Back in 2017 The Economist made a bold statement in declaring that the world’s most valuable resource was no longer oil but data, a commodity in a lucrative, fast-growing industry very much like oil was a century before.

The article was in fact about the dominance of the big tech companies Alphabet (Google’s parent company), Apple, Amazon, Facebook (now Meta) and Microsoft which were based on digital technologies and first understood how value could be extracted from the data they generate. The article went on to argue the necessity to regulate this market and make sure that none of these companies (or worse a cartel of them all) could have a complete dominance. Something that, after five years, I am not sure that we have in fact avoided.

It nevertheless attracted attention in many other sectors, including manufacturing, as to whether the same approach could be adopted to their processes on how to make them more digital and how to use the data generated in these new digital processes as a source of additional revenue for companies. They became progressively aware of the relevance of data and of the fact that, in order to generate it, products, machines and processes had to become ‘smart’. They also soon realised that generating data is a relatively simple task whilst the real challenge is extracting value from these data, which in turn requires having a multiplicity of them, storing them somewhere and using intelligence to analyse them in order to offer new added value services to customers.

In 2014, in what is considered the seminal paper on this subject, Michael Porter, a professor at the Harvard Business School and James Heppelmann, the then CEO of PTC, a leading maker of industrial software, introduced the concept of smart, connected products and explained how they were transforming competition among companies. Once made solely of mechanical and electrical parts, products (namely machines) have become complex systems that combine hardware, sensors, data storage, microprocessors, software and connectivity. These ‘smart, connected products’ made possible by vast improvements in processing power and device miniaturisation, and by the network benefits of ubiquitous connectivity, have unleashed a new era of competition.

What we are now seeing is an evolution in what we mean by the word machine; not only a piece of equipment capable of executing a certain task in a more or less automated manner, but a device enhanced by sensors, capable of generating data and is connected. Incidentally, smart and connected products, and the range of different technologies they incorporate, are the building blocks of the so called fourth industrial revolution or Industry 4.0 and of its future evolution into Industry 5.0. What we want to discuss here are some of these technologies such as IOT and the related other elements such as data cloud storage and computing, big data analysis and the new business models they enable.

IOT and servitisation

The Internet of Things or IOT, describes physical objects (or groups of such objects) with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communication networks. Although devices do not strictly need to be connected to the public internet, but only to a network and be individually addressable, extending their connectivity to the whole worldwide net expands the capabilities and potential of these objects. As a result, the definition of IOT, as opposed to the old-fashioned Internet of humans using computers, has been commonly adopted.

Connected devices that generate and exchange data can be anything  from our smartphones to cars, from home appliances to mobility infrastructures and from medical equipment to surveillance cameras. When we consider machines on a shop floor, we should rather use the term IOT to refer to a specific family of components and data transmission protocols that are compatible with a manufacturing environment. It is estimated that connected devices at the end of 2022 reached 13.14 billion (Statista 2022) and that this number will more than double by 2030 when we will have almost 30 billion devices in the network. It is an incredible amount that greatly surpasses the number of human beings on Earth.

So, what can a company do with this wealth of data that billions of connected machines gather and share? They can offer an impressive number of added value services to their customers in what becomes an effective bundle of machine and service. This is evolution towards servitisation, where the term is used to describe the transformation of a business to compete through a combination of services and products rather than products alone. Hence, it is the process of gaining multiple revenue streams by providing supporting services to produced goods.

Three main levels of servitisation exist. In a basic offering, it involves turning on basic services such as the provision of spare parts. At an intermediate level, the process aims at offering external services such as maintenance and repair tasks.

At the highest level of advanced service, it refers to the process of offering machines produced on a contractual basis where the output or use of the machine is focused upon. For machine suppliers this means moving from a transactional model in the interaction with customers in which all revenues are generated at a single time when the machine is sold, to a relational one where revenue streams are multiple and endure over time. Quite a radical change of mindset for companies in many sectors that, leveraging on modern technologies, are embracing this new business paradigm.

Atoms and bits

Upgrading a machine to make it smart is a transformational journey from atoms (hardware) to bits (data), from disconnected to connected, from the material to (almost) immaterial world. It all starts with the electronic and control architecture of the machine. Components at this level (electrical motors, electronic drives, input and output devices) are chosen for their capability of exposing data and signals about their status and their operational conditions. These basic elements are complemented by other sensors that can monitor an expanded set of conditions so that the machine can project a full data model of itself and its operations.

