Suggested Dissertations and Thesis Research Topics on Machine Learning, Artificial Intelligence, Smart Contracts, Blockchains, and Cloud Manufacturing for Industry 4.0 and Industry 5.0 Keywords applicable to this article: dissertation, thesis, topics, Machine Learning, Artificial Intelligence, Industry 4.0, Industry 5.0, Industrial Internet of Things, Big Data Analytics, Augmented Reality, Digital Twins, Additive manufacturing, 3D printing, Cloud Manufacturing, Network Manufacturing, Fog Computing, Edge Computing, Smart Logistics, Smart Supply Chain Management, Smart Manufacturing, Smart Production, Smart Transportation capabilities, Smart Warehousing capabilities, Smart Contracts, Blockchains, Supply Chain Intelligence, Cyber Physical Systems, Industrial Internet of Things, Industrial Internet, Global Value Chain Networking, and Collaborative Manufacturing; By: Sourabh Kishore, Chief Consulting Officer Please contact us at consulting@eproindia.com or consulting@eproindia.net to discuss your topic or to get ideas about new topics pertaining to your subject area. I am happy to present the fourth part of the article on dissertation and thesis topic development in the fields of Supply Chain Management, Industrial Engineering, Procurement, Inventory Management, Logistics, Distribution, and Retail. In this article, I have presented the evolving research areas of Machine Learning, Artificial Intelligence, Industry 4.0 and Industry 5.0, Blockchain, Smart Contracts, Augmented Reality, Digital Twins, and Cloud Manufacturing in these fields. This is an extension of our original three articles in logistics and supply chain management, accessible through the following links: Link to the first part of this article and Link to the second part of this article and Link to third part of this article for topic development in the areas of modern industrial engineering, logistics and supply chain management, Industry 4.0, procurement management, inventory management, sustainability, lean and agile capabilities, and dynamic capabilities; This article explores many newer topics of research in industrial engineering, logistics, supply chain management and its associated domains categorized under three broad research areas: Machine Learning and Artificial Intelligence. smart contracts and blockchains, and additive manufacturing (3D printing) and cloud manufacturing for Industry 4.0 and Industry 5.0. Each area presents opportunities for studying a number of practices and the factor variables (both mediators and moderators) associated with it, and their interrelationships. In addition to multivariate statistical studies, opportunities for studying application designs, software programming, and advanced systemic architectures exist in these areas. The studies proposed are mostly positivistic, deductive, and quantitative employing inferential statistical methods like ANOVA, MANOVA, Multiple Regressions, advanced Multivariate Statistical Modelling and Analysis comprising of Exploratory Factor Analysis using Principal Component Analysis, Confirmatory Factor Analysis, and Structural Equation Modelling, Application Characterisation Engine (ACE) Modeling in OPNET Modeler, programming in Java, Python, C++, and JavaScript, and System Dynamics Modeling in Vensim. Designing, Prototype Programming, and modeling and simulations of systemic algorithms offer significant research opportunities for numerous technical topics in the stated areas. Please visit our page on Multivariate Statistical Modelling and Analysis for further details on analysing and optimising the measurement constructs, OPNET Network Modeling and Simulation Services for further details on scope of such topics in Modeling and Simulations, and Software Development, Integration, Testing, and Training for our services in this stated context. You may also consider in touch programmes (action research), organisational ethnography, in-depth interviews, focus group discussions, and phenomenology as appropriate qualitative methods for deriving deeper knowledge about the variables and their possible interrelationships after completing the quantitative part (I mean, employing methodology and data triangulation using quantitative data and analytics).The descriptions of the areas and their associated practices are presented as the following: (A) Machine Learning and Artificial Intelligence for Industry 4.0 and Industry 5.0 Industry 4.0 and related topics were presented in the previous article on dissertation and thesis research topics in Industrial engineering, procurement, supply chain management, and logistics in the fourth industrial revolution. Industry 5.0 is the next industrial era in which, advanced intelligent automation levels, human-centric complete systemic controls, digital twins and augmented reality for human - machines collaboration, robotic societies, exoskeleton, and smart robots, machines, and systems/equipment are projected in academic studies. Industry 5.0 requires Industry 4.0 as its foundation. Hence, full implementation of Industry 4.0 capabilities are expected to drive Industry 5.0 capabilities upon them. Machine learning and Artificial Intelligence are at the core of comprehensive Industry 4.0 capabilities and their roadmap towards the Industry 5.0 capabilities. To implement them in production the complete digitalisation of all the machines, robots, vehicles, handling equipment, systems, pallets, racks, and even consignments is required such that they can transmit data sensed in the field processes to big data servers in standardised digital formats, such as MQTT (message queuing telemetry transport) data formats, eXtensible Markup Language (XML) format, JavaScript Object Notation (JSON) format, List textual format, and comma or tab separated formats. The data should be stored through the boilerplate data system in defined formats of the Application Programmable Interfacing (API) controllers. Examples of boilerplate access systems are Hadoop, Map Reduce, Hibernate, Java Persistent API (JPA) and Liquibase. The machine learning algorithms are designed to use the data collected from the field processes for detecting patterns, detecting anomalies, making predictions, classifying and categorising data classes, remote troubleshooting and maintenance, making choices and selections, and making operating level decisions. Machine learning may be viewed as the systemic layer comprising of learning algorithms whereas artificial intelligence may be viewed as the application layer for advanced planning, predictions and forecasting, auditing and security, automated monitoring and control, and automated decision making applications using the outputs of machine learning algorithms. To begin, I first want to distinguish between machine learning and artificial intelligence to establish a clear academic understanding. Machine learning is a set of algorithms designed for different purposes that learn from data sets and provide different outputs. The popular algorithms are K-means, support vector machine (SVM), decision tree, random forest, regressions (linear, polynomial, and logistic), K-nearest