Suggested Dissertations and Thesis Research Topics in Industry 4.0, Industrial Internet of Things (IIoT), Big Data Analytics, and Artificial Intelligence in Supply Chain Management, Inventory Management, and Logistics Keywords applicable to this article: dissertation, thesis, topics, Industry 4.0, Industrial Internet of Things, Big Data Analytics, and Artificial Intelligence in logistics, supply chain management, smart manufacturing, production, transportation capabilities, logistics, warehousing capabilities in the Industry 4.0 framework using Cyber Physical Systems enabled by Industrial Internet of Things and Industrial Internet in a Global Value Chain Networking 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. Mobile Friendly Version I am happy to present the third part of the article on dissertation and thesis topic development in the fields of Supply Chain Management, Inventory Management, and Logistics. In this article, I have presented the evolving research areas of Industry 4.0, Industrial Internet of Things, Big Data Analytics, and Artificial Intelligence in these fields. This is an extension of our original two articles in logistics and supply chain management, accessible through the following links: Link to the first part of this article , Link to the second part of this article and Link to fourth part of this article for topic development in the areas of logistics and supply chain management, procurement management, and inventory management, sustainability, lean and agile capabilities, dynamic capabilities, machine learning and artificial intelligence in Industry 4.0 and Industry 5.0, blockchains, smart contracts, additive manufacturing (3D printing), and cloud manufacturing. This article explores many newer topics of research in supply chain management and its associated domains categorized under four broad research areas: Industry 4.0, Industrial Internet of Things (IIoT), Big Data Analytics, and Artificial Intelligence. Each area presents opportunities for studying a number of practices and the factor variables (both mediators and moderators) associated with it, and their interrelationships. 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, and System Dynamics Modeling in Vensim. Please visit our page on Multivariate Statistical Modelling and Analysis for further details on analysing and optimising the measurement constructs and OPNET Network Modeling and Simulation Services for further details on scope of such topics in Modeling and Simulations. 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) Industry 4.0 Industry 4.0 is the name coined to the fourth industrial revolution. The third industrial revolution was about digitising manufacturing, operations, logistics, and supply chain management. This revolution is about enhancing their digital capabilities through integration of digital systems, convergence of cyber and physical systems (cyber-physical systems; CPS) , real-time visibility, predictability, self-awareness (cognitive abilities), location awareness, and artificial intelligence. The industrial automation and integration achieved through digitisation in the third industrial revolution involved proprietary technologies developed under three categories: Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition Systems (SCADA), and Distributed Control Systems (DCS). PLCs were designed to integrate a large number of sensors and actuators digitally using industrial bus designs and industrial digital communication protocols. SCADA was designed to integrate PLCs and DCS was designed to integrate SCADA systems as well as PLCs capable of running protocols for distributed control signalling. The primary domain in industrial automation and integration comprises of industrial sensors and actuators. It is called the sensor/actuator plane. The sensor/actuator plane has evolved through legacy process control and engineering systems; a number of proprietary protocols were designed decades ago (like, LONWORKS, BACNET, MODBUS, DC-BUS, DIRECTNET, OPC, DYNET, DNP3, XAP, CAN, etc.) that are still operational in many industrial and commercial applications. Please keep in mind that Industry 4.0 will largely not replace many of these. These protocols will remain in operation as is for many years from now. Hence, their knowledge will not be lost or rendered useless in the Industry 4.0 era. Many SCADA and DCS systems are still operating them for controlling millions of sensors and actuators. TCP/IP entered into process engineering in mid 90s when Embedded Java Beans (.JAR files) was developed by open source communities. Many of the sensors and actuators were consolidated into protocol converters before getting connected to SCADA and DCS. These protocol converters could convert signalling from proprietary protocols on RS232 or similar interfaces to .JAR files streams transmitted over TCP/IP links (Ethernet, Token Ring, FDDI, ATM, X.25, Frame Relay, etc.). Earlier, the protocol converters and the DCS/SCADA servers only were assigned IPv4 addresses given the limited address space of IPv4. With the advent of IPv6, now the sensors/actuators are also assigned individual IP addresses. This change has made monitoring and controls more effective and accurate because of plotting of sensors/actuators on 3D maps. This is the only change that has occured after introduction of the Industrial Internet of Things. The Industrial Internet of Things (IIoT) is more of an invention than an innovation. The name was given to the already running "physical" systems for decades, when the massive scale address space of IPv6 and its transmission control protocol was invented. Appropriate protocol conversion technologies from the proprietary industrial engineering protocols (LONWORKS, BACNET, MODBUS, DC-BUS, DIRECTNET, OPC, DYNET, DNP3, XAP, CAN, etc.) to TCP/IPv6 helped in transforming the "physical" systems into "cyber-physical" systems. RFID attachments to sensors/actuators and wireless sensor network protocol (ZigBee, based on IEEE 802.15.4) are the key innovations in its domain. IIoT architecture has four planes: The sensor/actuator plane, the data acquisition and control systems plane (primarily SCADA and DCS), the IT systems plane for pre-processing and any preliminary analytics (Edge IT plane), and the core IT systems for data warehousing, advanced data analytics and visualisations, and remote control apps (Core IT plane). All these planes have been used in for decades. In modern systems, the only change is that all these machine-to-machine communications are now done over IPv6 and the data analytics have come out of the traditional ACID-based structured relational databases controlled by SQL programming to become unstructured and non-relational big data databases controlled by Not-Only-SQL (NoSQL) programming. The dissertations and theses research topics in Industry 4.0 may be either in-depth industrial engineering designs and their simulations, or exploring many new factor and control variables that tie up very well with the traditional variables of the third industrial revolution (new conceptual frameworks or empirical formulations involving both new and old concepts), or exploring and confirming complex structural constructs showing significant influencesof Industry 4.0 factor and control variables on performance and behavioural variables in industrial engineering, or 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. The concepts of Global Value Chain through Digital Transformation and the role of Industry 4.0 framework 2. The changing roles of intelligent robotics and machinery control systems as Cyber-Physical Systems (CPS) in the Industry 4.0 framework 3. The evolving empirical models of Industry 4.0 and their applications 4. Designing and operating a global augmented reality architecture for integrating the five core Industrial Engineering Disciplines: Production, Inventory Control, Logistics, Supply Chain/Network Management, and Transportation. 5. Quality Assurance in Industry 4.0 6. Role of Industry 4.0 framework in 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.) 7. Role of Industry 4.0 in eliminating production defects, reworks and returns, logistics errors, and transportation accidents 8. Human resources challenges in Industry 4.0: HR policies, training and development of skills, and relationships between employers and employees 9. Aligning every policy, process, and tasks to the voices of customers 10. Role of Industry 4.0 in lean, agile, responsive, and flexible logistics and supply chain/network management 11. Role of Industry 4.0 in developing, enhancing, and maturing dynamic capabilities in the five core Industrial Engineering Disciplines: Production, Inventory Control, Logistics, Supply Chain/Network Management, and Transportation 12. Designing robust communication networks for Industry 4.0: few examples are - 5G LTE, Heterogeneous Networking (WiFi and ZigBee integration with 5G; Femtocells, and Local Area Networks for Sensors and Actuators on IPv6 such as 6LoWPAN, Near Field Communications, RFID, LPWAN, and Z-Wave), Gateway routing for Industrial Internet of Things, Network Aggregators, and Core Networking integration with Big Data and Artificial Intelligence Servers 13. Industrial Internet and Industrial Cloud for the Global Industry 4.0 framework 14. The evolving concept of Autonomous Robots and the Operator 4.0 integration with them within an Augmented Reality under Industry 4.0 15. Strategic Supplier Management in the Global Industry 4.0 framework 16. Smart Cellular manufacturing for building flexibility and dynamic capabilities in the Global Industry 4.0 framework 17. The Models of Early Awareness, Self-Configutation and Self-Optimisation, and Predictive Maintenance in the Global Industry 4.0 framework 18. Additive Manufacturing and 3D Printing technologies in the Global Industry 4.0 framework 19. Networked Manufacturing Designs and Operations, Inter-company integration, Holistic Digital Transformations and Engineering, and Intelligent Monitoring and Control SYstems in the Global Industry 4.0 framework 20. Smart Factory Modeling through integration of all Industrial Engineering disciplines using Industrial Cloud Computing and Industrial Internet in the Global Industry 4.0 framework 21. Smart Security for Smart Factories: building multi-layer deep cyber defense systems for protecting Industrial Cloud Computing and Industrial Internet in the Global Industry 4.0 framework 22. Building resilience to global supply chain disruptions in Industry 4.0 using continuous data collection and real-time trends radar systems 23. Real time visibility into global supply chain accidents and rapid rescheduling of consignments in the Global Industry 4.0 framework 24. Knowledge-driven smart predictive analytics of future risk events in global supply chains in the Industry 4.0 framework 25. Monitoring vital functions and machineries in a global supply chain in the Industry 4.0 framework 26. Horizontal and Vertical integration of smart manufacturing systems in Global Value Chain Networking in the Global Industry 4.0 framework 27. Continuous Engineering across the Value Chain in the Global Industry 4.0 framework 28. Accelerated Additive Manufacturing in a digitally transformed ecosystem of products and services following co-design, co-creation, and co-testing with smart partners and infuencers in the Global Industry 4.0 framework 29. Collaborative logistics, machine tools, and services-oriented supply chain