Prof. Nil Bajjio and Prof. Som are analyzing the seventh element of deep analytics is skill-style-support [Figure 1.11]. The workforce involved in top technological innovations are expected to develop different types of skills in technical, management and medical science domain such as research and development, knowledge management, new product development, process innovation, team management, design, protection of innovation, project management, supply chain management, sales and marketing, event management, construction, erection, testing, commissioning, product and service maintenance. The intellectual rights of technological innovations are protected through patents, trademarks, trade secrets and copyrights. The diffusion of the new technological innovation depends on the skills and capabilities of a group of firms in production, promotion and distribution of the new products and services globally.
The system administrators must have leadership skills in smart thinking, communication, coordination and change management. The workforce should develop skills through effective knowledge management programmes. An effective knowledge management system supports creation, storage, sharing and application of knowledge in a transparent, collaborative and innovative way. The life-cycle of a technological innovation also depends on the intelligence of marketing strategies such as branding, promotion, advertising, launching time, pricing mechanism, product quality, profit margin, compatibility and market share. It is important to analyze market segmentation, cost of advertising and promotion, reach, information content and duration of exposure.
The diffusion of top technology innovations needs the support of great leadership style; they are not only industry leaders but also political one. The style is basically the quality of leadership; the great leaders must have passion, motivation, commitment, support, coordination, integration and excellent communication skill. The leaders must be able to share a rational vision; mission and values related to top technology innovations among all the stakeholders honestly and appropriately in time. It is really challenging for the great leaders to implement top technological innovations physically and practically in the form of commercial products and services. They have to face and tackle threats from traditional industries. Top management must tackle the complexity of system implementation by developing a dedicated project team, a right mix of committed resources and talents like technical and business experts.
What should be the right organization model for top technological innovations? A traditional functionally centered organization model may not be suitable for supporting end-to-end business processes. Such process management is more than a way to improve the performance of individual processes; it is a way to operate and manage a business. An enterprise that has institutionalized process management and aligned management systems to support is a process enterprise. It is centered on its customers, managed around its processes and is aligned around a common, customer oriented goal. The business models of top technological innovations require the support of a process enterprise structure enabled with advanced information and communication technology. The structure should have project, design, production, supply chain management maintenance, human resource management, sales & marketing and finance cells. The structure should be governed by an executive committee comprising of CEO and directors. The process managers should be able to identify core processes in the value chain; communicate throughout the organization about these critical processes; create and deploy measures regarding end-to-end process performance and define process owners with end-to-end authority for process design, resource procurement, process monitoring for redesign and improvement. The structure of process enterprise requires a collaborative and cooperative work culture. Top innovations need proactive, reactive and preventive support for proper technology management.
Figure 1.11: Skill-style-support analytics
Innovation Model : What should be the innovation model for effective diffusion of emerging technology? Is it possible to adopt K-A-B-C-D-E-T-F model? The model is associated with following stakeholders.
Knowledge manager: The innovators should acquire the basic and fundamental concept through a differentiated course work; classify the primary, secondary and tertiary focus areas. Mandatory courses: Innovation, creativity and research methodology; communication. The depth and breadth of the course works should be traded off rationally. It needs proper guidance.
Activator: The activators should initiate the innovation process by identifying a good research problem through scope analysis. Random selection of research problem should be avoided by evaluating the strength, experience and skill of the innovators. The research problem should have potential business intelligence and social benefits.
Browser: The browsers should search for information; investigate throughout the process and find relevant data or information to start innovation. They may review and analyze the existing works through traditional sources of research data (e.g. digital library, books, papers, journals, magazines, industry reports, you tubes) and also through webinars, social networking and attending seminars, workshops and conferences. Random search may result wastage of time; a systematic and planned / guided search process may lead to good results.
Creator: The creators should analyze the gap and think of to-be system; generate new ideas, concepts and possibilities and search for new solutions.
Developer: The developers should transform the ideas of the creation phase into good solutions; turn the ideas into deliverables, products and services. They should collaborate with different research forums, industries and experts during this phase.
Executor: The executors should implement and execute the roadmap of the innovation.
Tester: The testers should do various types of experiments and laboratory works; verify system dynamics and monitor the performance of the deliverables. Advanced research laboratories are required for complicated testing and experiments.
Facilitator: The facilitators should define project plan, corporate governance policy, marketing plan, production plan, investment plan and cost-benefit analysis. They should be able to identify the revenue and profit making stream and fair, rational business intelligence. The government should provide financial assistance to the innovators in patent registration.
Project Management Skill & Style
Traditional approaches to project management focus on long-term planning and stability to mitigate various risks. But, complex technology innovation project management needs a mix of traditional and agile approaches to cope with uncertainties. 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, information and action oriented.
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. 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. It is not a simple task to tackle uncertainties and complexities in time and resource constrained project management for top technological innovations.
Novelty indicates how intensely new innovations are crucial aspects of a project. A 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 the project in terms of product, service and process. Pace indicates the urgency of a project which may be 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. The 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 end customers 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 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.
The feasibility and opportunities of a technology innovation project are estimated through real option, DEA, net present value (NPV) or internal rate of return (IRR).
But, it is hard to compute discounted cash flows due to inherent risks and uncertainties associated with an innovation of new technology. Data envelopment analysis combines qualitative and quantitative measures; it is basically a multi- criteria decision making approach.
Project Analytics : Classical models of resource constrained project scheduling problems are not adequate to solve real world problems due to increased complexities and uncertainties. Intelligent project analytics are essential for complex, fuzzy, stochastic, multi-mode, time and resource constrained project scheduling problems with multiple objectives. This work explores how to apply the concept of intelligent deep analytics for project management. Efficient project management requires coordination and integration among various elements. It is essential to define the scope of a project correctly through feasibility study, priority and cost-benefit analysis. It is a common practice to launch new projects with fake promises before an election. The society should be alert of such corruption.
Project Performance: KPIs and Data Visualization Strategy: It is essential for an efficient project manager to understand critical metrics and key performance indicators (KPI) and how to identify, measure, analyze, report and manage for the success of a project. KPIs and metrics are displayed in dashboards, scorecards and reports. Project metric is generic but KPI is specific. KPIs give early warning signs of poor project performance if the problems are not addressed appropriately. The project success is measured in terms of time, cost, performance and customer satisfaction. It is difficult to measure and monitor too many project performance metrics. Therefore, it is essential to consider optimal number of performance metrics and KPIs. It is possible to classify the performance metrics and KPIs into four categories.
Category 1 [ Operation ] : scope creep, project completion stage, flexibility, quality, cost, time, inventory, customer satisfaction; this category is associated with project success and element S2 and S3.
Category 2 [Finance] : revenue growth rate, cost reduction, profitability, ROI, payback period, NPV; this category is associated with element S3.
Category 3 [Human Resources (HR)] : performance, productivity, capacity utilization, skill; this category is associated with element S3.
Category 4 [Security intelligence] : It is essential to audit fairness and correctness (i.e. accuracy of estimate and measurement) of project plan computation and adjustment as per exceptions based on rationality.
Objects / entities: sustainable smart cities, smart villages, communities, smart world, smart universe;
Global security parameters: define a set of sustainable development goals. /* refer to scope and system analytics in sections 1 and 2 */
Skill: Knowledge mgmt. Technical , System admin, Management, Legal, Governance, ERP & SCM
Style : Leadership, Shared vision, Communication protocol, Risk assessment & mitigation, Organization learning ;
Support : Proactive, preventive and reactive support, system maintenance,