neighbours (KNN), Naive Bayes, Principal Components Analysis (PCA), hidden Markov models, DB Scan, agglomerative clustering, local outlier factor, continuous, and categorical. These algorithms can be classified as unsupervised, supervised, and reinforcement.The KNN, SVM, regression, decision tree, random forest, and Naive Bayes are all supervised machine learning algorithms. They need to be trained using labelled data to generate their intended predictive or judgmental outcomes. Further, K-means, DB Scan, agglomerative clustering, Markov models, and local outlier factor are unsupervised machine learning algorithms. They can be trained using unlabelled data to generate their intended outcomes. The continuous and categorical algorithms are reinforcement machine learning algorithms as they need agents-driven interactions with their environments to learn from them. Machine learning algorithms can be coded one at a time using their packages in the popular coding platforms (Python, C++, Java and others). Each machine learning runtime shall run one instance of the chosen algorithm with a desired purpose. However, business applications need outputs of several such instances to be analysed at a higher layer to execute the desired functionalities demanded by the business. The higher layer stated here is the artificial intelligence. An artificial intelligence application may use outputs of several machine learning instances to execute business requirements following the desired requirement specifications. Thus, a machine learning instance may be a runtime of a single code file whereas artificial intelligence may constitute an entire application using hundreds of runtimes of the machine learning code files. The artificial intelligence may use several runtimes of the same algorithm or a combination of them. For example, a complex artificial intelligence application may be designed to use outputs of 50 SVM instances, 50 K-means instances, 50 PCAs, and 50 KNNs each reading from different data sets in silos or a combination of them. Artificial intelligence may use some of the algorithms for data preparation before feeding into main analytical algorithms. For example, it may use SVM and KNN for data preparation and then use random forest for making predictions. To conclude, a designer may employ several machine learning algorithms for different purposes with dfferent inputs and outputs to meet the business requirements specifications of a larger artificial intelligence application. Machine learning and artificial intelligence can contribute to advanced capabilities in Industry 5.0, such as robots collaborating with other robots in an operating environment, self-, environmentally-, and cognitively-aware robots, humans collaborating with robots, organisational structures and hierarchies among robots, robots forming societies, and decision-making robots. Industry 5.0 is projected as human-centric using control systems formed by augmented reality and digital twins. Explained simply, Industry 5.0 shall facilitate human control over massive swarms of smart and collaborating machines and robots thus enabling the ability of a few individuals controlling entire manufacturing plants or even a network of manufacturing plants globally. You may picture this! If a swarm of machines, equipment, systems, and robots are able to collect data from their local processing capabilities, are able to digitalise the data, and send the data to big data servers where the data can be used for monitoring, control, troubleshooting, maintenance, making predictions, and making decisions, then the entire framework has Industry 4.0 capabilities. Riding on Industry 4.0 capabilities, when the robots can collaborate, self-inspect, self-heal, heal others, can interact with machines, can form different network configurations, can follow the artificial intelligence master, can reason, can make localised decisions, and can be controlled through augmented reality or digital twins in massive swarms in different network configurations, then the entire framework has Industry 5.0 capabilities. These capabilities are possible through localised robotic program control, robotic operations control, and robotic collaboration control operated by artificial intelligence. In fact, artificial intelligence may be viewed as collective shared brain of entire robotic swarms, robotic organisations, and robotic societies. The augmented reality and digital twins may be viewed as monitoring and control interfaces offered by artificial intelligence to the human operators and controllers. Digital twins are referred to indistinguishable digital equivalents of physical systems that are completely in sync with the physical systems by virtue of bidirectional real time transfer of state transition data. In reality, absolute indistinguishable digital equivalents having real time state transitions synchronised with their physical systems is practically very difficult. Hence, realistically digital twins should be considered more as digital siblings. The dissertations and theses research topics related to machine learning and artificial intelligence for Industry 4.0 and Industry 5.0 may be algorithm designs, industrial engineering designs and their simulations, software prototypes, exploring many new factor and control variables that tie up very well with the operating variables of the evolving industrial revolutions (new conceptual frameworks or empirical formulations involving both new and old concepts), exploring and confirming complex structural constructs showing significant influences of machine learning and artificial intelligence factor and control variables on performance and behavioural variables in industrial engineering in the Industry 4.0 and Industry 5.0 frameworks, and exploration of many real-world implementations through in-depth case studies and exploration of technology solutions offered by global multinational vendors in the fields of Industrial and Information Systems Engineering. Some of the key research areas are presented as the following: 1. Robotic collaboration system in smart manufacturing environments (multiple studies involving data collection on existing manufacturing processes and layouts and proposition of smart collaborative robotics in future) 2. Collaborative robotics controlled by artificial intelligence in warehousing (multiple studies possible in different industries) 3. The evolving empirical models of role of machine learning and artificial intelligence in Industry 4.0 and Industry 5.0, and their applications 4. Machine learning and artificial intelligence for Fault Tolerance in Production Automation (specific application areas may be studied, such as boilers, water pipelines, heat exchangers, furnaces, etc.; studies may involve programming in Python, Java, or C++) 5. Machine learning and artificial intelligence for automated Inventory Replenishment and Control (studies may involve programming in Python, Java, or C++) 6. Machine learning and artificial intelligence for Logistics Automation (specific application areas may be studied, such as allocation of assets, materials