Sharing Economy

Industry, services and the sharing economy

The so called „sharing economy“ is a very big deal, as the huge valuations of Uber, Airbnb and others attest. It is often conflated with Peer-2-Peer (P2P) Apps, however it is mainly about sharing assets that would otherwise be underutilized, whether or not some form of labor is included: cars, homes, computer systems, even cash. Private cars for example are on average utilized less than 20% of the time. Increasing utilization by providing use of the car by others for money, fundamentally changes the economics of car ownership and at the same time reduces the number of cars required to provide a given mobility capacity (so called deadweight costs). It is already having an effect on both sales volumes and product mix of car manufacturers. Incidentally such sweating of assets should shift income from goods producers to service providers and consumers, while boosting GDP –though that might take a while to make itself felt.

In B2B applications for industrial markets conceptually similar models have always existed in the sense that there have always been vendors using assets to provide services, whether in transportation, tools, energy plants (power, HVAC) or other areas, and essentially it is the idea behind “contract manufacturing” and various outsourcing approaches, including design or maintenance. What would be interesting however, is to see whether P2P applications can emerge. This will not be as easy because fixed asset utilization is generally higher in businesses (it is part of cost accounting, at least implicitly) as well as for obvious competitive reasons. Nevertheless some areas might be worthwhile exploring:

Parts pooling: This is often offered by OEMs, as a way to help customers reduce carrying costs of expensive parts as well as obsolescence risks, while providing timely service in case of need. However OEMs would naturally focus on their own parts, therefore there might be potential for an App which would enable customers to offer shared usage of selected parts stocks for a fee, irrespective of manufacturer.

Capacity pooling for specialized machinery: Initially this would be for fairly generic machinery, say presses, CNC machines or 3D printers or various mobile, non-dedicated/non-custom machinery. It should be possible to register an (excess) capacity in an App (in hours or pieces) and somebody could make use of it. This of course already exists in different forms, however Apps would significantly reduce transaction costs, standardize processes and speed things up.

People and expertise: Experts tend to be very expensive resources and utilization and productivity are hard to measure. Nevertheless, some organizations today do contract out experts to work on other organizations’ problems as a business sideline. The experts gain more experience and have the opportunity to see and work on different problems, thus avoiding Groupthink and bringing a new perspective to their own organization. Some Apps in that direction already exist, e.g. Innocentive.com, though it was probably perceived as a problem solving rather than a P2P sharing App. However Apps in this direction might become more of a mainstream, targeted and directed. Increasing utilization and productivity of expert resources would have positive economic effects overall. And experts also include managers.

Data: Of course data are a different story because, while they might be considered an asset the issue of utilization doesn’t arise: Many can utilize the same data at the same time, information can be copied indefinitely. However providing anonymized, yet contextual access to data streams to and from customers and equipment suppliers or between users of similar assets can generate both income streams as well as information and insights that can be highly beneficial. One of the main problems in asset management is that asset or plant operators usually lack sufficient asset technical skills, while product/system suppliers have deep knowledge about the performance of components and systems that they produce, but often fail to see their operational contexts and lack operational experience. Bi-directional sharing of data and information (with a plant operator providing individual asset data and receiving aggregated data from similar assets in return) can help drive new insights and productivity –providing of course there is sufficient capacity for analysis and interpretation. And as data analytics resources for industrial settings are still quite scarce, sharing these capabilities might also make sense.

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