Published on 11/08/2016 | Strategy
We’re in the early days of an Industrial Internet of Things (IIoT) revolution that will dramatically disrupt the manufacturing, oil and gas, agriculture, mining, transportation, healthcare, smart cities, smart grid and other industrial sectors of the economy.
How big is this potential impact? World Economic Forum estimates IIoT products will impact nearly two-thirds of the global gross domestic product (GDP). IDC estimates IIoT growth from $42.2 billion in 2013 to $98.8 billion in 2018 (18.6% CAGR). Top Markets estimates the IIoT market at $93.9 billion in 2014, growing to an annual $151.1 billion by 2020. GE estimates IIoT has the potential to add $10 to $15 trillion to GDP in the next 20 years. Cisco estimates the economic value of IIoT at $19 trillion by 2020. Accenture estimates IIoT has the potential to add $14.2 trillion to GDP by 2030.
The number of IIoT sensors shipped between 2012 and 2014 increased from 4.2 billion to an incredible 23.6 billion in three short years. Companies such as Cisco, GE, IBM, Intel, Microsoft, Amazon, Huawei, Qualcomm, Siemens, Philips, Autodesk, Mitsubishi Electric, Monsanto, Dupont, Dow Chemical, John Deere are beginning to offer enterprise class IIoT software platforms and IIoT sensors and control hardware.
The Economist estimates IIoT could result in 1% improvement in efficiency, which translates to $276 billion in savings over 15 years in just five industries – oil and gas ($90B), power ($66B), healthcare ($63B), aviation $30B), rail ($27B). Concurrently, incredible IIoT innovation continues at a break-neck pace by startups worldwide.
Most believe IIoT will eventually dwarf the multi-billion dollar consumer IoT market.
These market growth, sensor volume, and potential savings numbers are staggering. The economic value of these changes is “the largest growth in the history of humans,” says Janus Bryzek, known as the father of sensors. Given the large dollars being invested, what are the key benefits we can expect?
Increasing production efficiency, reducing wastage and production costs
Optimizing location and status of assets across an organization
Responding more rapidly to alarms due to realtime data and realtime analytics
Mitigating equipment failures and downtime through predictive maintenance
Creating new revenue streams through new products and services due to IIoT
Reducing operational errors, increasing worker productivity and safety
Sharing big data across industries can result in unexpected business relationships
Leveraging IIoT, manufacturers’ shop managers can now improve operations by capturing a vital metric called Overall Equipment Effectiveness (OEE) in realtime, which measures (a) availability, or down time, (b) performance, or machine efficiency, and (c) quality, or failure rate. Previously, OEE data was collected manually, with error rates sometimes exceeding 50%. After implementing IIoT and OEE on its manufacturing floor, Memex cites Magellen Aerospace more than doubled OEE from 36% to 85%, while Rose Integration raised OEE on their 30 industrial machines from 40% to 82%.
US Department of Energy indicates some companies are already experiencing benefits such as 12% savings on scheduled repairs, 30% on reduced maintenance costs, and 70% fewer breakdowns. World Economic Forum reports Thames Water, the largest provider of drinking and waste-water services in the UK, uses sensors, analytics and real-time data to anticipate equipment failures and respond quickly to critical situations, such as leaks and adverse weather events.
In short, IIoT provides visibility and transparency into industrial processes that were once opaque, enabling nimble companies to be increasingly competitive. IIoT will also fundamentally transform how humans, robots, and machines interact in these industrial sectors. Indeed, a game changer.
What could trip us up?
Security and data privacy Current security solutions protect just a few vulnerable ingress points. New security frameworks are needed to protect potentially tens of thousands of end points.
Uncertain responsiveness Internet response times are non-deterministic, often measured in seconds. Manufacturing, energy, transportation, and healthcare require deterministic sub-second responses, sometimes in the low milliseconds. IIoT solutions require innovative solutions, such as smart gateways and fog computing to address responsiveness.
Uncertain resilience Most current solutions are unproven. Are they designed to be fault tolerant with high availability? If solutions from multiple vendors are implemented, will errors from one solution cascade into others? Customers will come to rely on these IIoT solutions 24/7 with expected 99.999% reliability. In the meantime, solution providers must come up with implementation strategies while their solutions mature.
Uncertain ROI Uncertain return on investment due to immature and untested technologies and application of solutions into use cases that were not designed into the software.
Lack of interoperability Efforts to connect to industrial machines is a key IIoT growth driver. However, lack of interoperability continues to be problematic.
The remainder of this post focuses on challenges due to lack of interoperability.
Most industrial companies have invested in costly equipment with long life spans for decades. Much of this equipment was designed to be standalone, and not connected within an IIoT ecosystem. An enormous challenge is to provide seamless connectivity with this existing equipment, even while equipping next generation machines with IIoT connectivity.
World Economic Forum cites 67% respondents balk at implementing IIOT because of challenges to connect legacy equipment due to lack of interoperability.
Cisco estimates factories worldwide contain 60 million machines, with 90% of these machines residing in unconnected silos and 70% more than 15 years old.