services in the Global Industry 4.0 framework 30. Contracting and Negotiation for formation of Collaborative Virtual Organisation delivering Virtual Design and Engineering services in the Global Industry 4.0 framework 31. Challenges and solutions in collaboration among smart factories in the Global Industry 4.0 framework 32. Combining global supply networks through smart IIoT interactions in the Global Industry 4.0 framework 33. Building collaborative global supply networks with cyber physical systems and data rich analytical environments in the Global Industry 4.0 framework 34. Building a community of machines and their interactions with community of operators by strategically integrating machine-to-machine and human-to-machine communications in the Global Industry 4.0 framework 35. Building sustainability in global supply chains through dimensions of sensing and actuation of key triple-bottomline variables defined in the Global Reporting Initiative 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 Industry 4.0 framework. 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. OPNET Modeler and its Application Characterisation Engine for algorithmic interactions modeling is a suitable tool. In addition, modules can be written in Java, Python, or C++ following software development approach. This is a vast research area that requires significant contributions by students and professionals. Industry 4.0 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. 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 the first part of this article , second part of this article , and fourth part of this article for exploring more areas of logistics and supply chain 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) Industrial Internet of Things (IIoT) When capability of Internet connectivity is added to physical devices (such as household and utility devices), they are called Internet of Things (IoT). When the devices are attachments to industrial machinery, robotics, equipment, machine tools, service nodes, materials, control systems, and even workers, then they are called Industrial Internet of Things (IIoT). IIoT is viewed as an integral system in the Industry 4.0 framework. The Cyber-Physical Devices are more appropriately referred to as Cyber Physical Systems (CPS) in industries. IIoT can transform the physical systems into smart systems capable of collaborating, teaming, communicating, reporting, autonomous operations, and decision making. The key performance attributes of IIoT and IIoT-based systems are: cost effectiveness, low power consumption ensuring longer battery lives (six months to multiple years), high connections density, communication ranges sufficient to cover a plant area, efficient routing algorithms (like, Ant Colony, Greedy, Diffusion, SAR, GAF, etc.), low processing and storage capacities, capable of sustaining high latency shocks, and simple networking protocols and architectures for communications over IPv6 (commonly used are: ZigBee, 6LoWPAN, Near Field Communications, RFID, LPWAN, and Z-Wave). IIoT-enabled devices operate in two planes: Sensing and Actuating (please see some details in the introduction of Industry 4.0 above). The Sensing plane is designed to collect data from the attached physical systems related to individual process variables monitored by distant monitoring systems. The Actuating plane is designed to issue actuation commands to the physical systems received from distant control systems. The monitoring and control systems are integrated through an in-depth decision-making logic based on Artificial Intelligence using Machine Learning algorithms. Some decisions are issued by human operators operating as Operators 4.0 within an augmented reality space. However, the scope of human decision-making is reducing amidst digital transformation, automation, and smartness of integrated manufacturing systems. The manufacturing systems and their controlling processes are carefully integrated in a hierarchical fashion such that every process can utilise globally dispersed resources owned by a single large manufacturing organisation or by a consortium of manufacturers hooked to the cloud manufacturing system. IIoTs have major roles to play in this design. The CPS devices in manufacturing plants can exchange loads of data and consolidate them at strategically located big data repositories using Machine-to-Machine communications (M2M). Wherever manual intervention by operators working in the augmented reality setup is desired, Human-to-Machine (H2M) communication channels are opened. Mostly, human operators get access to cognitically aware system dynamics and control systems providing direct access to complex time-series enabled reporting for issuing bulk actuation commands. The bulk actuation commands are then splitted into thousands of individual actuation commands issued to the IIoTs for executing their respective actuation tasks. Again, the Automata running the entire system may not permit the bulk commands if they find conflicting actuations embedded into the algorithm. The Automata follows clearly defined deeply embedded rules that helps in identifying erroneous command sets or malicious attempts by industrial hackers. This is where the question arises: who defines those rules and how perfect they are to prevent minor or major industrial catastrophes? In modern IIoT-enabled cloud manufacturing systems, the generation of rules is also automated using Artificial Intelligence based on continuous learning from diverse mobile data captured in massive volumes in real time. In Industry 4.0 framework, IIoTs can be attached to every industrial engineering system involved for ensuring integrated manufacturing and delivery. For example, IIoTs may