preparation and handling, industrial buffers, in-plant transportation, packaging, storage, retrieval, etc.; studies may involve programming in Python, Java, or C++) 7. Machine learning and artificial intelligence for Strategic Supplier Management and Supply Chain Network Management (studies may involve programming in Python, Java, or C++) In the following topics, multivariate studies, focus group studies, and industrial archival studies are suggested; 8. Machine learning and artificial intelligence for Transportation Planning and Scheduling 9. Machine learning and artificial intelligence for Quality Assurance in Smart Manufacturing environments 10. Machine learning and artificial intelligence for sustainability enhancements of the Triple Bottomline Model (examples are, elimination of wastes, monitoring employees' health and safety, monitoring and controlling emissions and disposals, economic performance, etc.) 11. Machine learning and artificial intelligence for Lean Six Sigma 12. Role of machine learning and artificial intelligence in eliminating production defects, reworks and returns, reducing logistics errors, and reducing transportation accidents (multiple focussed topics are possible; multivariate research suggested) 13. Role of machine learning and artificial intelligence in aligning every policy, process, and tasks to the voices of customers 14. Role of machine learning and artificial intelligence in lean, agile, resilient, responsive, and flexible logistics and supply chain/network management (multiple focussed topics are possible; multivariate research suggested) 15. Role of machine learning and artificial intelligence in developing, enhancing, and maturing dynamic capabilities in the five core Industrial Engineering Disciplines: Production, Inventory Control, Logistics, Supply Chain/Network Management, and Transportation (numerous topics possible) 16. Role of machine learning and artificial intelligence for ensuring energy efficiency in industrial engineering 17. Role of machine learning and artificial intelligence in optimising production and logistics costs 18. Role of machine learning and artificial intelligence in securing industrial systems and networks 19. Role of machine learning and artificial intelligence in securing critical infrastructure and systems 20. Role of machine learning and artificial intelligence in provenance management and control 21. Role of machine learning and artificial intelligence in smart manufacturing for building flexibility and dynamic capabilities in the Industry 4.0 and frameworks 22. Artificial Intelligence models and algorithms for Early Awareness, Self-Configutation and Self-Optimisation, and Predictive Maintenance in the Industry 4.0 and 5.0 frameworks 23. Role of machine learning and artificial intelligence in additive Manufacturing and 3D Printing 24. Role of machine learning and artificial intelligence in building perception layer for real time supply chain tracking and tracing 25. Role of machine learning and artificial intelligence in automating robotics and smart factory processes through integration of all Industrial Engineering disciplines using Industrial edge computing and industrial cloud computing in the Industry 4.0 and 5.0 frameworks (several topics possible) 26. Role of machine learning and artificial intelligence in monitoring and controlling vital functions and human security in Industry 4.0 and 5.0 operating environments 27. Drivers of and barriers to success of machine learning and artificial intelligence in SME manufacturing sector 28. Real time visibility into global supply chain events and rapid scheduling, rescheduling, and rerouting of consignments using machine learning and artificial intelligence 29. Role of machine learning and artificial intelligence in smart predictive analytics of future risk events in global supply chains in the Industry 4.0 and 5.0 frameworks 30. Role of machine learning and artificial intelligence in Horizontal and Vertical integration of smart manufacturing systems with their supply chain networking 31. Role of machine learning and artificial intelligence in collaborative logistics and services-oriented supply chain services in the Industry 4.0 and 5.0 frameworks 32. Role of machine learning and artificial intelligence in collaborative production and logistics among smart factories in the Industry 4.0 and 5.0 frameworks 33. Role of machine learning and artificial intelligence for developing data rich analytical environments in the Industry 4.0 and 5.0 frameworks 34. Role of machine learning and artificial intelligence in building robotic communities and enabling their interactions operators; communities by strategically integrating machine-to-machine and human-to-machine communications in the Industry 5.0 framework 35. Role of machine learning and artificial intelligence in production and materials supply planning in multi-party additive manufacturing 36. Role of machine learning and artificial intelligence in building smart vision for human controllers in the Industry 5.0 framework 37. Role of machine learning and artificial intelligence in advanced materials discovery, replenishment, and management based on predictive analytics of consumptions 38. Role of machine learning and artificial intelligence in detecting and correcting systemic anomalies in logistics and supply chains 39. Role of clustering algorithms in machine learning to detect anomalies in IIoT data streams in Industry 4.0 and 5.0 frameworks 40. Role of clustering algorithms in machine learning to detect anomalies in provenance data streams in Industry 4.0 and 5.0 frameworks 41. Role of machine learning and artificial intelligence in detecting and correcting process inefficiencies and wastages in Industry 4.0 and 5.0 frameworks 42. Role of machine learning and artificial intelligence in smart controls and decision-making on logistics processes and systems 43. Role of machine learning and artificial intelligence in trackability and traceability of materials and finished products in global supply chain management 44. Role of machine learning and artificial intelligence in model-based business processes in Industry 4.0 and 5.0 frameworks 45. Role of machine learning and artificial intelligence in detecting causaility among industrial, logistics, and supply chain events 46. Role of machine learning and artificial intelligence in continuous process quality improvements in Industry 4.0 and 5.0 frameworks 47. Role of machine learning and explainable artificial intelligence in industrial systems security 48. Role of machine learning and explainable artificial intelligence in ensuring algorithmic transparency in Industry 4.0 and 5.0 frameworks 49. Role of machine learning and explainable artificial intelligence in tracing explainable quality and reliability issues in automated production, logistics, and supply chains 50. Role of machine learning and explainable artificial intelligence in blockchain integration The above list is a representative set of research opportunities based on current and projected trends in designing, planning, adopting, implementing, operating, controlling, and