Industrial Internet of Things (IIoT) and the Future of Manufacturing, Forecast to 2021

Industrial Internet of Things (IIoT) and the Future of Manufacturing, Forecast to 2021

IIoT Architecture Standardization to Create a $90 Billion Opportunity by 2021

RELEASE DATE
26-Dec-2017
REGION
North America
Research Code: K172-01-00-00-00
SKU: AU01621-NA-MR_21345

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Description

Industrial Internet of Things (IIoT) can be leveraged to transform the production aspect and future potential of the automotive manufacturing business. While evolving technologies play a key role in fostering the automotive business, it becomes crucial for automotive manufacturers to stay connected in order to amalgamate different technologies like machine learning, Big Data, sensor data, machine-to-machine communication, and automation. This will make the best use of IIoT, which would in turn result in gaining a competitive edge and open new revenue streams for the auto makers in the market.

Primary factors, like boosting operational efficiency, increasing productivity, and reducing the complexity of process in the industry plays a vital role in the adoption of IIoT by auto makers. Industrial Internet strives to create a connected enterprise by combining the information and operation department of the production facility. Cognitive systems and real-time analysis can interpret data from systems and components to valuable insights, with which automotive manufacturers can use it to improve the reliability of their output. This not only ensures quality but also improves overall efficiency while supporting manufacturers in introducing new value-added services to customers.

Automotive manufacturers are implementing IoT concepts to create automated and seamless transactions in their production process by deploying sensors, initiate communication with multiple devices, and implant advanced analytics solutions to derive actionable insights. Auto makers need to take multi pronged approach to create relevant business model that embraces IoT-enabled capabilities. Apart from this, maintaining and updating complex systems from in-house/remote locations, possible cyber attacks over the connected networks, adhering to standards of local networks for bandwidth and latency, maintaining process integrity and IP protection, and making immediate investments in evolving technologies are possible challenges to IIoT adoption in a manufacturing facility. However, continuous monitoring of transformation needs would help the auto maker develop a proactive approach to overcoming these challenges.

Key Issues Addressed

  • What are the various IIoT initiatives and how are the functional requirements best understood to implement IIoT? What are the prevailing market trends for IIoT across vertical and horizontal market?
  • What is the plausible future agenda of IIoT bodies? Who are the key participants and what are their IIoT platforms? What does their analysis reflect about their suitability for product manufacturing?
  • What is the system architecture comprised of and what are these different layers? How can end-to-end industrial architecture for IIoT applications be conceptualized?
  • What does the road map for cloud adoption in manufacturing look like? What are the stages of industrial cloud adoption? How is the IIoT market penetration set to grow during 2016 to 2021?
  • Security concerns are the number one barriers to cloud adoption. How is the industry trying to overcome this? Which regions are expected to dominate IIoT adoption?

RESEARCH: INFOGRAPHIC

This infographic presents a brief overview of the research, and highlights the key topics discussed in it.
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Table of Contents

Executive Summary—Key Takeaways

Executive Summary—Emerging Opportunities Across IIoT Architecture

Executive Summary—Technology Metamorphosis Adoption Cycle

Executive Summary—Technology Perspective

Executive Summary—Regional Perspective

Executive Summary—Industrial Cybersecurity Market Outlook

Executive Summary—Industrial Cybersecurity Customer Perspective

Research Scope

Research Aims and Objectives

Key Questions this Study will Answer

Research Background

Research Methodology

Industrial Internet of Things—The 4 Functional Facets

Market Definitions—IIoT System Architecture

Market Definitions—Different Layers

Market Definitions

Segmentation

Platform Industry 4.0

Impelling Objectives of IIoT Bodies

Global Standards Body—Overview of Alliances Landscape

Industrial Internet Consortium (IIC)

Regional Snapshot—Made in China 2025

Future Agenda for IIoT Committees

IIoT—Four Core Components

IIoT—Computing Requirements

Exploring Tools and Skills to Navigate IIoT

Analyzing the Industry Needs

Examining Challenges in IIoT Adoption

Conceptualizing IIoT Computing Requirements

IIoT Design Principles

IIoT Design Approach

IIoT in Enterprise System Hierarchy

Exploring IIoT’s End-to-End Industrial Architecture

Plant of the Future—Industry 4.0 Ecosystem

Sensing Layer—Key Sensor Type Classification

Sensors Market—Revenue Analysis and Key Trends

Emerging Sensors—The New Age IIoT Sensors

Device Layer—Overview of Communication Hardware

Device Layer—Synopsis of IIoT Gateway Unit Shipments

Gateway Matrix—Common Types and Key Features

Gateway Matrix—Common Types and Key Features (continued)

