Innovation Institute

Connected Productivity- Room for Improvement!

A rise in productivity has been a common theme connecting many manifestos of political parties, showing, irrespective of political leanings, the need to address the deficiency in productivity levels of many developed economies. Ostensibly, there are many ways in which to raise productivity including amongst others, higher level skills, better connectivity and lean techniques.

However, the question to be asked is: where is the growth going to come from, given the flat level of Gross Domestic Product (GDP - a measure of how fast an economy is growing), particularly amongst the G8 countries, with many experiencing issues of workforce demographics and talent shrinkage[1]?

Innovation and technology convergence has led to the emergence of digital productivity enhancers and accelerators such as robotics and automation, Internet of Things (IoT) and cloud computing, as well as Industrial Internet (or Industry 4.0), which is the current industrial automation and data exchange trend in manufacturing technologies that leverages technology convergence and connectivity to create the so called "smart factory".

Industrial Internet focuses on the non-consumer side of IoT, and applies the Internet of Things thinking to industrial settings. Industrial internet means combining complex physical machinery together with sensors, networks and software. It connects smart devices, machines and people, and aims to deliver better and faster decision-making via advanced analytics.

Sensor technologies are becoming increasingly inexpensive, more powerful and provide longer battery life.  Such technologies are also driving the cost of connectivity down even further. And with big data analytics and the uptake of machine learning by different sectors, data can now provide invaluable insights into any process. Such insights can provide productivity increases and cost reductions.

The ability of the Industrial Internet, although still emergent, to disrupt business models, reconfigure their supply chains and force new policies is evident. Manufacturing, healthcare, transportation, agriculture, mining and oil and gas account for the two thirds of the world’s economy, and these are the sectors where the Internet of Things and Industry 4.0 are already impacting performance outcomes.

Alongside increases in efficiency, Internet of Things and Industry 4.0 are putting a spot light on the value that data presents in this interconnected landscape. Monetization of data looks to extracting value from assets that were previously passive. By combining IoT with other emerging technologies such as deep learning - a subset of Artificial Intelligence – data can be analysed to provide prescriptive, predictive and eventually pre-emptive actions to improve performance and deliver additional benefits to the process or even the end-user.

The proliferation of smart things, from appliances, running shoes, watches, to cars and aeroplane cockpits offer a new value to end-users through their ubiquitous connectivity to devices and smart application logic.

Telecommunications technologies, which form the basis of connectivity are accelerating the rate of growth and convergence of both the consumer and the Industrial IoT[2]. However, there remains to be many challenges, chiefly amongst them is the aspect of standardization.

The diversity of IoT applications has resulted in the need for different requirements addressing different IoT systems’ configurations. This in turn, has brought about different IoT architectures with varied set of components and functionalities, as well as varying terminologies and conventions, comparable to that of the smart phones platform developments and evolution.

Although there are many players in the IoT platform development space, however, there are only a few leading players with closed interface platforms dominating this space – a situation that is not necessarily in the best interest of the customer.

Practices in asset management have evolved from various sources, converging to international consensus and formal standards, such as PAS 55 / ISO 55000 (specification for the optimal management of physical assets), ISO 55000, IEC 62264 (enterprise-control system integration) based on ANSI/ISA S95, ISO 15745 (industrial automation application integration framework), MIMOSA (Machinery Information Management Open System Alliance) / IEEE 1232, ISO 13374 (condition monitoring and diagnostics of machines), OPC UA (interoperability standard for data 91 exchange in the industrial automation space) and ISO 10303 / “STEP” (industrial automation systems and integration – product data representation and exchange)[3][4].

In today’s IoT field, products are targeted to specific sector and/or vertical market application, such as automotive or machinery, or to a horizontal consumer market, such as home automation and consumer electronics. So, cross-sectoral interactions will hopefully correct the market failure of only a few platform providers and enable a much faster spread and take up of IoT and Industrial Internet by all size of businesses. 

Many of the solutions that are currently available rely on various co-existing protocols, interfaces and platforms, either proprietary or standard. Diverse technologies are dedicated to a single application, and as such, existing solutions remain to be fragmented. Many emerging applications are still using their own standards, as those commonly agreed standards are being developed.

In summary, there remain to be a number of key challenges that require further developments to enable a faster uptake and wider spread of IoT and Industrial Internet to underpin productivity growth, including amongst others the following:

  • Standards & Architectures: the development of common architecture frameworks for connected system qualities and interoperability
  • Intelligent Processing: The development of IoT technologies that support the shift from just data collection to knowledge creation. In addition, it is important to manage a very high number of IoT devices that cannot be controlled individually, but need be run automatically.
  • User Acceptance: The development IoT value chain that reflects the full product life cycle (from sensor creation to user acceptance and disposal).
  • Security and safety: Security, trust and safety are key certification and validation issues with any IoT ecosystem, particularly in large scale projects such Smart Cities and eHealth. For example, validating user acceptability in car-to-car communications or enhanced assisted living for the purpose of relaying safety critical information. 
  • Legal: The role of legislations in the development of techno-legal frameworks that will inform policy and associative regulatory issues (i.e. insurance, liability as well as safety and security).

Figure 1 (above) highlights the interrelationships between technology development, convergence and applications, and, the need for standards that embrace architectures, communication gateways, user acceptance, safety and security, as well as legal and regulatory requirements within a connected ecosystem[5].  

This interplay will therefore, drive innovation and improve opportunities for truly raising the levels of productivity. Governments and political parties will have to invest smartly in education, technology and infrastructure as well as creating the regulatory frameworks and conditions that will enable a coexistence of technology and people in the workplace to combat stagnant productivity levels.

 

[1] worldeconomics.com/papers

[2] Gartner.com/newsroom

[3] IEEE Internet of Things Journal

[4] European Standardization. http://www.cencenelec.eu/standards

[5] Industrial Internet Consortium, Volume G1: Reference Architecture 2017

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