There is a distinct feeling that technology capabilities to monitor and capture ‘real time’ data using an ever increasing range of low cost sensors are getting out of ahead of the availability of Apps and Services that can provide ‘real time’ optimized responses. In the Industrial Technology and automation sector there is a long history of developing Machine to Machine responsive systems, but it would seem that for an IT sector based on historic transaction applications it’s a big paradigm (apologies) shift.
Service Engineer management, including Preventative Maintenance, is widely regarded as having excellent potential for substantial improvements in operating efficiency and direct cost saving. Achieving these goals requires more than capturing real time data, it requires an App that uses this data to make real time optimized responses.
Currently, whether, or not cloud based and mobile accessed, activities are planned in abstraction from reality on the basis of ‘historic’ data in ‘traditional’ IT applications. The justification for the adoption of IoT is to interact with ‘reality’ using a flow of real-time sensed data to drive a new generation of dynamically optimized ‘read and respond’ Apps.
Using IoT driven Service Maintenance changes activities from being planned on the basis of history, or responding to equipment failure, into being active optimized responses to the reality of the present, often with proactivity to developing situations.
But this cannot be achieved just by adding IoT sensing to the current traditional Service Maintenance Applications that were never built to include this kind functionality. Though admittedly better data input added to the overall data available can improve performance, but the answers will always be via historic reports, not ‘real time’ optimizations. ( ‘real time’ is a difficult term to define as in practice latency means nothing is real time, but in the context of IoT it means reacting to data flows, not historic data processing).
IoT driven Cloud based Apps such as Uber, the real time responsive Taxi Cab service, show how a new generation of Apps can provide real time optimization from IoT data inputs. These near real time read and responsive Apps are usually dubbed ‘Smart Services’; to distinguish them from current generation of Apps that may connect via the Internet and use Cloud services, but lack real time optimization to IoT data flows.
The costs and inefficiencies of associated with equipment failure are an issue across all Industry sectors so, not surprisingly, Service Management, with Preventative Maintenance have been an immediate target for applying IoT. Its not only break fix notifications for equipment failure, but being the ability to use complex event processing to predict that an imminent failure might occur.
Predictive Maintenance has always been the goal of any Service Management but to date the only possibility has been using historic failure records for guidance. Due to the time and costs of detailed record keeping and the need for a long term period of analysis this has only been possible for selected large value equipment. IoT sensing now makes it possible to provide absolutely accurate ‘real time’ data warnings across many items at low costs.
The benefits of Preventative Maintenance range from a less expensive fix of a simple wearing part thus avoiding wider spread damage to adjacent parts if left unattended. Ultimately, unaddressed failure could be of a catastrophic nature resulting in the need for a complete replacement unit. It is also important is being able to choose the time to carry out service work; Retailing, as an example, would prefer service work to be carried out of trading hours; whereas Manufacturing processes need to choose when to make planned shutdowns.
IoT sensing is one half of the game changing to Service Management, but it’s the complex event processing capability to make use of the real time data flows that makes preventative maintenance possible. IoT sensing brings the new unique capability to use real time data flows to establish relationships, and provide outcomes, that would not have been previously been possible. (See previous blog; event hubs or engines add react capability analytics to read real time IoT data).
As an example, IoT Complex Event Processing would interpret reporting rising changes in temperature, energy consumption, and vibration from individual sensors, as advance warning of a potential bearing failure in a rotating part. Reading the real situation will always be more accurate than even the best of historic time based operations.
An often-repeated industry story that illustrates the limitations of break fix actions with existing preventative maintenance routines tells of a unexpected breakdown in a heat pump being repaired with a new parts and a through overall service. Seven weeks later the annual time planned preventative maintenance service fell due and a different engineer dismantled the heat pump once again replacing the nominated ‘wearing’ parts in accordance with the instructions for an annual service.
Clearly ‘real time’ Smart Services using IoT data bring obvious benefits, but a Service Management and Preventative Maintenance package should be providing deeper operational aspects as well. It’s not just the equipment that benefits from real time dynamics, in this complicated working environment, engineering response and activity planning needs the same dynamic approach.
If an Engineer is on a site for one task, and another event occurs on the same site, then automatic re-planning of the service engineer’s day should occur. In turn this should lead to wider re-planning of the rest of field engineering teams activities for the day to ensure cohesive coverage.
Unexpectedly long repair times, traffic impacts on travel time, relationships to Customer Service satisfaction all require a fully integrated real time approach in a new generation of Service Management and Preventative Maintenance Services and Apps.
The commercial impact and importance of this market has not gone unnoticed by technology industry vendors, or equipment manufacturers, resulting in a wide range of announcements. See links to five vendors teaming IoT sensing with Service Management below. However, whilst all provide the core new capabilities outlined above there are noticeable differences in the capability to team the real time react events with an equally dynamic real time reactive Service Management environment.
As the shift to IoT sensor based Preventative Maintenance takes place and an increasing amount Service Management activities are driven by real time event responses, then the necessity for a similar shift to real time Service Management operations becomes clear. Service Management professionals are facing a similar transformation to Internet ‘reality’ operations as marketing professionals faced with the adoption of Internet ‘reality’ of Social Tools.
Salesforce focus on this aspect in their announcement last week, (15th March), of Field Service Lightening referring to it as 360-degree operations, the extent to which this is available in other Vendors offerings is less clear.
http://www.salesforce.com/service-cloud/features/field-service-lightning/
http://www.sap.com/pc/tech/internet-of-things/software/predictive-maintenance/index.html
http://www.ibm.com/internet-of-things/asset-management.html
https://blogs.microsoft.com/iot/2015/12/01/azure-iot-suite-predictive-maintenance-now-available/
https://www.bosch-si.com/solutions/manufacturing/predictive-maintenance/increase-machine-uptime.html
http://www.softwareag.com/us/solutions/manufacturing/iot/overview/default.asp