Device Layer—End-user Expectations

Network Layer—Industrial Spectrum Preferences

IIoT Industrial Spectrum—Data Consumption Analysis

Wireless Network—The Diverse Operating Frequencies

LPWAN—A New Reality in Connected Manufacturing

5G Technology—Set to Revolutionize IIoT by 2021

IIoT Software Segmentation—Prevailing Market

Discerning the Distribution Channel

Regional and Industry Vertical—Software Segment Overview

Growth Curve Analysis—Software Segment Overview

Roadmap for Cloud Adoption in Manufacturing

Analyzing Cloud Computing Models in Manufacturing

Cloud-based Models—Acute Focus on Data Sensitivity

On-premise vs. Cloud Based—Choosing the Right Deployment Model

Manufacturing Cloud System in Factory Automation

Value Proposition of Cloud in IoT

Fading Relevance of ‘One Size Fits All’ in Industrial Cloud

Industry’s Shift in Focus to Data Driven Services

Security Concerns—Cloud Adoption

Insularity on Industrial Cloud

Skills Gaps From Evolving Manufacturing Technologies

Future Course of Industrial Cloud

Overview of Manufacturing vs. Other Sectors

Manufacturing Analytics—A Gold Mine of Opportunities

Risk Reward Matrices—Production Line and Plant Level

Maintenance Analytics—Increase Equipment Uptime

Era of Advanced Machine Learning—A Proactive Approach

Big Data in Manufacturing

Regional Outlook—Key Dynamics Across Global Hotspots

Vertical Market Analysis—Discrete vs. Process

New Emerging Markets—Manufacturing Pollution Control

Size of the Pie—Summary of Key Market Opportunities

Introduction—IIoT and Industrial Cyber Security (ICS)

Key Industrial Network Implications

Smart Devices and Cybersecurity

Endpoint Security Implications

Security Challenges in the Factory of the Future

Case Example—Automotive Industry

Case Example—Power Industry

Security Opportunities—Factories of the Future

IIoT Ecosystem

IIoT Ecosystem—Key Vendors

Category 1—Industrial Big Data Analytics

Category 2—IoT Logistics

Category 3—IoT Connected Cars

IIoT Ecosystem Thrives on Partnerships and Technologies

IIoT Ecosystem to Customize Flexible and Reliable Production Processes

Digitization in Manufacturing—Key Elements of Automotive I4.0* Approach

Benchmarking Criteria

Industry 4.0 Platform Benchmarking

Bosch Software Innovations (SI)—Key Highlights and Partnerships

Bosch SI-Leading User and Solution Provider

Bosch Industry 4.0—Readiness and Software Solutions

General Electric (GE)—Key Highlights and Opportunities

GE—IIoT Platform

GE Predix Architecture Overview

GE Predix Machine, Edge Connectivity, and Predix Cloud Overview

SAP Software Solutions Accelerating Digital Innovation

SAP Industry 4.0 Solution Packages

SAP Automotive Solutions—Key Partnerships and Opportunities

IBM Products and Watson IoT Platform—Overview

IBM Industry 4.0 and Connected Manufacturing Suite—Key Pillars

Cisco Automotive Solutions—Key Partnerships and Opportunities

Cisco Solutions

SAP Industry 4.0 Solution Packages

Cisco DNA and Cisco Connected Factory for Industry 4.0

BMW Approach for Digitalization of Production Facilities

BMW Smart Factory Technologies—Implementation

BMW Smart Factory Technologies—Business Impact

GM IIoT Initiatives—Pilot Project to Reduce Factory Downtime

Smart Factory

IIoT in the Era of Digital Engineering

Traditional Supply Chain Framework in Manufacturing

Supply Chain Participants vs. Core Competency

IIoT-enabled ERP Systems

IIoT-enabled ERP Systems (continued)

Predictive Maintenance Structural Outline

Operational Predictive Maintenance Forecast

Predictive Maintenance—Cost Saving Analysis Assumptions

Cost Savings—Product Revenue vs. Maintenance Frequency

IIoT Shifts Power to Automation Vendors

Franchisee Model of Service Supply Chain

Toyota Operations Technology (OT) for Efficiency

GE Analogy—Embracing Brilliant Factories & Digital Twin

Bosch Analogy—A Smart Watch Solution

Stanley Black & Decker—Virtual Warehouse and Connected Factory

Jeep Transforms its US Factory Realizing IIoT

IIoT Growth Opportunities

Strategic Imperatives

The 3 Big Predictions

Legal Disclaimer

The Frost & Sullivan Story

Value Proposition—Future of Your Company & Career

Global Perspective

Industry Convergence

360º Research Perspective

Implementation Excellence

Our Blue Ocean Strategy

Related Research
Industrial Internet of Things (IIoT) can be leveraged to transform the production aspect and future potential of the automotive manufacturing business. While evolving technologies play a key role in fostering the automotive business, it becomes crucial for automotive manufacturers to stay connected in order to amalgamate different technologies like machine learning, Big Data, sensor data, machine-to-machine communication, and automation. This will make the best use of IIoT, which would in turn result in gaining a competitive edge and open new revenue streams for the auto makers in the market. Primary factors, like boosting operational efficiency, increasing productivity, and reducing the complexity of process in the industry plays a vital role in the adoption of IIoT by auto makers. Industrial Internet strives to create a connected enterprise by combining the information and operation department of the production facility. Cognitive systems and real-time analysis can interpret data from systems and components to valuable insights, with which automotive manufacturers can use it to improve the reliability of their output. This not only ensures quality but also improves overall efficiency while supporting manufacturers in introducing new value-added services to customers. Automotive manufacturers are implementing IoT concepts to create automated and seamless transactions in their production process by deploying sensors, initiate communication with multiple devices, and implant advanced analytics solutions to derive actionable insights. Auto makers need to take multi pronged approach to create relevant business model that embraces IoT-enabled capabilities. Apart from this, maintaining and updating complex systems from in-house/remote locations, possible cyber attacks over the connected networks, adhering to standards of local networks for bandwidth and latency, maintaining process integrity and IP protection, and making immediate investments in evolving technologies
More Information
No Index No
Podcast No
Author Jagadeesh Chandran
Industries Automotive
WIP Number K172-01-00-00-00
Is Prebook No
GPS Codes 9800-A6,9813-A6,9AA5-C1,9694,9968-A6,9AF6-A6,9B07-C1