automating systems and processes using machine learning and artificial intelligence in the Industry 4.0 and 5.0 frameworks. Each of these practices may be supported by a number of underlying factor variables acting as mediators and moderators. Please visit our page on Multivariate Statistical Modelling and Analysis for further details on analysing and optimising the measurement constructs. You may also consider studying these practices and their variables separately through in touch programmes (action research), organisational ethnography, in-depth interviews, focus group discussions, and phenomenology as in qualitative studies or investigating their interrelationships through hypothesis testing and testing of structural constructs (complex relationships models) in quantitative studies. Some of the above topics shall involve advanced modeling and simulations of smart industrial engineering systems. OPNET Modeler and its Application Characterisation Engine for algorithmic interactions modeling is a suitable tool. In focussed technical studies, you may develop prototypes by coding and integration using Java core, Java Spring Boot, C++, Python, or JavaScript frameworks (such as Angular JS, React Native JS, and Express JS). This is a vast research area that requires significant contributions by students and professionals. Industry 4.0 has gained some traction but is still an evolving field requiring significant research efforts as there are few empirical models and constructs and related theories in this field. Industry 5.0 is more-or-less at the conceptual stage. In addition to the suggestions above, please contact us at consulting@eproindia.com or consulting@eproindia.net to get more topic suggestions and to discuss your topic. We will be happy to assist you in developing your narrow research topic with an original contribution based on the research context, research problem, and the research aim, and objectives. Further, We also offer you to develop the "background and context", "problem description and statement", "aim, objectives, research questions", "design of methodology and methods", and "15 to 25 most relevant citations per topic" for three topics of your choice of research areas at a nominal fee. Such a synopsis shall help you in focussing, critically thinking, discussing with your reviewers, and developing your research proposal. To avail this service, Please Click Here for more details. Dear Visitor: Please visit the page detailing SUBJECT AREAS OF SPECIALIZATION pertaining to our services to view the broader perspective of our offerings for Dissertations and Thesis Projects. Please also visit the page having TOPICS DELIVERED by us. Please visit the first part of this article, second part of this article, and third part of the article for exploring more areas of industrial engineering, logistics, supply chain, and related topics pertaining to lean, six sigma, sustainability, performance, integration, aggregation planning, effectiveness, efficiency, IT and technologies in supply chain management, and cloud supply chains and manufacturing. With Sincere Regards, Sourabh Kishore. Apologies for interruption; please continue reading. (B) Smart Contracts and Blockchains in Industry 4.0 and Industry 5.0 The story of the roles of machine learning and artificial intelligence in Industry 4.0 and Industry 5.0 has not ended yet. When we consider advanced applications in smart contracts and blockchains, and additive and cloud manufacturing, the story becomes even more interesting. Let us take these two areas one by one. In this section, let us evaluate the research opportunities in smart contracts and blockchains for dissertations and theses research projects. Contracting is an integral part of manufacturing, logistics, and supply chains. A manufacturing organisation runs the operations with the help of hundreds of concurrent contracts for supplies and services. This is the reason the procurement function evolved as a significant contributor in such organisations. It consumes majority of the capital and running costs and hence, is at the centre of management focus. There are three main challenges, almost always conflicting, facing procurement function: reducing costs as much as possible, reducing losses as much as possible, and gaining maximum value from the investments and expenses made by the procurement function. Strategies like vendor managed inventory, third party logistics, fourth party logistics, contract manufacturing, renting of equipment and machines, contract workers, urban consolidation, warehousing, milk runs, collaborative forecasting and replenishment, etc. have been tried by procurement managers for a long time. How much they have been successful in reducing the conflicts between their three main challenges is still a matter of debate and research. The problems of unused machines and equipment (but still paid for), elevated billings, material losses, unaccounted expenses, activity based expenses, poor optimisation, and poor / delayed tracking of works accomplished have always haunted the procurement managers. The concept of smart contracts and blockchains may be the exact solution that procurement managers may be looking forward to for a long time. To understand smart contract and blockchain, picture the following scenario in your mind: (a) You are the procurement manager in a large manufacturing organisation having several manufacturing plants distributed in a large geographical area; (b) You interact with five strategic suppliers providing machines and equipment, warehousing space, transportation, and related several logistics services; (c) The existing contract is to provide one time gross payments for all their supplies and services consolidated; but you do not have any visibility into their operations, supplies, and services deliveries; (d) You seek a change of mechanism where you want to pay for each supply and service as they happen by closely monitoring each transaction; your suppliers warn you that this mechanism will require significant monitoring and control overhead that is almost impossible to execute given your limitations of expertise and staffing; (e) You seek the blockchain as the solution. You collaborate with your suppliers to create a blockchain network in which, their peers interface through advanced cryptographic key exchanges and controls. You act as the main controller of the blockchain network. (f) Your suppliers are now required to sign contracts digitally and allocate their resources to the contracts in services-oriented mode (pay only when they are used). The contracts can be two party or multiparty. Each contracting party shall have a digitally signed, encrypted and protected copy of the contract saved on their private spaces on the blockchain. (g) The resources allocated to the contracts need to be enabled as cyber physical systems using Industrial Internet of Things such that they can send updates to a database tracking their events. A machine learning algorithm is assigned to track the events database to present status updates and artificial intelligence is tasked