be attached to the conveyor belt system, internal mobile cranes, internal mobile carrier of packages, internal storage bays, and to the packages. A 3D model of the entire warehouse may be developed and dynamically configured to capture every dockings, undockings, additions, removals, and movements in the warehouse. An inventory management software with augmented reality may be provided to the operator in such a way that it can interact with all the equipment operating in the warehouse and also with the packages arriving and despatching. Now, if the operator decides to change the priority rating of a set of packages, he/she simply needs to change the assignments by clicking those packages in the 3D space and assigning the values through a floating menu. As soon as this change is made, the entire smart inventory management system will realign its operations to execute the changed priority ratings. More resources will be assigned to those packages automatically. A massive matrix of sensing and activation information will be varied by the smart inventory management software to execute a simple decision-making by the operator. Many such scenarios can be imagined related to role of IIoTs in the Industry 4.0 framework. The production robotics can be made more ergonomically and cognitively aware by attaching IIoTs to every mobility and activation functions of a robot. Shape changing robotics designed to complete multiple industrial production tasks can be created and allocated to hundreds of queue processors under an Industry 4.0 compliant manufacturing plant. When an operator changes priority levels of a production queue, more robots can be allocated to it by simply changing their shapes and enabling them to complete the queue faster. The academic research studies for dissertation and research projects should focus on a narrow and focussed problem area. Hence, the topics related to IIoTs may be focussed on a specific process, technology, or automation challenge, or on specific variables related to IIoTs and their influence on known empirical variables (such as, performance variables of a supply chain). You may also combine Industry 4.0, IIoTs, and Big data in your research as long as the topic is focussed on a sufficiently narrowed research problem. Following are some of the suggested topic areas related to role of IIoTs in Industry 4.0 are the following: 1. Study of empirical reference architectures for integrating IIoTs with Industry 4.0 architecture 2. Modeling IIoTs and Industry 4.0 integration following the theories of Enterprise Architecture 3. Cognitive and Ergonomically-Aware designs of Industrial and Logistics Robotics using the IIoTs 4. Deploying IIoTs to build Augmented Reality for Operator 4.0 in Industry 4.0 5. Role of IIoTs in predictive forecasting and analytics, and real-time controls on supply chain performance variables 6. Effects of IIoTs and Industrial Internet on key performance variables related to effectiveness and efficiency of all Industrial Engineering disciplines: materials planning, materials handling, procurement, production, logistics, transportation, and distribution 7. Employing IIoTs for real-time visibility and controls in inventory management for capturing and meeting the demands effectively 8. Using IIoTs for eliminating order rationing, beer gaming, and bullwhip effect in retail supply chains 9. Using IIoTs for real time performance monitoring and preventive maintenance of machines and robotics 10. Using IIoTs for enhancing safety standards and prevention of industrial accidents in Industry 4.0 11. Contextualising Scenarios, Monitoring Situations, and Acting on predictive alerts and alarms - the foundations of predictive analytics for industrial safety using IIoTs 12. In-gateway and in-device analytics services - adding distributed cognitive abilities to IIoTs in Industry 4.0 13. Enhancements of reliability and performance of industrial assets in plants and machineries using IIoTs in Industry 4.0 14.Enhancements of quality, reliability, and performance of industrial processes and products using IIoTs in Industry 4.0 15. A study of use cases of using IIoTs in B2B industrial manufacturing and job working contracts 16. The relationships between Industrial Agility and Responsiveness and Adoption of Digital Transformation using IIoTs 17. Rules-based preventive maintenance and management of Industrial Assets (Machineries and Robots) using IIoTs 18. Understanding, Reasoning, and Learning based on data collected from IIoTs: Artificial Intelligence for Industrial Automation in Industry 4.0 19. Role of IIoTs in sustainable manufacturing and sustainable supply chain management 20. Using IIoTs for enabling cognitive and location-aware capabilities in industrial transportation 21. Integrating IIoTs with cloud computing for cloud manufacturing applications 22. Optimising health of equipment and safety of workers using integrated cyber-physical systems enabled by IIoTs 23. Protecting identities and authentication of cyber-physical systems enabled by IIoTs 24. Building trust networking of cyber-physical systems enabled by IIoTs 25. Building and managing a dynamic mesh topology supply chain network using cyber-physical systems enabled by IIoTs 26. Preventing attacks on Industrial Internet and cyber-physical systems enabled by IIoTs 27. Policy-driven network functionalities for building a dynamic supply networking using cyber-physical systems enabled by IIoTs 28. Unmanned Aerial Vehicles (Drones) for remote controlled supply chain distributions using cyber-physical systems enabled by IIoTs 29. Using IIoT-enabled unmanned aerial vehicles (drones) in Industry 4.0 30. Digital transformations in supply networking using cyber-physical systems enabled by IIoTs 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 Industrial Cyber Physical Systems using Industrial Internet of Things. 