As businesses begin to embrace the opportunities offered by the IoT, digitilisation and analytics, many are unsure what they are going to do with all that information. Within Service businesses, most of the thinking is centred on driving out cost through remote diagnostics and services. But this is just the tip of the iceberg. The digitalisation of products provides an even bigger revenue opportunity, often in ways not initially foreseen.
Many companies are turning to Service Thinking to identify the profit pools that they can exploit within their customer and industry value chains. For example in the haulage industry, the truck itself only represents perhaps 8% of the running costs. 50% is the fuel bill and 25% the driver. Truck manufacturers such as MAN have developed servitized business models based on Telematics technologies that improve fuel consumption and turn the Truck into an operating expense. This data driven business model has enabled early adopters such as MAN UK to grow their business by a factor 10 over the past 20 years against a declining market.
Although ‘Service Thinking’ will help identify the areas of priority, how do companies go on to figure out how to develop data driven solutions. For example take SAVortex, a UK SME who have developed a SMART connected hand dryer with remote diagnostics. The idea was to use connectivity to dramatically reduce maintenance costs for hand dryers in large office complexes. This they achieved, but in addition found that the data they had on the usage of the toilets was even more valuable to their customers. Nearly everyone who goes uses a toilet also washes and dries their hands. By monitoring the usage of the hand dryers, large facility managers could infer the footfall in different areas of the building, so optimising heating, light and cleaning costs. These savings could in certain cases far outweigh those achieved within the original business model.
The question is how can we help companies make this type of leap in imagination. A framework originally developed by IBM and reported in the Harvard Business review, can help companies explore the value of their digitalised assets. 5 patterns of innovation were identified that could be used to monetize data;
- Add new value to existing Products: This comes from understanding the data being produced by products and whether it is possible to generate insights from it. In particular whether these insights could add new value to us, our customers, our suppliers our competitors or players in another industry. The SAVortex hand dryer is a good example of this.
- Combining Data within and across industries: Is it possible to combine the product data with another data set to create new value? In the truck example the driving habits of the driver could be analysed by MAN through the telematics. When combined with the drivers names held by the haulage company, training could be recommended to improve the capability of drivers to optimise fuel efficiency enabling profitability to be doubled!
- Digitalising Assets: Which assets are digital in nature and how can this feature be used to increase their value? Is it possible to turn physical assets into digital assets? An example from the field service world is that some spare parts will not be held as physical stock, but as a digital drawing. When the part is required, the drawing is down loaded to a 3D printer at the point of need for the part to be produced. This has significant implications on the business model for spare parts and where value is created.
- Trading Data: Can data be structured and analysed to yield higher value information? Again the Savortex example is a good example where the usage information of the dryer is can be sold to the facilities company due to the inherent value.
- Codifying a Capability: Does a company have a significant capability that can be digitalised and which others value? Many industrial companies have a huge amount of intellectual property which if put on a digital platform can yield immense value to various stakeholders. For example the bearing manufacturer SKF has many industrial apps which their customers and channel partners can download to help make their equipment more effective.
Key to success is to embark on this process with a cross functional team, adequate resources and senior management support. With these in place, the next step is to know what data you have from your products and operations. What data can you access but are not capturing? Do others have data that would be helpful to you and how might you collaborate with them. Then, by examining each of the five patterns, ideas begin to emerge and develop.
The creative process is greatly facilitated by two further actions;
- Having a strong technology presence within the team who can understand how data can be extracted, exchanged and mashed up.
- Having input from external parties who can bring an Out-Side in perspective to the technology and business challenges
What is clear is that opportunities are growing for product companies to find new value from the data they generate. With an open mind-set, some determination and a structured approach, this provides industrial companies with a significant opportunity to grow through embracing the digital economy.
This article was first published in January’s edition of Field Service News
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