Let’s begin with the following story: Janne, a forest machine owner from Juupajoki, wakes up early in the morning, drinks his coffee and jumps into his truck. He is about to begin his workday in the forest, so he checks the location of his forest machine and navigates to it, by using the mobile app that he has been using for years. Janne notices that his co-worker Kaisa has transported the machine 15 kilometers north to a new work area. Thanks to the app, Kaisa can continue sleeping, as Janne locates the machine without needing to reach her.
This example shows how far the digital transformation has come, in just a few years. The change has been huge. 10 years ago, only a few people owned a mobile phone with a touch screen and applications that could be downloaded from the online application store. There were also no cloud-based solutions to report things like machine status. The equipment also had very limited, if any, connectivity capabilities to push the collected data to the backend system, which is the backbone of the whole digital service offering. There are now almost no equipment, including forest machines, mining equipment or factory machinery, that are not capable of collecting data and sending it to a backend system. Even cardboard boxes may have embedded sensor capabilities! In most cases, the utilization of data is at another level. Companies have used cloud environments and transformed the collected data into interesting graphs and reports. Businesses can no longer afford not to take advantage of data. Technology no longer sets limits on product and service development.
Industries have moved from data capturing projects towards data utilization projects. Being able to capture data and turn it into insight, has helped industries to increase the overall efficiency of their processes and operations. If we used to have technological limitations, we now need to be careful with data ownership, privacy and cybersecurity. Data has become a very valuable asset. Whoever is capable of collecting data and can utilize it the most beneficial way, will receive the biggest profits from the new business. However, there is also a downside. If the data is handled carelessly, like in the case of a GDPR violation, it could lead to serious consequences and in the worst case, bankruptcy. Furthermore, making a new digital business profitable has not been easy. Most companies can't yet cover the costs of digital solution development. However, if they don't have a digital solution development running, they will position themselves as followers, while their competitors harvest the gold. In most cases, making money by offering digital products or services is not the goal. Being relevant by supporting the service business and new product sales is much more important. Nevertheless, making money by utilizing digital capabilities is increasingly important. The change calls for restructuring established practices. It is very likely that the new digital-enabled product or service will require changes to the current business model, sales approach and customer support. As we can see, without significant investment in training and leadership, the target will be difficult to reach. We are not there yet. Many companies have revealed their digital products and services. Some of them have been successful. However, what is happening today is turning the focus from one-time-pilots and proof-of-concepts towards scaled and standardized solution development. That will eventually bring the greatest benefits.
If you know what you don't know, you are in a good position. In the process industry, where basic products evolve rather slowly, it has been clear for a long time what the important features of the product are. However, some of these characteristics were previously very difficult to measure. Sometimes secondary variables were measured or there was tedious sample taking, and laboratory testing was involved. Many of them can now be measured online or, at least, with highly automated sample taking tools and fast processing. The accuracy of process control devices has improved significantly. For example, accurate control valves and variable speed electric drives are now much more affordable. Meanwhile, the demand for increased product variations has led to fragmentation of product portfolios. Despite the efforts of many supply chain professionals, the cyclic nature of demand has forced many producers to shorten their production cycles. In some cases, products have even been customized in an unprecedented way. As identified earlier, there has been an increased demand for more agile production capabilities. At the same time, technology has created opportunities to turn existing value chains to follow customer demand more closely than ever before. However, it's not just about knowing more. It is also important to use the knowledge in productive ways. Data can now be processed in standard platforms, analytics tools are more readily available and they are easier to use. As a result, the volume of data is not a problem anymore. Sophisticated control algorithms that were developed when there was less data handling capacity, can now be replaced by more general use of artificial intelligence driven tools, which makes it much easier to adopt them as best practice. However the role of a human expert is still paramount. Setting targets, combining possible data sources and nursing applications to adulthood and productive use, still requires industry expertise and project management skills, not to mention collaboration capabilities and an understanding of commercial factors. The process industry will continue its evolution at a revolutionary pace in the coming years. Increasingly accurate and frequent measurements and other signals from the process, will be put to good use with powerful machine learning and analytics tools. This will enable the agility of the process to increase without major investments in hardware. The customers will get their unique products, without ruining the production process economics.
The digitalization wave has had an impact on all areas of business and operations in manufacturing industries. Companies have noticed that making the products intelligent has made possible to capture data and create new ways of helping customers conduct their operations more efficiently. Let's explore the topic by setting the scene with a forestry example. However, it can be also be applied to other similar industries.
In the old days, lumberjacks used a pencil and paper to mark down the number of trunks they had processed in the forest. They utilized a hand saw, axe and later a chain saw to cut and prune the trees. At the end of the day, the books were collected by the operation managers, who calculated the wages for each worker. The forest operations in the forest are now fully directed and monitored digitally. Forest companies purchase wood from the forest owners, by using a digital purchasing software. The information on the work area locations, tree types and tree assortments are already in the company’s system, before the actual forest operation even begins. Forest machines get instructions on the logging site, price matrices and tree storage locations automatically, before being transported to the site. Forest machine operators do not need to feed trunks to the correct cutting point, since the automation does it in a fraction of a second. While the forest operation is ongoing in the forest, the data flows automatically via the cellular or satellite network into the machine owner’s mobile phone, forest company systems and forest machine manufacturer’s data storage.
In this case, everyone in the value chain becomes informed at the same time. Companies are capable of optimizing their resources in the field and at the factory in real time. Equipment manufacturers are also able to conduct predictive maintenance, since they notice maintenance needs, hours before they actually happen! New business models have also become possible. In the past, if the machine was sold to a customer at a fixed price, it can now also be sold with a monthly fee. The cost doesn’t only include the machine. It also comes with a full-service maintenance contract. If manufacturers sold machines and service in the past, they now sell uptime. These outcome-based business models were impossible to implement 10-20 years ago. This is because it was not known how many service hours and spare parts a machine would require within a four-year period in South America or Russia. If these business models were previously based on enlightened guesses, they are now based on data and analytics.
The end of the digital transformation in process and manufacturing industries is not in sight. It is still happening now with full force. All the needed enablers are available, and emerging technologies support innovation. Digital platforms can be found in every company and customers are ready to adopt new digital industrial services. Data is also extensively turned into value, by utilizing sophisticated analytics. Companies are now capable of delivering services that were previously impossible to offer, without initiating digital product and service development. The future looks bright. As we've seen, there is obvious customer demand for better and more intelligent digital-enabled solutions. Without a doubt, process and manufacturing companies are capable of delivering these products and services in the 2020's! And as the needs of these companies evolve, so does our digital service offering. In the next few weeks, we will continue to explore the digital evolution in manufacturing and process industries from a customer-centric perspective, and offer further insight on how the industries can implement digital at scale to fully capture its potential over the following years.
Co-authors: Jarmo Hiljanen & Matti Ketonen
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