Thoughts on developing a mobile app integrated with a cloud-based intelligent analytic system (wireless sensor data+equipment data+user data+?) for optimal asset management, leasing and resale

Developing this app is basically a matter of understanding the asset owner's use case ... as a general rule, optimizing return on investment in major capital assets is basically a matter of always having that asset performing at its highest and best use ... if we look at the following list, we can see that optimizing return on investment is a matter of keeping the asset occupied by tasks toward the top of the list.

• performing the principal task that its owner purchased it for at peak throughput or optimal performance
• performing the principal task that its owner purchased it for at sub-optimal, non-quantitatively managed performance
• leased / rented to a qualified lessor / renter who needs it for its principal task, but a smaller project 
• being evaluated by a future buyer of the asset
• undergoing routine preventative maintenance 
• being set-up or changed over for the next production task
• undergoing corrective maintenance (e.g. replacement of sickle sections or wear parts)
• setting idle, in storage, in a controlled situation that preserves the asset's condition
• being transported or moved to its next highest / best use ... hopefully, without being damaged in-route
• under major repair or overhaul; being refurbished or brought into condition for higher/better use

A significant thing to consider, of course, is whether leasing or renting the asset to qualified lessors / renters is worth the hassle ... it probably isn't, unless the owner already has some form of established processes or a system for doing this UNLESS this particular app would actually facilitate the owner's ability to lease or rent spare capacity.  

Another thing to consider is how the asset owner wishes to sell the asset ... through eBay, through Ritchie Bros, through a preferred auctioneer, through a broker-dealer on commission, through a classified advertisment, to a lessor/renter on a lease-to/rent-to-buy arrangement, through another means ... the app should accommodate different sales strategies.

Another consideration is what kind of user, sensor, engineering and other data are necessary to support the asset owner's need to quantitatively optimize the asset's performance ... for example, quoted speeds and feeds from a machinist's handbook are always conservative -- with different tools, different materials, cutting fluids, etc it is generally possible to do significantly better than the quoted speeds and feeds to optimize throughput on a machine tool ... there is no need to take stupid risks, but being lazy and just playing it safe or sticking well within the factors of safety almost always entails giving away a significant portion of theoretical production capacity ... a typical factor of safety is 2X -- or potentially a sacrifice of 50% of throughput! ... the same thing applies to almost every kind of machinery and equipment, i.e. past production data [with sufficient data from comprehensive condition monitoring systems] from similar conditions allows one to safely push the envelope in an evolutionary fashion to find the optimal production performance frontier.   Obviously, an asset owner will not want to frequently push things until they break -- but if that owner NEVER pushes anything until it breaks, we know that the asset was never used at its actual capacity ... the ability to drive optimization by sensing the effects on the machine from pushing the machine [up to, and just a wee bit past its limits] are is why intelligent, predictive systems based on networks of condition monitoring sensors are potentially so valuable.