What to do? Luckily for us, brownfield development, which describes adding IIoT connectivity to legacy industrial machines, (versus greenfield development, which describes IIoT connectivity built into the original design of industrial machines), can leverage existing standards such as Modbus, OPC, and MTConnect, while forging ahead on development of next generation IPv6 TCP/IP and other standards.
Modbus. Modbus is a serial communication protocol, published in 1979, to transmit data between master and slave devices, and is widely used by many manufacturers in numerous industries. The most common use case is to transmit realtime and historical data from instruments and control devices to a main controller. A typical Modbus network has one master and up to 247 slave devices. Modbus is read-write, which allows reading of data from devices, and writing of control data to devices. Modbus data can also be sent on TCP/IP networks using an adapter that puts device data in a packet, and transmits the packet to a TCP/IP client. Modbus is often integrated into SCADA applications, which are software systems for remote monitoring and control of numerous types of industrial devices. Typical uses for SCADA are to monitor air quality in pharmaceutical manufacturing facilities and manufacturing shop floor equipment performance in continuous, batch repetitive, and discrete data operations.
OPC. OPC was developed in 1996 to transmit realtime plant data between industrial hardware devices and software applications. OPC is read-write, which allows reading of data from devices, and writing of control data to devices. Like Modbus, OPC supports retrieval and transmission of realtime and historical archived data, as well as transmission of alarms and events. OPC drivers are available for hundreds of industrial machines.
BACNet. Automation and control network protocol for smart buildings.
MTConnect. MTConnect, released in 2008, is commonly implemented in new industrial machinery. This royalty-free protocol is an open source, lightweight, open, and extensible protocol, based on standard Internet protocols, such as a RESTful interface and XML. MTConnect was designed for software applications to monitor and gather data from numerically controlled shop floor equipment. MTConnect is read-only, which only allows reading of data from devices, not writing of control data to devices. MTConnect Institute and OPC Foundation released an interoperability specification in 2013.
CAN bus. Protocol enabling microcontrollers and devices to communicate without a host computer. Initially designed for communication within automobiles, CAN has been adopted by other vertical markets, including industrial machines, smart buildings, cycling, and entertainment.
Proprietary standards. Unfortunately, there are more than 200 proprietary standards such as Rockwell CIP Energy, Siemens PRO Flenergy, Mitsubishi CCLink IE.
NOTE: Often, using existing standards to connect to legacy industrial machines requires a hardware adapter, e.g., programmable logic controller (PLC), and software driver/agent.
The plethora of today’s physical connectivity options can be confusing – WiFi, Bluetooth (Classic and Low Energy), Zigbee, Z-Wave, 2G, 3G, 4G, and soon 5G, Thread, Whitespace TV, 6LowPAN, NFC, Sigfox, Neul, LoRaWAN,
How to choose? It’ll depend on a customer’s application requirements – balancing range, data throughput, battery life, power demands, and security.
Internet protocol IPv6 is a likely candidate to race ahead of other connectivity options, as it enables an almost unlimited number of devices connected to wired and wireless private, public, and hybrid cloud networks.
For applications requiring extended wireless range, mobile networks, terrestrial broadcast or satellite communications may be the answer.
Fog Computing is a compelling IIoT industry initiative to solve IIoT connectivity issues, by moving intelligence to the edge. This intelligence is embodied in virtualized, secure, multi-tenant network computing and storage in equipment located close to end-users (and IIoT devices) (rather than in the cloud). Roadside assistance is one application of Fog Computing, in which Fog Computing devices reside near roadways. Cisco is shipping Fog Computing capable devices.
Another industry IIoT initiative named Swarm Computing describes a decentralized smart peer network in which each node is a member of a local swarm requiring local intelligence, content storage and data distribution. A swarm can communicate to Fog Computing devices, which may reduce Fog storage and computational requirements. Swarm Computing costs less and requires less bandwidth, as less data moves to the cloud.
However, even with these new initiatives and many physical connectivity options, a real challenge remains as there is no industry-wide agreement on payload semantics, which is required for true plug-and-play IIoT interoperability.
As an industry, though, we are making progress, as you can see from a sample of recent industry initiatives below.
- IETF and IEEE are taking steps to define IIoT semantic interoperability in 2016.
- Steering committee hosted by Cisco recently introduced a seven layer IoT reference model.
- Weightless standard for low-power, wide area M2M IIoT communications.
- Government bodies in Germany and China are sponsoring IIoT initiatives.
- Companies are now participating in consortia to develop IIoT interoperability, payload semantic standards, and security standards, e.g., Industrial Internet Consortium (IIC), AllSeen Alliance, and Open Interconnect Consortium (OIC), Connecting Lighting Alliance.
It’s clear we’re not yet there, and we need to move faster. Otherwise, we risk putting the brakes on a major game changer with the potential to increase nearly two-thirds of the global gross domestic product (GDP).
The stakes are too high not to invest the time and money to solve the challenges now and fully reap the benefits of plug-and-play IIoT interoperability.
This article was originally posted on LinkedIn.
