Table Of ContentBusiness 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
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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
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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
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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
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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
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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
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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
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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
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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
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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].
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