphasizing the socio-economic development through creating new employment opportunities and ground-breaking technologies and new products.
The biorefinery is the cornerstone of the integrated bioindustrial systems, where a biorefinery is a production plant that combines bioconversion processes biomass and devices such as bioreactors to generate biofuels, electrical energy, heat energy, and value-added biochemicals from biomass. The International Energy Agency, Bioenergy Task 42 on Biorefineries, has defined biorefining as the sustainable processing of biomass into a spectrum of bio-based products (food, feed, chemicals, materials) and bioenergy (biofuels, power, and/or heat). Considering that biomass is all organic matters -except fossil fuels- such as forest materials, agricultural crops residues, livestock manure, organic fraction of municipal solid wastes, fish processing wastes, and food processing wastes [4].
The concept of biorefinery has several objectives: (1) maximizing energy use efficiency, (2) maximizing resource use efficiency, (3) minimizing environmental problems, (4) creating new value-added products, and (5) creating new employment opportunities. However, there are some critical concerns such as the competing uses of materials, market demands, and production costs.
The biorefinery has several advantages: (1) through producing numerous products, a biorefinery takes advantage of the numerous components in biomass and their intermediates then intensifying the value derived from the biomass, and (2) through producing various low-volume, nevertheless high-value, chemical products such as nutraceuticals and pharmaceuticals and a low-value, nonetheless high-volume liquid transportation fuel such as biodiesel and bioethanol, (3) meanwhile generating electrical energy and heat, through combined heat and power (CHP) plant, and (4) creating new high value-added products maximizes the feasibility, where the high-volume fuel’s production meet the energy demands, and the electricity and heat production minimizes the energy costs and decreases the greenhouse gas (GHG) emissions.
However, the subsequent concerns should be considered: (1) risk of excessive consumption of edible crops, (2) risk of deterioration of organic and mineral content of soils, (3) risk of excessive utilization of chemical fertilizers and pesticides to advance the production levels, (4) risk of competition between food and biorefinery, and (5) risk of deforestation.
The following is an approach to transform the present agricultural systems (beef, dairy, and poultry farms as well as cereals and vegetable crops production) and agro-industrial systems (ethanol industry and fish industry) into integrated bioindustrial systems by altering their linear mode of production into a circular mode of production to create a coherent bioeconomy, where the bioeconomy includes the conversion of renewable bioresources and waste streams into value-added bioproducts, such as food, feed, pharmaceuticals, nutraceuticals, biomaterials, biochemicals, biofuels, and bioenergy.
Cereal and vegetable production encompasses the utilization of several inputs such as water, fertilizers, pesticides, seeds, and energy. The products are grains, fruits, and tuber/roots. However, the waste is agricultural crops residues (Figure 1). The concept of bioeconomy is to use the output i.e., waste, of an industry or production system as an input i.e., feedstock, in another new industry. Therefore, this waste is planned to be used as feedstock for a new forage industry, where the produced forages are used for feeding livestock in a new livestock production system that produces milk and meat. However, this industry generates animal waste i.e., manure, which is planned to be used as feedstock for a new biogas plant that produces biogas that fuels the cereal and vegetable crops production system. Besides, the generated sludge is used as a biofertilizer within the crops production system. Part of the generated sludge is used in a new compost facility and the produced compost is used within the crops production system as a biofertilizer. An important key issue is that 4 new industries were established and, therefore, 5 new products were produced, which are considered as economic development. It should be noticed that the core of all these newly planned industries and facilities, which were integrated with the crops production system, is creating new employment opportunities, which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development (Figure 2).
Linear mode of cereal and vegetable crops production (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, and the red circle designates the waste).
Cyclic mode of cereal and vegetable crops production through integrated bioindustrial systems (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, the red circle designates the waste, and the yellow wave designates the employment opportunity).
