Balancing Edge and Cloud in the Digital Factory
How can a cloud-based platform help a manufacturer with a mismatched collection of legacy equipment, limited connectivity or networking and minimal IT staff? To do this, the platform provider must have an edge solution or partners that specialize in edge intelligence. This edge or fog solution should not only help solve the initial connectivity and data extraction problem but also provide stream processing, provide instant analytics for immediate issues, and consolidate workloads on-premise.
Technology vendors targeting the manufacturing sector need to understand that deploying technology on the factory floor requires a significant amount of custom work, at least for now. Rarely can they simply upload data to the cloud and get results. Each factory floor has a different setup with a different combination of machines from different manufacturers that often require protocol translation and networking. Before they can hope to improve operations, they must figure out how to monitor operations. Some will require closer monitoring with more frequent sensor readings and some will not.
In almost all cases, the V’s of Big Data (volume, variety, velocity, etc.) will continue to increase with more sensors, more types of sensors such as vibration, acoustic and video, more connectivity, and more connected devices such as smart glasses. To flexibly adapt to clients’ needs and help them scale, Smart Manufacturing vendors must offer edge intelligence capabilities integrated with the cloud.
Smart Manufacturing vendors with a cloud focus have already started investing in edge solutions. Amazon announced AWS Greengrass back in November 2016, SAP launched its Edge Services in July 2017, Siemens introduced its Industrial Edge solution in April 2018, and Microsoft made Azure IoT Edge generally available in June 2018. In addition, more edge hardware vendors have increased the capabilities and processing power of their edge systems, including ADLINK, Cisco, Dell, Eurotech, Fujitsu and HPE, and more edge intelligence specialists have emerged within the sector, including FogHorn, SWIM.AI and Telit. Also, Software AG has provided increased edge capabilities to Siemens MindSphere, and PTC removes a huge pain point for clients with Kepware. Unlike the cloud, which came as a push from the vendors, the end users will pull for edge solutions, which feel more comfortable to many as they more closely resemble older, on-premises solutions. The “Company profiles” section contains analysis on the positioning, strengths, and weaknesses of the above vendors. This report examines the drivers, barriers, and potential of edge and cloud computing in manufacturing.
Table of Contents
- 1. EXECUTIVE SUMMARY
- 2. STRATEGIC GUIDANCE FOR EDGE VENDORS
- 3. INTRODUCTION TO THE INDUSTRIAL DATA NETWORK
- 3.1. Report Definitions
- 3.2. IoT intelligence: From Endpoint to Cloud
- 3.3. Industrial Automation and Control Subsystems
- 4. INDUSTRIAL EDGE TO CLOUD MARKET OVERVIEW
- 4.1. Example Use Cases and Case Studies
- 5. MARKET TRENDS
- 5.1. Drivers of Cloud
- 5.2. Drivers of Edge
- 6. MARKET FORECASTS
- 6.1. Cloud Architecture Evolution
- 6.2. The Analytics Value Chain
- 6.3. Data and Analytic Services Revenues by Country
- 6.4. Total Revenue Breakdown (Excluding Hardware), 2018 vs. 2026
- 7. COMPANY PROFILES
- 7.1. Cloud Platform Providers with Edge Components
- 7.2. Edge Hardware
- 7.3. Edge Intelligence Specialists
- 7.4. Professional Services