WHY SMART FIELD SERVICE AUTOMATION NEEDS SMART DATA
7 min. reading time
13 August 2021 ·
hen researching Smart Field Service Automation, the goal is to achieve leaner and more transparent processes, shorter response times, and outcomes such as increased customer satisfaction and efficiency in the departments or sectors concerned.
In Appointment Scheduling, Field Service, and Last Mile logistics, this plan can be realised thanks to the balancing of integrating predictive maintenance and predictive analytics with state-of-the-art technologies. It is characterised by intelligent optimisation of deadlines and resources in real time. This organisation of orders and their completion ensures flexibility, removal of human error, and significant cost savings. So why do these processes in particular require 'smart' data in the first place? And what even counts as 'smart'?
SMART DATA FOR EFFECTIVE FIELD SERVICE PROCESS PLANNING
In a modern company, increasing digitalisation means that a great deal of data can be gathered on all logistics processes. This data - smart data - concerns, among other things, technical components, journey times, costs, duration of repairs that have taken place and the distribution of orders over specific periods of time.
Values on customer history and the current condition of the assets can be determined. Predictive analytics obtains valuable information from this data, which is suitable for forward planning and the creation of alternative scenarios. A visualisation that can be individually set via key figures and dashboards supports the responsible persons in an intuitive and precise evaluation that shows important changes and soon upcoming tasks in real time.
In this way, Smart Field Service Automation can be used to reduce response times and provide more precise information for customer enquiries. Predictive analytics evaluates historical data using mathematical methods that discover trends and patterns and incorporate them into a calculation model for future predictions. The best-known methods for these evaluations include decision trees, regression and neural networks.
While decision trees and regression are relatively easy to model, neural networks require much more computational effort. They can be represented using AI (artificial intelligence) and allow very precise recognition of patterns and trends in real time. They can only be used effectively if a corresponding volume of smart data is available. Predictive analytics can be useful, for example, in retail, for logistics, for service providers, utilities and insurance companies, but also for government and public administration.
Field Service Control for an Excellent Customer Experience
Predictive maintenance can make supply chain processes run more smoothly and efficiently. This is particularly important for companies that serve customers or dependencies at different locations. A prerequisite for predictive maintenance is predictive analytics. Only when the collected data has been evaluated by an intelligent calculation can predictions be made about possible scenarios. Both customer behaviour and technical processes are taken into account. This could be regular orders for certain products or services.
Smart machines provide digitised key figures on their consumption and maintenance intervals. In addition to these "hard" facts, the seemingly subjective statements of customers can also be included in the evaluations. Are there frequent complaints about a certain product? How often do complaints occur and what comments can be read in the social media?
All this digital information can flow into Smart Field Service Automation if it is evaluated via predictive analytics and integrated into predictive order offers via predictive maintenance. An interesting example of a successful implementation with predictive analytics and predictive maintenance for smart field automation is provided by the implementation of the FLS VISITOUR product for Christ Wash Systems (Otto Christ AG). An already existing ticket system and a mobile app of the manufacturer for car washes were integrated into the tour planning software. It enables the SLA-compliant fully automatic planning of maintenance and interval appointments. With this solution, predictive maintenance ensures that all maintenance work that will soon be necessary is taken into account in the tours and, if possible, carried out on an optimised schedule.
Thanks to real-time information obtained on the basis of predictive analytics, the interaction between office and field staff has been significantly improved. All processes are now digitalised throughout and ensure maximum transparency of orders. The employees are more evenly utilised, with the scheduling process running fully automatically.
This Smart Field Service Automation was only possible because a great deal of data was already available in digitalised form or could be transferred to a digitalised solution. Instead of calling the technicians directly, customers use a digital ticket system, for example, which determines all information about the maintenance tasks in question. Manual intervention by team leaders is only necessary for rare escalations. Instead, they can concentrate more on maintaining customer relations and on looking after their staff.
CUSTOMER EXPECTATIONS INCREASE WITH IMPROVED SERVICE OFFERS
In the wake of digitalisation and increasingly responsive businesses, customer expectations for smooth, accurate and, if possible, prompt service are on the rise. This is reinforced by the offers of large retailers such as Amazon with delivery promises within the shortest deadlines already with end customers and is also increasingly in line with the demands of B2B customers.
These demands can be met with a smart service model and smart field service automation. One goal of Smart Field Service Automation is the complete planning of all processes and resources with as few dispatchers as possible. The most important effect is not only the saving of manpower, but also the significant acceleration and optimisation of the relevant processes for predictive maintenance. If AI and predictive analytics are used to determine the upcoming maintenance work before the service orders arrive, the system can send offers to the customers as a precaution. Predictive maintenance can thus be integrated into existing processes without additional administrative effort.
In conjunction with intelligent route management, further optimisations become possible. Before each deployment to a specific location, the Smart Field Service Automation system can check whether an inspection or maintenance is due at or near that customer. The service technicians are automatically informed about the work to be done and thus help the customers to reduce downtimes due to repairs and to ensure smooth operation of the systems. Their tours are shorter due to the optimisation and can be combined for several orders in a sensible way.
This saves time as well as fuel and wear and tear on service vehicles. All these advantages of Smart Field Service Automation can only be used for Predictive Maintenance if digitised or digitisable information on the relevant processes and needs of the customers is available. Modern systems incorporate existing sources of the most diverse structures for this purpose.
These can be customer databases, apps or measured values from machines. External data sources such as traffic jam detectors can also be integrated for Smart Field Service Automation in order to provide the best possible support to employees on the road. To take advantage of automation, it can also be worthwhile to digitise processes such as order acceptance, complaints or customer communication and integrate the corresponding data into the system.
ORGANISE FIELD DATA AND GET SMART WITH EFFECTIVE PROCESS PLANNING