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. 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. Industry 4.0 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. (C) Big Data Analytics and Artificial Intelligence in Industry 4.0 In the Industry 4.0 framework, Big Data Analytics and Artificial Intelligence have very crucial roles at the backend of the industrial systems. Big data refers to the data-intensive technologies capable of collecting and processing massive-scale volumes of data with high value, high variety, high veracity, and high velocity. Big data requires new techniques of data modeling and new designs of data holding and transmission infrastructures and services. Data collected is holistic in nature; from all lifecycle stages of processes and the variables controlled by them. The original concept of big data was to capture every possible form of data, like online transaction processing (OLTP) systems (such as ERP, CRM, MRP, and SCM applications), decision support systems (examples, batch queries and reports), structured data formats (like, data files, database objects, comma or tab-separated, and spreadsheets), data generating machines (like, point-of-sale devices, ATM machines, sensors, smart scanners, RFID, and smart metering), and unstructured data formats (like, images, word processing files, video and audio files, blogs, e-mails, social media, and Internet). Hence, roles of SQL-oriented databases (Oracle, SQL Server, and MySQL) and Not-Only-SQL-oriented databases (Hadoop File System, map Reduce, MongoDB, HBase, Cassandra, and ZooKeeper) were planned to be merged. However, as this technology has evolved it appears that Not-Only-SQL (NoSQL) has taken precedence over SQL databases significantly. New data analysis technologies, such as massive-scale parallel processing on cloud computing, and virtual in-memory analytics have also evolved. Now the question arises is what was so much original and unique about Big Data Analytics that was not available in traditional data analytics systems like Business Intelligence, Data Warehousing, and Multidimensional reporting of Online Analytical Processing (OLAP)? To understand the difference, the perspectives of the traditional database administrator and the traditional data warehousing ETL (Extraction - Transformation - Loading) processor would be needed. The secret is hidden in an abbreviation called ACID (Atomicity - Consistency - Isolation - Durability). ACID is the core standard that every relational database management system needs to comply with; irrespective of the size of the databases. All ACID compliant OLTP databases were designed to commit a unique transactional record in a data field, which overwrites the older (obsolete) record in that field. This clearly meant that ACID compliant databases were not designed to maintain historical time-stamped records. Thus, OLTP databases were not designed for data analytics as you could only get the latest committed records from them. This gap was solved by the database administrators by maintaining backups of what is called in-memory "Redo Log Segments" or "Rollback Segments" or "Transaction Log Segments". These segments maintained details of all the previous commits into the data fields with timestamps. Using these segments, the database administrators could restore the database state at a particular time of failure should any corruption occurs after that time. Given that these segments were formed inside the memory, their sizes were limited by the available RAM in the server. Thus, when any segments were filled up the oldest records were deleted automatically to make room for the latest records. To protect the historical records, the database administrators used to design scripts for writing these segments into the hard disk before they are overwritten. The segments become static after getting written into the hard disks and hence were called "Redo Log Archives" or "Rollback Archives" or "Transaction Log Archives". In heavy duty OLTP applications, these segments gets filled up too frequently and hence archiving was enabled as a continuous feature. All the Redo Log, Rollback, or Transactional log archives were part of the incremental backup strategy. Ideally, a database administrator would take one full backup (all data files, control files, and procedures) and several incremental backups (of these archives) daily in tape libraries. These backups were the bread and butter for the Decision Support Service (DSS) specialists. For decision support, these backups were restored on separate servers and the data warehousing specialists used the ETL processing to build time-series data strings (tables with timestamped records) for every data type. The time-series data strings were packaged into multi-dimensional cube reports in a presentation system called "Online Analytical Processing (OLAP)". These time-series data strings in the form of OLAP cube reports were used for various decision-support tasks, like sales forecasting, products and market performance assessment, operations performance assessment, customer satisfaction measurements, promotional planning, future planning, forming new business strategy, etc. However, ETL was such a slow and tedious process that it may take weeks for the data analysts to get access to their latest outcomes. This means that there was always a time lag of weeks to a month between the OLTP and DSS databases. The bottomline: there was predictive analytics and future planning to some extent but no such capability enabling real-time visibility into the business. The time lag between the OLTP