to keep track of the status updates to make decisions on the quality and acceptability of the tasks accomplished such that they can be recorded as accomplished in the blockchain (in a space called "general ledger" allocated to track and close the contractual terms as and when they are completed in the perspective of the artificial intelligence). (h) When the tasks are marked as completed in the blockchain, the invoices are raised automtically by the contracts as per the agreed terms. Your job is to check the status updates in the blockchain records and make payments whenever the invoices are due. In the scenario above, the blockchain formed is a closed group blockchain (unlike the open blockchains used for cryptocurrency trading) and the digitally signed contract is a smart contract because it can track the execution of its terms automatically and ensure that the payments are made promptly on time. All state changes are recorded in general ledger in the form of blocks protected digitally by hash algorithms. State changes are immutable and are transparently visible to all contracting parties. There are no unnecessary costs paid on idle or even unallocated resources because the operations happening to execute the contracts are transparent to the prourement manager. In this architecture, the procurement manager can allocate multi-party contracts (such as, multiple transport and crane operators clubbed to serve a single contract). There is no longer a need to assign large fixed period and fixed value contracts to third and fourth party logistics providers. The payments can be strictly mapped with the value derived and the suppliers bidding the lowest prices can be prioritised. This arrangement appears to be the best dream of every procurement manager. However, how effectively and efficiently and to what extent this architecture can be realised is a matter of debate and research. The academic research studies for dissertation and research projects should focus on a narrow and focussed problem areas in smart contracts and blockchains. Hence, the topics related to blockchains may be focussed on a specific process, technology, algorithm, artificial intelligence design or automation challenge, or on specific variables related to data collection, tracking, and tracing through blockchains and their influence on known empirical variables (such as, performance variables of the suppliers). You may also combine blockchain, smart contract construction, machine learning algorithms, artificial intelligence designs, Industry 4.0, Industry 5.0, IIoTs, and Big data in your research as long as the topic is focussed on a sufficiently narrowed research problem captured from the industries. The studies may be about solving multivariate constructs for statistical significances or coding and prototyping of solutions using open source blockchain frameworks (such as Hyperledger and Corda). Following are some of the suggested topic areas related to smart contracts and blockchains in Industry 4.0 and 5.0 are the following: 1. Smart assets selection for allocating to smart contracts using artificial intelligence 2. Constructing smart contracts using a library of templates comprising terms, fields, and flows 3. Multiparty semi-private blockchain design for smart contracting in logistics and supply chains 4. Secured transaction processing models in supply chains using blockchains 5. Multinational logistics coordination and collaboration through blockchains 6. Provenance tracking and tracing using blockchains in materials planning, materials handling, procurement, production, logistics, transportation, and distribution (multiple topics related to specific areas can be formulated) 7. Assets and capacity management through smart contracts in logistics blockchains 8. Using blockchains for multi-party warehousing integration and management 9. Real time quality control in multi-party smart contracts executed through blockchains 10. Managing smart contracts in urban distribution logistics using blockchains 11. Orchestration of multiparty resources in multiparty smart contracts executed through blockchains 12. Predictive auditing and events analysis using smart ledger data in logistics blockchains 13. Supply chain performance enhancements using smart contracts and blockchains 14.Fraud detection and prevention of thefts in global supply chains using blockchain smart contracts 15. Digital passport system for authenticating shipped goods using blockchain smart contracts 16. Role of blockchains in achieving industrial agility and responsiveness 17. Role of blockchains in supply chain risk management 18. Integrating multiparty logistics networks through blockchains 19. Ensuring privacy and trust in supply chain contracts using blockchains 20. Role of blockchains in efficient routing, scheduling, and vehicle tracking in real time for industrial transportation 21. Customer and network value generation through blockchain smart contracts 22. Network configurations and process models for logistics blockchains 23. Role of artificial intelligence in blockchain smart contracts for automated allocation of logistics resources to contracts 24. Blockchain for supplier networking with high levels of trust and privacy 25. Enablers of a barriers to adoption of blockchain smart contracts in logistics and supply chains 26. Procurement value generation through strategic supplier integration using blockchain smart contracts 27. Policy-driven supplier network functionalities using blockchains and smart contracts 28. IIoT-enabled data collection design for executing blockchain smart contracts in logistics and supply chains 29. Immutable consensus through digital ledgers in logistics smart contracts 30. Counterfeit protection in global container shipments using blockchain smart contracts 31. Role of blockchain and smart contracts in securing healthcare supply chains 32. Role of blockchain and smart contracts in securing vaccine cold chains 33. Role of blockchain and smart contracts in managing hospital networks 34. Role of blockchain and smart contracts in hospitality networks 35. Role of blockchain and smart contracts in food and beverages supply chains The above list is a representative set of research opportunities based on current and projected trends in designing, planning, adopting, implementing, operating, and controlling systems and processes in the domain of blockchains and smart contracts. Each of these practices may be supported by a number of underlying factor variables acting as mediators and moderators. One may consider studying these practices and their variables separately through in touch programmes (action research), organisational ethnography, in-depth interviews, focus group discussions, and phenomenology as in qualitative studies or investigating their interrelationships through hypothesis testing and testing of structural constructs (complex relationships models) in quantitative studies. Please visit our page on Multivariate Statistical Modelling and Analysis for further details on analysing and optimising the measurement constructs. Some of the above topics shall