Machine makers in many established markets sell their machines directly or through dealer networks. At times, they create additional revenue streams by offering technical after-sales support. They often hope that the installed base of their machines together with limited incompatibility with competitors’ products provides some lock-in mechanism.
They also seek to build-in some technology obsolescence into their product cycles. Some also provide finance, directly or jointly with a finance entity, to help potential customers overcome the barrier of the upfront cost.
Many traditional robot suppliers fit the description above. Integrators often install a robotic or automated solution and provide after-sale technical support. They make it difficult to integrate competitors’ robots with their solutions and offer regular hardware and software updates.
New and emerging robotic firms however do not easily fit this bill.
They are in fact challenging the established norms. This is sometimes through will and sometimes through necessity. The trend towards alternative models is evident across all sectors that new robotics seeks to impact. This includes retail, agriculture, logistics, delivery, security, cleaning, transport, and so on.
In the new few paragraphs we outline some trends and drives in each sector. To get the complete picture please see the IDTechEx Research ‘New Robotics and Drones 2018-2038: Technologies, Forecasts, Players’. This report covers both existing as well as emerging applications. Indeed, it provides twenty-year forecasts in value and unit numbers for 46 categories, painting a comprehensive and quantitative picture of this major transformation.
Autonomous robots can provide automated precision weeding. Robotic intelligent implements can provide precision spraying or weeding too. The upfront machine or fleet costs are often high today. The technology risk for end users are also high. Users are often afraid that expert operators and repair persons will be needed.
They worry that the technology is not tried and tested, especially in an agricultural environment. They fear that the technology is likely to rapidly evolve, exposing them to serious obsolescence risks. Crucially, they require seasonal services and are accustomed to paying wages and not making significant capital investments into machines with low utilisation rates.
To address these challenges many companies are positioning as a RaaS- robotic as a service. They essentially become weeding service providers. They operate or monitor their own machines. They charge the customer per acre, a metric with which they are likely familiar.
They absorb the technology risk. Crucially, they give their robots extensive field practice and will have the chance to gather data and feedback. This is important because the design of these products and services is still in a state of flux with many further iterations anticipated.
This positioning changes the nature of their business. Companies will require additional working capital and staff to absorb the service costs and to offer a sufficiently scaled service network. They cannot simple build to order to balance their cashflows. This is where partnerships will become important. This is also where early capital investments in case of start-ups becomes a necessity as most will operate heavily in the red in the early years of their operations.
With time and technology maturity the model may revert back to a traditional arrangement, or will it? This is an ongoing debate because traditional heavy agricultural machine makers will also need to adapt their models. This is inevitable because as vehicles become more autonomous, in navigation and task, the machine becomes the services, blurring the boundary between equipment sales and service provision.
The whole value chains will need to adjust and even the dealers will need to find their sweet spots evolving their technical support into full blown remote robot operations? To learn more please read ‘New Robotics and Drones 2018-2038: Technologies, Forecasts, Players’.
Last mile delivery
Many small robots are appearing worldwide to solve the productivity problem present at the last stage of the delivery process: the last mile. These small slow robots autonomously deliver small payloads to their final destinations. At the level of individual machines, there are highly unproductive. However, at the level of a large fleet, without a driver overhead per unit, they can become productive and commercially viable.
Here two business models have emerged. Some follow the traditional model of trying to sell their robots. Others are positioning as delivery firms staffed mainly by autonomous robots. This latter model is adopted for many good reasons. It is envisioned that the hardware will in the future become modular, standardised, and highly commoditised. Essentially the same fate as consumer drones awaits the hardware platform. Competing in such a business would not be easy for start-ups especially those based in California and similar start-up hubs.
Crucially also the robot companies require practice data. This is because they will need to improve their delivery and navigation algorithms so that one day they can operate large fleets in complex environments with high speed units. The data loop would be cut if they just sold a machine and walked out. The data acquisition is a fundamental part of product improvement without which the company would likely stall. It will also open up the door to offering high value-added analytics services.
The technology is still immature. As such, it will require close monitoring and likely regular manual interventions to fix issues. As such, most players will, as a minimum, be forced to add a strong 24/7 service element to their business.
Robotic firms are emerging to enable autonomous robotic picking. These robots combine autonomous mobility with autonomous picking skills. Here too companies are frequently positioning themselves as a service provider, charging a monthly subscription fee or a $ per pick rate.
In this case too robotic companies require the data.
Their picking algorithms are based on deep learning and as such without training data their product roadmap will likely stall. This would be very dangerous to their business prospects because today’s generation of products only manage to slowly pick regularly-shaped known objects in simple environments. The future however is fast picking of novel randomly-shaped items in complex environments.
To traverse this competency gap, data will be indispensable. The users too will require ongoing support. They too will prefer not to absorb the technology risk especially since the technology- both hardware and software- are rapidly evolving. As such, a service model can prove win-win.
Autonomous mobile robots are developed to perform various security related tasks. These robots are being designed for indoor, outdoor and even rugged terrain operation. They are essentially sensors-on-a-wheel. Some versions can have more than 50-onboard sensors, generating nearly 100 tera bytes of data per year per machine. These robots can be deployed wherever some type of security and monitoring is required.
Here too firms are not always adopting an outright equipment sales model. It is common to seek a subscription model for giving customers access to the machine, the interface, the data plan, the 24/7 support, etc. Here too such arrangements can be win-win.
The suppliers will retain that crucial data loop in their business models, enabling them to improve their products, for example, by offering specialised algorithms able to detect, recognise, and analyse specific situations, e.g., from car number plate recognition to detection of dangerous gas leakages in an industrial site. Customers too will take this arrangement because it is closer to an end solution and makes it easier for them to test the technology and the new ways of working that it might enable.
Autonomous robots are also finding their way into retail stores, seeking to automate tedious tasks. In particular, they are being offered essentially as automated data acquisition tools, capturing data about items on the shelves with higher speed and accuracy than humans.
Here firms are positioning as full solution providers. This has many advantages. This future-proofs their business against hardware commoditisation. They can accumulate hard-to-obtain and hard-to-copy knowhow and data which can then underpin their value-added data analytics services.
Their customers too will be interested in a final solution and not another alien technology looking for a problem to solve. At the end of the day, they are interested only in an impact on the bottom line, be it higher stock availability, better stock positioning on shelves, or leaner inventories. As such, data-centric service-orientated models can be win-win propositions.
This shift towards non-traditional business models permeates every sector. It is happening even with cars where the rise of mobility is fuelling serious debates about the future of mobility and the role of autonomous taxi fleets and shared facilities. In general, even if the business models are not radically redrawn the profit pool within the value chain will be re-balanced. This will change the winners and losers and will demand that all participates begin looking ahead and planning now.