and DSS databases was acceptable because markets and competition were sluggish to changes with very less dynamism. With rapid dynamism caused by rapid changes in the markets, customers' expectations, disruptive innovations, and competitive landscapes, businesses realised that the ETL-enabled decision-support systems (OLAP, business intelligence, and data warehousing) provided them analytics reports too late to respond to the rapid dynamism in the markets, customers' expectations, disruptive innovations, and competitors' activities. Further, the scope of ETL was limited because of high hardware and storage costs and limited real-estate spaces provided to the data centre infrastructures. A replacement of ETL was needed in the OLTP database itself such that the entire ETL process can be replaced by some kind time series data readiness within the transactional databases with capabilities to build OLAP cubes within the memory used by the databases. The Not-Only-SQL Big Data system is the new innovation that has made it possible. The Hadoop, HBase, MongoDB, and Cassandra database systems have a feature that new data records do not overwrite the older ones but get appended to them tied to their respective date and timestamps. This concept may be visualised as "Data Streaming" instead of "Data Commits". This feature defies ACID compliance, but ensures that time series of each data type is readily available within the database itself and OLAP cubes can be dynamically built within the memory of the running databases. Thus, multi-dimensional reports in the OLAP cubes are now readily available at the same time when the transactions are happening making the dream of real-time visibility into the business a reality. However, it can be interpreted readily that Big Data cannot be the business of the standalone server systems whatever capacities they are provided. Even the cluster computing solutions will be insufficient after some time to hold the Big Databases. Seemingly, businesses needed endless computing power, endless data storage capacities, and endless memories. The solution was Virtualisation and Cloud Computing. Please visit our page Modern IT Systems Topics to learn about research opportunities in Virtualisation solutions and Cloud computing. Virtualisation and Cloud computing had some distinct features that supported Big Data: unlimited hardware can be clustered to form unlimited pools of memory, CPU, storage, and local area networking, and any hardware can be hot swapped while the cloud is running. Clouds can be scaled to indefinite capacities as big databases grow. Modern cloud computing services offered by Amazon (Elastic Compute), Google (Apps and App Engine), IBM (Blue services), Microsoft (Azure) etc. are capable of hosting big databases for global manufacturers, retailers, logistics service providers, supply chain service providers, etc. in their Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offers. Several Software as a Service (SaaS) applications for big data analytics are available on the cloud computing marketplaces at unimaginable low costs. In fact, even small businesses can also gain access to big data applications those couldn't have ever been able to afford the costs of ETL infrastructures. Big databases can also be limited later to build archives after a period (like, after five years) because the increasing levels of dynamism in the marketplaces and competitive landscapes might make data strings obsolete after such a period. One of the capabilities added to the big data analytics applications is the Artificial Intelligence. In simple terms, Artificial Intelligence may be viewed as the Machine Learning capability provided to the big data analytics applications enabling them to automatically analyse time series data and povide decisions or suggestions. Artificial Intelligence can also organise and categorise data types by calculating semantic distances. Algorithms like Naïve-Bayes, K-Nearest Neighbours, Support Vector Machines, and Random Forests can automatically categorise data into classes based on Input Feature Vectors and Biases defined by the AI programmers. More advanced algorithms, like Deep Learning, Recurring Neural Networks (RNNs) with or without Long-Short-Term Memories (LSTMs), Convolutional Neural Network (CNNs) and Deep Boltzmann machines (DBMs) are capable of providing predictive values of entire data sets based on comparisons between their historical data values (training data) and current data values (test data). In Industry 4.0, Artificial Intelligence has been assigned a higher role as they can automatically analyse time series data strings collected from the sensors and send actuation commands to the machineries and robotics. Industry 4.0 has allowed Artificial Intelligence to take control over industrial controllers (PLCs, SCADA, and DCS), over performance monitoring and maintenance of equipment, and over quality assurance. Artificial Intelligence can interact with human operators through natural language processing. In the process of researching the role of AI in the Industry 4.0 framework, you will need to know the current challenges that manufacturing organisations are facing in fully leveraging its capabilities. First of all, it needs to be accepted that AI is not like human brain by any means: both structurally and functionally. AI does not process information the way human being does. For example, you cannot train a human brain with big data as most of it will be forgotten by the human memory. Humans will always make decisions based on intuitions, perceptions, biases, and assumptions. AI can only recognise patterns of data shown to it (training data) such that it highlights the ones "most occuring" in the training