These raw data are then processed by a local brain, a firmware code that is executed on what is known as an edge device. Preprocessed data are then sent through a connectivity device via the Internet to a cloud infrastructure where they are stored. Finally, a set of API (application programming interfaces) make these data available to apps (applications) that process them, often using Machine Learning algorithms to extract patterns that detect, for example, potential machine failures that can then be predicted before they occur. This is the classical case of an advanced predictive maintenance service that is offered to the customer to totally avoid the risk of machine stoppages due to failures. The process the machine executes (for example cutting) is no more important than the data generated in the process and that can be used to increase the OEE (overall equipment efficiency) of the machine itself, optimise its performance and fine tune its working parameters. In this sense they create an additional value to the supplier offering.

The data value chain

In this entire process we can see how, at each step in the pipeline, the value of data grows in what we can define as a ‘data value chain’. Initially, they are created and extracted by the various onboard sensors in the machine; then data are produced (preprocessed) and initially analysed. Wherever data exist, especially in mission critical operations, their integrity and security must be guaranteed and this requires the adoption of special equipment and of adequate software provisions. Data are then valorised using software applications based on AI and ML to make them eventually available for use at the end of this value chain by machine operators, plant managers, other decision makers or, in a more extreme case, the machine itself. This last situation may sound exotic, but there is a growing interest for a scenario called ‘machine customer’, where the machine, using the predictive capabilities stemming from the gathered data and the ML algorithms, can anticipate the failure of its components, order the needed spare part autonomously and get it delivered before the failure occurs.

The purpose of this data value chain is, therefore, through this extraction of value, to take decisions and improve interaction with the products (machines) and the bundled services. However, the full implementation of this data-centric paradigm relies on two more pillars, data sharing and digital culture. Data sharing enables new business models based on the value of information exchanged within the company or towards partners, suppliers and customers, a digital ecosystem that surrounds and incorporates the individual machines. Finally, this evolution is not possible without the creation of a digital culture within the company that supports people and organisations in developing competences and digital processes.

New business models

How does all this reflect on company business models in a classical relationship between a machine supplier (OEM) and its customer? Let’s look at what happened in the past. As we have already mentioned, the business model was transactional, with the customer and the OEM at opposite ends of the value chain. The OEM sells the machine, the customer uses it. When something goes wrong and a failure or an accident occurs, the customer sends a support request. The OEM (or some other entity designated by the OEM) analyses the problem, identifies the cause and eventually takes care of the repair or replacement of the broken part or, if necessary, the entire machine. In this scenario the only value for the OEM is the product (the machine) and typically the service becomes a mere cost that, in a final balance, erodes the revenue margin generated when the machine was sold.

Recently, we have seen an evolution in this model and, today, OEMs are closer to their customers in the sense that the terms and conditions upon which the OEM guarantees the continuity of operation by the machine is contractually defined and well structured, for instance, with a service contract that specifies a set of regular maintenance or repair services scheduled in time. In this case the product (the machine) still takes the largest share of the value for the OEM, but the service contract becomes an element of differentiation and of enduring relationship with the customer.

Where then is this digital transformation leading companies in relation to evolving their business models? Eventually this will end up in a fully relational model in which OEM and customer strengthen their relationship with the goal of eliminating completely all machine down times and of maximising its performance. Services like real time monitoring, insight diagnostics to spot anomalies or even predictive insight to anticipate them, form the background of a framework in which the OEM and the customer become actual partners; in this scenario they aim for a long-term collaboration based on the life cycle extension of the system. This is the full exploitation of the servitisation model where product and service generate value together in an intimate bundle.

Elaborating further on this model we can imagine in the future a situation where a ‘pay per purchase’ of the machine is replaced by a ‘pay per use’ or a ‘pay per output’ and the OEM is rewarded for continuity of the manufacturing service it offers to its customer and on the number of items per time that are produced. This is no less than a Copernican revolution for conventional machine suppliers that want to venture along this challenging path. It requires a radical shift in company mindset, the calculation of the economics of the new business model, the elaboration of a set of different contractual frameworks, a restructuring of the customer service and machine maintenance functions, and an upskilling of human resources in the company. Not an easy journey for sure but the reward at the end is likely to compensate for the effort.

In conclusion we may ask ourselves whether for machine suppliers this is an option or an inescapable choice. To answer this question, we should consider the market situation that companies face nowadays. A redesigned globalisation scenario, a scarcity of supplies, a growth in the cost of raw materials and components and fiercer competition are all eroding the profit margins that derive from the sale of the machine. Complementing this with additional revenue streams, based on longer and stronger relationships with their customers and doing that before their competitors do is the only way to stay ahead and  secure the future of their businesses.

CREDIT: SHUTTERSTOCK / FORMATS

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