Table Of Content

Business Analytics for Top Technology Innovations 1st Edition, 2018 By Sumit Chakraborty Scope System Strategy Skill-Style-Support Structure Preface Staff-Resources Security Smart grid Solar power Electrical & & AI electronics hybrid vehicles Artificial Biomedical Cancer rainfall instrumentation prevention IIoT, Astronomical SCADA, ICS hazards Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 1 Preface Deep analytics does not only mean statistics or data mining or big data analytics, it is a complex multi-dimensional analysis through ‘7-S’ model based on rational, logical and analytical reasoning from different perspectives such as scope, system, structure, staff-resources, skill-style-support, security and strategy. This e-book presents a deep analytics model through a consistent and systematic approach and highlights its utility and application for reasoning seven technology innovations today: (1) solar power electronics, (2) electrical and hybrid vehicles, (3) Smart grid, (3) IIOT for SCADA and industrial control system, (4) Biomedical instrumentation, (5) Cancer prevention mechanism, (6) Artificial rainfall & cloud computing and (7) Real-time search for astronomical hazards. The reality is that every stakeholder is impacted by the challenges and opportunities of innovation ecosystems today. The concept of deep analytics is still relatively new; it has now emerged as a powerful tool for business analytics and a real world theme in the modern global economy. The target audience of this e- book includes academic and research community, corporate leaders, policy makers, administrators and governments, entrepreneurs, investors, engineers, producers and directors interested in production of documentary films, news and TV serials. I am excited to share the ideas of deep analytics with you. I hope that you will find them really value adding and useful and will share with your communities. It is a rational and interesting option to teach business analytics in various academic programmes of Electrical and Electronics Engineering (e.g. B.Tech, M.Tech, B.E.E, M.E., Ph.D.) and also business management (e.g. Technology Management, Strategic Management and Analytics for BBA, MBA, PGDM, PGDBM). This e-book is the online version and the summary of the original draft, Edition 1 dated 24. 07. 2018: published by Business Analytics Research Lab, India; Price : Rs. 10,000 (per copy of online version). This e-book contains information obtained from authentic sources; sincere efforts have been made to publish reliable data and information. Any part of this book may be reprinted, reproduced, transmitted or utilized in any form by any electronic, mechanical or other means, now known or hereafter invented, including photocopying, microfilming and recording or in any information storage or retrieval system with permission from relevant sources. Sumit Chakraborty Fellow (IIMC), Bachelor of Electrical Engineering (JU), Business Analytics Research Lab, India 24 July,2018 Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 2 Content SL no. Book Chapters Page no. Part I 1 Deep analytics for technology innovation management Part II 2 Solar power electronics : Deep analytics 3 Electrical & hybrid vehicles : Deep Analytics 4 AI enabled smart grid : Deep Analytics 5 IIOT, SCADA & ICS Part III 6 Cancer prevention mechanism : Deep learning 7 Bio-medical instrumentation challenges Part IV 8 Artificial rainfall, cloud physics & collaborative resource sharing 9 Real-time Search for astronomical hazards 10. Conclusion Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 3 Deep Analytics for Technology Innovation Management 1. Introduction Business analytics for technology innovation projects Requirements Technology Technology engineering schema management schema Scope System Structure Security Strategy Staff Skill, Style, support Critical success Computing schema Project analytics factors analysis Networking schema Financial Value chain Data schema investment model analysis Application schema Organization Business Process Application structure mapping integration Figure 1.1: Business Analytics for technology innovation projects Let us highlight the organization of our work. The work starts with the problem of technology innovation project management. It presents various models of business analytics such as Deep analytics ‘7-S’ model, SWOT Analysis and Technology life cycle analysis in terms of S-curve. Next, the business analytics framework is decomposed following a decision tree like structure (Figure 1.1.)  Technology requirements engineering schema o Scope o System  Technology schema o System  Computing schema  Networking schema Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 4  Data schema  Application schema o Structure o Security  Technology management schema o Staff–resources o Skill o Style o Support o Project analytics mechanism o Innovation ecosystem Next we have applied the aforesaid deep analytics to reason a set of test cases associated with top seven technology innovations today. o Solar power electronics (chapter 2) o Electrical and hybrid vehicles (chapter 3) o AI and smart grid (chapter 4) o IIOT, SCADA & ICS (chapter 5) o Deep learning for cancer prediction and prevention (chapter 6) o Biomedical instrumentation challenges (chapter 7) o Artificial rainfall and cloud physics (chapter 8) o Real-time search for astronomical hazards (chapter 9) A technology innovation project is associated with a network of organizations that satisfies the demand of ultimate customer by producing values in the form of products and services. Project management is a novel management paradigm; the basic objective is to fulfill ultimate customer demands by integrating a network of organizational units through systematic coordination of material, information and financial flows [11,15]. A project chain includes all the stages involved directly or indirectly with a project like suppliers, manufacturers, distributors, project service providers, consultants and customer. Each stage performs different processes and interacts with other stages of the chain; there is a flow of material, information and funds between different stages. Integration of organizational units and coordination of flows of material, information and funds are the challenges of today’s project