data sets in their predictive outcomes. Quite naturally, if AI is trained on data sets reflecting human perceptions, it will simply "highlight the perceptions having highest frequencies". This means that that the accuracy of AI predictions totally depends upon the how the categories and classifications in the training data have been defined. Thus, if the AI system of a company is giving inaccurate predictions, it is not at fault; rather the data engineers feeding the categories and biases into its training data set are to be blamed. Given that those categories and classifications are defined through inputs from human perceptions and biases, the intelligence of AI cannot be shielded from them. Simply stated, AI can be as good or as bad as the human intelligence governing its "training program". Hence, the decisions made by AI should be vetted by human experts, especially in applications having high risks to safety and security, and to quality assurance of products. In manufacturing, logistics, and supply networking, every AI system needs to traverse a maturity path before it is given autonomy of activations. AI will mature with hundreds of cycles of learning, each time with newer data. Hence, AI needs a massive fleet of Industrial Internet of Things for collecting data continuously for its continuous training and enabling it to improve continuously. Further, it needs to be realised that an AI system cannot be made multi-disciplinary. For example, an AI trained for ten years in car manufacturing cannot be used in the fast food industry. In fact, AIs trained in one company in an industry may not do well in other companies in the same industry unless they are allowed to "unlearn" certain things and "relearn" their replacement facts. When a manufacturing company installs AI for the first time, the management should treat it as a software with only the industry standard baselines preloaded and should not expect it to deliver results quickly. Perhaps, perfection of AI may take longer than what ERPs used to take earlier. Cloud computing may accelerate it though as it offers ready access to more than a quintillion bytes of knowledge about manufacturing processes. Even medium-sized companies can access that data for rapid training of their AI systems. However, all the decisions made by AI should be vetted by their older experts till the time AI has learned sufficiently from their own manufacturing environments using the data collected by the IIoTs in big databases. The researh opportunities in applications of Big Data and Artificial Intelligence in Industrial Engineering 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. Incorporation of Static and Mobile agents in Big Data processing in Logistics and Supply Chain systems 2. New set of performance measures, indicators, their measurement methods using Big Data Analytics and Artificial Intelligence in Logistics and Supply Chain Management (multiple focussed topics can be formed in this research areas) 3. New ways of supplier performance measurements using Big Data Analytics and Artificial Intelligence (multiple focussed topics can be formed in this research area) 4. Designing a life cycle of Big Data Analytics and Artificial Intelligence for Supply Chain performance measurements: planning, implementation, monitoring, control, and reporting 5. Designing and testing of Conceptual Frameworks defining complex multivariate relationships between the enabling factors of Big Data Analytics and Artificial Intelligence and the variables related to Logistics and Supply Chain performance attributes 6. New practices and their factor variables related to Big Data Analytics and Artificial Intelligence 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 IIoTs, Big Data Analytics, Artificial Intelligence, and Industry 4.0 8. Economics and Cost Savings achievable using Big Data Analytics and Artificial Intelligence 9. Dynamic capabilities and Market orientation achievable using Big Data Analytics and Artificial Intelligence 10. Competitive edge and advantage achievable using Big Data Analytics and Artificial Intelligence 11. Excellence in engineering, processes, and tasks achievable using Big Data Analytics and Artificial Intelligence 12. Continuous improvements in Industrial production, logistics, and supply chain management using Big Data Analytics and Artificial Intelligence 13. Supply chain agility, flexibility, responsiveness, and resilience achievable using Big Data Analytics and Artificial Intelligence 14. Autonomy, socialisation, responsiveness, and proactiveness in demand fulfillment: new performance attributes in the era of Industry 4.0, IIoTs, Big Data Analytics, and Artificial Intelligence (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? 16. Predictive Analytics and Real-time visibility into the supply chain echelons: How IIoTs, Big Data Analytics, and Artificial Intelligence are shaping supply chain performance under Industry 4.0? 