Case Study: Power BI and ‘four box’ analysis within the UK Lift Industry

The Stakeholders

UK Lift Maintenance Contractors, Lift Owners and Managing Agents

The Issue

The lift industry in the UK has a variety of Maintenance Contractors who are striving to collect ‘Big Data’ on the lifts within their portfolios through the use of new technologies such as the Internet of Things ‘IoT’ and Remote Monitoring.

A huge amount of data has been and is currently being collected with teams of people creating machine learning algorithms and analysing data continuously to improve maintenance regimes and reduce breakdowns.

How this data is used will determine how maintenance in the UK lift industry develops, however, from a client’s perspective the data can be too onerous to review and should be simplified to highlight key areas. For example; the top 5 worst performing lifts on their portfolios, where investment is required to improve asset lifecycles and alerts when a lift has broken down.

A UK Standard

Outside of the UK, performance is typically measured by Mean Time Between Failures (MTBF) whereas, in the UK, the Maintenance Contractors appear to report on availability %, downtime, call types, callback rates etc.

The average callback rate has been the topic of debate for many years and is believed to be 4 callouts per year within the UK.

Whilst the information provided by the UK Lift Contractors is required for in-depth and root cause analysis, Clients require consistency with reporting to compare the performance of their lifts and the performance of the Maintenance Contractors.

A lot of research has been completed on analysis of data such as Michael Porter’s ‘four box analysis’ and Dr Ben Carson’s ‘Boston Box’ approaches. These conclude that; whilst we seem to believe ‘more is better’ sometimes a simple clarification of issues or problems has a bigger impact on the Clients we provide this information to.

As an independent VT consultancy, D2E look to influence the industry to provide consistent and relevant information to Clients using the data collected via each of the key players’ IoT aspirations.

The Solution

Power BI is a powerful Microsoft tool which can be used to analyse the ‘Big Data’ provided by Lift Contractors and create user-friendly outputs that can assist with the management of lift portfolios. With a large amount of data being collected by Maintenance Contractors, Power BI can be used to show simple statistics such as MTBF, callback rates and availability of equipment.

The Future Outcome

With the UK Lift Industry focussing on IoT developments and collecting large amounts of data to present clients with, this is not being governed and data cannot be accurately compared between different Maintenance Contractors.

There is a range of dashboards provided by various Contractors throughout the UK Lift Industry such as; Schindler’s ‘Dashboard’ and ‘Ahead’, Otis’ ‘E-Service’ and ‘One’ and Kone’s ‘Online’ and ‘24/7’ systems. However, these dashboards report on different definitions of ‘performance’. 

The Contractors should continue to develop their systems; however, more thought should be given to how data is presented to the Client and work towards a universal format that can be used to compare against each other’s performance.

Small portions of this data can be presented using smart visualisations tools like Power BI, to provide relevant information which will assist with the management of lift portfolios and equipment alike. This system allows a multitude of buildings, Contractors, geographical locations and equipment types to be compared under the same definition of ‘performance’ which can assist with Clients expectations and management of their VT portfolios.

Simple data analysis, UK wide consistency with performance monitoring and smart visualisation of outputs should all be strived for within the UK lift industry. That is the ‘vision’.

Case study was written by Paul Clements