The market

Industrial IOT technology driven smart factory implementation gained significant momentum in the year of 2018 and continues to grow in 2019 in the manufacturing economies of the world. More action can be seen in the Asia-Pacific region due to it being the hub of advanced manufacturing.

The smart factory market is estimated at a value of US$153.7 billion in 2019 and to reach US$ 244.8 billion by 2024, at a CAGR of 9.76% during the period of 2019 – 2024, as per the study reported by MarketsandMarkets. The growth factors according to the report includes growth in usage of industrial robots, evolution of IIOT and demand for smart automation solutions.

In terms of IIOT connections, Juniper Research estimates 46 billion industrial and enterprise device connections by 2023. Installed base of wireless IoT in industrial automation are estimated to reach 21.3 million in 2018, according to Berg Insight. Asia-Pacific has the largest concentration of new IIOT connections.

Robots programmed to do certain specific tasks are seeing double-digit growth. ABI forecasts the global revenue of collaborative robotics shipments to see a compound average growth rate of 49.8% from 2016 to 2025. This is significantly higher compared to 12.1% for Industrial robots and 23.2% for commercial robotics. Small and medium businesses are seeing a lot of benefits in employing robots. The total market value for business process automation tools by 2020 is estimated at $10 billion US.

Connectivity and open standards

One of the factors enabling smart factory progress is wider deployment of 4G and other wireless communication protocols. Along with the speed of the data, the cost of the data- communication is falling. Unlicensed networking protocols which are less expensive and does not require service providers have emerged to address market demand of lower cost connectivity solutions. With 5G on the horizon, a multiple connectivity option is an enabler.

Inside a production floor most of the connections are still wired connections using DSL cable modems and Ethernet with wireless connections constituting around 25%. Cellular wireless is significantly used in M2M communications. Due to cost and other limitations of cellular networks, Low-Power Wide Area Networking (LPWAN) technologies are increasingly deployed. Sigfox and LoRa are fast-growing LPWAN in I-IOT.

LPWAN is used when the distance between communication points increases from a few meters to hundreds of meters or even kilometers. The competition between cellular frequency based LPWANS such as narrowband IOT and long-term evolution for machines (LTE-M) and unlicensed LPWANs such as LoRa, Sigfox, and RPMA (Random Phase Multiple Access) which uses publicly available ISM bands is growing. Many telecom service providers announced NB-IoT / LTE-M roll-outs that compete with Sigfox and LoRa in year 2017 which resulted in a high level of competition between these two in year 2018. We will see higher competition between these two in 2019.

Short-range wireless protocols such as Wi-Fi and Bluetooth continue to grow and are evolving with the new requirements. Short-range wireless finds more usage in wireless sensing in close proximity to the system. Despite the competition, Zigbee and RFID still find some space in this market.

5G is expected to enable a massive change in industrial IOT networking where it replaces wired connections with higher bandwidth. Trials have already started in 5G deployment. It yet to be seen whether 5G deployment can really replace usage of LPWAN networks.

With the multiple standards and multiple connecting protocols, the real enabler is interoperability. It is a fundamental challenge for successful IOT communications.

Industrial IOT implementation is getting highly complex due to lot of proprietary offerings and multiple platforms. Industry is looking for open standards so that a newly procured smart machine will operate with the other systems. The leading players in this domain ABB, Bosch Rexroth, B&R, CISCO, General Electric, KUKA, National Instruments (NI), Parker Hannifin, Schneider Electric, SEW-EURODRIVE and TTTech are jointly promoting OPC UA over Time Sensitive Networking (TSN) as the unified communication solution between industrial controllers and to the cloud. Based on open standards, this solution enables industry to use devices from different vendors that are fully interoperable. The participating companies intend to support OPC UA TSN in their future generations of product. These companies started an open technical collaboration under the umbrella of the Industrial Internet Consortium (IIC) and the OPC Foundation.

OPC UA TSN which is a combination of enhanced OPC UA Publisher/Subscriber (Pub/Sub) technology with the IEEE TSN Ethernet standards, offers all of the open standard building blocks required to unify communication for industrial automation. With a standardized information technology (IT) and operation technology (OT) it will be easier and faster to implement Industrial Internet of Things (IIoT) and Industry 4.0.

Cyber-security and safety are big issues and will be covered in a separate article, the point to make here is Cyber-security and safety is the most important aspect of IIOT powered smart factory.

Return on Investment

Return on Investment is the first thing to evaluate when a manufacturing business invests to transform into industry 4.0. Though there are lot of successful case studies, it is possible to invest more than what is required, selecting a vendor whose solutions may not be the right fit for your type of requirements. Sometimes you may select very cost-effective solutions, but it may not be the best solution in terms of security and reliability. All this demands a lot of evaluation before selecting a solution. Prioritizing what's important for your environment is also important.

In terms of the direct cost effectiveness of IOT, it’s impact can be found in human resources, energy resources, material resources and time. Though resource saving is one aspect of smart factory technology, the real-time analytics helps to manage assets efficiently and gain operational intelligence through data. Step by step identification of pain points and implementing IoT solutions in monitoring, operation and servicing/maintenance is a better approach for pre-existing operating plants. Direct and full investment in smart factories makes sense in terms of cost benefits for new plants. The knowledge of various hardware, software, security and safety components of smart factories is a must to the top management and key players. Human skills required in operating such systems is another area to invest.

Preventing unscheduled production cuts

unscheduled repairs and maintenance effects production flow. Human interface based predictive maintenance can be replaced by IOT based data analysis/intelligence using smart systems. This system monitors and analyzes the information from various sensors and the machines production and maintenance data to alert the user and supplier in advance for predictive maintenance. There are a lot of cases where it is becoming a must. To give an example, oil and gas exploration or any such similar industries can be remotely managed very cost effectively.

The necessity to improve uptime and efficiency of machines enables IIOT usage. Presently running expensive but older equipment can achieve reliability levels of new equipment by IoT-ifying them.

Cut in energy consumption

Real-time energy consumption management through IoT analytics can bring down energy wastage to zero level. Along with controlling the end-equipment and optimizing use of energy sources cuts energy costs. High-power motors and heating elements can be very efficiently operated along with slightly less power consuming units such as lighting.

Need of real time data

In most cases, the decision makers in the company are in need of real time data to make various decisions. Top management want to know production data throughout the day. The Artificial Intelligence (AI) based IOT system provides that automatically without human involvement. With real time data and past data analyzed, adapting to market and other changes is easier and less risky. Real-time data enables the virtual visibility of operation through a combination of VR and real time data, what is known as ‘digital twin’. Data-analytics provides insights into the data generated by the system itself without human interface.

Cloud for storage is expected to gain further popularity in industrial IOT. Security is one of the major issues with cloud storage. Security is ensured through encryption or only by sending raw basic no-header data which does not provide any intelligence about the machine. With the generation of huge amounts of data, companies are employing big data analytics and artificial intelligence tools to give extremely valuable insights. AI at both cloud and edge level started yielding results matching human like smart data analysis and inference. With built in AI at node or edge level, self-asset management can improve production while reducing unplanned downtime. AI powered smart computers/processors can be embedded in robots, fleet vehicles, and distributed microgrids so that the analytics carried locally by them to ensure security, privacy, data-related cost, and meet regulatory constraints.