Beef and dairy production encompass the utilization of several inputs such as water, forages, and energy. The products are milk and meat. However, the wastes are slaughter waste, manure, and whey (Figure 3). The concept of bioeconomy is to use the output i.e., waste, of an industry or production system as an input i.e., feedstock, in another new industry. Therefore, the slaughter waste is used as feedstock in a biorefinery to produce biofuels, biochemicals, pharmaceuticals, and nutraceuticals. Additionally, manure and whey are planned to be used as feedstock for a new biogas plant, where the produced biogas is used for fueling the beef and dairy production system. The biogas plant generates sludge, which is used as biofertilizer for a new crops production system that produces grains and tuber/roots. Besides, the generated crop residues are used as feedstock for the forage industry, which produces forages for beef and dairy production. Part of the generated crops residues is used in a new compost facility and the produced compost is used in fertilizing the crops production as biofertilizer. An important key issue is that 5 new industries were established and, therefore, 9 new products were produced, which are considered as economic development. It should be noticed that the core of all these newly planned industries and facilities, which were integrated with the beef and dairy production system, is creating new employment opportunities, which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development (Figure 4).
Linear mode of beef and dairy production (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, and the red circle designates the waste).
Cyclic mode of beef and dairy production through integrated bioindustrial systems (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, the red circle designates the waste, and the yellow wave designates the employment opportunity).
The poultry industry encompasses the utilization of several inputs such as water, forages, and energy. The products are meat and eggs. However, the wastes are slaughter waste and manure (Figure 5). The concept of bioeconomy is to use the output i.e., waste, of an industry or production system as an input i.e., feedstock, in another new industry. Therefore, the slaughter waste is used as feedstock in a biorefinery to produce biofuels, biochemicals, pharmaceuticals, and nutraceuticals. Additionally, poultry manure is planned to be used as feedstock for a new biogas plant, where the produced biogas is used for fueling the poultry production system. The biogas plant generates sludge, which is used as biofertilizer for a new crops production system that produces grains and tuber/roots. Besides, the generated crop residues are used as feedstock for the forage industry which produces forages for the poultry farms. Part of the generated crops residues is used in a new compost facility and the produced compost is used in fertilizing the crops production as biofertilizer. An important key issue is that 5 new industries were established and, therefore, 9 new products were produced, which are considered as economic development. It should be noticed that the core of all these newly planned industries and facilities, which were integrated with the poultry production system, is creating new employment opportunities which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development (Figure 6).
Linear mode of poultry production (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, and the red circle designates the waste).
Cyclic mode of poultry production through integrated bioindustrial systems (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, the red circle designates the waste, and the yellow wave designates the employment opportunity).
The fish processing industry encompasses the utilization of several inputs such as water, feed, and energy. The product is canned fish. However, the wastes are a large amount of fish waste and a large amount of wastewater (Figure 7). The concept of bioeconomy is to use the output i.e., waste, of an industry or production system as an input i.e., feedstock, in another new industry. Therefore, a large amount of wastewater is planned to be used as feedstock for a new wastewater treatment plant, where the treated water is used as input water in the fish processing industry. Further, this plant generates sludge, which is planned to be used as feedstock for a new biogas plant that produces biogas that fuels the finish processing industry. Besides, the generated sludge is considered a new product as biofertilizer. On the other hand, the large amount of fish waste is used as feedstock for a new biorefinery that produces fish meal and fish silage, pharmaceuticals (proteins, jadomycin, collagen, lactic acid, glycerol, proteases, lipases, and collagenases), nutraceuticals (omega-3, amino acids, fish oil, fatty acids, carotenoids, isoflavones, and lutein), chemicals (1,2-propanediol and 1,3-propanediol, dihydroxy-acetone, and methanol), biofuels (biodiesel, bioethanol, and biohydrogen). An important key issue is that 6 new industries were established and, therefore, a multitude of new products were produced, which are considered as economic development. It should be noticed that the core of all these newly planned industries and facilities, which were integrated with the fish processing industry, is creating new employment opportunities, which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development (Figure 8).
Linear mode of fish industry (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, and the red circle designates the waste).
Cyclic mode of fish industry through integrated bioindustrial systems (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, the red circle designates the waste, and the yellow wave designates the employment opportunity).