involve advanced modeling and simulations. OPNET Modeler and its Application Characterisation Engine for algorithmic interactions modeling in blockchains is a suitable tool. Additionally, blockchain programming tasks can be undertaken using open source blockchain frameworks such as Hyperledger and Corda to develop and test prototypes. These framework codes are available in multiple programming languages and can be installed, configured, and programmed in both Windows and Linux. Please visit our services on software development, integration, testing, runtime, and training. This is an emerging research area requiring significant contributions by students and professionals. It has its potential in all the operations in Industry 4.0 and Industry 5.0. Currently, there are few empirical models and constructs and related theories in this field. In addition to the suggestions above, please contact us at consulting@eproindia.com or consulting@eproindia.net to get more topic suggestions and to discuss your topic. We will be happy to assist you in developing your narrow research topic with an original contribution based on the research context, research problem, and the research aim, and objectives. Further, We also offer you to develop the "background and context", "problem description and statement", "aim, objectives, research questions", "design of methodology and methods", and "15 to 25 most relevant citations per topic" for three topics of your choice of research areas at a nominal fee. Such a synopsis shall help you in focussing, critically thinking, discussing with your reviewers, and developing your research proposal. To avail this service, Please Click Here for more details. (C) Additive Manufacturing (3D Printing) and Cloud Manufacturing in Industry 4.0 and Industry 5.0 Additive manufacturing (3D Printing) is a generic term given to the manufacturing processes in which, the products are created through multilayered deposition and binding of materials using modern technologies such as Stereolithography (masked and apparatus based), Computed Axial Lithography, Viscous Lithography Manufacturing, Electron Beam Melting, Directed Energy Deposition, Wire Arc Melting, Poly/Multi Jetting Fusion, Digital Light Processing/Synthesis, Bound Metal Deposition, Fusion Deposition Modeling, Direct Metal Laser Sintering, Selective Heat Sintering, and Selective Laser Sintering/Melting. These technologies use unconventional materials, which are different from the traditional subtractive manufacturing technologies. Examples of the new materials are Photo-sensitive liquid resins, other forms of resins, Plastics, fine powdered particles of Ceramic and Metal (binded by wax and polymer binders), other Powdered materials such as Inconel 625, Inconel 718, Acrylonitrile Butadiene Styrene, Titanium and their alloys, Aluminum, Alumide, Copper, Aluminum and Copper alloys, Nickel, Bronze, Chromium and Cobalt alloys, and Steels of various types, Cementitious materials, Polyvinyl alcohol plastic, Polycarbonates, Nylon, Graphite, Carbon, Paper, Graphene, and Polylactic acid. Detailed knowledge about these materials is the subject matter of advanced materials science and technology. In industrial engineering, logistics, and supply chain studies, a general understanding of these materials and the processes followed in the additive manufacturing machines and robotics (commonly referred to the class of 3D Printers) is sufficient. Before proceeding further, the name "3D printing" is clarified. Why should we call it 3D printing? Why not 3D production and 3D manufacturing? The answer is in the fundamental design of the manufacturing technology that is inspired by the 2D image printing technologies (laserjet and inkjet). The 2D printing is carried out in tiny blocks of pixels. The printing software divides the image into hundreds of tiny blocks of pixels. The printing head is prepared in steps to print one block at a time. At the time of printing a block, the printing head is provided all the information needed such as colour combinations, white spaces, and shades of grey. The printing information of several 2D blocks ahead is loaded into the printing buffer memory from where, the head pulls the information sequentially. At each 2D block, the printing head is adjusted automatically to feed the colors needed in right quantities and combination to print the block based on the information of that 2D block of pixels fetched from the buffer memory. The speed of printing is rapid such that it appears to be continuous. However, it is discrete as the printing head prints 2D blocks of pixels sequentially. The same principle is followed in additive manufacturing although with very complex design settings. 3D printing is done with the help of 3D models of the products created in 3D modeling software, such as Sketchup and AutoCAD 3D. The software format preferered for 3D printing is .STL (STL is the abbreviation for stereolithography). The .STL file is sent to the slicing software in 3D printers that divides the 3D model of the product to be created in a layer of slices, geometrically, and generates the printing instructions interpretable by the printing control system such that each slice can be printed separately, one above another sequentially, and then binded togather to create the finished product. Normally, 3D printing of the prototype product is done in several combinations of printing methods and materials. Thereafter, the best one tested for quality and reliability is used as the reference for mass production. 3D printing has gained popularity because of its rapid prototyping feature. A design idea can be tested quickly within a few hours on a 3D printer owned by the designer thus improving the design cycle significantly. The rapid prototyping carried out in the 3D printing industry is not only about realising and testing the 3D models of products but also of the printing process through materials jetting and fusions. Getting the best product outcome depends upon optimised specifications related to the process followed. Examples of the specifications are: laser/electron beam strength (power), temperature maintained, speed of beam movement (scanning) on the substrate, powder deposition speed, jetting speed, fusion speed, injection speed of fusion glues, quality and quantity of fusion glues applied, fire injection speed and intensity, oxygen pressure maintained, substrate growth rate, extrusion speed, etc. The designers need to maintain records these specifications of their best performing printed model. Models can be tested for tensile and compressive strengths, hardness, and endurance before their rupture points to identify the best performing one. Based on the best and moderately performing outcomes, designers can develop the expectancy specifications defining the ranges of acceptance of different performance variables based on which, the products can be sent for larger volume productions for consumption. The volumes of additive manufacturing are not as high as that of subtractive manufacturing given the slow pace of printing and several processes needed for preparing