management. A lack of coordination occurs if information is distorted as it moves across the project chain or if different stages of the chain focus on optimizing their local objectives. Efficient coordination and integration depends on choice of partners or strategic alliance, inter-organizational collaboration and leadership. Effective use of information and communication technology, integration of project planning and enterprise resource planning system and process orientation ensure improved Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 5 coordination of different flows in project management. Collaborative planning, forecasting and replenishment is a strategic tool for comprehensive value chain management of a project. This is an initiative among all the stakeholders of the project in order to improve their relationship through jointly managed planning, process and shared information. The ultimate goal is to improve a firm’s position in the competitive market and the optimization of its own value chain in terms of optimal inventory, improved sales, higher precision of forecast, reduced cost and improved reaction time to customer demands. Collaborative intelligence highlights the importance of sharing appropriate strategic data for greater transparency and accuracy of resource constrained, multi-objective and stochastic project planning. Information technology allows project chain partners to interconnect, but trust is also important. The interplay between trust and technology encourages the commitment of collaboration among the organizations. The partners of a project chain are often reluctant to share their private information. It is an interesting option to explore how privacy can be ensured in exchange of strategic information for collaborative intelligence. The trading agents must define points of collaboration for information sharing in terms of DCOR, CCOR and SCOR plans. DCOR plan analyzes project scope, requirements engineering, design, coding, configuration and customization. CCOR plan analyzes the strategies of erection, testing, commissioning, maintenance and performance optimization. SCOR plan analyzes demand, inventory, production and capacity, sourcing, distribution, warehousing, transportation, reverse logistics and evaluate supply chain performance in terms of lead time, cost, quality and service. The agents also analyze the intelligence and feasibility of various types of contracts such as service, sourcing, push-pull, revenue sharing and buy back contract. The project analysts assess and mitigate risks of uncertainties in delivery and capacity planning adaptively and resiliently; adjust reference project plan against exceptions and compute revised plan (P’). The outcome of collaborative intelligence is a set of intelligent contracts. Collaborative planning is a common approach for a group of decision-making agents (DMAs) to reach mutually beneficial agreements. This is an important conflict management and group decision-making approach for making an intelligent contract by a set of agents. The agents exchange information in the form of offers, counter-offers and arguments and search for a fair consensus. Efficient coordination mechanisms are essential to achieve a set of rational structured plan. The coordination mechanisms get competitive intelligence from a set of strategic business intelligence moves. The trading agents start interaction with their initial plans; call a specific set of moves; share strategic information Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 6 but do not disclose private data; negotiate and finally settle the desired output plans. The basic objective is to improve the financial and operational performance of a project plan through systematic coordination of the flows of information, resources and funds. The present work is organized as follows. 2. Deep Analytics - 7-S Model This section presents a model of deep analytics for technology innovation project management. It is basically an integrated framework which is a perfect combination or fit of seven factors or dimensions. Many technology innovation projects fail due to the inability of the project managers to recognize the importance of the fit and their tendency to concentrate only on a few of these factors and ignore the others. These factors must be integrated, coordinated and synchronized for effective project management [15,16,17]. Novelty Objectives Constraints Technology Complexity Priority Scope (S): System (S): 1 2 Application, Primary & Computing, Data, secondary scope, Networking, Applicati on Portfolio rationalization & Security schema Secu rity (S ): Structure (S): 6 3 Strategy (S): Threa t analytics 7 Activities, Nodes Shared vision Risks assessment Ordering constraint Risk m itigation Strategic alliance Time & resources Performance metrics Skill & Style (S): Staff-Resources (S ): 5 4 Pace, Planning, Execution 5M – Machine, Material, ERP, SCM, CRM, Support, Method, Money, Man Leadership style (Technical, Mgmt., Legal) Figure 1.2: Deep Analytics ‘7-S’ model Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 7 Traditional approaches to project management focus on long-term planning and stability to mitigate various risks. But, complex technology innovation project management requires a mix of traditional and agile approach to cope with uncertainties [8,10,12,13]. The intension driven role develops collaboration. The event driven role integrates planning and review with learning. The other important roles of the project managers are to prevent major disruptions and maintaining forward momentum continuously. They must acknowledge the emergence of a problem and then try to minimize the frequency and negative impact of unexpected events in a dynamic environment. They must be people oriented, information oriented and action oriented [14]. Traditional project management approach follows four steps such as definition, planning, execution and termination. But, no projects are so linear. Once project execution starts, reality may demand exception management i.e. the adjustment and amendment in the planning or definition phases. Each industry has a different profile of risk. Deep analytics is applicable to both adaptive and linear project management approaches for technology innovation. Many projects fail due to conventional approach which