17. Achieving Capability Maturity in strategic data management and operations data analysis using Big Data Analytics and Artificial Intelligence 18. Achieving the scientific level of data-driven business by building centres of excellence in Logistics and Supply Chain Management using Big Data Analytics and Artificial Intelligence 19. Building Descriptive, Predictive, Prescriptive, and Automated decision-making capabilities in Industry 4.0 settings using Big Data Analytics and Artificial Intelligence 20. Advanced data engineering skills required in Industry 4.0 for planning, designing, operating, controlling, and maintaining Big Data Analytics and Artificial Intelligence systems in modern data-driven industries 21. Studying the digital transformations of the traditional operations and controlling models in manufacturing, logistics, and supply chain management using Big Data Analytics and Artificial Intelligence (multiple focussed topics can be formed in this research area) 22. An evolving digital economy based on collaborative forums and consortiums for manufacturing, logistics, and supply networking using Big Data Analytics and Artificial Intelligence 23. Contextualising and Conceptualising big data using artificial intelligence in the Industry 4.0 framework related to all the Industrial Engineering Disciplines (multiple focussed topics can be formed in this research area) 24. Building agility and flexibility capabilities in globally spread manufacturing facilities in the Industry 4.0 framework using IIoTs, Industrial Internet, Big Data Analytics and Artificial Intelligence 25. New organisational cultures and employee performance monitoring and control systems using Big Data Analytics and Artificial Intelligence in the Industry 4.0 framework 26. Architectural Design, Positioning and Interactions between Components, and Algorithms for designing an Industry 4.0 Ecosystem using IIoTs, Industrial Internet, Big Data Analytics and Artificial Intelligence (multiple focussed topics can be formed in this research area) 27. Subscription models and selection of services in Cloud Computing for Big Data Analytics and Artificial Intelligence in the Industry 4.0 28. How traditional industries can transition to the Data-Driven Ecosystem by adopting Science and Technologies enabling data-intensive and data-centric manufacturing, logistics, and supply chain management models (multiple focussed topics can be formed in this research area) 29. How Big Data Analytics and Artificial Intelligence can be modelled to achieve an Ecosystem of Structured, Semi-Structured, and Unstructured Data Systems for Logistics and Supply Chain applications (multiple focussed topics can be formed in this research area) 30. Identifying and illuminating digital shadow zones in Industry 4.0 using Big Data Analytics and Artificial Intelligence (digital shadow zones, formed mostly due to communication shadows, are serious problem areas identified to be addressed by Industry 4.0) 31. Changing organisational structures and management models in the era of IIoTs, Industrial Internet, Big Data Analytics and Artificial Intelligence 32. Complex Events Processing and Visibility in Logistics and Supply Chain Management using Big Data Analytics and Artificial Intelligence 33. Monitoring and Controlling unpredictable mobility of assets and events using Big Data Analytics and Artificial Intelligence 34. Security and Safety threats and risk management in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 35. Evolving roles of IT Management and IT Teams in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 36. IT Governance and Enterprise Risk Management in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 37. Enterprise Architecture designs and models in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 38. Quality Management standards, designs, and models in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 39. Information Security Management System and Privacy in the era of IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (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 IIoTs, Industrial Internet, Big Data Analytics, and Artificial Intelligence under Industry 4.0 framework (multiple focussed topics can be formed in this research area) 41. The scope and feasibility of transportation fleets with AI-enabled automatic drivers and automatic traffic control in localised supply networks 42. The scope and feasibility of fleets of Unmanned Aerial Vehicles with AI-enabled automatic navigation and air traffic control 43. Models for designing training data sets for AI for logistics and supply chain automation (multiple focussed topics can be formed in this research area) 44. Designing an augmented reality environment for testing AI-driven vehicles within a facility handling large-scale in-plant movements 45. Designing an augmented reality environment for testing collision-avoidance of AI-controlled robotic movements delivering parcels to despatch collection channels 46. Investigating AI-driven models of warehouses and despatch centres free of any on-floor human involvement 47. Investigating the designs and models for mapping physical locations to virtual reality 3D models for achieving optimum augmented reality accuracy for movement of in-plant vehicles 48. Investigating the designs and models for locating consignments in a large-scale container depot or shipping yard the augmented reality replicas of the physical locations 49. Developing an architecture and automation algorithm for a consortium of suppliers supplying to common customers through a market exchange 50. Role of Big Data Analytics and AI in shaping the economic and operational sustainability of a global manufacturing organisation 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 Industrial Big Data Analytics and Artificial Intelligence for Industrial Automation. 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. 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. Industry 4.0 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. You may like to access the article by clicking the following link: Link to the first part of this article and Link to the second part of this article for topic development in the areas of logistics and supply chain performance, integration, aggregation planning, effectiveness, efficiency, IT and technologies in supply chain management, cloud supply chains and manufacturing, lean, six sigma, and sustainability. PreviousArticle Next Article Copyright 2020 - 2026 EPRO INDIA. All Rights Reserved