the materials and environment before starting the process. Materials wastage is definitely low but printing costs are high. Normally, additive manufacturing is preferred for mass producing of small products' parts or complete products only. Additive manufacturing has not yet been optimised for producing large parts and products requiring sophisticated engineering. Hence, additive manufacturing should not be viewed as a future replacement of subtractive manufacturing in the industrial engineering field. Supply chain and production logistics operations of additive manufacturing is different compared to those of subtractive manufacturing. The suppliers of additive manufacturing require different capabilities and competencies. For example, the raw materials supplier of metal 3D printing should be specialised in providing fine powdered metals suitable for jetting, laser processing and fusion binding processes. Given the low volumes of production the supply chains and suppliers have low capacities and the competition environment is not yet developed. Standalone 3D printing companies can hardly sustain their businesses in absence of demands and sufficient revenue generation. Hence, 3D printing companies are increasingly networking in large numbers to build a global network manufacturing consortiums. This is being accomplished through an evolving concept called cloud manufacturing. Cloud manufacturing, as the name implies, comprises of hosted systems and applications with coordination and collaboration systems for executing service-oriented manufacturing projects. A network of 3D printing companies can collaborate with another network of design companies and intellectual property owners of products to produce and deliver products on demand for global customers. Customers across the world can place orders for standard and customised products selected from hosted catalogues. The designs of the products are stored on the cloud manufacturing servers in the STL format. Designers may make changes as per the customisation demands, if any. Thereafter, the projects can be hosted for bidding by 3D printing companies located closest to the customers. The winners of bidding gain access to the STL files for preparing the slices and printing environment, and finally creating the product. The quality assurance tests can be carried out locally by the 3D printing company such that the test results can be published on the cloud manufacturing portal. After final acceptance by the quality managers, the products can be packaged and shipped to the customers. On final acceptance by the customers, the payments are released to the 3D printing company. Cloud manufacturing can be implemented as open manufacturing networks for collaboration between designers, product owners, and 3D printing companies, or closed manufacturing networks owned by large global companies interested in outsourcing their design and manufacturing tasks to global small businesses. In either configuration, a registration system needs to be configured in which, all the cloud manufacturers owning 3D printing facilities are registered. The facilities, their 3D printers, and their production logistics machines and robots need to be registered as clearly identifiable and traceable assets on the cloud manufacturing portal. Identification and traceability can be ensured using Industrial Internet of Things attached with every asset as sensors of parameters running in processes. The portals can gain massive sizes having thousands of registered designers and the production logistics assets and 3D printers owned by additive manufacturers. Artificial intelligence may have a major role in learning from the experiences and track records of the manufacturers such that appropriate manufacturing facilities can be selected and allocated to the incoming orders. The manufacturing contracts can be executed as smart contracts stored on blockchains such that all the printing status updates can be tracked and recorded automatically using data streams arriving from the Industrial Internet of Things attached with the printers. The suppliers to the additive manufacturing companies should be mostly local or at most regional such that they can deliver the materials faster and at low costs. Long distance supply routes are not feasible in additive manufacturing given the low volumes of customised products. The designers normally maintain their own small 3D printing facilities for rapid prototyping and testing of their design ideas. Once they are sure of performance and success of their design files, they post them on their design libraries on cloud manufacturing. After publishing the design files, they are mostly used by commercial manufacturers. As mentioned above, the designers publish their design files as well as the specifications of the best performing manufacturing parameters tested by them. If there are any variations in real manufacturing projects, they are recorded as experiential knowledge for future enhancements. The researh opportunities in applications of additive manufacturing and cloud manufacturing in the Industrial Engineering and supply chain disciplines are offered largely through the Industry 4.0 framework. Following are some of the suggested research opportunities in these fields for your dissertation and thesis projects: 1. Effects of Additive Manufacturing on Logistics and Supply Chain systems 2. Effects of Additive Manufacturing on Warehousing and Inventory Control 3. Changes in Procurement and Strategic Supplier Management in the new era of Additive Manufacturing 4. Supplier performance measurement and control in Additive Manufacturing 5. Conceptual Frameworks defining complex multivariate relationships in additive manufacturing processes and their influence on business performance 6. New practices and their factor variables related to Additive Manufacturing in networked manufacturing consortiums and their contribution to efficiency and effectiveness of Logistics and Supply Chain Management (multiple focussed topics can be formed in this research area) 7. New rules of Strategic supplier relationships in the era of Additive Manufacturing 8. Economics and Cost Savings achievable using Additive Manufacturing as a replacement of Subtractive manufacturing (multiple focussed topics can be formed in this research area in different industries) 9. Dynamic capabilities and Market orientation achievable using Additive Manufacturing 10. Competitive edge and advantage achievable using Additive Manufacturing 11. Excellence in engineering, processes, and tasks achievable using Additive Manufacturing 12. Continuous improvements in Industrial production, logistics, and supply chain management in the era of Additive manufacturing 13. Supply chain agility, flexibility, responsiveness, and resilience achievable in the era of additive manufacturing (multiple focussed topics can be formed in this domain) 14. Autonomy, socialisation, responsiveness, and proactiveness in demand fulfillment: new performance attributes in the era of Additive Manufacturing and Cloud Manufacturing (multiple focussed topics can be formed in this research area) 15. Orchestration and Synchronisation of Logistics and Supply Chain assets to allocate them where they are needed the most: Can Industry 4.0, IIoTs, Big Data Analytics, and Artificial Intelligence ensure optimal allocation of assets in Cloud Manufacturing? 