may not adapt to a dynamic business environment. Deep analytics is essential to understand the nature of a technology innovation project and identify the gaps between as-is and to-be capabilities in a systematic and compelling way. The 7-S model uses seven factors to analyze a project. The first dimension is scope. At first, it is very crucial to identify the scope of a project rationally through feasibility study and cost-benefit analysis. It is essential to identify the primary and secondary scopes through portfolio rationalization and analysis of priority, novelty, objectives and constraints of a set of projects. Perception based emotional and readymade thoughts may affect the correctness of scope analysis. Scope creep is a serious concern in project management. The second dimension is system; it is essential to decide appropriate technology. For instance, an information technology project requires sound computing, networking, data, application and security schema. The third dimension is structure of a project which is explored through complexity analysis of a graphical project network in terms of nodes, activities, estimation of time and resources and ordering constraints (e.g. sequential, parallel). The fourth dimension is staff-resources which estimates resource allocation plan in terms of man i.e. human resources (e.g. technical, management and legal staff), machine, material, method and money (fund or capital). The fifth dimension is skill and style; an efficient technology innovation project management approach demands multiple skills of the stakeholders such as pace or time management, resource planning, supply chain and customer relationship management, group dynamics and leadership style. The sixth dimension is security which is focused on threat analytics, risk assessment and mitigation techniques. Finally, the seventh Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 8 dimension is strategy which defines shared vision, communication protocol, strategic alliance, collaboration and performance measurement in terms of KPIs. ‘7-S’ model highlights a set of intelligent strategic moves to tackle uncertainties and complexities in time and resource constrained project management. Novelty indicates how intensely new innovations are crucial aspects of a project. A technology innovation project should be assessed on the scale of sophistication of technology, which may be low, medium or high. Another critical factor is the complexity of project in terms of product, service and process. Pace indicates the urgency of a project – normal, fast, time critical or blitz. Different projects have varying degrees of newness or novelty. A derivative product development project may have low risk and few future concerns. A new version of an existing product needs detailed analysis and market research. Breakthrough product development projects face high risks. Each project is unique, but not in every respect and may have some common features. The uncertainty in a project is a measure of the mix of new and mature technology and existing knowledge base; it may arise from technological aspects, new service offering or new market segments. High technology projects are subject to time delays, cost overruns and risks of product failure. The complexity base measures three different types of complications within a project such as assembly (low), system (medium) and array (high). High complexity requires efficient coordination and integration among various phases and systems of a project. Pace indicates a sense of urgency and time sensitivity. The failure of time critical projects results from the violation of milestone deadlines and related opportunity loss; blitz projects are crisis projects with extremely urgent timing. There are various strategies for optimal pace management such as contingency plans, alternative solutions in parallel, resilient approach and business cases to manage emergency and to overcome uncertainties and unexpected surprises. A technology innovation project may be delivered on time and budget through the efforts, skill and professionalism of the project managers. But, it may not meet the needs of the customer due to uncertainty and misunderstanding. The basic objective of the deep analytics is to figure out the actual structure of a project as compared with the existing capabilities, the gap and the measure of project success in terms of efficiency, impact on the customer, impact on the team, business success and preparation for the future. It is rational to take both short and long term view of a project plan since success may change during the life-cycle of a project with the change of environmental parameters and information. Does anything change from a future point of view? Does a project have sufficient flexibility to adapt to new requirements of a dynamic business environment? Are the incentives aligned properly with customer satisfaction, system performance, deadline and budget requirements? The deep analytics is useful to find the gaps between as-is Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 9 and to-be requirements of a project, efficient resource planning, uncertainty and risk management. Correct use of deep analytics clearly highlights low-medium- high benefit opportunity and low-medium-high risk difficulty. 3.SWOT Analysis Strength Opportunities (High efficiency) (Growth, Profit) Weakness Threats (High Cost) (Environmental pollution) Figure 1.3 : SWOT Analysis It is rational to evaluate strength, weakness, opportunities and threats for a strategic option on technology innovation. There may be major and minor strengths and weaknesses. Strength indicates positive aspects, benefits and advantages of a strategic option. Weakness indicates negative aspects, limitations and disadvantages of that option. Opportunities indicate the areas of growth of market and industries from the perspective of profit. Threats are the risks or challenges posed by an unfavorable trend causing deterioration of profit or revenue and losses. 4. Technological life cycle analysis Figure 1.4 : Technology life –cycle analysis No element in this universe exists eternally. Similarly, each technology emerges, grows to some level of maturity and then declines and eventually expires [19-25]. Reference : EBOOK/ BAV1.0/24072018 by Sumit Chakraborty Page 10