The bioethanol industry encompasses the utilization of inputs such as energy and raw cellulosic materials. The product is bioethanol. However, the waste is broth (Figure 9). The concept of bioeconomy is to use the output i.e., waste, of an industry or production system as an input i.e., feedstock, in another new industry. Therefore, this waste is planned to be used as feedstock for a new processing industry that produces wastewater and biofertilizer, where these products are used in a new hydroponics system that produces biowastes (crops residues). These wastes i.e., crop residues, are planned to be used as feedstock for a new forage industry that produces forages for a new livestock production system. However, this industry generates animal waste i.e., manure, which is planned to be as feedstock for a new biogas plant, which produces biogas that fuels the bioethanol industry. Besides, the generated sludge is used as biofertilizer for a new crops production system. Part of the generated sludge is used in a new compost facility and the produced compost is used within the crops production system as biofertilizer. The produced crops residues from the new crops production system as feedstock in a new compost industry, which produces a biofertilizer. An important key issue is that 6 new industries were established and, therefore, 7 new products were produced which are considered as economic development. It should be noticed that the core of all these newly planned industries and facilities, which were integrated with the bioethanol industry, is creating new employment opportunities, which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development (Figure 10).
Linear mode of bioethanol industry (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, and the red circle designates the waste).
Cyclic mode of bioethanol industry through integrated bioindustrial systems (the orange oval designates the input, the blue rectangle designates the industry, the green hexagon designates the product, the red circle designates the waste, and the yellow wave designates the employment).
Digitalization is essential to the advancement of the bioeconomy. Digitalization is promoting intelligibility throughout the value chains and facilitates to scrutinize the conformity with afforded standards. Digitalization modifies the route for expanding traditional bioeconomy and is converting the bioeconomy into a progressively multi and interdisciplinary proficient sector.
The digital revolution in the bioeconomy has 3 unique aspects: (1) the utilization of digital tools as a tool for monitoring. For instance, real-time monitoring of farming operations such as crops, and livestock can provide timely and feasibly added value. Likewise, in forestry, monitoring provides added value by processing data, optimizing the conservation and use of forest products, (2) data aid the development of value chains in terms of reusing, recycling, and repairing. Digitalization provides data analysis for biorefineries or bioindustry can assist in identifying new products evolving from what was formerly considered as biowaste, and (3) data-driven at its core, biosciences are growing precipitously owing to the expanding repository of information. Its application can be observed through a wide range of products and services such as the usage of genomes for therapeutics, personalized medicine, and pharmaceuticals. It can be noticed as well in the advancement of biochemicals as alternatives for petrochemicals.
Digital tools offer a variety of prospects within the traditional bioeconomy sectors such as farming, fisheries, and forestry. For farmers, the ability to track and monitor their livestock and crops boosts daily operations and grants for accurate development. There are also prospects for improved precision, as data is pooled promptly throughout the value chain from forage to dairies, slaughterhouses, products manufacturing, marketing, and consumption. Within the forestry industry, digital tools can be used for monitoring, forecasting, and management of forests.
Digitalization is encouraging practices innovation by boosting both supply and value chains in the circular bio-based economy. Thus, digitalization is able to play a role as a facilitator of circular bioeconomy procedures by for instance altering business patterns. Manipulating data to detect gaps for improving manufacture, or even to pinpoint how to help obtain value from both current production lines and bio-based waste streams are components of this development. At this point, streams of the circular bio-based economy, for instance, biowaste streams, are employed in different approaches since the data-driven procedures are strengthening the bioeconomy.
Digitalization is a component of the circular bioeconomy, where the bioindustrial systems are aiming at applying the circular economy standards that broaden the lifecycle of biowaste by recycling them as feedstock for bioenergy generation. Digitalization, smart algorithms, and advanced computer modeling guarantee resource boosting in the bioindustrial systems, raise the value of green production and are a factor in energy trade-off. Applications include open innovation platforms providing data access, which is open for research and development (R&D) as well as business. Digitalization can be used to create higher-value products in the circular bio-based economy. Digital tools can be implemented for making new value-added bioproducts. For instance, the production of novel and high-value bioproducts using existing bioresources.