16. Cloud Manufacturing network architecture for establishing global manufacturing network of 3D printing organisations 17. Supply chain excellence in additive manufacturing industry 18. A review of materials and their supply chains for additive manufacturing (multiple topics can be formed focussed on different industries) 19. Building Descriptive, Predictive, Prescriptive, and Automated decision-making capabilities in additive manufacturing using Artificial Intelligence (multiple topics in multiple industries are possible) 20. Advanced engineering for planning, designing, operating, controlling, and maintaining additive manufacturing processes in cloud manufacturing ecosystems (multiple focussed topics are possible) 21.Management, monitoring, and control of 3D printers and related production logistics machines and robots in network manufacturing 22. An evolving digital economy based on collaborative forums and consortiums for manufacturing, logistics, and supply networking using 3D printing collaborations 23. The role of machine learning and artificial intelligence in design validation of custom manufacturing projects in cloud-based additive manufacturing (multiple industry focussed topics can be formed in this research area) 24. Building agility and flexibility capabilities in globally spread manufacturing facilities in the additive manufacturing framework using Artificial Intelligence for cloud manufacturing 25.Changing materials supply chain management in the paradigm shift from subtractive to additive manufacturing 26. Architectural Design, Positioning and Interactions between Components, and Algorithms for designing an Industry 4.0 Ecosystem using artificial intelligence in additive manufacturing on Cloud Manufacturing ecosystems (multiple focussed topics can be formed in this research area) 27. Enablers of and barriers to additive manufacturing adoption in automotive parts manufacturing 28. Enablers of and barriers to adoption of cloud manufacturing by small and medium sized manufacturing organisations using additive and subtractive manufacturing systems 29. Collaborative supply chain network design for additive manufacturing in [Name of Country] (multiple country-specific focussed topics can be formed in this research area) 30. The advantages and limitations of additive manufacturing adoption in job shops producing sophisticated engineered parts 31. Models of service-oriented cloud manufacturing for 3D printing 32. Cloud manufacturing application designs for 3D printing (multiple technical topics can be formed) 33. Modeling of additive manufacturing resources allocation using smart contracts in cloud manufacturing blockchains 34. Security and Safety threats and risk management in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 35. Evolving roles of IT Management and IT Teams in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 36. IT Governance and Enterprise Risk Management in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 37. Enterprise Architecture designs and models in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 38. Quality Management standards, designs, and models in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 39. Information Security Management System and Privacy in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 40. Applying COBIT framework, NIST standards, and ISO 27000 series of standards in the era of additive manufacturing and cloud manufacturing (multiple focussed topics can be formed in this research area) 41. Rapid prototype development and testing processes in 3D printing industry for rapid product launch 42. Using augmented reality, digital twins, and digital siblings in additive manufacturing 43. Cloud Manufacturing designs for multiparty networking of 3D printing companies 44. Service-Oriented Manufacturing following the Cloud Manufacturing Model for networking of 3D printing companies 45. Design innovation and rapid prototyping using Cloud Manufacturing network of 3D printing companies 48. Cloud manufacturing innovations in the Industry 5.0 transition 49. Human-centric networked manufacturing using cloud manufacturing in the Industry 5.0 50. Hyper automation in Industry 5.0 using cloud manufacturing: enablers and barriers The above list is a representative set of research opportunities based on current and projected trends in designing, planning, adopting, implementing, operating, and controlling systems and processes in the domains of additive manufacturing networking using cloud manufacturing. Each of these practices may be supported by a number of underlying factor variables acting as mediators and moderators. One may consider studying these practices and their variables separately through in touch programmes (action research), organisational ethnography, in-depth interviews, focus group discussions, and phenomenology as in qualitative studies or investigating their interrelationships through hypothesis testing and testing of structural constructs (complex relationships models) in quantitative studies. Some of the above topics shall involve advanced algorithmic modeling and software development of smart industrial engineering systems using. OPNET Modeler and its Application Characterisation Engine for algorithmic interactions modeling is a suitable tool. This is a vast research area that requires significant contributions by students and professionals. additive manufacturing is still an evolving field requiring significant research efforts as there are few empirical models and constructs and related theories in this field. Please visit our page on Multivariate Statistical Modelling and Analysis for further details on analysing and optimising the measurement constructs. In addition to the suggestions above, please contact us at consulting@eproindia.com or consulting@eproindia.net to get more topic suggestions and to discuss your topic. We will be happy to assist you in developing your narrow research topic with an original contribution based on the research context, research problem, and the research aim, and objectives. Further, We also offer you to develop the "background and context", "problem description and statement", "aim, objectives, research questions", "design of methodology and methods", and "15 to 25 most relevant citations per topic" for three topics of your choice of research areas at a nominal fee. Such a synopsis shall help you in focussing, critically thinking, discussing with your reviewers, and developing your research proposal. To avail this service, Please Click Here for more details. In the first and second parts of this article, you will find many more research areas and opportunities that are still highly pursued in higher education in the field of supply chain management and its associated domains. 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