Big data is cornerstone in developing biosciences. In the health sector, for instance, big data is accelerating encouraging results in biomedical research. At this point, the quick leap of data-driven analysis is anticipated to reach a higher level of personalized medicine and pharmaceuticals. High levels of digitalization such as blockchain and artificial intelligence coupled with its application in, for instance, agriculture, aquaculture, and forestry, brand-new bioproducts, and recycling of by-products are projected to occur. The intersecting role of data for R&D as well as an invention in bioeconomy is applied in contemporary waste management such as the use of bacteria in biowaste degradation.
Data analysis is crucial for a profitable green transition. Numerous biorefineries implement data in fostering the applications of biosciences in utilizing, for instance, forest by-products. Biomaterials such as lignin were found to be valuable feedstock in the production of food, feed, and adhesives. Technologies such as pyrolysis use biological but inedible feedstock and produce liquid bio-oils. The bio-oil is consistent with the current fossil oil infrastructure, and thus fills one of the gaps arising between the bio-based economy and the petroleum-based economy. The rapid leap of data analysis is able to accelerate finding solutions for global challenges.
A digital transformation is in progress in the circular bio-based economy. Guaranteeing that rural communities realize the profits of this transformation necessitates a re-outlining of the discussion to emphasize not only the digitalization itself but the growth potential it offers. This prospective is comprehensive and involves the formation of innovative bioproducts, services, and bioindustries. While based on rural resources, these opportunities necessitate additional collaboration that reinforces rural–urban relationships. The digital revolution of the circular bio-based economy likewise retains the capability to carry out businesses in conventional circular bio-based economy sectors attracting a wider cross-section of communities. This leads to create new employment opportunities for rural communities.
Generally, the applications of digital tools include prototyping electronic boards, internet of things (IoT) platforms, software, and cellphone applications to control the operation of the bioproducts production systems as well as compute the input materials and energy on the one hand and the output materials and energy on the other hand. Similar applications include livestock farming, for example detecting the activity and health of the animals and informing the animal owner. Further applications include operating the cooling/heating systems based on detected indoor conditions in greenhouses and livestock barns. Another application is in precision farming to control the farming operations conducted by agricultural machinery connected to satellites. Further application is that digital tools can control the interoperability of agricultural systems e.g., control the soil-based sensors to be consistent with the tractor. Additionally, the role of mechatronics is highly foreseen in these applications. Finally, a further application is the use of QR-codes (Quick Response code) to boost comprehensibility across the value chain. For instance, QR-codes are used to track livestock, allowing consumers to trace the food they consume from its source through the route to the retailer. Several applications in this context were developed as cell phone applications [5] and desktop software [6, 7, 8, 9].
Nanotechnology and laser radiation have been implemented in the production process of several bioproducts [10, 11, 12, 13, 14, 15, 16]. Besides, the implementation of life cycle analysis (LCA) and environmental impact assessment (EIA) methodologies are of high importance to analyze the life cycle of bioproducts and to determine the environmental impact of the production processes [17, 18, 19, 20, 21]. A key issue is to conduct a techno-economic assessment (TEA) of the used technologies in the production process [22].
This study provides an approach to convert the present agricultural systems (beef, dairy, and poultry farms as well as cereals and vegetable crops production) and agro-industrial systems (ethanol industry and fish industry) into integrated bioindustrial systems and biorefineries through amending their linear mode of production into a circular mode of production to develop a sustainable bioeconomy. This development includes the bioconversion of biowaste streams from the existing agricultural and agro-industrial systems into value-added bioproducts, such as food, feed, pharmaceuticals, nutraceuticals, biomaterials, biochemicals, biofuels, and bioenergy where these novel bioproducts are considered as economic development. Whereas the core of the planned bioindustries is creating new employment opportunities, which is considered as social development. Furthermore, these integrated bioindustrial systems have zero-waste, zero-emission, and efficient resources and energy use, which are considered as environmental development. An important key issue is that digitalization guarantees resource boosting in the bioindustrial systems, where applications include the development of electronic boards, internet of things (IoT) platforms, software, and cellphone applications for monitoring and controlling the operations, computing input and output materials, and energy, and fostering comprehensibility across the value chain. Figure 11 summarizes the fields of science related to bioeconomy.
The fields of science related to bioeconomy.
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